Identifying protein complexes is an essential task for understanding the mechanisms of proteins in cells. Many computational approaches have thus been developed to identify protein complexes in protein-protein interaction (PPI) networks. Regarding the information that can be adopted by computational approaches to identify protein complexes, in addition to the graph topology of PPI network, the consideration of functional information of proteins has been becoming popular recently. Relevant approaches perform their tasks by relying on the idea that proteins in the same protein complex may be associated with similar functional information. However, we note from our previous researches that for most protein complexes their proteins are only similar in specific subsets of categories of functional information instead of the entire set. Hence, if the preference of each functional category can also be taken into account when identifying protein complexes, the accuracy will be improved.To implement the idea, we first introduce a preference vector for each of proteins to quantitatively indicate the preference of each functional category when deciding the protein complex this protein belongs to. Integrating functional preferences of proteins and the graph topology of PPI network, we formulate the problem of identifying protein complexes into a constrained optimization problem, and we propose the approach DCAFP to address it. For performance evaluation, we have conducted extensive experiments with several PPI networks from the species of Saccharomyces cerevisiae and Human and also compared DCAFP with state-of-the-art approaches in the identification of protein complexes. The experimental results show that considering the integration of functional preferences and dense structures improved the performance of identifying protein complexes, as DCAFP outperformed the other approaches for most of PPI networks based on the assessments of independent measures of f-measure, Accuracy and Maximum Matching Rate. Furthermore, the function enrichment experiments indicated that DCAFP identified more protein complexes with functional significance when compared with approaches, such as PCIA, that also utilize the functional information.According to the promising performance of DCAFP, the integration of functional preferences and dense structures has made it possible to identify protein complexes more accurately and significantly. Protein complexes are biomolecules that contain a number of proteins interacting with each other to perform different cellular functions . Hence, Laboratory-based techniques have been developed to identify protein complexes with laboratory experiments, such as chemical cross-linking ,3, AP-MSTo avoid the problems of laboratory-based techniques, a number of different computational approaches have been proposed as a complementary tool of identifying protein complexes and they are, by and large, developed based on the use of different graph clustering algorithms. In general, by representing a PPI network as a graph where vertices represent proteins and edges are the interactions between proteins, the resultant graph clusters are considered as the identified protein complexes. To do so, computational approaches that purely rested on the graph topology of PPI network discovered graph clusters by following certain topological structures, such as density -12, k-coRecently, the functional information of proteins has become available and easy-to-access due to the development of online resources -22, thusObviously, the aforementioned approaches make use of the functional information of proteins by designing different measures so as to weight the PPIs or proteins from different perspectives, such as similarity -26, depeProteins in the same protein complex are densely connected from the topological perspective;Proteins in the same protein complexes are at least similar in specific subsets of functional categories from the perspective of functional information.To do so, we propose DCAFP that can identify protein complexes based on the following two intuitive properties:It is seen that the first property requires that graph clusters of interest should have a dense topological structure which has been widely adopted to identify protein complexes and the second property is to emphasize the necessity of considering the individual preferences of functional categories when identifying protein complexes from the perspective of functional information.Regarding the implementation of DCAFP, we employ a likelihood matrix to represent to what extent pairwise proteins are likely to be identified in the same protein complex. If the likelihood between two proteins is high, it means that they are more likely to be identified in the same protein complex by DCAFP. Another benefit of using the likelihood matrix is that we may be able to identify overlapping protein complexes. To indicate the preference of each functional category when forming protein complexes, we assign a corresponding preference vector to each of proteins. With this preference vector, the individual preferences of functional categories can be quantitatively indicated when identifying the protein complex the corresponding protein belongs to. Then following the intuitive properties aforementioned, we formulate a constrained optimization problem to identify protein complexes based on the likelihood matrix and the preference vectors of proteins. This optimization problem is addressed by DCAFP adopting the strategy of alternatively optimizing the likelihood matrix and preference vectors through an iterative procedure. This procedure initially starts from a random guess of both the likelihood matrix and the preference vectors of all proteins and then iteratively improves the quality of the clustering until convergence.The performance of DCAFP has been evaluated by using five PPI networks from two species of Saccharomyces cerevisiae and Human and the three functional categories of GO. The extensive experimental results not only show that DCAFP has a promising performance when compared with state-of-the-art approaches but also demonstrate the ability of DCAFP to identify overlapping protein complexes.vi} (1 ≤ i ≤ nV) is a set of nV proteins, E = { eij} denotes all the nE interactions, and Λ = {Λp, Λf, Λc} is a set of the three functional categories, i.e., biological processes, molecular functions and cellular components. An interaction eij ∈ E connects the two proteins vi and vj in G. To represent the topology of G, we use an adjacency matrix tij = 1 if eij ∈ E, tij = 0 otherwise. For an arbitrary functional category, say Λp ∈ Λ, we define a domain dom (Λp) as a set of possible values that can be taken by Λp. In the GO database, dom (Λp) is the set of GO terms in Λp.To represent a PPI network, we use a 3-element tuple G = {V, E, Λ}, where V = {W = [wij]  to represent the likelihoods of being grouped in the same cluster for all pairwise proteins in G. By the definition of W, we have wij ∈  denoting how likely vi and vj are being identified in the same cluster. The larger the value of wij is, the more likely vi and vj are grouped in the same cluster.We use a likelihood matrix A = {Ap, Af, Ac} is adopted. Taking p and vi and vj in terms of Λp.To indicate the similarity of proteins in each functional category, a set of similarity matrices W and A, we also have another matrix D = [dij]  used to represent the similarity between any two proteins from the perspective of topological structure. Assuming that for vi we have Vi = {vk|eik ∈ E} representing the set of interacting proteins of vi and so is Vj for vj, the value of dij is the percentage of common proteins found in both Vi and Vi. Obviously the more interacting proteins vi and vj have in common, the larger the value of dij is.In addition to vi. In ri, each element is a non-negative value and we have the constraint rip + rif + ric = 1. With ri, we are able to quantify to what extent functional categories in Λ are preferred when we determine whether another protein vj should be grouped in the same cluster as vi from the perspective of functional information. In other words, regarding the clustering related to vi, Λp will play a more important role if rip is assigned a larger value. To represent the preference vectors of all proteins in V, we use the preference matrix To show the individual preferences of functional categories during clustering, we use a preference vector of functional categories for each of proteins and denote it as p} (Cp ⊆ V) from G. In each cluster of C, proteins are densely connected and they are similar in specific subsets of Λ. Since DCAFP is capable of identifying overlapping protein complexes, we may have ∃ Cp, Cq ∈ C : Cp ∩ Cq ≠ ∅.The problem of identifying protein complexes is to identify a set of clusters C = {CT, A and D, we target to find appropriate W and R so that the resultant clusters can best satisfy the aforementioned intuitive properties. Following this idea, we formulate an optimization problem as:WT = T ∘ W is the entrywise product of T and W, WD = D ∘ W is the entrywise product of D and W, Tr is the trace expression of the corresponding matrix, W, R, 1 is a column vector with a proper size and each element of 1 is 1, and nV × nV matrix each cell of which is defined as Given The optimization problem as described by  consistsTo confirm the topological structures of clusters identified, we constraint our analysis on the first term of  and rewrD, we know that a large value of dij indicates that vi and vj have a large percentage of proteins in common. For a third protein vk, wikwjk denotes the degree of being grouped in the same cluster with both vi and vj while tiktjk ensures that vk contributes to the value of Tr((WT)TWDWT) only if both eik and ejk are found in E. It is not difficult to conclude that if two proteins have many common proteins most of which are also likely to be grouped in the same cluster as the two proteins we concern, the value of  to categories where large similarity scores ,,4), we wmaximize . In 3),,vi ∈ V W and R respectively. For W, we use W are moving toward the maximum value . The term R.After discussing the appropriateness of the first two terms of  as beingW and R that can maximize (1), we adopt the strategy of optimizing W and R alternatively. That is to say, at each iteration, DCAFP first updates R while keeping W fixed and then use the updated R to update W. Assuming that we are now at the (l + 1)th iteration with Wl)( and Rl)( and Rl + 1)( are presented as below.To determine J(R|W) to denote that J is about to be maximized by updating R with a fixed W. First of all, we formulate a sequence of quadratic subproblems for approximating the maximization of J(R|W) as:To facilitate understanding, we now use max J(ri|W) is designed to maximize J(R|W) in terms of ri. Therefore, the problem of updating R is divided into several subproblems each of which is to update the corresponding ri as a part of the solution of , we employ the primal-dual active set strategy [ri. In particular, given Γ≥ is the set of greater-than constraints of ri, l + 1)th iteration, l + 1)th iteration.To solve max strategy  that is J(ri|W), we can obtain J(ri|W) is optimized over the active constraints. However, a full step in the direction ri. To avoid it, we have to find the maximum step m ∈ Γ≥, if ri as defined in  to represent the optimization problem of J in terms of W by fixing R. Observing  as:J(wij|R) is given in  is converted into a sequence of subproblems w.r.t. wij. In fact, the subproblem max J(wij|R) is essentially a maximization issue as indicated by  will be obtained when wij ≤ 1. Assuming that wij that satisfies the equation l + 1)th iteration, the solution to the subproblem From , the procated by . Becausej2=−1<0,  is a congiven in .11\documRl + 1)( and Wl + 1)( are able to be derived from Rl)( and Wl)( with (l + 1)th iteration, we will explain the details of DCAFP in the next subsection.So far, (l) with  and 11)R(l + 1)J, 2) identifying base clusters, and 3) obtaining C by merging these base clusters.DCAFP has three steps: 1) finding the optimal solution of max J. At the (l + 1)th iteration, the previous results of R and W, i.e., Rl)( and Wl)( and Wl + 1) is reached or the procedure is now at the maximum number of iterations lmax. Regarding the convergence of max J, the difference between J(Wl + 1) and J(Wl) should not be more than a predefined threshold, i.e., δ. Once converged, the current matrices of W and R will be taken as the solution to max J and represented as W∗ and R∗ respectively. A complete description of the first step of DCAFP is given in Figure In the first step, DCAFP adopts an iteration procedure to search for a local optimum of the optimization problem of max rding to  and 11)J. W∗, we now define base clusters as subgraphs where interacting proteins have a likelihood value not less than a predefined threshold, i.e., wmin. That is to say, two proteins vi and vj are grouped in the same base cluster iff wijtij ≥ wmin. To find all base clusters in G, DCAFP performs a heuristic search that is similar to [Given milar to . In partp and BCq , the overlapping score between them is defined as:The final step of DCAFP merges base clusters with a high degree of overlap in their proteins. For two base clusters BCosmax, between base clusters serves as the terminating criterion. This clustering algorithm is incremental and order independent. This step creates clusters that are more coherent and also reduces the fragmentation of the resultant clusters in C.In essence, DCAFP merges the base clusters using the equivalent of a single-link clustering algorithm  where a With the three steps mentioned above, DCAFP can complete the task of identifying protein complexes by obtaining C.For the purpose of performance evaluation, DCAFP has been tested with five sets of real PPI network data, including Krogan PPI network , Gavin PThe data of PPI networks of Krogan, Gavin and Collins was provided by the BioGRID database  with verRegarding the functional information of proteins, the GO terms in each functional category were obtained from the GO databases  for eachFor the ground truth of protein complexes in Saccharomyces cerevisiae, we used the curated protein complexes published by MIPS/CYGD  and CYC2For the purpose of performance evaluation, we compared DCAFP with the state-of-the-art approaches including GMFTP , PCIA 1, MCL 1010, MCODEwmin and osmax varying from 0 to 1 with an interval 0.1 when tuning the performance of DCAFP and performed 10 trials for each combination of wmin and osmax. We noted that the performance of DCAFP did not change much with proper values of lmax and δ. It is recommended to set lmax = 100 and δ = 1. In the experiments, matrices Ap, Af, Ac and D were determined by Jaccard index.Regarding the parameter setting for each approach, we either adopted the default values provided by the corresponding software or performed many experimental trials to identify the values that obtained the best performance. The strategies of parameter setting for all approaches are listed in Table f-measure, Accuracy [We used three independent evaluation measures, Accuracy ,27 and MAccuracy , to compf-measure, following [f-measure are given as:For ollowing ,19, a clf-measure where the minimum matching rate has to be specified in advance, the measures of Accuracy and MMR offer a natural and intuitive way to compare the predicted protein complexes with the ground truth protein complexes. In particular, Accuracy is defined as a geometric average of sensitivity [Unlike sitivity  and posisitivity  while MMf-measure, Accuracy and MMR, we also adopted the function enrichment test to demonstrate the advantage of DCAFP with the introduction of functional preferences when compared with approaches such as PCIA that also made use of functional information to identify protein complexes. Given a threshold of p-value, an identified protein complex is functionally significant if at least one GO term shared among the proteins of this complex is found to be significantly enriched according to the functional enrichment test. In the experiments, for protein complexes identified in each of PPI networks, we used GO::TermFinder [Besides rmFinder  to perfof-measure, Accuracy and MMR when applied to identify protein complexes from PPI networks used in the experiments. As can be seen from Table Table Regarding the number of protein complexes identified, MCODE tended to discover the fewest clusters for each of PPI networks and accordingly MCODE obtained a higher score of precision when compared with the other approaches. In contrast to MCODE, the number of protein complexes identified by DCAFP was subject to the density of PPI network. That is to say, when compared with the other approaches, DCAFP identified more protein complexes from dense PPI networks, such as Gavin and Collins, According to Table f-measure was better than the other approaches. In particular, DCAFP obtained the best f-measure scores for the PPI networks of Krogan, Gavin and DIP Hsapi and the second best f-measure scores for the remaining two PPI networks. When looking into the PPI networks of Collins and DIP Scere, we found that the difference between DCAFP and the approach with the best score of f-measure was much small, as DCAFP was only worse by 2% and 4% than the best approaches in Collins and DIP Scere respectively in terms of f-measure. Regarding Accuracy, DCAFP obtained a promising and stable performance in all PPI networks, as its score of Accuracy was always in the best three approaches with only a few exceptions. Similar results were also observed in the measure of MMR, where DCAFP also got competitive scores in each of PPI networks.For DCAFP, although its scores of Precision and Recall were at the average level among all approaches according to Table f-measure and MMR with all PPI networks used in our experiments. When it came to Accuracy, GMFTP performed slightly better than DCAFP with all PPI networks expect with DIP Hsapi where DCAFP performed better. To understand why this was the case, we noted from the details of the clustering results that GMFTP tended to identify a small set of clusters for each of PPI networks. Hence, it is able for GMFTP to obtain a relatively higher positive predictive value especially when the size of protein complexes to be identified is also relatively small. Since this was indeed the case with the protein complexes in Saccharomyces cerevisiae, GMFTP was able to perform better in terms of Accuracy. However, in the case of protein complexes in Human, the Accuracy of GMFTP was not as good as the size of protein complexes in Human was much larger than that in Saccharomyces cerevisiae.Concerning the effort of functional information to improve the performance of identifying protein complexes, we concentrated the discussion on DCAFP, PCIA and GMFTP, all of which additionally made use of functional information for clustering. From Table Overall, we noted that across all PPI networks, DCAFP yielded a promising performance. Comparing DCAFP with approaches that only considered the graph topology of PPI network, we found that DCAFP achieved a better performance than most of them for each of PPI networks as it additionally made use of functional information to improve the performance of identifying protein complexes. Moreover, DCAFP was better than both PCIA and GMFTP which also considered the functional information of proteins on average. Hence, if we intend to find an approach that can identify protein complexes more accurately, DCAFP is preferred.f-measure, Accuracy and MMR were given in Table In this section, we evaluated the impacts of the inclusion of information relating to density and functional preferences on the performance of DCAFP. To do so, we performed additional experiments with three versions of DCAFP and they were DCAFP with density only, DCAFP with functional preferences only and DCAFP with both. In particular, DCAFP with density only considered the density property while ignoring the property of functional preferences, DCAFP with functional preferences only considered the property of functional preferences while ignoring the density property, and DCAFP with both was the complete version of DCAFP. These three versions of DCAFP were tested with all of PPI networks and their results of Based on the results shown in Tables When comparing the performance of DCAFP with density only with DCAFP with functional preferences only, we found that DCAFP with density only obtained a better performance in dense PPI networks, i.e., Gavin and Collins. However, the advantage of DCAFP with density only in sparse PPI networks was not as obvious as in dense PPI networks. In particular, the performance of DCAFP with density only was comparable to and worse than that of DCAFP with functional preferences only in DIP Scere and DIP Hsapi respectively. From this observation, we can say that the significances of density and functional preferences have to be considered differently when identifying protein complexes. However, for the current version of DCAFP, we do not take into consideration this point as it is yet to explore that which of them should be weighted more heavily than the other according to their significances given a PPI network. Since we believe that it is possible for us to improve the performance of DCAFP by considering the weight for each of term in (1), we would like to propose to investigate it as part of our future work.In Table Despite the differences between Human and Saccharomyces cerevisiae PPI network data, DCAFP identified much more complexes with functional significance than PCIA in all PPI networks as indicated by both number and percentage of protein complexes that passed the p-value tests. That is to say, even PCIA obtained a better performance of accuracy for some PPI network , the complexes identified by PCIA were less significant than those identified by DCAFP. The reason why DCAFP performed well in functional enrichment tests can be ascribed to the introduction of functional preferences, which is capable of emphasizing the functional homogeneity for each of attributes.Also, the larger percentages of functional significant complexes identified by DCAFP can be an indicator that the complexes identified by DCAFP are real ones that could have been missed by laboratory-based identification techniques especially when DCAFP recalled the ground truth complexes well.wmin and osmax, we concentrated on analysing the sensitivity tests of wmin and osmax on the performance of DCAFP in this section. During the experiments, we found that the effects of wmin and osmax were quite similar across all PPI networks, hence we took the PPI network of Krogan as an example to demonstrate how wmin and osmax affected the performance of DCAFP. Figures wmin and osmax on the performance of DCAFP in terms of Precision, Recall, f-measure, Accuracy and MMR respectively.As the performance of DCAFP is more concerned with the parameters wmin or osmax generally improved the precision of DCAFP, but such improvement was more sensitive to the change of wmin when compared with that of osmax according to the difference between the slope of the surface along wmin axis and that along osmax axis. When wmin is increasing, the condition of grouping a protein into a base cluster becomes more restricted and accordingly complexes identified by DCAFP become smaller in terms of the number of proteins. Similar to wmin, an increasing osmax will make the condition of merging base clusters more restricted, thus resulting in smaller identified complexes. Since it is much easier for a small identified complex to have a ground truth complex matched, the precision of DCAFP can thus be improved.In Figure wmin play a more important role than osmax when affecting the performance of DCAFP in terms of Recall. In particular, the recall performance of DCAFP was improving with a falling wmin, but changing the value of osmax did not have much effect on that. A possible reason for the weak effect of osmax is that the overlapping between base clusters was rarely found in Krogan.In contrast to Figure f-measure performance of DCAFP in Figure wmin and osmax were similar to what we concluded from Figure wmin and osmax when compared with Precision. As f-measure considered Precision and Recall equally according to (13), its performance was more easily influenced by the one with high sensitivity. It was for this reason that f-measure and Recall scores of DCAFP reacted in a similar manner in Krogan. Hence, in the PPI network of Krogan, a better f-measure score of DCAFP was obtained with a small wmin and a large osmax. Similar conclusions can also be made for Accuracy and MMR from Figure Regarding the wmin on the performance of DCAFP is more predictable, as wmin has an explicit impact to the size of complexes identified. But for osmax, its effect on the performance of DCAFP is subject to the degree of overlapping found between base clusters. To put it more concretely, osmax will play a more important role in adjusting the performance of DCAFP if overlapping is more frequently observed in the base clusters; otherwise, it only has limited influence on the performance of DCAFP.In sum, the effect of To demonstrate the advantages of DCAFP when applied to predict protein complexes, we selected two examples of overlapping protein complexes identified by DCAFP from Krogan and DIP Hsapi respectively and illustrated them in Figure In the PPI network of Krogan, the network structures of three protein complexes, DNA-directed RNA polymerase I complex (DRP I), DNA-directed RNA polymerase II complex (DRP II), and DNA-directed RNA polymerase III complex (DRP III), were depicted in Figure p and Λc. Specifically speaking, when looking into the informative GO annotations that passed p-value test with a threshold of 0.01, we noted from the results that TAF10 and RET1 shared 27 out of 42, 1 out of 26 and 19 out of 35 annotations in the attributes of Λp, Λf and Λc respectively. For TAF10 and RET1, the number of annotations shared in the attribute of either Λp or Λc was much more than that in Λf.Another point worth noting is about the protein TAF10 highlighted with grey colour in Figure Given a detailed literature review regarding the protein TAF10 ,46, we nW. In particular, based on the optimized result of W, the base cluster of TNBSC was originated from the protein NFKBIB while that of IIC started from the protein CHUK. Since the overlapping score between these two base clusters was too small to be merged in the last step of DCAFP, they were thus identified as the protein complexes. As a result, for TNBSC all proteins except POLR1E and POLR2L were identified by DCAFP, and IIC was completely identified by DCAFP. The reason why DCAFP could not identify POLR1E and POLR2L was that none of interactions involving these two proteins were found in the PPI network of DIP Hsapi.Another example of overlapping protein complexes is from the human PPI network DIP Hsapi as depicted in Figure In this work, we have addressed the problem of identifying protein complexes by developing a new approach that considers the graph topology of PPI network and the functional information of proteins simultaneously. For the use of functional information, as we observed from the previous researches that proteins in a protein complex are rarely similar in all the categories of the functional information but instead they are normally found to be similar in specific subsets of the functional categories, functional preferences are thus introduced to emphasize such difference when identifying protein complexes. We then formulate the problem of identifying protein complex into a constrained optimization problem integrating the properties of functional preferences and dense structures of clusters. This constrained problem is then addressed by DCAFP in an iterative manner.Experimental results on five PPI networks from the two species show the promising performance of DCAFP when applied to identify protein complexes. The comparison to the state-of-the-art approaches revealed that with the integration of functional preferences and dense structures, DCAFP exhibited improved performance with both in terms of accuracy of the identified complexes as well as in functional enrichment tests.Regarding the future works, we would like to unfold it from two aspects. The first aspect is to consider assigning a weight to each of terms in the optimization problem of (1). As we found that the properties of density and functional preferences had different impacts to the performance of identifying protein complexes, the performance of DCAFP can be possibly improved if we make use of such difference by assigning different weights to the terms related to density and functional preferences. The other aspect is to implement DCAFP in a parallel manner so that the efficiency of DCAFP can be raised.http://www.comp.polyu.edu.hk/~cslhu/resources/dcafp.The supporting datasets and the DCAFP software are available in aDensity-based Clustering Approach with Functional Preferences.


Interaction of proteins with other molecules plays an important role in many biological activities. As many structures of protein-DNA complexes and protein-RNA complexes have been determined in the past years, several databases have been constructed to provide structure data of the complexes. However, the information on the binding sites between proteins and nucleic acids is not readily available from the structure data since the data consists mostly of the three-dimensional coordinates of the atoms in the complexes.DataBase of Binding Pairs in protein-nucleic acid interactions, http://bclab.inha.ac.kr/dbbp). DBBP contains 44,955 hydrogen bonds (H-bonds) of protein-DNA interactions and 77,947 H-bonds of protein-RNA interactions.We analyzed the huge amount of structure data for the hydrogen bonding interactions between proteins and nucleic acids and developed a database called DBBP (Analysis of the huge amount of structure data of protein-nucleic acid complexes is labor-intensive, yet provides useful information for studying protein-nucleic acid interactions. DBBP provides the detailed information of hydrogen-bonding interactions between proteins and nucleic acids at various levels from the atomic level to the residue level. The binding information can be used as a valuable resource for developing a computational method aiming at predicting new binding sites in proteins or nucleic acids. Protein-nucleic acid interactions play an important role in many biological activities. Site-specific DNA-binding proteins or transcription factors (TFs) play important roles in gene regulations by forming protein complexes . These pProtein-RNA interactions also have important roles in a wide variety of gene expression . For insAs the number of structures of protein-DNA/RNA complexes that have been resolved has been increased plentifully for the past few years, a huge amount of structure data is available at several databases -10. HoweDataBase of Binding Pairs in protein-nucleic acid interactions). The database shows hydrogen-bonding interactions between proteins and nucleic acids at an atomic level, which is not readily available in any other databases, including the Protein Data Bank (PDB) [In this study we performed wide analysis of the structures of protein-DNA/RNA complexes and built a database called DBBP (nk (PDB) . The binThe protein-DNA/RNA complexes determined by X-ray crystallography were selected from PDB. As of February, 2013 there were 2,568 protein-DNA complexes and 1,355 protein-RNA complexes in PDB. After extracting complexes with a resolution of 3.0 Å or better, 2,138 protein-DNA complexes  and 651 protein-RNA complexes (the DS2 data set) remained.Different studies ,12-14 ha<2.5 Å, (2) the donor-hydrogen-acceptor (D-H-A) angle >90°, (3) the contacts with the donor-acceptor (D-A) distance <3.9 Å, (4) H-A-AA angle >90°, where AA is an acceptor antecedent. These are the most commonly used criteria for H bonds. In particular, the criteria of H-A distance <2.5 Å and D-H-A angle >90° are essential for H bonds [As for the criteria for a binding site between proteins and nucleic acids, we use a hydrogen bond (H-bond), which is stricter than the distance criteria. The locations of hydrogen atoms (H) were inferred from the surrounding atoms since hydrogen atoms are invisible in purely X-ray-derived structures. H-bonds between proteins and nucleic acids were identified by finding all proximal atom pairs between H-bond donors (D) and acceptors (A) that satisfy the following the geometric criteria: (1) the hydrogen-acceptor (H-A) distance  H bonds . If therAs an example, Figure P (+) be the probability that an amino acid is a binding site and P (−) be the probability that an amino acid is a non-binding site in protein-nucleic acid interactions (Equations 1 and 2).Let P(A|+) is the probability that the binding amino acid is A. Likewise, the conditional probability P(A|−) is the probability that the non-binding amino acid is A. Equation 5 is the log-likelihood ratio of P(A|+) and P(A|−).Then, the conditional probability DHA >90°, We obtained H-bonds from 2,068 protein-DNA complexes (DS3) and 637 protein-RNA complexes (DS4) using HBPLUS ,20 with If an atom of DNA acts as a hydrogen acceptor, an atom of protein should be a hydrogen donor. Hence, the number of DNA acceptors  is the same as the number of protein donors , and the number of DNA donors  is the same as the number of protein acceptors . Likewise, the number of RNA acceptors  is the same as the number of protein donors  and the number of RNA donors  is the same as the number of protein acceptors .Figure Figure DBBP shows binding pairs at various levels, from the atomic level to the residue level. When it shows detailed information on H-Bonds, it shows the donors and acceptors of each H-bond. A same type of atom can play a role of hydrogen donor or acceptor depending on the context. We generated XML files for binding sites of protein-DNA/RNA complexes. Users of the database can access the XML file via PDB ID.Figure DataBase of Binding Pairs in protein-nucleic acid interactions). DBBP provides the detailed information of H-bonding interactions between proteins and nucleic acids at various levels. Such information is not readily available in any other databases, including PDB, but will help researchers determine DNA (or RNA) binding sites in proteins and protein binding sites in DNA or RNA molecules. It can also be used as a valuable resource for developing a computational method aiming at predicting new binding sites in proteins or nucleic acids. The database is available at http://bclab.inha.ac.kr/dbbp.From an extensive analysis of the structure data of protein-DNA/RNA complexes extracted from PDB, we have identified hydrogen bonds (H-bonds). Analysis of the large amount of structure data for H-bonds is labor-intensive, yet provides useful information for studying protein-nucleic acid interactions. The protein-DNA complexes contain 44,955 H-bonds, which have 3,657 hydrogen acceptors (HA) and 41,298 hydrogen donors (HD) in amino acids, and 41,298 HA and 3,657 HD in nucleotides. The protein-RNA complexes contain 77,947 H-bonds, which have 18,151 HA and 59,796 HD in amino acids, and 59,796 HA and 18,151 HD in nucleotides. Using the data of H-bonding interactions, we developed a database called DBBP (Byungkyu Park implemented the databse and prepared the first draft of the manuscript. Hyungchan Kim drew figures and prepared the manuscript together. Kyungsook Han supervised the work and rewrote the manuscript. All authors read and approved the final manuscript.


Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only.kd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn.This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors  to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (PThe proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. Protein-protein interactions (PPIs) regulate a wide range of biological processes, involved in almost every cellular function. Majority of the proteins in living cells interact with partner proteins and form a complex to regulate proper functions. PPI employs transport mechanisms, muscle contractions, regulations of gene expression and signal transductions ,2. PPIs dK). Binding affinity is crucial in drug developments and therapeutics, and thus, many approaches have been developed to measure the binding affinity. Generally, these approaches are categorized into two groups. The first group identifies the binding affinity using scoring functions and two hybrid systems, surface plasmon resonance and forster resonance energy transfer [et al. using sequence descriptors to develop a prediction method SMO using support vector machines (SVM) to discriminate high and low binding affinity of heterodimeric protein complexes [Binding affinity defines the strength of PPIs, and is represented by a dissociation constant  for 200 heterodimeric protein complexes and obtained correlation coefficient of 0.34 and a mean absolute error of 1.4. Contribution analysis of prediction has been used to select top-ranked features. The top-two physicochemical properties apparent partition energy [Nearly 4,000 PPIs exist and the growth of PPIs in size increases speedily. It is a challenging task to accurately predict the binding affinities of PPIs based on sequence information only. This work proposes a SVM-based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes by predicting their binding affinity. SVM-BAC using SVM with an optimal feature selection method, an inheritable bi-objective combinatorial genetic algorithm (IBCGA) , can iden energy  and prinn energy  are effeWe have classified heterodimeric protein complexes by predicting their binding affinities. A dataset consisting of 108 and 108 complexes with high and low binding affinity was used, respectively. All the sequences were encoded into 580 sequence descriptors. SVM-BAC incorporating with the optimal feature selection algorithm IBCGA selected a set of 14 informative sequence descriptors to discriminate the high and low binding affinity complexes.SVM-BAC achieved the training , test accuracies and Matthews correlation coefficient (MCC) of 85.80%, 83.33% and 0.71 respectively, slightly better than the SMO method  with 76.Classifier performance of using the ROC curve is shown in Figure Furthermore, we evaluated individual effect of these 14 features on prediction accuracy using knock-out analysis. Removing of an informative feature makes a significant decrease between 8 and 18% in terms of prediction accuracy, shown in Figure Kd). The binding affinity dissociation constant depends on many factors, such as structural features, interface properties and physiological factors, which are not easily obtained from primary sequences only. We made an attempt to estimate the binding affinities using the promising features of amino acids that were used to predict high and low binding affinity complexes. There were 200 heterodimeric protein complexes used to estimate the binding affinities, which covered various ranges of binding affinity values (Pkd) and functions. Support vector regression (SVR) was used as a prediction model to estimate binding affinities. Our model was trained using the 14 sequence descriptors and the PKd value. The proposed sequence based model using SVR yielded the correlation coefficient of 0.34 and mean absolute error of 1.4  are apparent partition energies calculated from Chothia index (GUYH850105)  and prinThe property of GUYH850105 is described as "Apparent partition energies calculated from Chothia index ". Chothi0) correlated with hydrophobic buried area. Upon adding hydrophobic buried surface at their interface leads to gaining of free energy Δ (ΔG0) = -15 ± 1.2 cal/molA2. Statistical and experimental estimations state that the increase of hydrophobic buried surface enhances the protein binding affinity [The buried hydrophobicity nature of protein-protein interactions has been extensively studied. Protein-protein binding directly correlates with total buried hydrophobic surface area and the binding energy increases with the increment of interfacial buried surface area . Mutatioaffinity ,25. We taffinity . We founThe property of SNEP660104 was described as "Relations between chemical structure and biological activity in principal component analysis IV". Sneath calculated the correlations of amino acids for the use in principal component analysis. Four vectors  were derived from the 20 amino acid correlations [2 ΔASA, and maximum and minimum numbers of hydrogen bonds in heterodimeric complexes were 1.89 and 0.29, respectively. Xu-et al analyzed hydrogen bond and salt bridge specificity, and charge distribution at protein-protein interfaces [Hydrogen bonds and salt bridges are one of the major contributors to protein-protein interactions. Polar and non-polar side chains significantly contribute to stabilization of the complexes. Polar side chains stabilize the protein complexes through hydrogen bonds. In general, protein interfaces are more hydrophilic than interior residues and form more hydrogen bonds at interfaces . In trypterfaces .We measured the numbers of hydrogen bonds at protein-protein interfaces in high and low binding affinity complexes. The average numbers of hydrogen bonds in high and low binding affinity complexes were 22.83 ± 19.70 and 19.42 ± 17.91, respectively. The hydrogen bonds at their interfaces were more enriched in high binding affinity complexes than in low binding affinity complexes. Contribution of these hydrogen bonds in overall protein-protein interactions is various and crucial to pinpoint. The numbers of hydrogen bonds at interfaces in high and low binding affinity complexes are shown in Figure The property of CHOP780101 is described as "Normalized frequencies of beta turn". Beta bends/ turn is formed by the polypeptide chain folds back on itself by 180 degrees. Beta turns shows three conformations based on their phi, psi values, and two major types exists i.e., types 1 and 2 . Amino aTo examine the beta turn participation in heterodimeric complexes, we calculated the number of beta turns in the used dataset. Totally, 4,528 beta turns participate in the 216 heterodimeric complexes. On average, every high and low binding affinity complex possesses 23.78 ± 16.89 and 18.27 ± 12.42 beta turns, respectively. Notably, the mean number of beta turns in high binding affinity complexes is significantly larger than that in low binding affinity complexes where the p-value of student's t-test is 0.003. The difference accuracy of the beta turns property CHOP780101 was 12.35% using the knock-out analysis  of 200 heterodimeric complexes with mean absolute error of 1.4 and it can be further refined by considering the categorization of functional types. Further physicochemical analysis revealed that buried hydrophobicity, beta turns, and hydrogen bonds are influential factors in protein binding affinity. The property analysis would be helpful to understand the underlying mechanism in the protein binding affinities. Though, protein binding affinities depend on various factors, we attempted to find out the contribution of sequence properties in binding affinity prediction.Characterizing the physicochemical properties influencing the protein binding affinity has a significant role in protein-protein interaction studies. We developed amino acid based predictor named as SVM-BAC to classify the high and low binding affinity complexes by identifying 14 informative properties. Moreover, the SVR-based prediction method using the 14 features was investigated to examine the ability of predicting the binding affinities for the whole set of complexes of various functional types. Our model estimated the binding affinities , we used the same 216 heterodimeric complexes which were used to predict the high and low binding affinity complexes. There were 16 complexes removed from the 216 complexes because there is no absolute value of binding affinity available. Finally, 200 protein complexes have been considered for the estimation experiment. Binding affinity values were collected from different sources [-3 to 10-15 M. The 200 complexes possess diverse functional groups such as antibody/antigen, enzyme-inhibitor, enzyme-substrate, G-protein containing, receptor-containing and other enzymes.For estimating the binding affinity value  of a complex for the ith amino acid ai of 20 amino acids to encode the protein sequence of variable length into the feature vector of length 580.Step 2: Calculate the composition th pphysicochemical property, TPCP(p), of a protein complex, where p = 1, 2, ..., 580.Step 3: Calculate the feature value of the p(ia) is the value of the i aamino acid of the th pphysicochemical property.where PCPC. IBCGA selects a small set of m features from a large number of n candidate features while optimizing the prediction performance. IBCGA is an efficient global optimization algorithm comprising an intelligent evolutionary algorithm which uses orthogonal array crossover to efficiently solve large parameter optimization problems. The inheritable mechanism can conserve the features that can improve the predication accuracy in the searching procedure.In this work, the inheritable bi-objective combinatorial optimization genetic algorithm (IBCGA)  is used n = 580 binary genes i bfor selecting informative features and two 4-bit GA-genes for tuning the parameters C and γ of SVM. The ith property is excluded from the SVM classifier if i b= 0, otherwise it will be included. This method can encode the 16 values of γ and C ∈ {2-7, 2-6, ..., 28}. In the SVM classifier, digitalized and normalized protein sequences in the training data set were used as input. In this work, the range of the size of candidate feature set selected by IBCGA is from start r= 10 and endr= 20. The feature selection algorithm IBCGA is described as follows.In using IBCGA, the parameter setting of SVM and feature selection were encoded into binary genes to be optimized simultaneously. In this work, the commonly used genetic algorithm (GA) terms are gene and chromosome, represent as GA-gene and GA-chromosome for the discrimination. The GA-chromosome consists of  randomly generate an initial population of individuals.Step 1:  Evaluate the fitness value of all individuals using the fitness function that is the prediction accuracy in terms of 10-fold cross validation.Step 2: (Selection) Use a conventional method of tournament selection that selects the winner from two randomly selected individuals to generate a mating pool.Step 3: (Crossover) Select two parents from the mating pool to perform orthogonal array crossover operation.Step 4: (Mutation) Apply a conventional mutation operator to the randomly selected individuals in the new population. Mutation is not applied to the best individuals to prevent the best fitness value from deterioration.Step 5: (Termination test) If the stopping condition (reaching a prespecified number of generations) for obtaining the solution is satisfied, then output the best individual as the solution. Otherwise, go to Step 2.r <end, rrandomly change one bit in the binary GA-genes for each individual from 0 to 1; increase the number r by one, and go to Step 2. Otherwise, stop the algorithm.Step 5: (Inheritance) If m features) and parameter settings (γ and C) of SVM are done by using IBCGA, the binding affinity prediction method SVM-BAC can be implemented. SVM is an effective method used in the two-class classification and regression problems [i, xjx) between any two objects i xand jx:After the feature selection  and Shinn-Ying Ho (SYH) designed the system, participated in manuscript preparation, and carried out the detail study. Jyun-Rong Wang (JRW), Ming-Ju Tsai (MJT), Kai-Ti Hsu (KTH), Phasit Charoenkwan (PCW), Wen-Lin Huang (WLH), and Hui-Ling Huang (HLH) participated in the design of the system, implemented programs, and discussed the results. All authors have read and approved the final manuscript.kd values. The additional file contains 200 protein-protein complex PDB ids and corresponding Pkd values. (*.pdf).Protein-protein complex PDB ids and corresponding PClick here for fileStatistics of the 14 physicochemical properties for 216 heterodimeric complexes. The additional file contains statistics description for 216 heterodimeric complexes. (*.xls).Click here for file


Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics.In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques.Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus “C protein” binds to membrane docking protein, while “X protein” and “P protein” interacts with cell-killing and metabolic process proteins, respectively.The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model. Viral pathogens affect their eukaryotic host partly by interacting with the proteins of the host cells In this paper, we have made an attempt to predict viral-host (inter-species) PPIs based on three well-known supervised machine-learning methods, namely SVM, Naïve Bayes and Random Forest using significantly diverse biological information like protein sequence, domain-domain associations, disorder regions, degree and amino-acids composition of viral and human proteins. The viral-host PPIs dataset were obtained from VirusMINT, a viral protein interaction database ftp://mint.bio.uniroma2.it/pub/virusmint/MITAB/current/2012-10-26-mint-viruses-binary.mitab26.txt) http://weizhong-lab.ucsd.edu/cdhit_suite/cgi-bin/index.cgi?cmd=cd-hit-2d) at 85% sequence identity level to find the homologous proteins present in the training and testing sets The dataset used were obtained from “VirusMINT: a viral protein interaction database” -based method is defined over a vector space where the problem is to find a decision surface that maximizes the margin between data points in the two classes. We have used SVMNaïve Bayes model computes subsequent probabilities for a given hypothesis (present/absence) assuming that the features that describe data instances are conditionally independent. Its performance is comparable to other supervised learning methods. We used Waikato Environment for Knowledge Analysis (WEKA) machine learning tool box to perform Naïve Bayes classification Random Forest (RF) classifier “grows” several Decision Trees (DTs) simultaneously where each node uses a random subset of the features. Each tree in the Random Forest classifies the new object, and “votes” for that class. The forest elects the classification based on majority vote . We obtained Random Forest (RF) classifier from the WEKA machine learning tool box. Optimal parameters were used for evaluation of the method.We focused on forty-four features of protein pairs to produce feature vectors . First, http://www.uniprot.org/) We inferred viral and host domain-domain associations from interacting protein pairs. Our goal was to find the frequency of a certain viral and host domain-domain association present in protein pairs. We collected all protein related “InterPro” domains from Protein Knowledgebase, UniProtKB . The beta coefficient value is a measure of how strongly each “predictor variable” influences the “criterion variable”. The higher beta coefficient value implies greater impact of the “predictor variable” on the “criterion variable”.We used 5-fold cross-validation to estimate performance of all methods. In 5-fold cross-validation, the dataset has been partitioned into 5 equally  sized segments or folds. Consequently, 5 times of training and testing were performed such that each time a different fold of the data is held-out for testing while the remaining four folds are used for training. The overall performance of a method was calculated using average performance over five folds.Sensitivity , specificity, accuracy, PPV , Matthew’s correlation coefficient (MCC) and F1 score were computed on 5-fold cross validation step. All the performance measures were based on a balanced dataset of 1∶1 positive vs. negative examples. Sensitivity, specificity, accuracy, PPV, MCC and F1 score were calculated by the following equations:, area under ROC curve was computed on 5-fold cross validation step.From Receiver Operating Characteristic (ROC) plotHierarchical clustering analysis was done using TIBCO Spotfire software The Database for Annotation, Visualization and Integrated Discovery (DAVID) web server was used to identify significantly enriched gene ontology (GO) annotation terms in predicted interacting human protein partners of hepatitis B and E viruses We started with 44 features of a specific viral-host protein pair and tried with different subsets of features in order to achieve maximum accuracy with nearly equal sensitivity and specificity of our proposed method , S7. IntIn order to achieve optimal sensitivity, specificity and accuracy, we tried different kernels and parameters using SVM. The linear and polynomial kernel function showed high specificity, but low sensitivity, whereas the sigmoid kernel function exhibited poor sensitivity . In contIn order to avoid bias in the performance of our proposed model, we tested it on blind dataset, not used in training or testing. Consequently, 204 protein pair between viral proteins and non-human species  was considered as a blind dataset. We used the same parameters and cut-off (threshold) for each approach. As shown in Dyer et al. developed a method to predict HIV-human PPIs using SVM classifier with linear kernel on different combinations of protein features, including domain profiles, protein sequence k-mers and properties of human proteins in a human PPI network Cui et al. worked on a similar problem of HCV-human and HPV-human PPIs using an SVM model with RBF kernel and relative frequency of amino acid triplets of a protein sequence. They have used 11 HCV (lead to 695 interactions) and 9 HPV proteins (lead to 252 interactions) Hepatitis B virus and hepatitis E virus proteins were used in order to predict unknown viral-human PPIs. All possible combinations of hepatitis B virus and human protein pairs (17615 * 4) were predicted by our proposed model . The preFinally, the human proteins present in high confidence (red area) of hierarchical clustering analysis Shown in  were useSimilar study as hepatitis B virus proteins was also done with hepatitis E virus proteins Shown in -S9, wherHere, we proposed three supervised machine learning-based techniques for predicting viral-host (across species) PPIs by incorporating potential biological information of protein pairs including domain-domain associations score, degree, percentage of disorder regions and amino acid compositions. Initially, we started with 44 features and predicted four best features, which were domain-domain association and methionine, serine and valine amino acid composition of viral proteins using categorical regression model . There are biological interpretations of these residues of viral proteins for their importance in viral-host PPIs. For example, methionine, serine and valine may be involved in stabilization of the protein structure, serine/threonine protein phosphatases and modulating syncytium formation during infection, respectively. It was observed that Random Forest perform better in terms of accuracy, MCC and area under ROC curve, while the proposed SVM method performs better in terms of sensitivity and F1 score. Performance of the proposed SVM method was evaluated on the blind dataset of 204 viral-host protein pairs , which achieved a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown HBV-human and HEV-human PPIs were predicted using optimised SVM model and were grouped by HCA and further validated by GO enrichment analysis. Hepatitis B virus interacting human proteins show distinct GO biological process terms; for example, “X-protein” probably interferes with cell defence mechanism, whereas “P-protein” binds to metabolic pathways. The predicted viral-human PPIs give us hint about the possible role of viral proteins in the pathogenesis process.Figure S1Hierarchical clustering of highly predicted SVM score of HEV-human protein pairs. Hierarchical clustering analysis was done using TIBCO Spotfire software with complete linkage clustering method, cosine correlation distance measure, average value ordering weight, scale between 0 and 1 normalization and empty value replace by 0 for both (row and column) dendrogram. The high, average and low SVM predicted scores are marked in red, white and blue, respectively.(PDF)Click here for additional data file.Figure S2A network of HBC-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Figure S3A network of HBP-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Figure S4A network of HEORF1 (Genotype 1)-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Figure S5A network of HEORF2 (Genotype 1)-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Figure S6A network of HEORF3 (Genotype 1)-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Figure S7A network of HEORF1 (Genotype 4)-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Figure S8A network of HEORF2 (Genotype 4)-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Figure S9A network of HEORF2 (Genotype 4)-human protein interactions predicted by our proposed method. The network visualized by Cytoscape 3.0.2 (PDF)Click here for additional data file.Table S1Statistic of homologous protein present in the training and testing sets as well as from the blind datasets.(XLSX)Click here for additional data file.Table S2Positive interactions dataset used in this study to build optimal model for prediction. The positive interactions dataset used in the study were obtained from VirusMINT.(XLSX)Click here for additional data file.Table S3Negative interactions dataset used in this study to build optimal model for prediction. The negative interactions dataset used in the study were chosen using random protein pairs which are not found in interacting protein pairs.(XLSX)Click here for additional data file.Table S4Positive interactions dataset used in this study as a positive blind dataset. The positive blind dataset used in the study were obtained from VirusMINT.(XLSX)Click here for additional data file.Table S5Negative interactions dataset used in this study as a negative blind dataset. The negative blind dataset used in the study were chosen using random protein pairs which are not found in interacting protein pairs (positive blind dataset).(XLSX)Click here for additional data file.Table S6All 44 input features.(XLSX)Click here for additional data file.Table S7SVM performance measures based on different subsets of features. Optimal parameters were used for respective subset of features.(XLSX)Click here for additional data file.Table S8Several SVM kernel-wise performance measures (sensitivity and specificity) on different models. Optimal parameters and threshold were used for respective kernel. In Model 1, 1st, 2nd, 3rd and 4th folds were used for training and 5th fold was kept for testing. In Model 2, 1st, 2nd, 3rd and 5th folds were used for training and 4th fold was left out for testing. In Model 3, 1st, 2nd, 4th, 5th folds were used for training and 3rd fold for testing. In Model 4, 1st, 3rd, 4th, 5th folds were used for training and 2nd fold was used for testing. In Model 5, 2nd, 3rd, 4th, 5th folds were used for training and 1st fold was kept aside for testing.(XLSX)Click here for additional data file.Table S9Different parameters used in Random Forest using WEKA.(XLSX)Click here for additional data file.Table S10Predicted scores of HBV-human protein-protein association by proposed optimal model.(XLSX)Click here for additional data file.Table S11HEV-human protein-protein association predicted scores by proposed optimal model.(XLSX)Click here for additional data file.Table S12GO enrichment analysis on interacting human protein partners of HBV X proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S13GO enrichment analysis on interacting human protein partners of HBV C proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S14GO enrichment analysis on interacting human protein partners of HBV P proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S15GO enrichment analysis on interacting human protein partners of HEV proteins using DAVID server. Significant biological process annotation terms were filter by FDR <0.05.(XLSX)Click here for additional data file.Table S16GO enrichment analysis on interacting human protein partners of HEV ORF1 (Genotype 1) proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S17GO enrichment analysis on interacting human protein partners of HEV ORF2 (Genotype 1) proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S18GO enrichment analysis on interacting human protein partners of HEV ORF3 (Genotype 1) proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S19GO enrichment analysis on interacting human protein partners of HEV ORF1 (Genotype 4) proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S20GO enrichment analysis on interacting human protein partners of HEV ORF2 (Genotype 4) proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.Table S21GO enrichment analysis on interacting human protein partners of HEV ORF3 (Genotype 4) proteins using DAVID server. Significant biological process terms were chosen by PValue<0.05.(XLSX)Click here for additional data file.


We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false-positive and false-negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer -related or damaging SNPs. Coev2Net can be downloaded at  Protein-protein interactions (PPIs) play a critical role in all cellular processes, ranging from cellular division to apoptosis. Elucidating and analyzing PPIs is thus essential to understanding the underlying mechanisms in biology. Indeed, this has been a major focus of research in recent years, providing a wealth of experimental data about protein associations -9. CurreAkin to sequencing of the human genome, complete high-confidence descriptions of PPIs is a fundamental step towards human interactome mapping ,25. Alsoet al. [et al. [Several attempts have been made to characterize the quality of the interactions obtained from HTP experiments ,24,28-31et al.  and Cusi [et al. , the lowRecently, a number of algorithms have been developed to predict protein interactions by integrating complementary data such as sequence features and structural features ,34-42. AIn this paper, we introduce a general framework to predict, assess and boost confidence in individual interactions inferred from a HTP experiment. Our contribution is three-fold: 1) we develop a novel computational algorithm to quantitatively predict interactions, given just the protein sequences; 2) we show how the algorithm can be used in a general framework to quantify confidence in observed interactions; and 3) we demonstrate the utility of our structure-based framework in providing biologically significant additional information about binding sites, which is not provided by any other HTP method . We first validate our method on a high-confidence network in the recently investigated human mitogen-activated protein kinase (MAPK) interactome ,68. We eWe developed Coev2Net Figure , a frameInputs to the framework are a high-confidence network  and the interactions identified from the HTP experiment for which one wishes to quantify confidence. For every pair of interaction in the HTP screen, Coev2Net provides a score to assess their likelihood of being co-evolved from interacting homologous sequences . To do this, Coev2Net first predicts a likely interface model for the two proteins, by threading  the sequWe first benchmark Coev2Net on SCOPPI , a proteFurthermore, Coev2Net also performs well on SCOPPI family pairs not having more than two non-redundant complexes, indicating Coev2Net's ability to deal with limitations of both structural and sequence training data  is significantly higher than that reported by other prediction methods based on conservation, genomic data, gene ontology annotation and literature extractions (approximately 14% to approximately 28%) , althouget al.  [P-value < 0.01, Fisher's test) with those deemed likely true positives from an experimental standpoint. Encouragingly, the percentage of our framework's predicted true positive interactions that are confirmed positive  in the Vinayagam dataset is roughly 52% . To assess significance, we divided our predictions into high confidence and low confidence based on the probability cutoff of 0.6. To categorize interactions as true positive or true negative in the Y2H screens, we assumed the cutoffs employed in Schwartz  assays) . We thenThe confidence scores given by our framework can be used to design additional experiments to enhance the quality of the initial interactome. We tested 19 randomly chosen high confidence interactions (confidence score > 0.6) using a complementary assay (LUMIER) . Each paet al. [Eight out of the 19 interactions were discarded in this way as they registered a z-score of less than 1.5 and were deemed too variable. For additional experimental details we refer the readers to a more comprehensive interactome mapping analysis in . Notablyet al. , in whicet al. , anotherP = 0.0075, Fisher's exact test; Figure P = 0.06). We further analyzed the distribution of the SNPs in terms of their density at the interface and non-interface. Here again, we find that damaging SNPs are preferentially located on the interface. We find that the average density of damaging SNPs at the predicted interfaces is significantly higher than their density at non-interface positions , a bias also observed by Wang et al. recently [P < 1e-10, Mann-Whitney test). These analyses show that there is an evolutionary pressure to admit only benign SNPs at the interface, since any potentially damaging SNP will hinder the interaction.In addition to the confidence scores, Coev2Net also provides a putative interface for the interaction. These interfaces can yield novel mechanistic insights into the PPI and provide hypotheses about disease-associated mutations that occur at the interface. Missense SNPs occurring at the interface can potentially disrupt the interaction between the proteins, leading to abnormal functioning of the cell. We analyzed the predicted interfaces for existence of PolyPhen2 annotated missense mutations in dbSNP (build 131) . PolyPherecently . For benP < 10e-20, Mann-Whitney test; Figure To investigate the structural distribution of annotated mutations, we analyzed somatic mutations characterized in cancer to see if there is any preference for their location on the protein. We analyzed annotated mutations in the coding region deposited in the Cosmic database for their predicted location . We onlyFinally, we looked at the predicted locations for some of the un-annotated mutations in kinases (from the MoKCa database ). As an Phosphorylation sites have been observed to be enriched at interfaces in solved structures . This obWe have proposed a novel structure-based computational approach to identify PPIs on a genome-wide scale. Using structural features, we have demonstrated that our method can not only identify true-interactions better than previous approaches, but also provide key biological insights that are absent from HTP experiments.While it has been shown previously for some families that residues in and around the interface have correlated evolutionary histories, extracting such robust correlation signals for predictive purposes on a genome scale has remained difficult due to limited known interacting homologs. In the context of homology search for only monomers, enriching a multiple sequence alignment with artificial sequences has proven to be effective in the case of limited homologs ,88. UtilAs more and more HTP data for mapping the interactome are gathered, there would be a necessary demand for automatic protocols to evaluate the data quality and estimate the confidence in individual interactions. In particular, transient interactions have been notoriously difficult to elucidate and validate. We have shown that confidence in PPIs investigated through HTP techniques can be quantified and enhanced by our proposed complementary structure-based PPI prediction algorithm. Our PPI predictions on recent HTP human MAPK interactomes and further experimental validations have indicated the efficacy of our predicted confidence scores. Moreover, since our framework requires only the sequences of the two candidate proteins, it can be used as a complementary feature to other methods that rely on additional features ,89.Limited studies have been undertaken to link structural features to genome-wide interactomes to gain a mechanistic understanding of underlying biological processes. Our threading-based approach enables us to extend coverage of structure-based studies further than that possible by homology models . As a result, the predicted structures are more reliable and provide a sound basis for mechanistic hypotheses. We provide an anecdotal example by analyzing the distribution of annotated missense SNPs in our predicted models. In agreement with a recent study , we showConventional homology-based methods such as interPrets , IBIS 7 and PRISThe Coev2Net algorithm can be roughly divided into three distinct stages: 1) identification of the putative binding interface; 2) evaluation of the compatibility of the interface with an interface co-evolution-based model (see 'Construction of the interface profile through simulated co-evolution' below); and 3) evaluation of the confidence score for the interaction.The two query sequences are each threaded against a complex template library to search for the best template. We use a top-performing threader program 'RAPTOR' ,90 to loThe predicted interface is evaluated by computing the log-likelihood of the interface residues with respect to the interface profiles described below - a PGM for interacting pairs ('positive') and another graphical model representing background correlations ('negative'). A high log-likelihood with respect to the 'positive' PGM implies that the protein sequences show co-evolution at the interface, compatible with the model, and are hence likely to interact.p is the probability of interaction (or our confidence score), then:Once we have the compatibility scores for the predicted interface, we use these as features to predict our confidence in the interaction. A logistic-regression classifier is trained on a high-confidence network, and is used to predict our confidence score for the interaction, which is the output of the classifier. Both alignment features (from stage 1: Identification of interface) and interface features  are used as features in the classifier. If Xi are the alignment features for each protein in the interacting pair ; Yi is the size of the interface; L+ is the log-likelihood score of the predicted interface with respect to the positive tree, and L- the log-likelihood score of the predicted interface with respect to the negative tree. The α, β1, β2, βi, β+ and β- are coefficients of the classifier.where To construct an interface profile for a SCOPPI family, which consists of a family of protein complexes; we exploit the biological intuition that interacting proteins exhibit co-evolution at the interface. This co-evolution has been detected even in residues within 10 to 12 Ångströms at the interface ,64,91-94We start the simulation from known complexes within a SCOPPI family. We first align the interfaces using a contact map alignment program, CMAPi . CMAPi eFor each pair of aligned seed sequences (full proteins forming the complex), additional sequences are constructed via random mutations according to a probability distribution  representing the corresponding protein families, and the alignment scores computed. They are then accepted or rejected in a stochastic manner, based on their joint fitness score. The mutation and stochastic selection of interacting sequences can be viewed as a Markov chain Monte Carlo (MCMC) algorithm  for a hiOnce we have sufficient sequences , we encode the pairwise correlations observed in these 'interacting' sequences using a PGM. Our motivations for introducing a PGM are twofold: 1) analogous to a sequence profile, a PGM is a 'profile' that can be used to score predicted interfaces; and 2) to explicitly capture long-distance correlations (non-contact-based) at or near the interface residues. We select 1,000 interacting sequences per training complex as our interacting set . To model the correlations between residues of these interacting proteins, we use the Sanghavi-Tan-Willsky algorithm  to constThe individual methods were evaluated based on their ability to correctly predict true-positives and true-negatives. To do this, we plot receiver operator characteristic (ROC) curves for each method. In our ROC curves, sensitivity is defined as True-positives/ and specificity is defined as True-negatives/. For a high-confidence true-positive and true-negative dataset, we perform five-fold cross-validation (CV) tests for each method , and plot the average sensitivities (at particular specificities) for these five runs. For Coev2Net, we run the MCMC sampling 5 times, and average the performance across these 25 curves (5 MCMC × 5 CV). To compare against interPrets, we used a cutoff on the z-score computed by the algorithm to classify a prediction as positive or negative. Since there is no training required here, there was no need for a cross-validation. For the computationally intensive IBIS , we compHMM: hidden Markov model; HTP: high-throughput; MAPK: mitogen-activated protein kinase; MCMC: Markov chain Monte Carlo; PGM: probabilistic graphical model; PPI: protein-protein interaction; ROC: receiver operator characteristic; SNP: single nucleotide polymorphism; Y2H: yeast-2-hybrid.The authors declare that they have no competing interests.RH, JB, and BB conceived and designed the study. RH designed and implemented the algorithm; JP and RH provided the proof. JP, JB and BB helped in designing the algorithm and interpretating the results. JP, AV, JX and US provided tools, protocols and reagents. AV and US did the LUMIER experiments. AV, NP and US provided feedback on the manuscript and suggested applications for the algorithm. RH, JP, JB and BB wrote the manuscript. All authors read and approved the final manuscript.Supplementary methods on the algorithm, results on benchmarking and comparison with other methods.Click here for file


Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research. As the basic unit of life, cells represent complex biological entities, whose normal function revolves around a delicate interplay between multiple diverse biomolecular systems. Proteins are vital components of these systems, acting as molecular machines, sensors, transporters, and structural elements (among others), with interactions between proteins, hereinafter called protein–protein interactions (PPIs), being key to their function.et al, Protein–protein interactions are inherently dynamic in nature, adjusting in response to different stimuli and environmental conditions. This provides considerable flexibility in function and allows cells to adapt in a measured way to changing circumstances. Even a subtle dysfunction of PPIs can have major systemic consequences, perturbing interconnected cellular networks and producing disease phenotypes , and whether the interaction is transient or stable  needs to be considered.Specialized Equipment and Expertise. Finally, it is important to ensure that all necessary resources and knowledge required to fully take advantage of a particular method are available. Although the majority of methods are straightforward, some do require specific instrumentation and expertise. Most methods, especially those that attempt to study interactomes on a genomewide scale, also require strong bioinformatics support for analysis and data cleaning.While numerous methods are available for the large‐scale study of PPIs, there is no one “perfect” method for all situations, and each has its own strengths and weaknesses. When selecting a suitable method to study interacting partners of a protein of interest, the following factors should be considered: While many PPI assays exist, we present below some of the newer and more widely used approaches, providing a concise overview of their key principles, advantages, and limitations. Key references for each technique, including examples of their large‐scale application, can also be found in Table Originally developed 25 years ago , which means that proteins confined to particular cellular environments  cannot be studied in their full‐length form. The proteins used in this method are also often overexpressed, which can lead to non‐specific interactions. Altogether, these effects can lead to a high false‐positive rate, necessitating careful follow‐up analysis to identify true, biologically relevant interactions. The readout of this method is also indirect, preventing spatial or temporal analysis of PPIs  formats. It is easy to establish in any laboratory environment and requires no specialized equipment. The assay is performed et al, Membrane yeast two hybrid suffers from some of the same disadvantages as the classical Y2H, including the problems associated with the expression, modification, and interaction of non‐native proteins in a yeast host, and artifacts resulting from protein overexpression. Also, MYTH can only be used with membrane proteins that have at least one terminus in the cytosol (where the necessary deubiquitinating enzymes are located). Additionally, soluble proteins cannot be used as baits in the MYTH system, unless they are exceptionally large or anchored to intracellular structures (thereby preventing diffusion of the bait‐transcription factor into the nucleus and interaction‐independent activation of the reporter system). The readout of this method is also indirect, preventing spatial or temporal analysis of PPIs . The LUMIER assay must be carefully controlled, to normalize for differences in transfection efficiency and expression, and minimize background signal. The assay is not ideal for studying how PPIs change spatially, over time or in response to different environmental conditions  to the plasma membrane requires that PPIs occur in the cytoplasmic submembrane region, preventing detection of interaction with preys localized to other subcellular compartments. This anchoring (and the large size of the bait tag) may also block certain PPIs due to steric issues beyond those occurring in many other methodologies  upon interaction of proteins to which they are fused leads to release of an artificial transcription factor, and subsequent expression of a reporter system . The method is best suited for the detection of binary PPIs . Additionally, the termini of the membrane protein fused to Cub must be cytosolic, in order to provide access to the deubiquitinating proteases responsible for cleavage and release of transcription factor. The method is also not suitable for spatial or real‐time temporal analysis of PPIs . In addition, for a strong FRET readout, close spatial proximity of the fluorophores is required for the energy transfer to occur. FRET also has decreased sensitivity compared to other fluorescence‐based approaches like BiFC or BRET, as there tends to be strong background autofluorescence in cells upon sample illumination. For this reason, many controls are necessary to quantify the changes in fluorescence intensity in the presence and absence of energy transfer, and particularly weak interactions producing a signal close to background may be difficult to detect. Depending upon the fluorophores selected, photobleaching can also result in loss of signal over time , while its interacting partner is fused to either green or yellow fluorescent protein . When donors and acceptors are brought into close proximity (< 100 Å) by interaction of their fusion partners, energy transfer occurs, producing fluorescent signal which is monitored to detect the PPIs . Additionally, issues associated with bait/prey stability and disruption of interactions upon cell lysis are avoided. The method is also well suited for identifying weak or transient interactions and is amenable to temporal regulation . When the Proximity Probes are brought close to one another , the DNA strands serve as a template to direct ligation of two subsequently added oligonucleotide fragments into a circular molecule. This circular DNA is then amplified using rolling circle amplification (RCA) primed by one of the original proximity probe oligonucleotides, resulting in a long DNA sequence physically linked to the corresponding antibody (and thus the interacting protein pair). This new DNA sequence contains many repetitive elements, which are then bound by fluorophore‐labeled complementary oligonucleotide probes, allowing visualization of interactions, at the specific sites where they occur, using a fluorescence microscope , making the approach expensive and highly dependent on enzyme activity and stability. The required use of antibodies in PLA is another potential drawback, as antibodies are often costly, and may also not be readily available against particular proteins of interest . TriCEPS may also not be effective in detecting receptor–ligand interactions in situations where ligand binding requires association to other cell surface structures  with a low false‐positive rate . For instance, a PPI may occur only during specific time periods  or if specific PTMs are present. The nature of protein–protein interaction is thus an inherently dynamic process that changes with time, environments, and at different stages of the cell cycle. Recently, dynamic protein interaction networks have been constructed by using proteomic, genomic, and transcriptomic methodologies. In the previous section, we touched briefly on the suitability of some techniques for mapping dynamic interactions. Examples of some of specific proteomic‐based approaches employed include Y2H and AP‐mass spectrometry , and the frequency of false negatives as the false negative rate  or sensitivity . The main strategies for assessing FDR and sensitivity have involved testing detected interactions by multiple methods and comparing against interactions from literature. FDR has been, arguably, a greater focus in interactome studies than sensitivity. In small‐scale screens, FDR can be assessed and minimized by testing all reported interactions using multiple methods. However, the FDR of small‐scale screens may still be uncertain. Edwards et al  experiments need to be described with a controlled vocabulary and recorded in a common format, (ii) thousands of articles have to be curated and resulting information has to be easily accessible, and (iii) proteins need to be unambiguously identified. The first task, creation of standard vocabularies and formats for PPI data, was addressed through the Human Proteome Organization Proteomics Standards Initiative (HUPO‐PSI) by the Molecular Interaction (MI) workgroup. They created a common controlled vocabulary for experimental techniques, molecular features, and interaction types , the way information is acquired , and the sources of information—peer‐reviewed articles (primary sources) or other databases (secondary sources). Manual curation of articles is the most trusted method for acquiring data and is carried out by most databases. Several approaches have been used to assist with curation: curation guidelines have been established, an automated syntax checker was implemented to test for compliance with accepted formats, and guidelines were created for reporting PPIs in papers, so that curation of papers is easier and more accurate  assigning confidence scores to detected interactions, (ii) identifying complexes by clustering PPI networks or analyzing additional data, and (iii) evaluating resulting complexes by comparing with gold standard datasets. The first step assigns confidence scores to detected interactions; scores can be used to filter interactions or can be included as input to clustering algorithms. Any of the scoring approaches described earlier may be used , is valuable in this selection process, and can help identify candidates based on a more complex range of user‐defined criteria.et al, et al, et al, The specific validation experiments to be performed also vary on a case‐by‐case basis. Typical initial characterization experiments involve disrupting the level of individual members of an interaction pair , and then looking for changes in the properties or function of the other member. For example, if studying the interactions of a particular receptor, one could investigate the effect of altering gene expression levels of identified interactors on downstream signaling cascades controlled by the receptor. Alternatively, effects on protein stability, protein trafficking, posttranslational modification, or responsiveness to known ligands/substrates could be probed. Investigations can be as specific as centering on molecular effects on individual proteins, or more broadly explore general phenotypic change . Mutational analysis of proteins can also be useful in identifying regions important for mediating and regulating interactions. For examples of proteomics screens followed by functional follow‐ups, we refer the reader to several recent studies (Babu The importance of performing these validations cannot be understated, as they provide a clear demonstration of the usefulness of any newly generated interactome in providing a solid starting point for the identification and characterization of biologically important processes. If an interactome cannot easily provide this information, then it is unlikely to be of widespread value to the scientific community, and further improvements are necessary. It is also critical to note that carrying out proper validations may represent a significant effort, and researchers must take this into account when planning and implementing any interactome mapping project, regardless of scale.et al, High‐throughput PPI mapping and analysis enables researchers to generate data and investigate biological processes on a previously unprecedented scale (Yao The authors declare that they have no conflict of interest.