2.4.3 Societal perspective

The societal perspective leads to the estimation of the costs estimated from both the payer and patient perspectives and the losses of societal productivity caused by the given diseases. In other words, the costs estimated based on this perspective include the costs of lost labor and productivity due to patients taking leaves of absence or dying prematurely. These costs may also encompass the costs of declines in quality of life and the psychological suffering of patients.

The table below summarizes the differences among these perspectives (Table 3).


Some years later, Loose *et al*. (2006) developed a similar but more sophisticated *de novo* method, the linguistic model. According to this model, AMPs seem to be a formal language with grammar composed of several rules (patterns) and a vocabulary (amino acids). Instead of using alignments to define the patterns, the TEIRESIAS algorithm was used for pattern discovery (Rigoutsos & Floratos, 1998). Thus, ~700 grammars sequences were established, and then all possible grammar sequences with 20 amino acid residues were written out. Sequences with at least 60% of identity with natural AMPs were removed, resulting in 12 million remaining sequences. Next, by removing sequences with at least 70% of identity, 41 candidates were obtained. From these, one peptide was insoluble, but 18 had MIC at maximum of 256 μg. ml-1 and the remaining peptides showed no activity against *E. coli* and *Bacillus cereus*. Through this method, novel antimicrobial peptides were designed without any information about their structures (Loose *et al*., 2006). In fact, as well as generating novel AMPs, this work was of great importance in that it explained some results of physicochemical methods of rational design. For each grammatical peptide, a nongrammatical peptide was designed with the same amino acid composition, by shuffling the sequence. Giangaspero *et al.* (2001) had already used this strategy and the shuffled peptide showed a reduced activity. However, Loose *et al*. (2006) generated shuffled peptides with no grammars, expecting that they had no activity because they were non-grammatical peptides. As result, only two shuffled peptides were active. From this, it could be seen that there are no direct relations between scalar physicochemical properties and antimicrobial activity, because shuffled and grammatical peptides have the same charge, hydrophobicity, size and molecular mass (Loose *et al*., 2006). This explains why the conclusions of physicochemical methods were not completely correct, and in some cases, controversial (*i.e.,* equal MICs but different physicochemical properties and *vice-versa*). The scalar physicochemical properties had led researchers to false conclusions because they have a secondary role in activity.

On the other hand, a property widely used in physicochemical methods does change when the sequence is shuffled, and that is the hydrophobic moment, a vector property. Loose *et al*. (2006) also used the hydrophobic moment. In a second step of rational design, the best designed sequence was submitted to a redesign process to increase its activity using a heuristic approach, and one of proposals of this redesign process was to "improve the segregation of positive and hydrophobic residues based on a helical projection" or, in other words, to improve the hydrophobic moment. The fact that no structural information is needed is certainly an advantage, but this method has some limitations, such as the difficulty in designing larger proteins with complex structures. So this method is restricted to generating AMPs similar to those that are deposited in the main data set, *i.e.*, the two most active peptides obtained through this method have 50 and 60% of identity to natural AMPs.

All these methods have been helpful, in their time, in reaching a better understanding of relationships between sequence, structure, physicochemical properties and antimicrobial activity. Overall, these methods have been effective in designing potent AMPs able to kill bacteria at low concentrations. Furthermore, they have also been helpful in the development of antimicrobial prediction tools, as will be seen in the next section.

#### **3. Methods to predict antimicrobial activity**

The understanding of antimicrobial peptides' behaviour led some groups to propose different approaches to predict antimicrobial activity, and this field saw much progress in last years. Several methods of antimicrobial activity prediction emerged from studies of rational design, mainly the physicochemical and *de novo* methods. The rules extracted from rational design methods can be extrapolated to other sequences with good reliability by computer-aided predictions. As a result, several tools have been developed, such as prediction tools from Collection of Antimicrobial Peptides (CAMP, Thomas *et al*., 2010) and AntiBP Server (Lata *et al*., 2010). Overall, there are two main strategies for predicting AMPs, the empirical methods and the supervised machine learning ones.

#### **3.1 Empirical methods of AMP prediction**

The empirical methods are qualitative, being based only on characters of AMPs without taking into account peptides without antimicrobial activity. In fact, these models are based on rules or patterns correlated to antimicrobial activity. However, the methods cannot be extrapolated to other classes of AMP, being restricted to the class that generated the model. Moreover, they have no standard accuracy measurement, since there is no larger set of nonantimicrobial sequences to test them. In fact, there is no accuracy value, making it complicated to compare the methods, which are summarized in Table 1.

The most simple prediction method is that employed by the Antimicrobial Peptides Database prediction tool (Wang & Wang, 2004). In this case no artificial intelligence was used. It is based only on logical questions about the sequence. It returns a positive prediction whenever a sequence is less than 50 amino acid residues in length, has hydrophobicity below 75% and a cationic net charge. Moreover, the prediction is also going to be positive if the sequences present an even number of cysteines. This method seems to be merely based on rules extracted from physicochemical studies. However, this method neglects some AMPs (*e.g.,* anionic and hydrophilic antimicrobial peptides).

Despite these clear limitations the APD prediction tool was used by Nagarajan *et al.* (2006) for validating their prediction method. This method was developed in order to mine protein data sets, being based on Fourier transformations and Euclidian distances. Comparisons were made with a power spectrum generated by the Fourier transformation of five indices. The indices were based on hydrophobicity, charge, polarity, cysteine content and amino acid distribution. For analysis, the method was applied to six antimicrobial peptides with 16 amino acid residues. In all cases, the power spectrum shows a peak at period 5, and the

to generating AMPs similar to those that are deposited in the main data set, *i.e.*, the two most active peptides obtained through this method have 50 and 60% of identity to natural

All these methods have been helpful, in their time, in reaching a better understanding of relationships between sequence, structure, physicochemical properties and antimicrobial activity. Overall, these methods have been effective in designing potent AMPs able to kill bacteria at low concentrations. Furthermore, they have also been helpful in the development

The understanding of antimicrobial peptides' behaviour led some groups to propose different approaches to predict antimicrobial activity, and this field saw much progress in last years. Several methods of antimicrobial activity prediction emerged from studies of rational design, mainly the physicochemical and *de novo* methods. The rules extracted from rational design methods can be extrapolated to other sequences with good reliability by computer-aided predictions. As a result, several tools have been developed, such as prediction tools from Collection of Antimicrobial Peptides (CAMP, Thomas *et al*., 2010) and AntiBP Server (Lata *et al*., 2010). Overall, there are two main strategies for predicting AMPs,

The empirical methods are qualitative, being based only on characters of AMPs without taking into account peptides without antimicrobial activity. In fact, these models are based on rules or patterns correlated to antimicrobial activity. However, the methods cannot be extrapolated to other classes of AMP, being restricted to the class that generated the model. Moreover, they have no standard accuracy measurement, since there is no larger set of nonantimicrobial sequences to test them. In fact, there is no accuracy value, making it

The most simple prediction method is that employed by the Antimicrobial Peptides Database prediction tool (Wang & Wang, 2004). In this case no artificial intelligence was used. It is based only on logical questions about the sequence. It returns a positive prediction whenever a sequence is less than 50 amino acid residues in length, has hydrophobicity below 75% and a cationic net charge. Moreover, the prediction is also going to be positive if the sequences present an even number of cysteines. This method seems to be merely based on rules extracted from physicochemical studies. However, this method

Despite these clear limitations the APD prediction tool was used by Nagarajan *et al.* (2006) for validating their prediction method. This method was developed in order to mine protein data sets, being based on Fourier transformations and Euclidian distances. Comparisons were made with a power spectrum generated by the Fourier transformation of five indices. The indices were based on hydrophobicity, charge, polarity, cysteine content and amino acid distribution. For analysis, the method was applied to six antimicrobial peptides with 16 amino acid residues. In all cases, the power spectrum shows a peak at period 5, and the

of antimicrobial prediction tools, as will be seen in the next section.

the empirical methods and the supervised machine learning ones.

complicated to compare the methods, which are summarized in Table 1.

neglects some AMPs (*e.g.,* anionic and hydrophilic antimicrobial peptides).

**3. Methods to predict antimicrobial activity**

**3.1 Empirical methods of AMP prediction**

AMPs.

major contribution to power spectrum is given by hydrophobicity index. Those power spectra were used to generate a reference power spectrum used in further comparisons. A set of 10,000 random peptides with 16 amino acid residues were generated by PERL scripts. The power spectrum of each random sequence was obtained and compared to the reference through Euclidian distance. From 10,000 random sequences, only three hits were obtained. Two of three hits had positive charge and were predicted as AMPs by the APD prediction tool. However, the three hits showed at least 30% of identity to a known AMP.

Similarly, Fernandes *et al.* (2009) developed a classification method based on fuzzy modelling, also focusing on data set mining. This approach is based on the linguistic model developed by Loose *et al.* (2006) and also on the peptide's amphipathicity. It made each screening into a data set, searching for sequences with a defined pattern. The found sequences were then classified by fuzzy modelling. This consists of a surface plot generated by two membership functions: a triangular function relating the ratio of polar to charged residues and a Gaussian hydrophobicity membership. The best candidates fall into a region between 2:1 and 1:1 polar to charged residues and the regions of moderated hydrophobicity in Gaussian membership, identifying the amphipathic sequences. Assuming hydrophobicity to be low or the ratio to be lower than 1, the sequence is a weak AMP; if hydrophobicity is medium and the ratio is adequate the peptide is a specific AMP; and if hydrophobicity or the ratio is high, the peptide is non-specific. The system was tested in NCBI's nonredundant protein data set (NR) and the seed sequence was Cn-AMP1 (Mandal *et al*., 2009). Through this, three sequences were obtained from a total of 7,153,872 sequences in NR.

Another method that involves patterns is the multidimensional signatures developed by Yount & Yeaman (2004). This method is based on recognition of sequence patterns and motifs in three dimensional structures to correlate them to antimicrobial activity. It was successfully applied to cysteine-stabilized peptides. In this work, a γ-core motif was recognized by the patterns "X[1,3]GXCX[3,9]C", "CX[3,9]CXGX[1,3]" and "CX[3,9]GXCX[1,3]", where X corresponds to any natural amino acid and the numbers between brackets represent sequence variations (*i.e.,* X[3,9] represents an extension of three to nine residues, being composed of any natural amino acid residue). Based on a data set of 500 antimicrobial peptides with length of up to 75 amino acids residues and cysteine content, prototypic sequences were chosen as representative of their classes. The conserved motif GXC was identified by visual inspection of multiple alignments. However, in some sequences this motif was inverted, so the three patterns of γ-core motif were proposed. Structurally the three patterns are absolutely conserved, corresponding to an antiparallel βsheet composed of two strands. In order to validate the model, two peptides without previously reported antimicrobial activity were selected, the sweet-tasting protein brazzein and the toxin charybdotoxin, both containing the γ-core motif in their 3D structures. These two peptides exerted direct antimicrobial activity against bacteria and fungi. The method was also validated by identification of γ-core sequence into well-known antimicrobial peptides without known 3D structure. The γ-core motif was identified in tachyplesins before its structure became available. Its three-dimensional structure really exhibits the motif of two antiparallel β-strands.

Jenssen *et al.* (2007) also developed and tested their model *in vitro*. They constructed a mathematical model for prediction based on the statistical methods, principal component analysis (PCA) and partial least squares (PLS). This model was filled up with three major classes of descriptors, (i) amino acid (charge, hydrophobicity and size); (ii) a series of contact energy for each pair of amino acids and (iii) 78 biophysical inductive and conventional quantitative QSAR descriptors. These data were extracted from a single-substitution Bac2alibrary containing 228 peptides. This model was capable of predicting 84% of tested peptides.

Table 1. Empirical antimicrobial prediction methods.

In fact, the last two methods are the most important among the methods discussed so far, mainly due to the *in vitro* validation of predictions. Without this kind of validation, these methods become only good hypotheses, without contributing much knowledge. However, they can achieve a more accurate prediction when they are more restricted to some class of AMP, without a generalization model.

#### **3.2 Supervised machine learning methods of AMP prediction**

Supervised machine learning methods for predicting antimicrobial activities have a wellestablished validation procedure, allowing these methods to be compared. The reliability of these methods is evaluated by several parameters, the main three being calculated as follows:

$$Sensitivity = \frac{TP}{TP + FN} \times 100\tag{2}$$

$$Specificity = \frac{TN}{TN + FP} \times 100\tag{3}$$

$$Accuracy = \frac{\tau p + TN}{TP + TN + FN + FP} \times 100\tag{4}$$

TP corresponds to the number of true positives; FN, the false negatives; TN, the true negative; and FP, the false positives. However, the evaluation of precision on positive predictions can be done by calculating the positive predictive value (PPV), given by the following equation:

classes of descriptors, (i) amino acid (charge, hydrophobicity and size); (ii) a series of contact energy for each pair of amino acids and (iii) 78 biophysical inductive and conventional quantitative QSAR descriptors. These data were extracted from a single-substitution Bac2alibrary containing 228 peptides. This model was capable of predicting 84% of tested

**Method How is the evaluation done? References**  APD Prediction Tool Well-known AMP properties Wang & Wang, 2004 Fourier Transformation Physicochemical properties Nagarajan *et al*., 2006

> Amino acid descriptors, pairs of amino acid descriptors and QSAR descriptors

Signatures Sequence and structure motifs Yount & Yeaman, 2004

In fact, the last two methods are the most important among the methods discussed so far, mainly due to the *in vitro* validation of predictions. Without this kind of validation, these methods become only good hypotheses, without contributing much knowledge. However, they can achieve a more accurate prediction when they are more restricted to some class of

Supervised machine learning methods for predicting antimicrobial activities have a wellestablished validation procedure, allowing these methods to be compared. The reliability of these methods is evaluated by several parameters, the main three being calculated as

TP corresponds to the number of true positives; FN, the false negatives; TN, the true negative; and FP, the false positives. However, the evaluation of precision on positive predictions can be done by calculating the positive predictive value (PPV), given by the

physicochemical properties Fernandes *et al*., 2009

Jenssen *et al.*, 2007

(2)

(3)

(4)

Fuzzy Modelling Sequence motifs and

**3.2 Supervised machine learning methods of AMP prediction**

Table 1. Empirical antimicrobial prediction methods.

peptides.

follows:

following equation:

PCA/PLS

Multidimensional

AMP, without a generalization model.

$$PPV = \frac{Sensitivity}{Sensitivity + (100 - S specificity)} \times 100 \, \therefore \, \frac{TP}{TP + FP} \times 100 \, \tag{5}$$

Here, comparisons among the methods are going to be made based on PPV and accuracy values (Table 2), since for discovering novel AMPs it is more important that the probability of a positive prediction be true. Overall, these methods require two data sets, the training set and the blind set. The training set is composed of two subsets, the positive data set (the AMPs) and the negative data set (the non-AMPs). Through these sets the algorithm is trained and then, tested against the blind set. From the results against the blind set, the true positives, negative and false positives and negatives are estimated and then the parameters (*e.g.,* accuracy) are calculated.

There are two major challenges in the usage of supervised machine learning to predict antimicrobial activity: the AMPs' size variation, and the absence of a dataset for nonantimicrobial peptides (Lata *et al.*, 2007). There are at least two choices of positive set, APD (Wang & Wang, 2004) and CAMP (Thomas *et al.*, 2010). Nevertheless, there are no nonantimicrobial data bases to use as a negative set. Another difficulty is the variation in size of AMPs, since the machine learning techniques need fixed length input vectors. Several strategies have been developed in order to overcome these problems.

Lata *et al.* (2007) developed the first supervised machine learning methods for prediction of antimicrobial activity. In this pioneer work, three algorithms were tested: SVM, ANN and quantitative matrices (QM). The positive data set was composed of 436 AMPs from APD and the negative set was composed of an equal number of non-secretory proteins randomly selected from SwissProt. Initially, an SVM model using amino acid composition of whole sequence was built with 20 inputs, one for each amino acid. This model achieves the highest accuracy of all generated models in 5-fold cross validation (89.04%). However, the authors proposed that is impossible to utilize this approach to search for AMPs in genomes or proteins due to the enormous size variation. Thus, it was decided that a fixed length would be used, using binary patterns, where each amino acid is represented by one binary pattern. SVM models with the 5, 10, 15 or 20 first N-terminal residues were constructed. The best accuracy observed in 5-fold cross validation was in SVM model with 15 residues (87.85%). Therefore, another two approaches using SVM were developed, the C-terminal approach (with 15 Cterminal residues) and the N+C-terminal approach (with 30 residues, 15 from N-terminal and 15 from C-terminal). The C-terminal approach achieves an accuracy of 85.16 %, while the N+Cterminal one achieves 92.11% in 5-fold cross validation. The three approaches were applied to QM and ANN. In both cases, the N+C-Terminal approach achieved the best accuracies in 5 fold cross validation, 90.37% and 88.17%, respectively. In a blind data set composed of 24 mature sequences extracted from SwissProt, the N+C-terminal approach had the higher performance in all algorithms, achieving a PPV of 91.66% for all algorithms.

In 2010, this system was improved, but only the SVM was used (Lata *et al*., 2010). In this new version, the positive data set was composed of 999 AMPs and the negative data set was constructed with an equal number of non-secretory proteins extracted from SwissProt. The blind set was composed of 466 AMPs from SwissProt, none of which were present in the positive set. The N+C-terminal approach continued to show higher accuracy (91.64%). Despite the drop in precision (92.11 to 91.64%), the improved version was more reliable because the number of sequences used in training and testing were higher than the previous version.

Thomas *et al.* (2009) used two other methods in addition to SVM. Random forest (RF) and discriminant analysis (DA) were implemented. RF showed the finest accuracy (93.2%), followed by SVM (91.5%) and DA (87.5%). The positive data set was composed of 2578 AMPs and 4011 sequences derived from SwissProt or randomly generated sequences. 70% of each set was used for training the machines and the other 30% composed the blind set. The algorithms were trained with 275 features, including composition, physicochemical properties and structural characteristics of each amino acid. In contrast to that of Lata *et al.* 2007 and 2010, the method developed by Thomas *et al.* (2009) was able to predict antimicrobial activity for sequences with variable size.

Table 2. Supervised machine learning methods of antimicrobial prediction.

Our group has developed an SVM model based on physicochemical properties for prediction of peptides with cysteine knot motifs (Porto *et al.*, 2010). Despite the absence of direct correlation between antimicrobial activity and physicochemical properties, their use solved the problem of size variation. However, it generated another problem, which is that shuffled sequences have the same scalar properties, since they are simple averages and the order of residues does not imply average modifications. That problem is avoided by including the hydrophobic moment, since the modification of sequence clearly modifies the hydrophobic moment. For the second challenge, a set of predicted transmembrane proteins was used as the set of non-antimicrobial peptides, since the transmembranes are non-secretory proteins. Through this approach, an overall accuracy of 83% was observed in a blind dataset. This model can be helpful to predict antimicrobial activity of a wide number of cysteine-stabilized peptides, such as conotoxins, proteinase inhibitors, metallothioneins, defensins and cyclotides. The only requirement is the presence of disulphide bonds in the peptide structure.

Thomas *et al.* (2009) used two other methods in addition to SVM. Random forest (RF) and discriminant analysis (DA) were implemented. RF showed the finest accuracy (93.2%), followed by SVM (91.5%) and DA (87.5%). The positive data set was composed of 2578 AMPs and 4011 sequences derived from SwissProt or randomly generated sequences. 70% of each set was used for training the machines and the other 30% composed the blind set. The algorithms were trained with 275 features, including composition, physicochemical properties and structural characteristics of each amino acid. In contrast to that of Lata *et al.* 2007 and 2010, the method developed by Thomas *et al.* (2009) was able to predict

**Set Negative Set Accuracy (%) PPV**

SwissProt and Random Sequences

SwissProt and Random Sequences

SwissProt and Random Sequences

Random Transmembrane Sequences

Non-AMP

Table 2. Supervised machine learning methods of antimicrobial prediction.

The only requirement is the presence of disulphide bonds in the peptide structure.

SVM APD SwissProt 92.11 92.11 Lata *et al.*, 2007 QM APD SwissProt 90.37 90.65 Lata *et al.*, 2007 ANN APD SwissProt 88.17 88.17 Lata *et al.*, 2007 SVM APD SwissProt 91.64 90.43 Lata *et al.*, 2010

ANN CAMP SwissProt 89.20 88.60 Torrent *et al.*, 2011

Our group has developed an SVM model based on physicochemical properties for prediction of peptides with cysteine knot motifs (Porto *et al.*, 2010). Despite the absence of direct correlation between antimicrobial activity and physicochemical properties, their use solved the problem of size variation. However, it generated another problem, which is that shuffled sequences have the same scalar properties, since they are simple averages and the order of residues does not imply average modifications. That problem is avoided by including the hydrophobic moment, since the modification of sequence clearly modifies the hydrophobic moment. For the second challenge, a set of predicted transmembrane proteins was used as the set of non-antimicrobial peptides, since the transmembranes are non-secretory proteins. Through this approach, an overall accuracy of 83% was observed in a blind dataset. This model can be helpful to predict antimicrobial activity of a wide number of cysteine-stabilized peptides, such as conotoxins, proteinase inhibitors, metallothioneins, defensins and cyclotides.

**(%) References**

93.20 95.13 Thomas *et al*., 2010

91.50 93.42 Thomas *et al*., 2010

87.50 87.45 Thomas *et al*., 2010

83.02 96.54 Porto *et al.*, 2010

Library 86.50 92.94 Fjell *et al.*, 2009

antimicrobial activity for sequences with variable size.

**Method Positive**

RF CAMP

SVM CAMP

DA CAMP

SVM APD

ANN AMP

Library

Also using physicochemical properties, Torrent *et al.* (2011) developed an ANN with eight properties: isoelectric point (pI), peptide length, -helix, β-sheet and turn structure propensity, *in vivo* and *in vitro* aggregation propensity and hydrophobicity. The main data set was composed of 1157 AMPs from CAMP and 991 non-AMPs from SwissProt. The training set was composed of 1074 peptides, while the testing and validation sets contained 537 peptides. The system achieved an accuracy of 90%. Indeed, the aggregation propensity was seen to be crucial for this method in much the same way as the hydrophobic moment in the method developed by Porto *et al.* (2010). The aggregation propensity changes if the sequence is shuffled, but the other six properties do not. When the aggregation parameter is removed, the system's reliability decreases.

The methods discussed so far show that AMP size variation problem is easy to solve, by using fixed sizes or physicochemical properties. Both strategies achieve similar accuracies. On the other hand, the non-AMP data set only seems to be easier to solve by using random proteins or proteins from SwissProt. However, comparing AMPs to randomly selected proteins from SwissProt is almost the same as comparing oranges to strawberries; it is relatively easy to distinguish each, generating high accuracies. Moreover, as shown in section 2, two peptides with high identities and subsequently similar properties can have different activities, as is the case of peptides derived from pardaxin (Thennarasu & Nagaraj, 1996) and LL-37 (Sigurdardottir *et al*., 2006).

Lately, a combined approach of QSAR and machine learning techniques has been developed (Fjell *et al*., 2009). Through 44 QSAR descriptors, an ANN was built based on 1433 random nine-mer peptides. The ANN was trained to predict sequence activity in relation to the control peptide Bac2A. For model evaluation, a library of nine-mer peptides composed of approximately 100,000 sequences was screened *in silico*. These sequences were divided into four classes: (I) most likely to be more active than the control; (II) likely to be more active than the control; (III) likely to be less active than the control; and (IV) most likely to be less active than the control. The topmost 50 positions of each class were synthesized and tested. For class I, an accuracy of 94% was observed, although the overall accuracy was around 85%.

The methods discussed here show that the great difficulties in antimicrobial activity prediction are the absence of a non-antimicrobial database and the enormous variation in sequence size. The greatest challenge for prediction methods is perhaps the heterogeneity of AMPs, which are part of a group with different sequences, structures and mechanisms of action.

#### **4. Conclusions and prospects**

In the future, novel treatments against resistant bacteria should be developed, including strategies that use unnatural AMPs as their basis. The development of unnatural AMPs can be carried out by various methods, including those discussed here. This kind of study brings new knowledge and also generates novel AMPs, in turn boosting the development of prediction methods that can help evaluate rational designed AMPs. A more accurate prediction model may be developed when the patterns of the linguistic model can be used to train machine learning techniques. In addition, a more efficient approach to pattern recognition is needed, since a single sequence is insufficient for patterns identification. In this view TEIRESIAS could be used once two or more sequences were need by this approach. This methodology will be helpful not only for novel AMPs development, but also for other protein chemistry fields. The development of this new methodology is a real challenge and could reduce current limitations, leading us to develop novel and more potent antimicrobial peptide agents.

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### *Edited by Eshel Faraggi*

Since the dawn of recorded history, and probably even before, men and women have been grasping at the mechanisms by which they themselves exist. Only relatively recently, did this grasp yield anything of substance, and only within the last several decades did the proteins play a pivotal role in this existence. In this expose on the topic of protein structure some of the current issues in this scientific field are discussed. The aim is that a non-expert can gain some appreciation for the intricacies involved, and in the current state of affairs. The expert meanwhile, we hope, can gain a deeper understanding of the topic.

Protein Structure

Protein Structure

*Edited by Eshel Faraggi*

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**Author details**

Manpreet Jaidka1 \*, Shikha Bathla1 and Ramanjit Kaur2

1 Krishi Vigyan Kendra, Langroya, Punjab, India

2 Indian Agricultural Research Institute, New Delhi, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. 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.

**101**

*Improved Technologies for Higher Maize Production DOI: http://dx.doi.org/10.5772/intechopen.88997*

[1] Shukla GN, Kumar A, Jha A,

2022; 2013

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*Improved Technologies for Higher Maize Production DOI: http://dx.doi.org/10.5772/intechopen.88997*


*Edited by Rashid Ali*

Cyclodextrins (CDs) have attracted great interest from the research community as well as industries in an array of sectors because of their unique structural features. This book provides a comprehensive overview of CDs, beginning with their historical background. Chapters address such topics as the structure and physiochemical properties of CDs, advancements in the field, and potential applications of these materials in fields such as drug delivery and sensing. This book reveals new frontiers in the CD world and is a useful resource for organic, analytical, and supramolecular chemists as well as scientists engaged in biological and material sciences.

Published in London, UK © 2023 IntechOpen © anusorn nakdee / iStock

Cyclodextrins - Core Concepts and New Frontiers

Cyclodextrins

Core Concepts and New Frontiers

*Edited by Rashid Ali*