Negatome is derived by manual curation of literature and by analyzing three-dimensional structures of protein complexes. The main methodological innovation in Negatome 2.0 is the utilization of an advanced text mining procedure to guide the manual annotation process. Potential non-interactions were identified by a modified version of Excerbt, a text mining tool based on semantic sentence analysis. Manual verification shows that nearly a half of the text mining results with the highest confidence values correspond to NIP pairs. Compared to the first version the contents of the database have grown by over 300%.Knowledge about non-interacting proteins (NIPs) is important for training the algorithms to predict protein–protein interactions (PPIs) and for assessing the false positive rates of PPI detection efforts. We present the second version of Negatome, a database of proteins and protein domains that are unlikely to engage in physical interactions (available online at  Extensive protein interaction maps have been derived for a number of model organisms by modern high-throughput techniques such as yeast two-hybrid assay. While being indispensable tools for systems biology research, these maps are still far from complete and the number of experimentally determined protein–protein interactions (PPIs) continues to grow rapidly, with no saturation in sight. For example, as of this writing the IntAct database  lists 48Knowledge about non-interacting proteins (NIPs) is as important for training various PPI prediction algorithms as gold standard datasets of positive interactions. It is also indispensable for assessing the false positive rates of PPI detection efforts. However, an experimental method to detect NIPs at proteomic scale remains yet to be invented. A commonly used approach to predict negative interaction data by choosing pairs of proteins that are localized to different cellular compartments has been shown to be biased in terms of the function and amino acid composition of the selected proteins . Alternahttp://phylogenomics.blogspot.de/2009/11/worst-new-omics-word-award-negatome.html), although we are not exactly sure why we deserved this honor.In 2009, we made available the first version of a database of mammalian NIP pairs that we call Negatome  created http://www.nlm.nih.gov/bsd/medline_lang_distr.html). The number of 3D protein structures available in the PDB database increased from 62 112 to 93 043 (mid-2013). Here, we present Negatome 2.0, an updated database of high-quality NIP pairs that has been derived by combining text mining and literature curation with protein structure analyses  and the emergence of systems for negation detection in molecular events such as Negmole .Length of the sentence (longer sentences are more likely to be wrongly interpreted).Type of the relation .The word indicating the negation (might be tagged wrongly if not a common negation word is used).Whether the agent and the theme are the same entity, which increases likelihood that the text mining system made a mistake. However, in this case the sentence is kept because it is still possible that it contains another non-interaction.To increase the precision of the analysis PAS were restricted to a narrower set of verbs specifically referring to interactions or binding . This reduced the set of candidate sentences to 2134. In addition, a confidence score was developed based on simple features of the sentence and the PAS:A sample of the results was again evaluated manually. The filter increased the precision of the approach, with more than 50% of the non-interactions classified correctly. The confidence score, in spite of its simplicity, proved to be very informative about the annotation quality. Among the 20 top scoring sentences the precision of text mining was 95%, while for the median 20 and the bottom 20 sentences it was 45% and 15%, respectively.We found that the main remaining source of errors was the ambiguity of protein symbols and compounds. Since the same names are often used for both kinds of entities, a large number of non-interactions between two compounds as well as between a compound and a protein were detected as protein–protein non-interactions. Attempts to overcome this problem by applying disambiguation algorithms and heuristics at the sentence level proved unsuccessful, probably because proteins and compounds act in a similar fashion and are hence described by similar sentences. We therefore decided to leave the compounds in the dataset and annotate them manually along with the protein–protein non-interactions.In addition to the precision analysis described above we also examined the recall of our method, i.e. how many of the non-interactions described in the literature were found by Excerbt, by investigating how well Negatome 1.0 can be reproduced by text mining. Only 1 of 40 randomly chosen NIP pairs from Negatome 1.0 was also identified by Excerbt. Around 80% of the cases are currently not reachable for text mining as they are described in the main body of full-text articles not available through PubMed and PMC, in figures and in tables. Five non-interactions from the test sample were not found because Excerbt assigns semantic roles with respect to every verb within a sentence. However, biological events can also be described without using a verb, for example, by using ellipsis, subordinate clauses where the verb is omitted since it can be inferred from the context , or by using nominalizations (‘the non-interaction of X and Y’). Furthermore, Excerbt currently does not resolve words, which refer to entities mentioned elsewhere in the text . In one occasion a term not covered by the Excerbt ontology was used. Only 1 of the 40 sample non-interactions was not found due to an error of Senna. In a further case Excerbt correctly omitted a non-interaction described in the Negatome 1.0, which was found erroneous upon careful inspection.Since the evaluation described above was dominated by a large amount of non-interactions inaccessible to text mining we analyzed a second test sample of 20 non-interactions from Negatome 1.0 described in the sentences reachable to Excerbt. There were only five non-interactions in this dataset, which could have been potentially found by Excerbt, and three of them were indeed identified while two were missed. One further case was a misclassification of the Negatome 1.0. The remaining 14 cases are not currently addressable by Excerbt as they involve ellipsis (2 cases), anaphora (4 cases), nominalization (2 cases), nested events (1 case), information spread over multiple sentences (1 case) and four protein names not covered by the Excerbt ontology.Our limited evaluation of the text mining results indicates that there is still considerable potential for increasing the recall (amount of non-interactions found). This can be accomplished by incorporating more sophisticated linguistic analyses capable of resolving more complex sentences. However, the precision (percentage of correctly identified non-interactions in the set of delivered non-interactions) was sufficient to allow for a significant speed up of the manual annotation process.The results obtained by text mining were manually verified by examining the original publications, analogously to the annotation of PPIs and protein complexes previously performed by our group ,22. To fExcerbt proposed 2134 protein–protein non-interactions, which were manually annotated. The annotation yielded 895 protein–protein and 119 protein–compound non-interactions. If the annotators came across NIPs while looking for additional information in the articles proposed by Excerbt, e.g. about the used methodology, these NIPs were also annotated. Such additional annotations accounted for ∼10% of the 895 NIPs. As seen in NIP pairs were derived from 3D structures of PDB biological units  as descrde novo predictions that have not yet been described in the literature.Negatome 2.0 contains all 809 structure-based non-interacting pairs from Negatome 1.0, which is expected since we applied the same method to the current PDB database. As for the data generated by manual annotation the overlap between Negatome 1.0 and Negatome 2.0 was limited to only 15 protein pairs due to entirely different approaches taken. In Negatome 1.0, papers containing NIPs were identified based on a simple keyword search in the PubMed database. These papers were then thoroughly studied by biological curators and most of the non-interactions were found in figures and tables. In contrast, in Negatome 2.0, text mining already provided candidate sentences, which were then evaluated by the annotators. Thus, in Negatome 2.0, the focus shifted away from the time-consuming analysis of figures and tables toward a more high-throughput automated approach. The overlap between structure-based and manually annotated NIP pairs is also very low, only 36 pairs. This can be explained by the fact that the structure-based dataset contains Analogously to Negatome 1.0 we provide more stringent subsets of our datasets obtained by removing those NIP pairs that are reported as interacting in the IntAct database. As seen in International Research Training Group ‘Regulation and Evolution of Cellular Systems’ [GRK 1563]; Funded through the Joint Technology Platform within the Helmholtz Alliance for Systems Biology and the Federal Ministry of Education, Science, Research and Technology . Funding for open access charge: Institute for Bioinformatics and Systems Biology/MIPS, HMGU – German Research Center for Environmental Health.DFG Conflict of interest statement. None declared.


Phylogenetic profiling is a technique of scoring co-occurrence between a protein family and some other trait, usually another protein family, across a set of taxonomic groups. In spite of several refinements in recent years, the technique still invites significant improvement. To be its most effective, a phylogenetic profiling algorithm must be able to examine co-occurrences among protein families whose boundaries are uncertain within large homologous protein superfamilies.Partial Phylogenetic Profiling (PPP) is an iterative algorithm that scores a given taxonomic profile against the taxonomic distribution of families for all proteins in a genome. The method works through optimizing the boundary of each protein family, rather than by relying on prebuilt protein families or fixed sequence similarity thresholds. Double Partial Phylogenetic Profiling (DPPP) is a related procedure that begins with a single sequence and searches for optimal granularities for its surrounding protein family in order to generate the best query profiles for PPP. We present ProPhylo, a high-performance software package for phylogenetic profiling studies through creating individually optimized protein family boundaries. ProPhylo provides precomputed databases for immediate use and tools for manipulating the taxonomic profiles used as queries.ftp://ftp.jcvi.org/pub/data/ppp/.ProPhylo results show universal markers of methanogenesis, a new DNA phosphorothioation-dependent restriction enzyme, and efficacy in guiding protein family construction. The software and the associated databases are freely available under the open source Perl Artistic License from  For a given protein, the evolutionary history of its family is reflected in its present day taxonomic distribution [Phylogenetic profiling is an established and widely known technique for inferring biological roles for unknown proteins . The tecribution . TherefoA phylogenetic profile in its simplest form is a series of binary characters , 1's or Nonetheless, phylogenetic profiling can be very successful in identifying cohorts of protein families likely to cooperate in the same cellular processes, and for making strong predictions of biological roles or molecular functions on unknown proteins when combined with other types of evidence. The technique has been used for various genetic and phenotypic traits that can be coded as binary characters . Some exThe success of phylogenetic profiling is affected by considerations in four components of the method, presented here in the order in which they are applied. The first is the selection of target species. Although the effect of species selection in the success of phylogenetic profile methods is largely unexplored, it is now generally believed that the prediction accuracy increases with increasing numbers of species ,26 whileIt appears that the most leverage for improvement of the method is found in the second of these: phylogenetic profiling works best when the working definition for each protein family comes as close as possible to the full set of homologs among which some specific function is conserved. Typically, however, molecular function varies within protein superfamilies, and a particular function is shared only within a narrow subset. Automated protein clustering methods or fixed sequence similarity cutoffs used for sequence homology searches cannot reliably achieve granularities that represent protein family sizes optimally. This shortfall can limit the effectiveness of available profiling methods, especially when they need to evaluate subgroups found within larger superfamilies.We describe here ProPhylo, a distributable software framework for performing phylogenetic profiling studies. It includes the first distributable tool for performing Partial Phylogenetic Profiling (PPP), an algorithm that overcomes such limitations by optimizing protein family size simultaneously with scoring co-occurrence. Since first introducing the principle , we haveftp://ftp.jcvi.org/pub/data/ppp/.ProPhylo is a framework written in Perl and designed to perform high-performance phylogenetic profile searches using arbitrary profiles on a desktop computer. A version is also available to run on computer clusters (available on request). The software comes with a database of pre-built BLAST searches of ~1500 selected prokaryotic reference genomes for immediate phylogenetic profile search. Users additionally have the option to create their own databases. The software comes with tools for the creation and manipulation of phylogenetic profiles. At present, the software is configured to run profile searches using our own PPP algorithm, but it is designed in such a way that any scoring function can be used as a plug-in. We have plans to include several other scoring functions with future versions of the software. The software and the associated databases are freely available under the open source Perl Artistic License from ftp://ftp.jcvi.org/pub/data/ppp/. The current version provides all vs. all BLAST result data for ~1500 complete prokaryotic genomes. These genomes were manually selected to remove excessive strain-level redundancy, while keeping the maximum possible taxonomic breadth. The peptide files for these genomes were downloaded from NCBI and an all vs. all search using NCBI BLAST was performed. Results were processed using a script supplied with ProPhylo to produce files suitable for reading by ProPhylo. These parsed BLAST result files are stored separately for each genome. BLAST results for a user-selected target genome, therefore, can be downloaded separately from other genomes, keeping the download size small.For a phylogenetic profile search, ProPhylo requires two databases: the cross-genome all vs. all BLAST search results (or a slice of this data set for at least one target genome), and the NCBI taxonomy database. The pre-formatted, ready-to-use databases can be downloaded from the ProPhylo distribution site, http://www.sqlite.org/) database of the NCBI taxonomic tree can be downloaded from the ProPhylo software distribution site. Alternatively, scripts supplied with the package can automatically download the NCBI taxonomic tree from the NCBI website and create the necessary SQLite database on the user's desktop machine.ProPhylo depends on the NCBI taxonomy tree for extraction of phylogenetic information. Thus, a local installation of the NCBI taxonomy tree is essential to run the software. A pre-formatted SQLite  algorithm . The algP =  ∈ {0,1}n is defined over a collection of taxonomic identifiers T = {1t,...,nt}, with negative examples being T0 = {i:Pi = t0} and positive examples being T1 = {i:Pi = t1}. We iterate through each gene in a genome. For each gene (G) we get its BLAST result (R), which is a collection of tuples  for j = 1,...,m, where j gis a GenBank identifier (GI) and j sis a measure of significance, where only the most significant match from each taxon is considered. For each such R, we iterate through the GIs sorted in decreasing order of significance. For each j gwe get its taxonomic ID, ju∈T While iterating over j = 1,...,m the algorithm calculates,A profile j h= |{u1,...,ju}∩T1|, i.e., the number of profile's positive genome seen up to index j. The value j pis, therefore, simply the probability, according to the binomial distribution, of finding at least j hhits  after j distinct taxa have been encountered in the BLAST hits list. The parameter p is the prior probability either provided by the user or calculated as the relative frequency of positive taxa in the input profile: p = |T1|/(|T0|+|T1|)where, G is then reported as p* = minjp, that is, the most significant probability computed for any depth in the BLAST hists list. For a genome, the results are presented as a sorted order of scores for each gene along with the values of p*, and its corresponding j and jh.The score of a particular gene A variation of the algorithm is double partial phylogenetic profiling (DPPP). This allows iterative searching starting with only a single given sequence. The algorithm iteratively goes down the BLAST hit list of a given protein, creating a new version of the taxonomic profile each time it finds a new taxon. These sets of query profiles are then searched using the PPP algorithm described above. The significant hits for each search are then collected and a sorted list of hits is generated as output. DPPP automates the generation of the optimized PPP data sets needed to guide systems biology discovery and protein family construction. However, presently we do not provide any post processing tools to automate building protein families according to DPPP results.To accelerate the speed of the search we developed several heuristics, such as pre-calculation of the maximum binominal probability given a particular profile. A read-ahead mechanism determines whether it is possible to improve on the existing best score, given the remainder of the hit list, and stops the search if there is no chance of such improvement.p; increasing this value increases the penalty for mismatches to the query profile and thus helps to find only-if relationships even for proteins that are relatively rare.The sensitivity of the algorithm can also be modulated at the user's discretion. One such mechanism is collapsing terminal branches of the taxonomic tree. The software fully understands the NCBI taxonomic tree; a search can be performed using higher taxonomic levels such as genus or family rather than the strain-level identifier from NCBI taxonomy tree. The algorithm will walk up the taxonomic tree to the level specified by the user and count that node only once for scoring. Another user option is to override the default calculation of ProPhylo is written in the Perl computer language and, therefore, theoretically can run on any platform where Perl is available, however at present we support only MacOSX and Linux officially. The software runs searches in parallel taking full advantage of multi-core processors frequently available in modern desktops. The software also comes in a flavor that runs on compute clusters (available on request).In addition to a collection of scripts for various tasks related to phylogenetic profile comparison, ProPhylo contains an object-oriented Perl library. Components of the search mechanism, such as BLAST results, phylogenetic profiles, algorithms for comparison of profiles, are represented as software classes and thus are extensible and readily modifiable. One advantage of such a design is apparent in the case of the implementation of the algorithm modules, with each algorithm being a plug-in. Any new algorithm, currently not supported by ProPhylo, can be easily supplied by inheriting from an interface and providing the concrete implementation of a few virtual methods.The user can download the software and the required databases from the distribution site. Detailed documentation is provided with the package for correct installation of the package and the database. The user also has the option to either create the databases on local machine or to use the pre-made databases available on the FTP site.p (see detailed discussion below), and/or specify a taxonomic level at which to filter the profile. Then, given a target genome to search and the query profile, the software iterates through every protein in the target genome, finding that protein's optimal correlation to the query profile. It orders its results by the statistical significance of each individually optimized score, and reports the numbers of matches and mismatches to the profile for each protein where its optimum was found  rather than 0 (non-methanogen).In the TIGRFAMs collection of protein family definitions , we haveth best hit by PPP to the methanogenesis profile.In the PPP results, the top-scoring twenty-nine proteins Table  Figure 2In Table PPP performs an excellent enrichment for proteins directly involved in methanogenesis in the set of proteins earning top score. Thirty-four of the top forty genes returned by PPP are members of previously identified methanogenesis marker families built in TIGRFAMs and identified within Genome Properties . These and best hit by PPP.The core computational function in PPP picks a score cutoff for a sorted list of BLAST hits in order to optimize a working definition of a protein family, based on which the family's match to the query profile can assessed. BLAST, however, suffers certain limitations. First, there is a limit in sensitivity. Second, in any protein family, both heterogeneity in the rates of evolution in different lineages and regions of unusual sequence composition can interfere with the ability of the evolutionarily most closely related proteins to outscore more distantly related proteins. Limitations of BLAST explain the absence of the MtrF protein, tetrahydromethanopterin S-methyltransferase F subunit (GI:294496428) from the list of proteins in Table ppp_hmmer.pl for analyzing HMMER3 search results files. It uses the compute engine of PPP, reading GI numbers ordered by HMM score rather than BLAST score. It finds and reports an optimal depth in an HMM hits list for matching a query profile, which aids in setting proper score cutoffs. This utility should be useful in workflows that begin with PPP search results and aim to develop new HMM-based protein family identification rules.HMMs based on protein multiple sequence alignments regularly outperform BLAST, both in sensitivity and in the discrimination of protein family members from non-members. An HMM (TIGRFAMs model TIGR02507) made from the alignment of just three MtrF sequences, for example, finds the complete MtrF family without a false positive. Constructing tools such as model TIGR02507 that cleanly identify molecular markers in large data sets often is a goal when phylogenetic profiling is performed. The ProPhylo software package, therefore, provides the utility e.g. absolutely restricted) to another can outscore the background if an appropriate scoring system is used. PPP scoring depends on the binomial distribution; changing its parameter p will determine which scores better, 25 "YES" genomes out of 75, or 7 out of 7. A command line switch that forces use of a different probability p from what is calculated naively from the query profile can steer a PPP search toward better detection of certain types of relationships. Using a larger value of p increases the effective penalty for each mismatch, and makes it easier to detect even rare proteins that approximate an "only if" relationship to the query profile. Figure p lowers the penalty and steers PPP towards the identification of families absolutely including the set described by the profile, but also including genes from other genomes.)PPP results show candidate protein families whose correlations to the query profile outscore the background from uninteresting correlations, as from similar taxonomy  or from similar environmental pressures . A rare protein meaningfully correlated (Streptomyces lividans [p = 0.048). Artificially raising p approximately 5-fold to 0.25 and running PPP on the genome of Pseudoalteromonas haloplanktis TAC125 yields the DptFGH genes as the top hits after the components of the DPT system itself. This is despite the DptFGH genes being present in roughly 1/4 of the genomes encoding the DPT system. Additional, yet-uncharacterized DPT-dependent systems should be expected having distinct subset distributions, and some of these may be relatively rare. PPP can be used with a raised value of p to enrich its results for protein families that are subsets of the DPT profile and likely candidates for such systems.The DNA phosphorothioation (DPT) system presents a challenge to phylogenetic profiling that can be addressed by application of this technique. The DPT system catalyzes a post-replication sulfur modification of the phosphate backbone of DNA, first identified in lividans . The prolividans . There iSerratia odorifera 4Rx13, using the DPT profile with p set to 0.4 yields, in addition to the DPT system components and the DptFGH genes, the protein GI:270263649 despite its occurring in only seven genomes , while TIGR04095 describes a subfamily of PF04851, "type III restriction enzyme res subunit," (n = 26), apparently the second discovery of a DPT-dependent restriction enzyme. Both of these families include members located adjacent to DPT system genes.A PPP search of In the TIGRFAMs collection of protein families, models that describe equivalogs (proteins conserved in function since their last common ancestor) can vary in sizes, ranging from universal (100%) to less than 1% in coverage of prokaryotic reference genomes. When building a protein family to represent one component of a new and previously unknown complex system, the proper protein family size cannot be known in advance. DPPP addresses this limitation by taking a query protein and systematically varying the numbers of its closest matches by BLAST as the basis for constructing query profiles for use in PPP Figure . If a prShewanella sp. MR-4, was selected as an example. DPPP searched a range of depths in the hits list to the query protein, from 10 genomes to 930 and the best score for any protein other than itself occurred when the BLAST score cutoff gave a query profile with 200 "YES" genomes of the 1466 in ProPhylo , significant signal-to-noise can be realized. For instance, the high information content of the DNA phosphorothiolation profile enables the identification of real relationships even when the overlap between profiles is very small, by using ProPhylo's ability to modulate the expected probability factor, ProPhylo is a software package for high-performance phylogenetic profile searches on desktop computers. The package implements Partial Phylogenetic Profiling (PPP) and Double Partial Phylogenetic Profiling (DPP), algorithms to facilitate protein family construction using phylogenetic profiles. The computational advances represented by the PPP and DPPP algorithms have the potential to take phylogenetic profiling beyond the limited correlation of pre-formed protein families, and to remove much of the insensitivity of previous methods due to the vagaries of protein cluster calculations. With ProPhylo, users will be able to generate and modify custom profiles, iterate and refine hypotheses, and tune parameters to perform real-time profiling research in a manner distinctly more powerful than is currently available.ftp://ftp.jcvi.org/pub/data/ppp/; Operating system(s): Platform independent; Programming language: Perl; Licence: Open Source Perl Artistic License.Project name: ProPhylo; Project home page: MKB created the ProPhylo software and the associated databases. JS implemented prototype versions of PPP algorithms. DH performed the biological investigations. All authors made contributions in improving the algorithms, writing the manuscript and read and approved the final manuscript.Supplementary Table 1. Table of the utility scripts and their uses, supplied with ProPhylo software.Click here for file


One of key issues in the post-genomic era is to assign functions to uncharacterized proteins. Since proteins seldom act alone; rather, they must interact with other biomolecular units to execute their functions. Thus, the functions of unknown proteins may be discovered through studying their interactions with proteins having known functions. Although many approaches have been developed for this purpose, one of main limitations in most of these methods is that the dependence among functional terms has not been taken into account.We developed a new network-based protein function prediction method which combines the likelihood scores of local classifiers with a relaxation labelling technique. The framework can incorporate the inter-relationship among functional labels into the function prediction procedure and allow us to efficiently discover relevant non-local dependence. We evaluated the performance of the new method with one other representative network-based function prediction method using E. coli protein functional association networks.Our results showed that the new method has better prediction performance than the previous method. The better predictive power of our method gives new insights about the importance of the dependence between functional terms in protein functional prediction. E. coli currently have experimental evidence indicative of a biological role [Currently the sequencing of many genomes has brought to light the discovery of thousands of possible open reading frames which are all potentially transcribed and translated into gene products. For many proteins, little is known beyond their sequences, and for the typical proteome, between one-third and one-half of its proteins remains uncharacterized. For example, yet despite being the most highly studied model bacterium, a recent comprehensive community annotation effort for the fully sequenced reference K-12 laboratory strains indicated that only half (~54%) of the protein-coding gene products of It is widely known that proteins seldom act alone; rather, they must interact with other biomolecular units to execute their function. Protein-protein interactions operate at almost every level of cellular functions . Thus, iBasically, there are two types of automatic function prediction paradigms by analyzing the entire set of functional associations recorded between gene products in the context of a network. The first one is to use the set of associations in the network to propagate the functional labels from well characterized protein nodes to those with limited or no annotations. Many functional prediction studies following this paradigm are often focused first on sub-grouping or clustering the interaction networks into functional modules based on the pattern or distribution of protein nodes and interaction links which can be highly suggestive of shared functions. These modules may be distinct or overlapping. Any unannotated gene products in a given module can be subsequently assigned the most common functional annotation(s) associated with its interacting partners or neighbors . This 'uAlternate computational methods have been devised to automatically assign functional labels, such as gene ontology (GO) terms , to the χ2 test. The prediction accuracy is comparable to another protein function classifier based on the support vector machines with a diffusion kernel. Hu et al. [Supervised computational methods for automatically assigning functional terms to previously uncharacterized genes based on the categorical properties of their annotated interaction partners have been widely developed -10. SchwThese methods can be roughly categorized as either simple, local, guilt-by-association techniques or global, network optimization procedures. While both approaches often achieve similar performance , there aIn this study, we proposed a relaxation labelling method for network-based protein function prediction. We explored to incorporate the inter-relationship among functional labels into the function predictions. The relaxation labelling is employed to iteratively update each node's likelihood for a given function by taking its non-local dependencies into account. The advantages of the method are demonstrated by a recently generated Ecoli protein functional association network .O is the set {o1, ..., on} of n proteins to be labelled. L is the set {l1, ..., lm} of m possible functional labels (GO terms) for the proteins. Let Pi(lk) be the probability that the label lk is the correct label for protein oi and each probability satisfies 0 ≤ Pi(lk) ≤ 1 where Pi(lk) = 0 implies that label lk is impossible for protein oi and Pi(lk) = 1 implies that this labelling is certain.In a functional association network, let us assume that The relaxation labeling process includes three steps :Pi(lk)(0) of protein oi for function lk . This can be done by assigning an initial and perhaps arbitrary probability for each label and protein. In this study, we used the weighted voting approach as proposed by McDemott et al. [oi and calculated the probability Pi(lk) (0) of protein oi for function lk given by:1) Initialize the probability t et al. . We consaij is the association score between protein oi and oj. N is the set of proteins neighbouring oi. We introduce a discrete value function θ, which takes value 1 if protein oj has function lk and 0 otherwise. If protein oi has no neighbours with function lk, we estimate Pi(lk)(0) based on the function's prior in GO gold standard we constructed.where Pi(lk) of protein oi for function lk . This is done by considering the probabilities of labels for neighbouring proteins. Let us assume that we have changed all probabilities up to some step, T, and we now seek an updated probability for the next step T + 1. We can estimate the change in confidence of Pi (lk) by:2) Update the probability Cij  is the correlation between protein functional labels defined as the conditional probability that protein oi has a label lk given that protein oj has a label ll, i.e. Cij  = P(lk|ll). We estimate Cij based on semantic similarity between functional terms in GO (discussed below). N is the set of proteins neighboring oi, and ωij is a factor that weights the labellings of these neighbours, defined in such a way that Pi(lk) in generation T + 1 can be computed from the values from generation T usingwhere 3) The process of step 2) is repeated until the labelling method converges or stabilises. This occurs when there is little or no change between successive sets of probability values.We used an integrated functional association network which merged the physical interaction network and predicted genome context association network . The resThe most widely adopted gold standard system is the Gene Ontology (GO) database , which uWhile expert curators manually assign terms based on published experimental evidence, most terms are electronically inferred based on sequence similarity to other well-studied gene products or other criteria. Each term is assigned an evidence code stating how the annotation is supported , which aWe created the gold standard for the function predictions based on Gene Ontology . In ordeDifferent methods have been developed to determine the similarity of two GO terms based on their distances to the closest common ancestor term and/or the annotation statistics of their common ancestor terms -18. Therli and lk, which are represented as two directed acyclic graphs (DAGs): li (lk) and all of its ancestor terms in the GO graph, and li and lk is defined asAssume that there are two GO terms: q associated to term li . Here where re < 1 measures the contribution of edge e ∈ q'. SV(lk) and SV(li) and where 0 <For the 32 GO biological process terms we discussed above, we used the software G-SESAME to calculate Wang's semantic similarity between any pair of GOs .Classifier performance is evaluated by five-fold cross-validation. Briefly speaking, all labelled proteins are arbitrarily split into five representative sub-groups. Each sub-group is kept to have approximately equal number of labelled proteins in each functional GO term. The computational algorithm is then trained on n-1 of the groups, followed by testing on the remaining holdout group. This procedure is repeated 5 times, each time using a different sub-group of gene products as the test set. We quantify the performance of each GO term based on the area-under-the-curve (AUC) in Receiver Operating Characteristic (ROC) curves drawn by plotting sensitivity versus specificity at different thresholds. Each threshold yields one pair of sensitivity and specificity values and, thus, one point on the curve. The single statistic (AUC) provides a quantitative indication of how well a particular functional classifier performs.To evaluate the overall prediction performance of our algorithm, we plot the precision against recall at different thresholds, which can be calculated as follows: Given a threshold, if a protein has predicted probability larger than it in a function category, we assign the function to the protein, so we computemi is the number of predicted annotations for protein i, ni is the number of known annotations for protein i, ki is the number of correctly predicted annotations for protein i.where Based on the pairwise semantic similarity among the 32 GO terms calculated using formula (4), we made a heat map representing this GO term similarity matrix, which is shown in Figure As we can see from the plot, some highly correlated GO terms  are grouped into a single block.To compare the performance of the weighted voting and relaxation labelling classifiers, we performed five-fold cross-validation on the built gold standard data. The comparisons of the AUC scores of the 32 GO terms generated by the two classifiers are shown in Figure In this study, we used the GO structure-based method to quantify the similarity between GO terms and demonstrated its advantage by incorporating this information in E. Coli protein function prediction. We are exploring to compare the performance of this method with information-content-based approaches to measure GO similarity in protein function prediction. In the future, we will apply the new method to protein functional association networks generated in other species and compare its performance with other representative methods in detail.In this study, we have proposed a new method for efficiently exploring relevant non-local dependencies of functional labels in the protein function prediction task. Our approach is able to take into account the correlations among GO terms. It includes two major steps: first, initial label assignment is made by a local classifier; second, the dependencies among GO labels are taken into accounted and propagated using an iterative relaxation procedure. The potential trade-off is that additional error or uncertainty may have occasionally been introduced by assuming functional similarity among more loosely connected proteins. We evaluated the performance of the new method with one other representative network-based function prediction method using E. coli protein functional association networks. Our results showed that the new method has better prediction performance than the previous method. The better predictive power of our method gives further insight about the importance of the dependence between functional terms in protein functional prediction.The authors declare that they have no competing interests.PH designed and implemented the algorithms. PH drafted the manuscript. HJ and AE provided helpful suggestions on the algorithm development. AE and HJ monitored the study. All authors read and approved the final manuscript.


Aedes aegypti. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host – dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies.The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito,  Aedes aegyptiInfection with one of the four serotypes of dengue virus can result in dengue fever and the life-threatening sequalae, dengue hemorrhagic fever and dengue shock syndrome Flaviviridae family, which includes the West Nile and Yellow fever viruses Dengue belongs to the Aedes albopictus cell line to identify protein interactions involving 19 mosquito proteins and four dengue proteins including capsid, envelope protein, NS2A, and NS4B A more detailed picture of how dengue virus interacts with its hosts has begun to emerge from studies aimed at identifying interactions between dengue proteins and host proteins. Several studies have focused on specific genes or pathways that relate to dengue pathogenesis. For example, an interaction between dengue capsid and human Death-domain Associated protein (DAXX) was identified and shown to increase Fas-mediated and CD137-meditated apoptosis of liver cells Despite the valuable insights that can be derived from protein interactions, the available data suggest that the dengue-host interactome is still noisy and incomplete. Protein interaction data from high throughput screens and even from literature-curated databases can include many false positive interactions that have no biological relevance To identify interactions with dengue proteins we subcloned open reading frames (ORFs) from dengue virus serotype 2 (strain 16681) into the yeast two-hybrid bait vector for expression of the proteins with an N-terminal LexA DNA binding domain (DBD). We constructed a total of 14 baits . These iAedes aegypti using mRNA pooled from ten stages of development ranging from egg to adult . We used a library mating assay to screen the mosquito library with each of 14 individual dengue bait proteins . To verify two-hybrid interactions, we subcloned the mosquito cDNAs from initial positives into new AD vectors and retested for interaction with the original dengue bait proteins. At the same time we tested for interactions with baits unrelated to the dengue baits to identify proteins that may nonspecifically interact with random proteins. In all we identified 102 interactions that were reproducible and specific by this definition , myosin v (AAEL009357), long form paramyosin (AAEL010975), and a hypothetical protein (AAEL014104) . AlthougWe identified 16 capsid-interacting mosquito proteins. Three of these were identified using the anchored capsid bait that contained the C-terminal membrane-spanning domain, while the others were identified using the mature capsid bait. We tested all of the capsid-interacting proteins to see if they were capable of interacting with each capsid bait protein and found that all but two proteins were capable of interacting with both baits . The twoWhile dengue-human protein interactions have been explored more extensively than dengue-mosquito protein interactions, the lack of overlap among validated interactions from different screens suggests that the dengue-human interactome is still incomplete. To complement the dengue-mosquito interactome and other dengue-human studies, we conducted two-hybrid screens using the 14 dengue protein baits  and a cDOur library screens identified four putative conserved interactions, where both human and mosquito orthologs were identified as interacting with the same dengue proteins . 54 out Combined, our human cDNA library screens and directed assays of mosquito orthologs identified 52 interactions involving 47 human proteins and three dengue proteins, capsid, NS3 and NS5  , as were the dengue-interacting proteins identified by other studies   . The den.4×10−7) . It has We identified a number of potentially relevant human NS3 interactors. CALCOCO2  is a component of Nuclear Domain 10 (ND10) bodies, which play a role in the intrinsic cellular defense mechanisms against some viruses The capsid interactors were isolated using the anchored capsid bait or the cytoplasmic capsid bait . We testAmong the human NS5 interactors, “unfolded protein response (UPR)” is the top enriched GO annotation . This enYeast two-hybrid studies frequently detect false positive interactions that have no biological relevance. One way to gain confidence in a two-hybrid interaction is to detect it using additional assays. We used two approaches to test the confidence of interactions that we identified in the library screens. First, we reasoned that biologically relevant virus-host protein interactions are likely to be conserved across the four dengue serotypes. There is 63–68% amino acid sequence homology among the four serotypes Drosophila cells along with NTAP-tagged dengue proteins . We purified the tagged dengue proteins and tested for co-purification of the host proteins by immunoblotting with myc antibodies  that we observed was only slightly higher for interactions that were positive by co-AP assays (average 3.4) than for interactions that were negative in co-AP assays (average 2.9), but this difference was not statistically significant. Thus, the numbers of active reporters and their levels in the two-hybrid assay was not a strong predictor of reproducibility by co-AP assay.Next we employed an orthogonal assay, co-affinity purification (co-AP), to test most of the dengue-host interactions that we identified by yeast two-hybrid assays. We expressed myc-tagged versions of the mosquito and human proteins in cultured tibodies . If one tibodies  and S6. It is often noted that a virus such as dengue with only 10 proteins of its own should need to interact with a number of host proteins to carry out its replication cycle. Our study combined with other large-scale and small-scale studies The second criterion proposed here is based on the fact that many biologically relevant PPI are conserved Drosophila genes that were required for DENV2 propagation in Drosophila cells and then tested human orthologs of a subset of the hits in a similar assay. That study provided functional evidence supporting several protein interactions that had been detected only by yeast two-hybrid assays, including ENOX2 – NS3 Drosophila ortholog of AAEL004783, ornithine decarboxylase antizyme (Oda), inhibited propagation of dengue in Drosophila cells For the above analysis we considered only evidence of direct protein-protein interactions. An alternative approach would be to consider other forms of evidence that may support the involvement of specific host proteins in dengue infection. Strong supporting evidence for a functionally relevant protein interaction, for example, can come from experiments showing that the host protein is required for efficient infection or propagation of the virus. Below we consider a number of protein interactions that, although detected by only one method, are supported by such functional data. Khadka et al. Drosophila marker gene mini-white and the phiC31 phage attachment site (attB) enabling targeted integration into specific attP sites engineered into Drosophila chromosomes. To construct pHZ12, a PCR fragment from pAS1 Drosophila translational start sequence followed by codons for 6 histidines and 3 myc tags, and recombination target sequences, 5RT (5′-TTGACTGTATCGCCG-3′) and 3RT (5′-CCGGAATTAGCTTGGCTGCAG-3′); these sequences allow insertion of open reading frames (ORFs) using recombination in E. coli or yeast XbaI and inserted into pHZ12 and pHZ13 digested with PmeI and XbaI. The ligations were used to transform E. coli, OmniMAXII (Invitrogen). Transformants with plasmids containing a Gateway cassette were selected on LB-Chloramphenicol/Ampicillin media. pJZ4_attR is a gateway destination plasmid containing an activation domain for yeast two-hybrid assays pHZ12 and pHZ13 are vectors containing the Gal4-responsive upstream activating sequence (UAS) driving expression of proteins with an N-terminal myc tag or TAP tag. Both vectors were made by modifying pUASattB CACCATTCCATTTTCTGGCGTTC, H87 TGGCGTTCTGTGCCTGGAATGAT and H241 TCAACAACACCAATCCATCTTGCGG - using SuperScript II Reverse Transcriptase (Invitrogen) according to the product manual. Individual viral genes or gene fragments were PCR amplified from the cDNA with specific primers  according to the manufacturer's protocol. The entry clones were sequenced with M13F(-21) and M13R sequencing primers. Each viral gene or gene fragment was transferred to the following destination vectors: pNLex_attR and pJZ4_attR for two-hybrid assays Dengue virus prototype strains for serotypes 1 (Hawaii), 2 (16681), 3(H87) and 4 (H241) were cultured in C6/36 cell lines. Viral RNA was extracted from the culture supernatant with the QIAamp Viral RNA Kit (Qiagen). cDNAs encoding each dengue virus 2 protein were generated from an infectious clone, a gift from Dr. Nopporn Sittisombut . cDNA synthesis was performed with primers targeting the 3′ end of each viral genome – Hawii and 16681  primers . Each 5′Aedes aegypti colony was established and maintained according to a published protocol XhoI site, 5′- GAGAGAGAGAGAGAGAGAGAACTAGTCTCGAGTTTTTTTTTTTTTTTTTT -3′. Next, the cDNA was ligated with an EcoRI adapter, 5′-OH-AATTCGGCACGAGG-3′3′-GCCGTGCTCCp-5′An XhoI, fractionated on Sepharose CL-2B gel filtration medium and then ligated into the yeast two-hybrid activation domain (AD) vector, pRF4-5o E.coli, MegaX DH10B™ T1R Electrocomp™ Cells (Invitrogen), by electroporation. 188 E. coli transformant colonies were randomly picked for colony PCR. 64% of the colonies had inserts with various sizes from 300 to 4,000 bp. The average size of the inserts was 1,400 bp. More than 107E. coli colonies were collected for plasmid extraction with a QIAGEN Plasmid Giga kit.The cDNA was digested with 8 transformant colonies were harvested and frozen in 1 ml aliquots at −80°C. Similarly, a human cDNA library (Origene Technologies) with cDNA from peripheral blood leukocytes (PBL) cloned into the AD vector pJG4–5 8 transformant colonies were harvested and frozen in aliquots. Bait strains were created by transforming RFY206 , which also contained the lacZ reporter plasmid, pSH18-34(URA3+), with pNLex bait plasmids expressing dengue proteins. Each screen was carried out by mating a freshly grown aliquot of a bait strain with a thawed aliquot of yeast containing the human or mosquito libraries. About 2×108 colony-forming units (cfu) of the bait strain were mated with 0.5–1×108 cfu of the library strains on a YPD plate. Diploids were collected, induced in liquid galactose medium for 4 hours at 30°C, and plated on galactose medium lacking leucine. cDNA inserts from galactose-dependent Leu+ yeast were PCR amplified and re-cloned into the AD vector in fresh RFY231 yeast by gap repair Drosophila Eip63E (FBgn0005640) and Cyclin J (FBgn0010317). cDNAs that passed the tests were PCR amplified and digested with AluI to identify unique digestion patterns, which may represent unique genes The LexA version of the two-hybrid system from the Brent laboratory D. melanogaster S2R+ cells were obtained from the Drosophila Genomics Resource Center at Indiana University. The cells were cultured in Schneider's media (GIBCO) supplemented with 10% FBS (Gemini) and 1%Gentamycin (GIBCO). S2R+ cells were incubated at 25°C. The cells were passaged once per week at 1∶10 dilution. Co-AP assays were performed essentially as described Aedes aegypti cDNA, pHZ13 plasmids containing dengue ORFs, and the expression driver plasmid pMT-GAL4 using the tranfection reagent, Effectene (Qiagen). pMT-Gal4 expresses Gal4 from the Cu+2-inducible metallothionine promoter 6 cells were seeded onto each well of a 12-well cell culture plate 18–24 hours before transfection with 250 ng of each plasmid, pHZ12, pHZ13 and pMT-GAL4. 12–18 hours after transfection the media was supplemented with 1 mM CuSO4 to induce protein expression. Three days after CuSO4 induction the cells were harvested and lysed. Expression of fusion proteins was determined by Western blot analysis of cell lysates using anti-NTAP  and anti-Myc (Santa Cruz Biotechnology) antibodies for proteins expressed from pHZ13 and pHZ12, respectively. For lysates with detectable expression of both the Myc-tagged and NTAP-tagged proteins, IgG beads were used to purify the NTAP-tagged protein complexes, the complexes were run on SDS-PAGE gels, and the gels were immunoblotted with anti-Myc to probe whether the Myc-tagged protein was co-purified. For interactions involving proteins with undetectable expression, the co-AP assay was attempted by swapping the inserts between pHZ12 and pHZ13. Interactions were only considered tested if both tagged proteins could be detected in cell lysates.Aedes aegypti mosquito dengue interactors, a gene ontology annotation (GOA) file was downloaded from UniProt-GOA (ftp.ebi.ac.uk/pub/databases/GO/goa/proteomes/31436.A_aegypti.goa) www.geneontology.org/ontology/obo_format_1_2/gene_ontology_ext.obo) Aedes aegypti interactors identified in our screen. For each gene set the degrees of all the genes were calculated and averaged. Next, random sets of genes of equal size to each of the three gene sets were picked and the average degrees for each random set were calculated. This was repeated for one million random sets. The distribution of these averages is plotted in p-value significance of the average degree for each gene set was calculated from the respective distribution using a two-tailed test.For enrichment analysis of Table S1Dengue-mosquito interactions found in this study.(XLSX)Click here for additional data file.Table S2Mosquito proteins with human orthologs that interact with proteins from other viruses.(XLSX)Click here for additional data file.Table S3NS3 domain analysis.(XLSX)Click here for additional data file.Table S4Gene Ontology (GO) and domain enrichment for mosquito proteins.(XLSX)Click here for additional data file.Table S5Capsid domain analysis.(XLSX)Click here for additional data file.Table S6Dengue-human interactions found in this study.(XLSX)Click here for additional data file.Table S7Human proteins that interact with proteins from other viruses.(XLSX)Click here for additional data file.Table S8Gene Ontology (GO) and domain enrichment for human proteins.(XLSX)Click here for additional data file.Table S9Oligonucleotide primers used in this study.(XLSX)Click here for additional data file.Table S10Dengue-host protein-protein interactions supported by multiple forms of evidence.(XLSX)Click here for additional data file.Table S11Yeast two-hybrid screen details.(XLSX)Click here for additional data file.Figure S1Intraviral protein-protein interactions. Interactions were identified by the galactose-dependent growth of diploid yeast expressing two dengue proteins. Each panel is a group of four indicator plates: Glucose complete minimal (CM) lacking leucine (–leucine) (top-left), Galactose CM –leucine (top-right), Glucose CM +X-gal (bottom-left) and Galactose CM +X-gal (bottom-left). An interaction is indicated by galactose-dependent growth on the plates lacking leucine (top two plates in each panel) or galactose-dependent blue colony color on the X-Gal plates (bottom two plates in each panel). Drosophila melanogaster Cyclin Y and Eip63E were used as a positive interaction control while D. melanogaster Cyclin Y and Cyclin J were used as a negative control. All media lack uracil, histidine, and tryptophan to select the two-hybrid plasmids.(PDF)Click here for additional data file.Figure S2Dengue-interacting host proteins are enriched for hubs. Enrichment analyses for proteins with many interacting partners in three dengue-host interactomes: human interactors of dengue baits identified in this screen (A), human interactors of dengue baits identified in other screens (B), and Aedes aegypti interactors of dengue baits in this screen (C).(PDF)Click here for additional data file.Figure S3Co-AP assays for dengue-host protein interactions. Additional co-AP results that were not shown in (PDF)Click here for additional data file.Data S1Cytoscape files containing all of the interaction data. Four Cytoscape files combined into a single compressed file include all of the two-hybrid interactions detected between dengue and either human or mosquito proteins, as well as the interactions supported by multiple forms of evidence. The interactions are annotated with information about how they were detected, whether they were conserved among the four serotypes, and whether they were confirmed by co-affinity purification assays.(ZIP)Click here for additional data file.


Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate.We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence.Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein.http://homppi.cs.iastate.edu/.Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners. Protein-protein interactions are central to protein function; they constitute the physical basis for formation of complexes and pathways that carry out virtually all major cellular processes. These interactions can be relatively permanent or "obligate"  or "transient" . Both the distortion of protein interfaces in obligate complexes and aberrant recognition in transient complexes can lead to disease .With the increasing availability of high throughput experimental data, two related problems have come to the forefront of research on protein interactions: i) prediction of protein-protein interaction partners; and ii) prediction of protein binding sites or protein-protein interfaces (PPIs). Although most effort to date has focused on one or the other of these problems, it is possible to use information from predicted protein-protein interaction networks as input for interface prediction methods, and predicted interface residues can be used as input for interaction partner predictions, a concept explored in a recent study of Yip et al. . In the Several different genetic, biochemical, and biophysical methods have been used to identify and characterize protein interfaces . These eTo distinguish interface residues from non-interface surface residues, a wide range of sequence, physicochemical and structural features have been investigated -18, and Several sequence-based protein-protein interface prediction methods have been explored in the literature ,22-28. MA number of structure-based methods ,18,29-31The use of structure-based methods, however, is limited to proteins for which the structure of the query protein is available, and the number of solved structures significantly lags behind the number of protein sequences . Even whThe relationship between sequence conservation and various aspects of protein structure, interaction, expression, and function has been the focus of many studies over the past decades ,40, and Despite the disagreement regarding whether interface residues are conserved or not, several researchers have used conservation of residues to predict protein-protein interfaces with varying degrees of success. For example, the Evolutionary Trace (ET) method ,60 and iwithout specifying a specific interaction partner . The results of this analysis indicated that interfaces in obligate complexes are, in general, more highly conserved than those in transient complexes. In a complementary set of experiments, we examined interfaces in complexes between specific pairs of proteins . In contrast to the results for NPS-interfaces, by focusing on the interface of each query protein with a specific binding partner, we discovered a high degree of sequence conservation in transient PS-interfaces. This analysis revealed that transient interfaces tend to be highly partner-specific.Against this background, we study a class of sequence homology-based methods for protein-protein interface prediction. We introduce a novel measure of interface conservation that captures the degree to which interface residues in each protein are conserved among its sequence homologs. First, we describe the results of our analysis of the interface conservation among homologous sequences using several large non-redundant datasets of protein-protein interfaces extracted from the Protein Data Bank (PDB) , includiSecond, based on the results of protein interface conservation analysis we propose HomPPI, a class of sequence homology-based approaches to protein interface prediction. We present two variants of HomPPI: (i) NPS-HomPPI (non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. The performance of both HomPPI methods was evaluated on several benchmark datasets, including a large non-redundant set of transient complexes. Due to the increasing importance of intrinsically disordered proteins in understanding molecular recognition mechanics and in rational drug design and discovery -75, we aFinally, we compare the performance of HomPPI with that of other web-based servers for interface residue prediction, using several performance measures that assess the reliability of correctly predicting, on average, interface and non-interface residues in a given protein. We discuss the relative advantages and limitations of homology-based methods for interface residue prediction.Methods for additional details).To define conditions under which it should be possible to infer protein-protein interface (PPI) residues using conservation of interfaces in homologous proteins and/or complexes, we systematically examined the relationship between interface residue conservation and sequence similarity . Our analyses are based on the following datasets: Nr6505 (a large non-redundant dataset of protein chains extracted from PDB ), Oblig9EVal) ≤ 10 from the resulting set of homologs, we eliminated those that were nearly identical to the query sequence (to ensure an accurate estimate of conservation). To ensure that the interface residues of the homologs could be reliably determined, we retained only those homologs that were part of complexes with resolution 3.5 Å or better. For each query-homolog pair in sequence alignments generated by BLASTP, we used the interface residues of the homolog(s) to predict the interface residues of the query protein. We calculated the correlation coefficient (CC) between the predicted and actual interface residues of the query protein, and refer to this value as the interface conservation (IC) score, i.e., the degree of conservation of interface residues between the query protein and its homologs (see Methods for details).First, we examined the conservation of PPI residues in the absence of knowledge of interaction partners. For this study, we analyzed interfaces in putative homologs  of each protein in a large non-redundant dataset, Nr6505. After removing chains with interfaces containing fewer than 3 amino acids, we were left with 5853 chains. For each of the 5853 remaining proteins, we extracted homologs from the nr_pdbaa_s2c database using BLASTP  with expEVal), Identity Score, Positive Score, Local Alignment Length (LAL) and two Alignment Length Fractions (LAL/Query Length) and (LAL/Homolog Length). The EVal is a statistic that estimates the number of hits expected by chance when searching database of a particular size; the lower the EVal value, the more significant the score. The Identity Score is a measure of the degree of sequence identity between two amino acid sequences. The Positive Score returned by BLASTP is the number of positive-scoring matches in an alignment. It takes into account observed substitutions that preserve the physicochemical properties of the original residue. The LAL is the length of the local alignment; Alignment Length Fractions are LAL normalized by the length of the query or the length of the identified homologous sequence. We represent each query-homolog pair as a six dimensional vector defined by these six variables.We examined the dependence of the interface conservation score on six NCBI BLAST alignment statistics: Expectation value (IC) scores and the six BLAST alignment statistics. PCA, which is a dimensionality reduction technique, is typically used to represent dimensions that explain maximum variability and provide a simple and parsimonious description of the covariance structure [As a first exploratory step, PCA  was applied to visualize the relationships between the interface conservation  Dark Zone: containing query-homolog pairs with poorly conserved interface residues (blue and green data points), corresponding to low values of the CC between predicted and actual interfaces and thus low IC scores; (ii) Twilight Zone: containing pairs with moderately conserved interfaces (yellow and orange data points); and (iii) Safe Zone: containing pairs with highly conserved interfaces (red data points).Figure Positive Score and Identity Score. Explanatory variables that are highly correlated with each other make similar contributions to the IC score.The PCA analysis allows us to identify highly correlated explanatory variables. In Figure IC score for each query-homolog pair is plotted against log is shown in Figure log is a good indicator of protein interface conservation. When log > -50 the median values of IC scores cluster around 0 (low conservation). In the region of log ≤-50  the medians of IC scores increase as the log  decreases. When log < -100 the medians of IC scores tend to be greater than 0.5 (strong conservation).We studied the relationship of each individual variable with interface conservation. A scatter plot in which the IC score for each query-homolog pair plotted against the log of its LAL value. We can clearly see that when log(LAL) is larger than 4, the medians of IC score show a strong positive correlation with log(LAL). When the LAL is shorter than 55 residues (log(LAL) <4), the probability that interface is conserved in these homologs is low (the medians of the IC scores are ~0). We define this region as the Dark Zone. When the LAL is longer than 700 residues (log(LAL)>6.55), interface conservation is high . We define this region as the Safe Zone.Figure IC scores and the Positive Scores of query-homolog alignments is shown in Figure IC scores begin to increase at a BLAST Positive Score of ~ 90%.The relationship between IC score with the Identity Score, and the Local Alignment Length Fractions (LAL/Query Length) and (LAL/Homolog Length). As expected, the Identity Score results were similar to those for the Positive Score. The IC score was not as strongly linearly related to LAL fraction as it was to the log(LAL) (data not shown). Taken together, these results provide guidelines for choosing sequence similarity thresholds that reflect the degree of conservation in NPS interfaces.We also studied the relationship of IC scores  complexes , it is is Figure  tend to EVal ≤ 10 Query and homolog proteins with interfaces containing fewer than 3 amino acids were removed, as were homologs that were nearly identical to the query proteins. We extracted 43,115 query-homolog pairs containing chains that participate in transient interactions and 24,212 pairs containing chains that participate in obligate interactions.To compare protein interfaces in transient and obligate complexes, we used the Trans135 and Oblig94 dataset obtained from , which ilog, Identity Score, Positive Score, log(LAL), and alignment length fractions (LAL/Query Length) and (LAL/Homolog Length). The PCA biplots in Figure IC scores (different colors) are partially segregated, indicating that the six alignment statistics can distinguish query-homolog pairs with highly conserved interface residues (red) from those in which interface residues are not conserved (blue or green).In agreement with previous studies , our anaIC scores plotted as a function of log (LAL) are more frequently above 0 for pairs that involve obligate binding proteins  for the obligate dataset has a larger coefficient (0.095) than that for the transient dataset (0.052), which confirms that protein interfaces are more conserved in the obligate complexes than in transient complexes analyzed in this study.The results in Figure s Figure  than fors Figure . RegressIC score with decreasing log. In contrast, Figure IC scores cluster around 0, indicating that log has little relation to interface conservation in transient complexes.Figure Positive Score is a good indicator of interface conservation in the case of proteins from obligate complexes; however, this is not the case for proteins from transient complexes. For obligate binding proteins, when the Positive Score exceeds 45%, the medians of IC scores begin to show an increasing trend . That is, the interface residues of a query protein represent the complete set of its interface residues with EVal≤10 against the nr_pdbaa_s2c database. We removed homologs that are part of complexes with resolution worse than 3.5 Å. If query proteins A and B form a complex A-B, and have homologs A' and B' that interact in a complex A'-B', we consider A'-B' as a homo-interolog of A-B. To ensure an accurate estimate of conservation, from the resulting set of homo-interologs, we eliminated those that were within the same PDB complex as the query proteins, and those that were nearly identical to the query pairs . For each protein chain in a query pair, we use the interface residues of its homolog in a homo-interolog to infer the PS interface residues of the query protein chain. Thus, we use the interface residues of A' in the homo-interolog (A'-B') of query pair A-B to infer the interfaces of A with B, based on the sequence alignment between A and A' obtained using BLASTP. We measure the similarity between a pair of interacting proteins A-B and its homo-interolog A'-B', in terms of the metrics for the quality of sequence alignment between A and A' and between B and B', using the six BLAST alignment statistics described above.To examine the conservation of partner-specific (PS) interfaces in transient protein complexes, we again used the Trans135 dataset of protein pairs that participate in transient interactions . For eacIC score) and the six alignment statistics computed from the predicted PS interfaces, e.g., of chain A when it interacts with B, using known interfaces of A' with B'. This analysis revealed that much of the observed variance in IC scores is explained by three factors: (i) the average log ; (ii) the average Positive Score of the homo-interolog and (iii) the alignment fractions AFrac, AFrac', BFrac, and BFrac' computed from the alignments of constituent chains (A with A' and B with B') .We used PCA of 3, 456 candidate homo-interologs to explore the relationship between interface conservation . In contrast, the, logLAL, which is the average of alignment length between A and A', and between B and B', is not strongly correlated with interface conservation for PS-interfaces  to infer the interfaces of the query protein. Otherwise, the process is repeated to search for homologs in the Twilight Zone or the Dark Zone. If no homologs of the query protein can be identified in any of the three zones, NPS-HomPPI does not provide any predictions. The Safe, Twilight, and Dark Zone homologs of the query protein sequence to be used for interface prediction are identified by searching the nr_pdb_s2c database using BLASTP with thresholds based on the interface conservation analysis (see Methods Section for details) .1. complexes i.e., the homo-interologs of A-B. PS-HomPPI labels a residue of the query sequence as an "interface" residue (with respect to its putative binding partner) if a majority of the residues in the corresponding position in homologous complexes are interface residues, and as "non-interface" residues otherwise. PS-HomPPI uses homo-interologs in Safe and Twilight Zones to make predictions. The PS-HomPPI prediction process is thus analogous to that for NPS-HomPPI, using thresholds for "close homo-interologs" based on the results of interface conservation analysis of PS-interface conservation .2. PS-HomPPI - Given the sequences of a query protein A and its putative binding partner B, PS-HomPPI searches the nr_pdbaa_s2c database to identify homologous We report several performance measures that provide estimates of the reliability of interface (and non-interface) residue predictions obtained using the HomPPI family of predictors. We compare the performance of HomPPI predictors with several state-of-the-art interface prediction methods on a benchmark dataset. We evaluate the effectiveness of HomPPI in predicting the interface residues of disordered proteins. Finally, we compare the partner-specific and non-partner-specific versions of HomPPI.protein (See Methods for details). However, because several of the published studies report performance measures that assess the effectiveness of the methods in reliably assigning interface versus non-interface labels, on average, to any given protein residue, we also include results using "residue-based" performance measures in Supplementary Materials (See http://homppi.cs.iastate.edu/supplementaryData.html).We focus our discussion on results using several performance measures that assess the effectiveness of the methods in reliably predicting, on average, the interface and non-interface residues of any given Among the 180 protein sequences in the Benchmark180 dataset (taken from ), 125 seWe also evaluated the prediction performance of NPS-HomPPI using homologs with different degrees of sequence homology. In Table Direct comparison of NPS-HomPPI with other methods described in the literature is complicated by the limited availability of implementations of the underlying methods (many of which are available only in the form of servers), and differences in the choice of training and evaluation datasets, evaluation procedures and evaluation measures . Hence, Promate samples the protein surface using circular patches around a set of anchoring dots and estimates the probability that each surface dot belongs to an interface, based on the distribution of various physicochemical properties within interface and non-interface patches. Cons-PPISP is a consensus method that combines six neural networks trained on six datasets. Meta-PPISP is a consensus method that combines the output from cons-PPISP, Promate, and PINUP . PIER reWe compared the performance of NPS-HomPPI with all five PPI servers on a subset of the Benchmark180 dataset , specifisurface residues are interface residues. In contrast, sequence-based methods have the more challenging task of identifying interface residues from the set of all residues. In other words, structure-based methods can trivially eliminate all non-surface residues from the set of candidate interface residues. Viewed in this light, the observed predictive performance of NPS-HomPPI, a purely sequence-based method, suggests that it is possible to make reliable non-partner-specific interface residue predictions using only the sequences of a protein by taking advantage of the conservation of interfaces in the context of non-partner-specific interactions.For the two transient complex types, enzyme-inhibitors Figure  and tranIntrinsically disordered proteins (IDPs) and proteins containing intrinsically disordered regions (IDRs) are attractive targets for drug discovery . The lacFigure At present, NPS-HomPPI has relatively high prediction coverage for long disordered proteins , but lower coverage for short disordered proteins . This is in part due to that fact that many disordered proteins available in the PDB have only NMR structures, which were excluded from the current study. Incorporation of data from NMR structures in the future can be expected to increase the coverage of NPS-HomPPI for disordered proteins.Our analysis of the conservation of PS-transient interfaces described earlier suggests that many interfaces in transient protein complexes are highly partner-specific. Thus, we implemented a variant of HomPPI, designated PS-HomPPI, to evaluate the possibility that prediction of interface residues, especially in transient complexes, can be improved by using sequence information about specific binding partners, when available.We first evaluated the performance of PS-HomPPI on a transient complex dataset, Trans135 (dimers from the dataset in ). PS-HomMethods for details).To investigate whether the partner information is, in fact, helpful in predicting interfaces we directly compared the performance of PS-HomPPI with NPS-HomPPI on the Trans135 dataset. In Trans135, there were 139 out of 270 chains that for which predictions could be generated by both NPS-HomPPI (using homologs) and PS-HomPPI (using homo-interologs) from the Safe or Twilight zones  when the homologs are selected taking into account measures of quality of sequence alignment, specifically NCBI BLAST sequence alignment statistics. The HomPPI methods presented here use simple linear combinations of BLAST sequence alignment statistics, determined using PCA analysis of the relationship between the statistics and the IC score. It would be interesting to explore optimal, perhaps non-linear, combinations of parameters to maximize the desired performance criteria .Because we do not know the Our results are consistent with previous studies , in thatCompared with its performance on transient binding proteins in the Benchmark180 dataset, NPS-HomPPI performs much better on interfaces of disordered proteins in the S1 and S2 datasets. This is consistent with the conclusion of Meszaros et al.  that intThe conservation of interfaces in IDPs may contribute to the generally successful application of interface residue predictors to interfaces in IDPs. Several groups have developed methods for predicting disordered binding regions, including PONDR VL-XT ,93, ANCHThe fact that disordered interfaces can be reliably inferred by NPS-HomPPI indicates that disordered interfaces are non-partner-specific, which is consistent with findings that these proteins are able to bind a broad range of ligands through common binding regions ,96.Our results show that whenever the interfaces of the close sequence homologs of a query protein are available, NPS-HomPPI outperforms several state-of-the-art protein interface prediction servers (many of which take advantage of the structure of the query protein), over a broad range of sensitivity and specificity values. In the case of transient complexes Figure  and 8b, The HomPPI methods for interface residue prediction do have an important limitation, however, in that they rely on the availability putative homologs for which experimentally-determined structures of bound complexes are available in the PDB. One may ask whether the coverage of the HomPPI family of protein-protein interface prediction methods is broad enough to be sufficiently useful in practice. We address this question below.The current coverage of HomPPI protein interface prediction methods can be assessed from our results as follows:• Benchmark180 dataset: NPS-HomPPI found at least one homolog that meets the similarity thresholds for Safe or Twilight Zones for 73% (83/114) of the obligate binding chains (homo and hetero-dimers). Among these, 82% (68/83) were predicted with both sensitivity and specificity ≥0.50, simultaneously. Similarly, at least one homolog was found for 62% (42/66) of transient binding chains (enzyme-inhibitors and non-enzyme inhibitors) in this dataset. Among these 55% (23/42) were predicted with both sensitivity and specificity ≥0.5.• Trans135 dataset: In the case of transient query proteins in the Trans135 dataset, NPS-HomPPI found at least one homolog that meets the similarity thresholds for Safe or Twilight Zones for 75% (202/270) of chains. Among these, 37% (74/202) were predicted with both sensitivity and specificity ≥0.5.• Disordered protein datasets S1 and S2: In the case of disordered proteins, NPS-HomPPI found at least one homolog that meets the similarity thresholds for Safe or Twilight or Dark Zones for 50% (26/52) of interfaces of disordered proteins in S1, the short disordered protein set, and 75% (30/40) of interfaces of disordered proteins in S2.• Trans135 dataset: PS-HomPPI found at least one homo-interolog that meets the Safe or Twilight similarity thresholds for 60% (162/270) proteins in the Trans135 dataset. Among these, 80% (130/162) where predicted with sensitivity and specificity ≥0.5, simultaneously.Based on these results, we estimate that, at present, the coverage of the HomPPI protein interface prediction methods is in the range of 60-70% of all query proteins. As the structural genomics projects currently underway generate increasing numbers of structures of protein-protein complexes , we can The current default parameters for HomPPI are intentionally rather stringently set based on the results of our statistical analysis of interface conservation using Trans135, which is a dataset of transient binding proteins. Our analyses suggest that NPS-HomPPI has wider Safe and Twilight Zones for obligate binding proteins than for transient binding proteins. Furthermore, even Dark Zone homologs yield interface predictions that are accurate enough to be useful in practice, with average specificity of 0.47 and sensitivity of 0.66 for hetero-obligate dimers, average specificity of 0.44 and sensitivity of 0.47 for homo-obligate dimers  predictions and protein interaction (partner) predictions answer closely related, but different questions. Non partner-specific protein interact -101. Altp et al.  have proReliable partner-specific interface predictions can be used to restrict the search space for protein-protein docking by specifying the contacts that need to be preserved in the docked conformation. It is also possible to rank the conformations produced by docking, based on the degree of overlap between the interface of a query protein and its binding partner in the docked conformation with the interface generated by a partner-specific interface prediction method, e.g. PS-HomPPI. In related work, we haveWe studied a large number of sequence alignments between protein pairs with known interfaces to explore the conditions under which conservation of protein interface residues, as determined by the alignment of a query sequence against its homologs/homo-interologs, can be used to reliably predict protein-protein interfaces. Based on the results of these analyses, we developed HomPPI, a simple sequence-based method for predicting interface residues based on the known interface residues in homologous sequences. HomPPI has two variants: NPS-HomPPI (for predicting interface residues of a query protein with unspecified interaction partners) and PS-HomPPI (for predicting interface residues of query proteins with a specified putative interaction partner).Our systematic evaluation of NPS-HomPPI showed that, when close homologs can be identified, NPS-HomPPI can reliably predict interface residues in both obligate and transient complexes, with a performance that rivals several state-of-the-art structure-based interface prediction servers. NPS-HomPPI can also be used as a reliable tool for identifying disordered binding regions. In this regard, NPS-HomPPI has an advantage over structure-based interface predictors, which cannot be used to predict binding sites in disordered regions of proteins because they do not form stable structures in their unbound state. In addition, the HomPPI family of interface prediction methods are fast enough for proteome-wide analyses.in silico identification of protein interfaces have been published in the past decade. However, despite the fact that many proteins are very specific in their choice of binding partners, the majority of studies focus on only one side of the bound complex. In this study, we implemented a novel partner-specific protein interface prediction method, PS-HomPPI, which infers interface residues based on known interfaces in the homo-interologs, i.e., complexes formed by homologs of the query protein and its putative interaction partner. When homo-interologs can be identified, PS-HomPPI can reliably predict highly partner-specific transient interfaces.Many studies on Although our focus in this study was on prediction of protein-protein interfaces, these methods could be useful in other settings, such as sequence-based prediction of protein-DNA, protein-RNA, and protein-ligand interfaces, and the prediction of B and T cell epitopes.http://homppi.cs.iastate.edu/.Both NPS-HomPPI and PS-HomPPI have been implemented in a server available at: Five datasets were used in this paper:• Nr6505 - For analyzing the protein interface conservation.• Oblig94 and Trans135 - For comparing the degree of conservation of protein interfaces in transient/obligate binding proteins.• Benchmark180 - For evaluating the prediction performance of HomPPI.• S1 and S2 - For evaluating the performance of NPS-HomPPI on interfaces of disordered proteins.• nr_pdbaa_s2c - For BLASTP searching for close sequence homologsWe extracted a maximal non-redundant set of known protein-protein interacting chains from the Protein Data Bank (PDB)  availabl1. Extract all the X-ray derived protein structures with resolution 3.5 Å or better in PDB. Remove proteins with less than 40 residues. We obtained 102,853 protein chains.2. Remove redundancy of the resulting dataset in step 1 using PISCES. All theThis dataset of 94 obligate protein-protein dimer complexes and the dataset of 135 transient dimer complexes was obtained from a large non-redundant dataset of 115 obligate complexes and 212 transient complexes  previously generated by Mintseris and Weng  to studyWe tested NPS-HomPPI on a benchmark dataset manually collected and used as evaluation dataset by Bradford and Westhead . This daWe evaluated the performance of NPS-HomPPI on a non-redundant disordered dataset that has been recently collected by Meszaros et al . S1 consThis dataset is used for BLASTP searches. We used the fasta files from S2C database  to generhttp://protInDB.cs.iastate.edu.This paper adopts a stringent definition of protein-protein interfaces. Surface residues are defined as residues that have the relative solvent accessible area (RASA) at least 5% . InterfaWe label the protein sequences as interface or non-interface residues (according to the definition of interface residues given above) as follows: We first calculate the relevant distances between atoms using the atom coordinates in ATOM section in PDB files. Then, by associating the ATOM section to residues in the SEQRES section, we can map the corresponding residues to protein sequences. However, various errors in PDB files make this a non-trivial task. Hence, we used the mapping files from S2C database, which offers corrected mapping information from ATOM section to residues in the SEQRES section of PDB files, to map interfaces determined in structures to full sequences.The amino acid substitution matrix and gap cost are essential parameters that need to be specified in BLAST searches. In this study, we used the substitution matrices and gap costs recommended for the different query lengths  See Tab.i, we calculate the corresponding performance measures for each protein i as follows:To evaluate the extent to which protein interfaces are conserved in query-homolog pairs and to estimate the performance of HomPPI and other predictors that we compare with in predicting the interface residues of a novel protein , we consider several standard performance measures including sensitivity , specificity (precision), accuracy and Matthews correlation coefficient (CC) . SpecifiiTP, iFP, i TNand i FNare respectively the number of interface residues of protein i that are correctly predicted to be interface residues, the number of residues of protein i that are incorrectly predicted to be interface residues, the number of residues of protein i that are correctly predicted to be non-interface residues, and the number of residues of protein i that are incorrectly predicted to be non-interface residues.where protein-based overall performance measures as follows:We calculate the N is the total number of test proteins.where These measures describe different aspects of predictor performance. The overall sensitivity is the probability, on average, of correctly predicting the interface residues of a given protein. The overall specificity is the probability, on average, that a predicted interface residue in any given protein is in fact an interface residue. The overall accuracy corresponds to the fraction of residues in any given protein, on average, that are correctly predicted. The overall Matthews correlation coefficient measures of how predictions correlate, on average, with true interfaces and non-interfaces.Often it is possible to trade off one performance measure  against another  by varying the threshold that is applied to the prediction score to generate the binary (interface versus non-interface) predictions. Hence, we include of the overall sensitivity against overall specificity for different choices of the threshold. The resulting specificity-sensitivity plots or precision-recall plots show the trade-off between sensitivity and specificity and hence provide a much more complete picture of predictive performance.protein. It is worth noting that most of the papers in the literature on interface residue prediction report performance measures by averaging over residues (as opposed to proteins). The residue-based overall performance measures are calculated as follows:The performance measures described above provide an estimate of the reliability of the predictor in predicting interface residues of a novel R and specificityR is obtained by varying the threshold applied to the prediction score. The residue-based performance measures provide an estimate of the reliability of the predictor in correctly labelling a given residue. However, in practice, it is useful to know how well a predictor can be expected to perform on a given protein sequence as opposed to a residue. sensitivityP, specificityP, accuracyR, and CCP are more informative than their residue-based counterparts. Hence, in this paper, we report results based on the protein-based measures although, for the purpose of comparison with other published methods, we include the results based on the residue-based measures in Supplementary Materials in HomPPI website.Residue-based specificity-sensitivity plots in this case show how the trade-off between specificityIC) score.In protein interface conservation analysis, we used the CC (defined above) as a measure of the extent to which the interface residues in query protein are similar to those in a putative homolog. For clarity, we refer this measure as the Interface Conservation  from the BLASTP statistics for the alignment of each homolog with the query sequence. For a given query sequence, at most K closest  are selected from the alignment of the query sequence with its homologs to be used to infer the interface residues of the query sequence. In our experiments, K, the maximum number of homologs used in the prediction was set equal to 10. At most K homologs of the query sequence are determined by ranking the homologs in the alignment in decreasing order of their predicted IC scores and choosing (at most) K Safe zone homologs (or Twilight zone homologs if no Safe zone homologs exist or Dark zone homologs if neither Safe nor Twilight zone homologs exist). Once the (at most) K closest homologs to be used for predicting the interface residues of the query sequence are chosen, each residue in the query sequence is labelled as an interface or non-interface residue based on the majority (over the set of at most K closest homologs of the query sequence) of the labels associated with the corresponding position in the alignment. More specifically, each of the at most K homologs provides a positive vote for a given position in the query sequence if the corresponding residue of the homolog is an interface residue; and a negative vote if it is a non-interface residue. The prediction score of NPS-HomPPI for that position in the query sequence is simply the number of positive votes divided by the total number of votes. A query sequence residue with a HomPPI score ≥0.5 is predicted to be an interface residue  to infer the interfaces of the query protein. Otherwise, the search is repeated for homologs in the Twilight and Dark Zones. If NPS-HomPPI cannot find homologs in any of the three zones, it does not provide any predictions. The default zone boundaries used by NPS-HomPPI (and hence the parameters used in NPS-HomPPI search for homologs of a query sequence) is based on our interface conservation analysis on the dataset of transient dimers Trans135 . We discard the whole PDB complex that contains A-B, to ensure an objective assessment of the reliability of our prediction procedure. For query A-B and its homologous interacting pair A'-B', we also discard the interacting protein pair A'-B' if A and A' or B and B' share ≥95% sequence identity and belong to the same species.PS-HomPPI predicts the interface residues in a protein chain based on the known interface residues of its closest homo-interologs. Given a query protein A and its interaction partner B, PS-HomPPI first identifies the set homo-interologs of A-B using BLASTP to identify the homologs of A and homologs of B. From the BLASTP results, we identify a set of homo-interologs that meet sequence similarity thresholds . In our experiments, K was set equal to 10. Given a query-partner pair A-B, we label each position in the amino acid sequence of protein A as an interface or non-interface based on whether or not a majority of the corresponding positions of the homologs of A within the homo-interologs of A-B are interface residues. More specifically, each of the at most K homo-interologs provides a positive vote for a given position in the query protein sequence A if the corresponding residue of its homolog A' in its homo-interolog is an interface residue; and a negative vote if it is a non-interface residue. The prediction score of PS-HomPPI for that position in the query sequence is simply the number of positive votes divided by the total number of votes. A residue in the query protein A with a prediction score ≥0.5, is predicted as interface, otherwise, it is predicted as non-interface.PS-HomPPI predicts whether an amino acid in query sequence A is an interface residue or not based on the corresponding position in its alignment with (at most) logEVal, Positive Score, AAFrac' and BBFrac', whereWe built a linear model for PS-interface conservation based on the important sequence alignment statistics identified in the PCA analysis: AAEVal' and BBEVal' are the EVal between A and A', and between B and B'. AApositiveS' and BBpositiveS' are the BLAST Positive Score between A and A', between B and B'. The model isA-B is query protein pair and A'-B' is the homo-interolog of A-B. Variables, parameter estimates and coefficients are shown in Table * Project name: HomPPIhttp://homppi.cs.iastate.edu/* Project home page: * Programming language: PerlAll authors of this research paper have directly participated in the design, implementation, or analysis of this study.