In the past few years, the field of metagenomics has been growing at an accelerated pace, particularly in response to advancements in new sequencing technologies. The large volume of sequence data from novel organisms generated by metagenomic projects has triggered the development of specialized databases and tools focused on particular groups of organisms or data types. Here we describe a pipeline for the functional annotation of viral metagenomic sequence data. The Viral MetaGenome Annotation Pipeline (VMGAP) pipeline takes advantage of a number of specialized databases, such as collections of mobile genetic elements and environmental metagenomes to improve the classification and functional prediction of viral gene products. The pipeline assigns a functional term to each predicted protein sequence following a suite of comprehensive analyses whose results are ranked according to a priority rules hierarchy. Additional annotation is provided in the form of enzyme commission (EC) numbers, GO/MeGO terms and Hidden Markov Models together with supporting evidence. Viruses are the most abundant biological agents and comprise the majority of the biodiversity on Earth -3. HowevThe recent development of faster and cheaper next generation sequencing technologies has contributed to an exponential growth of metagenomic sequencing data, transforming our view of the microbial world. Despite the advancements in sequencing technology, functional annotation of metagenomic sequences is still very challenging. Metagenomic data originate from heterogeneous microbial communities, are usually noisy and partial, and reads frequently contain truncated open reading frames (ORFs). Complicating this landscape, the vast majority of viruses isolated from environmental samples are novel and consequently most of their genes do not have homologous sequences in the public databases, making functional annotation even more difficult.Currently, there are a number of publicly available bioinformatics tools for the taxonomic  ,GreengenHere we describe a viral metagenomic annotation pipeline (VMGAP) that is currently utilized at the J. Craig Venter Institute (JCVI) for the functional annotation of viral metagenomic datasets. This pipeline incorporates a number of HMM and PSSM searches and makes use of a suite of specialized databases to improve the functional identification of viral genes. Results can be imported into JCVI Metagenomic Reports (METAREP) , an openThe VMGAP requires a protein multi fasta file as input and the local installation of several open source programs, packages and public databases. The required software and packages are HMMER , NCBI-toab initio gene finder program that uses empirical data including sequence-based composition, distance and orientation of genes of completely sequenced genomes to identify protein coding genes. Once uploaded, protein sequences are used to query several databases to identify protein features and similarities as schematically represented in The JCVI VMGAP consists of two consecutive steps: (1) database searches and (2) functional assignments. The pipeline uses as input a multifasta file containing the translations of all open reading frames (ORFs) predicted in a metagenomic sample. Protein coding genes are predicted using the structural annotation pipeline , that is-5.The non-redundant protein database encompasses several public protein databases  where each set of redundant peptides are condensed into a single database entry without losing useful information recorded in the fasta headers, such as EC numbers, product names, and taxon identification number. The VMGAP reports the top 50 hits with e-values ≤1x10-5 are reported.ACLAME is a public protein database of mobile genetic elements (MGEs), including bacteriophages, transposons and plasmids . Protein-3.The VMGAP queries three different environmental composite databases at the amino acid level: (i) ENV_NR, a GenBank non-redundant protein database that includes many environmental datasets, (ii) an in-house database (SANGER_PEP) composed of proteins coded by Sanger-based viral metagenomic samples not represented in ENV_NR  and ACLAME-HMMs (a compilation of HMMs that describe each of the protein families found in ACLAME). PFAM/TIGRFAM HMM searches are carried out in two different ways, either requiring a global or local alignment to the HMMs. Local HMM alignments increase sensitivity in the detection of conserved protein domains, particularly when the predicted peptide is truncated and extends to the end of the read, which is noted frequently in metagenomic datasets. All HMM hit with e-values ≤l1x10-5.The NCBI Conserved Domain Database (CDD) database is a collection of position specific scoring matrices representing conserved protein domains, protein families and superfamilies compiled from NCBI-curated domains , PFAM/TITo discover transmembrane proteins and signal peptides that could be associated with the surface of viral particles, the VMGAP utilizes two programs, SignalP for the identification of signal peptides, and TMHMM, a program that detects candidate transmembrane domains.-10.To aid in the metabolic reconstruction of metagenomes, the VMGAP makes use of PRIAM, a collection of PSSMs where each matrix represents an enzymatic function and is assigned to a particular EC number. Metagenomic samples are scanned for the presence of these PSSMs with RPS-Blast recording only those hits with e-values ≤1x10-10. Although proteins from ACLAME DB are also included in the non-redundant protein database, entries in the former have a higher priority since they are curated and therefore provide better functional annotation. Hits against HMMs describing ACLAME protein families and PFAM/non-equivalog TIGRFAM HMMs comprise the 4th and 5th layers of functional evidence, giving higher priority to those HMMs representing protein families against those describing conserved protein domains. Ranked 6th and 7th in the rule list are respectively RPS-BLAST hits with at least 90% coverage, percent identity ≥ 35% and e-value ≤ 1x10-10 against NCBI-CDD profiles and local-local hits against PFAM/TIGRFAM HMMs with e-values ≤ 1x10-5. Finally, low-confidence BLASTP hits with at least 70% coverage, percent identity ≥ 30% and e-value ≤ 1x10-5 against ACLAME DB and the non-redundant protein database occupy tiers 8 and 9 in the priority list respectively. Proteins that lack the evidence types described above, but still contain some other evidence such as hits against the environmental DBs are named “hypothetical protein”. Otherwise, proteins are labeled as “unknown protein”.Functional assignments of predicted peptides are carried out by retrieving the functional information produced from the results of the analyses performed in the previous steps following a series of pre-defined rules . Rules pThe VMGAP consists of three major modules implemented in Perl : (i) theThe heart of the VMGAP is the compute module . This moSmall files can be easily imported and analyzed in Excel. For extremely large files (more than a million entries), we recommend users to import the data into METAREP  for furtIn recent years and with the advancement of next generation sequencing platforms, metagenomic studies have become more affordable to the scientific community. This has triggered an exponential growth in the amount of metagenomic sequencing data available within public repositories and stresses the necessity for specialized highly efficient computational tools to cope with the functional annotation of these massive datasets. There are currently a variety of metagenomic annotation tools that are available to the general public through the web. Among the most popular resources is MG-RAST, an annotation tool that offers many advantages to the user: (i) it does not require a high-throughput computer facility, (ii) it uses reads instead of proteins as input and therefore there is no need for gene predictions, and (iii) the results are classified into functional categories facilitating the analysis of data. Perhaps most importantly, the functional distributions can be compared against other datasets that were annotated with MG-RAST.While MG-RAST is capable of providing meaningful taxonomic and functional annotation of microbial metagenomes, it is limited in its capacity to annotate viral metagenomes due to its inherent dependence of FIGfams. In order to quantitatively assess the utility of VMGAP for the functional annotation of viral metagenomic data, we ran an identical set of ~300,000 peptide sequences from a marine viral metagenomic library or their respective coding ORFs through the VMGAP and MG-RAST respectively. Analysis of the results showed that the VMGAP could assign functions to almost 16% more sequences compared to MG-RAST  and their results are stored in a single sqlite file. The storage of data in an sqlite database, however, might present some loading speed challenges when the data volume is very large and the speed of the storage device where the database resides is not fast enough . At JCVI, sqlite databases typically reside in 15,000 RPM SAS drives, with bandwidths of ~ 500 MB/sec. For slower systems, we recommend avoiding the usage of these databases and rather parse the results directly from the raw outputs of the analyses to generate the annotation and evidence files.The organizational format of the output tab-delimited files, annotation and evidence are also advantageous. Since the first column of these files contains unique protein identifiers, all of the annotation and supporting evidence for any protein or group of proteins can be retrieved using the Unix grep utility directly from the command line. These files can be also imported into Excel for their inspection and analysis. Lastly, the VMGAP pipeline can be easily updated and customized to meet the specific needs and objectives of the user through the addition of additional virus-specific databases as they become available or the inclusion of more specialized boutique databases (e.g. RNA virus specific datasets) respectively.


Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native) structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets.Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels  Protein-protein interactions which constitute the “interactome” of the cell are found in the majority of cellular processes and are known to regulate responses of organisms to the varied environments. Protein molecules in isolation cannot perform any function in the cell; it is the ensemble and their organisation into complexes which maintain the cellular integrity There are many docking algorithms available like DOCK When the two components forming the complex interact with each other there is a formation of protein-protein interface. These complexes can be divided into two types based on the nature of interacting partners, homodimers where the two interacting partners are identical and heterodimers where the interacting partners are different. Various properties of this interface formed upon interaction can be utilised to identify the native complex from the pool of proposed docked poses. Here we are describing a new objective scoring scheme named DockScore which takes into account several interface parameters. The weights for these parameters are optimised to improve the accuracy in identifying native or near-native pose.DockScore aims to determine the optimal interactions by taking into account various parameters of the interface. These parameters include surface area, spatial clustering, conservation of residues, presence of short contacts, hydrophobic residues and positively charged residues at the interface. The scoring method was first trained on a set of 30 non-homologous protein-protein complexes (15 homodimers and 15 heterodimers) to optimise the weights for different parameters. Further, performance of DockScore was assessed on 30 different protein-protein complexes and the native structure (deposited in PDB) or near-native structure was assigned as the best ranked complex from the pool of docked poses.We further identified some examples of the protein families where different modes of dimerizations are known and tested DockScore for its ability to distinguish these native dimerization modes. It was also tested on 10 of the CAPRI targets.An objective scoring scheme, DockScore, was developed in order to find the optimal interactions between the two interacting proteins. The scoring scheme was first trained on a set of 30 protein-protein complexes (listed below) and then applied on another set of 30 protein-protein complexes to recognise the best docked pose.Various features were taken into account and each is described below.β-Cβ distance cut-off of 7 Å.The interface residues were identified using inter-chain CA dataset of 30 protein-protein complexes was selected from previous studies, which includes 15 homodimers The same protocol as above was followed to obtain a dataset of 30 protein-protein complexes (15 homodimers and 15 heterodimers) and their docked poses were used to test the performance of DockScore. The chains within and across the two datasets (test and training) do not share more than 25% sequence identity.Surface area (S.A.) of the interface was calculated using Surface areas of the monomer and the complex were calculated using NACCESS  with default parameters and 1.4 Å radius for the probe molecule.The parameter was scored by assigning maximum score of 1 to the docked pose with highest surface area and minimum score of 0 to the docked pose with minimum surface area .(2)i).Conserved interface residues were identified using ConSurf i) (Scoring based on conservation was performed by assigning high score to the presence of high extent of amino acid conservation at the interface (Ci)  and normShort contacts here imply that two atoms do not closer than the sum of their VanDer Waals' radii. This is to avoid steric clashes and stabilise the complex as a whole. Our program CoilCheck highest is the highest energy value in the pool of 100 poses, Energylowest is the lowest energy value in the pool of 100 poses and Energycomplex is the energy value of the pose being scored.Here Energyβ-Cβ distance cut-off of 14 Å were considered as spatially clustered residues. Coefficient of clustering (θ) was calculated using It was employed as a measure of the compactness at the interface. It was calculated for the interface, by computing the pairwise distances between the interface residues between the two chains and the residues with a Ci is the number of hydrophobic  interface residues and N is the number of interface residues.Protein-protein interfaces of permanently interacting partners are known to be largely hydrophobic in nature i is the number of positively charged residues interface residues  and N is the number of interface residues.In the special case of interaction between DNA-binding proteins or transcription factors, we penalise the presence of positively charged residues at the protein-protein interface using +) or negative (Cl−) ions were added to achieve charge neutrality. Following this, docked poses were subjected to energy minimisation using software GROMACS 4 The docked complex was solvated in cubic water box with space water model and box edges 8 Å from molecule periphery. For large complexes, water box with box edges 10 Å from molecule periphery was used. Positive i is the rank of ith parameter).The weights were assigned to all the seven parameters employing two approaches. Firstly, the ranking of the parameters was done by using Leave-one-out approach and then assigning weights using Rank sum method , antigen-antibody (2) and other protein-protein complexes (5).DockScore was first trained on a set of 30 protein-protein complexes  and 2 anDockScore takes into account several parameters of the interface. Firstly, it identifies the interface residues and then scores for conservation, short contacts, spatial clustering and the nature of residues at the interface see .When the energy minimised poses (obtained using GROMACS v 4.5) were used, and the weights were assigned using one parameter at a time . HoweverSubsequently, the poses were examined without being subjected to energy minimisation and the performance of scoring was assessed by enumerating the complexes where DockScore accurately assigned to rank to the native form. Assigning equal weights to all the parameters resulted in an accuracy of 77% . Next, the scores were assigned by removing one parameter at a time  and alsoAfter training the scoring scheme on a set of 30 protein-protein complexes, we selected another set of 30 complexes  and 5 toDockScore was able to distinguish the native pose from a pool of docked poses in 26 of the complexes in the test dataset . In the cases, where non-native pose was ranked higher than the native, there was a small difference (0.02–0.1) in the scores as assigned by DockScore. The non-native top scoring poses were observed to be structurally similar to the native pose . We furtWe further compared the performance of DockScore with two of the previously reposrted scoring methods namely dDFIRE DockScore was further assessed for its performance on the four unrelated superfamilies whose members are known to possess different dimerization modes: GAF proteins, IL-8 superfamily proteins, DJ-1 superfamily and H-NS histone-like proteins superfamily.AnaebaenaMus musculusS.cerevisiaeGAF domains are present in cGMP regulated cycilc nucleotides phosphodiesterases, certain adenylyl cyclases and bacterial TF FhlA (formate hydrogen lyase transcription activator) DJ-1 superfamily of proteins is known to exhibit different dimerization modes SCOP also documents different modes of dimerization in certain superfamilies, like H-NS histone like proteins superfamily and IL-8 superfamily. We selected proteins with different modes of dimerization known in these superfamilies .Using one chain as receptor and another as ligand, 99 docked poses were generated for each of the different complexes, employing FRODOCK. These 99 docked poses and the native structure, were subjected to DockScore.For two of the superfamilies, IL-8 like and H-NS histone like proteins superfamily, DockScore accurately scores the native pose as the top ranked pose. In the case of GAF domains, out of the three different dimerization modes tested, DockScore accurately scores native pose as top-ranked pose in two modes. In the DJ-1 superfamily, out of the four different modes tested, native poses in two modes were accurately ranked as the top pose using DockScore. These cases, where non-native pose was ranked higher than the native, were further analysed.For the DJ-1 superfamily, the top-ranked pose and the native pose were very similar and they differ in score by only 0.1  and the In order to examine the effect of conformational changes and large-scale rotations that might accompany during interactions with other proteins, we next chose five proteins that are available in the bound as well as protein-unbound form as compiled in ComSin database DockScore performance was further evaluated on 10 of the earlier CAPRI targets . This waWe were able to assign the top-most rank to the native structure in 8 out of 10 targets. In remaining two cases (3R2X and 1TA3), where some docked pose obtained a higher score than native, were further analysed for the structural proximity of high-ranking docked pose to the native pose. These poses were observed to be well-superposed with the native pose  and the Since accurate structure determination of the macromolecular complexes is highly challenging and biologically important, prediction of protein-protein interactions through molecular docking is highly appropriate. However, the implementation of molecular docking for studying the interactions between a pair of proteins poses a challenge to identify the best docked pose out of the pool of various poses suggested by the docking program. For identifying the best docked pose, we devised an objective scoring scheme, named DockScore, which takes into account several interface parameters and hence ranks the docked poses.The interface residues are first identified for each pose and the docked poses are ranked based on the parameters like interface surface area, spatial clustering at the interface, presence of hydrophobic, positively charged residues, conservation of residues and undesirable short contacts at the interface. The contribution of the energy of each pose was also tested by subjecting the energy minimised poses for scoring to DockScore. Before testing the performance of the scoring scheme for selecting the best pose, it was first trained on 30 protein-protein complexes. The weights were assigned, using one parameter at a time, indicating the importance of each parameter in the final score and thereby accuracy could be tuned close to 96%. Subsequently, it was tested for its accuracy on a test dataset, which comprised of 30 other complexes.There were several tests adopted to assess the performance of DockScore:The scoring was considered accurate, when DockScore was able to rank the native structure (deposited in PDB) or near-native structure as the best rank pose.If the non-native poses were ranked higher than the native pose, we further analysed the difference in their scores assigned by DockScore and the fraction of overlapping interface residues.DockScore was further tested on four superfamilies, where different dimerization modes are known, for its ability to distinguish the native pose from a pool of docked poses.This scoring scheme when further applied on 10 of the earlier CAPRI targets was able to distinguish the native pose from the pool of 100 docked poses in all 10 targets.In the present study, FRODOCK has been used to obtain close to 100 docked poses. However, the objective scoring scheme devised can be used in general to rank the docked poses suggested by any docking program and for a variety of protein-protein interactions. In the near future, DockScore would be tested on other protein-protein complexes and other docking programs can also be used to test the accuracy of DockScore.These kinds of studies will aim to provide detailed insights into the interactions amongst proteins which are quite direct in nature. Also, the existence of interactions among protein factors regulating the direct interactions will provide an additional level of regulation on one hand, whereas it will also lead to the combinatorial diversity of regulatory complexes. With different combinations of these factors, regulation of diverse numbers of genes can be achieved. Therefore, studying the physical interactions amongst proteins will provide useful insights into unravelling the basis of this combinatorial diversity in eukaryotes.Figure S1Principal component analysis of 15 homodimers in the training set (corresponding to ) and 15 homodimers in the test set (corresponding to ). High dispersion shows that there is no bias or high sequence identity across the training and test dataset.(TIF)Click here for additional data file.Figure S2Principal component analysis of 15 heterodimers in the training set (corresponding to ) and 15 heterodimers in the test set (corresponding to ). High dispersion shows that there is no bias or high sequence identity across the training and test dataset.(TIF)Click here for additional data file.Table S1Weights assigned to different parameters using energy minimized structures.(DOC)Click here for additional data file.Table S2Weights assigned to different parameters removing one parameter at a time.(DOC)Click here for additional data file.Table S3Five cases (both bound and unbound form) from ComSin database, which were subjected to DockScore. All the five cases are validated using PISA as well. (*starting from unbound monomeric forms).(DOCX)Click here for additional data file.Table S4Results of the application of DockScore on 10 CAPRI targets.(XLS)Click here for additional data file.


Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species.We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM) model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV) and hepatitis C virus (HCV), our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO) annotations of proteins, we predicted new interactions between virus proteins and human proteins.Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1) it enables a prediction model to achieve a better performance than other representations, (2) it generates feature vectors of fixed length regardless of the sequence length, and (3) the same representation is applicable to different types of proteins. A variety of viruses cause diseases in humans, and viral infections affect millions of people each year. The treatment and prevention of viral infections and their associated diseases are the main public health challenges. Common examples of viruses associated with human diseases include HIV-1, influenza virus, human papillomavirus (HPV), herpes virus, and hepatitis A, B, C, D and E viruses. Different viral species have different infection mechanisms, and identifying host cell proteins that are attacked by a virus will certainly help better understand the infection mechanism and to design new antiviral strategies. Recently, proteome-wide studies of viral interactions with human proteins were performed, but comprehensive analysis of the interactions between virus proteins and human proteins involved in viral infection has not yet been investigated.et al. [Helicobacter pylori by combining a linear discriminant classifier and cloud points. You et al. [So far, most computational studies of protein-protein interactions (PPIs) have focused on the interactions between proteins of the same species. For example, Bock and Gough  created et al.  used theet al.  predicteu et al.  used a mIn this paper, we propose a representation method and a support vector machine (SVM) model to predict the interactions between two types of viruses (hepatitis C virus and human papillomaviruses) and human proteins. Although substantial progress has been made in clinical and basic research on the hepatitis C virus (HCV) and human papillomaviruses (HPV), interactions between these viruses and human proteins are not yet fully understood. Identifying more interactions between them should help elucidate the interaction mechanism of HCV and HPV with host cells, and can be helpful in designing molecules that target the new interacting proteins.amino acid triplet. For example, in the amino acid sequence TVAVTVA, there are four overlapping amino acid triplets, TVA, VAV, AVT and VTV. To reduce the dimension of the vector space of feature vectors, we represent an amino acid sequence using the class of amino acids. Based on the biochemical similarity of amino acids, twenty amino acids were classified into six categories: {IVLM}, {FYW}, {HKR}, {DE}, {QNTP}, and {ACGS} [One of the challenges in predicting protein-protein interactions from sequences is to find a way of fully encoding the important information content of protein sequences. In addition, the amino acid sequences of different lengths should be transformed into a feature vector of the same length. We represent a protein sequence using three consecutive amino acids called d {ACGS} ,11. Usinv is then mapped to a relative frequency vector i d, which is defined by equation 1.We use a binary space  to represent a protein sequence, in which V is a vector space of feature vectors with a fixed number of features and F is a vector space of frequency vectors. A protein sequence of variable length is first mapped to a feature vector of fixed length. A feature vector i fis the frequency of the i-th triplet type in the sequence. The value of i dranges from 0 to 1.714.where et al. [et al. [There are two differences between our representation and that of Shen et al. , namely  [et al.  clustereet al. [While the relative frequency in the representation of Shen et al.  has a vaet al. , it rangIn addition to the relative frequencies of amino acid triplets, a feature element representing the types of virus proteins (11 types of HCV proteins and 9 types of HPV proteins) were included in a feature vector. Each feature vector was labelled +1 for interaction and -1 for non-interaction. By encoding the type of a virus protein, the SVM model can find a human protein interacting with the virus protein.http://www.csie.ntu.edu.tw/~cjlin/libsvm/ with the radial basis function (RBF) as a kernel function, which is defined byA support vector machine (SVM) has been applied to several biological problems such as prediction of protein-protein interactions -6, homolu and v are two data vectors and γ is a training parameter. A smaller γ value makes the decision boundary smoother. The regularization factor C controls trade-off between a low training error and a large margin [where e margin . In thisWe tried several other kernel functions with our data. The linear and polynomial kernel functions resulted in high sensitivity , but low specificity (about 50%). The sigmoid kernel function showed poor performance both in sensitivity and specificity (about 50%). The radial basis function was the only one that showed reasonably good sensitivity and specificity, and chosen as the kernel function of the SVM model.Hepatitis C virus (HCV) is a small enveloped virus with a single-stranded RNA genome encoding a single open reading frame . The polhttp://www.hprd.org, we identified a total of 695 interactions between HCV proteins and human proteins. The 695 protein-protein interactions formed a positive data set for an SVM model. Both positive and negative data are required to train an SVM model. Unlike positive data, negative samples are not readily available from protein-protein interaction data. We randomly selected 695 human proteins from HPRD, which were not included in the positive data set, and generate a negative data set with them. Our method of generating a negative data set is similar to that of Gomez et al. [We obtained the interaction data between HCV proteins and human proteins from the infection mapping project (I-MAP) . I-MAP pz et al. , which aFor evaluating an SVM model, we divided both the positive and negative data sets into training and test sets. We randomly selected 500 positive data and 500 negative data for a training set. The remaining 195 positive data and 195 negative data were used to construct a test set. To keep the same proportion of human proteins interacting with each virus protein in both training and test sets, we selected training data byi Tis the i-th virus protein , N(iT) is the number of human proteins interacting with the i-th HCV protein, N(Training) is the total number of positive training data, and N is the total number of HCV-human protein interactions.where Table http://www.ncbi.nlm.nih.gov/biosystems/). After removing redundancy, we identified a total of 252 interactions of HPV proteins with human proteins, and obtained Gene IDs from HPRD http://www.hprd.org. A training set and test set for HPV interactions were constructed in the same way as for the HCV interactions. Table Human papillomavirus (HPV) is a member of the papillomavirus family of viruses that is capable of infecting humans. HPV types 16 and 18 cause 70% of cervical cancer . So far,We evaluated the performance of the SVM model in terms of sensitivity, specificity and accuracy.True positives (TP) are actual interacting proteins that are predicted correctly. True negatives (TN) are non-interacting proteins that are predicted correctly. False positives (FP) are non-interacting proteins that are predicted as interacting proteins. False negatives (FN) are interacting proteins that are missed.We did not perform cross validation to evaluate the SVM model. Instead, we prepared independent test sets that were not used in training the SVM model. In general, cross-validation shows a much higher performance than testing a prediction model on a new data set that was not used in training. As shown later in this paper, some virus proteins have a very small number of human protein interaction partners to perform cross validation. For example, the HCV NS4A and HCV NS4B proteins have only 7 and 1 interaction partners, respectively. The HPV E4 protein has only 2 interaction partners, and the HPV E8 protein has only 7 interaction partners. Thus, we tested the SVM model on new, independent data sets that were not used in training the model.et al. [Due to the randomness in drawing negative data from HPRD and positive data from the data set of HCV-human protein interactions for the training set, we prepared three independent test sets and evaluated the SVM model with the sets , we ran BLAST http://www.ncbi.nlm.nih.gov/BLAST/ with the known HHCV as query sequences against the human proteins in GenBank http://www.ncbi.nlm.nih.gov/genbank/. The E-value was set to 10-20 when running BLAST. After removing redundant sequences with the 695 known HHCV proteins, we obtained a total of 4,209 human proteins as the initial candidates of HHCV  cellular component terms [HCV for each HCV protein. After this refinement, we obtained a total of 456 candidates of HHCV. For instance, the HCV E2 protein has 29 known HHCV proteins, and the 29 HHCV proteins have a total of 15 GO cellular component terms. The SVM model predicted 33 HHCV proteins as interacting partners of the HCV E2 protein, and 10 out of the 33 candidates were left as reliable candidates of HHCV, since they have the same GO cellular component terms as the known HHCV proteins. Figure HCV proteins predicted by our method.In the 4,209 human proteins, the SVM model predicted 1,180 proteins as potential candidates of HTo evaluate the performance of the model with the HPV datasets, we prepared three training sets and three test sets , we ran BLASTP http://www.ncbi.nlm.nih.gov/BLAST/ with the E-value ≤ 10-20 against the human proteins in GenBank http://www.ncbi.nlm.nih.gov/genbank/. After removing redundant sequences with the 252 known HHPV proteins, we obtained a total of 560 human proteins as the initial candidates of HHPV  model and its representation method for predicting the interactions between viruses and human proteins.We represented a protein sequence using three consecutive amino acids called amino acid triplet. We mapped a protein sequence of variable length to a feature vector of fixed length, and then mapped the feature vector to a frequency vector that represents the relative frequency of each triplet within the protein sequence. The SVM model showed an average accuracy of 81.6% in predicting human proteins that interact with HCV proteins, and a similar accuracy (83.3%) in predicting human proteins that interact with HPV proteins. The performance of our SVM model was better than that of the other method  in both Our experiment on 2 different types of viruses showed that encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for protein sequences when finding protein-protein interactions across different species. The representation method has several advantages. First, it enables a prediction model to achieve better performance than that of other representations. Second, it generates feature vectors of fixed length regardless of the sequence length. Third, the same representation is applicable to different types of proteins.Elucidating virus-host interactions is important for understanding viral infections and for identifying new targets for rational drug discovery. In the future, we plan to construct virus-host protein interaction networks to achieve more viral species and perform further comparative analysis of the interaction networks to discover interaction patterns central or specific to them.The authors declare that they have no competing interests.Chao Fang did the early work with HCV data, and Guangyu Cui finished the work with the HPV data and prepared the first draft of the manuscript. Kyungsook Han supervised the work and rewrote the manuscript.Three data sets used for predicting human proteins interacting with HCV proteins. Three training and test sets of human proteins (HPRD IDs) that were used for the results of Table Click here for fileThree data sets used for predicting human proteins interacting with HPV proteins. Three training and test sets of human proteins (HPRD IDs) that were used for the results of Table Click here for file


Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important  Virtual interactomics has been developed in the last thirty years, and it is in fact based on gradual bioinformatic processing of experimental data. These data were usually obtained from individual studies of interactions, and various large-scale experimental methods such as the two-hybrid system, phage display library studies reverse interactomics, SPOT arrays or microarray studies, and extended sequence studies [Many important findings in pharmacology, cell biology, and pathobiology have been achieved with the aid of virtual interactomics including computer-aided structural analysis, prediction and  studies –7. In addition to sequence data, three-dimensional (3D) structures are ever more frequently required for interactomic predictions. X-ray crystallography or nuclear magnetic resonance studies represent the most frequent sources of 3D structures, whereas combination of electron microscopy of molecular complexes with X-ray crystallography turns out to be interesting for the same purpose –11. Altein vitro generated or rapidly evolved structures , and (iii) binding sites of antigen receptors expressed in specific immune cell clones, which are usually developed during the regular recombination process, and later also at the time of immune response.Virtual interactomic studies are mostly realized via computer programs including numerous online accessible tools. Three types of computer data processing are important for online interactomic predictions on molecular level, that is, (i) structure comparisons, (ii) molecular docking studies, and (iii) reevaluation of current database data, and accessible or proposed protein interaction networks. Similarly, three types of interacting structures can be distinguished with respect to their different molecular origin. This concerns (i) conserved structures, (ii) randomly/quasi-randomly In contrast to reactomics, interactomics described here deals exclusively with interactions, and thus concerns only interactions of enzyme active sites but not their subsequent reaction mechanism. Consequently, modeling of enzyme reactions exceeds the topic of this review. In addition, since we dealt here with protein interactomics, this paper also does not contain information about interactions of DNA with nonpeptide ligands.By using various structurally based programs, many protein interactions can be predicted based on the occurrence of phylogenetically conserved or convergently developed functionally important common structural features . However, diversification within protein families, and superfamilies causes losses of interacting structures or disables their accessibility. On the other hand, new interactive pairs frequently appear in cases of disordered protein regions, that is, peptide segments naturally occurring in multiple conformation variants –21. The During the last twenty years, conventional sequence-based search for conserved structures frequently combined different evaluations of sequence similarities. The corresponding protocols usually combined double sequence, and multiple sequence comparisons like BLASTP, PSI-BLAST, RPS-BLAST, Clustal W and MUSCLE , 23. In Input of 3D coordinates or their transformed representations was necessary for other structural studies predicting also functional interaction sites. The corresponding research yielded two servers with different 3D-BLAST programs enabling us to compare folds and fold families , 42 for. AlternaSimilarly to multiple sequence alignment, various forms of multiple structural alignments (MSTA) have been generated. Older attempts at MSTA were based on secondary structural alignment –53. On tIn spite of the increasing possibility of database reevaluation, some structurally based programs predicting interacting sites became at least relatively autonomous. A large number of specific and relatively autonomous programs concerned the prediction of epitopes , and thus mimic other peptides or organic compounds in their functionally important interactions. Mimotope development is based on synthetic peptide or phage display libraries, whereas natural development of specific cell clones is necessary for epitope recognition by specific antigen receptors. This means that both epitopes and mimotopes can sometimes considerably differ from the usual conserved structures mentioned above. In spite of the described difference, a unifying point between mimotopes and epitopes exists, because mimotopes were originally defined as peptides mimicking epitopes , formingMimotopes were originally derived in studies with a phage display library. This phage technology was discovered in the eighties . The corin vitro evolution of phage display libraries, (ii) offered many integrating programs, and (iii) made it possible to find all mimotope sets that have the 3D structure of a target-template complex . Id MimoDB –108). Bad MimoDB . This poThe recent status of epitope prediction is still far from resolved due to insufficient extent of the datasets, and still requires continuous improvement of database organization , 111. InAn interesting input simplification has been achieved with a novel web server CBTOBE. This server uses learning of support vector machines based on physicochemical profiles, and makes it possible to predict conformation epitopes based on a sole sequence input . A recenCombined predictions of epitopes presented to T-cell receptors have integrated class I MHC (major histocompatibility complex) peptide binding affinity, TAP transport efficiency and prediction of proteasome cleavage . NeSince production of various vaccines, and detection kits with mononoclonal antibodies requires high efficiency of preparations, special searches for conserved epitopes have been developed for this purpose. Though the meaning of crucial term “conserved epitope” exceeds primary structure relationships, the repertoires of the corresponding structures appear to be sometimes considerably limited. In fact, structural or regulatory adaptations of pathogenic microogranisms and viruses cause less stability of immunologically important, and promising promiscuous (cytotoxic or helper) T-cell epitopes broadly cross-reacting with different MHC antigens, different frequencies of T-cell epitopes specific usually only for certain unique MHC molecule, as well as losses of immunogenicity or even absence of immune response. In addition to current evaluation, drug effects (cf. effects in docking studies described below) and accompanying structural variability  have to be considered with respect to possible peptide epitope modifications, possible bias, or improvement of therapeutical design. It is a question whether conserved spatial structures following from multiple structural alignments mentioned above can be also interesting for prediction of conserved conformation of B-cell epitopes. The second part of The first more complex prediction of HIV epitopes was based on sequence conservativeness, secondary structure, solvent accessibility, hydrophilicity and flexibility . The stulock-and-key” type of protein interactions. Its original variants appeared in the eighties and reassumed interpretations of older molecular graphics [hand-in-glove” analogy was found to be more appropriate for molecular docking than the “lock-and-key” one [Molecular docking is a method, which predicts the preferred reciprocal orientation of two molecules when they bound to each other to form a stable complex . In casegraphics –152. Thekey” one . In addikey” one , that iskey” one . Alternakey” one . Affinitkey” one .in silico experiments. For instance, aa involvement in enzyme interactions was proved with simulated mutation -polymerase was tested in a large-scale virtual docking screening together with 300 000 added organic compounds with the aid of the program Lead Finder , whereass method , 161) whs method ).In silico selection resulted in a set of 106 small molecules, two lead compounds were confirmed to specifically block IL-2 secretion by DR1301-restricted T-cells in a dose-dependent and reversible manner [In addition to current molecular docking simulations , certaine manner .Pockets opening to protein surface represents potential sites for ligand binding or protein-protein interactions that were indeed identified in some cases –168. ConSome docking studies comprised phylogenic aspects of protein interactions. It was observed that except for antibody-antigen complexes , the surBioinspired algorithms were also applied to molecular docking simulations including neural networks, evolutionary computing and swarm intelligence . Though Protein interaction networks (PINs) are usually represented by graphs. Nodes of these graphs denote interacting molecules, whereas each edge linking two nodes indicates the corresponding interaction. The prevailing part of PIN is usually constituted by protein-protein interaction network (P-PIN). In addition to P-PIN, we can observe a record of protein interactions with (i) nonpeptidic hormones or mediators and drugs, (ii) processed, targeted, and functional complexes forming RNA, and (iii) recombining, hypermutating, repairing, replicating, twisting/untwisting, and transcribed DNA. These nonpeptidic compounds represent in fact inputs, outputs, interaction-stabilizing agents, or relay batons of P-PIN. In cases of DNA, and RNA, some papers appeared dealing with special protein-RNA, and protein-DNA interaction networks –208. ProLike in other related networks, clusters are recognized in P-PIN. Two main types of P-PIN-related clusters can be distinguished, that is, protein complexes and functional modules , 210. PrSaccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans, and various bacterial species [Eukaryota, whereas DNA repair proteins prevailed in conserved prokaryotic subgraphs. Incorporation of literature-curated and evolutionarily conserved interactions also allowed to develop an interaction network for 54 proteins providing a tool for understanding Purkinje cell degeneration [Much attention has been paid to the identification of small conserved subgraphs, particularly those occurring significantly often within the biological networks. These conserved subgraphs were referred to as network motifs or simply as motifs (similarly to primary structure motifs), and were considered as basic building blocks of complex networks including P-PIN , 213. An species , 215. Prneration . This reRecent integrative approaches combined at least transcriptional data with P-PIN analyses, and at most five layers including phenotype association with single-nucleotide polymorphism, disease-tissue, drug-tissue and drug-gene relationships, in addition to the topical P-PIN record  see als. Two-layFlaviviridae [The majority of proteins reciprocally interact via one or two interactions. This number substantially increases to tens, hundreds and more, when considering multi-interactive proteins denoted in PIN as hubs . In agreiviridae , 227. Iniviridae –230. Hubiviridae . In addiiviridae ) were reiviridae , whereasiviridae .Extended P-PIN research yielded many tools allowing P-PIN-based prediction of protein complex formation –237, ideA detailed evaluation of accuracy of all the above approaches would require a separate review. The obstacles consist first of all in presence of complementary information in articles inaccessible for biologists and on web pages. Such state complicates mining of accuracy data first of all in the case, when server-related papers represent only the final step of author's efforts. Accuracy evaluation or even the corresponding references are also rarely present in the papers concerning new online databases. In addition, some authors also use other criterions related to accuracy to evaluate the corresponding performance value, whereas only some of such evaluations are widely known as valid accuracy substitution, for example, AUC (area under the ROC curve) discussed below.In spite of the obstacles, we can mention several examples of high accuracy concerning the above approaches. Excellent accuracy values higher then 0.90 were found in the cases of Conserved Domain Database  more rapid and precise molecular modeling of protein complexes , (ii) more complex evaluation of structural similarities , (iii) extensive assembly of large-scale PIN and assembly of PIN or PIN subgraphs with dynamical properties, and (iv) restriction of conserved PIN subgraphs and clinically interesting responsive functional modules. The integrative tendencies then comprise (i) establishment of structurally functionally, and purposefully oriented databases and associated data mining servers, (ii) questions concerning phylogenic relationships between structural differences in PIN subgraphs, and the differences in protein structures of subgraph forming molecules, (iii) PIN prediction and revision/reevaluation of empirically proposed PIN using comparative molecular modeling, docking and bio-inspired algorithms, (iv) comparative differential multilayer network approaches including clinical treatment cases, and (v) attempts to construct global networks within a single cell, and hopefully, within the system of different reciprocally communicating cell types . Since interactomics simplifies reactomic simulations, it can also markedly and rapidly complete reactomic results, for example, in prediction of enzyme-inhibitor effects and specificity of protein kinase interactions mentioned above. In the end, we also dealt here with accuracy, which represents an important parameter of tool choice together with reasonable tool accessibility, and possibilities to test several alternatives before prediction, simulation and network analysis or to use two or several independent methods. In conclusion, many interesting and important interplays occur inside the virtual interactomic area as well as in its boundary lines and performance evaluation.


Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI) data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected.Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility.We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to discover novel complexes. We show improved performance over previous approaches in terms of precision, recall, and number and quality of novel predictions. We present and visualize two novel predicted complexes in yeast and human, and find external evidence supporting these predictions. Since protein complexes are groups of interacting proteins, many methods have been proposed to discover complexes from protein-protein interaction (PPI) data, which has been made available in large amounts by high-throughput experimental techniques. Typically, complexes are predicted based on topological characteristics in the PPI network. For example, many approaches search for regions of high density or connectivity -5. Otheromplexes .Saccharomyces cerevisiae (yeast), this complex is composed of ten proteins. Figure The performance of these complex discovery algorithms is reliant on the quality of the protein interaction data, which is often associated with substantial numbers of spuriously-detected interactions  and missing interactions . Furthermore, many protein pairs that actually do interact with each other are not located in the same complex, for example, protein pairs that bind temporarily to perform a function. We refer to such interactions as transient interactions. Finally, not all proteins in the same complex may interact with each other, making its PPI subgraph far from complete. Figure Many algorithms have been developed to assess the reliability of high-throughput protein interactions -15 or pret al. [Researchers have also proposed integrating heterogeneous data sources with supervised approaches to predict co-complex protein pairs (protein pairs that belong to the same complex), using a reference set of training complexes. Data integration leverages on the fact that diverse data sources other than PPI can also reveal co-complex relationships, while a supervised approach targeted at predicting co-complex protein pairs can be trained to discriminate between actual co-complex interactions and spuriously-detected or transient interactions. Qiu and Noble  integratet al.  integratWe propose a method to address these challenges of complex discovery: first, the PPI network is integrated with other heterogeneous data sources that specify relationships between proteins, such as functional association and co-occurrence in literature, to form an expanded, composite network. Next, each edge is weighted based on its posterior probability of belonging to a protein complex, using a naive-Bayes maximum-likelihood model learned from a set of training complexes. A complex discovery algorithm can then be used on this weighted composite network to predict protein complexes. Our method offers several advantages over current unsupervised or non-integrative weighting approaches. First, a composite protein network constructed from multiple data sources is more likely to have denser subgraphs for protein complexes, as it not only reduces the number of missing interactions, but also adds edges between non-interacting proteins from the same complex, because such proteins are likely to be related in ways other than by physical interactions. Second, learning a model from training complexes not only provides a powerful method to assess the reliability of interactions, but also allows the discrimination between transient and co-complex interactions. Third, utilizing multiple data sources to assess the reliability of interactions is likely to be more accurate than using just PPI data.Our choice of a naive-Bayes maximum-likelihood model also offers several advantages over other supervised data-integration approaches. Firstly our model is transparent, in that learned parameters can be validated and analyzed, for example to reveal the predictive strengths of the different data sources. Furthermore, for a predicted complex, the learned parameters can then be used to visualize the component evidences from the different data sources, allowing human judgment of the credibility of the prediction. Second, maximum-likelihood models are known to be robust and have low variance, even when few training samples are available. Although we describe our experiments using yeast and human, this is important when we apply our approach to less-studied organisms with fewer known complexes available for training. Finally, we utilize different clustering algorithms as well as a simple aggregative clustering strategy to evaluate the performance of our method, and show that we improve the performance of complex prediction compared to other weighting methods.u, v) with score s, u is related to v with score s, according to that data source. For both yeast and human, the following data sources are used:Heterogeneous data sources are combined to build the composite network. Each data source provides a list of scored protein pairs: for each pair of proteins  based on the number of shared neighbors of u and v. Interactions between proteins that have no shared neighbors are regarded as unreliable and are discarded. Protein pairs that do not directly interact but have shared neighbors are also scored; such pairs with scores above 0.1 are added as new interactions, and are called Level 2 or L2-PPIs. We consider PPIs and L2-PPIs as two separate data sources.• PPI data is obtained by taking the union of physical interactions from BioGRID , IntAct u and υ (or their respective genes) using the following evidence types: gene co-occurrence across genomes; gene fusion events; gene proximity in the genome; homology; coexpression; physical interactions; co-occurrence in literature; and orthologs of the latter five evidence types transferred from other organisms . Each evidence type is associated with quantitative information (e.g. the number of gene fusion events), which STRING maps to a confidence score of functional association based on co-occurrence in KEGG pathways. The confidence scores of the different evidence types are then combined probabilistically to give a final functional association score for . Only pairs with score greater than 0.5 are kept.• Predicted functional association data is obtained from the STRING database   is scored by the Jaccard similarity of the sets of papers that u and υ appear in:• Co-occurrence of proteins or genes in PubMed literature (data downloaded in January 2012). Each pair  ID of x as well as the word "cerevisiae"; for human that would be the papers that contain the gene name or Uniprot ID of x as well as the words "human" or "sapiens".where While there seems to be overlap between STRING's use of PPI and literature co-occurrence data with our use of them as separate data sources, note that STRING uses these data as only as component evidences for functional association and scores them accordingly. Thus we treat the STRING data as a representation of functional association between proteins, regardless of how this association was derived. Table u and v if and only if there is a relationship between u and v according to any of the data sources.In the composite network, vertices represent proteins and edges represent relationships between proteins. The composite network has an edge between proteins u, v) is weighted based on its posterior probability of being a co-complex edge (i.e. both u and v are in the same complex), given the scores of the data source relationships between u and v.Next, each edge  between proteins u and v of the composite network is cast as a data instance. The set of features is the set of data sources, and for each instance , feature F has value f if proteins u and υ are related by data source F with score f. If u and v are not related by data source F, then feature F is given a score of 0. Using a reference set of protein complexes, each instance  in the training set is given a class label co-complex if both u and υ are in the same complex; otherwise its class label is non-co-complex Learning proceeds by two steps:We use a naive-Bayes maximum-likelihood model to derive the posterior probability. Each edge (1. Minimum description length (MDL) supervised discretization  is perfoco-complex and non-co-complex:2. The maximum-likelihood parameters are learned for the two classes f of each feature c F. nis the number of edges with class label co-complex, co-complex and whose feature F has value f, non-co-complex, and non-co-complex and whose feature F has value ffor each discretized value e with feature values F1 = f1, F2 = f2, . . . is calculated as its posterior probability of being a co-complex edge:After learning the maximum-likelihood model, the weight for each edge Z is a normalizing factor to ensure the probabilities sum to 1. Although the second last equality makes the assumption that the features are independent, naive-Bayes classifiers have been found to perform well even when this assumption is false [where is false . Specifik edges are used by a clustering algorithm to predict protein complexes. We use the following clustering algorithms in our study:After the composite network is weighted, the top Markov Cluster Algorithm (MCL) [hm (MCL)  simulateRestricted Neighborhood Search Clustering (RNSC) [g (RNSC)  is a locIPCA [IPCA  expands Clustering by Maximal Cliques (CMC) [overlap_thres, then CMC checks whether the inter-connectivity between the two cliques exceeds a second threshold merge_thres. If it does, then the two cliques are merged; otherwise, the clique with lower density is removed.es (CMC)  first geHierarchical Agglomerative Clustering with Overlap (HACO) [A is later merged with another cluster B, it also tries to merge the remembered constituting clusters of A with the cluster B, and keeps the (possibly overlapping) resultant clusters if they are highly connected.p (HACO)  first coClustering with Overlapping Neighborhood Expansion (ClusterONE) [sterONE)  greedilyCMC, MCL, HACO, and ClusterONE are able to utilize edge weights in their input networks, whereas RNSC and IPCA do not; in this case, the selection of the top k edges provides less noisy networks as inputs to the algorithms.merge_thres produces too many clusters consisting of merged cliques. Thus, we run these algorithms with a range of values for their respective parameters, so as to obtain a more comprehensive picture of their performances across different weighting approaches. We run ClusterONE, RNSC, and IPCA with mostly default or recommended parameters. The parameter settings used in our experiments for the six clustering algorithms are given in Table CMC, MCL, and HACO utilize parameters whose optimal values are at least partly dependent on the input networks' distribution of edge weights. For example, given an input network with high edge weights, using CMC with too low a C produced by any of these clustering algorithms, we define its score as its weighted density:For any cluster COMBINED, which takes the union of the clusters produced by the six algorithms above. If two or more clusters are found to be similar to each other, then only the cluster with the highest weighted density is kept, and its score is defined as its weighted density multiplied by the number of algorithms that produced the group of similar clusters; otherwise its score is its weighted density as usual. We define two clusters C and D to be similar if Jaccard >= 0.75, where Jaccard is the Jaccard similarity between the proteins contained in C and D:We also use a simple voting-based aggregative strategy X Vis the set of proteins contained in X.where In our main experiment, we compare the performance of five weighting approaches:1. SWC: supervised weighting of composite network (our proposed method)2. BOOST: supervised weighting of composite network using LogitBoost 3. TOPO: unsupervised topological weighting of PPI network with Iterative AdjustCD [4. STR: network of predicted and scored functional associations from STRING   in the network is given a class label co-complex if u and v are in the same training complex, otherwise its class label is non-co-complex. For SWC and BOOST, learning is performed using these labels, and the edges of the entire network are then weighted using the learned models. TOPO, STRING, and NOWEI require no learning, so the labels are not used; instead, for TOPO the edges of the network are weighted with topological scores, for STRING the edges are weighted with functional association scores, and for NOWEI all edges are given weight 1. The top-weighted k edges from the network are then used by the clustering algorithms to predict complexes. For NOWEI we use We perform random sub-sampling cross-validation, repeated over ten rounds, using manually curated complexes as reference complexes for training and testing. For yeast, we use the CYC2008  set whicWe designed our experiment to simulate a real-use scenario of complex prediction in an organism where a few complexes might already be known, and novel complexes are to be predicted: in each round of cross-validation, the training complexes are those that are known and leveraged for learning to discover new complexes, while the test complexes are used to evaluate the performance of each approach at this task. Thus we use a large percentage of test complexes P is said to match a complex C at a given match threshold match_thres if Jaccard >= match_thres. Each cluster C is ranked by its score. To obtain a precision-recall graph, we calculate and plot the precision and recall of the predicted clusters at various cluster-score thresholds. Given a set of predicted clusters d are defined as follows:to evaluate the predicted clusters. First, a cluster The precision of clusters is calculated only among those clusters that do not match a training complex, to eliminate the bias of the supervised approaches (SWC and BOOST) for predicting training complexes well. The precision-recall area under curve (AUC) is used as a summarizing statistic for each method's performance. Besides evaluating the performance of complex prediction, we also evaluate the performance of edge classification, in which the edge weights are used to classify edges as co-complex or non-co-complex edges.To evaluate the quality of novel predicted complexes, we define three measures of semantic coherence for each complex: its biological process (BP), cellular compartment (CC), and molecular function (MF) semantic coherence. These are calculated from the proteins' annotations to Gene Ontology (GO) terms, which span the three classes BP, CC, and MF . We use We first evaluate each approach in classification of co-complex edges. Here, each weighting approach is used to weight the network edges, and the edges are classified as co-complex by taking a threshold on their weights. We obtain precision-recall graphs  by taking a series of decreasing thresholds; at each recall level, we also indicate the proportion of test complexes covered by at least one predicted edge .Figure On the other hand, SWC is more accurate than STRING in predicting co-complex edges with high weights, because many proteins that are highly functionally associated are not co-complex, while SWC's supervised learning approach produces weights that are targeted at predicting co-complex edges, so highly-weighted edges are likelier to be co-complex. However, to retrieve even more co-complex edges by lowering the weight threshold, STRING's precision rises above SWC's, indicating that finding co-complex edges in this region might be better served simply by functional association.BOOST integrates the same data sources as SWC, but uses LogitBoost instead to learn to classify co-complex edges. Its points in the graph are clustered in two regions: one set of edges are given high scores, achieving about 40% recall and 35% precision , while the remaining edges are given low scores. Thus BOOST performs classification in a categorical manner, whereas SWC produces co-complex scores that reflect a wide range of confidence.Finally, the performance of NOWEI, which uses unweighted PPI edges, appears as a single point on the graph, and shows that the PPI edges cover only 53% of co-complex edges, with a precision of 5%.Figure Compared to TOPO, SWC has lower precision along TOPO's entire recall range. However, once again TOPO's predicted edges are clustered in fewer complexes, giving lower complex coverage: for example, to cover 80% of complexes requires TOPO to recall 22% of edges at a precision of 8%; SWC has to recall only 13% of edges at a higher precision of 11% to cover the same amount of complexes. Thus, for human as well as yeast, SWC is able to predict co-complex edges for a wider range of complexes compared to TOPO, whose range is limited to fewer complexes that are densely connected.For human, STRING's functional association scores are the least accurate for predicting co-complex edges, giving the lowest precision among all the weighting approaches.Just like in yeast, BOOST performs classification in a categorical manner: a set of edges are predicted as co-complex with high scores, achieving 7% recall and similar precision levels as SWC, while the remaining edges are predicted as non-co-complex with low scores.k = 20000, but outperforms all other approaches). STRING achieves higher AUC compared to BOOST for all clustering algorithms except for CMC, while BOOST outperforms TOPO for all algorithms except for HACO. Finally, NOWEI performs dismally in all clustering algorithms except for RNSC. The COMBINED strategy achieves higher AUC compared to using each individual clustering algorithm, for all weighting approaches except for NOWEI . Using the COMBINED strategy, SWC achieves the highest AUC, followed by STRING, BOOST, TOPO, and finally NOWEI.We compare the performance of the five weighting approaches in complex prediction, when each of the six clustering algorithms is used separately, and when all the clustering algorithms are used together with the COMBINED strategy. Figure We analyze the clusters from the COMBINED strategy to determine how it achieves greater complex-prediction performance by aggregating clusters from the different clustering algorithms with simple voting. Figure k that achieves the highest AUC .Figure match_thres = 0.75. Thus it outperforms all other weighting approaches, especially at predicting complexes with ne granularity.SWC recalls the most test complexes, with the highest precision at almost all recall levels, especially with the stricter match_thresh = 0.5, STR achieves almost the same recall as SWC with only slightly lower precision levels, but its recall and precision are much worse at a higher match_thresh = 0.75. Since STR classifies co-complex edges across a large range of clusters, it is able to recall many test complexes; but its lower accuracy in edge classification means that many of its clusters include extra or missing proteins, causing them not to be matched at a stricter matching threshold. BOOST achieves similar recall as STR but with substantially lower precision levels at both match thresholds. Since it classifies edges categorically, many edges have similar scores that do not vary with classification accuracy; thus the ranking of clusters (based on their weighted-densities) does not correlate as well with their correctness, giving lower precision levels. TOPO achieves the lowest recall of all approaches. While its precision for its highest-scoring clusters is comparable to SWC's at match_thresh = 0.5 (at the extreme left end of the graph), it drops rapidly for the remaining clusters. This is because TOPO classifies co-complex edges accurately for a limited number of complexes which are thus easy to predict, while the remaining complexes' edges are not as accurately classified, creating many false positive clusters and low recall. Finally, although NOWEI achieves slightly higher recall than TOPO, it generates a great number of false positives, giving extremely low precision.At match_thresh = 0.75. Nevertheless, it is clear that SWC outperforms all the other weighting approaches. Using each clustering algorithm's best parameter settings for each approach, SWC achieves substantially higher AUC than all the other approaches, for all clustering algorithms. TOPO has higher or similar AUC compared to BOOST for all clustering algorithms except MCL, while STRING performs worse than BOOST for most clustering algorithms except IPCA and RNSC, where their performances are about even. The COMBINED strategy shows less clear benefits for human complexes, in terms of AUC: it gives an improvement for SWC only for match_thresh = 0.75, and actually gives worse performance for STRING compared to using CMC, IPCA, RNSC, or HACO alone. Figure Figure k that achieves the highest AUC .Figure match_thresh, with higher precision at all recall levels (except that TOPO's top-scoring clusters has slightly higher precision at match_thresh = 0.5). The performance advantage is even more pronounced at match_thresh = 0.75, where SWC recalls 50% more test complexes compared to the other approaches, and maintains almost twice the precision throughout its recall range. BOOST attains the next highest recall, but with substantially lower precision at all recall levels. Just as in yeast, its categorical edge classification reduces the correctness of the ranking of its clusters, giving lower precision levels.SWC attains the highest recall at both match_thresh = 0.5 its precision for its high-scoring clusters is higher than that of BOOST, and even comparable to SWC's for its highest-scoring clusters. Once again, TOPO's high accuracy in classifying edges for a limited number of complexes means it is only able to predict a few complexes well at rough granularity.TOPO achieves lower recall, but at match_thresh = 0.5, with a similar recall at the higher match threshold.Unlike in yeast, here STR performs extremely poorly with the lowest recall and precision levels of all weighting approaches. This is not surprising given that STR performs poorly in edge classification as well. Indeed, even NOWEI achieves higher recall and precision at k edges are used to predict complexes with the COMBINED clustering strategy, which combines clusters predicted by the six clustering algorithms. We use k = 20000 for SWC, BOOST, and TOPO, k = 10000 for STRING, and k = all edges for NOWEI.We evaluate the five weighting approaches  on the number and quality of high-confidence novel complexes predicted in yeast and human. For the supervised approaches (SWC and BOOST), we use the entire reference set of complexes  for training. Next, the edges of the entire network are weighted, and the top match_thres = 0.5, then the complex with the lower score is removed. Next, only novel predictions are kept: if any predicted complex matches any reference complex with match_thres = 0.5, then that predicted complex is removed. Finally, only high-confidence predictions are kept: for each weighting approach, using the cross-validation results, the score of each predicted complex is benchmarked to a precision value, and predicted complexes whose estimated precision are less than a confidence threshold are removed. For yeast, this confidence threshold is 0.5; for human, since much fewer complexes are predicted with high precision, we use a 0.4 confidence threshold.We filter the set of predicted complexes to obtain a set of unique, novel, high-confidence predictions. First, complexes that are too similar are removed: if any two predicted complexes match with p < 0.05) except for NOWEI ; however, the reference complexes of CYC2008 still have much higher BP and CC coherence (p < 0.0005). The MF coherence for SWC, STRING, and BOOST complexes are similar to that of the reference complexes, and are much higher than that of TOPO, NOWEI (p < 0.0005). Thus, weighting by SWC generates a larger number of novel yeast complexes with greater BP and CC semantic coherence and similar MF coherence, compared to the other weighting approaches. To explore the functions of the novel predicted complexes, we select a set of eleven high-level BP terms, and annotate a novel complex with a BP if that BP is annotated to the most number and a majority of proteins in the complex. Some complexes may be annotated to more than one high-level term. Table Figure p < 0.0005), BOOST (p < 0.0005), and NOWEI , and have similar BP, CC, and MF coherence compared to those of STR. However, the reference complexes of Corum still have significantly higher BP and CC coherence (p < 0.0005), while the MF coherence of SWC and STR are significantly higher than that of the reference complexes (p < 0.05). Thus, weighting by SWC generates a larger number of novel human complexes, with greater semantic coherence than other weighting approaches except for STR.Figure Table Figures The likelihood ratio is a reflection of "co-complexness strength". In general, the likelihood ratios increase as the scores for the data sources (i.e. the x-axes) increase. For the PPI and L2-PPI data sources, protein pairs with higher scores have greater number of shared neighbors, and are likelier to be co-complex: when the score of PPI is close to 1, indicating that almost all of the protein pair's neighbors are shared, the pair is 40 times likelier to be co-complex in yeast and 35 times likelier to be co-complex in human. L2-PPI scores are imputed in edges whose proteins do not actually interact according to PPI databases, yet who share many interaction partners. These scores have corresponding lower likelihood ratios compared to PPI scores: with a score close to 1, the pair is less than 30 times likelier to be co-complex in yeast and less than 20 times likelier to be co-complex in human.For the STRING data source, only protein pairs with very high functional association scores are likelier to be co-complex: those with the highest scores are almost 40 times likelier to be co-complex in yeast and 50 times likelier to be co-complex in human, whereas protein pairs with lower functional association scores do not seem any likelier to be co-complex.For PubMed data, protein pairs that co-occur in literature, even infrequently, are already much likelier to be co-complex: about 20 times likelier in yeast and 10 times likelier in human. However, pairs that co-occur more frequently in literature are not any more likelier to be co-complex compared to pairs that co-occur less frequently.The likelihood ratios for the different data sources show that the co-complexness strength of each data source does not increase linearly with its score. Moreover, between the different data sources, the relationships between data score and co-complexness are different. Thus, combining data scores across different data sources without factoring their dissimilar co-complexness relationships is evidently unsound, while our supervised approach scales the heterogeneous scores to a uniform co-complexness score in terms of likelihoods, which can then be combined probabilistically using the naive-Bayes formulation.The high likelihood ratios for the data sources also demonstrate that they are indeed indicative of edges belonging to complexes: during cross-validation for both yeast and human, none of the data sources were removed by feature selection in any round.k = 20000 edges , but the majority of them are not connected to the other proteins in the complex, so they are unlikely to be clustered together. Moreover, BRE, BABAM1, and BRCC3 are still highly connected to the original complex proteins. Indeed, clustering this network produces both the cluster consisting of the four CORUM proteins (generated by CMC), as well as a larger cluster consisting of the four CORUM proteins plus the three new members and two extra proteins (generated by IPCA). The likelihood network shows that PPI edges with strong co-complexness induce a full clique between two CORUM complex members FAM175A and UIMC1 with the three new members and an additional protein FAM175B; on the other hand, the four CORUM complex proteins themselves are less strongly connected, via two functional associations with high co-complexness and a few low co-complexness PPIs. This provides ample evidence that the three new proteins belong to this complex, while the inclusion of two extra proteins BRCA2 and FAM175B is likely due to their participation in other complexes that overlap with the BRCA1-A complex.Figure We select two novel complexes predicted with the COMBINED strategy using the SWC network, with the entire reference set of complexes for training.One high-scoring novel yeast complex, generated by all six clustering algorithms, is composed of four proteins, MMS1, MMS22, RTT101, and RTT107, and is annotated with two high-level BP terms, DNA metabolic process and response to stress. Figure Figure In this paper, we introduce a maximum-likelihood supervised approach for weighting composite protein networks for predicting protein complexes, called SWC (Supervised Weighting of Composite networks). First, we construct a composite protein network using three heterogeneous data sources: PPI, predicted functional association, and co-occurrence in literature abstracts. Next, we weight each edge of the composite network based on its posterior probability of belonging to a protein complex, using a naive-Bayes maximum-likelihood model learned from a set of training complexes. The weighted composite network is then used by clustering algorithms to predict new complexes. We also propose a simple aggregative clustering strategy that combines clusters generated by multiple clustering algorithms, using simple voting. We evaluate our weighting scheme using six clustering algorithms, as well our aggregative clustering strategy, on the prediction of yeast and human complexes. We demonstrate that our proposed method outperforms a supervised data-integration approach using boosting, a predicted functional-association network from STRING, an unsupervised approach using a topological function to weight PPI networks, as well as a baseline approach using unweighted PPI networks: our approach predicts more correct complexes at higher precision levels, and generates more high-confidence novel complexes with similar or better semantic coherence. Using a few example complexes, we show that our approach increases the density of the complexes' subgraphs, and filters them to remove extraneous edges. Furthermore, our approach allows visualization of the evidence of predicted complexes, using learned likelihood parameters to express strengths of co-complex relationships of each data type. This aids human evaluation of the credibility of predicted complexes.Finally, we present two novel predicted complexes: a four-protein yeast complex possibly involved in DNA metabolism and stress response, and a four-protein human complex possibly involved in transport processes. We show that these predictions appear credible from their evidences, being supported by diverse data sources with strong co-complexness. Indeed, a recent paper presents the predicted yeast complex as the Cul8-RING ubiquitin ligase complex, while the Uniprot database provides evidence that the predicted human complex may exist as a potassium channel complex.http://compbio.ddns.comp.nus.edu.sg/~cherny/SWC/.SWC software package and data files are available at The authors declare that they have no competing interests.CHY derived and implemented the algorithms, performed the experiments, and drafted the manuscript. GL and CHY designed the experiments. HNC and CHY selected and prepared the data sources. LW conceived and directed the study. All authors read and approved the final manuscript.Novel, unique, high-confidence predicted yeast complexes.Click here for fileNovel, unique, high-confidence predicted human complexes.Click here for file