Cellular activities are governed by the physical and the functional interactions among several proteins involved in various biological pathways. With the availability of sequenced genomes and high-throughput experimental data one can identify genome-wide protein-protein interactions using various computational techniques. Comparative assessments of these techniques in predicting protein interactions have been frequently reported in the literature but not their ability to elucidate a particular biological pathway.Escherichia coli catalogued in KEGG database using five protein-protein functional linkage prediction methods. These methods are phylogenetic profiling, gene neighborhood, co-presence of orthologous genes in the same gene clusters, a mirrortree variant, and expression similarity.Towards the goal of understanding the prediction capabilities of interactions among the specific biological pathway proteins, we report the analyses of 14 biological pathways of Our results reveal that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. These methods have predicted functional associations of proteins involved in amino acids, nucleotide, glycans and vitamins & co-factors pathways slightly better than the random performance on carbohydrate, lipid and energy metabolism. We also make similar observations for interactions involved among the environmental information processing proteins. On the contrary, genetic information processing or specialized processes such as motility related protein-protein linkages that occur in the subset of organisms are predicted with comparable accuracy. Metabolic pathways are best predicted by using neighborhood of orthologous genes whereas phyletic pattern is good enough to reconstruct central dogma pathway protein interactions. We have also shown that the effective use of a particular prediction method depends on the pathway under investigation. In case one is not focused on specific pathway, gene expression similarity method is the best option. Proteins are responsible for almost every cellular function of an organism such as behavior, metabolic activities and other phenotypic traits. Since proteins never work in isolation, identifying cellular function is crucial for understanding their role at system level. The cellular function of a protein is equivalent to the biological process/pathway in which it participates Reconstruction of biological pathways is possible through genome-wide physical/functional Protein-Protein Interactions (PPIs) predicted by these methods. However, only few studies have addressed the benefits and the limitations of each method to reconstruct biological pathways Escherichia coli K12 MG1655 (E. coli). Our results identify the best method suited for the prediction of biological pathway interactions. These results also provide pointers to the evolutionary and co-regulatory constraints on the proteins.Considering the independent evolutionary histories of pathways E. coli proteins for which pathway memberships were recorded in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database A schematic representation of five PPI prediction methods used in our analysis and overall approach is given in True Positives (TP) and False Positives (FP) were recorded at a series of interaction score thresholds generated for KEGG protein pairs by each prediction method. TP were the pairs with the proteins that share at least one KEGG pathway and their scores are equal to or above threshold. If they do not share KEGG pathway they were treated as FP. The positive examples were further categorized into 14 distinct pathways or functional categories with 50 or more proteins at 2nd level of KO definition, such as nucleotide and lipid metabolism . These 1Mycoplasma pneumoniaeMycoplasma pneumoniae show alternate transcript activity Prediction performance of five methods for detection of functional linkages among proteins belonging to eight metabolic pathways is shown in A set of other four pathways responsible for metabolism of carbohydrate, lipid, energy and non-standard amino acids was randomly predicted by all methods . As showOverall, our results suggest that GN is the best method to predict functional linkages among metabolic proteins. However, as discussed in the previous section , one shoCentral dogma of life related pathways with 50 or more protein components namely, translation, protein folding, sorting & degradation and replication & repair pathways are classified under genetic information processing term in KEGG . TranslaAnother housekeeping function performed during the protein synthesis is proper folding of nascent polypeptide followed by its transport to respective locations in the cell and degradation, if not folded properly or when their function is no longer needed. The best performing methods to predict folding, sorting and degradation pathways were PP and GM suggesting co-inheritance of the protein components and their physical association with each other . The AUCReplication and repair pathway protein components are involved in maintenance of chromosomes. This also includes one of the indispensable functions, the replication of DNA molecules during cell division which is common to life forms. The linkages among these proteins were mainly predicted by PP with AUC value of 0.71 followed by GM with 0.66. ROC curves are well separated and above right diagonal suggesting that the predictions could be achieved with reasonable accuracy . These rOverall, genetic information processing pathway proteins were maintained by strong co-inheritance patterns and they are possibly associated with each other by physical interactions in the cell.Signal transduction and membrane transport pathways were predicted with poor accuracy . The intOverall our results suggested that the prediction of signaling and membrane transport pathway PPIs is a daunting task for all methods.Cell motility related proteins are categorized under the term ‘Cellular processes’ in the KEGG database and are sub-divided into chemotaxis and flagellar assembly pathways. These PPIs were predicted relatively better by PP and ES methods with AUC values of 0.66 and 0.63 respectively . The outThe comparative assessment of five methods for their ability to predict protein-protein functional linkages of various biological pathways showed that every method is prone to erroneous predictions. The prediction of associations among the metabolic pathway proteins remains a challenging task for these methods, possibly due to their flexible/independent evolutionary histories. The lower performance of PP method has indicated an irregular phylogenetic distribution of proteins involved in the majority of metabolic pathways. These metabolic pathways are responsible for the turnover of carbohydrates, amino acids, lipids and non-standard amino acids. The PP method delineates the fact that, 1) the most of the metabolic genes are dispensable due to functional redundancy and this could be the underlying reason for weak correlation in their phyletic patterns, 2) Besides, each metabolic pathway has evolved around few core genes that are well conserved, whereas majority of other genes are versatile enough to offer a species/ecological niche specific framework for the environmental adaptation. Since, the proteins involved in metabolism of nucleotides, glycans, vitamins and co-factors are well conserved, PP and GN methods worked with greater accuracy.Majority of metabolic pathway protein linkages ranked well by gene neighborhood based method suggesting that the genes encoding them are in close proximity to each other on chromosome in one of the reference genomes. Such organization of genes involved in the extant metabolic pathways are probably shaped and arisen due to duplication of the same genes in-tandem. GN and GC methods could be used as powerful tools to predict functional linkages among the metabolic proteins. On other hand, GM method could make reliable predictions as far as nucleotide metabolic protein-protein linkages are concerned.As opposed to the above-mentioned lower performance in predicting metabolic protein linkages, these methods are extremely good in detecting interactions among proteins that are responsible for housekeeping processes and other subset of processes such as cell motility. Functional linkages between proteins that participate in the central dogma pathway can be predicted with reliable accuracy by PP, GM and ES methods. Higher prediction accuracies for these pathways were obvious since participating proteins form small complexes or molecular machines that are essential for genetic information processing. However, we suggest that these proteins not only show strong co-inheritance but also interact physically being co-expressed. Prediction of information processing pathways such as signal transduction and membrane transport remains the challenging task for these methods. None of the methods could show enhanced accuracy than the random predictor in detecting associations among these proteins.Metabolic and information processing pathway predictions with respect to the central dogma, consistently showed very small AUC values. These AUC values ranged mostly from 0.5 to 0.6 and can be rated slightly above random performance, indicating high proportion of false positive predictions. The acceptable range of AUC values from these methods have been achieved only for the central dogma pathways. Therefore, we believe the reported accuracies in all previous prediction analyses are likely to be the overestimated and resulted mainly from central dogma pathway predictions. These facts suggest that majority of methods used in this study are not enough to predict metabolic and information processing pathways with enough accuracy. Furthermore, the genome-scale predictions made by these methods are likely to have less coverage of protein linkages involved in these pathways. Furthermore, the careful selection of prediction methods along with pathway specific sequence features would enhance the prediction quality.In concluding remarks, overall AUC values of predictions are not very convincing for majority of the pathways. There is a differential tendency of various methods to predict various biological pathways. Therefore, we suggest that the potential use of a particular prediction method depends on the pathway under investigation. In making generalized prediction, ES method is the best option.ftp://ftp.ncbi.nih.gov/genomes/BacteriaEscherichia coli K12 MG1655 (E. coli) as a query/model organism. Orthologs of E. coli proteins were identified by reciprocal best hit search performed using NCBI Basic Local Alignment Search Tool (BLAST) Completely sequenced genomes of Bacteria available as on December 2007 at National Center for Biotechnology Information (NCBI) were downloaded from E. coli proteins were calculated using five PPI prediction methods which include GN, GC, ES, PP, and GM methods. In our previous study, we benchmarked methods other than ES against six reference genome sets  E. coli. The dimension of the each protein distance matrix would be equal to the number of genomes/species in which orthologs of that protein identified. Suppose, protein ‘X’ is identified in n species then the dimension of the matrix DX would be n x n. Each row or column of the matrix corresponds to a species under consideration. An element of the matrix DX  represents the distance calculated by comparing the amino acid sequences of orthologs of protein X from species i, and j. Similarly, distance matrix derived for protein ‘Y’ using MSA of its orthologs from n species. To correlate the distance matrices of protein X and Y, the distance between the species that were common to both matrices were retained. All the distance matrices were symmetric, hence upper or lower half of the matrix can be used for comparison. Since the distances that correspond to common species between two matrices were retained, one can use standard Pearson Correlation Coefficient (PCC) to calculate the extent to which two distance matrices are similar. The higher PCC value suggests the correlated amino acid substitutions in the orthologs of protein X and Y. Hence, it is likely that protein X and Y interact physically. There are several ways to exclude the speciation information or background noise due to the relatedness of species from these matrices DG which is similar to protein matrices where each row or column corresponds to a species/genome under consideration. An element of the matrix DG  represents the Genome Distance (GD) based on shared orthologs between species i and j which was calculated with the following equation,An and Bn is the total number of proteins present in species i and j, respectively. A∩Bn is the number of orthologs shared by species i and j. The orthologs were obtained for each of the species using a bi-directional BLAST searches against each other.GM or Genome distance-Mirrortree method is a variant Tol-mirrortree DG matrix  of protein X was subtracted from the  element of matrix DG). Prior to correction, all matrices were re-scaled due to the different scale of proteins and GD matrix. For this, we calculated PCC between DG matrix and every distance matrix of E. coli proteins. The highest PCC value obtained was 0.54 which was used to divide elements of each matrix before subtraction. This approach is referred as GD-Mirrortree (GM) and performed better than tol-mirrortree method Each protein distance matrix was corrected by subtracting its elements from the corresponding value in the E. coli orthologs E. coli protein and reference genome respectively i,j) element of the matrix represents bit score of alignment between protein i and its ortholog in reference genome j. Matrix was normalized with respect to protein and species divergence A set of 448 reference genomes was created by selecting one genome from a cluster obtained using the genomes that share more than 90% of E. coli protein coding gene pairs using PCC.Similarly, the gene expression data for 380 conditions was downloaded from M3D database To assess the performance of each PPI prediction method with respect to KEGG pathways, protein pair was considered as functionally linked or positive if both proteins shared at least one KEGG pathway, and unrelated otherwise i.e. negative. For analysis of individual KEGG pathways, however, two proteins were considered to be functionally linked or positive if they belonged to the same KEGG pathway, otherwise unrelated or negative with respect to pathway under consideration For a chosen threshold interaction score calculated by PPI prediction methods, the protein pairs with scores greater than or equal to the threshold and belong to positive examples are classified as True Positives (TP) and those with a interaction scores below the threshold are classified as False Negatives (FN). Similarly, the protein pairs with interaction scores greater than or equal to the threshold and belong to negative examples are classified as False Positives (FP) and those with a score below the threshold are classified as True Negatives (TN).http://www.r-project.org/) These labels were used to assess the performance of PPI prediction methods in the form of TP to FP ratios. ROC curves and AUC values were calculated using ROCR package in R language for statistical programming Click here for additional data file.Text S1Benchmarking details of physical and functional protein-protein interaction prediction methods.(PDF)Click here for additional data file.


Saccharomyces cerevisiae allow us to focus on a single species and to use all the available structures, correcting for redundancy, instead of using structural representatives. This allows us to control for potentially confounding factors that may affect sequence propensities. We find a significant contribution of main-chain atoms to protein-protein interactions. These include interactions both with other main-chain and side-chain atoms on the interacting chain. We find that the type of interaction depends on both amino acid and secondary structure type involved in the contact. For example, residues in α-helices and large amino acids are the most likely to be involved in interactions through their side-chain atoms. We find an intriguing homogeneity when calculating the average solvation energy of different areas of the protein surface. Unexpectedly, homo- and hetero-complexes have quite similar results for all analyses. Our findings demonstrate that the manner in which protein-protein interactions are formed is determined by the residue type and the secondary structure found in the interface. However the homogeneity of the desolvation energy despite heterogeneity of interface properties suggests a complex relationship between interface composition and binding energy.Most proteins attain their biological functions through specific interactions with other proteins. Thus, the study of protein-protein interactions and the interfaces that mediate these interactions is of prime importance for the understanding of biological function. In particular the precise determinants of binding specificity and their contributions to binding energy within protein interfaces are not well understood. In order to better understand these determinants an appropriate description of the interaction surface is needed. Available data from the yeast  Saccharomyces cerevisiae, which is the most comprehensively studied organism, the majority of proteins are involved in some sort of complex Protein-protein interactions (PPIs) underlie biological function at the molecular level. In the yeast There have been previous characterisations of several aspects of protein interfaces, for example studying protein-protein Knowledge of interface characteristics have been used in a variety of ways, for example the identification of protein interfaces Some broad trends of interface propensities have been identified. Interface patches must be highly accessible, even if most of their individual components are hydrophobic PPI interfaces differ between homo-complexes and hetero-complexes. These include differences in amino acid composition, interface size and contact preferences, Interestingly, PPI binding interfaces are heterogeneous, with individual residues making differing contributions to binding and a minority of residues contributing the bulk of the binding energy et al.,et al.,e.g.,Despite a degree of agreement, there are differences between previous studies. Chakrabarti S. cerevisiae. This limitation to a single species allows us to control for confounding factors associated with selection pressure on residue content, and so give an accurate picture of the relative propensities and roles of specific residue types.The large number of known protein-protein interactions from yeast, and the increase in the size of Protein Data Bank http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html) http://www.ebi.ac.uk/pdbe/pqs/index.html) Structures of protein complexes were extracted from the PISA (ftp://ftp.ncbi.nih.gov/genomes/Saccharomyces_cerevisiae/), or 3) did not have any hit (neither member of the pair compared) but had more than 80% identical residues. The rest of chains were assigned as being different proteins. We define homocomplexes as those complexes where all the chains were the same and heterocomplexes as those when all the chains were unique. We excluded those multimers with a mixture identical and different chains. In addition, homocomplexes can contain homo- and heterointerfaces depending on the orientation of the chains when interacting .In order to overcome the mentioned biases we used a different approach that consisted in using all the available structures. This permitted to analyse all the available PPIs and binding conformations. As not all proteins were equally distributed, we had to correct for the redundancy of the datasets. We identified two different sources of redundancy: 1) homocomplexes contain two or more identical chains (complex redundancy); 2) some chains are present in more than one structure (dataset redundancy). In order to not overestimate the contribution of any protein, we assigned a contribution for each chain equal to its redundancy factor (RF).c is the number of chains in the homocomplex and hd is the number of structures containing a specific protein at least once. In equations 2 and 3, the analysed protein is included in the count; so, if a protein were unique in the dataset, its contribution is 1, otherwise, it would be less. Obviously, heterocomplexes have a CR equal to 1.Propensities (p) show the enrichment or depletion of each feature in the interaction area or the rim compared to the whole of the protein surface.th feature in the rim or interaction area, and th feature in the protein surface.Frequencies are calculated as the total contribution of the feature in one of the areas compared to the contribution of all present features.th feature that lies in the interface or rim areas; th feature; and, For features based on residue counts , the contribution of each residue is equal to its redundancy factor. Individual amino acid propensities are based on the residue's accessibility  of DT The ΔGe.g., one species vs. several species, size of datasets, use of updated datasets) or to different methodologies . It is likely that the use of our larger data set derived from a single species and the ability for analysing variant interfaces has reduced some differences, at least with respect to the yeast interactome.We calculated the frequencies and propensities of interacting residues and those in the “rim” of the interface . The comAs observed previously There has been previous disagreement about the similarities or differences between the interface composition of homo- and hetero-complexes. Chakrabarti and Janin .We find that the frequency of each amino acid is very similar between homo and heterocomplexes in both the rim and interaction areas (Pearson's correlation (r) equal to 0.95 and 0.93, respectively). Moreover, propensities of interacting residues (r = 0.91) are also strongly correlated. However the correlation between hetero-rim propensities and homo-rim propensities is lower (r = 0.71). Together these results suggest that both types homo- and hetero-complexes use amino acids in a similar way to establish interaction contacts, while some small residues (e.g. Gly and Cys) combine with polar amino acids so as to establish a favourable neighbourhood so as to not interfere with the atomic contactsi.e., they must be desolvated) so as to establish interactions with their partners. We calculated the solvation energy, which is the amount of energy associated with the return of solvent molecules and the inverse of the desolvation energy.During the binding process, solvent molecules must be removed from the binding interfaces of monomers  and the non-interfacial protein surface see . The solWe next examined the types of atoms that are found in binding interfaces and the interactions they establish. Almost two thirds of interactions are side-chain to side-chain contacts, a larger proportion than previously reported , 2) Lys has many contacts involving backbone atoms despite its size, 3) the difference in surface area between Ile and Leu could be the reason for their differences in interaction type despite their similar volume, and 4) Pro has many side-chain interacting atoms despite being a relatively small amino acid. The functional differences between Arg and Lys have been discussed previously We tested whether different residues participate in interactions in similar ways. We find that the type of atoms involved in the interactions may depend on the volume of the residues see . Larger In a study of transiently-interacting heterocomplexes, Neuvirth et al Types of atomic interactions differ amongst the secondary structure elements . SpecifiOur analysis of protein interaction interfaces is, to date, based on the largest available dataset, and the study first based on a single species. Our analysis also differs from others in that we take into account that homo-complexes have homo-interfaces and hetero-interfaces. Around two thirds of atomic interactions occurring on yeast interface are between side-chain atoms. If we count the total proportion of side-chain atoms that take part in PPIs  we find that these comprise only 78% of interface atoms, with the remaining 22% of interacting atoms consisting of backbone atoms. Interestingly, these percentages are not evenly distributed among all the amino acid types or the secondary structure elements present in yeast interfaces. Those amino acids with large volumes are more likely to make side-chain interactions than smaller residues, probably because a larger proportion of those residues' atoms are in the side chain. We also find that α-helices are commonly found to make side-chain interactions. Within α-helices the side chains protrude outwards from the axis of the helix, shielding the majority of the main chain atoms from making interactions. At the ends of helices specific side chains often make capping interactions As previously seen vs hetero-interfaces and obligate vs transient interactions. Homo-oligomeric complexes are frequently obligate complexes, i.e., complexes that form soon after folding and remain bound for the lifetime of the complex. Hetero-oligomeric complexes may either be obligate complexes (for example the proteosome) or transient interactions . Obligate and transient interactions differ in many of their characteristics In contrast to previously published work More single-species studies will be possible in the future, making it possible to determine whether divergent species use the same recognition and stabilisation strategies for establishing protein-protein interactions. In the meantime, studies on the evolutionary conservation of the bonds may inform binding specificity. These combined efforts are likely to produce an improvement on the computational methods for predicting protein-protein interactions.File S1Structural data used from heterocomplexes. Interaction data is presented in three lines. Lines one contains the PDB code, the database we extracted the quaternary structure from and the name of the file (conformation) used. Lines two and three contain the interacting residues and the residues in the rim, respectively. Each residue is identified by the chain and the residue index it has in the original file. Note that quaternary structures databases can contain redundant chain names. Prior to the analyses, chains were renamed to avoid ambiguity; however, the information presented below refers to the chains as they appear in the original files.(TXT)Click here for additional data file.File S2Structural data used from homocomplexes. Interaction data is presented in three lines. Lines one contains the PDB code, the database we extracted the quaternary structure from and the name of the file (conformation) used. Lines two and three contain the interacting residues and the residues in the rim, respectively. Each residue is identified by the chain and the residue index it has in the original file. Note that quaternary structures databases can contain redundant chain names. Prior to the analyses, chains were renamed to avoid ambiguity; however, the information presented below refers to the chains as they appear in the original files.(TXT)Click here for additional data file.


Protein rigidity analysis is an efficient computational method for extracting flexibility information from static, X-ray crystallography protein data. Atoms and bonds are modeled as a mechanical structure and analyzed with a fast graph-based algorithm, producing a decomposition of the flexible molecule into interconnected rigid clusters. The result depends critically on noncovalent atomic interactions, primarily on how hydrogen bonds and hydrophobic interactions are computed and modeled. Ongoing research points to the stringent need for benchmarking rigidity analysis software systems, towards the goal of increasing their accuracy and validating their results, either against each other and against biologically relevant  parameters. We propose two new methods for modeling hydrogen bonds and hydrophobic interactions that more accurately reflect a mechanical model, without being computationally more intensive. We evaluate them using a novel scoring method, based on the B-cubed score from the information retrieval literature, which measures how well two cluster decompositions match.To evaluate the modeling accuracy of KINARI, our pebble-game rigidity analysis system, we use a benchmark data set of 20 proteins, each with multiple distinct conformations deposited in the Protein Data Bank. Cluster decompositions for them were previously determined with the RigidFinder method from Gerstein's lab and validated against experimental data. When KINARI's default tuning parameters are used, an improvement of the B-cubed score over a crude baseline is observed in 30% of this data. With our new modeling options, improvements were observed in over 70% of the proteins in this data set. We investigate the sensitivity of the cluster decomposition score with case studies on pyruvate phosphate dikinase and calmodulin.http://kinari.cs.umass.edu.To substantially improve the accuracy of protein rigidity analysis systems, thorough benchmarking must be performed on all current systems and future extensions. We have measured the gain in performance by comparing different modeling methods for noncovalent interactions. We showed that new criteria for modeling hydrogen bonds and hydrophobic interactions can significantly improve the results. The two new methods proposed here have been implemented and made publicly available in the current version of KINARI (v1.3), together with the benchmarking tools, which can be downloaded from our software's website,  As new generations of bioinformatics systems are released with new features and updated methods, it is vital to ensure that their results continue to match or improve upon previous generations. A number of protein rigidity analysis software systems have been built, including MSU-FIRST (now ProFlex) , ASU-FIRThe main goal in our research is to validate the predictive power of rigidity analysis systems. Towards this goal, we propose new modeling methods for incorporating noncovalent interactions that may improve accuracy. We also propose a general methodology for benchmarking protein rigidity analysis systems. Included in this a method to assign a score to a predicted cluster decomposition, compared with decompositions produced by some other method. This is an adaptation of the B-cubed score from the information retrieval literature, which is used as a comparative score on two clusterings of the same data .We use this evaluation method to benchmark our software, KINARI, against other previously available systems, MSU-FIRST and ASU-FIRST. In our benchmarking we use two data sets: the first is composed of several proteins used to validate the MSU-FIRST software ,5 and thProtein rigidity analysis. Rigidity analysis is a well-established method, implemented in several software systems, for analyzing flexibility of proteins. With this method, the molecule is modeled as a mechanical framework, in which distance and angle constraints are derived from chemical bonds and other inter-atomic interactions. A fundamental question in protein mechanics is to identify groups of atoms that move rigidly together, called rigid clusters, and the inter-connectivities that hold the protein together while permitting flexing. The pebble games are a family of combinatorial algorithms that work correctly and efficiently on certain families of mechanical structures. They has been implemented in several software packages  is thus crucial for an accurate representation of the flexibility characteristics of proteins".The power of a benchmark lies in fast hypothesis testing, which we demonstrated first by proposing new modeling methodologies for hydrophobic interactions and weak hydrogen bonds. The greatest improvement in B-cubed scores, most significantly in the small to medium sized proteins, resulted when both the modeling of hydrogen bonds and hydrophobic interactions were varied. This supports an earlier insight made by Gohlke Because we had access to decompositions that were well-validated against experimental studies, we were able to find combinations of parameters which maximized the B-cubed score and therefore the matching with the gold standard. Computing the B-cubed score at multiple cutoffs helps in choosing the best cutoff, so the score can be used to automatically tune parameters using a training set. To improve pebble game rigidity analysis as a predictive tool, the next steps should be toward finding the relevant features of a protein and its PDB structure which will lead to the best tuning of the parameters to capture biologically important characteristics in the decomposition. At a minimum, the features should include the size of the protein and whether the conformation is open or closed.A and D are used for the "acceptor" and "donor" atoms in a hydrogen bond. GS is used for "gold standard".DOFs is used for "degrees of freedom". H-bonds is used for "hydrogen bonds". The authors declare that they have no competing interests.NF designed and ran the experiments and wrote the supporting code. IS oversaw all work and designed the modeling scheme of the KINARI software. Both authors drafted the manuscript, and read and approved the final version.This article is an expanded version of an extended abstract that appeared previously, which focused on the modeling of hydrogen bonds. In this version, we have made substantial extensions in order to improve the quality and completeness of our contributions. We have proposed a new method for modeling hydrophobic interactions. We have added an analysis comparing results of our system, KINARI, with published results from an earlier protein rigidity analysis called MSU-FIRST. An additional 9 PDB files have been added to the to the data set in order to compare our system with MSU-FIRST, and additional case studies are presented. The new options for calculating hydrophobics with energies are now available from the KINARI-Web server, as well as a new feature to view histograms of the distribution of interactions by energy. We have made the scripts and dataset for performing the benchmarking available for download from the KINARI website.


The prediction of the network of protein-protein interactions (PPI) of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose here an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. We developed new estimators of the coevolution between proteins and combined them in an ensemble learning procedure.Escherichia coli and compared it to previously published methods. We show that our method allows prediction of PPI with an unprecedented precision of 95.5% for the first 200 sorted pairs of proteins compared to 28.5% on the same dataset with the previous best method.We applied this method to a dataset of known co-complexed proteins in E. coli.A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in  Protein-protein interactions are involved in most cellular processes. The knowledge of the complete network of protein interactions of a given organism (its “interactome”) helps to understand complex biological processes such as signalling cascades, metabolism or transcription control Proteins that interact, whether physically or not, are expected to be co-evolving: any evolutionary event affecting one protein might indirectly or directly impact all of its interacting partners ). Even though this new combined approach, applied to E. coli, gave better results than each prediction method taken independently, the number of False Positives (FP) and False Negatives (FN) remained worth considering. Moreover, because different prediction methods call for different types of data and independent implementation, the computation of all of the features is time-consuming and missing values are frequent.Recently, García-Jiménez mirrortree. Improved version of the basic Phylogenetic Profiles method have already been proposed . Concerning the mirrotree method, many improvements have been proposed: removal of the background similarity between the trees prior to the mirrortree analysis , this method does not rely on experimental data, but solely on genome sequences. It extracts features related to coevolution between proteins and uses a machine learning approach to combine them. We developed new features based on two published methods, namely PP and E. coli since we have a good dataset of its interactome. We then compared our set of predictions to those obtained using the mirrortree, tol-mirror and context-mirror methods with the same E. coli dataset.To challenge the efficiency of our method, we used it to detect PPI in context-mirror method We show that new features, directly inspired from old ones based on coevolution, associated with a powerful combination of classifiers in a learning procedure, allowed the prediction, with an unprecedented precision, of the interactions between proteins in organisms for which experimental data are not available. We obtained an area under the ROC curve (AUC) of 0.93 with our method, a value surpassing that of the E. coli, allowing detection of new links between them by previously unpublished interactions. Most of these new links are concordant with text-mining results and one of them is even confirmed by a binding experiment performed after the creation of the gold standard dataset used here. This demonstrates the validity of our method and gives new insights on a complex self-assembling nanomachine that allows bacteria to move in their environment and swim up chemical gradients.Finally, we analysed in detail the 50 best predicted pairs. We focused on 3 well-known complexes: chemotactic components, the flagellar apparatus and RNA polymerase complexes in mirrortreetol-mirrorcontext-mirrorWe present the results in terms of ROC curve, AUC, recall and precision curves, using (i) different methods proposed earlier to predict protein-protein interaction: mirrortree and tol-mirror approaches seem to perform poorly compared to the recent context-mirror method and to the method we propose here. This is clear from the ROC curves presented in mirrortree and tol-mirror methods respectively, while the context-mirror and our proposed method give AUCs of 0.87 and 0.93 (sd = 0.0028 for the latter) respectively , gave a prediction efficiency higher than each classifier taken independently . The bagtopology, matrix, tree and PP, see Material and Methods) taken independently to correctly rank the positive and negative pairs of the E. coli interaction dataset (We investigated the ability of each of the 4 classes of features ( dataset .topology and matrix classes have similar prediction efficiency (AUC = 0.78 and AUC = 0.80 respectively), while their combination (the tree class) improves the prediction efficiency . This shows that the features based on tree topology and those based on matrix comparisons measure different “aspects” of the coevolution between proteins. The PP class alone is good at predicting protein-protein interactions, with an AUC of 0.92 (sd = 0.0036). The AUC is however improved when combining the tree class and the PP class of features . Features of the a-priori on the number of pairs to remove, and it does not guaranty an optimal AUC of the remaining pairs, because it does not take advantage of the prediction of each classifier taken independently. Our proposed approach uses a threshold (α) associated with the percentage of classifiers predicting that a pair is not interacting .We developed a method for filtering out non-interacting pairs, that is removing the highest proportion of negative examples but at the same time disregarding the lowest proportion of positive ones. A simple solution to this problem would be to remove a pre-determined number of pairs at the end of the ranked list of pairs. However this has two limitations: it requires an α, from 0.01 (stringent filter) to 1 . Thus, our method filters out negative pairs without losing too many positive ones. The effect of the filtering strength on the quality of the ranking (AUC) for the different groups of features presented in In order to evaluate the ability of our method to correctly filter out negative pairs, we plotted the proportion of negative examples filtered out against the proportion of positive examples lost for different values of α between 0.01 and 1 , the AUC does not appear better with our method than with context-mirror. This could be seen as a decrease of the efficiency of the method when a moderate filter is applied because the overall ranking of positive and negative examples is not better with our method than with context-mirror in this case. This shows the limitation of using AUC for estimating the quality of a method. Indeed, whatever the value of α, the number of positive examples analysed is always higher than with the context-mirror method  but performs worse when α decreases. The combination of different classes of features thus results in a method whose behaviour in terms of AUC is almost uniform with respect to filtering.We compared the effect of filtering on the AUC when using the different classes of features presented in To estimate the efficiency of our approach, we focused on the nature of the pairs of proteins considered as non-interacting according to the goldstandard dataset we used (negative pairs). Among the 50 best predicted pairs, almost 50% of them (23 out of 50) are referenced in the STRING database rpoD, rpoE, rpoH, rpoS, rpoN, fecI and fliA, respectively) depending on physiological conditions. Unexpectedly, we detected a link between the housekeeping sigma factor RpoD (sigma 70) and the heat shock sigma factor RpoH (sigma 32). Such a link would be in accordance with the finding that the in vitro expression of the rpoH gene was found to require RpoD Brucella melitensisE. coli RpoH) is involved in the expression of the proximal rod protein FlgF. The two flagellar components FlgG and FliM that interact with FliA are themselves found to be connected to two chemotaxis proteins, cheW and cheA, respectively. The link between the C ring FliM and cheA could be a supplementary safety to the well known interaction between FliM and the phosphorylated form of CheY necessary to induce the switch in the clockwise/counterclockwise rotation of the flagellum. Indeed, CheY, the response regulator of bacterial chemotaxis, is phosphorylated by the histidine kinase CheA. It has been reported E. coli. More work is needed to investigate all of these pairs , is known to bind one of the seven sigma factors . Using only sequence similarity, we obtained an AUC between 0.52 and 0.53, depending on the method used for detecting homologous sequences.This value is very close to what would be expected if the ranking was done randomly (AUC = 0.5). These tests show that sequence similarity alone does not provide sufficient information to predict protein-protein interactions, and consequently that the high accuracy we obtained with our proposed method cannot be accounted for by redundancy in the data.Escherichia coli. Our method is conceptually based on coevolution of protein partners and uses features inspired by two methods developed earlier: mirrortreequality measures normally used in data mining approaches in order to detect coevolution based on pattern of presence/absence of proteins in the different species.We have designed a new machine learning method for the prediction of protein-protein interactions and used it to predict interacting proteins in the model organism Quality measures contributed to increase the predictive power of the method and confirmed that extended computations based on PP could give more information than the classical PP method as initially proposed by Pellegrini et al.context-mirror method, context-mirror method for the same dataset) and also filters out negative pairs with more accuracy, by losing fewer positive ones.It is difficult to estimate the independent contribution of each feature to the predictive power of a method such as the one we propose here. However, (i) the combination of features based on topology alone and matrix alone gave better prediction (higher AUC) than each group of features taken independently, proving that different aspects of coevolution between proteins can be extracted depending on the way the trees are compared. (ii) context-mirror, are conceptually and methodologically different. While our method is based on a machine learning approach, so that a dataset of known interactions is required for training the model, the other methods do not rely on such a process. Comparing the performance of machine learning methods like ours and ab-initio methods can still be done, but one has to keep in mind that their requirements and range of applicability are not the same.It is important to note that the method we propose here and the three methods we compare with, including α parameter. We believe that this is an important aspect of this work. Depending on whether the goal of the PPI detection is either to score the maximum number of pairs and to reconstruct the complete network of interaction, or to obtain a reduced list of pairs in which one wants to be sure of having only positive ones, the α parameter can be changed, from no filter (α = 1) to a strict filter. We see that with a very strong filter (α = 0.01), more than 99% of the negative pairs were removed with concomitant loss of only half of the positive ones. This means that we were able to go from a dataset including 0.817% of positive pairs to a dataset made almost exclusively of positive ones.The combination of the outputs of the 8 classifiers was made using an ensemble learning method. Each classifier is trained to predict if two proteins are interacting or not, and a degree of confidence is associated to each prediction. Ensemble learning is known to behave better than single classifiers positive pair and proteins not involved in a complex are negative ones. Thus, our method cannot differentiate between direct and indirect physical interactions. Disentangling these two types of interaction is a difficult task that has been subject to much work in the recent years. Having reliable candidates for putative direct interactions is at the basis of a class of computational methods aimed at predicting the 3-dimensional structure of protein complexes, called “docking”. Our work could therefore be seen as a promising first step for the detection of direct interactions and the subsequent docking of proteins, by limiting the number of interactions to be tested. These methods are indeed computationally very intensive, so reducing the number of pairs that are tested is beneficial.Reducing the size of the dataset is very important for going deeper into the detection of “direct” physical interaction between proteins. As in previous methods, our approach considers that any two proteins present in the same complex form a E. coli interactome. Our method is designed to be based on coevolution, without having to deal with the incorrect assignment of each pair in each class (pos or neg). We tested it with proteins that are known to interact in functional complexes (co-complexed proteins) as opposed to proteins that belong to gene regulatory or metabolic networks. Proteins belonging to the same complex are expected to coevolve more strictly than proteins involved in the same pathway. Indeed, methods that predict PPI based on coevolution have traditionally been better in detecting co-complexed proteins than proteins sharing a pathway  on the same dataset .For each protein in the eved see  or by theved see . These teved see  and 4 usE. coli interactome. Interacting proteins in E. coli were retrieved from the Ecid database We applied our method to the prediction of the pos) if the two proteins were part of the same complex and negative otherwise (neg). This led to a total of 628 positive pairs (0.82% of the complete dataset) and 76 202 negative pairs (99.18% of the complete dataset), for a total of 76 830 pairs analysed.A pair of proteins was considered positive  web server mirrortree and tol-mirror, were computed as proposed initially by their authors. For the mirrortree metric, each tree was converted into a pairwise distance matrix by summing the lengths of the branches separating two leaves in the tree . The tree similarity was then evaluated by computing the linear correlation coefficient between the two matrices. The second classical method is tol-mirror as proposed by Pazos et al.Amirror and Bmirror were obtained by computing the linear correlation coefficient between each individual protein tree (after transformation into a distance matrix) and the ToL. Note that the mirrortree approach we used here is similar to the one used in mirrortreecongI index congI index described previously. In total, 7 features based on the original mirrortree method were computed.Features in this first group were computed after each multiple alignment had been converted into a phylogenetic tree  of proteins, these measures are calculated using as inputs: the number of species where protein A and protein B have an ortholog (ABn), the number of species where protein A has an ortholog but protein B has not (B has an ortholog but protein A has not (A and B). These measures are duplicated in order to apply them in both directions.This second group of features does not require the construction of phylogenetic trees. Instead, the phylogenetic profile of each protein is obtained by looking at the pattern of presence/absence of orthologues of each protein in the other genomes. It is usually the case that if two proteins have similar phylogenetic profiles, they are likely to be interacting. However, the number of genomes in which these proteins have an orthologous protein relative to the total number of genomes we are looking at seems extremely important. The Phylogenetic Profile problem is similar to the problem of comparing the intersection between two sets in mathematics. Consider protein family computed  representree class, PP class, A and B. The tree class can be separated into two subclasses, namely the matrix class where the distance between the trees is computed by the linear correlation coefficient between the pairwise distance matrices extracted from the trees, and the topology class where topological distance between the trees is computed using the congI index A has an ortholog (An), the number of species where protein B has an ortholog (Bn), and the number of species where protein A and protein B have one ortholog (ABn). Note that these values are those used for the computation of the features based on PP comparison  and negative (neg) examples (protein pairs) was separated into three groups containing each the same proportion of examples in the pos and neg classes. Two groups were used for the training part of the method, and the remaining group was used for testing. Each of the three groups is alternatively used as the test-group. This allows us to score the entire data set. This operation was repeated 30 times to ensure that the method was reproducible and thus reliable.We used a 3 fold cross-validation (3CV) approach to test the ability of our method to correctly predict interacting pairs in the pos) and negative (neg) classes. These algorithms are present in the most recent version of weka We used a combination of 4 classical supervised classification algorithms to predict positive  (resp. negS(x)) the score associated to example x for the pos class (resp. neg). The calculation of the scores is detailed hereafter:C represents the set of classifiers and cP(x) represents the “confidence” that x is assigned to class pos by classifier c. posn (resp. negn) represents the number of classifiers that assign x to class pos (resp. neg) when considering 0.5 as the threshold that separate the two classes.This score is calculated as follows:pos with a high probability will have a high score, while a pair for which all classifiers assign the class pos but with a low probability (only slightly higher than 0.5) will be assigned a lower score.As a consequence, a pair for which all classifiers assign the class pos) and negative (neg) protein pairsWe give here a brief definition of the attributes used in this study to evaluate the quality of the ranking of positive (Precision is the ratio of the number of True Positive pairs (TP) that have been retrieved to the total number of pairs predicted as positives (TP+FP). This can be computed for each cut-off in the list of ranked pairs, by considering that this cut-off represents the separation between positive and negative examples. Doing so for each possible cut-off  allows a curve to be drawn representing the quality of the ranking. If the ranking is perfect, then all the interacting pairs (positive examples) will have the highest scores and thus this curve will remain at the value 1.0 until the cut-off reaches the actual number of positive pairs and decrease linearly afterwards.Recall is the ratio of the number of True Positive pairs that have been retrieved (TP) to the total number of positive pairs (TP+FN). As for the precision, the recall can be computed for each possible cut-off in the ranked list of pairs. It starts from 0 and, if the ranking is perfect, is expected to increase linearly to 1  and remain at 1 thereafter.ROC stands for the Receiver Operating Characteristic. It is a plot of the sensitivity  versus the False Positive Rate (FPR). A method able to perfectly separate positive and negative examples would lead to a ROC curve starting from position , going straight to the coordinates  along the y-axis (TPR) and then reaching the position . Conversely, a method that would not do better than random for ordering negative and positive examples would lead to a ROC curve close to the diagonal. The Area under the ROC Curve (AUC) can also be computed. It has the value 1 for a perfect prediction and the value 0.5 for a prediction not better than random.negP value, only for pairs s.t. posn = 0 so that if negP≤α, the pair was kept, otherwise we removed it.The machine learning method we propose here also allows filtering out the pairs that have a small score, thus enriching the final dataset in positive examples. We set a threshold for the α = 1 (no filter) for the overall evaluation of the method. We then varied the value of α from 0.01 to 1 in order to evaluate the effect of the strength of the filter on the trade-off between the proportion of negative examples filtered and the proportion of positive examples lost.Here we used We used the final sorted list of pairs to investigate the nature of negative pairs that were ranked highly (were assigned a high score) with our method. These represent protein pairs that have features in common with known co-complexed proteins. We focused on the 50 first predicted pairs  after the ranking of the pairs. In addition, we compared our approach with the context-mirror method proposed by Juan et al.E. coli. The principle of this method is to evaluate the similarity of each pair of protein trees in the light of the complete network of similarity between protein trees, using a linear correlation coefficient between the distance matrices extracted from the protein trees as an indicator of the similarity between trees. We used the program developed by the authors to perform this analysis, using as an input the same dataset as we used for testing our own approach. We used the default p-value threshold (p≤10−5) and considered the default levels proposed by the program: 1, 5, 10 and 25. For the AUC and ROC computations, we looked for the best ρ cut-off  in the other 2 176 proteins, using either HMMER3  with default parameters. Using a leave-one-out approach we assigned to each pair of proteins the label (positive or negative interaction) of their correponding pair of homologous proteins, if present. Finally, if p1 and p2 are two proteins and pp1 and pp2 are their corresponding homologous sequences, we assigned to the pair p1-p2 a score computed as:Similar (homologous) proteins might have the same protein interactors so that a redundant dataset could artificially explain a highly accurate prediction. In order to control for redundancy in the dataset, we predicted PPI using only sequence similarity. For each of the 2 177 proteins in the Table S1List of the 50 best ranked negative pairs detected in this study.(PDF)Click here for additional data file.


The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear.We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome.We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis. DrosophilaC. elegansPlasmodiumArabidopsisDirect, pair-wise (binary), physical protein-protein interactions (PPIs) are the foundation of all biological processes. Efforts to elucidate the interaction network of all proteins within a cell or organism — termed the interactome — has helped identify the architectural and functional blueprint of cellular processes in various model eukaryotic organisms, such as yeast in vivo: the yeast two-hybrid (Y2H) and its many derivatives There are two main methods of observing direct PPIs Saccharomyces cerevisiae have been extensively explored. There are currently three genome-wide high-throughput yeast two-hybrid (HT-Y2H) surveys The PPIs of the yeast 0, A1 and A2 that liberate the mature 18S rRNA from the pre-rRNA transcript We focused on mapping the PPIs of the small subunit (SSU) processome, a very large ribonucleoprotein complex comprised of ∼72 proteins and the U3 small nucleolar RNA (snoRNA). This biochemically well defined complex guides the endonucleolytic processing events at sites Ai) they reside in the nucleolus, the site of ribosome biogenesis, ii) their genetic depletion results in an 18S rRNA processing defect and iii) they co-immunoprecipitate the U3 snoRNA and/or another SSU processome protein component. There are currently 46 confirmed SSU processome proteins and 26 potential candidates suggested from partial data  studies ial data . Some ofial data . IdentifConsidering the SSU processome's well characterized and nearly complete component list, we sought to generate an up-to-date, comprehensive yeast SSU processome PIN by extracting and pooling protein interaction data from existing datasets. After retrieving both high-throughput and literature-curated binary protein interaction data, an interaction map was drawn using Cytoscape. The result is the most current protein interactome map of the yeast SSU processome to date, from which we identify additional interactions within the subcomplexes and some of the first potential interactions linking the various subcomplexes.http://www.ebi.ac.uk/intact/) http://thebiogrid.org/) i) provide downloadable data in a tab delimited format for every queried protein, ii) each contain PPIs from a different subset of genome-wide high-throughput studies, iii) each include PPIs from a different subset of LC studies, iv) pool interaction data from various organism-specific databases and v) are updated on a monthly basis to include novel interactions. We downloaded a total of 72 files from both IntAct and BioGRID databases, one for each of the 72 SSU processome proteins, totaling 144 spreadsheets by November 5, 2010. These files contained all known interactors — both binary and co-complex — for the query protein, the experimental method used to detect the interaction and the publication reference.For each SSU processome component, both IntAct  and Reference.All 144 spreadsheets underwent five editing stages to remove information unnecessary to this study and were streamlined into six columns: Bait, Prey, Experimental System , Literature Code .Proteins with missing names were labeled with the “Standard Name” Columns with information irrelevant to our study were deleted from both sets of data files. For IntAct, 32 data columns were reduced to five columns: bait ID, prey ID, interaction detection method, source (author) and PubMed ID. We also removed the extra columns from BioGRID, cutting nine columns down to the same five of the IntAct files.The 72 BioGRID and 72 IntAct files were merged into one large spreadsheet and duplicates entries were removed. These included identical interactions with the same experimental method and authors, a consequence of some, but not all interactions being reported in both BioGRID and IntAct. However, duplicate interactions identified via different experimental methods or by different research groups were kept.i.e., interactions between an SSU processome component and a non-SSU processome protein) were removed as a function of the SSU processome protein components having been relatively well catalogued biochemically. A “Literature Code” column was added to separate the data into Uetz et al.et al.et al.et al.All interactions involving only one SSU processome component , their interactors (prey), the experimental system used, the literature code, the source organism and the reference .C. elegans, D. melanogaster, H. sapiens, S. pombe, P. falciparum and M. musculus. Y2H interactions from organisms other than S. cerevisiae (non-yeast) were quarantined during Edit Stage 1 and underwent the remaining editing stages separately. BioGRID pre-categorizes interactions by organism; PPIs for non-yeast organisms were downloaded separately and edited as described above. In Edit Stage 5 following the IntAct and BioGRID merge, an “Organism” column was added to the master spreadsheet to enable sorting of yeast and non-yeast data. Protein nomenclature specific to the source organism was queried in Homologene (http://www.ncbi.nlm.nih.gov/sites/homologene) S. cerevisiae homologue. Proteins with available Homologene data were renamed as the S. cerevisiae homolog . BLAST analysis All downloaded IntAct files also included protein-protein interactions for et al.et al.et al.et al.et al.We used Cytoscape http://supfam.org/SUPERFAMILY/index.html) http://mips.helmholtz-muenchen.de/genre/proj/yeast/) http://pfam.sanger.ac.uk/) http://ca.expasy.org/prosite/) http://smart.embl-heidelberg.de/) http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) The motifs and domains present in the SSU processome proteins were identified using the SCOP Superfamily  et al. study et al. study et al. study (October 2008) et al. dataset  A systems biology study by Hazbun et al. in 2008 et al.The protein fragment complementation assay is an alternative method for identifying direct, physical PPIs. This strategy was used by Tarassov The SSU processome protein coverage of the aforementioned datasets was compared to coverage from literature-curated (LC) sources C. elegans, D. melanogaster, H. sapiens, S. pombe, P. falciparum and M. musculus.Conserved protein-protein interactions – or interologues – found in multiple organisms, as well as PPIs replicated by multiple studies or distinct experimental methods, carry a higher confidence value and are more likely to represent true interactions D. melanogasterC. elegans  some prey plasmids may replicate faster due to their smaller size, and can overtake the population in the pool by outcompeting larger prey plasmids that take longer or are more difficult to replicate, ii) some proteins, when over-expressed, may be toxic to the cell resulting in a dominant negative phenotype, while other proteins can enhance cell growth  and iii) there may be transformation and mating differences among different prey fusion protein plasmids et al.Among the genome-wide studies, the PCA dataset of Tarassov Protein interactions reported by more than one study, replicated via distinct methods or reported in different organisms are more likely to be authentic E. coli pull-downs and biochemical and biophysical assays . While conventional wisdom supports LC datasets to be accurate and of high-quality, some have remained skeptical, pointing to the poor overlap among the literature-curated studies, as well as protein name and species classification errors l assays .DrosophilaS. cerevisiae, the majority of retrieved PPIs were with non-SSU processome proteins or with proteins with no known yeast orthologues. Once the SSU processome components of various model organisms are better characterized, and their yeast orthologues determined, additional conserved interactions may be identified. However, our analysis suggests that the interactome coverage of C. elegans, D. melanogaster, S. pombe, P. falciparum, human and mouse may be even less than that of yeast. This is in line with a recent report suggesting that low interactome coverage, and not evolutionary divergence and loss of interologues, as the main obstacle to interactome network alignment The use of interologues in protein-protein interaction maps is rapidly increasing and constitutes a valid strategy for augmenting interactome coverage A few novel interactions previously undetected by HT-Y2H and LC studies surfaced in the PCA dataset: between t-Utp4 and t-Utp10, t-Utp5 and t-Utp8, t-Utp5 and t-Utp9, and t-Utp8 and t-Utp15 of the UtpA/t-Utp subcomplex and between Utp1 and Utp12 of the UtpB subcomplex compare  and 3. TNovel interactions were also reported between t-Utp4 of the UtpA/t-Utp and Utp18 of the UtpB subcomplexes. This interaction may suggest one of the first PPIs linking the various subcomplexes of the SSU processome, and is also a candidate for future validation studies. Interestingly, all genome-wide HT-Y2H screens A truly comprehensive interactome map of the SSU processome will provide us with insight into the complexities of the assembly, function and regulation of this large ribonucleoprotein complex. Since the SSU processome is required for the production of ribosomes in all eukaryotes, understanding its assembly is essential to elucidating its function in ribosome biogenesis. Our analyses of the existing databases indicates that ∼70% of the PPIs in the SSU processome have yet to be determined, and because of this we do not yet have an accurate picture of how this complex is assembled. The current lack of data includes both proteins with no known interactors, and missing PPIs between other connected proteins. Enhancing the experimental approaches to both the classic methods — such as the Y2H — and new methods — such as the PCA — are likely to be crucial for not only deriving an interactome map of the SSU processome, but a comprehensive and exhaustively screened yeast PPI map that covers the entire yeast proteome.This quantitative survey of existing databases for PPIs from HT-Y2H E. coli pull-downs and biochemical and biophysical methods that also test for direct binary interactions. This confirms that PPIs from LC sources, despite previously proposed skepticism, are largely credible.The LC dataset, which provided the highest coverage of the SSU processome proteins, contained PPIs that were confirmed by alternative methods, such as Although lacking many proteins and interactions, the up-to-date SSU processome interaction map compiled in this study can be applied to generate new hypotheses of subcomplex interactions, assembly and function. Additionally, approaches to experimentally determine the domain-domain interactions of the known PPIs Table S1The protein components of the SSU processome. The catalogued proteins are listed based on their membership in the known subcomplexes of the yeast SSU processome. Confirmed SSU processome components which have not been assigned to a specific subcomplex are listed as unclassified. Candidate SSU processome proteins are listed as unknown. The yeast SSU r-proteins  that are known components of the SSU processome et al.(DOC)Click here for additional data file.Table S2The SSU processome PPIs derived from the HT-Y2H, PCA and LC datasets.(XLS)Click here for additional data file.


Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details. Proteins bind with other molecules to bolster or inhibit biological functions. In all these protein and the binding partner (ligand) interaction, usually a few key residues are involved. It is important to identify these key sites in order to understand the function of the protein. Most of the computational approaches to recognize these functional sites in proteins can broadly be classified into sequence or structure based methods. There have been some magnificent works  in regarThe main strength of the sequence-based approaches for binding site prediction is that these methods have the ability to determine a ligand-binding motif in proteins that may not have same overall fold. However, if the binding site (pocket) is nonlocal or non-contiguous in sequence, motif based approach generally becomes ineffective. Homology-based methods require related proteins with significant identity to the query protein in the protein data bank (PDB) because the conservation of biochemical function drops rapidly for proteins sharing < 35-40% sequence identity . VariousEssentially, for methods based on sequence-based approaches, at first, the homologous sequences of a target sequence are collected and a multiple sequence alignment (MSA) is constructed. Then, using various approaches conserved residues are identified among all the sites in the MSA.Selection of homologous sequences to a query protein is a critical step in all sequence-based and sequence and structure based approaches for protein functional site prediction. Despite the fact, this problem has not been sufficiently addressed. In this regard, how to select appropriate homologous sequences for the identification of conserved residue is a contentious issue. Some of the recent approaches to address this issue are discussed below.As mentioned earlier, selection of homologous sequences is critical step in sequence-based and sequence and structure based approaches for protein functional site prediction. It has been empirically shown that certain degree of sequence divergence is required in an MSA for the identification of functional sites. However, there is no concrete objective criterion and selection of sequences for the MSA is unavoidably subjective .In this regard, Aloy et al.  developeMihalek et al.  also proRecently, Nemoto et al.  developeIn order to select the appropriate set of sequences for functional regions prediction, Nemoto et al.  developeThe DSPAC indicates the degree of spatial autocorrelation and is used as an index of the appropriateness of a set of homologous sequences. Precisely, a set of homologous sequences of a target protein is generated and divided into some subsets. Among the subsets, the subset with the maximum DSPAC is chosen. Finally, the set of sequences corresponding to the MSA that has the maximum DSPAC is adopted as the most appropriate set of sequences for the function region prediction of the query protein.The usefulness of the index was demonstrated by improved performance of functional region prediction in the FREPS program. It has to be noted here that the index for selecting appropriate homologous sequences is called DSPAC and the method that uses this index is called FREPS. FREPS is discussed in sequence and structure based method section.Next, we will discuss widely used methodologies and recent advances in structure-based approaches for functional site prediction.Based on the observation of existing protein-ligand complexes, it is quite evident that homologous proteins with similar global topology often bind similar ligands using a conserved set of residues . In thisA number of structure-based approaches for binding site prediction have been developed. These methods are broadly classified into: geometry based approaches and energetic based approaches. Geometry-based approaches identify binding residues by searching for pockets/cavities in a protein structure. Energetic-based approaches identify binding residues by using various interaction energies. Recently, there have also been recent developments of structure alignment based methods for functional site prediction.Due to the availability of large number of solved protein structures in databases like Protein Data Bank  and deveFINDSITE  is a thrGiven a query protein, FINDSITE first identifies a group of template structures user threading. Essentially, PROSPECTOR_3  threadinLocal alignment approaches are suited to detect locally conserved patterns of functional groups, which often appear in binding sites and have significant involvement in ligand binding. In this regard, ProBis enables ProBis is a local structure alignment (LSA) based algorithm developed at Janezic's lab to detect locally similar surface patches of proteins independent of the protein fold. The algorithm identifies structurally similar sites, whose residues may be scattered in the sequence space, but are close together in structure. Such patches are often related to ligand binding sites and searching for these sites exploits the fact that protein performing similar functions may share similar patterns of interactions of binding sites. ProBis uses a graph theoretical approach (clique based approach) by constructing protein graphs. Clique based algorithms have been previously applied to protein side-chain packing problems .Initially, protein surface residues are identified by calculating the solvent accessible surface atoms of the protein. The surface amino acid residues are then assigned to one of the five labels based on the physicochemical properties of the functional groups: hydrogen-bond donor, hydrogen-bond acceptor, mixed acceptor/donor, aromatic, and aliphatic groups. Each functional group is then substituted with one labeled point that represents potential interactions of this particular functional group with other molecules. Essentially, the vertices correspond to the functional groups of surface amino acid residues and the distance between pairs of adjacent vertices determines the edges. In this regard, this representation captures both geometric and physiochemical properties. Finally, different protein surfaces are compared by constructing protein product graphs, followed by a search for maximum cliques in these graphs. These maximum cliques in the product graphs correspond to protein surface similarities.The product graph obtained from the sub-graphs is then subjected to maximum clique finding algorithm and a maximum clique corresponds to a largest common vertex substructure. Alignment scores like surface vector angle, RMSD and E-values are calculated for each local superimposition. It is important to note that each of these pairwise comparisons may result in a number of different maximum cliques where clique represents the local structural similarities between the compared proteins. Finally, a search for similarities in flexible parts of the two compared proteins is conducted (Detail omitted here).In essence, ProBiS conducts searches for similar 3D structural regions in proteins without reference to known binding sites or co-crystallized ligands taking into account entire protein surfaces.COFACTOR  is a metMeanwhile, the binding pockets of template structures are scanned through the query protein structure to identify the best local geometric and sequence matches. The binding pose of the ligand in the query structure is predicted based on the local alignment of predicted and template binding site residues.The major difference between COFACTOR and the existing methods is the combination of global and local structural comparisons for identifying ligand-binding sites. It generally outperforms other cavity-based methods when only low-resolution protein models are available because the global topology comparisons can reliably identify the correct functional template. Furthermore, for proteins that have functional templates with different global topology but similar conserved binding pockets, the local structural alignment inside COFACTOR helps to recognize the ligand-binding residues unlike purely global structural comparison methods like FINDSITE .As discussed earlier, besides the structure alignment based methods, there are methods based on geometry. In this section, we will review some of the widely used geometric based approaches for protein ligand-binding site prediction.POCKET  algorithcs (cs= Connolly surface). Second, the pockets identified by the surface-solvent-surface events are re-ranked by the degree of conservation of the involved surface residues and this extension is called LIGSITEcsc [LIGSITE  is a metGSITEcsc   it intersects with no further atoms or ii) its radius drops below the minimum size. If it intersects with no further atoms the sphere is retained else the sphere is discarded. Once, the clefts on the surface have been filled by spheres, it is possible to cluster the spheres into separate regions and calculate a volume for each cleft. The approach is particularly useful for locating binding sites in proteins.As discussed earlier, besides geometric based approaches, there are a number of energetic based approaches. Some of the energetic-based approaches are discussed below.Q-SiteFinder  is a ligIndividual probe coordinates are then clustered according to the spatial proximity and the total interaction energies of probes within each cluster is calculated. The probe clusters are ranked according to their total interaction energies and the cluster with the most favorable is identified as the binding sites.A recent version of Q-SiteFinder  achievesPocket-Finder is a tool for identifying protein-ligand binding sites. Pocket-Finder implements LIGSITE. Like, LIGSITE Pocket-Finder measures the extent to which each grid point is buried in the protein. Each grid point has seven scanning lines passing through it . The grid points are initially set to zero. These points can have a value from zero (not part of a pocket) to seven (deep in the cavity) protein-site-protein event. Details are omitted here. Using a grid resolution of 0.9Å and a probe radius of 1.6Å and the threshold number of PSP events of 5, pockets are defined by cubes of retained grid points.The major difference between Pocket-Finder and Q-SiteFinder is that in clustering in Pocket-Finder the sites produced by the Pocket-Finder are ranked according to the number of probes in the site rather than by probe energy. Another difference between Q-SiteFinder and Pocket-Finder is the value of parameter for estimation of site volume. For Q-SiteFinder a value of 5.0Å is used and for Pocket-Finder a value of 3.0Å is used. These values reflect the fact that the probe site identified in Q-SiteFinder approach the protein within van der Waals (vdW) contact whereas sites approach the vdW surface of proteins in terms of Pocket-Finder.Various methods based on structural analysis coupled with surface hydrophobicity have been used to identify protein functional sites . In thisThe FOD hydrophobic force field is based on the assumption that the theoretical hydrophobicity distribution in proteins is represented by the 3-D Gaussian function. For this reason, these values can be considered equal to zero. The size of the molecule is expressed by the triple σx, σy, σz, which is calculated for each molecule individually provided that the orientation of the molecule with the longest possible inter-effective atoms distance is determined according to the appropriate coordinate system axis. The σ values are calculated as the 1/3 of the longest distance between two effective atoms calculated along each axis. The value of the Gauss function at any point of protein body is treated as the idealized hydrophobic density defining the hydrophobic core.The idealized hydrophobicity at any point of the “fuzzy oil drop” can be calculated according to the Gauss function for the molecule located with its geometric center as the origin of the coordinate system.The second component of this force field is an observed hydrophobicity distribution formed by the side chains of a protein molecule . The scoEssentially, the difference between the theoretical (expected) hydrophobicity and the observed values of hydrophobicity is used to characterize the functional sites. See  for the According to FOD, the hydrophobic residues tend to be placed in the central part of the protein molecule while hydrophilic residues on the protein's surface .This method to predict functional sites is based on the observation that there is a high discrepancy between observed and theoretical hydrophobicities within FOD in the area of the binding sites.The usefulness of the method for protein binding site detection was verified by comparing the method with SuMo  and ProFFTSite algorithm  is develSubsequently, consensus clusters are identified as sites in which different probe clusters overlap. The consensus clusters are ranked based on the total number of non-bonded interactions between the protein and all probes in the cluster. The consensus cluster with the highest number of contacts is ranked first whereby nearby consensus clusters are also joined with the cluster. Finally, the amino acids in contact with the probes of the newly defined cluster are predicted to be the top rank ligand-binding site. The stark difference of FTSite compared with other energy-based methods is that it uses multiple molecular probes rather than a single probe.As discussed above, there have been myriad of approaches to predict protein funcationl region based on sequence properties that have largely exploited sequence conservation or to accept fewer mutations relative to the overall protein. On the other hand, there are numerous approaches based on structural properties that have used geometric and energetic properties. In this regard, there have been various approaches to combine sequence and structure-based feature to improve protein functional site prediction. One of the seminal methods in this category is Evolutionary Trace.In this regard, ConCavity  falls inInitially, a grid of points surrounding the protein surface is scored by combining the output of a structure based pocket finding algorithm, mainly, LIGSITE, SURFNET1) Grid CreationGrid creation is similar to other grid creation ligand binding site prediction approaches like POCKET  and LIGSMajor difference between other grid based approaches and ConCavity is that ConCavity integrates evolutionary information directly into the grid creation step.2) Pocket ExtractionThe second step is to cluster groups of contiguous, high scoring grid points into pockets.3) MappingMapping residue is the third step in the pipeline and it uses the extracted set of pockets to generate ligand-binding predictions for residues.In essence, ConCavity uses the grid creation methods of LIGSITE , SURFNETComparison of ConCavity to several methods for ligand binding site prediction shows that ConCavity outperforms many of the existing methods for ligand binding site prediction.ConSurf  is a proGiven a sequence, initially, ConSurf searches for homologous sequences. As discussed earlier, selection of homologous sequence is subjective so ConSurf offers various choices for the uses. A MSA of the collected sequences is performed by MAFFT or other csc [csc only uses evolutionary conservation in a post-processing step to re-rank, hence we discussed LIGSITEcsc in the structure-based ligand site prediction section.LIGSITEcsc  can alsoThe SURFNET-ConSurf  is a metThe SURFNET  algorithThe second part involves discarding the spheres that are distant from highly conserved residues. The residue conservation scores are obtained from ConSurf-HSSP database version 1 , which pThis method was tested on a set of 244 proteins and in 75% of the proteins the ligand -binding pocket was identified correctly. It should be noteworthy that even though for a few cases the ligand binding sites that were identified by SURFNET were totally lost after the conservation filtering stage, this method proves the concept that a simple combination of conservation and binding site volume provides a reasonable improvement in binding site prediction.Functional Region Prediction of a protein by Spatial statistics (FREPS)  is a metThe method utilizes spatial statistics, which takes into account spatial position of the data. In addition, as discussed earlier, a novel DSPAC index is used to select the appropriate set of sequences.Based on two assumptions, viz. i) most of the functional regions of proteins are exposed or at least semi-buried on the molecular surface and ii) the amino acid residues conserved among homologues are abundant at the functional regions, due to functional constraints, FREPS identifies functional regions of a protein by examining the cluster of conserved residues on the protein surface with spatial statistics techniques. Essentially, a modified Local Moran's I score  called acsc test set for prediction of ligand binding sites. MetaPocket combines together four protein binding site predictors and proposes a meta-method to predict ligand-binding sites. The 4 combined methods are: LIGSITEcsc [There are some approaches where existing algorithms are combined to get the consensus among these algorithms. One of them is MetaPocket 2.0 . MetaPocGSITEcsc , PASS [4GSITEcsc , Q-SiteFGSITEcsc , and SURGSITEcsc .In essence, for each protein structure, four of the approaches mentioned above are used to identify pocket sites. It is not readily possible to combine or compare the results of these 4 methods as the pocket sites identified as these methods have different ranking scoring functions. Hence, in order to do that, a z-score is calculated separately for each site in different methods. Then, only the top three pocket sites in each method are taken into further consideration.Hence, a total of 12 pocket sites (3 from each of the 4 methods) are obtained and these sites are clustered using hierarchical clustering algorithm. Probes within a certain distance threshold (8Å) are grouped together as a cluster. Then, each cluster is ranked based on a scoring function that is the sum of the z-scores of the pocket sites in a cluster.Based on the benchmarking on various datasets 48 unbound/bound and 210 bound proteins, MetaPocket achieved slightly better results than the other approaches and correctly predicts the ligand the best overall success rate for all the top three predictions compared to each of the individual methods.In this section, we will describe the availability of these methods. Some methods are freely available for academic users as stand-alone version or some as web-servers and some as both. There are a myriad of approaches for protein functional site prediction. Most of these methods can be broadly classified into sequence-based, structure-based or sequence and structure based approaches. Here, we mostly discussed sequence-and-structure based approaches. As discussed earlier, protein functional site prediction is a very hot topic in structural bioinformatics and we will continually see a lot of algorithms developed in the area. Especially, we might see more development of meta-server type of approach combining more than one methods and methods that combine both sequence and structure features. Furthermore, as protein structure prediction protocols are becoming mature, we will also see method development for prediction of protein functional sites for ‘modeled proteins’.Moreover, most of these approaches use conservation as one of the important factor in determining the functional sites but these properties might be conserved because of the structural as well as functional reasons. Hence, methods like , 51 to d