The control of protein aggregation is an important requirement in the development of bio-pharmaceutical formulations. Here a simple protein model is proposed that was used in molecular dynamics simulations to obtain a quantitative assessment of the relative contributions of proteins’ net-charges, dipole-moments, and the size of hydrophobic or charged surface patches to their colloidal interactions. The results demonstrate that the strength of these interactions correlate with net-charge and dipole moment. Variation of both these descriptors within ranges typical for globular proteins have a comparable effect. By comparison no clear trends can be observed upon varying the size of hydrophobic or charged patches while keeping the other parameters constant. The results are discussed in the context of experimental literature data on protein aggregation. They provide a clear guide line for the development of improved algorithms for the prediction of aggregation propensities. The control of protein aggregation in the context of formulation development for biopharmaceuticals is a field of research with growing importance as the number of new bio-pharmaceutical drug candidates in various stages of development is steadily increasing. An interesting semi-empirical approach is the calculation of spatial aggregation propensities (SAP) as proposed by Trout and co-workers. Taken together the limited experimental evidence available so far does not allow for a clear conclusion regarding the relative importance of various structure based descriptors such as net-charge, dipole moment, and the presence of hydrophobic or charged patches. The fact that the structures and sequences of the proteins used in many published studies are not in the public domain is not helpful. Even if more experimental data were available it would still be difficult to quantify the relative impact of different properties from experimental data alone since numerous effects and parameters, including the presence of mutations, the buffer type and ionic strength, partial unfolding, the effect of osmolytes, etc, can be inter-dependent and in an experimental setup it is difficult or impossible to control all these parameters independently.To summarize, the available experimental data is difficult to compare and interpret. Here we address this issue and our results contribute to the understanding of the relative impact of a proteins physico-chemical properties on its colloidal interactions. The basis of the present study is a simple protein model that is used in Molecular Dynamics (MD) simulations. The model features physico-chemical descriptors with values inside the ranges typically found in small globular proteins. The descriptors considered here include the net-charge, the dipole-moment, and the hydrophobicity and polarity of surface patches, respectively. Using this model we determined the dependence of colloidal protein-protein interactions on each of the four descriptors. We find that variations of the net-charge and the dipole moment, both, have a clear and, importantly, comparable impact on the strength of these interactions. By contrast the variation of protein-protein interactions due to the presence of hydrophobic or charged patches is comparatively small and does not follow a clear trend. We interpret our results in the light of available experimental data and discuss their implications for the development of improved models for the prediction of protein aggregation propensities.The protein model and the simulation protocol used here were carefully designed to include important factors such as entropic contributions by using an explicit solvent model, to be simple enough to allow for the collection of semi-quantitative results for a range of test cases, and, most importantly, to allow for a clear assignment of effects to causes. Two major differences between this model and existing molecular simulation approaches As a basis for the PP model the structure of a In house software was used to generate about 36000 random topologies with protein-like surface charge distributions, as discussed in the previous Section. To allow for a comparison between real proteins and results calculated here PPs with net-charges and dipole moments within appropriate ranges need to be used. To establish such ranges we used a set of models of small globular proteins from the protein data base The charge distribution on a proteins surface can be expected to have an impact on protein-protein interactions, both, through interactions between charged patches, attraction through interactions between regions of charges with opposite or repulsion between regions with equal sign This is the sum of the squared partial charges on all atoms closer than a certain threshold As a measure for the colloidal interactions, and thus aggregation propensity, between a pair of PPs with a given topology we use mlssc, its smallest lssc value. The smaller this number, the lower the charge density of a patch of a particular size, and the more aggregation prone a particular topology is assumed to be. This assumption is based on the same concept as the SAP. Out of the random topologies three sets were selected. Details are summarized in mlssc and mlsc values were calculated for all topologies within set varPH with MD simulations as described in the Methods Section were performed for each topology in Comparison of panel C and D in For 22 cytokines and 3 IgG antibodies the variation of net-charge and dipole moment that can be achieved with mutation of a single residue are shown.In three of the 25 cases in As discussed in the introduction little is available in the public domain in terms of comparable experimental data for aggregation propensities of different proteins. We found two data sets that seem to be appropriate as test cases for our conclusions. The first, referred to as setA, is a subset of 18 proteins from a large set, comprising a large portion of the EColi strain K12 proteome. Structural models for the proteins in the two sets were generated as described in the Methods section and the net-charge, dipole-moment and nSAP, a variation of the original SAP-score, Recently setA Click here for additional data file.Figure S2Statistics of linear regression models discussed in the text. Output of the summary command in R.(EPS)Click here for additional data file.


We examine the biological relevance of our predictions using known signaling pathways and chart the abundance of interaction types in cell processes. Finally, we investigate the cross-relations between different interaction types within the network and characterize the discovered patterns, or motifs. We expect the resulting annotated network to facilitate the reconstruction of process-specific subnetworks and assist in predicting protein function or interaction.Protein-protein interactions (PPIs) govern basic cellular processes through signal transduction and complex formation. The diversity of those processes gives rise to a remarkable diversity of interactions types, ranging from transient phosphorylation interactions to stable covalent bonding. Despite our increasing knowledge on PPIs in humans and other species, their types remain relatively unexplored and few annotations of types exist in public databases. Here, we propose the first method for systematic prediction of PPI type based solely on the techniques by which the interaction was detected. We show that different detection methods are better suited for detecting specific types. We apply our method to ten interaction types on a large scale human PPI dataset. We evaluate the performance of the method using both internal cross validation and external data sources. In cross validation, we obtain an area under receiver operating characteristic (ROC) curve ranging from 0.65 to 0.97 with an average of 0.84 across the predicted types. Comparing the predicted interaction types to external data sources, we obtained significant agreements for phosphorylation and ubiquitination interactions, with hypergeometric p-value = 2.3e Protein-protein interactions (PPIs) play a key role in a diverse range of biological processes. As proteins interact in a specific manner, a large number of interaction types exists Several previous attempts were made to classify protein interactions into different types. One approach exploits protein structural information in order to predict protein docking mode and subsequently the interaction type Here we propose a method for predicting interaction type based on the experimental techniques by which the interaction was detected, using no prior biological information. We compiled a dataset of 180,353 PPIs and their corresponding detection methods from multiple databases and applied logistic regression to predict for each PPI its interaction types. We applied this method separately on ten interaction types including covalent binding, disulfide bond, protein cleavage and cleavage, deacetylation, dephosphorylation, methylation, phosphorylation, ubiquitination and ADP ribosylation. We validated our predictions using internal cross-validation and external data sources, obtaining high areas under the ROC curve. In addition, we chart the distribution of interaction types in biological pathways, showing that different cellular processes tend to be mediated by different interaction types. Finally, we illuminate inter-relations between types by analyzing recurrent network motifs spanning multiple types.‘association’ and its descendants we obtained 461,742 redundant interactions corresponding to 180,353 unique PPIs (see Methods). Each PPI record was associated with up to 20 detection methods and 5 interaction types with an average of 1.4 and 1.38 associations, respectively. When accounting for ancestors in the hierarchical structure, the average PPI-detection method and interaction type associations increased to 6.8 and 3.1 respectively. We compiled a human protein-protein interactions dataset from multiple sources utilizing the psi-mi format −18, 1.2e−17,1.1e−07,8.1e−07,1.5e−08 for enzymatic reaction, cleavage reaction, protein cleavage, dephosphorylation reaction and ADP ribosylation reaction, respectively). On the other hand, covalent binding and disulfide bond were both enriched with this method , suggesting that this detection method is less suitable for detecting transient enzymatic reactions, and is better suited for the detection of protein complexes. Notably, ‘ubiquitination reaction’ was also found to be enriched with affinity chromatography , reflecting the need for stable interactions to carry out ubiquitination reactions. Similarly, ‘two hybrid array’ was found to be underrepresented in phosphorylation and dephosphorylation interactions , while enriched in ubiquitination reactions , demonstrating again the subtle differences between these types of enzymatic reactions. Finally, ‘Comigration in gel electrophoresis’ showed enrichment in identifying covalent binding interactions  while being underrepresented in enzymatic reactions , in accordance with the technique prevalent use We hypothesized that different detection methods are better suited for different types. To test this hypothesis we examined the enrichment of interaction types detected by the different methods. We found that ‘affinity chromatography’, was underrepresented with enzymatic reaction and several of its descendant categories  covalent binding and (ii) enzymatic reaction see . To thisTo evaluate our prediction method, we used a 10-fold cross-validation setting. Our method obtained an area under the receiver-operating-characteristic curve (AUC) of 0.84 on average across the different interaction types see . To deteWe further validated our predictions against two external data-sources storing specific interaction types: (i) phospho.ELM −54. In a similar manner, comparing the classifier ranks for ubiquitination interactions to a set of 203 gold standard interactions retrieved from hUbiquitome yielded an AUC of 0.69. The set of predicted ubiquitination interactions was highly enriched in gold-standard PPIs, with hyper geometric p-value = 5.6e−28, retrieving 37% of gold standard proteins with an accuracy of 91%.To assess our predictions, we compared the set of predicted phosphorylation interactions to a set of 479 gold standard phosphorylation interactions stored in Phospho.ELM (see Methods), obtaining an AUC of 0.70. Choosing a cutoff as above, we were able to retrieve 75% of the gold standard interactions with an accuracy of 58%, yielding a hyper geometric p-value <2.3eNext, we tested the functional similarity of the proteins participating in the different interaction types. We compared the semantic similarity of proteins mediating each interaction type to the similarity of a randomly selected group of interacting proteins of the same size (see Methods). The similarity score distribution was significantly higher for proteins spanning the same interaction type .proteolysis involved in cellular protein catabolic process’; and ‘proteolysis’ categories. Both ‘disulfide bond’ and ‘covalent binding’ proteins were enriched in categories involving large complexes such as ‘ribosome’, ‘ribosomal subunit’, ‘ribonucleoprotein complex’ and more generally in the category ‘macromolecular complex’. ‘Ubiquitination reaction’ was enriched in ‘protein ubiquitination’ and ‘ubiquitin-protein ligase activity’. Proteins involved in phosphorylation reactions were enriched in eight GO categories directly engaging phosphorylation including ‘protein amino acid phosphorylation’, ‘phosphorylation’ and ‘protein kinase activity’.To analyze the categories enriched with each interaction type, we focused on the top 100 proteins with the highest network degree in each of the types (see Methods). All interaction types were highly enriched in GO categories, with at least 69 enriched categories per type and a total of 1758 enrichment over all types (FDR <0.05). Reassuringly, we found that most interaction types were enriched with terms directly describing their predicted type. For example, ‘protein cleavage’ were enriched in ‘−18) suggesting that different cell processes are mediated by different interaction types. Repeating the analysis using KEGG-type annotations revealed the same trend with an intra-subcategory correlation of 0.64 vs. inter-subcategory correlation of 0.32 .To assess the relevance of the predicted interaction types to cellular processes, we download KEGG pathways −35, 0.02 and 1.7e−16 for phosphorylation, dephosphorylation and ubiquitination reactions, respectively). Moreover, we found that the abundance of predicted interactions of type phosphorylation, dephosphorylation and ubiquitination across pathways were correlated with their abundance as annotated in KEGG pathways . Surprisingly, we observed a high correlation between interactions annotated in KEGG as ‘activation’ and all enzymatic reactions . Intrigued by this correlation, we wondered if our predicted interaction types can explain the mechanism of action for interactions annotated as ‘activation’/‘inhibition’ by KEGG, many of which are of unknown nature. One example for such an interaction is the inhibition of Notch by Numb in the ‘Notch signaling pathway’. It is known that Numb can bind to the intercellular domain of Notch, but the specific mechanism by which it inhibits Notch is still under investigation Next, we compared the predicted interaction types to KEGG’s annotations. KEGG interaction types consist of 13 annotations, from which five are molecular events and three were comparable with predicted types (see Methods). We first tested the enrichment of predicted types within the corresponding KEGG types. All three interaction types were enriched with the corresponding KEGG types . This suggests that our method relies on unbiased experiments and its strength might stem from the integration of multiple detection methods to tackle the prediction task.To the best of our knowledge, our method is the first to predict interaction functional type. We expect that as more interaction types will become predictable, the interaction profile of a protein could illuminate protein function as well as the way it mediates its role within the cell.We note that additional attributes can be exploited to improve interaction type prediction. For example, properties such as protein’s functional annotations can be used in the learning process, assuming that proteins with similar properties tend to interact in a similar manner.physical association”), see enzymatic study” descendant terms  which are designed to detect specific interaction types, to the corresponding interaction type, resulted in 1,656 unique interactions with known interaction type.Protein-protein interactions (PPIs) were downloaded from multiple sources using the molecular interactions query language (MIQL 2.5) of PSICQUIC In order to compile a complete detection method vector we exploited the hierarchical structure of the psi-mi format, to associate PPI with ancestors of the detection method to which it was annotated.In a similar manner PPIs were associated with interaction types ancestors, resulting in 27 redundant predictions of ‘disulfide bond’ and ‘covalent bond’ and 43 redundant predictions of ‘protein cleavage’ and ‘cleavage reaction’ see .enzymatic study”  which are designed to detect specific interaction types, were dismissed from the detection method vector. Additionally, rare detection methods associated with less than 200 interactions and high-level detection methods associated with all interactions  were dismissed from the analysis, resulting in a vector of 39 detection methods serving as feature vector to the logistic regression  of the known interaction type were annotated to this type making its prediction redundant and causing the lack of an appropriate negative set.We trained a generalized linear regression model, using a MATLAB classifier with binomial distribution. To avoid trivial prediction cases all descendants of interaction detection method psi-mi 0415 “sion see . For the‘physical association’ or ‘direct interaction’) as a part of the negative set. To gain a wider variety of interaction types and detection methods we chose the random interactions from the set of interaction associated with at least four detection methods. These 139,961 interactions (78% of the complete dataset) forms a representative group of reliable and well annotated interactions. The size of the negative set was set to the size of the positive set or at least 100 interactions, see The filtering process resulted in a set of 1,558 unique protein interactions with predictable type. The positive-training set for each interaction type included all interactions associated with that interaction type, while the negative set included interactions associated with different specific interaction types. Additionally, as interactions annotated to a specific type represent less than 1% of the interaction dataset, we accounted for random interactions not associated with any specific type Click here for additional data file.Table S2Detection methods used as features in the logistic regression.(XLSX)Click here for additional data file.


Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) have been used to demonstrate the work.Protein-protein interaction (PPI) networks (interactomes) of most organisms, except for some model organisms, are largely unknown. Experimental methods including high-throughput techniques are highly resource intensive. Therefore, computational discovery of PPIs can accelerate biological discovery by presenting "most-promising" pairs of proteins that are likely to interact. For many bacteria, genome sequence, and thereby genomic context of proteomes, is readily available; additionally, for some of these proteomes, localization and functional annotations are also available, but interactomes are not available. We present here a method for rapid development of computational system to predict interactome of bacterial proteomes. While other studies have presented methods to transfer interologs across species, here, we propose transfer of computational models to benefit from cross-species annotations, thereby predicting many more novel interactions even in the absence of interologs. We developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. The Mtb classifier gave a precision of 94% and a recall of 23% on a held out test set. The Mtb model was then run on all the 8 million protein pairs of the Mtb proteome, resulting in 708 new interactions (at 94% expected precision) or 1,595 new interactions at 80% expected precision. The CD classifier gave a precision of 90% and a recall of 16% on a held out test set. The CD model was run on all the 8 million protein pairs of the CD proteome, resulting in 143 new interactions (at 90% expected precision) or 580 new interactions (at 80% expected precision). We also compared the overlap of predictions of our method with STRING database interactions for CD and Mtb and also with interactions identified recently by a bacterial 2-hybrid system for Mtb. To demonstrate the utility of transfer of computational models, we made use of the developed Mtb model and used it to predict CD protein-pairs. The cross species model thus developed yielded a precision of 88% at a recall of 8%. To demonstrate transfer of features from other organisms in the absence of feature-based and interaction-based information, we transferred missing feature values from Mtb orthologs into the CD data. In transferring this data from orthologs (not interologs), we showed that a large number of interactions can be predicted.Rapid discovery of  bacterial interactome can be made by using existing set of GO and STRING features associated with the organisms. We can make use of cross-species interactome development, when there are not even sufficient known interactions to develop a computational prediction system. Computational model of well-studied organism(s) can be employed to make the initial interactome prediction for the target organism. We have also demonstrated successfully, that annotations can be transferred from orthologs in well-studied organisms enabling accurate predictions for organisms with no annotations. These approaches can serve as building blocks to address the challenges associated with feature coverage, missing interactions towards rapid interactome discovery for bacterial organisms.http://severus.dbmi.pitt.edu/TB and http://severus.dbmi.pitt.edu/CD respectively for browsing as well as for download.The predictions for all Mtb and CD proteins are made available at:  Mycobacterium tuberculosis (Mtb) causes tuberculosis which remains a leading infectious disease to this day, with about 2 million deaths annually worldwide [Clostridium difficile (CD) infection is the primary cause of antibiotic-associated diarrhoea. It has a property of undergoing mutation rapidly [C. difficile have emerged, that have been associated with severe disease outbreaks worldwide [The presence of about 500-1,000 bacterial species in the human gut flora of the intestines plays important role in immunity and nutrition [orldwide . A deadlorldwide . Clostri rapidly . In the orldwide . Understorldwide -11. Analorldwide . A betteorldwide ,14. Despbioinformatically by identifying interactions among orthologs  [Streptococcus pneumoniae interactome determined with Y2H was compared with that of E. coli, and a large number of conserved interactions were found among the orthologs between the two organisms [Newer techniques such as mass-spectrometry, yeast two-hybrid (Y2H), and tandem affinity purification have been designed to study the global interactions in an organism in a high-throughput manner. Y2H methods have a low recall, in the order of about 12% . This loerologs) ,18 or wierologs) . Gene fuerologs) . Gene orerologs) . Similarerologs) . Some meerologs) . Homologerologs) ,24. Someerologs) . Streptorganisms .Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) to demonstrate the method. The new interactions predicted for these two organisms are also made available. For this work, we selected STRING and GO data as features for our prediction method as these features are available for a large number of organisms. STRING database provides biophysical and functional interactions among proteins for 869 bacterial organisms [http://www.geneontology.org and BioGrid [With any of these methods, only a few potential interactions are discovered, making it necessary to devise other methods for discovering more of the hitherto-unknown interactions. In this work, we demonstrate that cross-species transfer of information may be carried out not only bioinformatically, but also by transfer of computational models across organisms. We show that a machine learning model developed for one organism may also be applied to predict PPIs of another organism based on the former's model and the latter's feature set. We used two specific pathogens rganisms . It giverganisms , http:// BioGrid ,29, thebinstance in unlabelled data) is classified as interacting or non-interacting based on the features describing that pair. A classifier (e.g. a random forest or a support vector machine) is learned from the features of the instances whose interactions are known (labelled data). We use these STRING and GO annotations in building features of protein-pairs. For labels, instances that have nonzero value for STRING experimental score are treated as known interactions. Simply sequencing the genome of a bacterium provides genomic context features in STRING. Several bacteria have at least partial annotations available in GO. Of the 1,707 genomes represented in GO, 1,194 have 50-70% of their genomes annotated and only 98 genomes have less than 50% annotated [Predicting PPIs is treated as a classification problem in machine learning. Each protein-pair . The features of protein-pairs in CD that contain these genes are computed by transferring corresponding annotations from Mtb orthologs. Table To develop a computational model for protein-protein interaction prediction, we assembled a dataset of unique proteins found as a union of those in STRING, GO for Mtb and CD. Seven features were computed for every protein-pair in the proteomes of both the organisms: Gene Ontology features for cellular component, molecular function and biological process, STRING features based on four genomic contexts, namely gene neighbourhood, gene fusion, gene co-occurrence and gene co-expression. Interactions with experimental evidence provided in STRING were used as the gold standard interacting labels. The total data collected for Mtb from the above sources consisted of 4,020 proteins and correspondingly, about 8 million protein pairs, and 3,439 known interacting pairs. CD data consisted of 4,147 proteins, about 8 million protein pairs and 3,414 known interacting pairs. It is common practice to consider randomly generated protein-pairs as non-interacting pairs for training and evaluation of PPIs (after removing any known interactions from this randomly generated set) because there is no authoritative dataset on non-interacting protein-pairs combined with the fact that only one in a thousand or so pairs is expected to interact.For classification of interacting pairs from non-interacting pairs, we chose to use a random forest classifier as it has previously been evaluated in comparison to Bayesian network models and support vector machines, and has been shown to be most suitable for PPI prediction ,33. For The random forest model evaluates each pair and outputs a distribution value ranging between 0 and 1, where larger values correspond to a higher confidence that the pair is interacting. By setting a threshold on this distribution and taking all protein pairs above the threshold, a binary classification value is assigned to each pair. Model accuracy is computed in terms of precision (what percentage of the predicted interactions are known to be true), and recall .Figure http://severus.dbmi.pitt.edu/TB and http://severus.dbmi.pitt.edu/CD At a very high precision (or selectivity), the number of novel interactions found are 708 for Mtb (with 94% expected precision) and 143 for CD (with 90% expected precision). Additional interactions may be found by lowering the threshold but at the expense of precision. Figure All the protein-pairs in each of the two organisms were evaluated by their corresponding random forest model. The complete list of the protein pairs and their random forest scores are made available for browsing as well as for download at While we are not in a position to make an authoritative estimate of the size of the interactome for the two bacteria, we need an approximate number for the expected number of interactions to put the state-of-the-art into context. It is generally expected that 1 in 500 to 1500 protein-pairs is an interacting pair . With this, the expected number of interactions for Mtb, CD, yeast and human would be as shown in Table STRING provides a score by integrating information from various other databases, and an overall-score ranging from 0 to 999 obtained as a combination of all these scores. A non-zero score means that at least one of these say that the pair is a functionally related, and possibly interacting. We found that 244,161 Mtb and 127,738 CD protein pairs have a non-zero STRING combined score , L-asparaginase involved in conversion of asparagine to aspartate is predicted to be interacting with Rv3676. The protein Rv3676 is transcriptional regulatory protein and belongs to CRP/FNR family. The functional significance involving orthologs of both the proteins have been studied in Escherichia coli [hia coli . cAMP reRv0350 - Rv3914: Rv0350 (dnaK) is a chaperone. It is a heat shock protein and is regulated positively by Rv3223c (sigH) protein and negatively regulated by Rv0353 (hspR). This protein is predicted to be interacting with Rv3914 (trxC). The protein Rv3914 is thioredoxin and participates in various redox reactions. In Mtb, it has been observed that under the conditions of oxidative and heat stress, sigH regulates the expression of thioredoxin and dnaK [and dnaK .The results of using cross-species computational model are shown in Figure To find the orthologs for CD in Mtb, we used the KEGG Orthology database . The numTraditional precision and recall plots were used to compare the performance associated with the three models. From Figure In the transfer of GO annotations with orthologous genes, a consistent improvement was observed  and Clostridium difficile (CD). For the first scenario, we developed random-forest classifiers which filtered functional linkages obtained from the STRING database for Mtb and CD, by adding gene ontology features to the STRING features. We could identify potential biophysical interactions amongst these linkages with a very high precision using the classifier. In the second scenario, we demonstrated computational transfer across species by predicting CD interactions using the classifier trained over Mtb interactome. This work demonstrates that with the availability of a comprehensive interactomes for a few organisms, interctomes for other organisms may be predicted not only bioinformatically but also by transfer of computational models. We also demonstrated cross-species information may be used not only for interolog transfer, but also for computational prediction of interactions where only one of the two proteins in the pair may have an ortholog, or when both pairs may have an ortholog but not necessarily their interaction. These methods can be used for bacterial proteomes for which few features and/or interactions are available.We presented three scenarios of availability of proteome annotations for bacteria and presented approaches for rapid development of a computational system for bacterial interactome prediction, even in scenarios where little or no interaction or feature information associated with protein-pairs is available, resulting in significant savings in cost, time and effort. The methods have been demonstrated with two candidate bacterial proteomes, namely that of http://severus.dbmi.pitt.edu/TB and http://severus.dbmi.pitt.edu/CDNovel interactions have been identified for both Mtb and CD, and have been made available online at The features that were used in the computational prediction are listed in Table hydrolase activity), whereas for some other proteins more specific information may be known . For use in some applications such as the current work, a smaller subset of vocabulary called GO Slims is available, which maps lower level (more specific) GO terms to a higher-level Slim category. In this work, the GO Slim annotations have been used. All GO terms associated with a protein are mapped to the GO Slim categories that were their closest ancestor in the GO DAG. For instance, Endoplasmic Reticulum (GO:0005783) is the closest ancestor of the term ER proteasome core complex, alpha-subunit complex (GO:0031605) and is also present in the GO-Slim as a broad cellular localization category. Thus GO:0031605 is mapped on to the broader cellular-localization term of Endoplasmic Reticulum. Using this approach, we assigned GO Slim categories to every protein, based on their GO term annotations. For molecular function and biological process, we made use of the GO-Slim OBO files provided by the gene ontology website, but for cellular localization, we used the GO Slim categories that we had previously created manually. The complete mapping of specific GO terms to each of the broader GO Slim terms or categories is provided for download at http://severus.dbmi.pitt.edu/TB/.Gene Ontology (GO) provides a controlled vocabulary to describe gene products, and the relations among the terms in the vocabulary. The terms are arranged in the form of a directed acyclic graph (DAG), with three major branches from the root corresponding to molecular function (MF), cell component (CC) and biological process (BP). Annotations of gene products by these terms are created by members of the GO project by curating literature. When the DAG is traversed away from the root, the terms describe more and more specific characteristics of the gene product, whereas when traversing towards the root they become more general. For some genes only broader information is known   in the training data. This is done separately for CC, MF and BP terms. From this, the feature value for a pair of proteins STRING database provides experimentally known interactions and pairs that are predicted to have functional linkages. It assigns a score to a protein-pair indicating the functional linkages that are present between them. STRING scores derived from gene neighbourhood, gene co-occurrence, gene co-expression and gene fusion were taken as provided by the database. These form the 4 features representing a protein-pair in our dataset. Additionally, the "combined score" provided by STRING was also downloaded for comparison with other methods but is not in our model. All protein-pairs with a non-zero STRING experimentally-derived score were considered to be interacting proteins.Orthologous genes were obtained from the KEGG Ortholog database .The Weka package (Weka 3.6.0)'s Random Forest implementation was used to build the classifier model from the training data .We used standard precision and recall plots to evaluate the model's performance. For varying thresholds, both precision and recall associated with the model was recorded, and a graph showing the recall on the x-axis and the precision on the y-axis was constructed. Thus, when different models are to be compared, one can do so, by observing their recall values for similar values of precision or vice-versa and see if for all values of precision, there is a general trend towards improvement in recall.The authors declare that they have no competing interests.MG designed and supervised the work. SA developed the Mtb interactome. RM and NS provided the biological context to the project. AK, AG and AH carried out the cross-species and ortholog transfer evaluations during their summer internships in 2011. Manuscript is written by SA, RM and MG with inputs from all the authors. All authors read and approved the manuscript.The file gives the GO annotations and KEGG pathway annotations, where available, for the two proteins in each of the 709 newly predicted interactions. The first sheet shows all the annotations, whereas the remaining three sheets show GO molecular function, biological process and KEGG pathways, respectively. In these three sheets, the rows are colored respectively in blue, green or grey depending on whether the two proteins in a pair have different annotations, same annotations, or whether the annotations are not known for either of the proteins.Click here for file


Aim. To develop a new invariant descriptor for the characterization of protein surfaces, suitable for various analysis tasks, such as protein functional classification, and search and retrieval of protein surfaces over a large database. Methods. We start with a local descriptor of selected circular patches on the protein surface. The descriptor records the distance distribution between the central residue and the residues within the patch, keeping track of the number of particular pairwise residue cooccurrences in the patch. A global descriptor for the entire protein surface is then constructed by combining information from the local descriptors. Our method is novel in its focus on residue-specific distance distributions, and the use of residue-distance co-occurrences as the basis for the proposed protein surface descriptors. Results. Results are presented for protein classification and for retrieval for three protein families. For the three families, we obtained an area under the curve for precision and recall ranging from 0.6494 (without residue co-occurrences) to 0.6683 (with residue co-occurrences). Large-scale screening using two other protein families placed related family members at the top of the rank, with a number of uncharacterized proteins also retrieved. Comparative results with other proposed methods are included. This is about 5% of the total structures in PDB. The Pfam database was recently reported to contain over 2200 gene families with unknown function [The Protein Data Bank .protein sequence, or the primary structure of the protein [secondary structure is formed by patterns of intermolecular bonding of hydrogen and is determined primarily by the location and the directions of these patterns [α-helixes, β-sheets, and turns. The overall 3D shape of the secondary structures determines the tertiary structure of the protein. When two or more chains combine to form a larger molecule, the whole structure is called the quaternary structure. Although proteins could vary significantly in their functions and 3D shapes, they also share a general common structure. Proteins are composed of 20-amino acids that are connected via peptide bonds . Each pr protein . This prpatterns , 15. Thistructural plasticity [A common method for protein function prediction is by annotation transfer from known homologous proteins . Functioasticity  has a coA classical approach for deriving the protein function is by first determining its 3D structure, which can then provide some ideas about its function . ProteinGiven the foregoing, it is not surprising that different methods have been proposed to characterize the protein surface. Popular examples include those based on surface shape distributions , Gauss iα atoms that have a predetermined accessible surface area, and adhere to defined constraints on the solvent vectors. Each patch is then described using six parameters, namely, solvation potential, residue interface propensity, hydrophobicity, planarity, protrusion, and accessible surface area. Ferrè et al. [α atom and the centroid of its side chain atoms. Surface patches are then compared for similarity by comparing the residue vectors for all possible pairs of residues from the query and target surface patch. Matches are determined based on the root mean square distance, and the residue similarity as determined using a standard substitution matrix. The results of using this method on a nonredundant list of protein chains as recorded in the SURFACE database [Much earlier, Jones and Thornton  analyzedè et al.  analyzeddatabase , a colledatabase , 31 for Distances, geometry, and topology have for long been used in the analysis of general protein 3D structures . Residue1 or 0, depending on whether the voxel is on the protein surface or in the interior. The 3D Zernike function is then applied to the voxelized 3D protein shape to obtain the 3D Zernike descriptors. Therefore, the problem of comparison of 3D surfaces is reduced to that of comparing two vectors representing the 3D Zernike descriptors for each protein surface. Several distance measures were tried, such as the Euclidean distance, Manhattan distance, and a correlation-based distance defined as the complement of the correlation coefficient between two Zernike descriptors. Venkatraman et al. [Following earlier work by Canterakis  on the un et al.  studied n et al.  used then et al. . This prA recent work  used theα atoms within each surface patch, conditioned on the specific residue at the center of the patch, and the particular residues found within the patch. Our method computes the residue-specific distance distributions, and residue-distance cooccurrences for the protein surface patches using only the Cα atoms on the protein surface. Residues in the interior of the protein are discarded. Unlike the approach in [The key difference in our method is the use of the local patch descriptors as defined by the distribution of distances between Croach in , we avoiWe present an invariant descriptor for characterizing protein surfaces. We start with a local descriptor of selected circular patches on the protein surface. For a given surface patch, the local descriptor is computed based on the residue distances from the center of the patch. The descriptor records the distance distribution between the central residue and the residues within the patch, keeping track of the number of particular pairwise residue cooccurrences in the patch. A global descriptor for the entire protein surface is then constructed from the local descriptors by combining information from local descriptors with similar central residues. The proposed descriptor is invariant to rotations of the surface and mirroring. Using a fixed patch size, we obtain a descriptor for the protein surface, independent of the size of the protein structure. Thus, the descriptor can facilitate the rapid matching of protein chains, and will eliminate the need for the exhaustive alignment of the protein 3D structures. For a given protein structure or protein chain from a database, such as the PDB, the proposed method can be summarize in the following steps:generate the Connolly surface  for the generate the surface patches and compute the local invariant descriptor for each patch on the surface;compute the global invariant surface descriptor for the protein chain, by combining information from the local patch descriptors; perform surface matching and comparison using the descriptors; classify the protein into its potential functional family, or perform protein surface retrieval using the invariant descriptors.uracil-DNA glycosylase, estrogen receptor, and cell division protein kinase 2. These are the same protein families used in a recently published work [epidermal growth factor (EGF) and cyclooxygenase-2 (COX-2), two protein families that are known to play a role in cancer. Below, we provide more details on the steps enumerated above.hed work . We alsoα atoms and their respective residue types by extracting the information from the PDB and the vertex files.For a given protein, we first generate its Connolly surface  at a givα position of a surface residue. For each residue on the surface , we construct a surface patch by recording its residue type, and consider all residues within a certain distance threshold (τp) as part of the patch ×(b + 1), where the rows correspond to the 20 distinct protein residue types, plus an extra row to describe the summary distance distribution within the patch. The columns represent the individual bins used to capture the distance distributions , plus an extra column to represent the summary of the residue cooccurrences. To reduce the computational time and space requirement, unlike in [The local invariant descriptor for the patch is created by calculating the distribution of distances between the central residue and all other surface residues within the patch. Additionally, the residue cooccurrences within the patch are also recorded as a part of the local descriptor. Each local descriptor is represented in a matrix nlike in  we definfirst form is a detailed distance distribution between the central residue in the surface patch and each of the other residues on the patch. To achieve this, a uniform distribution of the distances is assumed and the total number of bins b is used to estimate the probability distribution of finding a pair of residues at one of the b ranges. The second form is the global distance probability distribution. In this form we estimate the probability of observing any given residue within a patch in a particular distance range from the central residue. In this paper, we study the use of the global distance distribution in identifying similar protein surfaces, and possibly proteins with similar functions. Consequently, the question to be answered is, given a central residue of a specific type, what is the distance distribution for the residues around this central residue? That is, we seek Pr{d | Rc}, the probability of observing distance d between a central residue of type Rc and any other residue. We expect that the distance distribution should be similar for surface patches from functionally similar proteins.The basic idea of using the distance distribution is that similar functional proteins should have a similar distribution of distances between the residues on their surfaces. The patch descriptor captures the distribution in two forms. The Ri, given a central residue, say Rc. Again, we expect the probability Pr{Ri | Rc}, to be similar for protein surfaces from functionally similar proteins. We note that the surface co-occurrence does not depend on the specific distance between the residues involved, as far as Ri is within the patch. Given that surface structures are more conserved than sequence over evolution , 17, we sum C in matrix DA) and the residue cooccurrences  in describing the protein surface (see DRC = (sum C°sum RT), where ° is the concatenation operator and XT stands for the transpose of X. DRC is used to compute the conditional probability Pr{d | Rc, Ri}, that is, the probability of observing the distance d between residues Rc and Ri given that Rc is the central residue in the patch. We expect that the residue co-occurrence  should carry more distinctive functionally relevant information than the general distance distribution , since surface residue cooccurrences are likely to be more conserved over evolution. By combining both vectors, we can account for both the geometry of the protein surface and the distribution of specific residues within specific distances on the surface. Using both vectors brings in some biological relevance in the analysis and is likely to lead to improved results in the identification of functionally similar protein surfaces.The above have considered the distance and the co-occurrence separately. The DRC combines the general distance distribution , residue cooccurrences (RCs), or the distance-residue cooccurrences (DRCs).Given two proteins, say Protein 1 and Protein 2 we characterize them using their global descriptors, say Dg1d that is composed of the 20 global distance distributions represented by all sum C vectors from each descriptor. Dg1d is defined as Dg1d = (Dd1°  Dd2°⋯°Dd20), where Dd1, Dd2,…, Dd20 are the distance distributions from each residue type on the surface of Protein 1. Repeat the same process for Protein 2 to create Dg2d. Then we perform matching using the simple Euclidean distance: For matching using the distance distribution we create a vector Dg1c that combines the 20 residues co-occurrence vectors (denoted sum R), defined as Dg1c = (Dc1T°  Dc2T°⋯°Dc20T), where Dc1T, Dc2T  ,…, Dc20T represents sum R1T, sum R2T, and sum R20T. Similarly, we compute Dg2c. Matching is performed using the Euclidean distance between Dg1c and Dg2c.For Protein 1 we create a vector DRC that is comprised of all of the distance distributions as well as the residue cooccurrences. For Protein 1, we have DRC1 = (Dd1°  Dc1T° Dd2°  Dc2T°⋯°Dd1°Dc1T). Similarly we obtain DRC2 for Protein 2. Again for simplicity, matching is performed using the Euclidean distance. Clearly, other distance measures could be used.Here, we create a vector Having computed the surface descriptors and the distance between protein surfaces using the descriptors, one may be interested in determining whether a given unknown protein belongs to some known protein family. Using some training data, we can compute surface descriptors for the known family, and based on these perform the required classification. Classification is performed using Weka , 39, an uracil-DNA glycosylase, cell division protein kinase 2, and estrogen receptor. This was created by scanning the PDB and selecting the protein structures with protein chains belonging to one of the three families. We were able to extract 416 chains that belong to 243 proteins in the PDB. The dataset is distributed as follows: 91 chains from 46 distinct proteins for uracil-DNA glycosylase (Group1), 186 chains from 95 distinct proteins for estrogen receptor family (Group2), and 139 chains from 102 distinct proteins from cell division protein kinase 2 (Group3). We used DATASET-A basically to train the system, and perform initial testing. DATASET-B contained protein structures from two families, namely cyclooxygenase-2 (COX-2)  and epidermal growth factor (EGF) . We then extracted protein structures from the PDB that have 10 or less chains and ignored the rest. This resulted in a total of 15,386 protein chains form 6,261 unique proteins. DATASET-B included all structures in DATASET-A. We used DATASET-B for a more comprehensive scan of the PDB, in the quest for potentially novel structures that may be related to the two families. Experiments were performed using a SONY VAIO personal computer, with Intel Core 2 Duo Processor T8100, running at 2.10 GHz, with 2 GB of main memory. Programs were written using Matlab  with the Bioinformatics Toolbox. We set probe radius = 1.4 Å and patch distance threshold τp = 10 Å. For distance distributions, we used a fixed number of bins, b = 5. Classification was performed based on algorithms implemented in Weka [We performed experiments to test the performance of the proposed protein surface descriptor in two protein structure analysis tasks, namely, classifying proteins into their most likely functional groups, and ranking and retrieval of protein surfaces. We used two datasets for the experiments. DATASET-A contained information from three protein families: We divide DATASET-A into training and testing sets and apply different classifiers on the different descriptors proposed. In all our experiments, the training sets were kept very separate from the testing sets, with no overlap between the two. Classification performance is measured in terms of classification rate based on the three protein families in the dataset. We tested the method using various classifiers implemented in Weka, such as Naïve Bayes, logistic regression, and simple logistic classifier. We report results mainly for the logistic regression. First, we explore the impact of the size of the testing set and of the training set on the classification performance using the proposed approach. We varied the size of the training set (from 50 to 300), while keeping the size of the testing set fixed. We then checked the performance using fixed testing sets of size 100, 200, and 300. The figure shows that applying the distance distribution (DD2) alone resulted in the lowest performance accuracy as compared to using the residue cooccurrences (RCs) or distance-residue cooccurrences (DRCs). Yet, our definition of the distance distribution shows encouraging results. A steady improvement in performance with increasing training set size can be observed when using DD2 alone, peaking at about 87% with a training size of 200 and testing size of 100. The distinctiveness of our approach is the use of residue cooccurrences on the protein surface. This approach assumes that functionally similar surface proteins have similar residue cooccurrences within a small local surface region. n-fold cross validation, for different values of n. The use of distance-residue co-occurrence presents a steadier improvement in the classification rate. Using the DRC raised the accuracy rate to 99% using the simple logistic classifier on a training set of 150 and testing set of 100 (data not shown). We can observe the significant difference between the results of DD2 (which did not use information on residue cooccurrences) and RC and/or DRC (both of which used residue cooccurrences). uracil-DNA glycosylase family (Group 1)), while DD2 was worse than both RC and DRC). Overall, for Group 2 and Group 3, the Top 30 ranked proteins belonged to the corresponding family, while Group 1 was more difficult. In this section, we explore the effectiveness of our approach on the problem of search and retrieval of protein surfaces. Given a query protein, we study whether our approach has the robustness to place most of the functionally similar proteins in the top hits of the retrieved surfaces. Here, a query protein from each of the three groups is used to screen the entire DATASET-A (416 samples) and provide a ranking based on the similarity. Thus, each protein structure is ranked against the query, (from 1 to 416), where a lower rank  implies more similarity to the query. After that, we search over the retrieved proteins to find which ranks the functionally similar proteins  have attained. Nobs/Nexp, where Nobs = number of functionally similar proteins observed or retrieved by the system, and Nexp = number of functionally similar proteins expected by random selection. For an effective system, we expect that most of the functionally similar proteins should be observed after a small percentage of screening. That is, the top hits should contain mainly functionally similar proteins, and hence the enrichment factor should be high after a small percentage probe of the database, and gradually decrease towards 1 (which corresponds to random selection). We further measured the performance of our approach using the enrichment plot. The enrichment plot essentially measures how well a given ranking or retrieval system performs, when compared with a random selection of the data samples. At a given percentage of database screening, the enrichment factor is computed as the ratio , with members of the COX-2 and EGF families as the query. The main objective was to see how the proposed descriptors will perform on a large scale, and to see if the methods could predict potentially novel functional linkages between any of the families and other proteins in PDB. For this task, we used only PDB files with 10 or less chains, and ignored the rest. This resulted in a total of 15,386 protein chains from 6,261 unique proteins. EGF family as a query. COX-2 family. Results are shown only for the DRC descriptor. Generally, similar results were obtained using RC. We can notice that some of the unknown proteins (annotated as “uncharacterized”) were placed in the Top-50 rankings, implying a possible relationship with the respective families. Encouraged by the results in classification and ranking using the proposed descriptors, we now performed a larger scale experiment, by screening the entire protein structures in PDB, using the protein chains in DATASET-BThe use of distance distributions for protein surface analysis was studied by Binkowski et al. . As earlmin{rank, query group size}. This is similar to the definition used in [ used in . We perf used in , where t used in  proposedCygwin (a version of Linux for Microsoft Windows). Extraction of the protein chains and the Cα atoms was performed using Matlab Bioinformatics Toolbox , and required 32 seconds per PDB file. Construction of the descriptors after the above steps took an average of 0.7 seconds per PDB file. Querying DATASET-B  using the DRC descriptor required an average time of 0.28 seconds for each query PDB file.The most time consuming part was for preprocessing, as needed to construct the protein surfaces and extract the protein chains. The construction of the protein surface from the original PDB files required about 4.065 seconds, running on α to do the analysis, the need for surface alignment of the 3D structure is eliminated. Further, we do not need to perform any geometrical transformation to insure reliable matching. This is very important for rapid analysis over a large database, such as the PDB. We have introduced a novel approach to the description and characterization of protein surfaces. The proposed approach captures the surface structure of the protein by utilizing local patches defined only on the positions of surface residues, rather than over all surface vertices, or over all the surface atoms. We make residue cooccurrences on the surface a central part of the descriptor. The novelty of this approach can be observed by the ease of handling both local and global variation on the surface . Moving from local to global not only reduces the computational problem of matching 3D structures, but also facilitates direct comparison between protein structures of different sizes. By avoiding the construction of the complete 3D surface and retaining only the surface CCOX-2 and EGF family members showed that the proposed methods ranked related family members in the Top-20 hits, with a number of uncharacterized proteins also retrieved. It will be interesting to perform further biological lab experiments to verify if any of the retrieved uncharacterized proteins are truly related to the respective families to which they share similar surfaces (as determined by our surface descriptors).We showed results on the performance of the proposed methods in functional classification of proteins into their putative families, based on the surface information. We further compared the results using enrichment plots, and the standard measures of precision and recall. For the three protein families used, we obtained an area under the curve for precision and recall of 0.6494 (DD2), 0.6683 (RC), and 0.6676 (DRC). A screening of the PDB using


The coverage and reliability of protein-protein interactions determined by high-throughput experiments still needs to be improved, especially for higher organisms, therefore the question persists, how interactions can be verified and predicted by computational approaches using available data on protein structural complexes. Recently we developed an approach called IBIS (Inferred Biomolecular Interaction Server) to predict and annotate protein-protein binding sites and interaction partners, which is based on the assumption that the structural location and sequence patterns of protein-protein binding sites are conserved between close homologs. In this study first we confirmed high accuracy of our method and found that its accuracy depends critically on the usage of all available data on structures of homologous complexes, compared to the approaches where only a non-redundant set of complexes is employed. Second we showed that there exists a trade-off between specificity and sensitivity if we employ in the prediction only evolutionarily conserved binding site clusters or clusters supported by only one observation (singletons). Finally we addressed the question of identifying the biologically relevant interactions using the homology inference approach and demonstrated that a large majority of crystal packing interactions can be correctly identified and filtered by our algorithm. At the same time, about half of biological interfaces that are not present in the protein crystallographic asymmetric unit can be reconstructed by IBIS from homologous complexes without the prior knowledge of crystal parameters of the query protein. Protein interactions determine the outcome of most cellular processes and the analysis of protein interaction networks is crucial for understanding the mechanisms of cell functioning. The recent advances in experimental methods for identification of protein-protein interactions have provided extensive data on protein interaction networks. While for some organisms, such as yeast, the networks are close to completion and their reliability is relatively high One way to fill this gap and provide a more reliable and comprehensive biomolecular interaction network is to employ computational methods for protein interaction prediction and verification. There are many different computational approaches to predict protein interactions; some are based on genomic context, co-evolution, co-expression or co-occurrence patterns of potentially interacting proteins and their genes In this paper we tried to assess how the homology inference approach can be used to annotate the biological partners and interfaces of protein-protein interactions even if the native complex is not present in the structural database. We try to determine which factors influence the accuracy of such an approach. First, we find that the performance of the IBIS method for predicting protein interaction partners reaches 88% sensitivity and 67% specificity while performance for prediction of binding site locations is 72% recall and 70% precision. Interestingly a considerable increase in accuracy is observed if all available data on structures of homologous complexes is used, as compared to the approach where only a non-redundant set of structural complexes is employed. Second we show that there exists a trade-off between specificity and sensitivity if we use only conserved binding site clusters or clusters supported by only one observation (singletons). Finally we address the question of predicting the biological interfaces that are not present in the PDB asymmetric unit and need to be reconstructed by applying crystallographic symmetry operations. We show that almost half of such interfaces can be reconstructed by IBIS without the prior knowledge of crystal parameters of the query protein.We used the NCBI Molecular Modeling Database (MMDB) “binding site” or “interface”. In the current release of the Molecular Modeling Database (MMDB) A pair of interacting domains is defined if one of the domains has at least 5 residues in contact with the other. We define a residue to be in contact if there is at least one (heavy) atom of the residue within 4.0 Å of atoms of the other domain. The contact radius was chosen based on the mean number of inter-domain contacts formed by the non-redundant set of domain families. When we varied the contact radius from 2 Å to 6 Å the mean number of contacts showed a steep increase around 4 Å. The set of residues from one domain making contacts with the other domain constitutes a To ensure the biological relevance of binding sites, they are clustered based on their sequence and structural similarity. Here are the important details concerning the main steps in the processing, as shown in To infer interactions by homology for a given query protein with a known structure but unknown partners and binding sites, we first collect all protein domains/chains from known complexes which are structurally similar to a query and have at least 30% sequence identity to the query Step 1, . HereaftA and B we reindex the alignment between structure neighbor A and the query, with the alignment between the query and structure neighbor B T is the temperature factor, S is the similarity score between binding site i and binding site j in each cluster, C represents a cluster, |C| is the number of binding sites in the cluster C, and N is the total number of binding sites clustered. The temperature T constant (0.05) is chosen to correctly balance the energy-like and entropy-like terms in the function The binding sites of the homologous structure neighbors are clustered using a complete-linkage clustering algorithm, which calculates the distance between two clusters as the maximum distance between their members. A distance cutoff value to define the clusters is chosen using a pseudo-free energy function from a study which maximizes the mean similarity of members within a cluster and minimizes the complexity of the description provided by cluster membership conservZ).All binding site clusters are ranked in terms of biological relevance and similarity to the query Step 5, . First, contactZ). Another term in the ranking score accounts for the relevance of a given binding site cluster to the query. A position specific score matrix (PSSM) is constructed based on the binding site alignment using the implicit pseudo-count method PSSMZ). A higher sequence-PSSM score implies a higher probability of this site being biologically relevant for annotating the given query. In addition we calculate the average sequence identity between the query and all cluster members over the whole structure-structure alignment (not just binding sites) to estimate the evolutionary distance between the query protein and the group of homologous structure neighbors (pcntZ).Second, since the larger interfaces are more likely to be biological, the ranking score also includes the term corresponding to the number of interfacial contacts averaged over all homologous complexes  are converted to Z-scores and their weighted combination is used where weights were determined empirically.Interfaces present in PDB asymmetric units (ASU) are validated using the PISA  algorithm The sensitivity, specificity, precision and recall were estimated as follows. The sensitivity and error rates were estimated based on the number of true positives  and false positives . Sensitivity or Recall (TP/(TP+FN)) was defined as the number of true positives (TP) found divided by the overall sum of true positives in the test set. Error rate (FP/(FP+TN)) or specificity (one minus error rate) was estimated as the number of false positives (FP) divided by the sum of false positives and true negatives . Precision (TP/(TP+FP)) was also calculated to compare the performance of IBIS to other methods.To filter out fallacious interactions we have used the PISA algorithm. We regard the interactions occurring in an assembly predicted to be stable by PISA as biologically relevant, and the others occurring in the ASU but not validated by PISA as crystal packing interactions. After processing all 34846 protein X-ray complex structures having at least one observed inter-chain protein-protein interaction in the asymmetric unit (ASU), we found that 24089 (69%) of the structures are annotated to be multimeric, 6272 (18%) of structures are predicted to be monomers according to PISA and the remaining 4529 (13%) could not be processed due to various reasons such as incomplete X-ray data, for example . The disIt has been noted previously that correct assignment of biological units in protein complexes can add more domain-domain interfaces beyond those that are seen in the PDB asymmetric units We evaluated the accuracy of IBIS to predict protein-protein binding sites from three different perspectives. First we compared IBIS annotated sites with manually curated CDD annotated sites. The definition of false positives for protein-protein interaction predictions is rather ambiguous and none of the available test sets of true interactions can capture all possible biologically relevant interactions for a given protein. Therefore we evaluated separately the rate of true positives and false positives using two test sets: the test set of crystal packing pairwise interactions to evaluate the false positive rate (specificity) and a set of biological interactions to evaluate the true positive rate (sensitivity). Finally we performed comparisons with other available methods on the test sets reported previously. In this case the test sets provided the information not only on pairwise interactions but also on the locations of binding sites.The Conserved Domain Database (CDD) IBIS annotation of biologically relevant protein-protein binding sites on a query relies on the conservation of binding sites among homologs. Crystal packing interactions tend not to be found in conserved binding site clusters because the latter are most likely to be biological binding sites. We have evaluated the efficiency of IBIS to correctly identify non-biological crystal packing interactions (as true negatives) using a set of 76 known crystal-packing interactions published previously As shown in We also evaluated the ability of IBIS to identify biologically relevant PPIs using a set of known protein interactions, in total, 74 biological interactions between protein chains compiled from two previous studies Finally, based on the test set and results presented by Zhang et al 4S4 clusters coupled by two bridging cysteine thiols. C. pasteurianum is quite different from that found in other bacteria.The nitrogenase enzyme system catalyzes the nitrogen fixation reaction present in many free-living bacteria, it is composed of two components, molybdenum-iron (MoFe) protein and iron (Fe) protein. MoFe-protein is a hetero-tetramer (α2β2 subunit) with two copies of the FeMo cofactor C. pasteurianum has been crystallized as an α2β2 tetramer without the Fe-protein dimer (PDB 1MIO) C. pasteurianum MoFe-protein (Cp1) protein based on solved nitrogenase complexes from other bacterial systems. Similarly the α subunit  was queried in IBIS. We compared inferred sites with MoFe protein and Fe-protein binding sites obtained by docking from previous studies C. pasteurianum (Cp1) has a unique insertion of 50 residues in length and none of the homologs of Cp1 used to infer binding sites contained this insertion.The MoFe-protein from The coverage and reliability of experimentally determined protein-protein interactions remains quite limited especially for higher organisms, therefore it is important to determine how many of these interactions can be recovered and verified by computational approaches using available information accumulated for protein structural complexes. We present a method which is based on the assumption that the structural location and sequence patterns of protein-protein binding sites are conserved between close homologs. Even though functional annotation transferred from one homologous protein to another can result in incorrect functional assignment, and inference of protein binding interfaces is reliable only among close homologs, we showed that inferring protein binding sites from homologous complexes is a remarkable help to annotate protein binding sites for many unknown protein complexes. Indeed, more than 37000 protein domains are currently annotated with IBIS inferred protein-protein interactions (not counting observed interactions) showing an increase by almost 10000 domains since January 2010. Moreover the IBIS framework can be used to guide and complete the CDD binding site annotations, as we show currently 75% of CDD families are missing protein-protein binding sites which are in turn are present in the IBIS database. We also found that there exists a trade-off between specificity and sensitivity if we use only conserved binding site clusters or clusters supported by only one observation (singletons). It implies that different strategies of inference should be applied depending on a particular task of prediction.There are a few inference based approaches to predict protein-protein binding sites Knowledge of the true oligomeric assembly/state of a protein is critical for correct annotation of functional binding sites. Deciphering the correct state is tedious and experimental methods like analytical ultracentrifugation, gel filtration, mass spectrometry and others provide useful but still limited information on the biologically relevant assembly. The only way to study the atomic details of protein-protein interactions is to use structures present in the Protein Data Bank (PDB) Figure S1Distribution of oligomeric states of multimeric structures with observed interactions in IBIS. State “−1” correspond to structures that either could not be processed by PISA or no stable assembly was predicted by PISA.(TIF)Click here for additional data file.Figure S2Distribution of the number of chains for structures predicted as monomers by PISA but present as multimers in PDB ASU. Bin “1” corresponds to intra-chain domain-domain interactions. Other structures represent cases with potential crystal packing interactions.(TIF)Click here for additional data file.Figure S3Distribution of PISA predicted multimeric states for structures present as a single chain in PDB ASU.(TIF)Click here for additional data file.


Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.http://iimcb.genesilico.pl/RNP/In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at  Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, protein synthesis, replication of viral RNAs, and virus assembly (review: ). DefectDocking methods are widely used to predict structures of macromolecular complexes, starting from structures of the individual components . All docOne interesting and frequently neglected aspect of RNA structure and interactions is the presence of posttranscriptional modifications, which increase the basic set of four nucleotides  to more than 100 variants with altered base and/or ribose moieties . ModifieRecently, new statistical potentials for scoring protein-RNA complexes have been proposed: a distance-dependent all-atom potential developed by the Varani group , and a rIn this article we introduce two new, medium-resolution, knowledge-based potentials for scoring protein-RNA models: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State  potentiaWe compare the discriminative power of our new statistical potentials to the Varani and Fernandez potentials. For the bound docking test set our potentials discriminated native-like (with RMSD < 10 Å) structures of protein-RNA complexes, the potential developed by the Varani group recognized structures very close to the native (RMSD < 5 Å), whereas the Fernandez potential recognized near native structures only for some protein-RNA complexes. For the unbound docking test set, our potentials have the highest discriminative ability of alternative models. Our new knowledge-based potentials are a useful tool for scoring protein-RNA complexes generated by macromolecular docking methods, such as GRAMM or Hex.To search the space of possible orientations and conformations of the components, we employed the GRAMM method for medium- to low-resolution docking . As oppoThe van der Waals radius was used as a projection of an atom. The value of the grid step was set to 1.7 Å for complexes from the bound docking set and to the minimal value allowed by the program for complexes from the unbound docking set, a repulsion parameter to 10  and attraction double range to 0. Ligand structures were rotated by 10° angle intervals. If significant steric clashes were observed in a large fraction of models obtained in preliminary GRAMM runs, as assessed by visual inspection, the repulsion parameter was increased to decrease the volume of steric conflict, until docking decoys reached physically reasonable geometry , but "spatially complementary" models are much better approximation of the real-life sampling. The false models were not selected by the GRAMM scores, as we found that the GRAMM score alone is a poor predictor of the complex native-likeness (data not shown). We treated all 10000 decoys per complex as equal, both for training of our potential, and for evaluation with all four potentials analyzed in this work.ε between the united atom type from the protein i and the united atom type from the RNA j is calculated according to Formula (1)The distance and orientation-dependent knowledge-based potentials described in this article (DARS-RNP and QUASI-RNP) were generated using reverse Boltzmann statistics, where the interaction energy R is the gas constant, T is the temperature, obs  Nis the number of contacts between atom types i and j observed in the training set in a distance/angular bin d, while exp  Nis the expected number of contacts at the same distance/angle in the reference state. There are two types of bins, connected with different terms of the potential: a distance bin (1 Å) used in the distance-dependent term, and an angular bin (20°) used in the angular-dependent term. The energy is calculated for each pair of protein-RNA united atoms that are within the distance of 9 Å from each other. This threshold parameter was chosen based on the analysis carried out by the Shakhnovich group [where ch group , who ideOur two statistical potentials calculate the reference state in different ways. The reference state for the QUASI-RNP potential is calculated using mole fractions of residues:i Xand j Xare the mole fractions of atom type i and j respectively, while obs (d) Nis the total number of contacts in bin d. On the other hand, in the DARS approach [exp  Nis a normalized number of contacts between atom types i and j in a set of decoy structures that are considered as random models. 1000 decoys were generated for each native structure of protein-RNA complex from the training set by using the GRAMM docking program [where approach Nexp , an angular-dependent energy term (Ea), a site-dependent energy term (Es), and a penalty for steric clashes (Ep)  Figure :The site-dependent term assesses the probability of interaction of amino acid residues with edges of nucleotide residues: Watson-Crick, Sugar and Hoogsteen, as defined by Leontis at.al  Figure .obs common for DARS-RNP and QUASI-RNP, Nexp for DARS-RNP, and Nexp for QUASI-RNP, respectively, as a function of distance, angle and nucleotide edges, while Additional Files All four terms of the energy function exhibit comparable values  [In order to compare our QUASI-RNP and DARS-RNP potentials to the previously published Varani potential we used the same training set as the Varani group . There ag, 2bh2) . One of Our software uses an in-house modified variant of PDBParser from Biopython  to parse PDB files and ignore information about e.g. atom occupancy.To test the discriminatory power of the QUASI-RNP and DARS-RNP knowledge-based potentials we compared them with two existing statistical potentials developed by the Varani  and FernTwo types of test sets were used, based on bound structures (components isolated from complex structures), or on unbound structures .1cvj, 1ec6, 1fxl, 1jid, 1urn) using the docking module of ROSETTA, through the use of the protein side chain repacking algorithm [We used one set of molecules with unmodified (bound) conformations of entire RNA molecules and protein backbones, but with optimized protein side chains. This set was developed by the Varani group to perform decoy-discrimination tests of their all-atom potentials ,26. Theilgorithm ,28. For The second set contains all molecules in unmodified (bound) conformations, and was generated by ourselves using the high-resolution mode of the GRAMM docking program. For each of the five protein-RNA complexes from the Varani test set 10,000 alternative docking decoys were generated according to the procedure described above in the Docking section.2rkj, 1wsu, 1ooa, 2r8s, 2pjp, 1lng, 2pxv, 1e7k, 1wpu, 3bso, 2qux, 2jea), previously used by the Fernandez group to test their potential [The unbound docking test set was based on twelve native protein-RNA complexes  has demonstrated that the scoring functions alone may not be the best discriminators of native-like structures, and better results may be achieved by clustering well-scored suboptimal structures ,30. We hIn order to compare the ability to identify near-native protein-RNA complex structures by our two potentials (QUASI-RNP and DARS-RNP), the potential developed by Varani et al. , and theWe tested the DARS-RNP, QUASI-RNP, Varani, and Fernandez potentials for scoring of RNA-protein complexes on two bound test sets, and examined which potential gives the best correlation coefficient between scores and RMSD of the corresponding decoys. For the Varani test set, the DARS-RNP and QUASI-RNP potentials recognized most structures with ligand RMSD < 10 Å. We found strong correlation between DARS-RNP scores and RMSD, as well between QUASI-RNP scores and RMSD for models up to 10 Å from the native structure Figure , Table 1In the bound docking test set generated by the GRAMM program, there is a smaller number of near-native structures than in the Varani set, and some of them exhibit steric clashes. For this test set, our DARS-RNP and QUASI-RNP potentials exhibit lower values of the correlation coefficient than for the Varani test set . One way to avoid such situations is to identify (or predict) the RNA-binding site on the protein surface and filter the initial decoys . Both DARS-RNP and QUASI-RNP favored a structure that is very different from the native complex, even though they were able to recognize native-like structures for the same complex generated by Varani. The visual examination of models for the 1JID complex that were best-scored by our potentials revealed structures, in which the RNA backbone has entered a very deep and narrow groove on the protein structure far from the true RNA-binding site, leading to decoys with very big area of protein-RNA interactions, and hence with significantly more contacts than in the native structure (solvent-accessible surface area buried upon complex formation: ~2600 ÅLTREST3D ) to remoIt is worth to mention that the five structures in the bound docking test sets were excluded from the training set only for the QUASI-RNP and DARS-RNP potentials. The training set of the Fernandez potential contained three out of five assessed complexes, and the Varani training set contained all five complexes. Therefore, the ability of the Varani and Fernandez potentials to discriminate native-like models for these structures may be overestimated. In particular, the Varani potential has the best results for those decoys from the GRAMM-generated unbound set that are very close to native structure (RMSD < 5 Å). There, the Varani potential easily recognizes the structures close to those in its training set. Interestingly, for decoys from the same set, with RMSD up to 10 or 20 Å, our potentials still exhibit better results than the Varani potential, suggesting that they do have a power to discriminate between these medium-quality decoys and those that are totally native-unlike.The results of tests for the unbound docking set are more objective due to a complete separation of training and testing data, and because they simulated the predictive power of the potentials in a real docking experiment, where the bound conformations of components are unknown. Among the four methods tested, QUASI-RNP and DARS-RNP potentials have the biggest average correlation coefficients between the scores and the RMSD of the model from the native structure. However, it must be emphasized that even these "winners" of our benchmark were able to identify native-like structures only for four out of eight tested cases Figure  and 7. AAmong the four knowledge-based potentials tested here for their ability to identify native-like protein-RNA docking models, the high-resolution Varani potential exhibited the best ability to recognize models closest to the native structure (RMSD < 5 Å). This potential appears as the method of choice for high-accuracy docking methods that are able to generate structures very close to the native ones. It must be emphasized, however, that there are no computational tools, with which to reliably predict conformational changes upon binding. Therefore high-resolution docking is only applicable in situations, where the receptor and ligand structures exhibit very little conformational change between the bound and unbound forms. Unfortunately, whether the conformational change occurs or not cannot be reliably predicted. In most cases of protein-RNA binding, moderate conformational changes of protein and/or RNA molecules occur upon complex formation. There, low resolution methods that apply a coarse-grained energy model to a coarse-grained representation (i.e. without looking at the atomic details that change upon binding) have a chance to be practically useful.Among the four potentials tested in this work, the Fernandez potential has the weakest discriminatory power for identification of near-native structures, but we believe this potential may perform much better when combined with the FTDock scoring function, as originally intended by the Fernandez group. However, the combination of Fernandez and FTDock potentials is possible only for FTDock decoys, because the FTDock potential is not available as a standalone program, hence we could not apply it to the decoys generated in our study. FTDock is also unable to deal with modified residues in the RNA, which precludes it from applicability to many RNA-protein complexes, where RNA modifications play a critical role (e.g. interactions of tRNA with aminoacyl-tRNA synthetases).The main difference between all potentials considered in this article is the (sub)set of atoms taken into consideration. The Varani potential considers interactions between all atoms, with chemically similar atoms (based on the CHARMM atom definition) treated in the same way. It contains only a distance-dependent multiple bin term. The Fernandez potential calculates interactions between entire residues represented as single interaction centers, using only one bin (i.e. the interaction is either present or absent). Our potentials use multiple bins for distance as well as orientation, hence they take into account more information about the possible arrangements of amino acid and nucleotide residues, even though they use less atoms than the Varani method.In our study we have used both bound and unbound conformations for docking . The shapes of score-RMSD dependence plots show differences associated not only with the methods, but also with the type of docking. As expected, all potentials exhibit best results for bound docking experiment, where there are many near-native structures without steric clashes. This observation underlines the influence of decoy generation method on the ability to successfully identify native-like decoys in the generated dataset.Our study allows for direct comparison of the decoy-based and quasi-chemical approaches for calculating statistical potentials. The small, but significant average advantage of the DARS-RNP potential over the QUASI-RNP potential can be explained by the more realistic treatment of "random" protein-RNA interactions. In the DARS-based approach, the statistics of amino acid-nucleotide contacts are inferred from geometrically plausible, but biologically irrelevant decoys (pseudo-complexes), while the quasi-chemical approach predicts the occurrence of such contacts based on the frequency of individual residues. We expect this advantage of the DARS approach to hold also for other types of docking. The calculation of a DARS-based potential requires, however, the calculation of a large number of decoys for each complex in the training set, hence it requires considerably bigger computational effort, which may be prohibitive in case of large training sets.By definition, none of the rigid-body docking methods analyzed here is capable of predicting the structures of complexes that involve large conformational changes. We also found that the presence of extensive steric clashes in decoys deteriorates the discriminatory power of all potentials tested in our benchmark. Thus, we propose that the next step in the development of methods for modeling of protein-RNA complexes should be taken towards algorithms that enable simultaneous docking and (re)folding of protein and RNA components. Recently, a number of methods for modeling of RNA 3D structures have been reported that utilize very similar methodology to that used for protein modeling ,33. ThisAmong the four potentials tested in this work the QUASI-RNP and DARS-RNP potentials exhibit the highest discriminatory power for both bound and unbound docking experiments. The small average advantage of the DARS-RNP potential over the QUASI-RNP potential can be caused by the more realistic treatment of "random" protein-RNA interactions. None of the rigid-body docking methods analyzed here is capable of predicting the structures of complexes that involve large conformational changes. Our potentials recognized near native structures only for these complexes, whose components exhibit relatively small structural changes (RMSD < 3 Å) during formation of the protein-RNA complex.http://iimcb.genesilico.pl/RNP/; Operating system: Linux; Programming language: Python; Other requirements: Biopython and NumPy libraries; Licence: GNU GPL; No restrictions to use by non-academics.Project name: Statistical potentials for Protein-RNA docking; Project home page: RMSD: Root Mean Square Deviation; RNP: RNA-protein complexes; QUASI-RNP: QUASI-chemical potential for RNA-Protein complexes; DARS-RNP: Decoys As Reference State potential for RNA-Protein complexes.IT developed statistical potentials, performed testing procedures, drafted the manuscript and prepared the figures. JMB conceived of the project and edited the manuscript. Both authors analyzed and interpreted the data. All authors read and approved the final manuscript.Additional_figures_and_table.pdf. Files and tables with additional data illustrating the details of implementation, ordered according to their appearance in the text.Click here for fileEnergy_DARS.pdf. Energy for each distance, angle, and site bin, for each pair wise interaction in the DARS-RNP potential.Click here for fileEnergy_QUASI.pdf. Energy for each distance, angle, and site bin, for each pair wise interaction in the QUASI-RNP potential.Click here for fileObserved.pdf. Observed number of contacts in each distance, angle, and site bin, for each pair wise interaction.Click here for fileExpected_DARS.pdf. Expected number of contacts in each distance, angle, and site bin, for each pair wise interaction in the DARS-RNP potential.Click here for fileExpected_QUASI.pdf. Expected number of contacts in each distance, angle, and site bin, for each pair wise interaction in the QUASI-RNP potential.Click here for file