Protein-protein interfaces hold the key to understanding protein-protein interactions. In this paper we investigated local interaction network patterns beyond pair-wise contact sites by considering interfaces as contact networks among residues. A contact site was defined as any residue on the surface of one protein which was in contact with a residue on the surface of another protein. We labeled the sub-graphs of these contact networks by their amino acid types. The observed distributions of these labeled sub-graphs were compared with the corresponding background distributions and the results suggested that there were preferred chemical patterns of closely packed residues at the interface. These preferred patterns point to biological constraints on physical proximity between those residues on one protein which were involved in binding to residues which were close on the interacting partner. Interaction interfaces were far from random and contain information beyond pairs and triangles. To illustrate the possible application of the local network patterns observed, we introduced a signature method, called iScore, based on these local patterns to assess interface predictions. On our data sets iScore achieved 83.6% specificity with 82% sensitivity. Protein interactions are mediated by multiple physicochemical contacts at the interfaces between proteins. A considerable amount of research has focused on understanding the nature of such interactions The interfaces between proteins have been investigated in terms of the structural motifs they contain. In Many studies have also shown that amino acids with particular physicochemical properties tend to be in contact with complementary amino acids in the interface, for example via hydrogen bonds, electrostatic interactions, and aromatic ring stacking In In this paper we combine structural motifs, residue information and the patch idea to detect preferred patterns of interacting residues, and we illustrate our findings by providing a new scoring method for assessing interface predictions. The idea for this paper arose from our previous paper In this paper, we build on this work to investigate the small local network patterns termed labeled 4-tuples (pair-to-pair interactions) by considering both the structural information, the way a 4-tuple is wired, and the physicochemical properties, the amino acid composition of a 4-tuple. Four nodes can be wired as a connected graph in 6 different ways. If two contact sites from one protein and two contact sites from the other protein are connected in one of these 6 ways , these four contact sites form an inter-protein pair-to-pair interaction, called a 4-tuple. Each 4-tuple can be labeled by the amino acid types of its 4 nodes. Out of As reported in The background distribution depends on the database. In this paper we used three data sets, of homodimer, heterodimer, and domain-domain interfaces. For each of these data sets, we investigated the local network patterns of the interfaces in this paper, and found that the differences between these three data sets of interfaces are statistically significant. However, the profile based method gives very similar results across these three databases . As shown in To see how significant the coupling of, or dependency between, the residues in the interface is, we compare the background relative frequencies with the observed relative frequencies of the contact pairs and triangles. The greater the difference the more significant the coupling. These two relative frequencies are plotted against each other in There are 6 different ways to form a connected graph on 4 nodes; these can be found in When both intra-protein contacts are present, which is the case for A1, C1, D1, E1, F1, one would assume that the more inter-protein contacts, the less frequent the subgraph. However, this is not always the case: E1 is more frequent than C1 despite more contacts; C1 and D1 have the same number of inter-protein contacts, yet D1 is far more frequent. When only one intra-protein contact is present, as in A2, B1, D2 and E2, then D2 is more frequent than B1 despite requiring one more inter-protein contact. This indicates that there is a tendency to complete the “triangle” which is hidden in B1. These observations could be viewed as a “tendency to create triangles” at interface. When neither of the intra-protein contacts are present, as in A3 and C2, then we see that C2 is extremely rare, so that should rule out some inter-protein contacts in the prediction: if we predict C2 then we are most likely wrong. Since every site in our 4-tuples is an inter-protein contact site, subversion A2 only asks for an extra intra-protein interaction among those four sites, while A1 requires two of them. Each of those two sites in A1 with only intra-protein edge must have at least one inter-protein edge shared with some site other than those ones in this subgraph. This explains why we observed the A2 as the most frequent pattern at interface.Furthermore we considered labeled 4-node-subgraphs, with the label referring to the 7 categories. We note that there are We also counted the motif of ‘1-to-k’ with 1 residue on one protein and The chi-square goodness-of-fit test Local network patterns were summarized using iScore , and iScore was used to screen protein-protein interfaces. Among the 15 complexes in DOCKGROUND which have an interface given by only two chains and 100 decoys and 1-10 near native structures, the lowest iScore was a decoy for all 15 complexes; the highest iScore was a near-native structure for 10 of the complexes; the top 5 highest iScore's contained at least one near-native structure for 13 of the 15 complexes. The power of the method for selecting near-native structures can be measured by the average specificity and the average sensitivity for all 15 complexes. To see how this score works, we take 1KU6 as an example. In DOCKGROUND, there are 100 decoys for 1KU6 numbered from r-l_1 to r-l_100, and 10 near-native structures named as ‘r-l_30133’, ‘r-l_30306’, ‘r-l_31538’, ‘r-l_49723’, ‘r-l_71222’, ‘r-l_81617’, ‘r-l_94327’, ‘r-l_161170’, ‘r-l_182529’ and ‘r-l_207655’. The structure with the highest iScore was a near-native structure, r-l_182529, the structure with the lowest iScore was a decoy, r-l_51, and the true interface in 1KU6 was ranked within top 17. In this paper, we investigated the interactions of up to 4 residues in the interfaces between proteins from a statistical viewpoint. Considering the interfaces as networks with nodes of residues and edges of contacts, we examined labeled contact pairs, triangles, and 4-tuples. On our data set, the difference between the observed relative frequencies of those labeled subgraphs across the interfaces and the corresponding background relative frequencies gives an idea of how significant the existence of such preferred patterns is. These preferred patterns point to biological constraints on physical proximity between those residues on one protein which are involved in binding to residues which are close on the interacting partner (C2 is extremely rare). The statistical tests suggest that higher order labeled motifs have significantly more information than what can be inferred by lower order motifs. Computationally, we find that interaction interfaces are far from random and contain information beyond pairs and triangles. We exploited this fact by providing a signature method for interfaces. A chi-square score, called iScore, was calculated by comparing the profiles of the local network patterns built in this paper with those patterns in the predicted interfaces. The results suggest that the signal for the decoy established by the profile of the contact 4-tuples is stronger than that established by either the contact pairs or the contact triangles. When based on the contact 4-tuples the score achieves high specificity and sensitivity. The score demonstrates that the local network patterns studied in this paper do capture some characteristics of protein-protein interfaces. Future work will investigate alternative scoring methods.The main data sets used in this study were built as described in To assess the relevance of the preferred patterns, we proposed a scoring method for selecting the near-native structures from docking decoys. We selected all complexes with the interface given by only two chains, A and B, from DOCKGROUND yielding 15 complexes, which are listed in i.e one residue from each protein. The observed relative frequency of a contact pair of amino acid categories is defined as the ratio between the counts of this pair of amino acid categories occurring as a contact pair in our data set and the total number of all We defined a contact pair at the interface as two surface residues within 4.5Å of each other on different proteins, i.e.The number of occurrences, The number of occurrences For three surface exposed sites, two of which come from one protein and one from the other protein, contact triangles are defined when the distance between each pair of sites in the triangle is less than 4.5Å. The observed relative frequency of a contact triangle of amino acid categories isThe counts, andFor four surface exposed sites on two proteins, two sites from each protein, 4-tuples are defined if these 4 sites are connected in one of the ways listed in The counts, In a plot of observed relative frequency against background relative frequency, we note that theThe chi-square goodness-of-fit test The probability of 7 types of amino acid categories occurring at interface are denoted as The number of estimated parameters is The number of estimated parameters is also The number of non zero cells is Similarly, the test was carried out for edges to see whether the null hypothesis of contact triangles and contact 4-tuples at the interfaces can be explained by the frequency of contact pairs occurring at the interfaces can be rejected. As described above we have 28 different types of contact pairs. For a triangle For a 4-tuple where By comparing the observed local network patters in the interface of a protein complex with the local network profiles established on our data set, a chi-square score can be calculated for this interface. Taking the coordinates of the residues in the interface of interest as the input, we built its contact map as described before; and then, we counted different types of the contact pairs, the contact triangles, and the contact 4-tuples on this contact map. For example, for the contact 4-tuple The chi-square scores for all types of contact 4-tuples establish a chi-square signature for the interface of interest, and we also carried out the chi-square goodness-of-fit between the observed pattern in the interface of interest and the profile pattern established in this paper. The protein-protein interface established by a docking algorithm was scored in terms of the above chi-square scores for all types of contact 4-tuples at the interface as follows:Since the near-native structures form only 7.75% of all structures in the decoy set, the PROC  curve The ROC curve plots sensitivity versus specificity, while the PROC curve plots precision against recall.File S1Inter Protein/domain Contact Sites Predicted by Feature of 4-tuples at Interface.(PDF)Click here for additional data file.File S2Counts of 4-tuples in Data Sets of Domain-Domain Interfaces, Homodimer Interfaces, and Heterodimer Interfaces.(XLS)Click here for additional data file.File S3Comparison of Local Network Patterns Among the Data Sets of Domain-Domain Interfaces, Homodimer Interfaces, and Heterodimer Interfaces.(PDF)Click here for additional data file.


Drug-Protein Interaction Analysis (D-PIA) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called D-PIA performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.Given a database of drug-protein interactions, D-PIA, we have tested it with real drug-protein interaction data. D-PIA has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if D-PIA can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.To evaluate the effectiveness of D-PIA has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and D-PIA is therefore very fast to compute. D-PIA has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery. To facilitate drug design and discovery, it would therefore be very useful if we can predict whether or not a particular drug candidate may interact with a particular target protein based on its their structures at the molecular or sub-molecular levels.In many different and extremely complex ways, the chemical pathways in our bodies are affected by various diseases. When one is sick, it might be a mistake in one reaction in a pathway that stops an important protein from being produced or causes too much of it to be produced. To correct such mistakes, drug molecules can be developed to interact with Over the past decade, a lot of effort has been made to investigate into how drug and protein interact and the most notable among the work done are those related to protein-ligand docking . Ligand Instead of investigating into protein-ligand docking, there has also been some effort to look into the analysis of molecular substructures  and biolOther than finding privileged substructures, a variety of statistical methods have recently been proposed to predict drug-target or more generally, protein-ligand interactions ,6. ThereMost of the work mentioned above has been performed independently from the viewpoints of either ligands or proteins. Not much work has been done to investigate into how the chemical and biological space may interact with each other. In , the papD-PIA . Instead of relying on the availability of the 3D structural information of a target protein to predict if it may have any interaction with a certain drug candidate, D-PIA only makes use of the 2D molecular fingerprints of the protein in the prediction process.To identify ligand candidates efficiently for such applications as drug design and discovery, we need to be able to predict if a drug may interact with a protein without having to obtain full information of the 3D structures of protein molecules at an early stage. To do so, we propose to use a data mining algorithm called D-PIA performs its tasks by first breaking down drug molecules into substructures and proteins into their protein domains. By so doing, D-PIA attempts to determine if the drug substructures may interact or bound with the protein domains and if the strength of such interactions or bindings may determine if drugs can be designed for optimal compatibility with the human body and with other drugs [Proteins are molecules consisting of a long chain of amino acids with unique structures and substructures. A protein domain is a part of a protein chain that can evolve, function, and exist independently of the rest of the other parts of the chain . D-PIA per drugs .D-PIA makes use of a probabilistic measure to determine if a drug substructure and a protein domain are interdependent on each other and it does so in several steps: (i) for each drug in the database, the fingerprints of its molecular substructures are first obtained; (ii) for each protein in the database, the fingerprints of its protein domains are obtained; (iii) based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed; (iv) based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified; and (v) the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.Once the drug substructures and protein domains are identified, D-PIA has been tested with real data involving two thousand drugs and the proteins that they interact with. Our experimental results show that it can be very helpful for predicting drug-protein and protein-ligand interactions. It can also be used to address problems such as ligand specificity.M drugs {1, D2, ... Di, .. DMD} with each characterized by p substructure descriptors respectively. Suppose also that we have a set of N proteins {1, P2, ... Pj, ... PNP} with q protein domains descriptors identified in each of them respectively.Suppose that we have a set of M drugs can therefore be represented as i D= , where ix subis the xth substructure of the ith drug where i∈{1, 2,..., M} and x∈{1, 2, ..., p} and ix sub= 1 when the ith substructure exists in the drug, otherwise ix sub= 0. Similarly, each protein can be represented as j P= , where jy domis the yth protein domain of the jth protein, j∈{1, 2, ..., N}, y∈{1,2,..., q} and jy dom= 1 when the yth protein domain jy domexists in the protein, otherwise jy dom= 0. The existence of one of more interaction relationships between the given drugs and proteins are represented by a matrix I = T, where i α= , l∈{1, 2, ..., M}, k∈{1, 2,..., N}. lk α= 1 when there is an interaction between the lth drug and kth protein.Each of the ith substructure of a drug has a sufficiently strong interdependency relationship with the jth protein domain of proteins, we construct a contingency table , we can determine if a drug substructure i subhas an interdependency relationship with the protein domain jdom, at the 95% confidence level.ij adcan be positive and negative. When ij adis positive, i suband j domis interdepdent on each other and when ij adis negative, they are not.It should be noted that the value of weight of evidence measure for the patterns discovered above.Since the existing of drug substructure in a drug is important for determining the interaction between protein domains, it is necessary to ensure that they are utilized in the prediction of an interaction relationship between a drug and a protein. The interdependency relationships discovered by (2) determines only the interdependency between drug substructures and protein domains, but it does not measure how strong the interdependency is. For this reason, we introduce the j dom= 1 is found to be interdependent with i sub= 1. Then the weight of evidence provided by i sub= 1 in favor of j dom= 1 opposed to i dom= 0 can be defined as [Suppose that fined as :whereWoE can be used to be a positive or negative measurement for supporting or refuting the existence of an interaction relationship between a drug containing i suband a protein containing j domto have an interaction relationship. Hence, for a drug to be predicted to interact with a target protein, it should have sufficient support from its substructures in the sense that they should have a large enough degree of interdependency with the protein domains of the target protein.D-PIA is to see if it can correctly predict drug-protein interactions that it has no previous knowledge of. Here we propose to evaluate D-PIA by testing it to see if it can predict known drug-target interactions correctly.One way to evaluate the effectiveness of i Dand protein jP, the potential interaction between them can be estimated by determining if there is any significant interdependency between the substructures in i Dand the protein domains in jP. To do so, let us denote the set of substructures in i Das i DS= {1 s, 2 s, ..., as} and the set of domains in j Pas j PD= {1 d, 2 d, ..., bd}, where a is the total number of substructures in iD, and b is the total number of protein domains in jP. For ∀s' ∈ i DS∀ d' ∈ iPD, we consider the interaction between s' and d' as significant when (6) below is satisfied.Given a pair of drug i Dand jP, there are a × b possible significant interdependency relationship of substructures and protein domains in total. The potential interaction between i Dand j Pcan be estimated based on the interacting substructures between them. If there is only 1 significant interdependency between the substructures of a drug and protein out of the total a × b such possible relationships, we may consider that the potential interaction between i Dand j Pas very weak. On the other hand, if more than half of the associations are significant, we may consider that the potential interaction between i Dand j Pas high. Therefore we could assert that there is potential interaction between i Dand j Pas (7).For a pair of val(x) = 1 if |x| > 1.96, otherwise val(x) = 0.where iD, and the protein, jP, will be more significant if the value of w is higher than some user-supplied threshold, denoted as R, i.e if w >R, and if, at the same time, the WoE is also high, then it means that the interaction between i Dand j Pis not only just strong, but the strong interaction relationship is also supported with strong evidence.The interaction between the drug, D-PIA, we used the dataset from [To evaluate the effectiveness of set from  which coset from , i.e., eOther than the drugs, the dataset also contains information about 1554 proteins in total. According to the UniProt  and PfamD-PIA determines the adjusted residuals for the drug substructures and protein domains based on Equation (2) above. In Table D-PIA computes to determine if there is significant interdependency relationship between a drug substructure and a protein domain. As shown in the table, for example, the drug substructures of SUB840, SUB841, SUB861 are interdependent with the protein domains of PF00104 and PF00105.Given the drugs and proteins as described above, Equation  above. ID-PIA, we therefore try to determine if there is a strong enough drug-protein interaction between the drugs i Dand the protein j Pin our dataset based on the adjusted residuals obtained between the substructures of the drugs and the protein domains of the proteins as illustrated in Table R to 10% in our experiments and found D-PIA to be able to predict the existence of Drug-Protein interaction at an accuracy of 85.4%. A 5-fold cross-validation approach is used to evaluate the ability of D-PIA to determine if a drug interacts with a protein and this approach is described as follows:To evaluate the effectiveness of 1) We split the drug-protein interactions dataset into five subsets of equal size and take each subset in turn as a test set.D-PIA on the remaining 4 sets.2) We perform D-PIA attempts to predict the existence of interactions between drug and protein in the testing data and the accuracy over the five folds are computed.3) Based on the significant interdependency relationships determined between drug substructures and protein domains, A ROC (receiver operating characteristic) curve  based onw represents the existence of a significant interdependency relationship between a drug substructure m suband a protein domain ndom, it does not tell us how strong the interdependency relationship is. To find out, we compute, as discussed above, the WoE measure for the interaction between m suband ndom. We summarize the result of the interaction between the drug substructures m suband protein domain n domand we present some of the results in Table While true-positive vs false-positive for the prediction results of the experiments. The true-positive is concerned with the rate of correctly predicted drug-protein interactions whereas the false-positives is concerned with the rate of incorrectly predicted drug-protein interactions.The ROC in Figure D-PIA can be very accurate in predicting drug-protein interactions most of the ROC curve is much above the reference line (random prediction). The AUC (area under the ROC curve) score (which is 1 for perfect accuracy and 0.5 for random prediction) score for D-PIA is 0.7497 which shows that that it is much better than prediction at random.We can see from the chart that D-PIA can be used to predict how likely a drug candidate may interact with a particular protein. Based on the WoE computed as shown in Table These results show that D-PIA is proposed here to discover patterns in known drug-protein interaction to predict those that are unknown so that the protein-ligand docking problem can be more easily tackled without having to rely on any 3D information. D-PIA makes use of fingerprints of the known drug substructures and protein domains to infer the existence of interactions between corresponding drugs and proteins. Experimental results show that the D-PIA can work effectively and can infer drug-protein interaction with high accuracy and can be a promising tool for computer aided drug discovery.One common approach to drug discovery is to tackle the protein-ligand docking problem. To effectively do so, there is a need for information related to the 3D structures to be known. As such information is difficult and expensive to obtain, The authors declare that they have no competing interests.Weimin Luo carried out this study and drafted the manuscript. Keith CC Chan conceived this study and participated in its design and helped to draft the manuscript. All the authors read and approved the final manuscript.


Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.http://www.bioacademy.gr/bioinformatics/projects/ppireview.htmWhile results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at:  Proteins are the main actors responsible for virtually every function within a cell. While some proteins are characterized by a unique function, the majority of them operate in coordination with other proteins forming PPI networks to carry out processes in the cell. Such processes include cell cycle control, differentiation, protein folding, signaling, transcription, translation, post-translational modification and transportation. Trying to understand and predict protein functions at a systems level is neither a straightforward nor a trivial task. Due to such issues, which range from wet-lab technical challenges to the innate complexity of high dimensional data analysis, function prediction has become one of the most important and difficult challenges in current computational biology research.Some of the most well known techniques to reveal information about the interaction of proteins are the pull down assays  and tandIn addition to the direct experimental data, a wide range of large biological databases storing information about validated or predicted PPI data is also available. The Yeast Proteome Database--YPD , for exaProteins can either act individually or as a part of bigger system to perform an intricate process in the cell. Thus, proteins often collaborate and form stable associations, termed protein complexes ,18,19. IIn this study, we to go one step further than  and bencIn this section, we give a short description of the datasets that were used in this study. All of the current datasets hold information about unweighted PPI associations.This network consists of 7430 edges and 2262 vertices. A genetic interaction network was mapped by crossing mutations in several genes into a set of viable gene yeast deletion mutants scoring the double mutant progeny for fitness defects. The interactions of this network were produced by predicting the functions of the interactive elements. These elements are often produced by bringing together functionally related genes, components, or proteins that belong to the same pathway. The genetic network exhibited dense local neighbourhoods.Saccharomyces cerevisiae organism. In the original article, the MCL 36], wereNi indicates the number of proteins belonging to recorded complex i and Tj indicates the total number of members of j predicted complex assigned to all benchmark complexes. These metrics are widely used to measure the correspondence between the result of a classification and a reference and to provide an overview of how accurately the clustering techniques can detect the protein complexes from PPI data.Sn), if a method predicts very big complexes with many proteins, the Sn score will tend to have very high values. The PPV value on the other hand, does not evaluate overlapping clusters properly. In addition, all of the evaluation metrics described above assume that a complete set of real protein complexes is available, but this does not necessarily corresponds to the real experimental data.The aforementioned metrics come with their strengths and their limitations. In the case of sensitivity  and Positive Predictive Value (PPV) metrics.Figure We performed extensive experiments using four different algorithmic strategies to detect protein complexes in six PPI networks. For the evaluation process, two different evaluations sets were used. These were a) the golden standard of MIPS recorded protein complexes and b) the BT_409 dataset.In the cases where the MIPS dataset was used for evaluation, the spectral clustering algorithm achieved the highest performance with respect to the percentage of valid predictions compared to the other algorithms. On the other hand, the RNSC and MCL algorithms were the methods that clearly generated the most valid clusters. Regarding the BT_409 evaluation set  when the MIPS golden standard is used for evaluation, (b) when the BT_409 dataset is used for evaluation.Click here for file


Identification of such interactions leads to a greater insight into drug target prediction. Some recent studies have been conducted for computational prediction of new interactions based on the experimentally validated information stored in a HIV-1-human protein-protein interaction database. However, these techniques do not predict any regulatory mechanism between HIV-1 and human proteins by considering interaction types and direction of regulation of interactions,  are considered here to provide important additional information about the regulation pattern of interactions. We have also studied the biclusters and analyzed the significant GO terms and KEGG pathways in which the human proteins of the biclusters participate. Moreover the predicted rules have also been analyzed to discover regulatory relationship between some human proteins in course of HIV-1 infection. Some experimental evidences of our predicted interactions have been found by searching the recent literatures in PUBMED. We have also highlighted some human proteins that are likely to act against the HIV-1 attack.Here we present an association rule mining technique based on biclustering for discovering a set of rules among human and HIV-1 proteins using the publicly available HIV-1-human PPI database. These rules are subsequently utilized to predict some novel interactions among HIV-1 and human proteins. For prediction purpose both the We pose the problem of identifying new regulatory interactions between HIV-1 and human proteins based on the existing PPI database as an association rule mining problem based on biclustering algorithm. We discover some novel regulatory interactions between HIV-1 and human proteins. Significant number of predicted interactions has been found to be supported by recent literature. Human immunodeficiency virus-1 (HIV-1) causes acquired immunodeficiency syndrome (AIDS) in which human immune system begins to collapse. Progressive failure of the immune system leads to life threatening infection. At each stage of life cycle, HIV-1 virus hijacks the host cellular machinery for increasing the production of virus genomic material. HIV-1 virus contains a single stranded RNA genome, which codes for only 19 proteins; thus, it relies on human cellular functions. The RNA genome, consisting of seven structural landmarks  and nine genes , encode nineteen proteins. The prediction of possible viral-host interactions is one of the major tasks in Protein-Protein Interaction (PPI) research for antiviral drug discovery and treatment optimization. Predicting PPIs between viral and host proteins has contributed substantial knowledge to the drug design area. Recently, PPI prediction has been regarded as an promising alternative to the traditional approach to drug design[The computational approaches for predicting PPIs are mainly modeled as classification problems. In3]  3] a BayMost of the approaches were primarily focused to determine the PPIs in a single organism ("intra-species prediction"). But the prediction of PPIs between different organisms ("inter-species prediction"), more specifically in virus and the corresponding host proteins is now very important issue in development of new therapeutic approaches and design of drugs for these viral diseases. Recently some computational approaches are proposed by several researchers to predict and analyze some novel interactions between HIV-1 and human proteins.In a randomA similar biclustering approach is studied in to find In another study, a novelWith this observation we use an association rule mining approach for finding a set of rules by considering both the interaction types and the direction of regulations. For this, we have annotated each interaction with interaction type and divided the whole network into two annotated subnetworks depending on the regulation direction of interaction type. We have utilized Binary inclusion-Maximal (BiMax) biclustering algorithm on each In this section biclustering-based association rule mining approach is described. An outline of our method for analysis of bicliques and prediction of interactions has been visualized in FigureHP1_upregulates signifies that the HP1 protein is upregulated by some viral proteins and protein of type HP2_inhibitedby represents that the human protein HP2 inhibits some viral protein. We construct two binary matrices of human and viral proteins, HV_ positive of size 19 × 2564, and HV_negative of size 19 × 1271. An entry of ‘1’ in matrix HP_ positive and ‘-1’ in HV_negative denotes the presence of interaction between the corresponding pair of human and HIV-1 proteins, and an entry of 0 represents the absence of any information regarding the interaction of the corresponding human and viral proteins. An entry ‘X’ in both the matrices represents the interaction between the corresponding pair of human and HIV-1 proteins is two-way or bidirectional interaction. The whole process is described in detail in algorithm 1. The step-1 and step-2 are involved in preparing the datasets in the form of two matrices. In step-3 and step-4 our algorithm uses BiMax.Click here for fileAll type-1 and type-2 rules. All the predicted type-1 and type-2 rules are listed here.Click here for fileReferences of PUBMED entry. The references of the articles we find from PUBMED showing the proof of our predictions are listed here. Click here for filehttp://kucse.in/hiv/supplementary_bioinfo1/reference_of_pubmed.pdf.Click here for filePredicted interactions supported by more than one methodology.Click here for file


As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or “singleplex” proteins. Actually, multiple-location or “multiplex” proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the “multi-labeled learning” and “accumulation-layer scale”, a new predictor, called  Knowledge of the subcellular location of proteins is important as can be viewed from the following four aspects. (1) It can provide useful insights or clues about their functions; particularly, one of the fundamental goals in cell biology and proteomics is to identify the functions of proteins in the context of compartments that organize them in the cellular environment. (2) It can indicate how and in what kind of cellular environments the proteins interact with each other and with other molecules; this is especially important for the in-depth study of protein-protein interaction (PPI), one of the currently hot topics in proteomics. (3) It can help our understanding of the intricate pathways that regulate biological processes at the cellular level http://www.expasy.org/sprot/relnotes/relstat.html; meaning that the number of protein sequence entries now is more than 132 times the number from about 24 years ago.Although the knowledge of protein subcellular localization can be acquired by conducting various biochemical experiments, it is both time-consuming and costly by relying on doing experiments alone. Particularly, recent advances in large-scale genome sequencing have generated a huge number of protein sequences. For example, in 1986 the Swiss-Prot Facing the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely and effectively identifying various biological features for newly found proteins Actually, the problem of predicting protein subcellular localization is somewhat reminiscent of the efforts by many previous investigators because during the past 19 years or so, a series of methods have been developed on this topic  Only the integer numbers 0 and 1 were used to reflect the GO (gene ontology) 2) It was through an optimal threshold factor 3) Although a web-server for Euk-mPLoc has been established at http://www.csbio.sjtu.edu.cn/bioinf/euk-multi-2/, only one query protein sequence at a time is allowed when using the web-server to conduct prediction; for the convenience of users in handling many query protein sequences, such a rigid limit should be improved.Recently, a powerful predictor, called The present study was initiated in an attempt to develop a new and more powerful predictor by addressing the above three problems.st residue of the protein nd residue, …, Given a query protein sequence Thus, various non-sequential or discrete models to represent protein samples were proposed in hopes to establish some sort of correlation or cluster manner through which the prediction could be more effectively carried out.Eq.1 can be formulated by Eq.2, if using the ACC model to represent the protein The simplest discrete model used to represent a protein sample is its amino acid (AA) composition or AAC To avoid completely lose the sequence-order information, the pseudo amino acid composition (PseAAC) was proposed to represent the sample of a protein, as formulated by Actually, the PseAAC for a protein Eq.1). The form of Eq.4 can cover the PseAAC as originally formulated in Eq.4).To develop a powerful method for statistically predicting protein subcellular localization, one of the most important things is to find a formulation to reflect the core and essential features of protein samples that are closely correlated with their subcellular localization. However, this is by no means an easy thing to do because this kind of features is usually deeply hidden or “buried” in piles of complicated sequences. To deal with this problem, let us consider the following approaches via the general form of PseAAC (GO database Compression and reorganization of the existing GO numbers. The GO database (version 740 released 30 July 2009) contains many GO numbers. However, these numbers do not increase successively and orderly. For easier handling, some reorganization and compression procedure was taken to renumber them. For example, after such a procedure, the original GO numbers GO:0000001, GO:0000002, GO:0000003, GO:0000009, GO:00000011, GO:0000012, GO:0000015, …, GO:0090204 would become GO_compress: 0000001, GO_compress: 0000002, GO_compress: 0000003, GO_compress: 0000004, GO_compress: 0000005, GO_compress: 0000006, GO_compress: 0000007, ……, GO_compress: 0011118, respectively. The GO database obtained thru such a treatment is called GO_compress database, which contains 11,118 numbers increasing successively from 1 to the last one.Eq.4 with Using Use BLAST Those proteins which have http://www.ebi.ac.uk/GOA/ to find the corresponding GO number(s) Search the GO database at Eq.6 is given byThus, the elements in Eq.7, the GO formulation derived from the above steps consists of 11,118 real numbers rather than only the elements 0 and 1 as in the GO formulation adopted in As we can see from Eq.6 may become a naught vector or meaningless under any of the following situations: (1) the protein 2)its representative proteins do not contain any useful GO information for statistical prediction based on a given training dataset.Note that the GO formulation of Under such a circumstance, let us consider using the sequential evolution formulation to represent the protein Biology is a natural science with historic dimension. All biological species have developed continuously starting out from a very limited number of ancestral species. It is true for protein sequence as well Eq.4, here let us use the information of the PSSM (Position-Specific Scoring Matrix) To incorporate the sequential evolution information into the PseAAC of Eq.1), Eq.9 were generated by using PSI-BLAST According to Use the elements inEq.4 can now be formulated asEq.14 by following a given order, say from left to right and from the 1st row to the last as illustrated by following equation Eq.14 for Eq.15.Introduce a new matrix generated by multiplying Eq.6), then all the protein samples used to train the prediction engine should also be expressed in the GO formulation; if the query protein was defined in the form of Eq.15), then all the training data should be expressed in the SeqEvo formulation as well.Regardless of using which formulation to represent protein samples, the following self-consistency principle must be observed during the course of prediction: if the query protein Below, let us consider the algorithm or operation engine for conducting the prediction.In this study, let us introduce a novel classifier, called the multi-label KNN or abbreviated as ML-KNN classifier, to predict the subcellular localization for the systems that contain both single-location and multiple-location proteins.Without losing generality, let us consider a system or dataset on sites Fig. 1; Eq.17 as the codes of the 22 location sites; i.e., “1” for “acrosome”, “2” for “cell membrane”, “3” for “cell wall”, and so forth (Eq.17). Thus, we have Eq.6), and Eq.15), respectively; the only difference is that the corresponding constituent elements are derived from the amino acid sequence of Suppose Eq.19, when In sequence analysis, there are many different scales to define the distance between two proteins, such as Euclidean distance, Hamming distance K nearest neighbor proteins to the protein K nearest neighbor proteins in Suppose Now, for a query protein The number of how many different subcellular locations it belongs to will be determined by its nearest neighbor protein in Eq.20 that has (have) the highest score(s), as can be expressed by Eq.17. For example, if Eq.20 is Eq.20 are However, the concrete location site(s) to which ane” cf. Table 1.iLoc-Euk, which can be used to predict the subcellular localization of both singleplex and multiplex eukaryotic proteins. To provide an intuitive picture, a flowchart is provided in iLoc-Euk.The entire classifier thus established is called iLoc-Euk was established. Below, let us give a step-by-step guide on how to use it to get the desired results.For those who are interested in using the predictor but not its mathematical details, a web-server for http://icpr.jci.edu.cn/bioinfo/iLoc-Euk and you will see the top page of the predictor on your computer screen, as shown in iLoc-Euk predictor and the caveat when using it.Open the web server at site Example button right above the input box. For more information about FASTA format, visit http://en.wikipedia.org/wiki/Fasta_format. Different with Euk-mPLoc 2.0Either type or copy and paste the query protein sequence into the input box at the center of Submit button to see the predicted result. For example, if you use the three query protein sequences in the Example window as the input, after clicking the Submit button, you will see st query protein is “Extracellular”, that for the 2nd one is “Cytoplasm; Nucleus”, and that for the 3rd one is “Cytoplasm; Mitochondrion; Nucleus”. In other words, the 1st query protein (A0S865) is a single-location one residing at “extracellular” only, the 2nd one (P40057) can simultaneously occur in two different sites (“cytoplasm” and “nucleus”), and the 3rd one (Q05043) can simultaneously occur in three different sites . All these results are fully consistent with the experimental observation as summarized in the Online Supporting Information S1 Click on the Batch-example The maximum number of the query proteins for each batch input file is 50. After clicking the button Batch-submit, you will see “Your batch job is under computation; once the results are available, you will be notified by e-mail.” Note that if you submit a batch input file from an Apple computer, although it looks like in the FASTA format, your input might change to non-FASTA format in the server end and cause errors. Under such a circumstance, the safest way is to submit your input file with a pdf format.As shown on the lower panel of Citation button to find the relevant papers that document the detailed development and algorithm of iLoc-Euk.Click on the Data button to download the benchmark datasets used to train and test the iLoc-Euk predictor .Click on the To obtain the predicted result with the expected success rate, the entire sequence of the query protein rather than its fragment should be used as an input. A sequence with less than 50 amino acid residues is generally deemed as a fragment. Also, if the query protein is known not one of the 22 locations as shown in iLoc-Euk.In statistical prediction, it would be meaningless to simply say a success rate of a predictor without specifying what method and benchmark dataset were used to test its accuracy. As is well known, the following three methods are often used to examine the quality of a predictor: independent dataset test, subsampling test, and jackknife test However, even if using the jackknife approach for cross-validation, a same predictor may still generate obviously different success rates when tested by different benchmark datasets. This is because the more stringent of a benchmark dataset in excluding homologous and high similarity sequences, the more difficult for a predictor to achieve a high overall success rate Online Supporting Information S1 of http://www.csbio.sjtu.edu.cn/bioinf/euk-multi-2/Data.htm. The reasons we choose it as a benchmark dataset for the current study are as follows. (1) The dataset was constructed specialized for eukaryotic proteins and it can cover 22 subcellular location sites; compared with the other datasets in this area that only covered 5-10 subcellular locations, the coverage scope of the current dataset is much wider. (2) None of proteins included in the current benchmark dataset has Euk-mPLoc 2.0 on the current benchmark dataset have been well documented and reported is a recent paper In this study, the same benchmark dataset The dataset Euk-mPLocEuk-mPLoc 2.0Euk-mPLoc 2.0 was about 25% higher than that by Euk-mPLoc. Therefore, to demonstrate the power of the predictor proposed in this paper, it would be sufficient to just compare iLoc-Euk with Euk-mPLoc 2.0For such a complicated dataset containing both single-location and multiple-location proteins distributed among 22 subcellular location sites, so far only two existing predictors, i.e., Euk-mPLoc 2.0iLoc-Euk on the aforementioned benchmark dataset iLoc-Euk is over 79%, which is about 15% higher than that by Euk-mPLoc 2.0.Listed in Euk-mPLoc 2.0 and iLoc-Euk, the false positives (over-predictions) and false negatives (under-predictions) were also taken into account to reduce the scores in calculating the overall success rate. As for the detailed process of how to count the over-predictions and under-predictions for a system containing both single-location and multiple-location proteins, see Eqs.43–48 and Note that during the course of the jackknife test by iLoc-Euk was 5535/7766 = 71.27%.To provide a more intuitive and easier-to-understand measurement, let us introduce a new scale, the so-called “absolute true” success rate, to reflect the accuracy of a predictor, as defined byiLoc-Euk can achieve higher success rates than Euk-mPLoc 2.0 are as follows. (1)The GO formulation used to represent protein samples in iLoc-Euk is formed by the hit probabilities and hence contains more information than that in Euk-mPLoc 2.02) The accumulation-layer scale has been introduced in iLoc-Euk that is particularly useful and more natural for dealing with proteins having multiple subcellular locations.The reasons why iLoc-Euk is more powerful than the existing predictors in identifying the subcellular locations of eukaryotic proteins, there is much room for further improvement in future studies. As shown in iLoc-Euk for proteins belonging to “hydrogenosome” and “microsome” locations are still very low. This is because of that, compared with the most of the other 20 location sites, the numbers of proteins in the two sites are not sufficiently large to train the prediction engine in a more effective way. It is anticipated that with more experimental data available for the two sites in the future, the situation will be improved and the anticipated success rates by iLoc-Euk will be further enhanced.Finally, it should be pointed out that although Supporting Information S1The benchmark dataset (PDF)Click here for additional data file.


PPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking “unbound perturbation” (“unbound docking”) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScorePPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScorePPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScorePPI/FRODOCK will be successful.The distance-dependent knowledge-based DrugScore Protein-protein interactions have important implications in most complex cellular signalling processes Sampling of possible protein-protein configurations and scoring each configuration are the two main aspects for successful protein-protein docking. Whereas the configurational space of two (rigid) proteins can be successfully sampled in reasonable time, the reliable identification of near-native protein-protein complex structures from a set of generated configurations is still a major challenge PPI for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes (accessible at http://cpclab.uni-duesseldorf.de/dsppi/) PPI is based on the DrugScore approach that has proven successful already as a scoring and objective function for protein-ligand We recently developed the knowledge-based scoring function DrugScorePPI potentials in structure prediction of protein-protein complexes. In this study, DrugScorePPI was initially used as a scoring function to evaluate decoys of two non-redundant datasets of 54 protein-protein complexes by Gray et al.PPI was used as an objective function in connection with the fast spherical harmonics-based protein-protein docking algorithm FRODOCK PPI as an objective function was evaluated in bound and unbound docking on the ZDOCK benchmark 3.0 a priori if unbound docking with DrugScorePPI/FRODOCK will be successful.This provided the incentive for us to evaluate the DrugScorePPI was described in detail recently RNA scoring functions for protein-ligand and RNA-ligand complexes PPI potentials were derived from 851 crystallographically determined protein-protein complexes using an in-house mySQL database that contains structural information of all PDB entries . The dataset consists of 655 homodimers and 196 heterodimers. In all of the cases, the complexes had been resolved to at least 2.5 Å. PDB codes of all complexes used for deriving the potentials are listed in ref. The derivation of the distance-dependent pair-potentials of DrugScoreW between atom p with type Τp of a protein P and atom b with type Τb of the binding partner B, separated by a distance p,bd, results in the docking score ΔW for evaluating a protein-protein complex configuration (eq. 1). The upper distance for deriving the pair-potentials was set to 5 Å.Summing over the resulting specific interactions ΔW.Contrary to previous work on alanine scanning PPI potentials was assessed in terms of their ability to select native-like protein-protein complex configurations from datasets of pre-generated protein-protein docking decoys. Thus, the DrugScorePPI potentials were evaluated as a scoring function. For this we used two non-redundant datasets constructed by the Baker group using the protein-protein docking program RosettaDock PPI for these complexes can be directly compared to the validation study by Baker and coworkers Initially, the predictive power of the DrugScoreet al.PPI potential grids for approximating the binding energy upon complex formation ΔW (eq. 1). For distances p,bd smaller than the location of the first maximum of a pair-potential with respect to the origin plus 0.1 Å, a Gaussian repulsion term with a height of 280.000 at p,bd = 0 Å was added to the DrugScorePPI pair potentials as described in ref. PPI potentials. Each rectangular potential grid was located at the center of mass of a receptor. The size of the grid in the {x, y, z} direction was determined from the maximum difference of the {x, y, z} coordinates of two atoms plus the upper distance limit of the pair-potentials of 5 Å. The rotational and translational sampling resolutions were set to 5.6° (∼60.000 rotations) FRODOCK is a fast spherical harmonics-based protein-protein docking tool developed by Garzón PPI/FRODOCK approach, we used protein-protein complexes from the ZDOCK benchmark 3.0 prepared by Hwang et al.nat) and non-native (fnot) contacts of interface residues following CAPRI criteria nat ≥0.5 and (l_rmsd ≤1.0 Å or i_rmsd ≤1.0 Å)), medium accuracy ((0.3≤ fnat ≤0.5) and (l_rmsd ≤5.0 Å or i_rmsd ≤2.0 Å) or (fnat >0.5 and l_rmsd >1.0 Å and i_rmsd >1.0 Å)), acceptable accuracy ((0.1≤ fnat ≤0.3) and (l_rmsd ≤10.0 Å or i_rmsd ≤4.0 Å) or (fnat >0.3 and l_rmsd >5.0 Å and i_rmsd >2.0 Å)), and incorrect (fnat <0.1 or (l_rmsd >10.0 Å and i_rmsd >4.0 Å)) according to ref. α -i_rmsd <1.5 Å and fnot <0.4), medium , and difficult (Cα -i_rmsd >2.2 Å) cases For validating the DrugScorePPI potentials were derived. To test whether this leads to a training effect on the potentials, we re-derived the DrugScorePPI potentials from the knowledge base excluding the homologous complexes. When applied in the evaluation studies, the re-derived potentials did not lead to significantly different results (data not shown). This observation is consistent with results from a leave-homologous-complexes-out cross-validation study when applying the DrugScorePPI potentials for in silico alanine-scanning PPI potentials.The final benchmark set shares five homologous complexes with the knowledge base of 851 structures from which the DrugScorePPI/FRODOCK  was evaluated with respect to computational efficiency on three protein-protein complexes with different sizes  are required per atom−atom pair to obtain statistically significant potentials PPI show qualitatively similar shapes but quantitative differences with respect to the minima in the case of N.pl3-O.co2 and O.3-O.3. As such, for N.pl3-O.co2 interactions both a global minimum  values of ) and a local minimum  were found in the case of DrugScorePPI but only a global minimum in the case of DrugScore . Both the global minima reflect the typical distance for hydrogen bonding including salt bridges PPI derivation confirmed that indeed for many salt bridges between Asp or Glu and Arg one of the N.pl3-O.co2 interactions is shorter than the other one. Hence, the minima of the DrugScorePPI potential reflect the ability of proteins to diversify salt bridge interactions as a result of residue packing. The O.3-O.3 interactions at the minimum around 2.5 Å are much more favorable in DrugScorePPI (ΔW≈−6434) than in DrugScore (ΔW≈−3961). This is an interesting result because in the case of protein-protein interactions there are only three residues, Thr, Ser, and Tyr, that are able to form O.3-O.3 interactions. Regarding Ser and Thr, there is a need for small residues for tight interface packing; thus, the multiple polar interactions of Ser and Thr can appreciably contribute to protein-protein binding. Tyr is one of the most common hotspot residues in protein interfaces as it can form aromatic interactions in addition to hydrogen bonds ΔWDrugScore_PPI = −1023; ΔWDrugScore = −1419) are almost similar in both potentials.The pair-potentials shown in PPI potentials encode characteristic determinants of the molecular recognition of proteins, which differ from those observed in DrugScore pair potentials of protein-ligand interactions. This indicates that these knowledge-based potentials do not have a universal character; rather, the respective potential can be expected to show a high predictive power only when applied to cases that lie within the scope of the knowledge base used for its derivation. The generalizability of the potentials is expected to increase with the size of the knowledge base they are derived from even if the current knowledge base already yielded statistically significant potentials (see above). Considering that the derivation of DrugScorePPI potentials occurs in an automated manner, this suggests to re-derive a new DrugScorePPI version once the number of protein-protein complexes has increased markedly in the PDB. In addition to extending the knowledge base of native complexes, the knowledge base can also be extended by considering non-native complexes, i.e., docking decoys for using DrugScorePPI potentials as a scoring and objective function in structure prediction of protein-protein complexes. Applying linear programming, the pair-potentials can then be scaled with the objective to maximize the gap between scores of native versus non-native complexes In summary, the DrugScorePPI was used as a scoring function for ranking decoys of a non-redundant dataset of 54 targets for which “unbound perturbation” and “unbound docking” solutions have been generated by Baker and coworkers  solution(s) with rmsd <10 Å in the top 5 in 81.5% (57.4%) of the cases  in 100% (73.3%) of the cases (PPI yields 25.0% (20.8%). Thus, when compared to the probabilities for random selection, DrugScorePPI shows superior performance in ranking acceptable solutions on the top. Comparing these results to the ones of Baker and coworkers  energy landscape that is smooth as to not impair the efficiency of configurational sampling during docking RS was used as a quantitative measure to determine the correlation between the rmsd values and the scores of docking solutions In order to further analyze the properties of DrugScorePPI scores for 1000 “unbound perturbation” decoys of a serine protease/prosegment complex (PDB-ID 1PPE) and a trypsin/trypsin inhibitor complex (PDB ID 1SPB) are shown as a function of the rmsd with respect to the native structure. In both cases, a well-defined funnel-shape is obvious, although in the former case a decoy with rmsd = 7.11 Å is slightly favored over more near-native solutions. The RS is 0.75 (0.61) for the protease/prosegment complex (trypsin/trypsin inhibitor complex). When considering all 54 complexes, for 59% (44%) an RS of at least 0.3 (0.4) was found native binding modes.DrugScorePPI potential grids can be used as input. Eleven potential grids  For our purpose, FRODOCK was adapted such that precalculated DrugScorePPI/FRODOCK combination , convincing results were obtained with the DrugScorebination . A high bination , DrugScoPPI/FRODOCK. Ramaraj et al. reported that Tyr shows the highest abundance of all amino acids in the paratope-containing surface (PCS) of an antibody and also the highest presence in the PCS compared to the surface of the whole antibody PPI/FRODOCK: Three of them are antigen-antibody complexes , and two are other complexes . We will discuss these failures in more detail in the section “Influence of crystal packing”.Antigen-antibody complexes have been found to show major differences in the interactions compared to other protein-protein complexes PPI for rescoring the 2000 top-ranked decoys generated with the original FRODOCK implementation, a dramatic drop-off in the docking success rate was observed , no near-native solution could be sampled among the top 2000. Interestingly, however, a solution was identified on the first rank that has medium accuracy with respect to a structural arrangement that does not result in the biologically relevant interaction but one originating from crystal contacts  For contacts . This fiIn summary, these examples reveal that many “failures” in protein-protein docking can be understood if crystal packing effects are considered. Conversely, in our view, in the design of benchmarks for protein-protein docking, such potential influences should be taken into account, as has been done in the field of protein-ligand docking for the CCDC/Astex clean set PPI/FRODOCK approach again on the cleaned version of the ZDOCK benchmark 3.0, now using both protein binding partners in the unbound conformation  DrugScorePPI is still not “soft” enough to compensate for the missing explicit treatment of protein flexibility in the docking algorithm. Structural refinement of complex structures obtained from the docking and re-evaluation with DrugScorePPI could be a way to overcome this limitation PPI only evaluates interactions between receptor and ligand atoms that are <5 Å apart. Thus, interactions between atoms potentially being in contact in the native complex will not be considered in the unbound docking if the conformational changes between bound and unbound states are too large. Again, this limitation may be overcome by structural refinement of the initially generated complex structures. Second, the missing long-range interactions in DrugScorePPI may also lead to not recognizing encounter complex configurations, which are generally dominated by electrostatic interactions We noted that in the case of unbound docking the difference between the number of complexes for which a complex structure of a given accuracy is generated  is 20.0% (65.9%) considering only the “easy” and “medium” cases in the benchmark , i.e., ePPI/FRODOCK, four of which are classified as easy , two as medium , and seven as difficult  cases. Four of these complexes are antigen-antibody complexes ; one of the complexes has already been discussed above regarding crystal packing effects (PDB-ID: 1I4D). Visual inspection of the remaining eight complexes revealed pronounced conformational changes upon complex formation in terms of loop movements in the interface of five complexes (PDB-ID’s: 1BKD (α-i_rmsd<3.7 Å) and large domain movements for one of the complexes . Such large rearrangements in the protein-protein interface are apparently out of the scope of our rigid docking approach using knowledge-based potentials. One way to overcome this limitation without having to modify the rigid docking approach is to perform ensemble docking using ensembles of protein structures deformed along collective degrees of freedom For 13 complexes no solution with i_rmsd <10 Å was found in the top 2000 predictions by knowledge-driven docking with DrugScore’s: 1BKD , 1DE4, 1PPI/FRODOCK is higher for complexes with only small rearrangements (see above). This leads to the question if and how one can estimate a priori whether unbound protein-protein docking will be successful; for this, only information on the unbound binding partners should be used so as to mimic a real-life scenario. To this end, we applied a method developed by Marsh et al.rel) of an unbound protein in order to predict the magnitude of binding-induced conformational changes. Arel is the actual accessible surface area of a protein divided by the accessible surface area expected for a folded protein of the same molecular weight. Using this measure follows the rationale that binding partners in the unbound state with higher Arel values expose more surface area and adopt more extended conformations, thus, they are likely to be more flexible and, hence, show larger conformational changes upon binding et al. found a linear correlation between Arel and log(rmsd between the bound and unbound state) with r2 = 0.64. When computing Arel for those 88 binding partners in the cleaned version of the ZDOCK benchmark 3.0 where both of the proteins are in the unbound state, we find a linear correlation of r2 = 0.49 (p<0.001) with the logarithm of the all-atom rmsd value with respect to the bound conformation  correlation is weak for homomers The success rate of unbound docking with DrugScorerel to the results from our knowledge-driven unbound protein-protein docking, we find that for 80% of the complexes where the docking failed at least one protein had Arel >1.1  in the latter case. Compared to the results by Baker and coworkers, DrugScorePPI performs slightly inferior in the case of the “unbound perturbation” dataset but superior in the case of the “unbound docking” dataset. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScorePPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. In contrast, when used as an objective function for global unbound protein-protein docking, only fair docking success rates are obtained. They improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. These docking success rates are comparable to those of other state-of-the-art protein-protein docking approaches. Finally, we devised a highly accurate criterion based on the relative solvent accessible surface area (Arel) for a priori prediction when unbound protein-protein docking of heteromeric structures with DrugScorePPI/FRODOCK will be successful.In summary, the distance-dependent knowledge-based DrugScorePPI has been originally developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes. So far, no tweaking of parameters with respect to evaluating protein-protein complex structures has been done, in contrast to optimization procedures applied to other scoring functions for protein-protein docking PPI already balances very well several different types of (short-range) interactions important for protein-protein recognition. Our analysis of the unbound docking results suggests that augmenting DrugScorePPI by additional (long-range) terms, as done in other studies Our results are remarkable as DrugScoreFile S1DSPPI_PPD_suppinfo_final_correct.pdf contains additional information to the manuscript explaining datasets, methods, and results in further details. It consists of 36 pages, 14 tables and 5 figures.The file (PDF)Click here for additional data file.


Protein conformational changes are commonly associated with the formation of protein complexes. The non-catalytic death effector domains (DEDs) mediate protein-protein interactions in a variety of cellular processes, including apoptosis, proliferation and migration, and glucose metabolism. Here, using NMR residual dipolar coupling (RDC) data, we report a conformational change in the DED of the phosphoprotein enriched in astrocytes, 15 kDa (PEA-15) protein in the complex with a mitogen-activated protein (MAP) kinase, extracellular regulated kinase 2 (ERK2), which is essential in regulating ERK2 cellular distribution and function in cell proliferation and migration. The most significant conformational change in PEA-15 happens at helices α2, α3, and α4, which also possess the highest flexibility among the six-helix bundle of the DED. This crucial conformational change is modulated by the D/E-RxDL charge-triad motif, one of the prominent structural features of DEDs, together with a number of other electrostatic and hydrogen bonding interactions on the protein surface. Charge-triad motif promotes the optimal orientation of key residues and expands the binding interface to accommodate protein-protein interactions. However, the charge-triad residues are not directly involved in the binding interface between PEA-15 and ERK2. Protein conformational changes are fundamental biochemical phenomena in protein-protein interactions, and are crucial for mediating protein functions. Death effector domain (DED) belongs to a superfamily of death structural domains that primarily promote homotypic domain-domain interactions in the tumor necrosis factor (TNF)-mediated apoptotic cascade activated by the formation of the death-inducing signaling complex (DISC) 74, one of the charge-triad residues, of the PEA-15 DED was located at the binding interface with ERK2 19-R72-D74, and a number of other electrostatic and hydrogen bonding interactions are clearly identifiable 68, R71, and P73 on PEA-15 directly interact with ERK2 through hydrogen bonding and hydrophobic effects, whereas the charge-triad residues, R72 and D74, are not involved in the binding interface, but serve to stabilize the orientation of the interface residues DED proteins are also reported to interact with various proteins in distinct biological pathways to modulate biological processes in addition to apoptosis Based on the dynamic profiles of PEA-15 in the complex with ERK2, we previously proposed that the DED of PEA-15 undergoes a profound conformational change upon interaction with ERK2 2H, 13C, and 15N-labeled full-length PEA-15 and natural abundance ERK2 were expressed as described previously 3CT) in-phase anti-phase (IPAP) technique 12E5 (Fluka), and n-hexanol (Aldrich) in water, with a C12E5/hexanol molar ratio of r = 0.85 Uniformly http://www.pymol.org) with customized python scripts. Small molecule charge-triad analogs were surveyed with a substructure search that included a carboxylate group, a guanidine group, and another carboxylate group using the WebCSD server Surface polar interactions of PDB structures, 2LS7, 4IZ5, 4IZ7, 4IZA, and 2BBR, were performed using PyMOL version 1.5 . These distances are comparable to a recent crystal structure of the L-arginine amino acid (CSD Refcode TAQBIY) aK∼2.2) and one guanidine group (paK∼12.5) with N-H…O hydrogen bonding distances of 2.577 Å and 2.798 Å  3/D10-R85-D81, appears to mediate the interaction with PLD1, although it serve to stabilize the α1-α5-α6 segment when interacting with ERK2.It is notable that PEA-15, also referred to as phosphoprotein enriched in diabetes (PED-15), binds tightly to phospholipase D (PLD) isoforms 1 and 2, an interaction that ultimately leads to type II diabetes Compared to death domain (DD) structures, DEDs are more versatile and inherently more dynamic and flexible structural domains. In the PDB, a handful of DD proteins Figure S11H-15N) for PEA-15 DED in ERK2-bound form measured from two distinct media.Singular value decomposition (SVD) analyses of RDC data ((PDF)Click here for additional data file.Figure S21H-15N) for PEA-15 DED in ERK2-bound form measured from two distinct media.Singular value decomposition (SVD) analyses of RDC data ((PDF)Click here for additional data file.


The intricate molecular details of protein-protein interactions (PPIs) are crucial for function. Therefore, measuring the same interacting protein pair again, we expect the same result. This work measured the similarity in the molecular details of interaction for the same and for homologous protein pairs between different experiments. All scores analyzed suggested that different experiments often find exceptions in the interfaces of similar PPIs: up to 22% of all comparisons revealed some differences even for sequence-identical pairs of proteins. The corresponding number for pairs of close homologs reached 68%. Conversely, the interfaces differed entirely for 12–29% of all comparisons. All these estimates were calculated after redundancy reduction. The magnitude of interface differences ranged from subtle to the extreme, as illustrated by a few examples. An extreme case was a change of the interacting domains between two observations of the same biological interaction. One reason for different interfaces was the number of copies of an interaction in the same complex: the probability of observing alternative binding modes increases with the number of copies. Even after removing the special cases with alternative hetero-interfaces to the same homomer, a substantial variability remained. Our results strongly support the surprising notion that there are many alternative solutions to make the intricate molecular details of PPIs crucial for function. The number of known protein-protein interactions (PPIs) grows rapidly, yet their molecular details remain largely unknown. Over the last years, structural biologists have addressed this issue with an increased output of structurally resolved hetero complexes. This wealth now enables statistically significant quantitative statements about interface properties. Here, we addressed the question how interfaces differ when observing the same proteinprotein interaction twice. A new dataset derived from the entire PDB was analyzed employing different definitions for the “same interaction” and a range of interface similarity measures. The hypothesis was that the interface between the same pair of proteins stays the same irrespectively of how often it is measured. Although the results mostly confirm this hypothesis, the surprising finding was how often it was not true: for many comparisons of interfaces, the molecular details of the interaction differed importantly, often without the slightest change of amino acids. In addition, no matter how much “special cases” were sieved out, the essential message remained: interfaces appear immensely plastic. Hand-selected sample structures largely support this view. In general, we complement a series of recent studies focusing either on family-family interactions or exploring other aspects of protein-protein complexes. The study of high-resolution three-dimensional (3D) structures of proteins as deposited in the PDB, the Protein Data Bank Despite this wealth of high-resolution interaction data, the set of interactions for which the exact molecular mechanisms are known remains immensely incomplete An excellent recent work reviews various types of protein interactions For example, they found that two different SCOP domain families exhibit more than one orientation of binding about 24% of the time Another challenge for the analysis of large-scale data sets have been crystal contacts and the difficulties of automated methods to correct such problems . In other words, we look at external interactions corresponding to the same pair of protein sequences and estimate how often the interfaces are different (SameProt). However, we still maintain the comparison between essentially the same proteins, because we make sure that a sequence change does not go hand-in-hand with a change of the original protein (Interolog). In a dimer-dimer comparison on this Interolog-level, corresponding interactors still had a similar 3D structure, but their sequences could be very different. , we considered a pair of residues Rx and Ry as part of the interface if it contained at least one pair of atoms closer than 0.6 nm (6 Å) or if it met all three conditions: (i) both residues changed their accessible surface area upon binding (dASA: replacing the binding partner by water), (ii) Rx had no other interaction partner within 0.6 nm (6 Å), (iii) of all residues in protein chain Y that changed their accessible surface area (ASA), Ry was the closest to Rx. The latter included interactions that fell slightly above the 0.6 nm (6 Å) threshold but should still be considered interacting by their ASA change  different types of sequence divergence. This also addressed the redundancy immanent in the PDB in the form of, e.g., overrepresented protein families. We hierarchically clustered the hetero-dimers over three levels, corresponding to increasing levels of sequence divergence  and also to embrace this alternative grouping when trying to infer biologically meaningful similarity distributions. While the following procedure successfully reduced the bias stemming from overrepresented sequences and sequence families, we deliberately left Level SameSeq clusters unchanged in the assumption that all binding modes are biologically meaningful and that eliminating this redundancy would remove more biology than noise Finally, we want to investigate the diversity of binding modes between proteins from the same family pair, but different gene pairs  and whether or not these homo-oligomers had other external interfaces with the same interaction partner as in X′/Y′ (i.e. whether X′ had interfaces with Y′1/…/Y′m or Y′ interfaces with X′1/…/X′n). Having identified the set of all those sequence- or family-identical interfaces (including X′/Y′), they were compared to X/Y. Only if X/Y< = >X′/Y′ was the best match among all alternatives, the corresponding similarity was used. Otherwise, the entire comparison was discarded  comprised 37,338 hetero-dimers. We grouped and filtered them on three different levels with decreasing sequence redundancy . For insIn order to capture different facets of interface similarity, we introduced and evaluated nine different similarity measures , or only roughly assessed the conservation of the interface location (Sphere Radius Ratio). Taking into account two similarity measures simultaneously, small differences were observed in as many as 49% of all comparisons  returned distances in Å for the entire protein rather than for the interface alone. This perspective could capture conformational changes outside the binding regions that would be missed by other measures. L_rms found 64–69% of all “ligands”  not to exhibit conformational changes and to bind to the larger proteins at the same positions (RMSD <1.0 Å). Conversely, 10–14% of the interfaces differed very substantially between alternative experiments (>9.0 Å). Considering Face Position Similarity and L_rms at the same time suggested that about 1% of all comparisons did not differ by the first but differed substantially (>9 Å) by L_rms  and largely conserved in 80%  by the Face Position Similarity measure rose from 12% to 17%. In other words, about one interface pair in six differed substantially.Two hetero-dimers can differ by minor sequence variations and still correspond to the same external interaction. Comparing these slightly different pairs , interface conservation dropped significantly by both measures . NeverthWe hypothesized that families of interologs without alternative binding might have similar functions and that the same could be true for families with extreme binding diversity. Unfortunately, only for 18 Level Interolog clusters, interfaces were always maintained (Face Position Similarity >0.9 at 100%), while only 17 others always used very different interfaces (Face Position Similarity <0.5 at 100%). These numbers were too small to permit statistically significant analyses on the functional differences between those interactions. We still provided a list of those cases in Section S8 in With a special filter, we might remove all alternative binding of a protein to the same homomer from D-SameSeq to D-SameFam . ObviousOur finding that most interactions form identically when repeating the experiment might not be surprising. The observation that many interactions differed substantially, in contrast, appears much more counter-intuitive. Readers might attribute the difference to some mistake in the way we measure similarity or build our data sets. We addressed these concerns by expanding our analysis in many directions. On top, we analyzed ten case studies in more detail. Three are discussed in detail in the following , the othras in complex with the nucleotide exchange factor SOS . It needs the interaction with SOS in order to release GDP again after conversion. To this end, SOS provides a binding site that is highly specific for the ras-GDP complex. However, despite this specificity to ras-GDP, also SOS and ras-GTP can form a complex Our first example is the enzyme Related work mostly differentiates alternative binding modes by clustering approaches. This implicitly suggests the assumption that the system could fall into alternative minima. Our results seem to support this assumption: interface similarities follow an extreme distribution, i.e. either they are very similar or very dissimilar . When diThe dimeric interactions between cyclins and protein kinases (interface copy number 1) reveal a largely conserved binding cloud . AnotherFurther extending the interface variability of cyclins and protein kinases is the recently discovered interface between cyclin D1 and protein kinase 4 , blue. IHomomers can bind to another protein through alternative interfaces  is evident from the structure within seconds and homomerization might be essential . Filtering alternative binding to the same structural homomer , the onlWe had to exclude other interesting examples to limit the length of this work. These included the D-SameProt comparisons of two nitrogenase complexes  exhibiting alternating quaternary states While our examples confirmed the overall trends, they also suggested that the averages above reveal only the tip of the iceberg: if there is one reasonable measure for interface similarity by which two experimental solutions differ, then this observation suggests variability. To complicate matters further, we observe “rotational interfaces” (F1-ATPase) and see that ligand binding can have great impact on interface specificity (ras-SOS). On another note, our tests with alternative data set parameters, e.g. changing structural resolution, revealed that the variability that we see is not explained by experimental or procedural inaccuracies. Thus, the PDB structures clearly tell a tale of unexpected variability and dynamics of biological interactions.Empirically, we found several reasons for the same two proteins to have different interfaces (D-SameSeq). The simplest was merely technical: some experimental findings may not have been completely correct. We reduced this effect by excluding complexes with resolutions >2.5 Å, but even structures at 1.2 Å can contain errors There were various reasons why variability in binding was higher between sequentially modified proteins than for identical proteins. The modifications that preserved the original protein (D-SameProt) were usually point mutations . Others included protein tags at the N- or C-terminus (e.g. to facilitate protein purification), post-translational modifications (protein cleavage) and alternative splicing. For interologs (D-Interolog), finally, there was also evolutionary driven sequence divergence. As described before, however, the mere presence of insertions or deletions was not enough for low interface similarity: we reduced structures to common residues before comparing them. Thus, the increase in variability was actually the result of changes in the common parts of two structures.Using similar measures as we did, other groups hetero-multimer could be a requirement for the interaction with another protein. Studying which interactions remain after disabling the potentially highly complex hetero-multimer is much beyond the currently available data. Finally, also the original publication of a complex usually describes different interfaces to the same homomer as separate interfaces.As mentioned before, alternative interfaces might be due to the intrinsic capability of proteins to bind at different positions. This is often encountered among homo-oligomers Our results raise the question whether the molecular details of protein-protein interactions (PPIs) are crucial for function. Protein crystallography captures static views on those molecular details along with some information about the dynamic nature of PPIs. If the details always had to be the same to guarantee function, different experiments would identify the same interfaces. We applied many reasonable ways of measuring interface similarity in order to analyze the consistency of the molecular details of protein-protein interactions between different experiments. For sequence-identical pairs of proteins, i.e. the same biological interaction, most interfaces were almost completely conserved by all measures. However, all measures also revealed an unexpected variety. Depending on how much detail we required to be similar in order to consider two experiments to yield the same results, we found 11–37% of all observations to have significant differences, and up to 10% to be completely different. One important result was that this was a significant fraction of the difference observed between homologous PPIs. Put differently, over a third of the differences in the interactions between pairs of homologous proteins are also observed between identical proteins. These numbers may challenge the notion that the maintenance of the molecular details is crucial for function. At least, our results suggest that there appear to be many alternative solutions to maintain or actually enable the intricate molecular details: change seems an extremely frequent exception for protein-protein interfaces.Dataset S1In this file, we list all pairs of hetero-dimers leading to a Face Position Similarity below 0.9. Additional columns provide the copy numbers, L_rms, an indicator for the best homomer match and the type of sequence divergence.(ZIP)Click here for additional data file.Text S1In this text, we give details about various methods and analyses found in the main manuscript and provide additional results.(PDF)Click here for additional data file.