According to the method presented in this chapter, in order to perform the translation of a molecule A in reference to a fixed molecule B, the affine transformation is applied to A without using the rotation and scaling functions, for the parameter values *a* = 1, *rx* = *ry* = *rz* = 0. The values of *Tx*, *Ty*, *Tz* that satisfy a certain condition (Eqs. (36, 37)), are searched in the interval [−D, +D]. Usually, the value *D* = 200 was sufficient to complete the search successfully; otherwise, a larger D value was applied. Finally, the affine transformation is applied to molecule A for the found *Tx*, *Ty*, *Tz* values. According to **Figure 2**, translation occurs in two occasions in our method. First, the drug adopts the role of the molecule A undergoing translation, and the linker adopts the role of the fixed molecule B (step S5d). Later, the linker-drug complex adopts the role of the molecule A undergoing translation, and the cysteine adopts the role of the fixed molecule B (step S8d).

### **2.5. Axes**

As mentioned in Section 2.3, the rotation of a molecule A (i.e., the drug or linker-drug complex) in relation to its corresponding fixed molecule B (i.e., the linker or antibody, respectively) is performed for *a* = 1, *Tx* = *Ty* = *Tz* = 0 and the values of the variables *rx*, *ry*, *rz* that satisfy a certain condition. That condition is the minimization of the absolute value of the angle between the axes of the two molecules (Eqs. (28, 29)).

In the context of this project, the axis of each molecule has been defined by taking into account the desired final layout of the three molecules. In the lysine [34] and cysteine conjugation methods, the same kind of bond connects the linker with the drug. Therefore, in both cases, the drug axis is defined by the drug atoms participating in the hydrogen bond with the linker, which are a hydrogen (H) atom and the electronegative (EN) atom covalently bonded to it [Eqs. (16–18); **Figure 2**, step S4a]. The linker axis has been specified so as to connect the two most distant non-hydrogen linker atoms because that line is quite representative of the linker shape [Eqs. (19–21); **Figure 2**, step S4b]. Given the chemical structure C12S of the linker used in cysteine conjugation, those two atoms are always the sulfur (S) atom and its most distant carbon (C) atom. On a similar note, the axis of the linker-drug complex connects the two most distant non-hydrogen atoms of the linker-drug complex [Eqs. (22–24); **Figure 2**, step S7a]. The cysteine axis has been defined as the line going through the alpha carbon (Cα) and sulfur atoms (S) of the cysteine side chain [Eqs. (25–27); **Figure 2**, step S7b]. This line was chosen for its direction towards the exterior of the antibody, which is the desired direction for the linkerdrug complex. In the lysine conjugation of [34], the linker C15N was used, therefore the linker axis was defined by the nitrogen (N) atom with its most distant carbon (C) atom. Also, the lysine axis went through the alpha carbon (Cα) and nitrogen (N) atoms of the lysine side chain.

$$\left(p\_{d1} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{H \text{ atom of } dmg} = \left(\mathbf{x}\_{Hd}, y\_{Hd}, \mathbf{z}\_{Hd}\right) \tag{16}$$

$$\mathbf{p}\_{d2} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{EN about } \mathbf{q}' \text{ drug}} = \left(\mathbf{x}\_{\text{ENd}}, \mathbf{y}\_{\text{ENd}}, \mathbf{z}\_{\text{ENd}}\right) \tag{17}$$

$$\begin{aligned} L\_d \colon (\mathbf{x}, \mathbf{y}, \mathbf{z}) &= p\_{d1} + t\_d \ast \left( p\_{d1} - p\_{d2} \right) = \left( \mathbf{x}\_{Hd}, y\_{Hd}, \mathbf{z}\_{Hd} \right) + \\ t\_d \ast \left( \mathbf{x}\_{Hd} - \mathbf{x}\_{E \&d}, y\_{Hd} - y\_{E \&d}, \mathbf{z}\_{Hd} - \mathbf{z}\_{E \&d} \right), & -\infty < t\_d < +\infty \end{aligned} \tag{18}$$

$$\mathbf{p}\_{l1} = \begin{pmatrix} \mathbf{x}, \mathbf{y}, \mathbf{z} \end{pmatrix}\_{\text{NH atom of link}} = \begin{pmatrix} \mathbf{x}\_{\text{NH}}, \mathbf{y}\_{\text{NH}}, \mathbf{z}\_{\text{NH}} \end{pmatrix} \tag{19}$$

$$\left(\mathbf{p}\_{l2} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{S\text{ aow of } l\text{index}} = \left(\mathbf{x}\_{Sl}, \mathbf{y}\_{Sl}, \mathbf{z}\_{Sl}\right) \tag{20}$$

$$\begin{aligned} L\_{l}: \left(\mathbf{x}, y, z\right) &= p\_{l1} + t\_{l} \, \mathsf{\*} \left(p\_{l1} - p\_{l2}\right) = \left(\mathbf{x}\_{\text{NHH}}, y\_{\text{NHH}}, z\_{\text{NHH}}\right) + \\ t\_{l} \, \mathsf{\*} \left(\mathbf{x}\_{\text{NHH}} - \mathbf{x}\_{\text{Sl}}, y\_{\text{NH}} - y\_{\text{Sl}}, z\_{\text{NH}} - z\_{\text{N}}\right), & -\infty < t\_{l} < +\infty \end{aligned} \tag{21}$$

$$\mathbf{p}\_{\text{ld}1} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{NH atom of higher -drug}} = \left(\mathbf{x}\_{\text{NHId}}, \mathbf{y}\_{\text{NHId}}, \mathbf{z}\_{\text{NHId}}\right) \tag{22}$$

$$\mathbf{p}\_{\text{id}\,2} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{S\,2\,one\,off\,index-drug}} = \left(\mathbf{x}\_{\text{S\,2d}}, \mathbf{y}\_{\text{S\,2d}}, \mathbf{z}\_{\text{S\,2d}}\right) \tag{23}$$

$$\begin{aligned} L\_{\mathsf{M}} \colon (\mathsf{x}, \mathsf{y}, \mathsf{z}) &= p\_{\mathsf{M}1} + t\_{\mathsf{M}} \, \mathsf{\*} \left( p\_{\mathsf{M}1} - p\_{\mathsf{M}2} \right) = \left( \mathsf{x}\_{\mathsf{NHM}}, \mathsf{y}\_{\mathsf{NHM}}, \mathsf{z}\_{\mathsf{NHM}} \right) + \\ t\_{\mathsf{l}} \, \mathsf{\*} \left( \mathsf{x}\_{\mathsf{NHM}} - \mathsf{x}\_{\mathsf{SL}}, y\_{\mathsf{NHM}} - y\_{\mathsf{SM}}, \mathsf{z}\_{\mathsf{NHM}} - \mathsf{z}\_{\mathsf{SM}} \right), -\infty < t\_{\mathsf{M}} < +\infty \end{aligned} \tag{24}$$

$$\left(\mathbf{p}\_{\text{cyc1}} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{S atom of Cylinder}} = \left(\mathbf{x}\_{\text{Sc}}, \mathbf{y}\_{\text{Sc}}, \mathbf{z}\_{\text{Sc}}\right) \tag{25}$$

A Bioinformatics Method for the Production of Antibody-Drug Conjugates Through Site-Specific Cysteine Conjugation http://dx.doi.org/10.5772/62747 41

$$\left(p\_{cyl2} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{Ca atom of Quantum}} = \left(\mathbf{x}\_{\text{Cr}}, y\_{\text{Cr}}, \mathbf{z}\_{\text{Cr}}\right) \tag{26}$$

$$\begin{aligned} L\_{cys}: \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right) &= p\_{cys1} + t\_{cys} \, \ast \left(p\_{cys1} - p\_{cys2}\right) = \left(\mathbf{x}\_{\mathrm{Sc}}, p\_{\mathrm{Sc}}, \mathbf{z}\_{\mathrm{Sc}}\right) + \\ t\_{cys} \, \ast \left(\mathbf{x}\_{\mathrm{Sc}} - \mathbf{x}\_{\mathrm{Sc}}, y\_{\mathrm{Sc}} - y\_{\mathrm{Cr}}, \mathbf{z}\_{\mathrm{Sc}} - \mathbf{z}\_{\mathrm{Cr}}\right), & -\infty < t\_{cys} < +\infty \end{aligned} \tag{27}$$

$$(r\_{\times}, r\_{\times}, r\_{\pm} : \angle \left(L\_d, L\_l\right) = 0^{\circ} \tag{28}$$

$$\left(r\_x, r\_y, r\_z\right) : \angle \left(L\_{\text{td}}, L\_{\text{cyc}}\right) = 0^\circ \tag{29}$$

#### **2.6. Distances**

cysteine axis has been defined as the line going through the alpha carbon (Cα) and sulfur atoms (S) of the cysteine side chain [Eqs. (25–27); **Figure 2**, step S7b]. This line was chosen for its direction towards the exterior of the antibody, which is the desired direction for the linkerdrug complex. In the lysine conjugation of [34], the linker C15N was used, therefore the linker axis was defined by the nitrogen (N) atom with its most distant carbon (C) atom. Also, the lysine axis went through the alpha carbon (Cα) and nitrogen (N) atoms of the lysine side chain.

40 Bioinformatics - Updated Features and Applications

( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( )

=+ - = +

1 12 : , , \* , ,

*L xyz p t p p x y z t x xy yz z t*

=+ - = +

1 12 : ,, \* , ,

*L xyz p t p p x y z t x xy yz z t*

( )

*l NHl Sl NHl Sl NHl Sl l*

\* ,, ,

( )

*l NHld Sld NHld Sld NHld Sld ld*

\* ,, ,

=+ - = + - - - -¥ < < +¥

1 12 : , , \* , ,

*L xyz p t p p x y z tx xy yz z t*

*d d d d d Hd Hd Hd d Hd ENd Hd ENd Hd ENd d*

( )

\* ,, ,

1 ( ) ( ) = = , , , , *d H atom of drug Hd Hd Hd p xyz xyz* (16)

2 ( ) ( ) = = , , , , *d EN atom of drug ENd ENd ENd p xyz xyz* (17)

1 ( ) ( ) = = , , , , *l NH atom of linker NHl NHl NHl p xyz xyz* (19)

2 ( ) ( ) = = , , , , *l S atom of linker Sl Sl Sl p xyz xyz* (20)

1 ( ) ( ) , , , , - *ld* = = *NH atom of linker drug NHld NHld NHld p xyz xyz* (22)

2 ( ) ( ) , , , , - *ld* = = *S atom of linker drug Sld Sld Sld p xyz xyz* (23)

1 ( ) ( ) = = , , , , *cys S atom of Cysteine Sc Sc Sc p xyz xyz* (25)

(18)

(21)

(24)

As mentioned in Section 2.4, the translation of a molecule A (i.e., the drug or the linker-drug complex) in relation to its corresponding fixed molecule B (i.e., the linker or the antibody, respectively) is performed for *a* = 1, *rx* = *ry rz* = 0, and the values of the variables *Tx*, *Ty*, *Tz* that satisfy a certain condition. That condition is the minimization of the absolute value of the difference between the distance of the two molecules and a fixed value l (Eqs. (36, 37)). The definitions of the distance between two molecules and the fixed value are given below.

Taking into consideration the desired connectivity between the three molecules, in the context of this project, the distance between two molecules was defined as the distance between the atoms that will participate in the intended intermolecular bond (hydrogen or disulfide bond, **Figure 1**). As a consequence, the fixed value l was defined as the official length of that intermolecular bond (*lHb* = 1.5–2.5 Å for the Hydrogen bond, *lSSb* = 2.05 Å for the disulfide bond).

In more detail, in both lysine and cysteine conjugation, the distance between the drug and linker was defined by the drug hydrogen (H) atom and the linker electronegative (EN) atom participating in the H bond between the linker and drug [Eqs. (30–32); **Figure 2**, step S4c]. In regard to the distance between the linker-drug complex and the conjugation residue, in lysine conjugation, it was defined by the linker nitrogen (N) atom and the lysine hydrogen (H) atom bonded to the side chain nitrogen atom. In cysteine conjugation, however, it has been defined as the distance between the sulfur (S) linker atom and the sulfur atom of the cysteine side chain [Eqs. (33–35); **Figure 2**, step S7c]. The translation of the drug in relation to the linker is performed for the values of the variables *Tx*, *Ty*, *Tz* for which the distance between the two molecules takes the official length *lHb* = 1.5–2.5 Å of the hydrogen bond (Eq. (36)). The transla‐ tion of the linker-drug complex in relation to the cysteine is performed for the values of the variables *Tx*, *Ty*, *Tz* for which the distance between the two molecules takes the official length *lSSb* = 2.05 Å of the disulfide bond (Eq. (37)):

$$\mathbf{p}\_d = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{H \text{ atom of drug}} = \left(\mathbf{x}\_{\text{Hd}}, \mathbf{y}\_{\text{Hd}}, \mathbf{z}\_{\text{Hd}}\right) = \mathbf{p}\_{d1} \tag{30}$$

$$p\_{l1} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{EN \, atom } q' \text{ index}} = \left(\mathbf{x}\_{\text{EN}}, \mathbf{y}\_{\text{EN}}, \mathbf{z}\_{\text{EN}}\right) = \left(\mathbf{x}\_{\text{Cl}}, \mathbf{y}\_{\text{Cl}}, \mathbf{z}\_{\text{Cl}}\right) \tag{31}$$

$$D\_{d-l} = \ \ p\_d - p\_{l1} \tag{32}$$

$$\mathbf{p}\_{12} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{S: aou of } l \text{index}} = \left(\mathbf{x}\_{\text{Sl}}, \mathbf{y}\_{\text{Sl}}, \mathbf{z}\_{\text{Sl}}\right) \tag{33}$$

$$\mathbf{p}\_{\text{sys}} = \left(\mathbf{x}, \mathbf{y}, \mathbf{z}\right)\_{\text{S axiom of Cyzasia}} = \left(\mathbf{x}\_{\text{Sys}}, \mathbf{y}\_{\text{Sys}}, \mathbf{z}\_{\text{Sys}}\right) \tag{34}$$

$$D\_{cys-l} = \ p\_{cys} - p\_{t2} \tag{35}$$

$$\left| \left| T\_{\mathbf{x}}, T\_{\mathbf{y}}, T\_{\mathbf{z}} : \middle| D\_{d-l} - l\_{Hb} \right| = 0 \tag{36}$$

$$\left| \left| T\_{\ge}, T\_y, T\_z : \right| D\_{\text{cys}-I} - I\_{\text{SSb}} \right| = 0 \tag{37}$$

where the variables *Tx*, *Ty*, *Tz* of Eq. (36) refer to the translation of the drug (**Figure 2**, step S5d), while the variables of *Tx*′, *Ty*′, *Tz*′ Eq. (37) refer to the translation of the linker-drug complex (**Figure 2**, step S8d).

#### **2.7. Implementation**

The steps S1 and S2 (see the **Figure 2**) that involve the selection and preparation of the input files of the engineered antibody, the linker, the drug, as well as the selection of the conjugation cysteine residue, are carried out manually. The steps S3–S9 have been developed as a C++ program, and therefore can be executed automatically. In order to make the necessary data management, a number of classes and functions were created in regard to the PDB records used, the three-dimensional points and lines, as well as the affine transformation.

### **3. Results**

The method described in the previous section has been applied to various antibody and drug combinations, taking about 10–13 seconds for each TDC to be generated. In **Figure 3**, the basic stages of the production of a THIOMAB-drug conjugate are depicted. First, the drug and linker are formed in two independent PDB files. The axis of each molecule is depicted as a white line, and the initial angle between them is 98.2° (**Figure 3a**). The drug is rotated, and the angle between the two axes becomes 0.08° (**Figure 3b**). The initial distance between the atoms that will form the hydrogen bond (the C atom of the linker and the H atom of the drug, bonded covalently to an O drug atom) is 28.02 Å. The drug is translated and the distance between the two atoms becomes 1.51 Å (**Figure 3c**). The linker has remained fixed. The two molecules are merged into a linker-drug complex, whose new axis is depicted as a white line (**Figure 3d**). The residue valine of the antibody with PDB id 4GAG, with sequence number 206 on the light chain (LC-VAL206), is replaced by a cysteine, whose axis is calculated (**Figure 3e**). The initial angle between the axis of the linker-drug complex and the axis of the cysteine is 105.6°, and the distance between the sulfur atoms of the cysteine and the linker is 54.75 Å. The linker-drug complex is rotated and translated, so that the angle becomes 0.083° and the distance becomes 2.07 Å. Finally, the linker-drug complex is connected with the cysteine (**Figure 3f**).

1 ( ) ( ) ( ) = == , , , , , , *l EN atom of linker ENl ENl ENl Cl Cl Cl p xyz x y z xyz* (31)

*D pp dl d l* - = - 1 (32)

*D pp cys l cys l* - = - 2 (35)

,,: 0 *TTT D l x y z d l Hb* - - = (36)

,,: 0 *TTT D l x y z cys l SSb* - - = (37)

where the variables *Tx*, *Ty*, *Tz* of Eq. (36) refer to the translation of the drug (**Figure 2**, step S5d), while the variables of *Tx*′, *Ty*′, *Tz*′ Eq. (37) refer to the translation of the linker-drug complex

The steps S1 and S2 (see the **Figure 2**) that involve the selection and preparation of the input files of the engineered antibody, the linker, the drug, as well as the selection of the conjugation cysteine residue, are carried out manually. The steps S3–S9 have been developed as a C++ program, and therefore can be executed automatically. In order to make the necessary data management, a number of classes and functions were created in regard to the PDB records

The method described in the previous section has been applied to various antibody and drug combinations, taking about 10–13 seconds for each TDC to be generated. In **Figure 3**, the basic stages of the production of a THIOMAB-drug conjugate are depicted. First, the drug and linker are formed in two independent PDB files. The axis of each molecule is depicted as a white line, and the initial angle between them is 98.2° (**Figure 3a**). The drug is rotated, and the angle between the two axes becomes 0.08° (**Figure 3b**). The initial distance between the atoms that will form the hydrogen bond (the C atom of the linker and the H atom of the drug, bonded

used, the three-dimensional points and lines, as well as the affine transformation.

(**Figure 2**, step S8d).

42 Bioinformatics - Updated Features and Applications

**2.7. Implementation**

**3. Results**

2 ( ) ( ) = = , , , , *l S atom of linker Sl Sl Sl p xyz xyz* (33)

( ) ( ) = = , , , , *cys S atom of Cysteine Scys Scys Scys p xyz xyz* (34)

**Figure 3.** The basic stages of the computational production of a TDC. (a) Left: The linker molecule C12S. Right: The drug with NCI sequence number 9, before its reconfiguration. Angle between the two axes: 98.2°. (b) The linker and the rotated drug. Angle between the two axes: 0.08°. Distance between the H atom of the drug and the C atom of the linker: 28.02 Å. (c) The linker and the translated drug. Distance between the H atom of the drug and the C atom of the linker: 1.51 Å. (d) The linker-drug complex. (e) The engineered cysteine that has replaced the residue LC-VAL206 on the antibody with PDB id 4GAG. Initial angle between the axes of the cysteine and the linker-drug complex: 105.6°. Initial distance between the two S atoms: 54.75 Å. (f) The linker-drug complex conjugated to the engineered cysteine. Angle between the axes of the cysteine and the linker-drug complex: 0.084°. Distance between the two S atoms: 2.07 Å.

More examples of produced TDCs are demonstrated in **Figure 4**. The TDC composed by the antibody with PDB id 4GAJ, the linker molecule C12S and the drug with sequence number 5 (nci\_5.pdb) is depicted in **Figure 4(a)**. A hydrogen bond connects the drug with the linker, and a disulfide bond connects the engineered cysteine on the antibody with the linker-drug complex. The cysteine has replaced the solvent-accessible amino acid alanine with residue sequence number 114, at the heavy chain of the antibody (HC-ALA114). The same TDC is depicted from a closer distance at the right of the figure. In **Figure 4b**, a similar TDC composed by the antibody with PDB id 4GAJ, the linker molecule C12S and the drug with sequence number 7 (nci\_7.pdb) is depicted. The above technique can be used for the conjugation of TDCs with more than one drug molecules per antibody. **Figure 4c** depicts a TDC with a DAR of 2, composed by the antibody with PDB id 4GAG, to which two drug molecules with sequence number 1 (nci\_1.pdb) have been conjugated. The engineered cysteines that serve as conjuga‐ tion sites have replaced the solvent-accessible residues alanine with sequence number 114 at the heavy chain and valine with sequence number 206 at the light chain (LC-VAL206). The conjugation areas of the same TDC are demonstrated from a closer distance at the right of the figure. The linker and the drugs used in the examples are depicted in **Figure 5**.

**Figure 3.** The basic stages of the computational production of a TDC. (a) Left: The linker molecule C12S. Right: The drug with NCI sequence number 9, before its reconfiguration. Angle between the two axes: 98.2°. (b) The linker and the rotated drug. Angle between the two axes: 0.08°. Distance between the H atom of the drug and the C atom of the linker: 28.02 Å. (c) The linker and the translated drug. Distance between the H atom of the drug and the C atom of the linker: 1.51 Å. (d) The linker-drug complex. (e) The engineered cysteine that has replaced the residue LC-VAL206 on the antibody with PDB id 4GAG. Initial angle between the axes of the cysteine and the linker-drug complex: 105.6°. Initial distance between the two S atoms: 54.75 Å. (f) The linker-drug complex conjugated to the engineered cysteine. Angle between the axes of the cysteine and the linker-drug complex: 0.084°. Distance between the two S atoms: 2.07 Å.

44 Bioinformatics - Updated Features and Applications

More examples of produced TDCs are demonstrated in **Figure 4**. The TDC composed by the antibody with PDB id 4GAJ, the linker molecule C12S and the drug with sequence number 5 (nci\_5.pdb) is depicted in **Figure 4(a)**. A hydrogen bond connects the drug with the linker, and a disulfide bond connects the engineered cysteine on the antibody with the linker-drug complex. The cysteine has replaced the solvent-accessible amino acid alanine with residue sequence number 114, at the heavy chain of the antibody (HC-ALA114). The same TDC is depicted from a closer distance at the right of the figure. In **Figure 4b**, a similar TDC composed

**Figure 4.** (a) Left: TDC composed by the antibody with PDB id 4GAJ, the linker molecule C12S, and the drug with se‐ quence number 5. The drug has been conjugated through the linker to the engineered cysteine that has replaced the residue HC-ALA114. Right: The same TDC from a closer distance. (b) Left: TDC composed by the antibody with PDB id 4GAJ, the linker molecule C12S, and the drug with sequence number 7. The drug has been conjugated through the linker to the engineered cysteine that has replaced the residue HC-ALA114. Right: The same TDC from a closer dis‐ tance. (c) Left: TDC composed by the antibody with PDB id 4GAG, two linker molecules C12S, and two drug molecules with sequence number 1. The antibody conjugation sites are engineered cysteines that have replaced the residues HC-ALA114, and LC-VAL206. Right: The same TDC from a closer distance.

**Figure 5.** (a) The linker molecule C12S. (b) The cytotoxic drugs used in **Figures 3** and **4**. Top left: the drug with sequence number 1 (nci\_1.pdb). Top right: the drug with sequence number 5 (nci\_5.pdb). Bottom left: the drug with sequence number 7 (nci\_7.pdb). Bottom right: the drug with sequence number 9 (nci\_9.pdb).

### **4. Discussion**

This chapter has presented a computational method for site-specific formulation of ADCs, by conjugating the cytotoxin and the linker to engineered cysteines on the surface of the antibody. A hydrogen bond is employed to connect the linker with the drug, forming a linker-drug conjugate, which is then attached through a disulfide bond to the selected engineered cysteine. The change of the atomic coordinates of a molecule was implemented with an affine transfor‐ mation. The generation of the linker molecule C12S, as well as the cysteine replacement, was achieved through molecular modeling software [36, 42].

The present chapter proposes a general way to implement computationally ADC molecules using data from established databases. The common antibodies used do not target cancerspecific molecules, as opposed to antibodies that are parts of real ADCs. The employment of antibodies with cancer-specific antigens was not feasible, since their three-dimensional structures are not available. However, considering that the conjugation occurs in the preserved regions of the antibody, the same techniques could be applied using the PDB file of any full antibody. In addition, conventional anticancer drugs were used, since the three-dimensional structures of the more potent drugs used in ADCs currently are not available. It is important to note that the results of the presented method are not proposed as real anticancer drugs. The presented method is an attempt to simulate ADCs using programming techniques and available data.

Site-specific conjugation is a substantial goal in ADC development, since it gives the ability to control the pharmacokinetic and therapeutic properties of the produced ADCs. Aside from the replacement of residues with cysteines, mentioned in Section 1, a similar concept is the substitution of cysteines with serines in order to reduce the number of inter-chain cysteines available for conjugation. Another technique is the introduction of non-native amino acids in the antibody sequence, such as selenocysteine, acetylphenylalanine, para-acetylphenylala‐ nine, and para-azidophenylalanine, utilizing their ability to provide orthogonal conjugation chemistries that are not available from original residues. Enzymes have been used to help form bonds between the antibody, the linker and drug, a method called enzymatic conjugation. An additional strategy is the conjugation at the glycosylation sites of the antibody, leaving the amino acid sequence of the antibody and the cell culture condition intact. For example, sialic acid moieties can be introduced enzymatically to the antibody, which can then be oxidized to introduce aldehyde groups. The resulting antibodies can then form stable oxime bonds with drugs containing aldehyde-reactive aminooxy groups [22, 46–59].

A number of actions could be taken to improve and expand the work described in this chapter. Some of our future goals is the implementation of more site-specific conjugation ways, the use of antibody forms alternative to the full monoclonal antibody, such as scFvs, diabodies, and minibodies, as well as other non-cleavable and cleavable linker molecules. In addition, the automatic recognition of all amino acids available for conjugation given a specific antibody would be interesting, as well as the estimation of the best drugs and linkers given a conjugation method. Finally, the conditions and equations used could be modified in order to take into consideration other factors.

### **Acknowledgements**

**Figure 5.** (a) The linker molecule C12S. (b) The cytotoxic drugs used in **Figures 3** and **4**. Top left: the drug with sequence number 1 (nci\_1.pdb). Top right: the drug with sequence number 5 (nci\_5.pdb). Bottom left: the drug with sequence

This chapter has presented a computational method for site-specific formulation of ADCs, by conjugating the cytotoxin and the linker to engineered cysteines on the surface of the antibody. A hydrogen bond is employed to connect the linker with the drug, forming a linker-drug conjugate, which is then attached through a disulfide bond to the selected engineered cysteine. The change of the atomic coordinates of a molecule was implemented with an affine transfor‐ mation. The generation of the linker molecule C12S, as well as the cysteine replacement, was

The present chapter proposes a general way to implement computationally ADC molecules using data from established databases. The common antibodies used do not target cancerspecific molecules, as opposed to antibodies that are parts of real ADCs. The employment of antibodies with cancer-specific antigens was not feasible, since their three-dimensional structures are not available. However, considering that the conjugation occurs in the preserved regions of the antibody, the same techniques could be applied using the PDB file of any full antibody. In addition, conventional anticancer drugs were used, since the three-dimensional structures of the more potent drugs used in ADCs currently are not available. It is important to note that the results of the presented method are not proposed as real anticancer drugs. The presented method is an attempt to simulate ADCs using programming techniques and

number 7 (nci\_7.pdb). Bottom right: the drug with sequence number 9 (nci\_9.pdb).

achieved through molecular modeling software [36, 42].

**4. Discussion**

46 Bioinformatics - Updated Features and Applications

available data.

Funding was received through an IKY Fellowship of Excellence for postgraduate studies in Greece—Siemens Program. The authors confirm that the funder had no influence over the study design, content of the chapter, or selection of this book.

### **Author details**

Arianna Filntisi1,2\*, Dimitrios Vlachakis2 and George K. Matsopoulos1

\*Address all correspondence to<EMAIL_ADDRESS>
1 School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

2 Computational Biology and Medicine Group, Biomedical Research Foundation of the Academy of Athens, Athens, Greece

### **References**

[15] Carter PJ. Potent antibody therapeutics by design. Nat Rev Immunol. 2006;6(5):343– 357. DOI: 10.1038/nri1837

**References**

2014–03]

[Accessed: 2014–02]

48 Bioinformatics - Updated Features and Applications

s11523-012-0212-2

2013.03.004

mabs.1.3.8515

j.cbpa.2013.03.022

[1] National Cancer Institute at the National Institutes of Health. 2014. Available from:

[2] American Cancer Society. 2014. Available from: http://www.cancer.org/ [Accessed:

[3] Cancer Research Institute. 2014. Available from: http://www.cancerresearch.org/

[4] Li J, Chen F, Cona MM, Feng Y, Himmelreich U, Oyen R, et al. A review on various targeted anticancer therapies. Target Oncology. 2012;7(1):69–85. DOI: 10.1007/

[5] Adler MJ, Dimitrov DS. Therapeutic antibodies against cancer. Hematol Oncol Clin

[6] Sapra P, Shor B. Monoclonal antibody-based therapies in cancer: advances and challenges. Pharmacol Ther. 2013;138(3):452–469. DOI: 10.1016/j.pharmthera.

[7] Goldmacher VS, Chittenden T, Chari RVJ, Kovtun YV, Lambert JM. Antibody-drug conjugates for targeted cancer therapy. In: Desai MC, editor. Annual Reports in

[8] Casi G, Neri D. Antibody-drug conjugates: basic concepts, examples and future perspectives. J Control Release. 2012;161(2):422–428. DOI: 10.1016/j.jconrel.2012.01.026

[9] Kovtun Y V, Goldmacher VS. Cell killing by antibody-drug conjugates. Cancer Lett.

[10] Gerber H-P, Senter PD, Grewal IS. Antibody drug-conjugates targeting the tumor vasculature: Current and future developments. MAbs. 2009;1(3):247–253. DOI: 10.4161/

[11] Wu AM, Senter PD. Arming antibodies: prospects and challenges for immunoconju‐

[12] Perez HL, Cardarelli PM, Deshpande S, Gangwar S, Schroeder GM, Vite GD, et al. Antibody-drug conjugates: current status and future directions. Drug Discov Today.

[13] Alley SC, Anderson KE. Analytical and bioanalytical technologies for characterizing antibody-drug conjugates. Curr Opin Chem Biol. 2013;17(3):406–411. DOI: 10.1016/

[14] Iyer U, Kadambi VJ. Antibody drug conjugates-Trojan horses in the war on cancer. J Pharmacol Toxicol Methods. 2011;64(3):207–212. DOI: 10.1016/j.vascn.2011.07.005.

Medicinal Chemistry, Volume 47. Academic Press; 2012. p. 349–366.

2007;255(2):232–240. DOI: http://dx.doi.org/10.1016/j.canlet.2007.04.010

gates. Biotechnol. 2005;23(9):1137–1146. DOI: 10.1038/nbt1141

2014;19(7):869–881. DOI: 10.1016/j.drudis.2013.11.004

North Am.2012;26(3):447–481. DOI: 10.1016/j.hoc.2012.02.013

http://www.cancer.gov/ [Accessed: 2014–03]

[41] Sukumaran S, Gadkar K, Zhang C, Bhakta S, Liu L, Xu K, et al. Mechanism-based pharmacokinetic/pharmacodynamic model for THIOMABTM drug conjugates. Pharm Res. 2015;32(6):1884–1893. DOI: 10.1007/s11095-014-1582-1

[28] Boylan NJ, Zhou W, Proos RJ, Tolbert TJ, Wolfe JL, Laurence JS. Conjugation site heterogeneity causes variable electrostatic properties in Fc conjugates. Bioconjug

[29] Worldwide PDB (wwPDB) organization. 2003 [Updated: 2016]. Available from: http://

[30] RCSB Protein Data Bank. 2000 [Updated: 2016]. Available from: http://www.rcsb.org

[31] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucl. Acids Res. 2000;28(1):235–242. DOI: 10.1093/

[32] Chemical Computing Group Inc. Molecular Operating Environment (MOE). 2013.

[33] Filntisi A, Papangelopoulos N, Bencurova E, Kasampalidis I, Matsopoulos G, Vlachakis D, et al. State-of-the-Art Neural Networks Applications in Biology. Int J Syst Biol

[34] Filntisi A, Vlachakis D, Matsopoulos G, Kossida S. 3D structural bioinformatics of proteins and antibodies: state of the art perspectives and challenges. Int J Syst Biol

[35] Vlachakis D, Tsagrasoulis D, Megalooikonomou V, Kossida S. Introducing Drugster: a comprehensive and fully integrated drug design, lead and structure optimization toolkit. Bioinformatics. 2013;29(1):126–128. DOI: DOI: 10.1093/bioinformatics/bts637

[36] Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera-a visualization system for exploratory research and analysis. J Comput Chem.

[37] Toshimoto K, Wakayama N, Kusama M, Maeda K, Sugiyama Y, Akiyama Y. In silico prediction of major drug clearance pathways by support vector machines with feature selected descriptors. Drug Metab Dispos. 2014;42(11):1811–1819. DOI: 10.1124/dmd.

[38] Mitra A, Kesisoglou F, Dogterom P. Application of absorption modeling to predict bioequivalence outcome of two batches of etoricoxib tablets. AAPS PharmSciTech.

[39] Vlachakis D, Kossida S. Antibody drug conjugate bioinformatics: drug delivery through the letterbox. Comput Math Methods Med. 2013;2013(2013):282398.

[40] Scilligence. JSDraw - Antibody-Drug Conjugate [Internet]. 2014 [Updated: 2015]. Available from: http://www.elncloud.com/jsdrawapp/jsdraw/ADC.htm [Accessed:

Biomed Technol. 2013;2(4):63–85. DOI: 10.4018/ijsbbt.2013100105

Biomed Technol. 2013;2(3):67–74. DOI: 10.4018/ijsbbt.2013070105

2004;25(13):1605–1612. DOI: 10.1002/jcc.20084

2015;16(1):76–84. DOI: 10.1208/s12249-014-0194-8

DOI:http://dx.doi.org/10.1155/2013/282398

Chem. 2013;24(6):1008–1016. DOI: 10.1021/bc4000564

www.wwpdb.org/ [Accessed: 2015]

[Accessed: 2016]

50 Bioinformatics - Updated Features and Applications

[Accessed: 2014]

nar/28.1.235

114.057893

2016]

### **Databases and Algorithms in Allergen Informatics**

Kiran Kadam, Sangeeta Sawant, V.K. Jayaraman and Urmila Kulkarni-Kale

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/63083

#### **Abstract**

[53] Kiick KL, Saxon E, Tirrell DA, Bertozzi CR. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc Natl Acad

[54] Hofer T, Skeffington LR, Chapman CM, Rader C. Molecularly defined antibody conjugation through a selenocysteine interface. Biochemistry. 2009;48(50):12047–12057.

[55] Hofer T, Thomas JD, Burke TR, Rader C. An engineered selenocysteine defines a unique class of antibody derivatives. Proc Natl Acad Sci U S A. 2008;105(34):12451–12456. DOI:

[56] Li X, Yang J, Rader C. Antibody conjugation via one and two C-terminal selenocys‐

[57] Zimmerman ES, Heibeck TH, Gill A, Li X, Murray CJ, Madlansacay MR, et al. Produc‐ tion of site-specific antibody-drug conjugates using optimized non-natural amino acids in a cell-free expression system. Bioconjug Chem. 2014;25(2):351–361. DOI: 10.1021/

[58] Axup JY, Bajjuri KM, Ritland M, Hutchins BM, Kim CH, Kazane SA, et al. Synthesis of site-specific antibody-drug conjugates using unnatural amino acids. Proc Natl Acad

[59] Sochaj AM, Świderska KW, Otlewski J. Current methods for the synthesis of homoge‐ neous antibody-drug conjugates. Biotechnol Adv. 2015;33(6):775–784. DOI: 10.1016/

teines. Methods. 2014;65(1):133–138. DOI: 10.1016/j.ymeth.2013.05.023

Sci U S A. 2012;109(40):16101–16106. DOI: 10.1073/pnas.1211023109

Sci U S A. 2002;99(1):19–24. DOI: 10.1073/pnas.012583299

DOI: 10.1021/bi901744t

52 Bioinformatics - Updated Features and Applications

10.1073/pnas.0800800105

j.biotechadv.2015.05.001

bc400490z

Allergic diseases are considered as one of the major health problems worldwide due to their increasing prevalence. Advancements in genomic, proteomic, and analytical techniques have resulted in considerable progress in the field of allergology, which has led to accumulation of huge amount of data. Allergen bioinformatics comprises allergenrelated data resources and computational methods/tools, which deal with an efficient archival, management, and analysis of allergological data. Significant work has been done in the area of allergen bioinformatics that has proven pivotal for the development and progress of this field. In this chapter, we describe the current status of databases and algorithms, encompassing the field of allergen bioinformatics by examining work carried out thus far with respect to features such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment. This chapter illustrates concepts and algorithms in allergen bioinformatics, as well as it outlines the key areas for potential development in allergology field.

**Keywords:** allergen databases, allergen epitope prediction, allergenicity, allergenic proteins, bioinformatics

### **1. Introduction**

The immune system represents a very complex system comprising numerous biological molecules and processes, which combine to form body's defense against infectious agents and other threats. Immunity is basically divided into two types such as innate immunity and adaptive immunity [1]. Innate immunity also referred to as natural, native, or nonspecific immunity acts as a first line of defense against common harmful agents. Innate immune response provides

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

immediate protection and involves number of components such as monocytes, macrophages, neutrophils, cytokines, complement, and epithelial barriers. Adaptive or acquired immunity comprises highly specific immune responses that are elicited against particular pathogens or antigens. These immune responses are either cell mediated or antibody mediated (humoral) and executed by specialized lymphocytes or immunoglobulins, respectively. On certain occasions, the immune system produces immune responses that are harmful for the host organism. Autoimmunity denotes one such case wherein the body elicits immune responses against its own cells and tissues (self-antigens) which lead to development of autoimmune diseases. In some cases, immune system produces inappropriate immune responses known as hypersensitivity, which has deleterious effects on the host organism. Hypersensitivity reac‐ tions are categorized into four groups based on the type of immune response and the effector mechanism involved. These are (i) immediate hypersensitivity (type I), (ii) antibody-mediat‐ ed hypersensitivity (type II), (iii) immune complex–mediated hypersensitivity (type III), and (iv) cell-mediated hypersensitivity (type IV) [2].

Allergic reactions are type I hypersensitivity reactions, which are characterized by induction of specific class of antibodies known as immunoglobulin E (IgE). These reactions are elicited against specific type of antigens commonly referred to as allergens. An allergic reaction involves specialized cells and specific molecules of the immune system [3]. IgE antibodies induced by allergens upon allergic sensitization bind to effector cells such as basophils and mast cells via specific Fc receptors present on the surfaces of those cells. Subsequent exposure to the allergen causes cross-linking of membrane-bound IgE on these effector cells, which leads to their degranulation and release of pharmacologically active agents such as histamine. These pharmacological mediators are responsible for clinical manifestations of allergic reactions in the affected individuals. Immunogenicity in general refers to the potential of an antigen to elicit an immune response, while in case of allergens, allergenicity is considered as a reflection of its allergenic potential. Allergenicity indicates the capability of an allergen to induce clinical symptoms of allergy as well as to induce and bind to IgE antibodies [4]. The prevalence of allergic reactions has increased significantly in the last few years, especially in the developing countries [5]. This has resulted in considerable increase in disease burden as well as economic issues due to costs associated with these diseases. Therefore, the study of allergic diseases has gained tremendous importance as they represent one of the major health problems in urban and rural regions.

The field of allergy research has rapidly progressed in the last few years [6]. Recent advances in genomic, proteomic, and analytical methods have given rise to large amounts of data relevant to allergens. This data can be correlated with pathology of various allergic diseases based on experimental, clinical, and epidemiological data for allergic reactions. The continuous growth of data calls for the efficient archival, management, and analysis of data. This has led to the development of the field of allergen informatics, which comprises allergen specific databases/resources and computational methods/tools. Allergen informatics constitutes an important branch of immunoinformatics [7]. In this chapter, a review of the existing status of allergen informatics with respect to important aspects such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment has been presented (**Figure 1**).

**Figure 1.** Topics covered for allergen informatics in the current review.

### **2. Allergens and allergenicity**

immediate protection and involves number of components such as monocytes, macrophages, neutrophils, cytokines, complement, and epithelial barriers. Adaptive or acquired immunity comprises highly specific immune responses that are elicited against particular pathogens or antigens. These immune responses are either cell mediated or antibody mediated (humoral) and executed by specialized lymphocytes or immunoglobulins, respectively. On certain occasions, the immune system produces immune responses that are harmful for the host organism. Autoimmunity denotes one such case wherein the body elicits immune responses against its own cells and tissues (self-antigens) which lead to development of autoimmune diseases. In some cases, immune system produces inappropriate immune responses known as hypersensitivity, which has deleterious effects on the host organism. Hypersensitivity reac‐ tions are categorized into four groups based on the type of immune response and the effector mechanism involved. These are (i) immediate hypersensitivity (type I), (ii) antibody-mediat‐ ed hypersensitivity (type II), (iii) immune complex–mediated hypersensitivity (type III), and

Allergic reactions are type I hypersensitivity reactions, which are characterized by induction of specific class of antibodies known as immunoglobulin E (IgE). These reactions are elicited against specific type of antigens commonly referred to as allergens. An allergic reaction involves specialized cells and specific molecules of the immune system [3]. IgE antibodies induced by allergens upon allergic sensitization bind to effector cells such as basophils and mast cells via specific Fc receptors present on the surfaces of those cells. Subsequent exposure to the allergen causes cross-linking of membrane-bound IgE on these effector cells, which leads to their degranulation and release of pharmacologically active agents such as histamine. These pharmacological mediators are responsible for clinical manifestations of allergic reactions in the affected individuals. Immunogenicity in general refers to the potential of an antigen to elicit an immune response, while in case of allergens, allergenicity is considered as a reflection of its allergenic potential. Allergenicity indicates the capability of an allergen to induce clinical symptoms of allergy as well as to induce and bind to IgE antibodies [4]. The prevalence of allergic reactions has increased significantly in the last few years, especially in the developing countries [5]. This has resulted in considerable increase in disease burden as well as economic issues due to costs associated with these diseases. Therefore, the study of allergic diseases has gained tremendous importance as they represent one of the major health problems in urban

The field of allergy research has rapidly progressed in the last few years [6]. Recent advances in genomic, proteomic, and analytical methods have given rise to large amounts of data relevant to allergens. This data can be correlated with pathology of various allergic diseases based on experimental, clinical, and epidemiological data for allergic reactions. The continuous growth of data calls for the efficient archival, management, and analysis of data. This has led to the development of the field of allergen informatics, which comprises allergen specific databases/resources and computational methods/tools. Allergen informatics constitutes an important branch of immunoinformatics [7]. In this chapter, a review of the existing status of allergen informatics with respect to important aspects such as allergens and allergenicity,

(iv) cell-mediated hypersensitivity (type IV) [2].

54 Bioinformatics - Updated Features and Applications

and rural regions.

Allergens represent the most critical component of an allergic reaction, although IgE antibody, Fc receptors, mast cells, and basophils as well as pharmacological mediators such as histamine and heparin also play very significant roles. Allergens are ubiquitous substances, which arise from a variety of sources such as foods, plants, animals, or environment. An allergen can either be a chemical substance (e.g., penicillin) or a protein (e.g., albumin, profilin, etc.). Majority of the allergens are proteins or glycoproteins that possess high water solubility. Several bio‐ chemical and structural features of allergens such as stability, hydrophobicity, and ligandbinding domains are known to contribute to their allergenicity [8]. However, common molecular and structural features of allergens that are responsible for allergenicity have not yet been conclusively discovered.

Allergens are provided with a unique, unambiguous, and systematic nomenclature which has been developed and maintained by the World Health Organization (WHO) and International Union of Immunological Societies' (IUIS) "Allergen Nomenclature Sub-committee" [9, 10]. The nomenclature is based on the Linnean system, and an allergen, which satisfies certain bio‐ chemical and immunological criteria, is included in the WHO/IUIS nomenclature. An allergen name consists of an abbreviation of the scientific name of the allergen source organism. First 3–4 letters denote the genus name, while the subsequent 1–2 letters represent species, followed by an Arabic numeral that denotes the order of its identification. For instance, Der p 1 represents the first allergen to be characterized from the house dust mite *Dermatophagoides pteronyssinus*. An allergen may possess isoallergens or isoforms/variants, which are considered as multiple molecular forms of the same allergen. The WHO/IUIS nomenclature defines isoallergen as an allergen belonging to a single species, with a similar molecular size and identical biological function, and possessing ≥67% amino acid sequence identity while a variant or isoform corresponds to allergen sequences that differ by only a limited number of amino acid substitutions [11]. It is very important to archive and study the data on isoallergens and isoforms/variants in a differentiated manner as it has been shown that variations in allergens significantly affect their allergenicity and cross-reactivity as well as influence recognition of epitopes by T cells and IgE [12]. An allergen can be considered as a major or minor allergen based on the measure of its allergenicity. Major allergens are the ones to which >50% of patients with an allergy to its source are sensitized, while minor allergens are recognized by a limited number of patients [13].

Allergens display important features such as epitopes and cross-reactivity that are very critical with respect to understanding of allergic reactions and developing newer approaches for diagnosis and treatment of allergic diseases. Epitope or antigenic determinant refers to the immunologically active region of the allergen. An epitope can be an IgE-binding epitope or a T-cell epitope depending on whether it interacts with an IgE or a T-lymphocyte. An IgE epitope can be either sequential (linear) that consists of contiguous stretch of amino acids or confor‐ mational (discontinuous), which comprises amino acids present at different loci in an antigen. An antibody is said to be cross-reactive when it recognizes and binds to multiple antigens.

### **2.1. IgE-binding epitopes**

IgE-binding epitopes refer to the IgE recognition sites in allergens that are involved in specific interaction of allergens and IgE antibody. Inferences drawn from allergen–antibody complexes and other important studies have shown that majority of IgE-binding epitopes are conforma‐ tional in nature [14]. IgE epitopes possess some defining structural and immunological features such as they are more cross-reactive in nature and have higher intrinsic flexibility. These features make them distinct from other antibody epitopes and contribute significantly in the allergenicity [15, 16]. Identification and in-depth analysis of IgE-binding epitopes has the potential to contribute immensely in accurate diagnosis and allergen-specific immunotherapy of allergies, especially the food allergy [17, 18]. Large amount of data on allergen epitopes are generated by employing strategies based on the use of overlapping synthetic peptides, recombinant allergenic fragments, cocrystal structure complexes, etc. However, it is believed that insights obtained from study of allergen–antibody complexes will be the most helpful in understanding the role these epitopes play in allergic reactions [19–21].

### **2.2. T-cell epitopes**

molecular and structural features of allergens that are responsible for allergenicity have not

Allergens are provided with a unique, unambiguous, and systematic nomenclature which has been developed and maintained by the World Health Organization (WHO) and International Union of Immunological Societies' (IUIS) "Allergen Nomenclature Sub-committee" [9, 10]. The nomenclature is based on the Linnean system, and an allergen, which satisfies certain bio‐ chemical and immunological criteria, is included in the WHO/IUIS nomenclature. An allergen name consists of an abbreviation of the scientific name of the allergen source organism. First 3–4 letters denote the genus name, while the subsequent 1–2 letters represent species, followed by an Arabic numeral that denotes the order of its identification. For instance, Der p 1 represents the first allergen to be characterized from the house dust mite *Dermatophagoides pteronyssinus*. An allergen may possess isoallergens or isoforms/variants, which are considered as multiple molecular forms of the same allergen. The WHO/IUIS nomenclature defines isoallergen as an allergen belonging to a single species, with a similar molecular size and identical biological function, and possessing ≥67% amino acid sequence identity while a variant or isoform corresponds to allergen sequences that differ by only a limited number of amino acid substitutions [11]. It is very important to archive and study the data on isoallergens and isoforms/variants in a differentiated manner as it has been shown that variations in allergens significantly affect their allergenicity and cross-reactivity as well as influence recognition of epitopes by T cells and IgE [12]. An allergen can be considered as a major or minor allergen based on the measure of its allergenicity. Major allergens are the ones to which >50% of patients with an allergy to its source are sensitized, while minor allergens are

Allergens display important features such as epitopes and cross-reactivity that are very critical with respect to understanding of allergic reactions and developing newer approaches for diagnosis and treatment of allergic diseases. Epitope or antigenic determinant refers to the immunologically active region of the allergen. An epitope can be an IgE-binding epitope or a T-cell epitope depending on whether it interacts with an IgE or a T-lymphocyte. An IgE epitope can be either sequential (linear) that consists of contiguous stretch of amino acids or confor‐ mational (discontinuous), which comprises amino acids present at different loci in an antigen. An antibody is said to be cross-reactive when it recognizes and binds to multiple antigens.

IgE-binding epitopes refer to the IgE recognition sites in allergens that are involved in specific interaction of allergens and IgE antibody. Inferences drawn from allergen–antibody complexes and other important studies have shown that majority of IgE-binding epitopes are conforma‐ tional in nature [14]. IgE epitopes possess some defining structural and immunological features such as they are more cross-reactive in nature and have higher intrinsic flexibility. These features make them distinct from other antibody epitopes and contribute significantly in the allergenicity [15, 16]. Identification and in-depth analysis of IgE-binding epitopes has the potential to contribute immensely in accurate diagnosis and allergen-specific immunotherapy of allergies, especially the food allergy [17, 18]. Large amount of data on allergen epitopes are

yet been conclusively discovered.

56 Bioinformatics - Updated Features and Applications

recognized by a limited number of patients [13].

**2.1. IgE-binding epitopes**

T-cell epitopes are the antigenic determinants of allergens that interact with T-lymphocytes via specific T-cell receptors. T-cell epitopes of allergens have shown to be very important for the modulation of allergic response and thereby contributing to symptoms associated with allergic diseases [22]. They have enormous potential in the development of allergy vaccines as well as newer strategies in allergen immunotherapy, considering their fundamental role in allergic response [23, 24]. Recent findings have indicated that T-cell epitope repertoire in allergens is diverse than IgE epitopes, and it can be very useful in specific immunotherapy in allergy [25]. An analysis carried out on available epitope data has shown that T-cell epitopes are known to occur more commonly in the airborne allergens as compared to food allergens [26].

### **2.3. Cross-reactivity**

Cross-reactivity denotes a clinically and immunologically critical phenomenon displayed by allergens from various sources and is the cause of pollen-food syndromes, such as the one seen in case of birch and apple. Cross-reactivity is considered as a property of antibodies and it arises when an antibody or a subgroup of antibodies recognizes more than one allergen or epitope [27]. Two allergens are considered cross-reactive if they are recognized by a single antibody (or T-cell receptor). It has been stated that cross-reactivity among allergens at the level of B cells, T cells, and mast cells reflects clinical sensitivities and contributes very significantly in the regulation of allergic sensitization [28].

Cross-reactivity is predominantly an antibody defined phenomenon and IgE antibodies are shown to be more cross-reactive in nature. Affinity of the antibodies toward the allergen is known to play an important role in cross-reactivity. However, the properties of the allergenic protein are also very important and shared features on the level of both primary and tertiary structures of the cross-reactive proteins are found to be responsible for cross-reactivity [4]. Similarity at the level of sequence is an important indicator and cross-reactivity seems to require more than 70% sequence identity. In addition to this, other factors such as the host immune response against the allergen, dosage of allergen, and mode of exposure also con‐ tribute in clinical relevance of allergic cross-reactivity. Inferences drawn from studying a large number of allergens have led to the conclusion that structural similarity among proteins from diverse sources is the molecular basis of allergic cross-reactivity [29]. Considering the role it plays in the development of allergic symptoms, a detailed analysis of cross-reactivity has the potential to contribute in the development of new strategies in diagnosis and therapy of allergic diseases.

### **3. Allergen databases**

Last few years have witnessed substantial technological advances in the field of genomics and proteomics along with tremendous improvements in analytical methods. This has led to a significant progress in the area of allergy research. As a result of this, there has been a steady and continuous increase in the number of characterized protein allergens over the last few years. Efficient storage and management of data has become very important because of such incessant accumulation of molecular and clinical data on allergens. Therefore, allergen databases represent very crucial resources for basic allergy research as they are involved in archival of available allergen knowledge.

**Table 1.** Summary of major allergen databases and their relevant features.

Many allergen-specific databases have been developed in the past few years although they differ from each other with respect to their objectives, type of data archived, accessibility of contents, and the level of annotation and applications [30]. In addition to dedicated allergen databases, primary bioinformatics databases also document significant data on allergens. Examples of these databases include GenBank/GenPept [31, 32], UniProtKB [33], and Protein Data Bank (PDB) [34], which archive sequence and structure data on allergens along with its annotation. The Summary of allergen-specific databases is provided in **Table 1**. In the following section, the existing allergen databases are described.

### **3.1. IUIS Allergen database**

**3. Allergen databases**

58 Bioinformatics - Updated Features and Applications

archival of available allergen knowledge.

Nomenclature Subcommittee (http://www.

and Experimental Allergology, Italy (http://www.allergome.

Sealy Centre for Structural Biology, University of Texas, USA (https://fermi.utmb.edu)

National Institute of Health, Japan (http://allergen.nihs. go.jp/ADFS/)

Resource Program (FARP) (http://www.allergenonline.

logy and Allergy Research, Medical University of Vienna, Austria

(http://www.meduniwien.ac.

allergen.org)

IUIS Allergen [36] WHO/IUIS Allergen

Allergome [39] Centre for Clinical

AllergenOnline [46] Food Allergy and

org)

AllFam [48] Department of Pathophysio

at/allfam)

Structural Database of Allergenic Proteins (SDAP)

Allergen Database

[41]

[44]

For Food Safety (ADFS) org)

Last few years have witnessed substantial technological advances in the field of genomics and proteomics along with tremendous improvements in analytical methods. This has led to a significant progress in the area of allergy research. As a result of this, there has been a steady and continuous increase in the number of characterized protein allergens over the last few years. Efficient storage and management of data has become very important because of such incessant accumulation of molecular and clinical data on allergens. Therefore, allergen databases represent very crucial resources for basic allergy research as they are involved in

> Sequence (isoallergens/ isoforms), structure, allergenicity

Sequence (isoallergens/ isoforms), structure, clinical, epidemiological, cross-reactivity, etc.

Sequence, structure, structural models, IgE epitopes

Sequence, structure, IgE epitopes, small molecule

Allergen family data, cross-link to Pfam database

Sequence, allergenicity Yes 2016

allergens

AllergenPro [53] The National Agricultural Sequence, IgE epitopes Yes 2015

**tools (if any)**

– Updated

– Updated

Yes 2013

Yes 2016

– 2011

**Updates**

continuously

continuously

**Database Developed by (URL) Type of data archived Computational**

The IUIS Allergen Nomenclature Sub-Committee, under the auspices of the WHO, provides the systematic nomenclature of allergenic proteins and it has developed and maintained Allergen database [35, 36]. The database archives all of the WHO/IUIS–recognized allergens along with their isoallergens and isoforms (variants). In order to maintain a consistent allergen nomenclature for newly discovered allergens, researchers are required to submit newly described allergens to the Allergen Nomenclature Sub-Committee before submitting their manuscript to a journal for consideration for publication.

Each allergen in this database is provided with annotation that includes biochemical name, molecular weight, information on its allergenicity, reference, etc. Additionally, sequence data for allergens and isoallergens/isoforms are also stored in the database, along with crossreferences to GenBank [31], GenPept [32], and UniProtKB [33], as well as to PDB [34], for nucleotide, protein sequences, and 3D structure data, respectively. Allergen database can be searched by using allergen name, biochemical name, allergen source organism, taxonomic group, etc., as search criteria. The database is updated continuously with specific names assigned to newly discovered allergens and isoallergens/variants [37]. Allergen database does not exemplify the comprehensive allergen data although it documents majority of the char‐ acterized allergens. This is because there are a large number of allergens that have been reported in literature which are not recognized by IUIS-Allergen. The database does not archive data on allergen epitopes and cross-reactivity.

### **3.2. Allergome database**

Allergome [38] represents an extensive repository of information on allergen molecules causing IgE-mediated (allergic, atopic) diseases [39]. The database comprises comprehensive data on WHO/IUIS-approved allergens along with other non-recognized allergens. These allergenic molecules are selected and curated from the published literature and web-based resources. It also contains data on allergenic sources based on whether they possess identified molecules or not. Allergome documents information on allergen and isoallergens/isoforms along with their sequences. Cross-links to sequence and structure databases like UniProtKB [33] and PDB [34] are also provided.

Allergome can be searched by using basic and advanced search options. Basic search employs numerous search criterions such as allergen name, biochemical name, source organism, etc., while advanced search enables the user to search using specific attributes. Each allergen molecule is represented by a monograph which represents information about the three parts of allergen such as basic information, data on the native form, and its recombinant form. The most important and unique feature of Allergome platform is the presence of several support modules that deal with archival of specific aspects of allergen data. A couple of important modules are RefArray, for easy access to references stored in the Allergome, and Real Time Monitoring of IgE sensitization (ReTiME), for real-time data collection and storage of IgE sensitization data and the number of other utilities. Allergome is updated regularly and allergen data curated from literature is documented.

### **3.3. Structural database of allergenic proteins**

Structural Database of Allergenic Proteins (SDAP) [40] is an allergen database that prominently deals with structural aspects of allergens [41]. It houses comprehensive cross-referenced sequence data on allergens, IgE-binding epitopes, 3D structures, and models of allergens. Each allergen in SDAP is provided with cross-links to primary databases such as UniProtKB [33], PDB [34], as well as to important resources such as NCBI Taxonomy Browser [42] and PubMed [42] for literature references. SDAP also has a utility as a web server that integrates various computational tools, which assist structural biology–related studies dealing with allergens and their epitopes. It employs an algorithm based on the conserved properties of amino acid side chains to detect regions associated with allergenicity in novel sequences. The database consists of number of tools that can be used to assess potential cross-reactivity of allergens and also help in screening of IgE epitopes in novel proteins. The last update of the database was carried out on February 25, 2013. SDAP does not archive complete data for allergens that are not recognized by IUIS while data on allergen cross-reactivity is also not documented.

### **3.4. Allergen database for food safety**

reported in literature which are not recognized by IUIS-Allergen. The database does not

Allergome [38] represents an extensive repository of information on allergen molecules causing IgE-mediated (allergic, atopic) diseases [39]. The database comprises comprehensive data on WHO/IUIS-approved allergens along with other non-recognized allergens. These allergenic molecules are selected and curated from the published literature and web-based resources. It also contains data on allergenic sources based on whether they possess identified molecules or not. Allergome documents information on allergen and isoallergens/isoforms along with their sequences. Cross-links to sequence and structure databases like UniProtKB

Allergome can be searched by using basic and advanced search options. Basic search employs numerous search criterions such as allergen name, biochemical name, source organism, etc., while advanced search enables the user to search using specific attributes. Each allergen molecule is represented by a monograph which represents information about the three parts of allergen such as basic information, data on the native form, and its recombinant form. The most important and unique feature of Allergome platform is the presence of several support modules that deal with archival of specific aspects of allergen data. A couple of important modules are RefArray, for easy access to references stored in the Allergome, and Real Time Monitoring of IgE sensitization (ReTiME), for real-time data collection and storage of IgE sensitization data and the number of other utilities. Allergome is updated regularly and

Structural Database of Allergenic Proteins (SDAP) [40] is an allergen database that prominently deals with structural aspects of allergens [41]. It houses comprehensive cross-referenced sequence data on allergens, IgE-binding epitopes, 3D structures, and models of allergens. Each allergen in SDAP is provided with cross-links to primary databases such as UniProtKB [33], PDB [34], as well as to important resources such as NCBI Taxonomy Browser [42] and PubMed [42] for literature references. SDAP also has a utility as a web server that integrates various computational tools, which assist structural biology–related studies dealing with allergens and their epitopes. It employs an algorithm based on the conserved properties of amino acid side chains to detect regions associated with allergenicity in novel sequences. The database consists of number of tools that can be used to assess potential cross-reactivity of allergens and also help in screening of IgE epitopes in novel proteins. The last update of the database was carried out on February 25, 2013. SDAP does not archive complete data for allergens that are not

recognized by IUIS while data on allergen cross-reactivity is also not documented.

archive data on allergen epitopes and cross-reactivity.

**3.2. Allergome database**

[33] and PDB [34] are also provided.

60 Bioinformatics - Updated Features and Applications

allergen data curated from literature is documented.

**3.3. Structural database of allergenic proteins**

Allergen Database for Food Safety (ADFS) [43] is developed as a project of the Division of Biochemistry and Immunochemistry of National Institute of Health Sciences (Japan). The aim of the database is to archive allergenic proteins and their IgE epitopes with a special emphasis on food allergens and food safety [44]. Allergens archived in ADFS are grouped into eight categories such as pollen, mite, animal, fungus, insect, food, latex, and others, and each allergen entry is provided with the primary database accession numbers of their genes and 3D structure information. The database is also equipped with homology-based sequence search tool for the evaluation of allergenicity. One of the most distinct features of ADFS is the archival of data on small molecule, nonprotein (chemical) allergens. The database does not archive data on allergen cross-reactivity.

### **3.5. AllergenOnline database**

AllergenOnline [45] is a well curated allergen database that documents a peer reviewed allergen list, which is compiled from various resources such IUIS-Allergen, PubMed, scientific publications, and other allergen databases. The database was developed within the Food Allergy Research and Resource Program (FARRP) at the University of Nebraska [46]. For each allergen, data on source organism, common name, IUIS official nomenclature, protein length, class of allergen like food allergen, contact allergen, etc., and a link to the NCBI protein (GenPept) [32] database are provided. AllergenOnline also provides the utility for sequencebased searches for allergens, which include alignments by FASTA and an eight-amino acid short-sequence identity search. This utility can be very useful in the identification of proteins that may present a potential risk of allergenic cross-reactivity. AllergenOnline is updated every year and the last update that resulted in version 16 of the database was reported on January 27, 2016. It does not archive data on allergen epitopes as well as on allergenic cross-reactivity.

### **3.6. AllFam database**

AllFam [47] represents a very important resource for allergens as it is involved in classification of allergens into protein families [48]. This study has shown that allergens are distributed into relatively few protein families and possess a limited number of biochemical functions. The structural classification of allergens in AllFam is performed by using family information from PFam [49] and the Structural Classification of Proteins (SCOP) database [50], while biochemical functions of allergens were extracted from the Gene Ontology annotation database [51]. The database provides the option of browsing lists of allergen families based on allergen source (plants, animals, and fungi) and route of exposure (inhalation, ingestion, etc.) while search for specific protein families can also be performed. Each allergen family in AllFam is linked to a family fact sheet that describes the biochemical properties of the family members as well as a list of key references related to this family. The last update of AllFam was reported on September 12, 2011. AllFam does not archive data on molecular features of individual allergens although cross-link to IUIS-Allergen and Allergome is provided for each documented allergen.

### **3.7. AllergenPro database**

AllergenPro [52] is a recently developed allergen database that archives data on allergen sequences, structures, and epitopes from various sources. It is an integrated database which provides information about allergens in foods, microorganisms, fungi, animals, and plants [53]. It has been provided with a utility to search for allergens based on keywords as well as the sequence. AllergenPro is also equipped with a computational tool for the prediction of allergenicity. Prediction is based on three different approaches such as FAO/WHO guidelines (sequence)–based approach, motif-based approach, and epitope-based approach. The data‐ base was last updated on June 4, 2015. AllergenPro does not archive data on allergen crossreactivity while the literature references for documented allergens and epitopes have also not been provided.

### **3.8. Archival of allergen epitope data**

As mentioned earlier, epitopes denote very important feature of allergens as they play vital role in allergic diseases. Because the molecular characterization of allergens has risen im‐ mensely in recent years, the data on allergenic epitopes has also increased significantly. Therefore, it has become necessary to store and manage the epitope data for its efficient utilization.

Some of the existing allergen databases described above, such as SDAP, ADFS, and Allergen‐ Pro are involved in storage of allergen epitope data. There are few databases available that are dedicated for epitope data from all types of antigens, which also document information on allergen epitopes [54–56]. However, the allergy-associated epitope data stored in these databases may not be comprehensive. The Immune Epitope Database (IEDB) [57], which is a repository of immune epitope reactivity data, is also a major database of allergy-derived epitope data [58]. It archives extensive allergen epitope data along with biological assays associated with them, including IgE-binding as well as T-cell epitopes curated and compiled from allergy-related references. IEDB is also equipped with several strategies for efficient searching and visualization of data on allergy-related epitopes [59]. Therefore, it represents a very useful and user friendly platform to access and retrieve allergy-related epitope data for the community of allergists. In a study involving classification of all the epitope-specific literature in various immunological domains, it is stated that IEDB comprises relatively fewer references for allergy-derived epitopes as compared to Cancer and Infectious Diseases [60]. This indicates that there is considerable scope for more in-depth archival of allergen epitope data from literature. Another study on meta-analysis of the allergy-associated epitope data in IEDB has indicated that relatively lesser data is archived for allergen T-cell epitopes as compared to IgE epitopes [26].

### *3.8.1. AllerBase database*

Observations from the study of the existing allergen databases indicated that they archive significant data on various aspects of allergen and allergenicity, although the level of com‐ pleteness of data differs considerably for diverse allergen features. AllerBase [61] is a recently developed comprehensive database of allergens and allergen features which addresses some of the limitations associated with the existing allergen databases [Kadam et al. 2016, unpub‐ lished]. The database comprises extensive data on experimentally validated allergens and allergen specific features such as IgE-binding epitopes, IgE cross-reactivity, IgE antibodies, and evidences for experimental validation of allergens. AllerBase is provided with basic and advanced search utilities along with browse database option to retrieve desired allergen data. The Completeness Index, which represents availability of data for various features for each allergen and a structure visualization utility, denote important features of the database. AllerBase also provides cross-references to several immunological and allergen databases and represents a notable instance of integration of allergen data from number of resources.

### **4. Computational prediction of allergens/allergenicity**

**3.7. AllergenPro database**

62 Bioinformatics - Updated Features and Applications

been provided.

utilization.

**3.8. Archival of allergen epitope data**

compared to IgE epitopes [26].

*3.8.1. AllerBase database*

AllergenPro [52] is a recently developed allergen database that archives data on allergen sequences, structures, and epitopes from various sources. It is an integrated database which provides information about allergens in foods, microorganisms, fungi, animals, and plants [53]. It has been provided with a utility to search for allergens based on keywords as well as the sequence. AllergenPro is also equipped with a computational tool for the prediction of allergenicity. Prediction is based on three different approaches such as FAO/WHO guidelines (sequence)–based approach, motif-based approach, and epitope-based approach. The data‐ base was last updated on June 4, 2015. AllergenPro does not archive data on allergen crossreactivity while the literature references for documented allergens and epitopes have also not

As mentioned earlier, epitopes denote very important feature of allergens as they play vital role in allergic diseases. Because the molecular characterization of allergens has risen im‐ mensely in recent years, the data on allergenic epitopes has also increased significantly. Therefore, it has become necessary to store and manage the epitope data for its efficient

Some of the existing allergen databases described above, such as SDAP, ADFS, and Allergen‐ Pro are involved in storage of allergen epitope data. There are few databases available that are dedicated for epitope data from all types of antigens, which also document information on allergen epitopes [54–56]. However, the allergy-associated epitope data stored in these databases may not be comprehensive. The Immune Epitope Database (IEDB) [57], which is a repository of immune epitope reactivity data, is also a major database of allergy-derived epitope data [58]. It archives extensive allergen epitope data along with biological assays associated with them, including IgE-binding as well as T-cell epitopes curated and compiled from allergy-related references. IEDB is also equipped with several strategies for efficient searching and visualization of data on allergy-related epitopes [59]. Therefore, it represents a very useful and user friendly platform to access and retrieve allergy-related epitope data for the community of allergists. In a study involving classification of all the epitope-specific literature in various immunological domains, it is stated that IEDB comprises relatively fewer references for allergy-derived epitopes as compared to Cancer and Infectious Diseases [60]. This indicates that there is considerable scope for more in-depth archival of allergen epitope data from literature. Another study on meta-analysis of the allergy-associated epitope data in IEDB has indicated that relatively lesser data is archived for allergen T-cell epitopes as

Observations from the study of the existing allergen databases indicated that they archive significant data on various aspects of allergen and allergenicity, although the level of com‐ pleteness of data differs considerably for diverse allergen features. AllerBase [61] is a recently Allergens mainly comprise commonly occurring proteins in foods, pollens, and other biolog‐ ical entities in the environment. It has become necessary to assess the potential allergenicity of these proteins considering the health hazards associated with allergic reactions to them. In recent years, genetic engineering and food processing methods are routinely employed for modifying the existing proteins or introducing new ones. Analysis of allergenicity of such proteins/products along with newly introduced biopharmaceuticals is absolutely essential in order to avoid transfer of an allergenic molecule. Computational assessment or prediction of allergenicity represents the major approach to test for allergenicity, and numerous bioinfor‐ matics tools/methods have been employed successfully for this purpose [62]. The majority of these methods utilize the amino acid sequence of allergens along with its different features, while a very few approaches use structure information. **Table 2** denotes the list of computa‐ tional tools/servers available for the prediction of allergens/allergenicity. In the following section, the prominent approaches used for the computational assessment or prediction of allergens/allergenicity are described briefly.

*A*ROC: area under the receiver-operating curve; SE: sensitivity; SP: specificity; MCC: Matthews correlation coefficient.

**Table 2.** List of computational tools/servers for allergen/allergenicity prediction.

### **4.1. Sequence similarity-based approaches**

**No. Method (URL) Approach used Efficiency**

sequence and SVM

Sequence features and SVM, sequence motifs,

Sequence based descriptors, auto and cross-covariance, machine learning

DFLAP algorithm and SVM –

Iterative pairwise sequence similarity and SVM

Sequence based features

AFFP dataset, normalized BLAST *E*-values and

physicochemical descriptors,

Integration of methods based on FAO/WHO guidelines, sequence motifs and

Auto and cross-covariance, descriptor-based fingerprints

and SVM

SVM

SVM

Biochemical and

sequence features, subcellular locations, mRMR, SVM

Sequences as text documents, Naive Bayes classifier and SVM

epitopes, allergen representative peptides

Sequence motifs –

Accuracy = 85%, SE = 88%,

Accuracy = 85.3%, SE = 82.5%,

*A*ROC = 0.928, accuracy = 95.3%, SE = 83.4%, SP = 96.4%

MCC = 0.95, SE = 93%,

MCC = 0.97, SE = 98.6%,

Accuracy = 93.42%

Accuracy = 88%, MCC = 0.759

SP = 99.9%

SP = 98.4%

–

–

SP = 81%

SP = 88.1%

[67] of protein

(http://weballergen.bii.a-star.edu.sg/) [73]

64 Bioinformatics - Updated Features and Applications

(http://www.imtech.res.in/raghava/algpred/)

(http://www.ddg-pharmfac.net/AllerTOP)

(http://bioinformatics.bmc.uu.se/evaller.html)

(http://tiger.dbs.nus.edu.sg/AllerHunter/)

(http://jing.cz3.nus.edu.sg/cgi-bin/APPEL)

(http://gmobl.sjtu.edu.cn/PREAL/index.php)

(http://gmobl.sjtu.edu.cn/proAP/main.html)

(http://ddg-pharmfac.net/AllergenFP/) [103]

(http://sortaller.gzhmu.edu.cn/)

(http://allerdictor.vbi.vt.edu/) [99]

6 WebAllergen

7 AlgPred

[75]

8 AllerTOP

[83]

9 EVALLER

[86]

[89]

[91]

[93]

[96]

14 Allerdictor

15 proAP

[100]

16 AllergenFP

13 PREAL

12 SORTALLER

11 APPEL

10 AllerHunter

One of the first studies dealing with analysis of allergenicity was put forth by Metcalfe et al. [63]. They have proposed a decision tree–based approach for allergenicity assessment of foods derived from genetically modified crops. The first computational approach for the assessment of allergenicity was provided by "Codex Alimentarius Commission" of FAO/WHO [64, 65]. It stated that a protein can be regarded as an allergen if it consists of an exact match with at least six contiguous amino acids or showed more than 35% similarity over a window of 80 amino acids when compared with a sequence of known allergen. This approach has been widely used to predict allergenicity and there are number of web servers for allergen prediction, which are based on it. Allermatch [66], AllerTool [67], and AllergenPro [53] are some of the prominent web servers which employ these FAO/WHO guidelines for allergen prediction. Additionally, some of the major allergen databases such as SDAP [41] and AllergenOnline [46] also utilize this strategy for allergenicity prediction. A recent study performed by Verma et al. [68] has shown that the sequence similarity-based approach gives substantially better results when used in combination with other bioinformatics methods. However, results obtained by certain studies indicated that approaches based on these guidelines are not highly efficient for identifying allergenic proteins and many of times they lead to false or irrelevant allergenicity estimations [69–71]. As a result of these observations, it became necessary to discover and employ other strategies for the prediction of allergenicity.

### **4.2. Motif-based approaches**

In a study carried out by Stadler and Stadler [71], it was observed that the use of sequence motifs, which represent the secondary structures of proteins, performs significantly better than the approach based on FAO/WHO guidelines. This method employs MEME motifs of a length of 50 residues for the prediction of allergenicity by using pairwise sequence alignment with certain threshold. WebAllergen [72] is a web server for the prediction of allergenic proteins which is also based on specific detectable allergenic motifs in known allergens [73]. Further‐ more, a study carried out by Kong et al. showed that an approach based on search of multiple motifs is more specific and efficient than the conventional single motif search [74]. AlgPred [75] and AllergenPro [53] are important web servers for allergen prediction in which one of the prediction approaches is based on allergen-derived motifs. A recent study that employs computational approaches for comparison of allergens and metazoan parasite proteins stated that significant sequence and structure similarity exists between parasite proteins and allergenic proteins [76]. The analysis was carried out using sequence and structural motifs in allergens and a workflow was developed for the computational analysis of parasite proteins.

### **4.3. Machine learning–based approaches**

Recent years have witnessed tremendous increase in the application of machine learning methods for solving biological problems. Machine learning–based approaches have been widely used for predicting various aspects of protein function [77]. These methods are also employed routinely for the development of algorithms to predict allergenicity of novel proteins.

Although Support Vector Machine (SVM) is the most commonly used machine learning method for allergen prediction, other methods have also been frequently employed. One of the earliest methods was developed by Zorzet et al. [78] that utilizes a k-Nearest-Neighbor (kNN) classification algorithm for the prediction of allergenicity, while a Bayesian classifier was employed by Soeria-Atmadja et al. [79] for the same purpose. An approach based on the combination of hidden Markov model (HMM) and conserved motifs in allergen was also used to successfully predict protein allergenicity [80]. Dimitrov et al. [81] developed two artificial neural network (ANN)-based algorithms for allergenicity prediction, which utilize descriptors derived from amino acids that denote their structural and physicochemical properties. AllerTOP [82] is an online bioinformatics tool to perform the computational prediction of allergens [83]. This algorithm employs descriptors that denote the chemical properties of amino acids in allergen sequences and auto- and cross-covariance transformation along with five machine learning methods for classification. These methods are random forest, multilayer perceptron, logistic regression, decision tree, naïve Bayes, and kNN.

There are number of web-based tools/servers developed which use SVM for performing classification/prediction of allergens. AlgPred [84] is one of the earlier web servers developed for the prediction of allergenic proteins [75]. It employs SVM with amino acid and dipeptide composition as features of allergens to achieve accuracy of 85.02 and 84.00%, respectively. EVALLER [85] is another web server created for *in silico* determination of potential allerge‐ nicity with very good efficiency [86]. It performs detection based on filtered length–adjusted allergen peptides (DFLAP) algorithm and SVM. AllerTool [87] web server also applies SVMbased algorithm for the prediction of allergenicity and provides sensitivity and specificity of 86.00% [67]. AllerHunter [88] is an important web-based computational system for allerge‐ nicity assessment which uses a scheme based on iterative pairwise sequence similarity encoding along with SVM [89]. The method is very efficient with a sensitivity of 83.4% and a specificity of 96.4%.

A web-based tool APPEL [90] is developed for the prediction of allergenic proteins that employs physicochemical and structural features derived from allergen sequence in combi‐ nation with SVM [91]. Zhang et al. have developed an online allergen prediction tool titled SORTALLER [92], which is based on allergen family featured peptide (AFFP) dataset and employs SVM as a classifier [93]. An algorithm developed by Mohabatkar et al. [94] for the prediction of allergenic proteins utilizes pseudoamino acid composition (PseAAC) along with SVM and provides an accuracy of 91.19%. PREAL [95] is web-based tool that performs allergen prediction by using SVM along with feature selection methods such as maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS) [96]. A combination of hydrophobicity amino acid index and discrete Fourier transform along with an SVM classifier is employed for highly efficient prediction of allergenicity in a signal-processing bioinformatics approach [97]. Allerdictor [98] is web server that specializes in large-scale allergen discovery. It models protein sequences as text documents and employs SVM in text classification for carrying out allergen prediction [99].

### **4.4. Other approaches**

allergenic proteins [76]. The analysis was carried out using sequence and structural motifs in allergens and a workflow was developed for the computational analysis of parasite proteins.

Recent years have witnessed tremendous increase in the application of machine learning methods for solving biological problems. Machine learning–based approaches have been widely used for predicting various aspects of protein function [77]. These methods are also employed routinely for the development of algorithms to predict allergenicity of novel

Although Support Vector Machine (SVM) is the most commonly used machine learning method for allergen prediction, other methods have also been frequently employed. One of the earliest methods was developed by Zorzet et al. [78] that utilizes a k-Nearest-Neighbor (kNN) classification algorithm for the prediction of allergenicity, while a Bayesian classifier was employed by Soeria-Atmadja et al. [79] for the same purpose. An approach based on the combination of hidden Markov model (HMM) and conserved motifs in allergen was also used to successfully predict protein allergenicity [80]. Dimitrov et al. [81] developed two artificial neural network (ANN)-based algorithms for allergenicity prediction, which utilize descriptors derived from amino acids that denote their structural and physicochemical properties. AllerTOP [82] is an online bioinformatics tool to perform the computational prediction of allergens [83]. This algorithm employs descriptors that denote the chemical properties of amino acids in allergen sequences and auto- and cross-covariance transformation along with five machine learning methods for classification. These methods are random forest, multilayer

There are number of web-based tools/servers developed which use SVM for performing classification/prediction of allergens. AlgPred [84] is one of the earlier web servers developed for the prediction of allergenic proteins [75]. It employs SVM with amino acid and dipeptide composition as features of allergens to achieve accuracy of 85.02 and 84.00%, respectively. EVALLER [85] is another web server created for *in silico* determination of potential allerge‐ nicity with very good efficiency [86]. It performs detection based on filtered length–adjusted allergen peptides (DFLAP) algorithm and SVM. AllerTool [87] web server also applies SVMbased algorithm for the prediction of allergenicity and provides sensitivity and specificity of 86.00% [67]. AllerHunter [88] is an important web-based computational system for allerge‐ nicity assessment which uses a scheme based on iterative pairwise sequence similarity encoding along with SVM [89]. The method is very efficient with a sensitivity of 83.4% and a

A web-based tool APPEL [90] is developed for the prediction of allergenic proteins that employs physicochemical and structural features derived from allergen sequence in combi‐ nation with SVM [91]. Zhang et al. have developed an online allergen prediction tool titled SORTALLER [92], which is based on allergen family featured peptide (AFFP) dataset and employs SVM as a classifier [93]. An algorithm developed by Mohabatkar et al. [94] for the prediction of allergenic proteins utilizes pseudoamino acid composition (PseAAC) along with SVM and provides an accuracy of 91.19%. PREAL [95] is web-based tool that performs allergen

perceptron, logistic regression, decision tree, naïve Bayes, and kNN.

**4.3. Machine learning–based approaches**

66 Bioinformatics - Updated Features and Applications

proteins.

specificity of 96.4%.

A study carried out by Wang et al. evaluated sequence-, motif-, and SVM-based approaches for the computational prediction of allergens and also performed parameter optimization to obtain better performance [100]. The resulting methods from this study are integrated and made available as a web application titled proAP [101]. AllergenFP [102] is a recently devel‐ oped web server for allergenicity prediction that utilizes alignment-free descriptor-based fingerprint approach [103]. The descriptors used here are important properties of amino acid such as size, hydrophobicity, relative abundance, helix, and beta-strand forming propensities, etc. In a structure-based approach proposed by Bragin et al. [104], information derived from protein 3D structure is used for the representation of protein surface as patches designated as discontinuous peptides. It is observed that prediction of allergenic proteins based on this approach gave better accuracy. Vijayakumar and Lakshmi have developed a fuzzy inference system–based algorithm for allergenicity prediction that utilizes five different modules [105]. These modules consist of a machine learning classifier, motif search, sequence similarity, FAO/ WHO evaluation scheme, etc. FuzzyApp [106], a web server based on fuzzy rule–based system, is then developed for the prediction of allergenicity [107]. Jiang et al. performed an analysis of food allergens using a computational model that simulates gastric fluid digestion [108]. This study stated that food allergens could be classified as alimentary canal-sensitized and nona‐ limentary canal-sensitized allergens based on the digestibility of these allergens in simulated gastric fluid.

### **5. Computational prediction of allergen epitopes**

Epitopes represent distinctive amino acid residues on the antigens and are important deter‐ minants of an immune response. Identification of epitopes is considered a key aspect of designing highly effective multiple-subunit vaccines and developing efficient diagnostic and therapy methods against allergens. Although experimental methods have been very useful for the identification of epitopes, their usefulness is restricted because of their time- and costintensive nature and inability in dealing with large-scale elucidation of epitopes. Hence, computational approaches are considered to be very beneficial alternative as they are cost and time effective.

Large number of highly efficient algorithms and tools have been developed over the years for the computational prediction of epitopes. These methods deal with the prediction of both B-cell and T-cell epitopes as well as sequential (linear) and discontinuous (conforma‐ tional) epitopes. Based on the information (data) utilized for performing prediction, the methodologies can be grouped as sequence-based or structure-based approaches. Many se‐ quence-based linear epitope prediction methods for B cells have been developed and used since long time and majority of them are propensity scale and machine learning–based methods [109, 110]. Some of the major tools/servers that deal with the prediction of linear Bcell epitopes are listed in **Table 3**.

*A*ROC: area under the receiver-operating curve; SE: sensitivity; SP: specificity; MCC: Matthews correlation coefficient.

**Table 3.** List of major tools/servers for sequential (linear) epitope prediction.

Number of methods that utilize 3D structure of antigens for discontinuous epitope prediction have also been developed. These methods use different approaches for prediction such as solvent accessibility of surface residues [123, 124], solvent accessibility with propensity scores [125], and propensity scores with packing density of amino acids [126]. An account of major tools/servers that are involved in conformational epitope prediction is provided in **Table 4**.

both B-cell and T-cell epitopes as well as sequential (linear) and discontinuous (conforma‐ tional) epitopes. Based on the information (data) utilized for performing prediction, the methodologies can be grouped as sequence-based or structure-based approaches. Many se‐ quence-based linear epitope prediction methods for B cells have been developed and used since long time and majority of them are propensity scale and machine learning–based methods [109, 110]. Some of the major tools/servers that deal with the prediction of linear B-

Fixed length epitope patterns,

Amino acid anchoring pair composition (APC) and SVM

SVM classifiers with string

Physicochemical properties of

Sequence features and multiple linear regression (MLR)

A collection of tools based on

Large datasets of epitopes,

Tri-peptide similarity, propensity

various methods

scores and SVM

Number of methods that utilize 3D structure of antigens for discontinuous epitope prediction have also been developed. These methods use different approaches for prediction such as

*A*ROC: area under the receiver-operating curve; SE: sensitivity; SP: specificity; MCC: Matthews correlation coefficient.

**Table 3.** List of major tools/servers for sequential (linear) epitope prediction.

KNN, SVM

Antigen sequence features, SVM *A*ROC = 0.85

Bayes feature extraction and SVM *A*ROC = 0.84,

Antigen fragment score and SVM *A*ROC = 0.829

Accuracy = 65.93%, SE = 67.14%, SP = 64.71%

accuracy

*A*ROC = 0.809,

*A*ROC = 0.758

Accuracy = 58.7%

accuracy = 74.5%

*A*ROC = 0.728,

Accuracy = 86%

*A*ROC = 0.702,

–

SE = 81.8%, SP = 64.1%

SE = 80.1%, SP = 55.2%

= 72.94%

–

recurrent ANNs

epitope residues

Parker's hydrophilicity scale and HMM

kernels

**No. Method (URL) Approach used Efficiency**

cell epitopes are listed in **Table 3**.

68 Bioinformatics - Updated Features and Applications

1 ABCPred (http://www.imtech.res. in/raghava/abcpred/) [111]

iastate.edu/bcpreds/) [113]

4 BcePred (http://www.imtech.res. in/raghava/bcepred/) [114]

5 BepiPred (http://www.cbs.dtu.dk/ services/BepiPred/) [115]

7 Bayesb (http://www.immunopred.org/ bayesb/index.html) [117]

8 COBEpro (http://scratch.proteomics.

9 EPMLR (http://www.bioinfo.tsinghua. edu.cn/epitope/EPMLR/) [119]

10 IEDB Analysis Resource (http://tools.

2 APCpred (http://ccb.bmi.ac. cn/APCpred/) [112]

3 BCPreds (http://ailab.cs.

6 BEST (http://biomine.ece. ualberta.ca/BEST/) [116]

ics.uci.edu) [118]

iedb.org/bcell/) [120]

12 SVMTriP (http://sysbio. unl.edu/SVMTriP/) [122]

11 LBtope (http://www.imtech.res. in/raghava/lbtope/) [121]

*A*ROC: area under the receiver-operating curve; SE: sensitivity; SP: specificity; MCC: Matthews correlation coefficient.

**Table 4.** List of major tools/servers for conformational (discontinuous) epitope prediction.

Studies have shown that the analysis of antigen–antibody complex structures is very useful for the characterization of conformational epitopes [134]. A dedicated resource titled AgAbDb [135] that archives the interactions derived from antigen–antibody complexes is available, which can be very useful for the analysis of epitopes [20, 21]. Several algorithms have also been developed for the prediction of T-cell epitopes in antigens. These methodologies deal with the prediction of peptides that possess the ability to interact with specific major histocompatibility complex (MHC) molecules [136]. Machine learning–based approaches are very commonly employed for this purpose and are found to be very efficient [137]. The details of epitope prediction methods/tools for B cells and T cells have been reviewed elsewhere [138, 139]. Some of the important tools/servers that perform the prediction of T-cell epitopes are listed in **Table 5**. Recently, it has been shown that epitope prediction can be performed over the whole proteome by integrating multiple epitope prediction methods [149]. Antibody-specific epitope prediction has emerged as a significant alternative to the traditional antibody-independent epitope prediction methods [150].

**Table 5.** List of major tools/servers for T-cell epitope prediction.

Epitopes represent critical components of allergens from the perspective of allergic reactions and development of new diagnosis and treatment strategies. Therefore, the computational prediction of these epitopes in allergens is of immense importance. Due to limitations associ‐ ated with detailed archival of allergen epitope data and highly heterogeneous nature of the data, the number of tools available for allergen epitope prediction are far less, especially when compared with the number of tools available for allergen/allergenicity prediction. Therefore, the general epitope prediction methods listed above can be employed for epitope prediction studies in allergens. Kleter and Peijnenburg [70] developed a strategy to screen for the potential linear IgE-epitopes using sequence comparison with a minimal length of six amino acids. The approach was moderately effective and it showed that further verification of IgE binding of epitopes by experimental tests is necessary. AlgPred [84] developed by Saha and Raghava is one of the first and major tools for the computational assessment of IgE epitopes [75]. Here, a database of known IgE epitopes is created and it is used to accurately predict allergenic proteins. AllerPred is a SVM-based computational system for the assessment of overlapping continuous and discontinuous B-cell epitope binding patterns in allergenic proteins [151]. This approach is successfully used to predict allergenicity of novel proteins. Dall'Antonia et al. [152] have developed a software tool titled Surface comparison–based Prediction of Allergenic Discontinuous Epitopes (SPADE). The algorithm consists of a structure-based comparison of allergen surfaces and IgE cross-reactivity data and is able to predict IgE epitopes from three important allergen families. A recent work performed by Lollier et al. [153] on meta-analysis of IgE-binding epitopes provided some important findings regarding these epitopes. They computed the fraction of allergen amino acids that are involved in epitopes and modeled a relationship between the rising number of literature references and the amino acid fractions to assess the possibility of binary classification of epitopes and nonepitopes. A web-based tool LocAllEpi [154] is also developed for the visualization of allergen epitopes along the protein sequence and their structural features.

### **6. Computational prediction of allergenic cross-reactivity**

of the important tools/servers that perform the prediction of T-cell epitopes are listed in **Table 5**. Recently, it has been shown that epitope prediction can be performed over the whole proteome by integrating multiple epitope prediction methods [149]. Antibody-specific epitope prediction has emerged as a significant alternative to the traditional antibody-independent

**No. Method (URL) Approach used Efficiency**

SVM, ANN

residues

Epitopes represent critical components of allergens from the perspective of allergic reactions and development of new diagnosis and treatment strategies. Therefore, the computational prediction of these epitopes in allergens is of immense importance. Due to limitations associ‐ ated with detailed archival of allergen epitope data and highly heterogeneous nature of the data, the number of tools available for allergen epitope prediction are far less, especially when compared with the number of tools available for allergen/allergenicity prediction. Therefore, the general epitope prediction methods listed above can be employed for epitope prediction

integrated approach

QSAR approach based on proteochemometrics

Integrated method employing proteasomal cleavage, TAP transport efficiency, and MHC class I binding affinity

A method for all HLA class II molecules based on peptidebinding MHC environment

Based on specificity-determining

Scoring system based on position of

Algorithm based on HLA-DR binding

Combination of methods based on proteasomal cleavage, TAP transport and MHC binding

residue in the epitopes

pocket similarity

Cytotoxic T-lymphocyte epitopes,

Multi-step algorithm that employs

Accuracy = 75.2%

Accuracy = 60%

Accuracy = 89%

*A*ROC = 0.95

*A*ROC = 0.807

–

–

–

–

epitope prediction methods [150].

70 Bioinformatics - Updated Features and Applications

1 CTLPred (http://www.imtech.res. in/raghava/ctlpred/index.html) [140]

2 EpiJen (http://www.ddg-pharmfac. net/epijen/EpiJen/EpiJen.htm) [141]

3 EpiTOP (http://www.pharmfac. net/EpiTOP/) [142]

4 NetCTLpan (http://www.cbs.dtu. dk/services/NetCTLpan/) [143]

6 PREDIVAC (http://predivac. biosci.uq.edu.au/) [145]

5 NetMHCIIpan-3.0 (http://www.cbs.dtu.dk/ services/NetMHCIIpan-3.0/) [144]

7 SYFPEITHI (http://www.syfpeithi.de/bin/ MHCServer.dll/EpitopePrediction.htm) [146]

8 TEPITOPEpan (http://www.biokdd.fudan. edu.cn/Service/TEPITOPEpan/) [147]

9 WAPP (http://abi.inf.uni-tuebingen.de/

*A*ROC: area under the receiver-operating curve.

**Table 5.** List of major tools/servers for T-cell epitope prediction.

Services/WAPP/) [148]

Cross-reactivity plays an important role in allergic reaction from the immunological and clinical context. Therefore, the computational prediction of allergenic cross-reactivity has been considered of substantial significance. The prediction of cross-reactivity in allergens is associated with the prediction of allergenicity for the majority of the cases. This is mainly because the antigenic determinants that contribute to the cross-reactivity in allergens are also responsible for their allergenicity. As a result of this, many of the tools/algorithms that have been developed for the prediction of allergens/allergenicity also perform cross-reactivity prediction.

The criteria defined by FAO/WHO experts, which have been mentioned earlier, help to identify cross-reactivity in allergens [155]. AllerTool [87] is a web server that performs cross-reactivity prediction based on amino acid sequence and WHO/FAO guidelines [67]. It also provides a graphical representation of the published and predicted cross-reactivity patterns of allergens. Stadler and Stadler [71] developed a sequence-based approach and stated that motif-based strategy provides better results for the computational assessment of cross-reactivity than the FAO/WHO guidelines. SDAP [40], which is a specialized allergen database described before, also comprises a sequence-based tool for the identification of cross-reactivity among allergens [41]. AllerHunter [88] is a SVM-based web server that deals with efficient assessment of allergic cross-reactivity in proteins [89]. A recently developed fuzzy inference system–based algorithm for allergenicity prediction is also able to predict cross-reactivity in allergens [105].

### **7. Future perspectives and challenges**

Allergy represents a serious problem, as allergic diseases are known to affect millions of people worldwide. Advancements in genomic, proteomic, and analytical techniques have led to the generation of large amount of data related to allergy and allergens. Archival and analysis of these data denotes a major challenge in allergen bioinformatics. Data integration is one of the key limitations for efficient and useful storage of allergy associated data. This is mainly due to the heterogeneous nature of the data, which is derived from various sources such as molecular data from experimental characterization of allergic reactions, clinical, and epide‐ miological data from patients/populations. Bioinformatics resources and tools have an important role to play in overcoming this problem. In the wake of ever-expanding volume of data, it is vital to focus on developing databases/resources that will integrate information from different sources as well as from literature and provide rapid access to it. Analysis of such data can be further utilized to obtain important insights to understand allergic reactions. Structural features of allergens contribute significantly to their allergenicity and therefore this knowledge can be employed for developing more efficient methods for allergen/allergenicity and allergic cross-reactivity prediction. Recent advances in epitope prediction methodologies focus on antibody-specific epitope prediction approaches [150]. Application of such approaches for predicting IgE-binding epitopes will be extremely important in the development of newer and effective strategies for diagnosis and treatment of allergic diseases. Allergen immunotherapy (AIT), which is an individualized and allergens-based treatment approach, has been consid‐ ered as a prototype of precision medicine or personalized medicine [156]. Bioinformatics has the potential to play an important role in the development of novel approaches in AIT as well as contribute for further enrichment of the field of allergen informatics. This will surely aid in gaining better understanding of allergic diseases and positively influence upcoming research in the field.

### **Acknowledgements**

The work was supported under the Senior Research Fellowship (SRF) granted to KK by the University Grants Commission (UGC), New Delhi, India. UKK and SS would like to acknowl‐ edge the Centre of Excellence grant from the Department of Biotechnology (DBT), New Delhi, India. The authors would also like to acknowledge the Bioinformatics Centre, Savitribai Phule Pune University, for providing the infrastructure and resources.

### **Author details**

Kiran Kadam1 , Sangeeta Sawant1 , V.K. Jayaraman2 and Urmila Kulkarni-Kale1\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Bioinformatics Centre, Savitribai Phule Pune University, Pune, India 2 Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh, India

### **References**

**7. Future perspectives and challenges**

72 Bioinformatics - Updated Features and Applications

in the field.

**Acknowledgements**

**Author details**

Kiran Kadam1

Allergy represents a serious problem, as allergic diseases are known to affect millions of people worldwide. Advancements in genomic, proteomic, and analytical techniques have led to the generation of large amount of data related to allergy and allergens. Archival and analysis of these data denotes a major challenge in allergen bioinformatics. Data integration is one of the key limitations for efficient and useful storage of allergy associated data. This is mainly due to the heterogeneous nature of the data, which is derived from various sources such as molecular data from experimental characterization of allergic reactions, clinical, and epide‐ miological data from patients/populations. Bioinformatics resources and tools have an important role to play in overcoming this problem. In the wake of ever-expanding volume of data, it is vital to focus on developing databases/resources that will integrate information from different sources as well as from literature and provide rapid access to it. Analysis of such data can be further utilized to obtain important insights to understand allergic reactions. Structural features of allergens contribute significantly to their allergenicity and therefore this knowledge can be employed for developing more efficient methods for allergen/allergenicity and allergic cross-reactivity prediction. Recent advances in epitope prediction methodologies focus on antibody-specific epitope prediction approaches [150]. Application of such approaches for predicting IgE-binding epitopes will be extremely important in the development of newer and effective strategies for diagnosis and treatment of allergic diseases. Allergen immunotherapy (AIT), which is an individualized and allergens-based treatment approach, has been consid‐ ered as a prototype of precision medicine or personalized medicine [156]. Bioinformatics has the potential to play an important role in the development of novel approaches in AIT as well as contribute for further enrichment of the field of allergen informatics. This will surely aid in gaining better understanding of allergic diseases and positively influence upcoming research

The work was supported under the Senior Research Fellowship (SRF) granted to KK by the University Grants Commission (UGC), New Delhi, India. UKK and SS would like to acknowl‐ edge the Centre of Excellence grant from the Department of Biotechnology (DBT), New Delhi, India. The authors would also like to acknowledge the Bioinformatics Centre, Savitribai Phule

, V.K. Jayaraman2

and Urmila Kulkarni-Kale1\*

Pune University, for providing the infrastructure and resources.

, Sangeeta Sawant1

\*Address all correspondence to<EMAIL_ADDRESS>

[27] Aalberse RC. Assessment of allergen cross-reactivity. Clin Mol Allergy. 2007;5:1. DOI: 10.1186/1476-7961-5-2

[14] Pomés A. Relevant B cell epitopes in allergic disease. Int Arch Allergy Immunol.

[15] Bufe A. Significance of IgE-binding epitopes in allergic disease. J Allergy Clin Immunol.

[16] Aalberse RC, Crameri R. IgE‐binding epitopes: a reappraisal. Allergy. 2011;66:1261–

[17] Bannon GA, Ogawa T. Evaluation of available IgE‐binding epitope data and its utility in bioinformatics. Mol Nutr Food Res. 2006;50:638–644. DOI: 10.1002/mnfr.200500276

[18] Matsuo H, Yokooji T, Taogoshi T. Common food allergens and their IgE-binding

[19] Meno KH. Allergen structures and epitopes. Allergy. 2011;66:19–21. DOI: 10.1111/j.

[20] Ghate AD, Bhagwat BU, Bhosle SG, Gadepalli SM, Kulkarni-Kale UD. Characterization of antibody-binding sites on proteins: development of a knowledgebase and its applications in improving epitope prediction. Protein Pept Lett. 2007;14:531–535. DOI:

[21] Kulkarni-Kale U, Raskar-Renuse S, Natekar-Kalantre G, Saxena SA. Antigen–antibody interaction database (AgAbDb): a compendium of antigen–antibody interactions. In: De RK, Tomar N, editors. Immunoinformatics. New York: Springer; 2014. pp. 149–164.

[22] Cavkaytar O, Akdis CA, Akdis M. Modulation of immune responses by immunother‐ apy in allergic diseases. Curr Opin Pharmacol. 2014;17:30–37. DOI: 10.1016/j.coph.

[23] Larché M. T cell epitope-based allergy vaccines. In: Valenta R, Coffman R, editors. Vaccines against Allergies. Berlin, Heidelberg: Springer; 2011. pp. 107–119. DOI:

[24] Prickett SR, Rolland JM, O'Hehir RE. Immunoregulatory T cell epitope peptides: the new frontier in allergy therapy. Clin Exp Allergy. 2015;45:1015–1026. DOI: 10.1111/cea.

[25] Frazier A, Schulten V, Hinz D, Oseroff C, Sidney J, Peters B, Sette A. Allergy-associated T cell epitope repertoires are surprisingly diverse and include non-IgE reactive

[26] Vaughan K, Greenbaum J, Kim Y, Vita R, Chung J, Peters B, Broide D, Goodman R, Grey H, Sette A. Towards defining molecular determinants recognized by adaptive immunity in allergic disease: an inventory of the available data. J Allergy (Cairo).

antigens. World Allergy Organ J. 2014;7:26. DOI: 10.1186/1939-4551-7-26

epitopes. Allergol Int. 2015;64:332–343. DOI: 10.1016/j.alit.2015.06.009

2010;152:1–11. DOI: 10.1159/000260078

74 Bioinformatics - Updated Features and Applications

2001;107:219–221. DOI: 10.1067/mai.2001.112850

1274. DOI: 10.1111/j.1398-9995.2011.02656.x

1398-9995.2011.02625.x

10.2174/092986607780989921

DOI: 10.1007/978-1-4939-1115-8\_8

2011;2010:628026. DOI: 10.1155/2010/628026

2014.07.003

12554

10.1007/82\_2011\_131

action along with the tertiary structure information. J Proteomics Bioinform. 2012;5:84– 89. DOI: 10.4172/jpb.1000217

[57] The Immune Epitope Database (IEDB). 2010. Available from: http://www.iedb.org [Accessed: 2016-03-07].

[41] Ivanciuc O, Schein C, Braun W. SDAP: database and computational tools for allergenic

[42] NCBI Resource Coordinators. Database resources of the National Center for Biotech‐ nology Information. Nucl Acids Res. 2015;43:D6–D17. DOI: 10.1093/nar/gku1130 [43] Allergen Database for Food Safety (ADFS). Available from: http://allergen.nihs.go.jp/

[44] Nakamura R, Teshima R, Takagi K, Sawada J. Development of Allergen Database for Food Safety (ADFS): an integrated database to search allergens and predict allergenic‐

[45] AllergenOnline. Available from: http://www.allergenonline.org [Accessed:

[46] Goodman RE, Ebisawa M, Ferreira F, Sampson HA, van Ree R, Vieths S, et al. Aller‐ genOnline: a peer-reviewed, curated allergen database to assess novel food proteins for potential cross-reactivity. Mol Nutr Food Res. 2016;60:1183–98. DOI: 10.1002/mnfr.

[47] AllFam. Available from: http://www.meduniwien.ac.at/allfam [Accessed: 2016-03-07]. [48] Radauer C, Bublin M, Wagner S, Breiteneder H. Allergens are distributed into few protein families and possess a restricted number of biochemical functions. J Allergy

[49] Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, Heger A, Hether‐ ington K, Holm L, Mistry J, Sonnhammer EL, Tate J, Punta M. Pfam: the protein families

[50] Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol.

[51] Gene Ontology Consortium. The Gene Ontology (GO) database and informatics

[52] AllergenPro. Available from: http://nabic.rda.go.kr/allergen [Accessed: 2016-06-01]. [53] Kim CK, Seol YJ, Lee DJ, Jeong IS, Yoon UH, Lee JY, Lee GS, Park DS. AllergenPro: an integrated database for allergenicity analysis and prediction. Bioinformation.

[54] Saha S, Bhasin M, Raghava GP. Bcipep: a database of B-cell epitopes. BMC Genomics.

[55] Huang J, Honda W. CED: a conformational epitope database. BMC Immunol. 2006;7:7.

[56] Sharma OP, Das AA, Krishna R, Suresh Kumar M, Mathur PP. Structural epitope database (SEDB): a web-based database for the epitope, and its intermolecular inter‐

database. Nucl Acids Res. 2013;42:D222–D230. DOI: 10.1093/nar/gkt1223

resource. Nucl Acids Res. 2004;32:D258–D261. DOI: 10.1093/nar/gkh036

Clin Immunol. 2008;121:847–852. DOI: 10.1016/j.jaci.2008.01.025

1995;247:536–540. DOI: 10.1016/S0022-2836(05)80134-2

2014;10:378. DOI: 10.6026/97320630010378

2005;6:79. DOI: 10.1186/1471-2164-6-79

DOI: 10.1186/1471-2172-7-7

ity. Kokuritsu Iyakuhin Shokuhin Eisei Kenkyujo hokoku. 2004;123:32–36.

proteins. Nucl Acids Res. 2003;31:359–362. DOI: 10.1093/nar/gkg010

ADFS/ [Accessed: 2016-06-01].

76 Bioinformatics - Updated Features and Applications

2016-03-07].

201500769

[81] Dimitrov I, Naneva L, Bangov I, Doytchinova I. Allergenicity prediction by artificial neural networks. J Chemom. 2014;28:282–286. DOI: 10.1002/cem.2597

[68] Verma A, Misra A, Subash S, Das M, Dwivedi P. Computational allergenicity prediction of transgenic proteins expressed in genetically modified crops. Immunopharmacol

[69] Hileman RE, Silvanovich A, Goodman RE, Rice EA, Holleschak G, Astwood JD, Hefle SL. Bioinformatic methods for allergenicity assessment using a comprehensive allergen database. Int Arch Allergy Immunol. 2002;128:280–291. DOI: 10.1159/000063861

[70] Kleter GA, Peijnenburg AA. Screening of transgenic proteins expressed in transgenic food crops for the presence of short amino acid sequences identical to potential, IgE– binding linear epitopes of allergens. BMC Struct Biol. 2002;2:8. DOI:

[71] Stadler MB, Stadler BM. Allergenicity prediction by protein sequence. FASEB J.

[72] The web tool WebAllergen (http://weballergen.bii.a-star.edu.sg/) could not be accessed

[73] Riaz T, Hor HL, Krishnan A, Tang F, Li KB. WebAllergen: a web server for predicting allergenic proteins. Bioinformatics. 2005;21:2570–2571. DOI: 10.1093/bioinformatics/

[74] Kong W, Tan TS, Tham L, Choo KW. Improved prediction of allergenicity by combi‐

[75] Saha S, Raghava GP. AlgPred: prediction of allergenic proteins and mapping of IgE

[76] Tyagi N, Farnell EJ, Fitzsimmons CM, Ryan S, Tukahebwa E, Maizels RM, Dunne DW, Thornton JM, Furnham N. Comparisons of allergenic and metazoan parasite proteins: allergy the price of immunity. PLoS Comput Biol. 2015;11:e1004546. DOI: 10.1371/

[77] Kadam K, Sawant S, Kulkarni-Kale U, Jayaraman VK. Prediction of protein function based on machine learning methods: an overview. In: iConcept Press, editor. Genomics III - Methods, Techniques and Applications. Hong Kong: iConcept Press; 2014 . pp. 125–

[78] Zorzet A, Gustafsson M, Hammerling U. Prediction of food protein allergenicity: a

[79] Soeria-Atmadja D, Zorzet A, Gustafsson MG, Hammerling U. Statistical evaluation of local alignment features predicting allergenicity using supervised classification algorithms. Int Arch Allergy Immunol. 2004;133:101–112. DOI: 10.1159/000076382

[80] Li KB, Issac P, Krishnan A. Predicting allergenic proteins using wavelet transform.

bioinformatic learning systems approach. In Silico Biol. 2002;2:525–534.

Bioinformatics. 2004;20:2572–2578. DOI: 10.1093/bioinformatics/bth286

epitopes. Nucl Acids Res. 2006;34:W202–W209. DOI: 10.1093/nar/gkl343

nation of multiple sequence motifs. In Silico Biol. 2007;7:77–86.

Immunotoxicol. 2011;33:410–422. DOI: 10.3109/08923973.2010.523704

10.1186/1472-6807-2-8

78 Bioinformatics - Updated Features and Applications

on 2016-06-01

journal.pcbi.1004546

162. ISBN: 978-1-922227-096

bti356

2003;17:1141–1143. DOI: 10.1096/fj.02-1052fje

[109] Yang X, Yu X. An introduction to epitope prediction methods and software. Rev Med Virol. 2009;19:77–96

[96] Wang J, Zhang D, Li J. PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection. BMC Syst Biol. 2013;7:S9. DOI:

[97] Chrysostomou C, Seker H. Prediction of protein allergenicity based on signal-process‐ ing bioinformatics approach. In: Proceedings of the Engineering in Medicine and Biology Society (EMBC), 36th Annual International Conference of the IEEE 2014.

Chicago: IEEE; 2014. pp. 808–811. DOI: 10.1109/EMBC.2014.6943714

[98] Allerdictor. Available from: http://allerdictor.vbi.vt.edu/ [Accessed: 2016-03-07].

[99] Dang HX, Lawrence CB. Allerdictor: fast allergen prediction using text classification techniques. Bioinformatics. 2014;30:1120–1128. DOI: 10.1093/bioinformatics/btu004

[100] Wang J, Yu Y, Zhao Y, Zhang D, Li J. Evaluation and integration of existing methods for computational prediction of allergens. BMC Bioinformatics. 2013;14:S1. DOI:

[101] proAP. Available from: http://gmobl.sjtu.edu.cn/proAP/main.html [Accessed:

[102] AllergenFP. Available from: http://ddg-pharmfac.net/AllergenFP/ [Accessed:

[103] Dimitrov I, Naneva L, Doytchinova I, Bangov I. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics. 2014;30:846–851. DOI: 10.1093/bioinformatics/

[104] Bragin AO, Demenkov PS, Kolchanov NA, Ivanisenko VA. Accuracy of protein allergenicity prediction can be improved by taking into account data on allergenic protein discontinuous peptides. J Biomol Struct Dyn. 2013;31:59–64. DOI:

[105] Vijayakumar S, Lakshmi PTV. A fuzzy inference system for predicting allergenicity and allergic cross-reactivity in proteins. In: Proceedings of the Bioinformatics and Biomedicine (BIBM), IEEE International Conference; 18–21 December 2013. Shanghai:

[106] FuzzyApp. Available from: http://fuzzyapp.bicpu.edu.in/fuzzyapp.php [Accessed:

[107] Saravanan V, Lakshmi PT. Fuzzy logic for personalized healthcare and diagnostics: FuzzyApp—a fuzzy logic based allergen-protein predictor. Omics. 2014;18:570–581.

[108] Jiang B, Qu H, Hu Y, Ni T, Lin Z. Computational analysis of the relationship between allergenicity and digestibility of allergenic proteins in simulated gastric fluid. BMC

IEEE; 2013. pp. 49–52. DOI: 10.1109/BIBM.2013.6732458

Bioinformatics. 2007;8:375. DOI: 10.1186/1471-2105-8-375

10.1186/1752-0509-7-S5-S9

80 Bioinformatics - Updated Features and Applications

10.1186/1471-2105-14-S4-S1

10.1080/07391102.2012.691362

DOI: 10.1089/omi.2014.0021

2016-03-07].

2016-03-07].

2016-03-07].

btt619

[136] Weber CA, Mehta PJ, Ardito M, Moise L, Martin B, De Groot AS. T cell epitope: friend or foe? Immunogenicity of biologics in context. Adv Drug Deliv Rev. 2009;61:965–976. DOI: 10.1016/j.addr.2009.07.001

[123] Kolaskar AS, Kulkarni-Kale U. Prediction of three-dimensional structure and mapping of conformational epitopes of envelope glycoprotein of Japanese encephalitis virus.

[124] Kulkarni-Kale U, Bhosle S, Kolaskar AS. CEP: a conformational epitope prediction

[125] Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol.

[126] Qi T, Qiu T, Zhang Q, Tang K, Fan Y, Qiu J, Wu D, Zhang W, Chen Y, Gao J, Zhu R. SEPPA 2.0—more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen. Nucl Acids Res.

[127] Sweredoski MJ, Baldi P. PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure. Bioinformatics.

[128] Giacò L, Amicosante M, Fraziano M, Gherardini PF, Ausiello G, Helmer-Citterich M, Colizzi V, Cabibbo A. B-Pred, a structure based B-cell epitopes prediction server. Adv

[129] Ansari HR, Raghava GP. Identification of conformational B-cell epitopes in an antigen from its primary sequence. Immunome Res. 2010;6:6. DOI: 10.1186/1745-7580-6-6 [130] Ponomarenko J, Bui HH, Li W, Fusseder N, Bourne PE, Sette A, Peters B. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics.

[131] Rubinstein ND, Mayrose I, Martz E, Pupko T. Epitopia: a web-server for predicting Bcell epitopes. BMC Bioinformatics. 2009;10:287. DOI: 10.1186/1471-2105-10-287 [132] Liang S, Zheng D, Standley DM, Yao B, Zacharias M, Zhang C. EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server

[133] Liang S, Zheng D, Zhang C, Zacharias M. Prediction of antigenic epitopes on protein surfaces by consensus scoring. BMC Bioinformatics. 2009;10:302. DOI:

[134] Zheng W, Ruan J, Hu G, Wang K, Hanlon M, Gao J. Analysis of conformational B-cell epitopes in the antibody–antigen complex using the depth function and the convex

[135] AgAbDb. Available from: http://bioinfo.net.in/AgAbDb.htm [Accessed: 2016-03-07].

results. BMC Bioinformatics. 2010;11:381. DOI: 10.1186/1471-2105-11-381

Hull. PLoS One. 2015;10:e0134835. DOI: 10.1371/journal.pone.0134835

server. Nucl Acids Res. 2005;33:W168–W171. DOI: 10.1093/nar/gki460

Virology. 1999;261:31–42. DOI: 10.1006/viro.1999.9859

82 Bioinformatics - Updated Features and Applications

2012;8:e1002829. DOI: 10.1371/journal.pcbi.1002829

2008;24:1459–1460. DOI: 10.1093/bioinformatics/btn199

Appl Bioinform Chem. 2012;5:11–21. DOI: 10.2147/AABC.S30620

2014;42:W59–W63. DOI: 10.1093/nar/gku395

2008;9:1. DOI: 10.1186/1471-2105-9-514

10.1186/1471-2105-10-302

### **Bioinformatics for Membrane Lipid Simulations: Models, Computational Methods, and Web Server Tools**

S. W. Leong, T. S. Lim and Y. S. Choong

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62576

#### **Abstract**

[150] Sela-Culang I, Ofran Y, Peters B. Antibody specific epitope prediction—emergence of a new paradigm. Curr Opin Virol. 2015;11:98–102. DOI: 10.1016/j.coviro.2015.03.012

[151] Tong JC, Tammi MT. Prediction of protein allergenicity using local description of amino

[152] Dall'Antonia F, Gieras A, Devanaboyina SC, Valenta R, Keller W. Prediction of IgEbinding epitopes by means of allergen surface comparison and correlation to crossreactivity. J Allergy Clin Immunol. 2011;128:872–879. DOI: 10.1016/j.jaci.2011.07.007

[153] Lollier V, Denery-Papini S, Brossard C, Tessier D. Meta-analysis of IgE-binding allergen

[154] LocAllEpi. Available from: http://wwwappli.nantes.inra.fr:8180/LocAllEpi/ [Accessed:

[155] Aalberse RC, Stadler BM. In silico predictability of allergenicity: from amino acid sequence via 3‐D structure to allergenicity. Mol Nutr Food Res. 2006;50:625–627. DOI:

[156] Canonica GW, Bachert C, Hellings P, Ryan D, Valovirta E, Wickman M, De Beaumont O, Bousquet J. Allergen immunotherapy (AIT): a prototype of precision medicine.

World Allergy Organ J. 2015;8:31. DOI: 10.1186/s40413-015-0079-7

epitopes. Clin Immunol. 2014;153:31–39. DOI: 10.1016/j.clim.2014.03.010

2016-03-07].

10.1002/mnfr.200500270