An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners. The interplay between biopolymers is critical in directing and maintaining physiological processes. Whilst genome-sequencing projects are providing large amounts of protein sequence data from many organisms, our understanding of binding specificity between proteins, and how a protein selects partners from closely related alternatives, remains limited. The majority of work in identifying specificity determinants focuses on the sequences and structures of the proteins involved. Methods for identifying residues that determine specificity face challenges, often due to an absence of suitable experimentally determined structures or the lack of affinity data . Where sGenomic and proteomic studies have shown that most proteins belong to families of evolutionarily, and often functionally, related molecules . The numvia leucine zippers, where charge interactions modulate specificity on a relatively conserved steric framework [The present report examines specificity in paralogous protein–protein interactions from a structural viewpoint, combining atomistic-level detail, with rapid calculation of electrostatic interactions and surface burial. In computing interfacial properties, an empirical calculation approach is taken, using the solvent accessible surface area (SASA) approach of Lee and Richards  and a Deramework . The simThe key requirements for a system to be included in this study are the availability of binding data, and the presence of at least one representative complex in the protein structural database . After oi and qj, separated by a distance of r, interact with a 1/r Coulomb potential in a dielectric medium with the relative permittivity of water (80), modified by a Debye-Hückel factor at 0.15 M ionic strength [The estimated electrostatic energy of interaction for groups bearing net charge (NetQ) and changes in non-polar and polar solvent accessible surface areas (ΔSASAnp and ΔSASApol) are calculated for all complexes modeled as rigid structures, with the differences for surfaces denoting subtraction of the sum of the component values from the complex value. Each component may be one, or more than one, polypeptide chain . Surfacestrength .Experimental data obtained from literature are used to separate interactors from non-interactors, which are then coupled with template-based comparative models for the potential interacting pairs. In the case of bZIPs, a dataset of 127 strong interactions, 324 weak interactions, and 1214 non-interactions was assembled from a comprehensive study of leucine zipper dimerization . The autCaulobacter crescentus genome encodes three parE toxins and one pseudogene (parE2) and their corresponding parD antitoxins [Mycobacterium tuberculosis vapBC family, comprising 48 vapC toxins that interact on a 1:1 basis with their vapB antitoxins [The titoxins , whereastitoxins . The partitoxins , i.e. eatitoxins , which ptitoxins  for the titoxins  for the titoxins  and 3DBOtitoxins  for the As part of the ubiquitination pathway, ubiquitin-conjugating enzymes (E2s) interact with ubiquitin-ligating enzymes (E3s). Human E3 ubiquitin ligases are divided into three subgroups depending on the structure of the catalytic domain, the largest group being the RING-type E3s . In a geInteraction data on BH3 peptide interactions with antiapoptotic proteins, consisting of 48 IC50 values, was obtained from solution competition assays on the binding between five antiapoptotic proteins and BH3 peptides from 10 proapoptotic proteins . The 48 Before proceeding to comparative modeling, we examined the binding mode between different pairs of proteins within each system for conservation. Structural and other experimental data demonstrate that the binding modes within the Bcl-2 family and the E2—E3 system are highly conserved ,50. Our Where comparisons are made between sets of calculated properties, statistical significance is assessed with the two-tailed Mann-Whitney U test, a non-parametric test used to determine whether samples derive from populations with the same distribution. Use of multiple templates allowed us to assess the robustness of our results.A multiple sequence alignment between paralogues in protein families is used to perform comparative modeling with one or more template structures for a complex. 2) than non-interactors , whereas change in buried polar accessible surface area is similar for interactors  and non-interactors  . Weak interactors are located between interactors and non-interactors, although closer to interactors for ΔSASAnp and closer to non-interactors for NetQ. The ranking of p-values is the same when modeling with the 1T2K template, with Mann-Whitney test p-values for interactors compared with non-interactors of 6.99x10-14 for NetQ, 2.71x10-10 for ΔSASAnp and 2.62x10-5 for ΔSASApol.A challenge for studies that seek to understand interaction specificities between paralogue families of proteins is the availability of high quality experimental data. Such data are available for the basic leucine zipper transcription factors (bZIPs), upon which Newman and Keating have carried out a comprehensive binding study . After p 100 Å2) . The NetUbiquitination contributes to the regulation of many physiological processes . The tra-22). For interactors, ΔSASAnp, M = -661, SD = 77 Å2 compares with M = -610, SD = 102 Å2 for non-interactors (p = 1.31x10-22). For ΔSASApol, interactors give M = -370, SD = 98 Å2 and non-interactors M = -398, SD = 95 Å2 (p = 5.76x10-9).The majority of suitable templates in the Protein Data Bank (PDB)  represen-13. For ΔSASAnp, M = -806, SD = 99 Å2 for interactors compares with M = -767, SD = 100 Å2 for non-interactors (p = 9.50x10-18). For ΔSASApol, interactors give M = -419, SD = 74 Å2 and non-interactors M = -460, SD = 90 Å2, with p = 1.65x10-18.The largest available E3 structure suitable to be a template, a 59 residue-long RING domain bound to an E2 enzyme (4CCG) also gave separation of all three properties . NetQ fo-8). Buried non-polar surface is significantly larger in interactors  than non-interactors , whereas polar surface is not significantly different . Similar results are obtained with the 3JVZ template  being most discriminatory, followed by buried non-polar surface, with buried polar surface being least discriminatory. It is shown that the results are largely independent of the template, although there is a limit to the template-based modeling with our current methods, demonstrated by the bacterial toxin–antitoxin pairs. These systems have diverged sufficiently to seriously impact on the accuracy of the comparative modeling process. For example, the vapBng tools . However2+ to maintain the native protein structure. It has been found that preserving the template amino acid sidechain rotamer is beneficial in maintaining the stability of modeled antiapoptotic protein–BH3 peptide complexes during molecular dynamics simulations [Our sidechain replacement comparative modeling tool provides no opportunity to model insertions and deletions. Whether such changes can be modeled with sufficient accuracy and speed for large-scale analysis of complexes remains an open question. An available option is whether to repack all sidechains or to employ a more minimal repacking of only those sidechains that differ between model and template. The minimal repacking scheme has been used, since amino acid conservation could reflect an important role in maintenance of structure . For exaulations .High throughput experimental data for protein–protein interactions are key for the current study, but these data can be imprecise. For example, the largest dataset used, E2 –RING E3 interactions, derives from a yeast two-hybrid screen . Given tIn agreement with previous work , we findThis observation can be interpreted from the standpoint of the core and rim model, and co-evolution of sequences, at least in the case of BH3 peptide–antiapoptotic protein complexes. Anchoring hydrophobic resides are highly conserved in this system , with coversus polar/charged residues) on stability of each interacting partner could lead to a preference for less deleterious charge swaps over non-polar surface alteration. Second, in a view of the crowded macromolcular environment, complementarity of charge interactions could afford a mechanism for scanning of potential partners at moderately longer range than the solvent exclusion of non-polar interactions.We have established that charge-charge interactions contribute substantially to a fine-tuning of pair interaction specificity in the systems studied, and in one case show that this modulation is based largely in the rim of the core and rim model for protein-protein interactions. It is unclear why interactions evolve in this manner, although two lines of enquiry are apparent and could be further investigated. First, the effects of mutations (non-polar The empirical modeling pipeline could be trialed with a combination of charge and surface burial, or inclusion of volume-based descriptors , and wit


The protein interface is key to understand protein function, providing a vital insight on how proteins interact with each other and with other molecules. Over the years, many computational methods to compare protein structures were developed, yet evaluating interface similarity remains a very difficult task. Here, we present PatchBag – a geometry based method for efficient comparison of protein surfaces and interfaces. PatchBag is a Bag-Of-Words approach, which represents complex objects as vectors, enabling to search interface similarity in a highly efficient manner. Using a novel framework for evaluating interface similarity, we show that PatchBag performance is comparable to state-of-the-art alignment-based structural comparison methods. The great advantage of PatchBag is that it does not rely on sequence or fold information, thus enabling to detect similarities between interfaces in unrelated proteins. We propose that PatchBag can contribute to reveal novel evolutionary and functional relationships between protein interfaces. Understanding protein function is one of the key challenges facing molecular biologists. Generally, the protein sequence of amino acids determines a fold in space according to the physiochemical attributes of its residues. The specific fold exposes some residues to the surface of the structure, while other residues remain buried. The exposed residues are the protein surface, which is the part of the protein that potentially interacts with other molecules, such as DNA, RNA, small ligands, or other proteins. The interacting molecule binds a specific part of the surface, the interface, which matches the molecule’s binding preferences in terms of structure and physiochemical properties, e.g. electrostatics and hydrophobicity. Therefore, the protein sequence, the fold, the surface, and the interface – are all tied to protein function.1. Hence, a classic approach to infer functional similarity is to use a structural alignment, which is the process of identifying two equally sized substructures that are geometrically similar. The output of a structural alignment is the aligned substructures, superimposed on one another. One can measure the root mean square deviation (RMSD) between the sub-structures, to quantify their divergence from one another. Among the widely used structural alignment tools are CE2, DALI3, and FATCAT4. Indeed, many methods for protein function prediction rely on finding structural neighbors8. However, it has been shown that in some cases different folds can share similar functions, and in other cases proteins of the same fold can have different functions, e.g., interact with different substrates9. In such cases, the protein surface can shed new light on the protein function. In particular, the protein interface that mediates the interactions with other molecules is of interest13. Indeed, it has been shown that protein interfaces are far more conserved than the protein surfaces14.The three-dimensional (3D) structure of proteins is highly conserved in evolution and provides important information about their function17, Three-Dimensional Zernike Descriptors (3DZD)21, or an unordered collection of the three-dimensional (3D) coordinates of the surface atoms23. Some of these algorithms were implemented for a fast 3D comparison of protein surfaces25. In addition, algorithms were developed dedicatedly to compare interfaces - the functional part of the surface. The available interface-comparison and interface clustering algorithms are usually limited to specific interface types, such as protein-binding interfaces33, ligand-binding interfaces40 or nucleic-acid-binding interfaces41. Other methods based on local structural surface comparison have been employed for pocket comparison42 and interface prediction, for instance, predicting protein-protein interacting residues43 or finding ligand binding sites44.Several algorithms were developed to identify surface similarities, independent of the overall protein folds. These algorithms represent the surface shapes in various ways, such as Alpha Shapes and Delaunay Triangulations45. BOW algorithms were also used in the context of protein study, as for example in SVM-Fold46 to detect protein sequence homology, in FragBag47 to rapidly retrieve protein structures from the Protein Data Bank (PDB), and to discriminate native structures from structure prediction decoys23. In PatchBag, we define local surface patches by the coordinates of the C-alpha atom of an exposed residue and their nearest C-alpha atoms that are not consecutive along the protein chain.Here, we present a novel geometry-based approach, named PatchBag, for characterizing and comparing protein surfaces or sub-surfaces (interface) in an accurate and highly efficient manner. In contrast to other interface-comparison tools, Patchbag is applicable for comparing interfaces of any type, as it does not require any information from the specific partner of the protein of interest. PatchBag represents protein surfaces or interfaces as vectors that count the number of appearances of various geometrical types of small surface units, defined as surface patches. This is known as the bag-of-words approach (BOW), where the surface is described as an unordered collection of local features. The advantage of using BOWs is that it is magnitudes faster to compare vectors than to compare the original objects. Indeed, the BOW approach is extensively used in large search systems such as web search engines48. To test PatchBag’s ability to identify homologous interfaces we construct a homology-based network, connecting protein domains based on their PatchBag similarity of their known interfaces. We show that the interface similarity network derived from our PatchBag algorithm can recapitulate a high percent of the known interacting partners. Our results are comparable to the results of the algorithm presented by Cukuroglu et al.49, who applied the multiple protein structure alignment algorithm MultiProt50 to compare and cluster Protein-Protein Interfaces (PPIs). Furthermore, we demonstrate that PatchBag can identify homologous interfaces from randomly selected surface patches of the same size on the protein. Overall, PatchBag is highly efficient and can be used as an inverted index in future interface based search systems. We propose that PatchBag can help to identify similar interfaces regardless of their fold, providing a novel approach for protein function prediction based on their functional interfaces.We use PatchBag to model the interfaces of 1,070 protein domains from the database of three-dimensional interacting domains (3DID)n as a set of n 3D coordinates, representing the center of C-alpha atoms of an exposed residue calculated by the DSSP algorithm51 with a solvent accessibility threshold of 20% and its n − 1 nearest backbone residues, excluding the successor and predecessor residues along the backbone. We measure the distance between residues using Euclidean distance, and take n − 1 nearest neighbors with the minimum distance to the initial exposed residue. We eliminate successor and predecessor residues to capture the 3D space neighbors of the surface residue, rather than its backbone neighbors. In addition, we define the patch orientation by the direction of the normal vector pointing away from the protein core, calculated using the alpha shape algorithm15. We consider surface patches of sizes n = 4, 5, and 6 residues. For two surface patches of the same size, each represented by n C-alpha atoms, we define the distance between them as the minimal RMSD, calculated using Kabsch’s algorithm52 for all possible correspondences between the two sets of points. When comparing two surface patches, each represented by n C-alpha atoms, there are n! possible correspondences between the atoms in both patches ; we calculate the RMSD values of all possibilities in which the angle between the normal vectors is less than 90°, and take the minimum to be the true correspondence (for further details see Methods).PatchBag is a concise vector representation of protein surfaces or sub-surfaces , based solely on the 3D structure of the protein. The protein surface is represented by all protein residues that are exposed to the solvent, while interfaces are defined as the subset of residue that interact with a specific partner (See Methods). An outline of the method is shown in Fig. Next, we derive a library of patches by applying the surface patch similarity to a representative set of patches extracted from proteins surfaces. The patches library is essentially a set of patches resulting from clustering patches from a large set of representative protein patches (ranging from 4000 to 8000 randomly selected patches) from the SK.531 dataset (see Methods - Datasets). The clustering is based on the spatial distance between the patches, as measured by RMSD, and taking into account the surface orientation . We first generate different patches libraries that vary in their number of elements (denoted library_size), starting from a randomly selected set of 5000 patches. We then cluster the patches to libraries of 20, 50, and 100 bags with different patch sizes . We evaluate the quality of a clustering used to construct a library by the diameter of each cluster  and the distance between clusters  (see Methods for details). Table k is the library size, then the protein surface/interface will be represented by a vector of size k that counts for each library patch the number of times it is represented. We approximate the distance between two surfaces by the “PatchBag distance”, which is 1 – the cosine between their PatchBag vectors 2. Figure Further, to represent a protein surface/interface by a PatchBag vector, we consider the residues from the entire surface or the residues from a defined protein interface, for surfaces and interfaces, respectively, and extract all overlapping patches (as described in detail in the Methods section). Consequently, each patch extracted from the protein’s surface or interface is compared to the medioid of each bin in the library and is represented by the library patch most similar to it See Fig. . Eventuaequation  in Metho48 (see Methods – 3DID.1070 datasets). For each protein among the protein-protein interacting pairs we define the interface residues based on the reported interacting residues, and extract their interface patches as described above and detailed in the Methods section. Our working hypothesis is that if two proteins have similar interfaces then their true partners are likely to have similar interfaces as well54. We define an Interface Similarity Network (ISN), where nodes represent protein interfaces and edges connect nodes representing similar interfaces. For a pair of interacting interfaces A and A′, we define the interaction size of the node A to be the number of neighbors of A in the ISN that interact with the neighbors of A′ in the ISN , which we use in all the following tests.In PatchBag-ISN, we measure the similarity between two interfaces by their PatchBag score. The number of nodes in the network is the number of domains – 1,070 (one interface per domain), the number of edges varies, depending on the threshold lds Fig. . For eac−39, Mann-Whitney U test). To verify that the difference between PatchBag-ISN and Random ISN does not depend of the random model chosen, we generate the random network in three different ways: (1) as a randomly rewired network generated by permuting the nodes. In other words, we shuffle the interacting pairs and rewire the structural neighbors while maintaining the degree distribution. (2) as a randomly rewired network without permuting the nodes; here, we maintain the interacting pairs while rewiring the structural neighbors. (3) as an Erdős network57, in which each edge is included with a probability p = 1/|E|). Figure As shown in Fig. N randomly chosen scattered patches on the protein surface, where N is the number of residues on the original interface. The interaction rates depend on the node’s degree, which derives from the overall number of edges in the network. Thus, when comparing two networks generated by two comparison methods, it is important to set the threshold T such that the number of edges in each network is similar. In the Scattered-PatchBag-ISN we used T = 0.41, which yields 63,400 edges . As shown in Fig. −32 (Mann-Whitney U-test). Notably, the average interaction rate for the Scattered-PatchBag-ISN is higher than the equivalent value for the Random Networks, indicating that randomly chosen patches on the surface maintain some of the signal.To support that the PatchBag similarity is due to the similarity of the true interfaces, and not to the overall shape of the domains, we use a random set of surface residues as a control. We generate a Scattered-PatchBag-ISN, where similarity between interfaces is calculated based on the similarity between 50, customized to compare structural interfaces, as used by Cukuroglu et al.49, using the same network approach and set of residues as in the PatchBag-ISN. We construct the MultiProt-ISN from 63,316 edges (see Methods) with an average degree of 118.35 (±108.63) – similar to the average degree of the PatchBag-ISN. The average interaction rate of MultiProt-ISN is slightly higher than PatchBag-ISN, yet it has a similar distribution: 35.22% (±21.22%)  A and B have the same PFam classification, and (2) A′ and B′, the interacting proteins of A and B, have the same Pfam classification as well  using a computer running 8 Intel i7-2600K 3.40 GHz cores to run PatchBag in this dataset. The preprocessing of a single structure includes extracting its surface or interface and calculating its PatchBag vector according to the selected library. The preprocessing step takes 9 seconds per surface/interface on average, depending on the number of residues in the overall structure and on the surface or the interface. The preprocessing step of a dataset of 1,070 protein interfaces took 2.7 hours, once we had the PatchBag vectors, comparing all-vs.-all  took around one second. Running MultiProt for all-vs.-all comparison on the same computer took 5 hours, or 0.03 seconds per single comparison. Using MultiProt, as there is no pre-processing option, the querying time of the 3DID.1070 dataset is 32 seconds, and it will grow linearly with the size of the dataset.45 and in computer vision for comparing images58. The BOW approach has also been employed to compare protein sequences, where a word is a sequence k-mer46, or to protein structures, where a word is a short structural fragment on the protein backbone47. In PatchBag, the words are small structural patches on the protein surface, defined by the coordinates of 4–6 C-alpha atoms that are structural neighbors of a central exposed residue. The pitfall in defining a protein surface using a surface accessibility threshold is that proteins tend to have structural fluctuations. Sometimes, even a minor fluctuation can cause a buried residue to become exposed, and vice versa. Such an event is very common, and alters the surface structure. Defining a surface patch by a central exposed residue along with its spatial neighbors, which are not necessarily solvent accessible, renders PatchBag less sensitive to small structural fluctuations and thus more robust.PatchBag is a novel geometry-based method to compare protein surfaces and interfaces, based on the bag-of-words (BOW) approach. BOW is widely used in applications of automated search: For example, in textual search for efficiently comparing documents43, is sequence independent, as it only considers the three dimensional coordinates of the C-alpha atoms of the interface residues. This allows us to find interface similarities among non-homologous proteins. Moreover, as opposed to some state-of-the-art interface comparison methods, such as the method proposed by Brender and Zhang59, PatchBag does not require a prior structural alignment. This is especially useful to identify similar interfaces even if the overall structure of the proteins is different, thus enabling us to identify structurally similar interfaces lying on completely different protein folds. The strength of PatchBag lies in its robustness to represent any type of protein interface , and infer similarity of any kind. Most existing interface comparison methods are designed to compare the paired-interface of protein-protein complexes, which includes the interface of two interacting partners54. Paired-interface search is useful for docking tasks, or template based PPI search60, however, it is less suitable for finding similarities between protein interfaces that bind other ligands, such as nucleic acids. Other methods are uniquely designed to find similarities among small ligands binding interfaces or pockets61. The latter methods identify similarities between binding sites in different proteins that bind the same ligands, yet they are not applicable for searching protein interface similarity in general, as they require the convexity of the interface, which is not necessarily the case in PPIs.PatchBag infers interface similarity by the relative frequency of different structural patches existing on the interface. Another valuable characteristic of BOWs is that they can be used within an inverted index data structure, which stores each interface according to its patches, rendering search even faster. Interestingly, even though PatchBag, as other BOW-based methods, completely ignores the order of the words (patches), it can still recognize interface similarities. PatchBag, unlike other interface comparison methods62 or structure space64. We take advantage of protein similarity networks to validate our new interface similarity measure. Specifically, we evaluate PatchBag  by its ability to recapitulate known interactions from PPI data. The ISN approach allows us to examine a large dataset of interfaces, instead of studying a few examples. We further use this approach to compare PatchBag to MultiProt50, which has been used to compare paired-interfaces49, namely, the contact region between two interacting proteins. Here, we employ MultipProt to single interfaces and show that PatchBag performs on par with it though significantly more efficiently. We thus propose that ISNs can also serve to evaluate other interface similarity methods.The uniqueness of PatchBag is that it allows comparing any given region on a protein surface to any other surface region without any requirements or assumptions on the interfaces of interest, such as being on homologous proteins or knowing the interacting partner of the interfaces. The lack of compatible computational methods to compare interfaces led us to find a unique approach to evaluate PatchBag’s performance. Our approach assumes that two similar interfaces tend to have similar partners. Thus, we build an Interface Similarity Network (ISN), in which the nodes are protein interfaces, and edges connect similar interfaces according to a similarity measure. Protein similarity networks are commonly used to explore protein sequence space65. This index can be used as a powerful interface search engine that given a query interface will instantly filter all potentially similar interfaces. On the filtered set, which only contains a fraction of the interfaces in the database, one could apply a more accurate  method to refine the results. Such a tool would be useful for template based PPI prediction, by the process defined in the work of Muratcioglu et al.66. It can also be applied to search for a specific interface among a set of candidate interfaces on a protein’s surface, for example – for searching a DNA and RNA binding interfaces, given a set of candidate binding interfaces on a protein surface, or for searching a ligand binding interface from a list of potential interfaces given by a pocket prediction tool, such as CASTp35. By allowing a fast, sequence- and fold- independent search in large datasets of functional surfaces, PatchBag can boost the current comprehension of protein function, and reveal novel evolutionary relationships between protein surfaces and interfaces, which are yet unidentified due to lack of sufficient and efficient computational tools for surface comparisons.A great advantage of PatchBag is its pre-processing step, which allows us to calculate the PatchBag vectors of a large dataset in advance. Then, querying a pre-processed dataset using a new protein interface will take only a few seconds to prepare the PatchBag vector of the query interface, and a few nanoseconds to compare it to the entire dataset, as calculating the “PatchBag distance” of vectors is a rapid computation. Thus, whether the pre-processed dataset is of size 1,000 interfaces or 100,000 interfaces, its querying time remains the same, and it depends mainly on the queried interface, and less on the dataset size. PatchBag can be further employed to index interfaces from the PDB, as well as intra-domain interfaces, that is, areas within a domain that are often found to be evolutionary related to domain-domain interactions44. In this dataset each domain was taken from a different SCOP67 family, from 72 SCOP superfamilies and the structure similarity of each pair is less than a Z-score of 3.8 by the Combinatorial Extension (CE)2. To prepare the patches libraries we filter structures with less than 50 residues. In addition, we exclude domains that were removed from SCOP version 1.75B, structures solved by NMR, and structures with crystallographic resolution of 3.0 Å or worse. This results in 531 sequence non-redundant protein domains.We use a dataset which consists of 609 protein domains48. We filter out domains with 80% sequence identity or more using the CD-HIT software68 and take only sequences longer than 50 residues. Then, we extract the interfaces from each domain within the domain pairs by considering only the interacting residues, as defined in the 3DID database. Notably, we consider only pairs where both interfaces are larger than 15 residues. Overall, we have 535 such pairs, that is, 1070 interfaces.The 3DID dataset is a catalogue that contains all domain-domain interactions from the PDB69, where the SAS  of all vs. all alignments were recorded. SAS score is the ratio of the RMSD and alignment length times 10053. In our gold standard, two domains are considered similar if their SAS score is below a threshold T = 5 Å, a threshold on which sequence alignment similarity yields an AUC of 0.547.For surface comparison we use a dataset of 2,743 structurally aligned protein domains from CATH generated in a previous studysurface is the collection of surface accessible residues. We calculate the exposed residues using the DSSP algorithm51 with surface accessibility threshold of 20%. The protein interface is a subset of the protein surface, which includes the residues that interact with a substrate. In the 3DID dataset the interface residues are provided in the data files. We define a surface patch by a central exposed C-alpha atom and its nearest C-alpha neighbors, excluding the C-alpha atoms of the successor and predecessor residues along the polypeptide chain. In addition, we define the patch orientation by the direction of the normal vector pointing away from the protein core. To calculate the normal vectors we compute the alpha shape triangulation15 of the protein surface, and use the triangulation facets to compute the normal of each residue.The protein 51, and consider its surface patches. Then, we approximate each patch by its most similar counterpart in a library of patches. We randomly selected 4000, 5000, 6000, 7000 and 8000 patches from the 60,070 patches on the proteins in the SK.531 dataset (see Methods – Datasets), and used the k-means++ algorithm70, in each case clustering them into k = 20, 50, and 100 clusters. The mediods (the surface patches closest to the centroids according to the surface patches similarity score) of each cluster were collected to form the surface patch library.For each given protein domain we extract the surface residues using DSSPk-means++ algorithm initializes the mediods randomly it is expected that any two runs of the algorithm on the same data would yield different results. To choose the best clustering result we used the Dunn clustering score71, which is the ratio between the minimal inter-cluster RMSD to maximal intra-cluster RMSD, shown in equation  is the inter-cluster distance between cluster i and cluster j, measured by the distance between their mediods, and D(m) is the intra-cluster distance of cluster m, measured by the mean distance between each cluster member and its mediod. We clustered the data 50 times and chose the medoids with the maximal Dunn score as our patches library.Given that the L < patch_size, library_size > , we characterize a protein surface or subsurface P by its PatchBag vector of library_size entries; the vector represents the number of times each library-patch best approximates a surface patch in P. We calculate the similarity between two protein surfaces by the cosine distance of their corresponding PatchBag vectors – see equation . Overall, the PatchBag-ISN yielded 62,373 edges. Note, that when comparing ISNs generated by two different comparison methods, it is important to set the threshold T such that the number of edges is similar. Hence, to keep the number of edges similar in all ISNs, in Scattered-PatchBag-ISN, T was set to 0.41 yielding 63,400 edges, and in MultiProt-ISN we used T = 6.1, resulting in 63,316 edges.Interface Similarity Network, is a network in which nodes represent protein interfaces and edges connect nodes representing similar interfaces. In PatchBag-ISN each pair of interfaces (nodes) were connected by an edge if their “PatchBag distance” was below threshold Given that domains 〈A, A′〉 are interacting according to the 3DID database, we measured the interaction size of node A, that is, the number of neighbors of A whose interacting partner is in the neighbors group of A′. Finally, we recorded the interaction rate, which is the proportion between the node’s interaction size and its degree.A stand-alone version of PatchBag software is avaialble via request.Supplementary Information


Many protein–protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression).http://biocomp.chem.uw.edu.pl/CABSdock/). Based on the docking result, the protein–protein complex is reconstructed and refined.In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model.The results show that the CABS-dock method may be useful as the primary docking tool in specific protein–protein docking cases similar to EphB4-EphrinB2 complex—that is, where a short linear motif fragment can be identified. As much as 40% of protein–protein interactions (PPIs) in higher eukaryotes are mediated by short linear motifs  (SLiMs).Even if the localization of the interaction site is known, the prediction of protein–protein complex structure is still a demanding task . The maihttp://biocomp.chem.uw.edu.pl/CABSdock/) that employs an efficient peptide docking scheme. Various methods exist for peptide–peptide docking [We propose and test a new protocol for protein–protein docking based on the flexible docking of a SLiM fragment (peptide) to a protein receptor without using any information about the SLiMs structure or a binding site. To perform this step, we use the CABS-dock online docking server –15  random peptide conformations in coarse-grained representation is generated;peptide replicas are placed at random locations around the receptor at the distance of 20 Å from the receptor’s surface;docking simulation is run with the completely flexible peptide and the receptor restrained to near-native conformation by a set of distance constraints derived from the starting conformation, a set of 10,000 models is generated (1000 per replica);100 lowest energy states from each replica are selected for further processing;conformations of all 1000 (10 × 100) models are structurally clustered using the k-medoids procedure with L-RMSD  as the distance measure between them;10 top-scored models, representatives of 10 most dense clusters (density defined as the number of cluster elements divided by average L-RMSD between them), are reconstructed to all-atom representation and refined in a short simulation in Modeller .In the first and crucial modeling step, the SLiM sequence is docked to the receptor protein structure in the bound form  using the CABS-dock server –15. DuriIn the second step, a model of the complex is built. To do so, we use structures of both of the proteins, as well as information about the bound SLiM obtained with CABS-dock. In our study, we use the best binding pose of the SLiM peptide from the 10 top scored models provided by the CABS-dock server . The complex conformation is produced by performing RMSD-minimizing superposition of the peptide and its SLiM counterpart in the protein.Finally in the third step, the derived protein–protein complex model is refined using all-atom refinement procedures , 44.To analyze the applicability of our approach to the PPI interface prediction we use the interface-RMSD measure (iRMSD), which is calculated as the RMSD of the C-alpha atoms of the residues forming the SLiM interface. The interface is defined as the peptide together with the receptor residues within the 4.5 Angstroms cut-off .Another way of assessing the predicted structure is analyzing the fraction of the native contacts (fNC) that were correctly predicted in the model. fNC values are calculated as the number of correctly predicted native contacts present in the model structure divided by the total number of native contacts. The contacts were calculated with the COCOMAPS Tool  with defWe identified a pose with iRMSD value of 3.1 Å bound in the proximity of the native PPI interface .An improved method for complex reconstruction from the docked SLiM pose. The RMSD-minimizing superposition we use here could be replaced with a method that performs rigid-body docking guided by the SLiM pose that could attempt to generate acceptable structures (as many variants as possible) for the further scoring and refinement step.An improved refinement method that could include: large-scale domain movements  and small-scale and large-scale backbone movements at the complex interface .Further advances of our protocol are possible, including the incorporation of:In this work, we used the CABS-dock method to predict the binding site and pose of the fragment of EphB4-EphrinB2 protein–protein complex, which allowed further reconstruction of the complex. The results we presented show that CABS-dock peptide (SLiM) docking may be a useful tool for protein–protein docking. The presented protein–protein docking scheme, applied here to modeling the EphB4-EphrinB2 interaction see Fig. , can be


The annotation of protein function is a vital step to elucidate the essence of life at a molecular level, and it is also meritorious in biomedical and pharmaceutical industry. Developments of sequencing technology result in constant expansion of the gap between the number of the known sequences and their functions. Therefore, it is indispensable to develop a computational method for the annotation of protein function. Herein, a novel method is proposed to identify protein function based on the weighted human protein-protein interaction network and graph theory. The network topology features with local and global information are presented to characterise proteins. The minimum redundancy maximum relevance algorithm is used to select 227 optimized feature subsets and support vector machine technique is utilized to build the prediction models. The performance of current method is assessed through 10-fold cross-validation test, and the range of accuracies is from 67.63% to 100%. Comparing with other annotation methods, the proposed way possesses a 50% improvement in the predictive accuracy. Generally, such network topology features provide insights into the relationship between protein functions and network architectures. The source code of Matlab is freely available on request from the authors. Protein can carry out various biological functions, such as replication and transcription of DNA, catalysis of metabolic reactions, and transport of bioactivity molecules. The accurate annotation of protein function is therefore a vital step to elucidate the nature of life at the molecular level and has a great application in biomedical and pharmaceutical industry13At a cellular level, protein executes its function through cooperated interaction rather than autonomous working68910121314151617182122232426272829303132Over the past several decades, some traditional network topology features, such as degree, betweenness centrality and clustering coefficient, have been developed and utilized to address various biological and medical problems3435et al.N of protein sequence. Transitions have three values, which can also be acquired through the number of three combined indices “12”, “13” and “23” divided by the length N − 1 of protein sequence. Each specific index class contains 5 distribution values, which are acquired from the position percents in the whole sequence and treated as the first index, 25% index, 50% index, 75% index and 100% index, respectively. The detailed explanations and definitions can be found in reference39In order to build a high quality human PPI network, PPI data is downloaded from the HIPPIE databaseFor each protein, the corresponding annotation information of molecular function (MF) and biological process (BP) is retrieved from the UniProtKB/Swiss-Prot (release of July 2014). The function annotation information acquired from reliable Gene Ontology (GO) databaseThe selection of negative sample (i.e. protein does not perform a specific function) is especially important for the recognition of protein function. Unlike previous random selection method3Protein with GO annotation in the UniProtKB/Swiss-Prot is referred to as positive sample, with NoGO annotation as negative sample, without GO annotation in the above two databases as unknown sample. For a given GO term, a benchmark dataset is constructed by the selection of all positive samples and the random selection of negative ones. For those GO terms out of NoGO, the corresponding benchmark datasets are constructed by the choice of all positive samples and the random choice of unknown ones. The proportion of negative or unknown samples to positive ones is set to “1:1”, because it can overcome the limitation of a larger number of negative samples and result in unbiased prediction. In order to guarantee the dataset with statistical significance, the 227 benchmark datasets are finally constructed, in which the number of positive samples is at most 1237 and at least 50. In the corresponding 227 GO function terms, the 45 and 182 terms belong to MF and BP, respectively.L . Note that the maximal value of L is equal to 10, because the maximal path distance between any two vertexes is 10 (i.e. the distance of protein P51460 and protein Q9NV35 as well as that of protein Q9NV35 and protein Q9UBD3). On the basis of results derived from the breadth-first search, the 8 types of network topology features are then calculated to characterise proteins, and described as follows:APVW). For a given vertex i, APVW can be obtained by equation (1): vi(j) and vi(L)(j) denote the jth weight (i.e. jth protein primary structure descriptor) of the given vertex i and a specific vertex i(L), respectively. The path distance between the two vertexes is L, and i, i(1), …, i(L − 1), i(L) are involved in the path. The superscript “F” in i(L) is a protein with a specific function. NP is the number of path with distance L. For each weight and parameter L, a feature value can be obtained. Therefore, the topology feature has 17,670 (1767 × 10) values, which take into account the direct and indirect interaction information between a query protein and the proteins annotated with a specific GO term at the levels of protein primary structure descriptions.The average path vertex weight (PWPFP). The path distance between proteins and the given protein i is L. The kind of topology features can be calculated by equation (2): ei,i(1) is the edge weight between vertex i and i(1). The superscript “F” in i(L) is a protein with a specific function. FP is the number of path with distance L and the protein at the end of the path has a specific function. For each parameter L, a feature value can be calculated. Therefore, the feature comprises 10 values, which also consider the direct and indirect interaction information between a given protein and the proteins with a specific function at the level of interaction confidence score. The larger value of PWPFP means that the query protein can interact with more proteins with a specific function.The path weight proportion of a protein with a specific function to all proteins (APWPF). The type of topology features can be computed based on the equation (3): NF and |NF| indicate a set of proteins with a given function and the number of proteins in the set, respectively. The path distance between the proteins and a given protein i(L) is L. By the equation, the 10 features can be obtained when L changes from 1 to 10. The network topology features also consider the relationship between a given protein and the proteins with a specific GO term annotation. A higher value of APWPF implies that the query protein is more likely to have the specific function.The average path weight of proteins with a specific function (PINPFP). The type of features can be obtained by equation (4): N is a set of proteins with path distance L for the protein i. According to the definition, NF is a subset of N. EN denotes the number of edges between any two proteins in the set N. NF. Similarly, the 10 values can be obtained based on the various L. The topology features take into account the interaction information among direct and indirect neighbor proteins.The proportion of interaction number among proteins with a specific function to among all proteins (ADPF). The type of topology features can be calculated by equation (5): FDeg vi(L) denotes the degree value of a protein (i.e. vertex vi(L)) with a specific function. By changing L from 1 to 10, the 10 features can be obtained. A higher feature value of ADPF means that the given protein has more interactions with the proteins with a specific function.The average degree value of the proteins with a specific function (ADPWPF). The type of topology features can be obtained according to equation (6): L, and they consider the interactions with the proteins having a specific GO term annotation.The average degree and path weight of proteins with a specific function (PP). The type of topology features can be obtained by equation (7): NIF is the intersection of N and NF, and |NIF| denotes the number of proteins in the set. Based on the various values of L, the 10 descriptor values can also be acquired, and they consider the indirect correlations between the query protein and the proteins with a specific GO term.The proportion of protein (PPL). The type of topology features can be calculated by equation (8):L, in which initial and final vertexes are proteins with a specific function, middle vertexes consist of the given protein i. NPF is the number of paths, in which the first and the aftermost vertexes are also proteins with a specific function, but medial vertexes do not contain the given protein i. In the type of topology features, L is equal to 2, 3, 4, ……, 10, because the minimum length of path involving two proteins with a specific function and the given protein i is 2. According to the definition, the 9 descriptors can be calculated.The proportion of path length  and kernel parameter γ . The publicly available Libsvm softwareIdentification of protein function can be viewed as a problem of two-class classification, i.e., predict a given protein with or without a specific function. Considering the nonlinear relationship between functions and network topology features, therefore, SVM with radial basis function is employed to construct models for the recognition of protein functions. The grid search strategy is utilized to optimise the penalty constant http://penglab.janelia.org/proj/mRMR/index.htm.The large feature sets can fully characterise the network topology information, but it may result in over-fitting and massive computations. Therefore, mRMR algorithm is employed to select the optimal feature subsets. Based on the mutual information theory, mRMR algorithm ranks features according to their relevance to the class of samples and redundancy among the features. The top ranked features have the better trade-off between maximum relevance and minimum redundancy. The detailed description for mRMR can be obtained from the work of pengBased on the results of 10-fold cross-validation, accuracy (Acc), sensitivity (Sen), specificity (Spe), precision (Pre) and Matthew’s correlation coefficient (Mcc) are calculated to estimate the prediction performance of model. The receiver operating characteristic curve (ROC) is the plot of false positive rate against true positive rate with the various decision thresholds, and precision recall curve (PRC) reflects the relationship between precision and recall with the gradual change of thresholds. The corresponding areas under the curves of ROC (AUCR) and PRC (AUCP) are also calculated to assess the predictive ability. The area is always between 0 and 1, and the model with a higher area value gives a better predictive performance.For each of 227 benchmark datasets, 200 feature subsets containing 5, 10, 15, ……, 1000 features are generated by mRMR feature selection algorithm. Finally, the 227 optimised feature subsets are obtained based on the strategy of 10-fold cross-validation test and the value of Acc. The corresponding results are shown in As shown in P values of 0.6800 (Acc), 0.8257 (Sen), 0.3821 (Spe), 0.3941 (Pre), 0.6744 (Mcc), 0.5534 (AUCR) and 0.9062 (AUCP) are obtained. The results reveal that our method has no significant differences in the predictive performance for BP and MF terms.The predictive performance is also investigated for terms from BP and MF. As shown in In order to avoid tedious work, we only analyse 7 optimal feature subsets derived from the 7 largest benchmark datasets with the corresponding terms GO: 0007165, GO: 0007596, GO: 0010467, GO: 0044267, GO: 0044281, GO: 0044822 and GO: 0045087. The compositions of these best feature subsets are shown in APVW features, 4 PWPFP features, 5 APWPF features, 1 PINPFP feature, 1 ADPF feature, 2 ADPWPF features, 1 PP feature and 3 PPL features. In the 453 APVW features, 94, 59, 47 and 37 features are derived from the node weight of hydrophobicity, flexibility, polarizability, and charges, all of which are crucial for protein function. Hydrophobicity is an important factor for understanding protein folding and protein function. Flexibility plays a vital role when proteins respond to environmental changes, ligand binding and chemical modificationsFrom APVW features, 2 PWPFP features, 4 APWPF features, 2 PINPFP features, 2 ADPF features, and 2 ADPWPF features. Among the 468 APVW features, 100, 50, 48 and 38 are calculated by node weights of hydrophobicity, polarizability, flexibility and charge. For GO: 0007596, GO: 0010467, GO: 0044267, GO: 0044822 and GO: 0045087, the number of overall features and APVW features in the optimized feature subsets are 20 and 14, 20 and 13, 15 and 12, 15 and 13, 40 and 32, respectively. Compared with other features, APVW can effectively grasp the network topology information by taking account of physicochemical attributes of direct and indirect neighbor proteins, and it is the dominant one because most of features come from APVW (>60%). Except for GO: 0007165, the other 6 corresponding optimised feature subsets have no PPL feature, indicating that PPL has the least discrimination information. The contributions of PWPFP, APWPF, PINPFP, ADPF, ADPWPF and PP are roughly equivalent. Therefore, we can expect it is of great significance for predicting protein function if a new encoding strategy can take into account the physical and chemical properties of protein and more effectively consider direct and indirect interaction information in the context of PPI network.For GO: 0044281, the optimal feature subset consists of 468 In the current study, negative or unknown samples with the same size to positive ones are randomly selected to construct benchmark dataset. For each of GO terms, the corresponding 10 datasets through 10 times random sampling are generated to construct predictive models and evaluate their performances by 10-fold cross-validation test. Finally, the standard deviations of Acc, Sen, Spe, Pre and Mcc are calculated to evaluate whether it is rational to sample only once. The statistical results of standard deviations are illustrated in For most of GO terms, the corresponding standard deviations are extremely low. For Acc, Sen, Spe, Pre and Mcc, standard deviations of 204, 203, 200, 202 and 197 GO terms consistently fluctuate from 0 to 2.5%, suggesting that the results of 10 duplicated experiments are very close to each other and it is reasonable to perform the random sampling only once. We assume that the standard deviations of GO terms greater than 2.5% are mainly ascribed to the unknown samples involving in some false negative proteins . The number of false negative protein in the every benchmark dataset varies widely, resulting in a large deviations of predicted results. However, the problem can be solved through the accumulation and abundance of protein function data.In order to characterise the network topology information of protein, the 8 kinds of novel features are proposed based on the protein primary structure descriptions. Considering the effect of sequence similarity, proteins in the benchmark datasets with more than 40% sequence similarity are eliminated by CD-HIT softwareThe Acc values of 121 non-redundant datasets are lower than those of benchmark datasets, while the residual values between 108 benchmark datasets and the corresponding non-redundant datasets are lower than 5%. From Surprisingly, we find from In our study, some network topology features are proposed based on the degree value of protein. Therefore, the influence of degree distribution on model prediction performance is further investigated. Firstly, the new datasets are constructed by selecting positive and negative samples or positive and unknown samples according to the same degree values. Secondly, the optimal feature subsets are chosen (the corresponding results shown in the ADPF and ADPWPF.The 205 (205/277 = 90%) absolute residual values of Acc are lower than 4.63%, 201 (201/277 = 89%) of Sen lower than 5.45%, 194 (85%) of Spe lower than 3.80%, 198 (87%) of Pre lower than 4.04% and 205 (90%) of Mcc lower than 0.0926. The results reveal that most of absolute residual values are very low and the degree distribution of protein has a minor influence on the prediction results for the vast majority of datasets. It may be attributed to the fact that the optimal feature subsets have very few network topology features of About 2% of Acc absolute residual values, 2% of Sen, 5% of Spe, 5% of Pre and 2% of Mcc are higher than 13%, 16%, 11%, 12% and 0.28, respectively. In these datasets, the degree distribution of protein has some impacts on the prediction results, probably resulting from the false negative samples in unknown ones which bring about the unstable prediction performance.The proposed approach is compared with the existing methods, such as diffusion state distanceIn DSD, cDSD, STMs and PPIi methods, feature selection technique and machine learning method are not employed to identify protein function. In order to compare the proposed method with the existing methods fairly, therefore, we firstly calculate the features, and then use mRMR to select the optimized feature subsets. The selection of optimal feature subsets is not performed for PPIi because only 7 features are calculated according to the definition. Finally, we utilize SVM to construct models and adopt grid search to optimise parameters. The mean results and standard deviations of all GO terms are shown in From P values are listed in P values are always in the range of , indicating the significant difference between the current method and existing methods in the prediction performance. Therefore, it is anticipated that our method is superior to these approaches and the proposed network topology features have more useful information in predicting protein function.Wilcoxon rank sum test is also carried out, and the corresponding In order to demonstrate the practical application of the proposed method, we perform predictions of 7 GO terms for negative and unknown samples. The results are listed in For GO term of 0044281, 675 proteins with unknown function annotations are predicted with the function of small molecule metabolic process, and some of them have indirect evidence supports. For example, we find that 105 proteins have the function annotation with ancestor or descendant based on the GO database, implying that these proteins perform the functions related to the biological function and/or biological process. In addition, we also find that the 338 proteins have the neighbor proteins with the annotation, suggesting that these proteins are likely to have the function because the interacted proteins generally have similar or common functions. For GO terms of 0007165, 0010467, 0045087, 0044267 and 0007596, the 1,005, 684, 154, 656 and 77 proteins originally annotated as unknown functions are predicted to have the corresponding function of GO terms. In these predicted proteins, 250 (24.88%), 60 (8.92%), 17 (11.04%), 83 (12.65%) and 3 (3.90%) proteins have the corresponding GO terms with ancestors or descendants. Also, 529 (52.64%), 319 (46.64%), 83 (53.90%), 324 (49.39%) and 35 (45.45%) proteins have the neighbor proteins with the annotation of the corresponding GO terms. These results indicate that most of the predicted proteins have indirect evidences from GO annotations and human PPI network, and the predicted results are very reliable.The identification of protein function is an important step for deciphering the nature of life. In this study, a novel approach is developed to predict protein functions based on weighted human PPI network and graph theory. The novel network topology features are proposed to characterise proteins in the context of network. The performance of current method is assessed by 10-fold cross-validation test as well as the superiority is proved by comparing with the existing approaches. Also, the robustness of the current approach for redundant data and random sampling is verified by constructing non-redundant datasets and performing duplicated experiments. Our study gives a new insights for protein function mechanism at the scale of PPI network. It is anticipated that the proposed method may be a powerful tool for the annotation of protein function.How to cite this article: Li, Z. et al. Large-scale identification of human protein function using topological features of interaction network. Sci. Rep.6, 37179; doi: 10.1038/srep37179 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs.In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer.http://www.bi.cs.titech.ac.jp/megadock-web/.MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at The online version of this article (10.1186/s12859-018-2073-x) contains supplementary material, which is available to authorized users. Many proteins interact with each other in nature ; these iComputational PPI prediction methods can be categorized into three types: 1) those utilizing the amino acid sequence information of the protein –14, 2) tHowever, in order to utilize PPI predictions (including predicted 3D complex structures), it is necessary to archive prediction results and their 3D structures and make this information easily accessible. For example, some existing databases, such as PrePPI , HOMCOS In this study, we developed a database containing comprehensive PPI predictions by MEGADOCK among 7528 representative structures of human protein chains registered in the Protein Data Bank (PDB)  ; taxonomy of protein is H. sapiens; resolution of 2.5 Å or less; and nonredundant so that the sequence similarity is less than 90% using the PDB search function, which utilizes NCBI BLASTClust [This section explains the protein structures registered in MEGADOCK-Web. Since it is important for supporting drug discovery to integrate data for ASTClust .H. sapiens), protein name, and gene name using Bioservices [These PDB entries were divided into chain units, and we excluded those with less than 25 residues or with mutations. As a result, 7528 protein chain structures were subjected to PPI prediction. In addition, we annotated each protein chain structure with the ID of UniProt   are stored in MEGADOCK-Web.DockingScore) per specific protein rotation, and 3600 patterns of protein rotations are computed, resulting in 10,800 predicted conformations in total. To calculate the DockingScore for each position, we used methods described in our previous papers [DockingScore, MEGADOCK calculates the PPIScore, which describes the possibility of a PPI. PPIScore is defined as PPIScore = (Stop − μ)/σ, where Stop is the highest DockingScore in the protein pair, μ is the average of 10,800 DockingScores, and σ is the standard deviation of these scores [PPIScores indicate a higher possibility of a PPI. Although DockingScore cannot be compared between different pairs of proteins, the PPIScore, which is normalized DockingScore, represents how likely the PPI is to occur. The sensitivity of PPI prediction can be adjusted by tuning the threshold value of the PPIScore. Additional file The procedure for PPI predictions by MEGADOCK is as follows. First, hydrogens and small molecules in HETATM rows in PDB files, such as cofactors and small ligands, were excluded in the predocking process. Secondly, predicted protein-protein conformations were created with MEGADOCK. MEGADOCK outputs the three best translational positions that provide the best value of docking evaluation function  when the order of PDB input pair is switched. In order to avoid such redundancy for users, we stored the protein pairs that had higher PPIScores for prediction between the same PDB files. As a result, MEGADOCK-Web displays 7528C2 + 7528 (homo dimers) = 28,331,628 PPI predictions. The total computation time was approximately 500 CPU years.As a result, we calculated 7528Figure PPIScore and are paged per 10 items. The list contains information on the following three points for all the proteins in the database: 1) identification information, 2) PPIScores with the query protein, and 3) known PPIs for the query protein. Each row also has a “View” button to transit to the predicted complexes page  typed in the “Search Single Protein” window in the Top page  DockingScore, 3) a molecule viewer displaying the 3D structure of the predicted complex, and 4) a PDB file download link for the complex structure. Molmil [DockingScore (ZDOCK 3.0.1 and MEGADOCK output style).On the predicted complexes page, 10 predicted complexes based on the results of the MEGADOCK dockings between the two proteins are displayed with 3D molecule viewers. From the “Download Complex in this page” link, 10 predicted complex structures can be downloaded as PDB files together in zip format. The table includes the following four columns: 1) rank (up to 1000) of the . Molmil  is adoptAssuming that a user has a protein of interest known to affect a certain biochemical pathway with unknown interactions, this section describes how to discover PPI candidates in a specific pathway.PPIScore of greater than or equal to the user-defined threshold.First, as in the previous section, the user can search a query by typing in the “Search Single Protein” window at the top of the page . Lowering the threshold increases the sensitivity and decreases the specificity. In the pathway selection page, links to the KEGG pathways to which the predicted binders belong are displayed. Note that the query protein does not always exist on the KEGG pathway; obtaining many predicted binders on the pathway without the query protein indicates a possibility of unknown interactions of the query protein in the pathway. Furthermore, in order to display only pathways with specific functions, it is possible to filter the pathways by inputting a word or phrase in the “Pathway Filtering” window at the top of the pathway selection page. Clicking the link takes the user to the corresponding KEGG page, in which the query protein is colored in blue, and predicted binders are in red.The threshold of PPIScores of all combinations of pairs of those protein structures are shown in descending order in the lower part of the page. The upper table has information similar to that of the protein selection page. If the structures of proteins to be used have already been specified, the predicted complexes page  can be found easily. From the “View” link, it is also possible to transit to the predicted complex page  from several structures, users are taken to the PPI prediction list page Fig. . On thisBy following the above procedure, MEGADOCK-Web can facilitate the assessment of a specific PPI. In fact, the interaction between APC and Axin has been revealed experimentally .The potential for identification of PPIs was described in the previous section. Here, we demonstrate the ability to identify a feasible new PPI using MEGADOCK-Web. We explain how to search unknown PPIs against SOS protein, which is also associated with cancer, as an example.PPIScore between 1DBH_A and 1Q2H_B was 8.2, indicating a possibility of a PPI.First, the pathway selection page is chosen, as described in the previous section, and the list includes the neurotrophin signaling pathway (hsa04722). In this pathway, an adaptor protein containing Pleckstrin homology (PH) and Src homology-2 (SH2) domains (APS), also known as SH2B2, is adjacent to SOS Fig. . Second,The PPI database STRING DB stores experimental information and literature-based predictions . AnalysiMME and MTHFD1 are a pair of proteins that interact in prostate cancer cells ; howeverWhen MME and MTHFD1 are searched as queries, candidate structures are obtained. Here, 1R1H  describeSome existing databases contain 3D structural information similar to MEGADOCK-Web, such as PrePPI , HOMCOS First, MEGADOCK-Web contains a larger number of PPI predictions than other databases because it uses MEGADOCK, a rapid rigid docking tool. For example, MEGADOCK-Web provides 10 times more PPI predictions than PrePPI and 100 times more PPI predictions than Interactome3D. Second, MEGADOCK-Web supports both single protein and paired protein queries. Thus, it is possible to conduct searches from various perspectives. Furthermore, since PrePPI displays only one prediction model, users cannot discuss the predicted complexes on various interaction surfaces. In contrast, MEGADOCK-Web enables users to investigate the complexes because up to 1000 candidate complexes can be shown in the predicted complexes page. Finally, since proteins are mapped on the biochemical pathways (KEGG pathways), MEGADOCK-Web helps users to discuss the possibility of PPIs based on the biological roles of the predicted interactions.Saccharomyces cerevisiae, Escherichia coli and Mus musculus). Although the current version of MEGADOCK-Web provides only human proteins, we will expand MEGADOCK-Web to include other species in the near future.MEGADOCK-Web is expected to help to annotate proteins with unknown functions by providing comprehensive PPI predictions, irrespective of species. This program can be expected to help researchers discover new PPIs more broadly if we expand the database to include other species  that can use computing resources according to demand . We will release a service for on-demand PPI prediction in the near future.http://www.bi.cs.titech.ac.jp/megadock-web/.In this paper, we described our new database, named MEGADOCK-Web, which is an integrated database of high-throughput structure-based PPI predictions. MEGADOCK-Web is freely available for use at MEGADOCK-Web provides PPI predictions to users quickly by archiving exhaustive PPI predictions based on docking calculations. MEGADOCK-Web provides the following powerful functions: 1) searching PPI predictions by MEGADOCK, 2) providing annotations for each predicted protein pair with experimentally known PPIs, 3) visualizing candidate interactions with query proteins in biochemical pathways, and 4) visualizing predicted complex structures using a 3D molecular viewer. MEGADOCK-Web also gives comprehensive PPI prediction results in cooperation with biochemical pathways and enables users to easily and rapidly examine PPI possibilities. MEGADOCK-Web is expected to facilitate the discovery of new PPIs and protein functions.http://www.bi.cs.titech.ac.jp/megadock-web/.MEGADOCK-Web is freely available for use at MEGADOCK-Web is accessed from a web browser; therefore, it is platform-independent.MEGADOCK-Web has been extensively tested with Google Chrome, Safari, Mozilla Firefox, and Microsoft Edge.MEGADOCK-Web is free to use by everyone; there are no restrictions.Additional file 1:Supplementary Figures. PDF file containing various figures with detailed MEGADOCK performance and MEGADOCK-Web screenshots. (PDF 819 kb)


Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design. Drug action is a complex process. A drug starts to take effect on a biological system when it interacts with its targets. However, a drug rarely binds to a single target. Multiple target binding, i.e., polypharmacology, is a common phenomenon245678Computational complexity, due to the number of possible ligand conformations (for de novo methods) and the enormous size of compound libraries ,Inability to adequately normalize the objective function in order to properly rank numerous solutions .To date, few computational tools that support the discovery and application of multi-target therapies are available. The existing computational methods are tailored for single-target drug design and can be classified into two groups. The first group consists of methods that exploit structural information of a protein binding site, trying to synthesize a suitable compound de novo1012Recent years have seen the development of knowledge-based methods for protein-ligand interactions1516http://bioinfo.cs.uni.edu/COSINE.html.In this paper we present COSINE (COldStartINtEractions) - a statistical framework and a corresponding computational method for multi-target virtual screening via the “one-class collaborative filtering” technique. Our program exploits existing knowledge and databases of known interactions as well as the sequence similarities between proteins and structural similarities between drug molecules to suggest potential targets for new chemicals. Among unique aspects of our work are position specific weights, impute values, and a novel weighted-profile procedure for improving target prediction for novel chemicals. The accuracy of COSINE is validated in blind benchmarks that utilize well-known and publicly available resources. Our data shows that COSINE clearly outperforms numerous state-of-the-art methods for the same problem in several different tests and with respect to different accuracy measures. The algorithm is freely available at R of observed interactions, with m rows, representing targets, and n columns, representing chemicals, our algorithm decomposes R into a product of two lower dimension matrices of dimensions m × r and r × n, r ≪ min. The component matrices correspond to proteins’ and chemicals’ latent preferences. The assumption is that the set of proteins  under consideration can be divided into a relatively small number of subsets with different proteins from the same subset exhibiting the same preferences to chemicals. Our algorithm takes account of the fact that related proteins, such as those with similar amino-acid sequences or similar three dimensional structures, exhibit similar preferences to chemicals and vice versa .In a typical recommender system, user rating is expressed using different scores . However, the nature of available data for protein-chemical interactions is different. Often times, only “positive” data consisting of known and validated interactions is available but there is no straightforward way of distinguishing “negative” scores (no interactions) from the missing data. The underlying binary score system (1 for interacting pairs and 0 otherwise) necessitates a deviation of the computational models used for protein-chemical interactions from the classical recommender models. COSINE belongs to the category of one-class collaborative filtering methods24m and n represent the number of proteins and chemicals, respectively, and let R =  be a m × n matrix of protein-chemical interactionsCOSINE is a dual-regularized, one-class collaborative filtering method26R is typically incomplete. While each nonzero entry ri,j = 1 signifies a known interaction, the meaning of each zero entry ri,j = 0 is ambiguous in that there can be either no interaction between the target ti and the compound cj, or, alternatively, that an interaction exists but it has never been verified experimentally. Thus, the goal is to predict the missing entries .In protein-chemical interaction studies, the binary matrix FGT of two matrices F and G of dimensions m × r and n × r, respectively, where r ≪ min  represents the number of latent factors. While our program is capable of performing either linear or logistic factorization, in the rest of this paper we will focus on logistic factorization, because it allows for an elegant statistical treatment.Building upon the general low-rank matrix factorization framework, COSINE approximates the probability of each chemical interacting with each target by mapping both chemicals and proteins to a common latent space of reduced dimensionality. The assumption here is that the number of factors influencing protein-chemical interactions is relatively small or, more formally, that the matrix of protein-chemical interactions is of low rank and, therefore, that it can be written as the product Following Steckfi and gj denote the ith and jth row (latent vector) of the matrices F and G, respectively, wi,j are the position specific weights on interaction scores, qi,j are the so-called “imputation values”λF, λG are tunable parameters and where COSINE extends several other methods for the protein-chemical interaction prediction16ri,j = 0 but some new experimental evidence suggests that the chemical cj might interact with protein ti. We can utilize this new knowledge by setting qi,j = 1 while lowering the corresponding weight wi,j to account for any uncertainty in the imputed value. A more thorough justification of the objective function (2) is given below. For a less general case, we refer the reader to Johnsonet al.To provide insight into the motivation behind our method, consider, for instance, an ambiguous case where ei,j be the event that the compound cj interacts with the target ti. Assume that the probability distribution of ei,j is logistic. In other words, assume that the probability pi,j assigned to ei,j is given byTo derive the function (2) analytically, let wi,j reflects the confidence in the entry ri,j of the interaction matrix R. More precisely, higher weights are assigned to protein-chemical pairs  which are known to interact , while lower values of wi,j are given to pairs for which ri,j = 0. To put it differently, a high number of positive training examples corresponds to each interacting pair while a lower number of negative training examples corresponds to each non-interacting (or unknown) pair. Hence, the likelihood of ri,j + qi,j given fi and gj can be written asRecall also that or, at the matrix level,p can be derived through the Bayesian inferenceAs in SteckF and GFinally, we derive the loss function (2) by taking the negative logarithm of (6), while assuming the Gaussian distribution of the entries of the logistic loss function (used by default in our method) has an explicit probabilistic interpretation.M =  be the matrix of pair-wise target similarity scores, where each entry mi,j represents the similarity between the proteins ti and tj, and let N =  be the matrix of pair-wise compound similarity scores. The affinity of similar chemicals to bind similar proteins is accounted for by minimizing the protein homophilyTo increase the accuracy of protein-chemical interaction prediction, we further extend the loss function (2) to account for the fact that similar chemicals are likely to interact with similar targets. Formally, let and the compound homophilyλM and λN, our loss function becomesIncorporating the regularization terms (8) and (9) above into (2), and introducing two additional tunable parameters, mi,j of the matrix M typically represent the sequence similarity of the primary structures of proteins ti and tj, as measured, for example, by the normalized Smith- Waterman alignment score or by the PSI-BLAST e-valuemi,j can be chosen to represent the three-dimensional similarity of the proteins’ tertiary structures. Similarly, each ni,j represents the similarity score for the compounds ci and cj, as measured, for instance, by the Tanimoto scoreci′s and cj′s pharmacological profilesIn practice, the entries Note that the partial derivatives of (10) can be written aswhere et al.There are several ways to minimize the loss function (10)2526F) are computed as the sum of the latent preferences for that protein  and the latent preferences of J most similar proteins (those with available interaction data). More specifically, we set the ith row of the matrix F for the new target ti toThe most challenging task in protein-chemical interaction prediction is known as the “cold-start problem”. The goal is to predict interactions of chemicals (or targets) for which no interaction data is available. COSINE implements a modified version of the “weighted profile” method32fj is the jth row of F (representing the latent preferences of the target tj), v is the weight parameter and mi,j is the similarity score of the targets ti and tj. The normalization factor SM is set to where G) are computed in the same way, using the compound similarity scores ni,j, namely.The latent preferences for new chemicals . In the second iteration of the algorithm, the weight  of ri,j is increased by one if the computed probability of interaction pi,j is either too small or too large  is set to 1 if the probability of the interaction pi,j computed in the first step is high . We take max rather than pi,j since our underlying assumption is that the true interactions have been experimentally verified and hence the nonzero values of ri,j should be taken account of in the second step.COSINE minimizes the loss function (10) twice. The first time around, all imputation values  is high . OtherwiTo validate the algorithm, we compared it to a number of different methods for the same problem, namely KBMF2Ket al.We first tested the accuracy of our algorithm in the classic Yam08 benchmark designed by Yamanishi In order to compare COSINE directly to the methods previously tested in this benchmark  we performed a 5-fold cross-validation on the set of chemicals. More specifically, for each protein class, the set of chemicals was split into 5 subsets of approximately equal size and each subset was taken in turn as a test set. As described in van Laarhoven and Marchioriet al.Some methods for protein-chemical interaction prediction have been tested in Yam08 benchmark that uses 10-fold instead of 5-fold cross validation. To compare COSINE with those algorithms we modified the testing procedure and  the corresponding entries in the protein-chemical interaction matrix. To perform “cold start” analysis on ZINC data, we identified a set of chemicals having only one known target. The resulting set was further divided based on two criteria: 1) the number of chemicals the target proteins are associated with, and 2) the maximum chemical-chemical similarity score for the chemical in the dataset, with 0.1 increments. Each set was further subdivided into two subsets of approximately equal size, the test set  and the r% Recall = TP/CP, where TP and CP represent the total number of true and condition positives, respectively. Since there is a total of 3,500 targets, the r% of predictions include (35 · r)th or higher ranked target for each chemical. For instance, the True Positive Rate (TPR) of 0.7 at the 35th cutoff rank (top 1%) means that 70% of the total tested positive pairs were ranked 35th or better for the tested chemicals. Using TPR in place of AUC allows us to assess the performance of COSINE from a different angle. In particular, it is informative to compare COSINE head-to-head to NRLMF again, since, according to the results by Liu et al.r% of predictions for various values of r. The significant performance advantage of COSINE over REMAP illustrates the benefits of using local weights, logistic (as opposed to linear) factorization and a weighted profile approach for novel drugs and novel targets.To provide for a conceptually different test, the ZINC benchmark uses the True Positive Rate  at the top Nuclear Receptors test set) while a larger number of iterations (100–600) is needed to achieve comparable accuracy on larger data sets (such as Enzymes or ZINC test). As seen in We studied how the number of iterations in the matrix factorization step influences the accuracy of our algorithm. In our experiments, the convergence is attained after about 50 iterations for smaller data-sets  as a function of missing interaction data and as a function of incorrect interaction data. As shown in Historically rational drug design has been characterized through identifying a single disease associated target and discovering exquisitely selective drugs against that target. Unfortunately, this one-drug-one-gene approach has been of limited success. This failure is manifest in the current issues facing the drug industry with near empty pipelines and costly post-market withdrawals. New methodologies are called for. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigmIn-silico protein-chemical interaction prediction has proven useful in drug-repurposing (drug-repositioning), an area of drug discovery that aims to find new therapeutic indications for known, FDA approved drugs46Computational methods that can assist polypharmacology are of key importance in drug development. Selected statistical techniques, including recommender systems, known as “low-rank matrix completion” and “collaborative filtering”, have been successfully used to predict protein-protein interactionsWe introduce a computational method for predicting protein-chemical interactions based on matrix factorization. Our method builds upon “collaborative filtering” - a widely used statistical technique for making recommendations to utilize existing knowledge stored in the databases of known interactions. By incorporating the weighting and imputation of the interaction data, as well as the dual regularization from both chemicals and targets, COSINE is able to exceed accuracy of other state-of-the-art methods for the same problem.Our algorithm integrates chemoinformatics , bioinformatics (protein sequence similarity) and a drug-target network (in form of matrix completion) approaches. Utilizing chemical structural similarity has proven useful (and has been widely applied) in the drug discovery for single-target virtual screening. Incorporating protein sequence similarity has shown promises in the prediction of drug off-targets515253The publically available chemogenomics data, on which all of existing virtual screening methods are inherently based, is incomplete and noisy. The missing interaction data is predicted in COSINE by completing the input interaction matrix, while biased and noisy data is filtered out by selecting the objective function that minimizes the rank of the output matrix of predicted interactions.We recognize that, even though the ROC and PR curves may give a global estimation of the false positive rate for a prediction in the certain rank given by existing virtual screening algorithms, they may be not adequate for a risk-sensitive drug discovery application. In addition, in bio- and chemo-informatics applications, non-nested CV model is known to bias the parameters to the data set. Thus, other approaches to assessing reliability for specific new cases (including the label permutation and/or nested CV approach) will be extremely useful. We have developed several methods, e.g. ENTS56How to cite this article: Lim, H. et al. Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem. Sci. Rep.6, 38860; doi: 10.1038/srep38860 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.