Let's now look at the consequences on the network features. The first consequence is a multiplicity of the number of interactions in interfaces with local symmetry and when the sequences are identical and therefore an intrinsic increase of the network interconnectedness (Fig. 8B). As observed for the p53 case, such increase would lead to network sensitivity to rewiring which in terms of protein may introduce a vulnerability to chain dissociation or chain reorganization. The second consequence is the decrease of the number of distinct connected components (Fig. 8B). This again would increase the propagation of changes within the network upon mutation because the amino acids are not secluded from one another. It is interesting that local symmetry is enough to improve the communication within the network without altering significantly the average degree <*k*>. This means that even without hubs the protein interfaces become highly connected by long paths. This preliminary analysis suggests that interfaces made of domains with different sequences might be more resistant to fold plasticity because of an absence of sequence symmetry. Protein oligomers which undergo a transition to pathological assemblies (fiber or oligomers) probably have global and local properties that make them amenable to fold plasticity. How the local properties alter the global properties remain to be explored.

#### **5. Conclusion**

Let's consider three intermolecular networks, one with different amino acid sequences and no local symmetry, a second with identical sequence arranged in a parallel manner (horizontal axe symmetry) and a third with identical sequence arranged in an anti-parallel manner

**Figure 8. Effect of local symmetry on network features.A.** Protein interfaces may be formed by association of do‐ mains with identical sequences (left and right pannels) or different sequences (middle pannel). In the former cases, the two domains maybe aligned in a parallel or antiparallel manner (in register or out register arrangement). The iden‐ tical sequences will have an intrinsic symmetry in their amino acid pairing, namely if the residue 1 is in interaction with the residue 2 then the residue 2 is in interaction with the residue 1. Such symmetrical constraint will produce some motifs in the network which are not necessarily present in "asymmetrical" interfaces made of domains with two differ‐ ent sequences. This is illustrated on a simple network. **B. Connected components.** Considering a slightly more com‐ plex network one can see that the motifs results from the elements of symmetry, a horizontal axial or a rotational axesymmetry for a parallel or antiparallel arrangement, respectively. The dotted boxes indicated the connected components, namely the residues which are connected to each other. One can see the effect of the symmetry on the total number of connected component. **C. Propagation of changes.** The effect of a single node modification on the network, indicated by a M for mutation, is considered. Assuming there is an effect as long as there is a link between two nodes, the symmetry enables the changes (red link) to propagate within the network. **D.** The average degree *<k>*

(rotational axe symmetry) (Fig. 8).

348 Oligomerization of Chemical and Biological Compounds

of the nodes is given for each of the networks.

The novel results obtained by graph theory are that the layout of the interactions, called the network topology, is extremely important for understanding the formation of an interface and the plasticity of fold and quaternary changes. It is also important to understand that the keys are not in any hot spot features but are in the residues whose local properties spread enough global effects to regulate/affect the full-length chain structure. In other words, the formation of interfaces and the quaternary plasticity lay on the residues that control allosteric transitions, mechanisms now revisited using propagation measures in networks. This local to global transition is also investigated by mathematical concepts in the chapter by Laurent Vuillon and Claire Lesieur.

The take home message of the chapter is to exhibit the usefulness of computational approaches to efficiently complement experimental approaches and gain insight in protein assembly. Obviously, future challenges are on understanding how intramolecular and intermolecular interactions are coordinated and the determinants of allosteric transitions. Graph theory and networks approaches open new venues to explore such problems and are certainly going to provide important breakthrough. Briefly, graph theory can help in identifying intramolecular and intermolecular key interactions as well as in investigating their communication means by analyzing the topology of the networks, isolating appropriate clusters and determining propagation route (allostery).

Now, it may be yet too early to grasp what are the network measures most relevant to the problem of protein assembly and how they can be interpreted in terms of protein's needs. For example, proteins and protein interfaces have been described as random networks with Poisson degree distributions centered to a characteristic average <*k*> degree [178]. This means all nodes have on average the same number of links (or contacts) and there are no hubs. Proteins have also been described as single-scale network with exponential degree distribution and no hubs again [59, 84]. In some case, the random network is attributed to the backbone interactions while the single-scale network is attributed to the side chain interactions. A network made of a minimum number of contacts seems rather coherent as proteins probably minimize the number of links (bonds) per amino acid to reduce the "building" cost in terms of bonds and the sequence stringency.

**Author details**

Address all correspondence to<EMAIL_ADDRESS>
[1] Goodsell DS, Olson AJ. Structural symmetry and protein function. Annu Rev Bio‐

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Claire Lesieur\*

**References**

Simultaneously, proteins are described as small world because they have small average path length <*l*>. Small <*l*> generally indicates that most nodes, namely amino acids, of the network are within the reach of each other. Such node accessibility would suggest that a single modification anywhere in the network (ie any mutation in a protein) would easily spread changes in the whole network, a hazardous situation for a protein and in contradiction with the fact that protein folds and functions resist most mutations. Small world networks generally have hubs, highly connected nodes that govern the network communication routes. But proteins are random or single-scale networks and as such are not expected to have hubs, at least not hubs with many more links than other nodes. The absence of hubs is good as it reduces the protein vulnerability to mutation. We have measured <*l*> from 10 to 19 in protein interfaces for networks made of about 300 nodes (unpublished). For comparison the world wide web has similar <*l*>=19, but 800 million nodes. Maybe it just happens that some worlds are smaller than other.

It is therefore not so simple to deconvoluate the topology of a network with a small average <*l*> depending if it is a random, single scale or scale-free (power law degree distribution) network. Theoretical developments aiming at this understanding are proposed and allow considering distribution of connected components, distribution of clustering coefficient and approximation of <*l*>. Such work will help analyzing the network measures obtained for amino acid networks [179].

One problem of network is the number of interactions and nodes generated to describe a protein network and how to discriminate a hierarchy within these set of interactions to understand the determinant ones. To this goal, one elegant strategy is to experimentally measure kinetics and affinity to prioritize interactions in networks [180]. Such approaches would complement MD simulations and help discriminating the good from the bad.

#### **Acknowledgements**

A special thanks to Kave Salamatian and Laurent Vuillon for numerous fruitful discussions on network and graph theories. We thank the federation of research FR2914 MSIF (Modeliza‐ tion, Simulations, Interaction Fundamentals) for supporting our work on interdisciplinary research.

#### **Author details**

Claire Lesieur\*

all nodes have on average the same number of links (or contacts) and there are no hubs. Proteins have also been described as single-scale network with exponential degree distribution and no hubs again [59, 84]. In some case, the random network is attributed to the backbone interactions while the single-scale network is attributed to the side chain interactions. A network made of a minimum number of contacts seems rather coherent as proteins probably minimize the number of links (bonds) per amino acid to reduce the "building" cost in terms of bonds and

Simultaneously, proteins are described as small world because they have small average path length <*l*>. Small <*l*> generally indicates that most nodes, namely amino acids, of the network are within the reach of each other. Such node accessibility would suggest that a single modification anywhere in the network (ie any mutation in a protein) would easily spread changes in the whole network, a hazardous situation for a protein and in contradiction with the fact that protein folds and functions resist most mutations. Small world networks generally have hubs, highly connected nodes that govern the network communication routes. But proteins are random or single-scale networks and as such are not expected to have hubs, at least not hubs with many more links than other nodes. The absence of hubs is good as it reduces the protein vulnerability to mutation. We have measured <*l*> from 10 to 19 in protein interfaces for networks made of about 300 nodes (unpublished). For comparison the world wide web has similar <*l*>=19, but 800 million nodes. Maybe it just happens that some worlds are smaller

It is therefore not so simple to deconvoluate the topology of a network with a small average <*l*> depending if it is a random, single scale or scale-free (power law degree distribution) network. Theoretical developments aiming at this understanding are proposed and allow considering distribution of connected components, distribution of clustering coefficient and approximation of <*l*>. Such work will help analyzing the network measures obtained for amino

One problem of network is the number of interactions and nodes generated to describe a protein network and how to discriminate a hierarchy within these set of interactions to understand the determinant ones. To this goal, one elegant strategy is to experimentally measure kinetics and affinity to prioritize interactions in networks [180]. Such approaches

A special thanks to Kave Salamatian and Laurent Vuillon for numerous fruitful discussions on network and graph theories. We thank the federation of research FR2914 MSIF (Modeliza‐ tion, Simulations, Interaction Fundamentals) for supporting our work on interdisciplinary

would complement MD simulations and help discriminating the good from the bad.

the sequence stringency.

350 Oligomerization of Chemical and Biological Compounds

than other.

acid networks [179].

**Acknowledgements**

research.

Address all correspondence to<EMAIL_ADDRESS>
AGIM-FRE3405 UJF-CNRS, Grenoble, France

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**Chapter 12**

**Provisional chapter**

**Geometry and Topology in Protein Interfaces -- Some**

The present work is motivated by the biological problem of understanding and possibly predicting the assembly of biological molecules, in particular proteins. This is one of the most common processes in living cells thus it is essential to understand its key aspects, especially in relation to the implication in several pathologies, from bacterial infections (cholera, anthrax, ...) to protein misfolding diseases (Alzheimer, Parkinson, ...) [1–4]. The stable association of different subunits requires the formation of specific intermolecular bonds, thus constituting what is called an interface. Unfortunately, in spite of extensive analyses, the identification of the patterns, in the polypeptidic chain, responsible for the

Geometry has developed the ability to measure and characterize complex shapes but it is not a priori obvious that it may also reveal important aspects of the interactions. To understand

The main geometrical elements of the modern Golden Gate Bridge in S. Francisco and the ancient Roman aqueduct bridge Pont du Gard, in the Gard department in France, are arcs. But with a main difference: the three arcs that form the suspension system of the Golden Gate Bridge are concave upward while the many arcs that form the Pont du Gard are concave downward. Indeed, in the first case the arcs resist to longitudinal tension while in the second case resist to longitudinal compression. Stones are unsuited to resist to strong tension, while they perfectly resist to huge compression. Thus, architectural elements that have to undergo strong tensions are made of wood or steel, but not of stone. Notice that the simple observation of the geometrical form, the concave upward or downward aspect of the bridges, has lead us to understand the basic interactions and formulate constraints on the possible choices of materials. This argument can be pushed much forward: the structural analysis in architecture and engineering largely rely on euclidean geometry (diagrams of forces are diagrams of vectors). Even if the elastic properties of construction materials and the action of gravity are important, the main ingredient in studying the equilibrium of forces is geometry.

> ©2012 Feverati, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2014 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.

distribution, and reproduction in any medium, provided the original work is properly cited.

**Geometry and Topology in Protein Interfaces –**

**Tools for Investigations**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

establishment of an interface remains difficult.

this point, let consider the following examples.

**Some Tools for Investigations**

Giovanni Feverati

Giovanni Feverati

10.5772/58420

**1. Introduction**

http://dx.doi.org/10.5772/58420

#### **Geometry and Topology in Protein Interfaces -- Some Tools for Investigations Geometry and Topology in Protein Interfaces – Some Tools for Investigations**

Giovanni Feverati Giovanni Feverati

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2003:805-8.

17531981.

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58420 10.5772/58420

#### **1. Introduction**

The present work is motivated by the biological problem of understanding and possibly predicting the assembly of biological molecules, in particular proteins. This is one of the most common processes in living cells thus it is essential to understand its key aspects, especially in relation to the implication in several pathologies, from bacterial infections (cholera, anthrax, ...) to protein misfolding diseases (Alzheimer, Parkinson, ...) [1–4]. The stable association of different subunits requires the formation of specific intermolecular bonds, thus constituting what is called an interface. Unfortunately, in spite of extensive analyses, the identification of the patterns, in the polypeptidic chain, responsible for the establishment of an interface remains difficult.

Geometry has developed the ability to measure and characterize complex shapes but it is not a priori obvious that it may also reveal important aspects of the interactions. To understand this point, let consider the following examples.

The main geometrical elements of the modern Golden Gate Bridge in S. Francisco and the ancient Roman aqueduct bridge Pont du Gard, in the Gard department in France, are arcs. But with a main difference: the three arcs that form the suspension system of the Golden Gate Bridge are concave upward while the many arcs that form the Pont du Gard are concave downward. Indeed, in the first case the arcs resist to longitudinal tension while in the second case resist to longitudinal compression. Stones are unsuited to resist to strong tension, while they perfectly resist to huge compression. Thus, architectural elements that have to undergo strong tensions are made of wood or steel, but not of stone. Notice that the simple observation of the geometrical form, the concave upward or downward aspect of the bridges, has lead us to understand the basic interactions and formulate constraints on the possible choices of materials. This argument can be pushed much forward: the structural analysis in architecture and engineering largely rely on euclidean geometry (diagrams of forces are diagrams of vectors). Even if the elastic properties of construction materials and the action of gravity are important, the main ingredient in studying the equilibrium of forces is geometry.

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. © 2014 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.

©2012 Feverati, licensee InTech. This is an open access chapter distributed under the terms of the Creative

A second example is taken from Einstein's general relativity, where the notion of gravitational interaction and the space-time geometry are fully identified by the equivalence principle and the Einstein's field equations. The equivalence principle was first formulated by A. Einstein in 1907, when he recognized that the local behaviour of falling bodies is equivalent to the effect of being in an accelerated reference system (this holds for local effects only). The Einstein's field equations (1915) provide with a mathematical formulation of the principle. In summary, spatial and temporal distances fully inform on the gravitational interaction.

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367

http://dx.doi.org/10.5772/58420

cases, the prediction also takes into account the known geometrical constraints present in

Geometry and Topology in Protein Interfaces -- Some Tools for Investigations

Oligomeric proteins associate by forming an interface. Various descriptions of the interface

The simplest definition of interface between two adjacent polypeptidic chains *A* and *B* is provided by selecting the set of pairs of atoms, one from each chain, whose distance is lower than a given cut-off, typically fixed near 0.5 nm (cut-off interface). This definition does not provide any measure to distinguish pairs. As such, it provides little information since firstly physical interactions decrease when distance grows, secondly two interactions of equal strength may not play the same role if they are in different parts of the molecules, inserted in different local atomic environments. In another definition, the interface is identified to the surface buried between the two components *A* and *B*, namely to those atoms that belong to the surface of *A* and *B* and that loose solvent accessibility once the complex *AB* is formed [10] This makes use of the Van der Waals atomic radii, and leads to distinguish a rim (exposed to the solvent) from a core (inaccessible to the solvent). The interface can be defined also by constructing the Voronoi *α*-complex [10], namely the set of Voronoi restricted balls. The construction follows a precise mathematical procedure, and determines the volume in which

Both the buried surface and Voronoi restricted balls methods focus on the volume of the atoms and the importance of the specific chemical properties of each atom. They make use of a cut off and describe the interactions by using the Van der Waals radius. Differently from these descriptions of the interface, we felt the need to develop a stronger analysis of the structural organization of a protein interface, in order to evaluate the specific role of each

In [11], we shown that many aspects of the structural organization of a protein interface can be effectively described by a graph, namely the ensemble of nodes and edges, constructed following the precise geometrical analysis of the three-dimensional structure of the interface known as symmetrization or symmetric minimization of distances. The algorithm and the graph theory terms are described in Methods. The graph describes how the different atoms are connected. In fact, among its edges one recognizes the known hydrogen bonds present at the interface, that are obtained as a bonus, because the symmetric minimization does not make use of them (see Methods). An example of interaction graph is given in Figure 1.

Statistical analyses have been performed on the case of the *β* interfaces, that are formed by two adjacent *β* strands, one from each subunit [6–8]. In [8] the analysis has been extended to a dataset of 755 proteins. It is known that there are three possible orientations of the adjacent *β* strands: they can be anti-parallel (by far, this is the most common case), parallel or oblique. The latter actually includes all the cases that do not enter into the previous ones, for example perpendicular or oblique *β* strands. The most significant results of these statistical analyses are summarized here (please refer to the Methods for the precise definition of the motifs).

• Two typical interaction graphs have been observed, one for the parallel and one for the anti-parallel orientation. The anti-parallel case shows a BB graph of type ladder, were rungs are typically spaced of 2 amino acids. The parallel case shows a BB graph of type zigzag, in which one recognizes a separation of 2 amino acids in each oscillation of the

amino acids.

have been proposed.

an atom interacts more than its neighbours.

residue and the rules of pairings.

This connection between geometry and interactions also works at the atomic scale. The perfectly planar and hexagonal symmetric form of benzene molecules, compared to the non planar and less symmetric cyclohexane molecule, is clear indication of the different nature of the corresponding Carbon-Carbon bonds and of the sp2 or sp3 hybridization respectively.

In the early 50's, F. Crick [5] observed that the formation of the coiled-coil protein interface is due to the appropriate geometrical and chemical complementarity of the two interacting domains, as in a lock and key mechanism. The key has a particular geometrical form combined to some contact points which together provide it the capacity to associate to one lock.

Moving on from all these examples, in our group we have developed tools to investigate the geometry of the interfaces in multi-chain proteins. In essence, we measure shapes and compare measures between different proteins. Our input data are protein atomic positions, as those from the Protein Data Bank (PDB) repository of protein structural data.

In particular, we compare protein interfaces of similar geometrical form. From the previous examples, there is no surprise in claiming that the geometry will provide information on the interactions at the interfaces. In our previous publications [6–8], careful statistical analyses have been performed and have led to formulate constraints on the amino acid sequences and atom pairs that are compatible with a given geometrical form. The long term perspective of our work is to rationalize the interface, namely to establish a clear understanding of its sequence-structure relationship, in order to develop interface prediction tools and help to advance in interface design.

#### **1.1. Basic information on interfaces**

The shape and the function of proteins are normally encoded within their sequences, i.e. in their amino acid compositions but it is not yet possible, by simply reading the primary sequence of a protein, to predict its three-dimensional structure or the quaternary organization, in the case of an oligomeric protein. One of the difficulties is the non linear encoding of the information in the sequence, namely the fact that the three-dimensional structure is often generated and stabilized by bonds between residues that are not contiguous or even are very far apart, along the chain. Another difficulty is due to the degeneracy between sequences and structures, consisting in the observation that several sequences can code for the same shape, that indicates a versatile role of the amino acids. The secondary structures of proteins which are mainly composed of *α* helices, *β* structures and loops are partially understood, and several prediction programs are now available. Prediction of 3D structures is mainly based on homology, namely comparison of sequences that have similar three-dimensional elements. A rich collection of prediction tools is available on [9]. In some cases, the prediction also takes into account the known geometrical constraints present in amino acids.

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lock.

advance in interface design.

**1.1. Basic information on interfaces**

A second example is taken from Einstein's general relativity, where the notion of gravitational interaction and the space-time geometry are fully identified by the equivalence principle and the Einstein's field equations. The equivalence principle was first formulated by A. Einstein in 1907, when he recognized that the local behaviour of falling bodies is equivalent to the effect of being in an accelerated reference system (this holds for local effects only). The Einstein's field equations (1915) provide with a mathematical formulation of the principle. In summary, spatial and temporal distances fully inform on the gravitational interaction. This connection between geometry and interactions also works at the atomic scale. The perfectly planar and hexagonal symmetric form of benzene molecules, compared to the non planar and less symmetric cyclohexane molecule, is clear indication of the different nature of the corresponding Carbon-Carbon bonds and of the sp2 or sp3 hybridization respectively. In the early 50's, F. Crick [5] observed that the formation of the coiled-coil protein interface is due to the appropriate geometrical and chemical complementarity of the two interacting domains, as in a lock and key mechanism. The key has a particular geometrical form combined to some contact points which together provide it the capacity to associate to one

Moving on from all these examples, in our group we have developed tools to investigate the geometry of the interfaces in multi-chain proteins. In essence, we measure shapes and compare measures between different proteins. Our input data are protein atomic positions,

In particular, we compare protein interfaces of similar geometrical form. From the previous examples, there is no surprise in claiming that the geometry will provide information on the interactions at the interfaces. In our previous publications [6–8], careful statistical analyses have been performed and have led to formulate constraints on the amino acid sequences and atom pairs that are compatible with a given geometrical form. The long term perspective of our work is to rationalize the interface, namely to establish a clear understanding of its sequence-structure relationship, in order to develop interface prediction tools and help to

The shape and the function of proteins are normally encoded within their sequences, i.e. in their amino acid compositions but it is not yet possible, by simply reading the primary sequence of a protein, to predict its three-dimensional structure or the quaternary organization, in the case of an oligomeric protein. One of the difficulties is the non linear encoding of the information in the sequence, namely the fact that the three-dimensional structure is often generated and stabilized by bonds between residues that are not contiguous or even are very far apart, along the chain. Another difficulty is due to the degeneracy between sequences and structures, consisting in the observation that several sequences can code for the same shape, that indicates a versatile role of the amino acids. The secondary structures of proteins which are mainly composed of *α* helices, *β* structures and loops are partially understood, and several prediction programs are now available. Prediction of 3D structures is mainly based on homology, namely comparison of sequences that have similar three-dimensional elements. A rich collection of prediction tools is available on [9]. In some

as those from the Protein Data Bank (PDB) repository of protein structural data.

Oligomeric proteins associate by forming an interface. Various descriptions of the interface have been proposed.

The simplest definition of interface between two adjacent polypeptidic chains *A* and *B* is provided by selecting the set of pairs of atoms, one from each chain, whose distance is lower than a given cut-off, typically fixed near 0.5 nm (cut-off interface). This definition does not provide any measure to distinguish pairs. As such, it provides little information since firstly physical interactions decrease when distance grows, secondly two interactions of equal strength may not play the same role if they are in different parts of the molecules, inserted in different local atomic environments. In another definition, the interface is identified to the surface buried between the two components *A* and *B*, namely to those atoms that belong to the surface of *A* and *B* and that loose solvent accessibility once the complex *AB* is formed [10] This makes use of the Van der Waals atomic radii, and leads to distinguish a rim (exposed to the solvent) from a core (inaccessible to the solvent). The interface can be defined also by constructing the Voronoi *α*-complex [10], namely the set of Voronoi restricted balls. The construction follows a precise mathematical procedure, and determines the volume in which an atom interacts more than its neighbours.

Both the buried surface and Voronoi restricted balls methods focus on the volume of the atoms and the importance of the specific chemical properties of each atom. They make use of a cut off and describe the interactions by using the Van der Waals radius. Differently from these descriptions of the interface, we felt the need to develop a stronger analysis of the structural organization of a protein interface, in order to evaluate the specific role of each residue and the rules of pairings.

In [11], we shown that many aspects of the structural organization of a protein interface can be effectively described by a graph, namely the ensemble of nodes and edges, constructed following the precise geometrical analysis of the three-dimensional structure of the interface known as symmetrization or symmetric minimization of distances. The algorithm and the graph theory terms are described in Methods. The graph describes how the different atoms are connected. In fact, among its edges one recognizes the known hydrogen bonds present at the interface, that are obtained as a bonus, because the symmetric minimization does not make use of them (see Methods). An example of interaction graph is given in Figure 1.

Statistical analyses have been performed on the case of the *β* interfaces, that are formed by two adjacent *β* strands, one from each subunit [6–8]. In [8] the analysis has been extended to a dataset of 755 proteins. It is known that there are three possible orientations of the adjacent *β* strands: they can be anti-parallel (by far, this is the most common case), parallel or oblique. The latter actually includes all the cases that do not enter into the previous ones, for example perpendicular or oblique *β* strands. The most significant results of these statistical analyses are summarized here (please refer to the Methods for the precise definition of the motifs).

• Two typical interaction graphs have been observed, one for the parallel and one for the anti-parallel orientation. The anti-parallel case shows a BB graph of type ladder, were rungs are typically spaced of 2 amino acids. The parallel case shows a BB graph of type zigzag, in which one recognizes a separation of 2 amino acids in each oscillation of the

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**2. Methods**

**2.1. Symmetric minimization of distances**

Start: *R*<sup>0</sup> ←

*i* ← 0

**while** *Ri* �= { } **do:** min*A*(*a*) ← min

> *LA* ←

*LB* ←

Output: *S*0, *S*1, *S*2, ...

interface. Please see the caption of Figure 2.

*Si* ← *LA* ∩ *LB Ri*<sup>+</sup><sup>1</sup> ← *Ri* − *Si i* ← *i* + 1 **end while**

min*B*(*b*) ← min

Input: *A* ← atoms of the first subunit

*B* ← atoms of the second subunit

(*a*, *b*) : *a* ∈ *A*, *b* ∈ *B*

The description of the interface, that has been developed starting with [11] and that will be used in this paper, was introduced to help extract the structural organization of the interface. It focuses on the way atoms pair, keeping into account the local connectivity, namely the possibility that atoms interact with other atoms, according to their local arrangement. It is based on the notion of symmetric minimization of distance pairs, defined by the symmetric minimization algorithm, presented here in pseudocode. The flow chart is given in Figure 3. The needed mathematical explanations and demonstrations have been provided in [12].

*d*(*a*, *b*) ← A metric ; typically, the inter-atomic distance is used

) : (*a*, *b*′

, *b*) : (*a*′

(*a*, *b*) ∈ *Ri* : *d*(*a*, *b*) = min*A*(*a*)

(*a*, *b*) ∈ *Ri* : *d*(*a*, *b*) = min*B*(*b*)

The sets *R*0, *Ri* are sets of edges. The empty set is indicated with { }. The symbols − and

∩ indicate set difference and set intersection respectively. The symbols min*A*(*a*), min*B*(*b*) indicate the functions that give the shortest distances in the neighbourhood of the indicated point. The symbols *LA*, *LB* indicate the shortest edges relative to the given iteration *Ri*. The

In summary, the symmetric minimization is a recursive method that, at the first iteration, defines the lowest level of the interface as the pairs of atoms that are reciprocal nearest neighbours. The nearest neighbour condition must be verified for both the two subunit atoms, as the name itself suggests. These pairs form the lowest level *S*0, called symmetrized

reciprocal shortest edge sets are indicated with *Si*, and called symmetrized levels *i*.

) ∈ *Ri* and *b*′ ∈ *B*

Geometry and Topology in Protein Interfaces -- Some Tools for Investigations

, *b*) ∈ *Ri* and *a*′ ∈ *A*

*d*(*a*, *b*′

*d*(*a*′

**Figure 1.** Full interaction graph of the level 0 of the protein 1EEI interface (see Methods). The upper horizontal line represents the sub-unit D, the lower one the sub-unit E. With the crosses we indicate the residues that participate to the interaction graph; their name and membership to specific secondary structures is indicated, when available. The dots represent the residues that do not participate to the interface. The dotted-dashed lines represent pairs of atoms both from the backbone of the residues (BB graph). The solid lines indicate that at least one atom of the pair is from the side chain (SC graph).

zigzag. In some cases, the zigzag topology reduces to a simple vertex V (defined in Methods).

Both these specific topologies identified in the BB graph correspond to the known hydrogen bond graph between backbone atoms. While it is not surprising to find them in the BB graph, the surprise comes from the fact that the position of hydrogen atoms has not been used as input by our algorithms (in most cases it is not even given in the PDB files). Thus, the symmetric minimization algorithm is able to reconstruct the backbone hydrogen bonds network by the unique input of the backbone atoms *N*, *Cα*, *C*,*O* coordinates, with an accuracy of 90%. In other words, the backbone hydrogen bonds satisfy the mathematical property of symmetrically minimized distances and the backbone non-hydrogen atoms that engage in BB hydrogen bonds are reciprocal nearest neighbours, with the indicated accuracy. This identification has been obtained using the web server RING (see Methods) to calculate the hydrogen bonds and compare with the graphs (see also [6]). Among the 120 pairs of residues in the graphs (anti-parallel and parallel cases only), 108 are recognized by RING as hydrogen bonds and 12 are not. Thus, the symmetric minimization recognizes hydrogen bonds with accuracy of 90%.

• Interfaces of the oblique family have very small or absent BB graph, thus the two BB graphs are rather specific to the respective anti-parallel and parallel cases. It is also rare to find BB graphs in non *β* interfaces.

In fact, in these cases the BB graph is intra-chain, namely it develops between atoms of the same sub-unit and is almost absent in the interface.

• The amino acids are not randomly paired. Rather, the frequency with which residues are connected in the interaction graph clearly deviates from the expected frequency calculated from the average frequency of residues in the interface. This indicates that the edges in the interaction graph form in order to provide to the interface very specific features.

#### **2. Methods**

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D: T - 1 P - Q -

E: P - 93 T

Methods).

L α E - Y -

α Y <sup>α</sup> <sup>D</sup>

to find BB graphs in non *β* interfaces.

the same sub-unit and is almost absent in the interface.

<sup>α</sup> <sup>E</sup>

(BB graph). The solid lines indicate that at least one atom of the pair is from the side chain (SC graph).

S α Q α K α A α R α M α T α I α

<sup>α</sup> <sup>I</sup>

**Figure 1.** Full interaction graph of the level 0 of the protein 1EEI interface (see Methods). The upper horizontal line represents the sub-unit D, the lower one the sub-unit E. With the crosses we indicate the residues that participate to the interaction graph; their name and membership to specific secondary structures is indicated, when available. The dots represent the residues that do not participate to the interface. The dotted-dashed lines represent pairs of atoms both from the backbone of the residues

zigzag. In some cases, the zigzag topology reduces to a simple vertex V (defined in

Both these specific topologies identified in the BB graph correspond to the known hydrogen bond graph between backbone atoms. While it is not surprising to find them in the BB graph, the surprise comes from the fact that the position of hydrogen atoms has not been used as input by our algorithms (in most cases it is not even given in the PDB files). Thus, the symmetric minimization algorithm is able to reconstruct the backbone hydrogen bonds network by the unique input of the backbone atoms *N*, *Cα*, *C*,*O* coordinates, with an accuracy of 90%. In other words, the backbone hydrogen bonds satisfy the mathematical property of symmetrically minimized distances and the backbone non-hydrogen atoms that engage in BB hydrogen bonds are reciprocal nearest neighbours, with the indicated accuracy. This identification has been obtained using the web server RING (see Methods) to calculate the hydrogen bonds and compare with the graphs (see also [6]). Among the 120 pairs of residues in the graphs (anti-parallel and parallel cases only), 108 are recognized by RING as hydrogen bonds and 12 are not. Thus, the symmetric minimization recognizes hydrogen bonds with accuracy of 90%. • Interfaces of the oblique family have very small or absent BB graph, thus the two BB graphs are rather specific to the respective anti-parallel and parallel cases. It is also rare

In fact, in these cases the BB graph is intra-chain, namely it develops between atoms of

• The amino acids are not randomly paired. Rather, the frequency with which residues are connected in the interaction graph clearly deviates from the expected frequency calculated from the average frequency of residues in the interface. This indicates that the edges in the interaction graph form in order to provide to the interface very specific features.

A -

β E - R - <sup>G</sup> - L - S β E β T β Y β F - <sup>K</sup> - 23

I β A β I β S β M β A β N - <sup>103</sup>

#### **2.1. Symmetric minimization of distances**

The description of the interface, that has been developed starting with [11] and that will be used in this paper, was introduced to help extract the structural organization of the interface. It focuses on the way atoms pair, keeping into account the local connectivity, namely the possibility that atoms interact with other atoms, according to their local arrangement. It is based on the notion of symmetric minimization of distance pairs, defined by the symmetric minimization algorithm, presented here in pseudocode. The flow chart is given in Figure 3. The needed mathematical explanations and demonstrations have been provided in [12].

> Input: *A* ← atoms of the first subunit *B* ← atoms of the second subunit *d*(*a*, *b*) ← A metric ; typically, the inter-atomic distance is used

$$\begin{aligned} \text{Start:} & \quad R\_0 \leftarrow \left\{ (a, b) : a \in A, b \in B \right\} \\ & i \leftarrow 0 \\ & \text{while } R\_i \neq \{ \ \} \text{ do:} \\ & \text{min}\_A(a) \leftarrow \min \left\{ d(a, b') : (a, b') \in R\_i \text{ and } b' \in B \right\} \\ & \text{min}\_B(b) \leftarrow \min \left\{ d(a', b) : (a', b) \in R\_i \text{ and } a' \in A \right\} \\ & L\_A \leftarrow \left\{ (a, b) \in R\_i : d(a, b) = \min\_A(a) \right\} \\ & L\_B \leftarrow \left\{ (a, b) \in R\_i : d(a, b) = \min\_B(b) \right\} \\ & S\_i \leftarrow L\_A \cap L\_B \\ & R\_{i+1} \leftarrow R\_i - S\_i \\ & i \leftarrow i + 1 \\ & \text{end while} \end{aligned}$$

Output: *S*0, *S*1, *S*2, ...

The sets *R*0, *Ri* are sets of edges. The empty set is indicated with { }. The symbols − and

∩ indicate set difference and set intersection respectively. The symbols min*A*(*a*), min*B*(*b*) indicate the functions that give the shortest distances in the neighbourhood of the indicated point. The symbols *LA*, *LB* indicate the shortest edges relative to the given iteration *Ri*. The reciprocal shortest edge sets are indicated with *Si*, and called symmetrized levels *i*.

In summary, the symmetric minimization is a recursive method that, at the first iteration, defines the lowest level of the interface as the pairs of atoms that are reciprocal nearest neighbours. The nearest neighbour condition must be verified for both the two subunit atoms, as the name itself suggests. These pairs form the lowest level *S*0, called symmetrized interface. Please see the caption of Figure 2.

The algorithm can be repeated on all the edges that have not been retained at the lowest level. This produces a new set of reciprocally shortest edges, not contained in *S*0, that forms the symmetrized set *S*1, named level 1. The repetition of the algorithm up to exhaustion of all the atom pairs, provides a hierarchy of levels

$$\begin{array}{ccccccccc} \mathbf{S\_{0}} & \mathbf{S\_{1}} & \mathbf{S\_{2}} & \dots & \mathbf{S\_{M}} & & & & & & \mathbf{1} \end{array} \tag{1}$$

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A

B

Geometry and Topology in Protein Interfaces -- Some Tools for Investigations

*b*

NO

**Figure 2.** Example of symmetric minimization. The distance (*ab*) is the smallest for the atom *a* and, at the same time, is the smallest for *b*. Thus, the edge (*ab*) is retained in the symmetrized interface. The distance (*bc*) is the smallest for (*c*) but not for

Otherwise said, the geometrical action of ranking the pairs of an interface corresponds to the sequence of actions that one has to follow to construct the interface with minimum amount of steric effects. The sequence read in the opposite sense, from the maximum level *SM* to *S*0, indicates the steps to access the closest atoms of the interface by removing one layer after the

It is important to stress that each edge of *S*<sup>0</sup> behaves as a nucleation centre, or a bud, because every edge that appears at level 1 is attached to a level 0 edge; thus, level 0 edges act as the starting points of a growth process where, at each higher level, new edges attach to the bud. The growth occurs simultaneously from the various level 0 edges. Each bud is technically a cluster. At some level, an edge appears that joins two different buds; the edge length is the resolution threshold for the two buds. Certain edges do not start a growth process; this occurs when the atoms joined by these edges are not further connected to the rest of the interface. Also, a bud must be present at level 0 and cannot appear at higher level: *S*<sup>0</sup> already contains all the buds. In other words, the various parts of the interface are already contained in *S*<sup>0</sup> and a set smaller than *S*<sup>0</sup> is possibly insufficient to reconstruct the full interface. This provides the mathematical justification for calling *S*<sup>0</sup> a framework of the interface. Indeed, as we have empirically observed in our previous publications, *S*<sup>0</sup> is the smallest set that can describe the interface. This is the most important point of the whole construction and is

In summary, the symmetric minimization has been introduced to responds to the following

**Scalable.** The symmetric minimization may be applied to proteins or objects of any size, without size limits. Moreover, it can be applied also to objects at the human or interstellar

based on the theorems proved in [12] but is published here for the first time.

needs and possesses the following features [12].

*a*

*c*

(*b*) thus it is not retained.

other.

scale.

that define the symmetrized interface (SI). Thus, inter-atomic distances between the two sub-units are ranked according to levels. Notice that, as proteins are of finite and not of infinite size, there must be a maximum level. Also, the algorithm is free of ambiguities, even in case of regular structures. In Figure 1 and in previous papers the lowest level only was analysed, while it is a purpose of this paper to start the investigation of the higher levels.

The symmetric minimization of distances is a variant of the case *k* = 1 of the known *k*-reciprocal nearest neighbour method (kRNN), discussed in [13] and used now in the domain of hierarchical classification and object retrieval in images. A modern presentation is in [14]. Actually, the lowest level *S*<sup>0</sup> could be equivalently obtained with both methods but at higher levels the equivalence breaks down. The purpose of kRNN is to assert the relative proximity of several images containing the same object appearing in different scenes. Each image is a point in some very high-dimensional space. Using an appropriate metric, the closest images are found and agglomerated in a cluster. At the new iteration, new images will join the cluster. The method kRNN compares high-dimensional vectors. It can be applied to atomic coordinates. Once two atoms are found to be reciprocal nearest neighbours at some iteration, they are removed from the pool (and put in a cluster) before the next iteration could start. On the other hand, in the symmetric minimization, two atoms that are reciprocal nearest neighbours are not removed from the pool: the edge they form is removed but the atoms remain. This is the role of the sets *Ri* in the algorithm: at the next, the same atoms may be nearest neighbours with others. In kRNN, the sets of edges *Ri* would simply be replaced by some *Ai*, *Bi* where *Ai* or *Bi* would be obtained by removing from the initial *A* and *B* the atoms found at each iteration. The choice of working with edges comes from the goal of describing the interactions from a geometrical point of view. Moreover, the binding energy in a protein interface accumulates between all pairs of atoms, at least in a suitable range of distances, no matter if they are nearest neighbours or not. Using edges, the information about all the neighbours of an atom is recorded and used at one or other of the levels. Using atoms, part of the edges are not evaluated and some information seems lost, at least for purposes related to protein structure.

The classification of pairs into levels reminds one of perturbative calculations, very common in physics, where the lowest order contains the strongest interaction and the higher orders introduce weaker and weaker terms. The ranking, and the set of levels in equation (1), have been computed on the basis of inter-atomic distances: the higher the level, the larger the distance between atoms. Physically speaking, moving to higher distances implies a tendency to move to weaker interactions. Here, force fields and types of atoms have not been used in the symmetric minimization, thus rising to higher rank only indicates a tendency to weaker interactions and does not hold in a strict sense.

Imagining a mechanical model of balls and sticks, and a quantity of glue, can one mount a human size model of the protein interface? Yes, if the sequence in equation (1) is followed.

6 ime knjige

The algorithm can be repeated on all the edges that have not been retained at the lowest level. This produces a new set of reciprocally shortest edges, not contained in *S*0, that forms the symmetrized set *S*1, named level 1. The repetition of the algorithm up to exhaustion of

that define the symmetrized interface (SI). Thus, inter-atomic distances between the two sub-units are ranked according to levels. Notice that, as proteins are of finite and not of infinite size, there must be a maximum level. Also, the algorithm is free of ambiguities, even in case of regular structures. In Figure 1 and in previous papers the lowest level only was analysed, while it is a purpose of this paper to start the investigation of the higher levels. The symmetric minimization of distances is a variant of the case *k* = 1 of the known *k*-reciprocal nearest neighbour method (kRNN), discussed in [13] and used now in the domain of hierarchical classification and object retrieval in images. A modern presentation is in [14]. Actually, the lowest level *S*<sup>0</sup> could be equivalently obtained with both methods but at higher levels the equivalence breaks down. The purpose of kRNN is to assert the relative proximity of several images containing the same object appearing in different scenes. Each image is a point in some very high-dimensional space. Using an appropriate metric, the closest images are found and agglomerated in a cluster. At the new iteration, new images will join the cluster. The method kRNN compares high-dimensional vectors. It can be applied to atomic coordinates. Once two atoms are found to be reciprocal nearest neighbours at some iteration, they are removed from the pool (and put in a cluster) before the next iteration could start. On the other hand, in the symmetric minimization, two atoms that are reciprocal nearest neighbours are not removed from the pool: the edge they form is removed but the atoms remain. This is the role of the sets *Ri* in the algorithm: at the next, the same atoms may be nearest neighbours with others. In kRNN, the sets of edges *Ri* would simply be replaced by some *Ai*, *Bi* where *Ai* or *Bi* would be obtained by removing from the initial *A* and *B* the atoms found at each iteration. The choice of working with edges comes from the goal of describing the interactions from a geometrical point of view. Moreover, the binding energy in a protein interface accumulates between all pairs of atoms, at least in a suitable range of distances, no matter if they are nearest neighbours or not. Using edges, the information about all the neighbours of an atom is recorded and used at one or other of the levels. Using atoms, part of the edges are not evaluated and some information seems lost, at least for

The classification of pairs into levels reminds one of perturbative calculations, very common in physics, where the lowest order contains the strongest interaction and the higher orders introduce weaker and weaker terms. The ranking, and the set of levels in equation (1), have been computed on the basis of inter-atomic distances: the higher the level, the larger the distance between atoms. Physically speaking, moving to higher distances implies a tendency to move to weaker interactions. Here, force fields and types of atoms have not been used in the symmetric minimization, thus rising to higher rank only indicates a tendency to weaker

Imagining a mechanical model of balls and sticks, and a quantity of glue, can one mount a human size model of the protein interface? Yes, if the sequence in equation (1) is followed.

*S*0, *S*1, *S*2, ..., *SM* (1)

all the atom pairs, provides a hierarchy of levels

purposes related to protein structure.

interactions and does not hold in a strict sense.

**Figure 2.** Example of symmetric minimization. The distance (*ab*) is the smallest for the atom *a* and, at the same time, is the smallest for *b*. Thus, the edge (*ab*) is retained in the symmetrized interface. The distance (*bc*) is the smallest for (*c*) but not for (*b*) thus it is not retained.

Otherwise said, the geometrical action of ranking the pairs of an interface corresponds to the sequence of actions that one has to follow to construct the interface with minimum amount of steric effects. The sequence read in the opposite sense, from the maximum level *SM* to *S*0, indicates the steps to access the closest atoms of the interface by removing one layer after the other.

It is important to stress that each edge of *S*<sup>0</sup> behaves as a nucleation centre, or a bud, because every edge that appears at level 1 is attached to a level 0 edge; thus, level 0 edges act as the starting points of a growth process where, at each higher level, new edges attach to the bud. The growth occurs simultaneously from the various level 0 edges. Each bud is technically a cluster. At some level, an edge appears that joins two different buds; the edge length is the resolution threshold for the two buds. Certain edges do not start a growth process; this occurs when the atoms joined by these edges are not further connected to the rest of the interface. Also, a bud must be present at level 0 and cannot appear at higher level: *S*<sup>0</sup> already contains all the buds. In other words, the various parts of the interface are already contained in *S*<sup>0</sup> and a set smaller than *S*<sup>0</sup> is possibly insufficient to reconstruct the full interface. This provides the mathematical justification for calling *S*<sup>0</sup> a framework of the interface. Indeed, as we have empirically observed in our previous publications, *S*<sup>0</sup> is the smallest set that can describe the interface. This is the most important point of the whole construction and is based on the theorems proved in [12] but is published here for the first time.

In summary, the symmetric minimization has been introduced to responds to the following needs and possesses the following features [12].

**Scalable.** The symmetric minimization may be applied to proteins or objects of any size, without size limits. Moreover, it can be applied also to objects at the human or interstellar scale.

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**2.2. Interaction graphs**

a weighted graph.

the analysis focuses on the interface.

and not from individual atoms.

(*a*, *b*)*F*(*a*′

, *b*′

nodes) in this description that can be called full atoms.

theory this procedure is called fusion. If (*a*, *b*),(*a*′

acid resolution). By consequence, the graphs *Saa*

let's call *F* (fusion) the equivalence relation such that

Then, the following quotient set defines equivalent classes

when two residues are connected by more than one pair of atoms.

The previous subsection demonstrates how the data are analysed. By construction, each set in equation (1) is a collection of edges, the extrema of which are points in some metric space. Mathematically, this corresponds to the notion of graph, namely a set of nodes (here the points in the metric space) and a set of edges joining some of the nodes. Thus, from now on each set representing a level will be naturally identified with a graph. In the present case it is important to stress that the graph has been obtained by evaluating distances, thus each edge is associated to the physical distance of the endpoints. Mathematically speaking, this is

Geometry and Topology in Protein Interfaces -- Some Tools for Investigations

As the graph nodes were initially atoms of one or the other of two adjacent subunits, the graph is automatically bipartite: each edge has an endpoint on one subunit and the other on the second subunit. Edges between atoms of the same subunit are not considered here, as

By construction, a pair of atoms can be connected by a single edge only, as there is just one distance value between them. Thus parallel edges are absent (edges with the same end

Since the first paper on the subject [11], the levels *Si* have been coarse-grained in order to facilitate the human interpretation of the data and to transfer the information to the residue scale. This is actually the scale used by biological entities to store and transfer information: DNA and RNA code for amino acids, not for atoms, and proteins form from residue chains

In the coarse-grained representation the information appears at the amino acid resolution: all the atoms of a residue are identified and represented with the residue name itself. All edges ending on the atoms of the residue are now referred to the residue itself. In graph

> *Saa <sup>i</sup>* <sup>=</sup> *Si*

whole elements are all the edges that start and end on the same residues (*aa* means amino

The subsequent analyses will always refer to this coarse-grained graphs, while the initial

represented in a very effective way: the first subunit residues are represented as an horizontal line of equispaced dots; similarly, the second subunit residues appear as dots in an horizontal line below the previous one. Clearly, the two horizontal lines represent the chains of residues connected by the peptide bonds. Being the graph bipartite, edges can only join a node from one horizontal line to a node of the other horizontal line. In Figure 1 there is an example.

symmetric minimization has always been done with atoms. The graphs *Saa*

, *b*′

) ⇐⇒ *a* and *a*′ belong to the same residue and *b*, *b*′ belong to the same residue

) ∈ *Si* are edges of one of the graphs,

*<sup>F</sup>* (3)

*i* could have parallel edges; this happens

(2)

*i* can be

**Figure 3.** Flow chart of the symmetric minimization algorithm.

#### **2.2. Interaction graphs**

8 ime knjige

Construction of *R*<sup>0</sup> (set of atom pairs, one from sub-unit *A*, one from sub-unit *B*)

Iteration counter: *i* = 0

From *Ri*, construction of the sets *LA* and *LB LA* : for each atom of *A*, the closest partner in *B*, within *Ri LB* : for each atom of *B*, the closest partner in *A*, within *Ri*

Symmetrized level: *Si* = *LA* ∩ *LB*

**Figure 3.** Flow chart of the symmetric minimization algorithm.

**Scale-free.** No length scale has been imposed from outside.

dilute packing.

scale for the interface.

The rest: *Ri*<sup>+</sup><sup>1</sup> = *Ri* − *Si* Is *Ri*<sup>+</sup><sup>1</sup> empty?

**Local.** It is based on the local arrangement of atoms (or points) and not on global features. So, it captures the differences occurring in situations like a dense atomic packing or a

**Intrinsic scales.** It defines a set of characteristic scales, intrinsic to the interface itself. Indeed, the symmetric minimization allows to divide the interface in clusters (the buds previously presented). The first edge that joins two different clusters (two buds) is a characteristic

**Metrics independent.** It is independent on the explicit distance function adopted. Namely, the actual distance used can be different from Euclidean distance, non euclidean

geometries being allowed if they use positive definite metrics (a distance).

Yes

End

No

*i* → *i* + 1

The previous subsection demonstrates how the data are analysed. By construction, each set in equation (1) is a collection of edges, the extrema of which are points in some metric space. Mathematically, this corresponds to the notion of graph, namely a set of nodes (here the points in the metric space) and a set of edges joining some of the nodes. Thus, from now on each set representing a level will be naturally identified with a graph. In the present case it is important to stress that the graph has been obtained by evaluating distances, thus each edge is associated to the physical distance of the endpoints. Mathematically speaking, this is a weighted graph.

As the graph nodes were initially atoms of one or the other of two adjacent subunits, the graph is automatically bipartite: each edge has an endpoint on one subunit and the other on the second subunit. Edges between atoms of the same subunit are not considered here, as the analysis focuses on the interface.

By construction, a pair of atoms can be connected by a single edge only, as there is just one distance value between them. Thus parallel edges are absent (edges with the same end nodes) in this description that can be called full atoms.

Since the first paper on the subject [11], the levels *Si* have been coarse-grained in order to facilitate the human interpretation of the data and to transfer the information to the residue scale. This is actually the scale used by biological entities to store and transfer information: DNA and RNA code for amino acids, not for atoms, and proteins form from residue chains and not from individual atoms.

In the coarse-grained representation the information appears at the amino acid resolution: all the atoms of a residue are identified and represented with the residue name itself. All edges ending on the atoms of the residue are now referred to the residue itself. In graph theory this procedure is called fusion. If (*a*, *b*),(*a*′ , *b*′ ) ∈ *Si* are edges of one of the graphs, let's call *F* (fusion) the equivalence relation such that

(*a*, *b*)*F*(*a*′ , *b*′ ) ⇐⇒ *a* and *a*′ belong to the same residue and *b*, *b*′ belong to the same residue

(2)

Then, the following quotient set defines equivalent classes

$$S\_i^{aa} = \frac{\mathcal{S}\_i}{F} \tag{3}$$

whole elements are all the edges that start and end on the same residues (*aa* means amino acid resolution). By consequence, the graphs *Saa i* could have parallel edges; this happens when two residues are connected by more than one pair of atoms.

The subsequent analyses will always refer to this coarse-grained graphs, while the initial symmetric minimization has always been done with atoms. The graphs *Saa i* can be represented in a very effective way: the first subunit residues are represented as an horizontal line of equispaced dots; similarly, the second subunit residues appear as dots in an horizontal line below the previous one. Clearly, the two horizontal lines represent the chains of residues connected by the peptide bonds. Being the graph bipartite, edges can only join a node from one horizontal line to a node of the other horizontal line. In Figure 1 there is an example.

In fact we found it very convenient to distinguish two sub-graphs. The dotted-dashed lines represent pairs of atoms both from the backbone of the residues (BB graph). The solid lines indicate that at least one atom of the pair is from the side chain (SC graph). Mathematically speaking, this is called an edge-labelled graph, the two possible labels being BB and SC. In principle, there is a third edge label; indeed, the nodes of the same sub-unit form a sequence connected by the peptide bond. Albeit this type of edge is not explicitly shown in the graphs, it is implicitly present and motivates the choice of organizing the residues along straight lines.

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rungs and separation *δ* = 2. In Figure 22 the residues C, S, F (47, 49, 51) of chain A and the residues Y, E, E (110, 108, 106) of chain B form a BB ladder of separation *δ* = 2. The two most common separations are *δ* = 1 or 2, ladder1 and ladder2 respectively. Cases with more than 3 rungs are known. In Figure 14, the residues from 98 to 102 (of the chain

Geometry and Topology in Protein Interfaces -- Some Tools for Investigations

**Multiple edges:** presence of parallel edges in the graph (edges with the same endpoints). The term multiple edges seems more adequate to the present case, given the use of the word parallel to indicate the orientation of the *β*-strands in the interface. In Figure 8, the residues E and F are connected by 4 edges, three of type SC and one of type BB. In Figure

It is important to look for these motifs starting from the largest elements, to avoid useless multiple countings. Especially, it is obvious that a zigzag of 8 nodes contains all the shortest sizes, from 4 to 7. There is no need to record all of them, the largest one being the most informative. Also, a V3 or zigzag motif automatically contains the simplest vertex V, and often more than once. Thus, the vertex V is not counted when it appears inserted in a V3 or a zigzag. In Figure 33, the zigzag5 (residues V, M, I, V, A) contains three times a V. In Figure

These topological elements may appear alone or in combination and have been chosen, as in other publications, because they represent the most common motifs in interface graphs. It may happen that a graph contains more than one motif, in which case all of them will be recorded. For example, in a common situation one of the nodes of a zigzag5 is also a vertex V3. In that case, both motifs are registered. The main reason is that a systematic classification of graphs needs to be accurate and free of ambiguous search criteria, thus it is definitely preferable to accept a redundancy in the identification of motifs than introducing untested criteria. For example, often a ladder has separation of 2 in one part of the graph and separation of 1 in another part, with a edge (a rung) in common between the two parts. Thus, it is recorded as part of both a ladder of 2 and ladder of 1. Indeed, so far, no acceptable criterion has been found to discriminate if a edge must be considered part of a ladder with separation of 1 or of 2. In Figure 28, there is a BB ladder formed by M, I, K, I (chain A) and V, L, I, E (chain B). Residues M and I are separated of 2 while residues I and K, K and I are

In [6] a dataset of 39 oligomeric proteins was chosen on the basis of the presence, in the three-dimensional structure, of a well recognizable *β* interface. Here the same set will be used. It is listed in Table 1 with the indication of the chains and the intervals participating to the *β* interfaces. All proteins are homomeric of stoichiometry from 3 to 8. This set is characterized by the absence of sequence homology, structural homology or functional homology. Viral and membrane proteins are absent, given their specificities. Thus, the set can be considered representative of the general behaviour of *β* interfaces, without reference to specific classes of proteins. In the dataset there are anti-parallel, parallel and oblique orientations of the adjacent *β* strands. Actually, the oblique family will be considered for comparison only and not deeply investigated. In fact its graphs are less structured and it is

D) with the residues from 29 to 25 (chain E) form a ladder1 with 5 rungs.

22, the residues S and E are connected by three BB edges.

30, the vertex V3 (residue H) contains three V motifs.

difficult to find similarities between proteins.

separated of 1.

**2.4. The dataset**

Notice that while the full atom graphs are a fortiori different, *Si* ∩ *Sj* = {}, this is no longer true for the fused graphs: it is not a priori granted that they are different.

In summary, the sets in equation (3) characterize the interaction between subunits; each set is a bipartite and edge-labelled graph where parallel edges may occur.

The amino acid resolution graph of the interaction between subunits has shown to be extremely effective in interpreting data and designing statistical analyses. Notice that the choice of marking non interacting residues with a dot and interacting residues with a cross is purely conventional and does not add information.

#### **2.3. Topological analysis of the graphs**

The analysis will proceed with the inspection and comparison of the interaction graphs of the level 1 of the interface, with some additional information from the level 2. The focus will be on the topology, namely on the organisation of edges in the graphs, whose importance has already been shown in the previous publications [6, 11]. As already stated, the amino acid resolution will be systematically adopted.

The interface graphs will be analysed using the following motifs.

rungs and separation *δ* = 2. In Figure 22 the residues C, S, F (47, 49, 51) of chain A and the residues Y, E, E (110, 108, 106) of chain B form a BB ladder of separation *δ* = 2. The two most common separations are *δ* = 1 or 2, ladder1 and ladder2 respectively. Cases with more than 3 rungs are known. In Figure 14, the residues from 98 to 102 (of the chain D) with the residues from 29 to 25 (chain E) form a ladder1 with 5 rungs.

**Multiple edges:** presence of parallel edges in the graph (edges with the same endpoints). The term multiple edges seems more adequate to the present case, given the use of the word parallel to indicate the orientation of the *β*-strands in the interface. In Figure 8, the residues E and F are connected by 4 edges, three of type SC and one of type BB. In Figure 22, the residues S and E are connected by three BB edges.

It is important to look for these motifs starting from the largest elements, to avoid useless multiple countings. Especially, it is obvious that a zigzag of 8 nodes contains all the shortest sizes, from 4 to 7. There is no need to record all of them, the largest one being the most informative. Also, a V3 or zigzag motif automatically contains the simplest vertex V, and often more than once. Thus, the vertex V is not counted when it appears inserted in a V3 or a zigzag. In Figure 33, the zigzag5 (residues V, M, I, V, A) contains three times a V. In Figure 30, the vertex V3 (residue H) contains three V motifs.

These topological elements may appear alone or in combination and have been chosen, as in other publications, because they represent the most common motifs in interface graphs. It may happen that a graph contains more than one motif, in which case all of them will be recorded. For example, in a common situation one of the nodes of a zigzag5 is also a vertex V3. In that case, both motifs are registered. The main reason is that a systematic classification of graphs needs to be accurate and free of ambiguous search criteria, thus it is definitely preferable to accept a redundancy in the identification of motifs than introducing untested criteria. For example, often a ladder has separation of 2 in one part of the graph and separation of 1 in another part, with a edge (a rung) in common between the two parts. Thus, it is recorded as part of both a ladder of 2 and ladder of 1. Indeed, so far, no acceptable criterion has been found to discriminate if a edge must be considered part of a ladder with separation of 1 or of 2. In Figure 28, there is a BB ladder formed by M, I, K, I (chain A) and V, L, I, E (chain B). Residues M and I are separated of 2 while residues I and K, K and I are separated of 1.

#### **2.4. The dataset**

10 ime knjige

straight lines.

In fact we found it very convenient to distinguish two sub-graphs. The dotted-dashed lines represent pairs of atoms both from the backbone of the residues (BB graph). The solid lines indicate that at least one atom of the pair is from the side chain (SC graph). Mathematically speaking, this is called an edge-labelled graph, the two possible labels being BB and SC. In principle, there is a third edge label; indeed, the nodes of the same sub-unit form a sequence connected by the peptide bond. Albeit this type of edge is not explicitly shown in the graphs, it is implicitly present and motivates the choice of organizing the residues along

Notice that while the full atom graphs are a fortiori different, *Si* ∩ *Sj* = {}, this is no longer

In summary, the sets in equation (3) characterize the interaction between subunits; each set

The amino acid resolution graph of the interaction between subunits has shown to be extremely effective in interpreting data and designing statistical analyses. Notice that the choice of marking non interacting residues with a dot and interacting residues with a cross

The analysis will proceed with the inspection and comparison of the interaction graphs of the level 1 of the interface, with some additional information from the level 2. The focus will be on the topology, namely on the organisation of edges in the graphs, whose importance has already been shown in the previous publications [6, 11]. As already stated, the amino

**Zigzag:** it is a path that alternates from one to the other of the two sub-units, namely from one to the other of the two nodes subsets of the bipartite graph, like in a zigzag seam. In principle, the smallest recognizable zigzag visits three residues; the experience with data has clearly shown that it is not useful to consider such a path in the zigzag family but it is better to classify it in the vertex family (see later). Thus, the smallest zigzag seam considered is the one with 4 residues visited, zigzag4. It is visible in Figure 5, involving the residues A, I, E, A (BB graph). Zigzag paths with 5 or more residues are

**Vertex:** it is a residue connected with two or more different amino acids. In the smallest case, the residue is connected to two other residues and indicated V, as in Figure 4, residues F, L, K. The case of more than two residues connected with the same vertex will be comprehensively indicated with V3. Three exemplars are present in Figure 28. Especially, the residue I on chain B has four connections, three of type BB and one of type SC. **Ladder:** This motif occurs when 2 (or more) amino acids at separation *δ* on the first subunit, namely at positions *M*, *M* + *δ*, are connected with 2 (or more) amino acids *N*, *N* − *δ* on the second sub-unit, respectively. Notice that the second sub-unit runs oppositely to the first. The two groups of amino acids are the ladder rails and the edges are the rungs, from which the name was adopted. In Figure 3, residues E, Y and E, W form a ladder with 2

comprehensively indicated as zigzag5. See Figure 27, residues T, E, L, V, A.

true for the fused graphs: it is not a priori granted that they are different.

is a bipartite and edge-labelled graph where parallel edges may occur.

is purely conventional and does not add information.

**2.3. Topological analysis of the graphs**

acid resolution will be systematically adopted.

The interface graphs will be analysed using the following motifs.

In [6] a dataset of 39 oligomeric proteins was chosen on the basis of the presence, in the three-dimensional structure, of a well recognizable *β* interface. Here the same set will be used. It is listed in Table 1 with the indication of the chains and the intervals participating to the *β* interfaces. All proteins are homomeric of stoichiometry from 3 to 8. This set is characterized by the absence of sequence homology, structural homology or functional homology. Viral and membrane proteins are absent, given their specificities. Thus, the set can be considered representative of the general behaviour of *β* interfaces, without reference to specific classes of proteins. In the dataset there are anti-parallel, parallel and oblique orientations of the adjacent *β* strands. Actually, the oblique family will be considered for comparison only and not deeply investigated. In fact its graphs are less structured and it is difficult to find similarities between proteins.

#### **2.5. Residue interaction network generator (RING)**

It is a web server [15] with software for rendering a protein structure into a network of interactions. Nodes represent single amino acids and edges represent the non-covalent bonding interactions that exist between them. In particular, this web server has been used to calculate the hydrogen bonds for the proteins of the dataset. The hydrogen bonds have not been supplied to the symmetric minimization method but just used to compare some results. 10.5772/58420

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In the parallel orientation, the zigzag topology and the vertices V3 are absent at this level. On the contrary, these are the most common topological elements at the lower levels. At this level we just find the ladder motif with separation of 1 or 2 amino acids. In some

Geometry and Topology in Protein Interfaces -- Some Tools for Investigations

**R4. Other orientation.** In interfaces other that the anti-parallel or parallel *β*, it was systematically observed in previous publications and is confirmed here that the BB graphs are rare and poorly structured, as composed of one or two edges. This obviously holds

**R5. SC graph.** These graphs are more elaborated and very rich of a variety of elements. One may recognize that the motif of the ladder is present in nearly half the graphs, at all the various levels examined (0, 1, 2). The V, V3 and zigzag elements are very common in all the three orientations. A more complete analysis of the SC graphs will be realized in

The participating amino acids are actually very similar: in [11], paper fully dedicated to

detected. At the level 0, the difference is more in terms of the description that emerges. In the buried surface or Voronoi cells based interface approach, is very natural to distinguish between rim and core: a solvent molecule that tries to penetrate the interface first has to visit the rim, and after may force into the core. The symmetrized levels instead present the growth of the interface, as in a budding process, from the set *S*0. The hierarchy in (1) indicates the dynamical sequence of events that may construct the interface with minimal steric effects. Somehow, this introduces the notion of time in an otherwise static view. The distinction between rim and core is possibly given by the number of the connections of a residue: a highly connected residue must be in the core and cannot be in the rim. Vice versa, a poorly connected residue is in the rim. Indeed, the part of a residue surface that is exposed to the solvent will not be connected to other atoms while an atom that is completely buried will have connections with all its neighbours. This indicates that a minimal distinction between rim and core may appear if one compares few close levels, like *S*0, *S*<sup>1</sup> and *S*2. The point is that the more an atom is present at different levels, the more is connected thus the rim must correspond to those atoms that appear few times across the levels, the others being in the

The importance of how the information circulates in an interface has been first shown in [8].

approaches based on the contact surface (buried surface or Voronoi cells based interface). The motifs that allow this transfer are the zigzags, especially the long ones, and the vertices V, V3. On the contrary, a pure ladder topology, in BB or SC or both, does not have ways to correlate far apart atoms. The frequent presence of zigzag, V and V3 motifs implies the existence of constraints on the positions or the physico-chemical properties of non neighbouring atoms, and sometimes of very far apart atoms. Indeed, in Figure 27, the mere establishment of the path formed by the residues S2, E123, L5, L124, I7, V127 (where BB and SC are both present)

*i* show the presence of long range correlations that are not easily detected with

<sup>0</sup> , with published interface data more than 85% of similarity has been

cases the BB graph reduces to one edge.

In Methods, four definitions of interface have been introduced.

true also at levels 1 and 2.

future publications.

compare the level 0, *Saa*

**4. Discussion**

core.

The graphs *Saa*

#### **3. Results**

The dataset has been described in Methods and its proteins are listed in Table 1.

The graphs of the level 0 were provided as Supporting Information to the publication [6] and are downloadable in open access, as well as the publication itself.

In this section the description focuses on the level 1 and, to a minor extent, on the level 2. The graphs of the level 1 are provided in Figures 3 to 26, and 27 to 34. Given their minor role, the graphs of the level 2 will be provided on demand.

In the following description, the results are grouped and numbered from Result 1 (R1) to Result 5 (R5).

**R1. BB graph at level 1.** The level 1 graphs are not identical to the level 0 ones.

In all the anti-parallel cases (Figures 3 to 26), the presence of a level 1 BB graph is observed, of size comparable with the one observed at level 0, namely with a similar number of edges. It is very small in just one case (2ojw level 1), where a single edge is present.

Similarly, in all the parallel cases (Figures 27 to 34), there is a level 1 graph, with a number of edges similar to the one found at level 0.

**R2. BB graph structure at level 1.** In the anti-parallel orientation, the graphs present a ladder structure in 21 out of 24 cases. Its separation is of 2 amino acids in 20 cases out of 24 and of 1 amino acid in 15 out of 24 cases1. The zigzag connection is absent. The vertex V is present in 13 graphs on 24. The V3 motif is present in 9 out of 24 cases. The zigzag4 is counted 12 out of 24 times. Just 4 multiple edges are observed. In all cases at least one ladder motif shows up at level 0 or at level 1.

In the parallel orientation, the graphs shows a common presence of the zigzag5 topological element, in 4 out of 8 graphs, often accompanied by one or more V3, in 5 out of 8 graphs. The zigzag5 motifs are probably not completely independent from those that appear at level 0, as they always show up together. This aspect needs a larger statistics to be confirmed or disproved. The zigzag opening is of 1 or 2 amino acids. There are also 4 ladders of separation 1. Multiple bonds appear once out of 8 cases2.

**R3. BB graph structure at level 2.** A level 2 BB graph is observed in all cases, often less populated than the lower levels graphs.

In the anti-parallel orientation, there are 13 ladders out of 24. The other cases show a V or zigzag4 motif, and in one case a zigzag5 is found.

<sup>1</sup> At level 0 the ladder separation is of 2 residues (23 cases out of 24). There are multiple edges in 21 cases out of 24.

<sup>2</sup> At level 0 there are 5 zigzag5 out of 8. After, one finds 2 V and 1 zigzag4. Multiple bonds are absent.

In the parallel orientation, the zigzag topology and the vertices V3 are absent at this level. On the contrary, these are the most common topological elements at the lower levels. At this level we just find the ladder motif with separation of 1 or 2 amino acids. In some cases the BB graph reduces to one edge.

#### **4. Discussion**

12 ime knjige

**3. Results**

Result 5 (R5).

present.

**2.5. Residue interaction network generator (RING)**

It is a web server [15] with software for rendering a protein structure into a network of interactions. Nodes represent single amino acids and edges represent the non-covalent bonding interactions that exist between them. In particular, this web server has been used to calculate the hydrogen bonds for the proteins of the dataset. The hydrogen bonds have not been supplied to the symmetric minimization method but just used to compare some results.

The graphs of the level 0 were provided as Supporting Information to the publication [6] and

In this section the description focuses on the level 1 and, to a minor extent, on the level 2. The graphs of the level 1 are provided in Figures 3 to 26, and 27 to 34. Given their minor

In the following description, the results are grouped and numbered from Result 1 (R1) to

In all the anti-parallel cases (Figures 3 to 26), the presence of a level 1 BB graph is observed, of size comparable with the one observed at level 0, namely with a similar number of edges. It is very small in just one case (2ojw level 1), where a single edge is

Similarly, in all the parallel cases (Figures 27 to 34), there is a level 1 graph, with a number

In the parallel orientation, the graphs shows a common presence of the zigzag5 topological element, in 4 out of 8 graphs, often accompanied by one or more V3, in 5 out of 8 graphs. The zigzag5 motifs are probably not completely independent from those that appear at level 0, as they always show up together. This aspect needs a larger statistics to be confirmed or disproved. The zigzag opening is of 1 or 2 amino acids. There are also

**R3. BB graph structure at level 2.** A level 2 BB graph is observed in all cases, often less

<sup>1</sup> At level 0 the ladder separation is of 2 residues (23 cases out of 24). There are multiple edges in 21 cases out of 24.

<sup>2</sup> At level 0 there are 5 zigzag5 out of 8. After, one finds 2 V and 1 zigzag4. Multiple bonds are absent.

In the anti-parallel orientation, there are 13 ladders out of 24. The other cases show a V

4 ladders of separation 1. Multiple bonds appear once out of 8 cases2.

**R2. BB graph structure at level 1.** In the anti-parallel orientation, the graphs present a ladder structure in 21 out of 24 cases. Its separation is of 2 amino acids in 20 cases out of 24 and of 1 amino acid in 15 out of 24 cases1. The zigzag connection is absent. The vertex V is present in 13 graphs on 24. The V3 motif is present in 9 out of 24 cases. The zigzag4 is counted 12 out of 24 times. Just 4 multiple edges are observed. In all cases at least one

The dataset has been described in Methods and its proteins are listed in Table 1.

**R1. BB graph at level 1.** The level 1 graphs are not identical to the level 0 ones.

are downloadable in open access, as well as the publication itself.

role, the graphs of the level 2 will be provided on demand.

of edges similar to the one found at level 0.

ladder motif shows up at level 0 or at level 1.

populated than the lower levels graphs.

or zigzag4 motif, and in one case a zigzag5 is found.

In Methods, four definitions of interface have been introduced.

The participating amino acids are actually very similar: in [11], paper fully dedicated to compare the level 0, *Saa* <sup>0</sup> , with published interface data more than 85% of similarity has been detected. At the level 0, the difference is more in terms of the description that emerges. In the buried surface or Voronoi cells based interface approach, is very natural to distinguish between rim and core: a solvent molecule that tries to penetrate the interface first has to visit the rim, and after may force into the core. The symmetrized levels instead present the growth of the interface, as in a budding process, from the set *S*0. The hierarchy in (1) indicates the dynamical sequence of events that may construct the interface with minimal steric effects. Somehow, this introduces the notion of time in an otherwise static view. The distinction between rim and core is possibly given by the number of the connections of a residue: a highly connected residue must be in the core and cannot be in the rim. Vice versa, a poorly connected residue is in the rim. Indeed, the part of a residue surface that is exposed to the solvent will not be connected to other atoms while an atom that is completely buried will have connections with all its neighbours. This indicates that a minimal distinction between rim and core may appear if one compares few close levels, like *S*0, *S*<sup>1</sup> and *S*2. The point is that the more an atom is present at different levels, the more is connected thus the rim must correspond to those atoms that appear few times across the levels, the others being in the core.