Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved.A comprehensive map of the humanWe propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how “distant” they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions.Our method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and diversity of the template dataset, but, given the rapid accumulation of solved protein complex structures, its scope and utility are expected to keep steadily improving.The online version of this article (doi:10.1186/s12859-017-1550-y) contains supplementary material, which is available to authorized users. Mycobacterium tuberculosis (MTB), the underlying causative agent, present scientific and strategic challenges [Tuberculosis (TB) continues to pose a serious global health problem , 2. The allenges –6. Overcallenges . Dissectallenges , 8.Multiple changes are known to occur in the physiology of the macrophage following phagocytosis of virulent MTB –9. Thesein vivo in disrupting endosomal trafficking which in turn promotes bacterial survival. One limitation of such a high-throughput screening approach is that, possibility of false detections  cannot be ruled out. Estimates suggest that Y2H has a sensitivity of only about 20% [Low throughput experimental studies have so far uncovered and characterized around 40 binary protein-protein interactions between MTB and human, and these have helped shed some light on the pathophysiology of the disease . A recenbout 20% , and, asbout 20% , severalbout 20% , 24.Several computational approaches to predicting PPIs, both within and between species, are based on the use of sequence information of the participating proteins –27. ThesSequence-based methods, in general, harness the “universe” of experimentally known PPIs, both within and across genomes, which serve as templates to search against –27, 30. if information about the details of the interaction (i.e. the specific regions involved in the interaction interface) is not taken into account. Given that resolved crystal structures are available for only a small fraction of the PPIs known to date [BLAST is an example of heuristic local alignment , and ess to date , this soOn the other hand, imposing stringent filters on the coverage of the template sequence in the alignment in order to avoid the above caveat might turn out to be too restrictive. This constraint could cause potential interactions to be missed out, if the query and template proteins happen to share a highly conserved region that is sufficient for the template interaction to occur.Such pitfalls underscore the need to incorporate additional information about the structural interface actually mediating the template interaction into sequence-based search for viable PPI candidates, which can improve the quality of the predictions. Assuming that functional domains are independently folding modular units of protein structure and mediate inter-protein interactions, restricting the template PPIs to the subset for which structural information is available provides a more targeted approach to search for potential PPIs. We propose leveraging the structurally resolved protein complexes deposited in the Protein Data Bank (PDB), and using their interacting portions (domains) instead of the full length protein sequences as templates in a local alignment-based search for viable human-MTB PPIs. Combined with other sources of information such as functional annotation, cellular localization and cell type-specific gene expression data , such anH. sapiens and the virulent M. tuberculosis strain H37Rv along with functional meta-data for the proteins were downloaded from UniProt (Sept 2015). All human proteins were retained for the subsequent search. On the other hand, the MTB proteome was restricted to a smaller subset composed of proteins for which there is direct or indirect link to infection/adaptation inside the host . In order to construct this subset, we merged the following sources of contextual information for MTB: (1) proteins annotated with a select set of relevant keywords or GO cellular component terms in UniProt ; (2) all proteins detected in the culture filtrate in the proteomic study by de Souza et al. [The proposed method screens for physical protein-protein interactions between human and MTB proteomes. Complete reference proteomes for a et al. ; (3) alla et al. ; and (4)a et al. . The uniInformation about domain-domain interactions in PDB protein complexes was obtained from the iPfam (version 1.0) and 3DID (version 2015_02) databases , 42. BotIn order to highlight the potential limitations of local sequence alignment for PPI inference, we identified Reciprocal Best Hits (RBH) for every protein in the MTB set using NCBI protein BLAST search  against the SwissProt database. RBH provides the best match for a query protein in every other annotated proteome, and this approach is routinely employed in comparative genomics to screen for orthologs –46. StatWe used Smith-Waterman (SW) local alignment implemented in the EMBOSS command-line tool   is available. This procedure is outlined in Additional file The various steps involved in our methodology have been summarized as a flow chart in Fig. We randomly selected 50 domains that each occur 30–60 times among the proteins comprising the large scale PPI dataset (see above). Differences among the members of every domain family were quantified in terms of their pair-wise sequence similarity values , and the pair-wise differences in sequence length scaled by the average length for that family. The distributions of these metrics are illustrated for the particular example of the Ulp1 protease family C-terminal catalytic domain (PF02902) in Additional file E. coli. Briefly, the frequency of occurrence of every domain pair in the positive set (i.e. among the PPIs) was compared with its incidence in the complement set . Statistical significance of its over-representation in the PPI set was assessed in terms of the p-value estimated by one-sided Fisher’s exact test. There could be protein pairs for which no interaction evidence has been reported because they are present in different cellular locations or not co-expressed under in vivo conditions, but nevertheless could still be compatible to physically interact in vitro. Thus, not all members of the complement set are expected to be bona fide non-interactions. In order to minimize the influence of this confounding factor on estimation of likelihood ratios, and refine the complement set to better reflect the “true” negative set, we retained only those E. coli protein pairs in the complement set which were co-localized to the cytosol (as indicated by their GO cellular component annotation), and showed correlated expression (pearson correlation coefficient > 0.5) in the M3D compendium of microarray profiles [We estimated the statistical association between the interacting domains (as well as domain combinations) listed in iPfam/3DID and a large scale physical PPI network for profiles .Domain-domain interactions can be specific, and thus, it is quite likely that every instance of a particular pair of domains, for which interaction evidence is found in a few PDB complexes, will not engage in a detectable interaction . We presLocal alignment of two sequences looks for matching subsequences in longer full length proteins. Consequently, even matches which cover only a fraction of the template protein sequence can score high in terms of the statistical significance  or percentage sequence similarity of the aligned portion. If the template PPI set contains a substantial proportion of proteins having more than one functional domain, the possibility of ascribing spurious interactions to protein pairs exists in the absence of structural details about the template interaction Fig. . In ordeAnother way to depict the differences between proteins related by RBH is shown in Fig. iPfam and 3DiD [Knowledge about interacting domains extracted from functional complexes with structural information can be used to suggest potential interactions between proteins for which experimental interaction evidence is lacking. We have made use of the set of structurally resolved protein complexes listed in two databases, and 3DiD , 42, as iPfam and 3DiD taken together provide a list of 3265 binary domain-domain (inter-chain) interactions involving 1356 Pfam domains. Out of these, 1034 domains have at least one instance of occurrence in the human/filtered MTB proteomes, and they collectively yield a total of ~ 82470 pairs of MTB and human proteins that could potentially interact.We have restricted our search for potential human-MTB PPIs to a filtered set of ~ 1000 MTB proteins which may have some functional role to play in the infection process (Methods). single domain pair for which interaction evidence has been found in only a few (or just one) PDB structures. Therefore, we suggest a simple scoring scheme to sort these plausible interactions, using the evidence from the known DDI interfaces as a prior. The sequences corresponding to the interacting DDI instances have been extracted from the corresponding PDB files and used as templates to scan the human and MTB proteomes. Each “hit” has been scored in terms of the coverage of the template sequence in the resulting alignment (required to be > 90%) and sequence similarity with the template in the alignment. For different thresholds of varying stringency, the search yields a small set (10–500) of prioritized MTB-host protein pairs which could represent functional PPIs  across species. The pattern of domain-domain interactions mediating the PPI is also assumed to be conserved, and this provides additional template sequences to compare against  showing interaction evidence in PDB complexes also occur in non-interacting protein pairs listed in the Negatome database . This cup-value > 0.05) with the set of known PPIs among the co-localized, co-expressed proteins in E. coli (see Methods). Such a lack of statistical enrichment is found not only for domain pairs but also for some pairs of domain combinations . Our scoring scheme based on sequence similarity provides a simple way to factor in this heterogeneity, and sort the list of host-pathogen protein pairs according to the empirical evidence for domain-domain interactions currently available in the PDB.We have arrived at a non-redundant, prioritized list of MTB-human protein pairs, each of which contains at least one pair of subsequences closely resembling a known DDI instance on the sequence level. The complete list of predictions may be found in Additional files We further sought circumstantial evidence for the in vivo relevance of the prioritized interactions. To this end, we prepared a list of 462 relevant human proteins by combining datasets on host dependency factors , host ceOur approach picks out a few potential interactions which could be of some interest. The secreted Hypoxia response protein 1 of MTB (Rv2626c) is predicted to interact with CLCN3 (H(+)/Cl(−) exchange transporter 3 (Chloride channel protein 3)) through their CBS domains (PF00571). This latter protein is an antiporter associated with the endosomal/phagosomal membrane, and contributes to the acidification of the endosomal lumen thereby possibly affecting vesicle trafficking . GO annoGenome-based computational methods have been employed for several years now to help reconstruct the protein interactome underlying the functional landscape in a number of organisms –27, 30. M. tuberculosis. Our targeted approach may be viewed as setting an upper bound on the performance of any comparative sequence-based method for identifying PPI candidates that relies purely on sequence alignment. We have proposed reducing the occurrence of false positives (esp. in the case of multi-domain proteins) and negatives, and increasing specificity, by prioritizing the candidate interactions on the basis of their domain-level sequence similarity with the template proteins. On the other hand, since our approach is extrapolatory in nature, only those candidate protein pairs which share similar subsequences with the template interactors get high scores. Thus, the number of high-scoring predictions that can be made, i.e. the sensitivity of the method, is limited by the size and diversity of the template dataset used.  Howeven so far , 22.Some earlier studies have also proposed the use of domain-level interaction information to screen for potential PPIs, but those approaches essentially treat all members of a domain family on the same footing, disregarding the differences in interaction ability among them , 66–69. The candidate interactions could have been prioritized in other ways, besides the one we have adopted. For example, the joint similarity metric could be calculated based on only those positions in the sequence alignment that correspond to the directly interacting residues in the template complex  that might underlie changes in the transcriptional profile at various stages of the infection –73. WithM. tuberculosis, it is of general applicability, and should provide a useful data-driven approach to predicting and prioritizing potential PPIs between any pathogenic microbe and its host that leverages the existing genomic and structural datasets available in the public domain.Our analysis of local sequence alignment applied to host-pathogen PPI prediction highlights the possibility of drawing spurious inferences , if structural details about the template interactions are not available/not taken into account. We have proposed making use of the structurally resolved complexes in the Protein Data Bank for more targeted search for novel PPI candidates between human and MTB proteins. The use of domain-domain interaction information reduces the chances of false positives/negatives from local sequence alignment-based PPI prediction. Our knowledge-based approach, which looks for similar sequences in the vicinity of known DDI templates, acknowledges the inherent diversity within domain families and DDI interaction specificity, for which we have provided different lines of supporting data. Although we have illustrated our methodology with the specific case study of Additional file 1: Figure S1.Figure S2. Pair-wise comparisons between MTB proteins and their putative orthologs listed in the Integr8 (upper figure), eggNOG (lower left) and KEGG Orthology/KO (lower right) databases. Note the relatively smaller number of orthologs assigned by Integr8. Figure S3. Using the interolog approach to extend the limited set of DDI templates provided by PDB. Evolutionarily conserved PPIs (involving orthologs) are assumed to share a common underlying pattern of domain-domain interactions. This allows interacting domains to be inferred for PPIs for which structure information is not directly available . Figure S4. Diversity in terms of length and sequence composition across the polypeptide sequences assigned to the Ulp1 protease family C-terminal catalytic domain (PF02902). The scatter plot follows from all-vs-all pairwise sequence alignment using the Smith-Waterman method. Pairs coming from the same proteome (blue) or from different proteomes (cyan) have been assigned different colors. Figure S5. Statistical over-representation of interacting Pfam domain pairs (from iPfam/3DID) in a PPI network for co-localized, coexpressed cytosolic proteins in E. coli. Several domain pairs do not show significant association with the PPI set. The darker horizontal line represents the p-value < 0.05 threshold. (PDF 1100 kb)Histograms summarizing the occurrences of Pfam-A functional domains in the human (blue) and MTB (red) proteomes. Nearly 30% of the proteins in both species have been assigned more than one domain. Additional file 2:iPfam. Column headers: 1) Pfam ID of domain 1; 2) Pfam ID of domain 2; 3) PDB chain ID of domain 1; 4) PDB chain ID of domain 2; 5) Uniprot accession of PDB chain 1; 6) Uniprot accession of PDB chain 2. (TXT 6425 kb)Complete list of domain-domain interactions from Additional file 3:Complete list of domain-domain interactions from 3DID. Column headers: 1) Pfam ID of domain 1; 2) Pfam ID of domain 2; 3) PDB chain ID of domain 1; 4) PDB chain ID of domain 2; 5) Uniprot accession of PDB chain 1; 6) Uniprot accession of PDB chain 2. (TXT 6075 kb)Additional file 4:Prioritized list of human-MTB protein pairs obtained by comparison with PDB DDI templates, sorted according to their joint similarity scores (>0.7 only). Colum headers: 1: Uniprot accession of MTB protein. 2: Uniprot accession of human protein. 3: Alternate symbols for MTB protein. 4: Alternate symbols for human protein. 5: Template sequence (PDB chain ID) mapping to MTB protein. 6: Template sequence (PDB chain ID) mapping to human protein. 7: Pfam ID of template mapping to MTB protein. 8. Pfam ID of template mapping to human protein. 9: Pfam domain name of template mapping to MTB protein. 10. Pfam domain name of template mapping to human protein. 11. Uniprot accession of template mapping to MTB protein. 12: Uniprot accession of template mapping to human protein. 13: Joint similarity score from comparison with the template DDI. 14: Total number of DDIs between the two interacting proteins to which the template DDI belongs. (TXT 7871 kb)Additional file 5:Prioritized list of human-MTB protein pairs obtained by comparison with the interlog-derived template set, and sorted according to their joint similarity scores (>0.7 only). Colum headers: 1: Uniprot accession of MTB protein. 2: Uniprot accession of human protein. 3: Alternate symbols for MTB protein. 4: Alternate symbols for human protein. 5: Template sequence (SwissProt ID|Pfam ID|sequence) mapping to MTB protein. 6: Template sequence (SwissProt ID|Pfam ID|sequence) mapping to human protein. 7: Pfam domain name of template mapping to MTB protein. 8. Pfam domain name of template mapping to human protein. 9: Joint similarity score from comparison with the template DDI. 10: Total number of DDIs between the two interacting proteins to which the template DDI belongs. (TXT 204 kb)Additional file 6:Summary of comparison of our predictions with those from four most recently published computational studies on host-MTB interactions. (PDF 49 kb)


S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen  Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein’s function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions.The functions of roughly a third of all proteins in IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins. Campylobacter jejuni .Despite the proven value of large-scale protein interaction data, relatively little is known about the interactome of the human pathogen s of age , 15. A bS. pneumoniae are still uncharacterized, while many more proteins have only very general or predicted annotations, such as “membrane protein” or “ABC transporter,” without known specificity (per the UniProt reference proteome data available in June 2016). The fraction of uncharacterized proteins in S. pneumoniae is similar to that in other bacteria, reflecting a need for protein function studies in bacteria in general. For instance, our study of the Treponema pallidum interactome  of the 2,109 proteins of 9% of theeractome . While peractome , such inSimple statistical methods such as the “majority rule” can predict the function of uncharacterized proteins by considering the majority of functions of their interaction partners –21. For –S. pneumoniae proteins. This allowed us to predict functions for many poorly characterized proteins by connecting them to known proteins and pathways. To further enhance our ability to predict protein functions, we augmented the experimentally derived S. pneumoniae protein network with interactions predicted from other bacteria. Notably, we found that prediction accuracy increased with the addition of a protein interaction network that combined the experimental data with the bacterial meta-interactome. We further show that such prediction characteristics increase as a function of a given protein’s degree (number of interactions) in the original and augmented networks. The predictions appeared functionally consistent in comparisons of results in the original and augmented networks. Finally, we utilize our bacterial meta-interactome to predict the functions of 299 poorly characterized genes in S. pneumoniae.We have previously shown that use of multiple variants of the Y2H approach can produce more-comprehensive coverage of interactions for any set of proteins, allowing discovery of many protein interactions that would be missed with the use of one experimental approach alone –25. As a–S. pneumoniae, a Gram-positive bacterium with 2,109 predicted open reading frames (ORFs)  see , 27  see . Combiniproteome .10.1128/mSystems.00019-17.1TABLE S1 TABLE S1, XLSX file, 0.1 MB.All available ORF clones were obtained from the Pathogen Functional Genomics Resource Center (PFGRC) at the J. Craig Venter Institute (JCVI); all clones were used as baits and preys. Download Copyright © 2017 Wuchty et al.2017Wuchty et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00019-17.2TABLE S2 TABLE S2, XLSX file, 0.1 MB.All bait clones screened by the LexA Y2H system. Download Copyright © 2017 Wuchty et al.2017Wuchty et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00019-17.3TABLE S3 TABLE S3, XLSX file, 0.1 MB.All PPIs found by both the Gal4 and LexA screens; reciprocal hits represent PPIs that were found when bait clones were used as prey and vice versa. Download Copyright © 2017 Wuchty et al.2017Wuchty et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. pneumoniae. To build the meta-interactome, we included experimentally derived protein interactions from C. jejuni, M. loti, Synechocystis, B. subtilis, T. pallidum, H. pylori, E. coli, and M. tuberculosis, whose interactomes have been determined on a large scale . This protein participates in the SMC condensin complex in B. subtilis, organizing and compacting chromosomes during growth . However, a limitation of the experimentally derived network for protein function prediction is that it contains a large number of functionally uncharacterized proteins. In the combined network, the functions of 342 of 918 (37.2%) proteins were unknown  28). Gi. Gi28). e scale 16, 11, 12d script , we deteeractome . To assenotation ; Table 1g growth –32. Whiltivities ; Table 1S. pneumoniae, we randomly picked 80% of all functionally annotated proteins 1,000 times to predict the functions of the remaining 20% in each random run. In particular, we utilized functional annotations from the EggNOG database , suggesting that the augmentation of the original network with interactions from other bacteria significantly improved the quality of functional predictions  open reading frames using a Gal4-based yeast two-hybrid (Y2H) system. In the second step, we focused on proteins of unknown function, using a LexA-based Y2H system. On the basis of ample previous experience (–We used a two-step approach to study protein-protein interactions (PPIs) in perience –25, we kS. pneumoniae were shuttled from pENTR221 entry clones  into Y2H bait plasmid pDEST22  using Gateway cloning.In the Gal4-based Y2H screen, full-length ORFs of Individual bait plasmids were transformed into haploid yeast strain CG-1945 and prey plasmids into Y187  as described previously .S. pneumoniae entry clone library individually in selective Luria Broth (LB) medium, followed by pooling and plasmid isolation. The resulting entry clone plasmid pool was shuttled into the pGADT7g and pDEST22 prey plasmids by the use of a Gateway LR reaction (Invitrogen). The reaction mixture was then transformed into electrocompetent E. coli DH10B  and grown in selective LB medium, and the plasmids were isolated. Plasmid pools were then transformed into Y187 as described previously  of 2, were mixed. Selection of Y2H positive diploids was carried out on plates (15-cm diameter) containing agar medium with SD medium  without the amino acids Leu, Trp, and His and supplemented with 0.1 mM 3-amino-1,2,4-triazole (3-AT). The screening plates were incubated for 3 to 5 days at 30°C. To check the mating efficiency, a 1:10,000 dilution was plated on -Leu-Trp SD agar in parallel to the screens and the number of diploid colonies was determined. A screen was repeated if the number of colonies was <200,000. For autoactivating baits, screens were repeated on 1 mM and 10 mM 3-AT.Yeast bait and prey libraries were grown and mated as described in reference S. pneumoniae strain TIGR4 (control).Y2H-positive preys were identified by colony PCR after zymolyase (amsbio) treatment using BIOTAQ Red DNA polymerase (Bioline) following enzymatic purification performed as described in reference Streptococcus pneumoniae proteins that were annotated as “hypothetical.” We started with the same 1,297 sequence-validated Streptococcus pneumoniae Gateway entry clones described above (constructed by the PFGRC at JCVI). We successfully subcloned 1,219 of these open reading frames (ORFs) into activation domain (AD) vector pJZ4attR  classes, i.e., functional annotations derived from the EggNOG database  is the discrete δ function, where δ = 1 if unclassified proteins i and j have the same function  and vice versa. As a consequence, the first term allows us to optimize the number of interactions between unclassified proteins if they are predicted to have the same function. Depending on the function assigned to an otherwise unclassified protein, the second term aims to optimize support for the assigned function of protein i. In particular, we determine the number of classified proteins hi(σi) that interact with unclassified protein i with the same function σ that was assigned to unclassified protein i.We modeled the prediction of a functional class σ of a protein ts model . In partdatabase . All proE, we applied a simulated annealing approach that features an effective temperature T. After initially assigning random functions to all unclassified proteins, we randomly selected a protein, changed its function to a different class, and determined the energy of the new configuration. If the difference of energies ΔE = ≤0, the new configuration was accepted. If ΔE = >0, the new configuration was accepted with probability p = −ΔE/Te. To obtain stabilized functional configurations, we repeated such a Monte Carlo step 10,000 times (T by 0.01 in each step and repeated such Monte Carlo steps. Since minimum energy solutions are not unique, we repeated such runs of simulated annealing 100 times and considered the fraction of times that an unclassified protein i was observed in a certain functional state σ to be an estimate of the probability that protein i belongs to class σ.To minimize 00 times . SubsequS. pneumoniae 1,000 times and utilized the remainder to predict the functions of the sampled proteins in the protein interaction network of S. pneumoniae as well as in the augmented network. Each gene in the sample was assigned the most probable function. As such, we determined ni→j, the number of times that the original gene in the network of S. pneumoniae was predicted to have function i while its function changed to j in the augmented network. For each transition from function i to j, we determined its probability N is the total number of genes that were considered. For a null model, we determined an expected probability of transitions from function i to jni→ is the number of times that genes were found to have function i in the original protein interaction network of S. pneumoniae, while nj→ is the number of times that genes were found to have predicted function j in the augmented network. Combining these probabilities, we determined a log-odds ratio a = ni→nj→ and b = N2. We calculated a P value for the significance of a link between two classes by a Z test, where P = <0.05.We randomly sampled 20% of all functionally annotated proteins in pi is the fraction with which a given protein was assigned to functional class i. Such a measure tends to reach a value of 1 if one function dominates the distribution of fractions and vice versa.The Simpson s-index considers the fractions with which a given protein was assigned to a functional class. In particular, we calculated its heterogeneity of functional fractions as a Simpson diversity  index deS. pneumoniae, we calculated the fraction of correctly predicted functions in bins of proteins with a given number of interaction partners in the original network of S. pneumoniae. Since each protein was assigned to a functional class with a certain probability, we labeled each protein with the most probable function. We defined the enrichment of accuracy in a given bin of degree k as fk is the fraction of correctly predicted functions of proteins with degree k in the original network of S. pneumoniae. In turn, fk,m reflects the rate of correctly predicted functions using the augmented network.To compare the prediction results that we obtained with the original network and the augmented network of protein interactions in


RNA–Protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA–Protein interactions and binding sites from experiments and predictions, RNA–Protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA–Protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA–Protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA–Protein interactions. RNA and protein are two important components of organisms, and the interactions between them are crucial for a large number of cellular processes, such as protein synthesis and regulation of gene expression ,3,4,5,6.A list of RNA–Protein interaction database resources is given in With the rapid increase of RNA–Protein interaction resources, certain interaction databases have been developed to integrate various data sources into one framework: a manual review of literature, experiments and prediction. Mus musculus and Drosophila melanogaster, including protein-coding RNAs, tRNAs, rRNAs, miRNAs and viral RNAs. Furthermore, each interaction entry contains detailed information curated from other resources, including binding sites, Gene Ontology (GO) terms, protein/RNA motifs, detected methods and biological functions. PRD is a good reference of RNA–Protein interactions and may be helpful for the study of RNA–Protein interaction networks.Protein–RNA interaction database (PRD)  is a datHomo sapiens, Mus musculus and Saccharomyces cerevisiae). Wherein, RNA–Protein interactions (more than 8000) are collected from literature mining. A local the University of California, Santa Cruz (UCSC) Genome Browser was integrated for H. sapiens, M. musculus and S. cerevisiae. Additionally, the current version provides human and mouse gene function prediction based on the interactions and gene co-expression scores between interacting molecules. The database is a helpful resource of the ncRNA interactome.The noncoding RNAs and protein-related biomacromolecules interaction database (NPInter)  curates RAID  is a resCLIPdb  is a CLICaenorhabditis elegans). Taking advantage of UCSC’s TrackHub feature, all target site information for RNA-binding proteins or miRNAs are integrated into the local installation of the UCSC genome browser as additional local tracks. The combinatorial action of RNA-binding proteins and miRNAs on target mRNAs form a post-transcriptional code. The database is helpful to understand the post-transcriptional regulatory network.doRiNA  is a datC. elegans. Of which, the Ago protein binding sites are used to obtain CLIP-supported miRNA target sites of high confidence. In addition, miRFuction and ceRNAFunction web servers were provided to predict the function of ncRNAs and protein-coding genes based on miRNA-mediated regulatory networks. It will be a valuable resource for all RNA interactions that involve RNAs and proteins as regulators.starBase (sRNA target Base)  decodes C. elegans, D. melanogaster, E. coli, H. sapiens, M. musculus and S. cerevisiae) shows that the tool has high prediction accuracy. RPI-Pred can be applied for developing a reliable ncRNA–Protein interaction network, which will contribute to the understanding of ncRNA’s function.RNA–Protein interaction predictor (RPI-Pred)  is a ncRThe resources in the section focus on the binding sites among RNAs and proteins based on their structures and sequences . In dataThe Protein–RNA Interface Database (PRIDB)  is a datThe RNA-Binding Protein DataBase (RBPDB)  is a resThe RNA binding site Data Base (RsiteDB)  is a datBefore searching for information, users want to know which database meets their needs and whether the data in the database is reliable. Such an introductory document should present the purpose of the database, the types of interactions, the data source, statistics of the data, the date of the last update and the functions provided.The Search and Browser modules are the bridge between the data and the users. In general, the simple/advanced search and simple/advance browse panels are necessary. Users can overview the information in the database by a simple search/browse and further choose what they interested in by an advanced search/browse. The clear and well-organized presentation of search/browse results is also essential. If the number of data is large, breaking up detailed information into small pieces will be very useful. The help page is convenient for users to use the databases better. Although each database is a whole work, the correlation among databases is inevitable and important. Therefore, the extra links to other resources will help users understand data better. The process of developing and updating a database is a time-consuming and nerve-wracking task. The collection and organization of known RNA–Protein interactions is the first and most important step to establish a comprehensive interaction database. The database should provide a centralized and reliable data repository allowing users to search and browse RNA–Protein interactions systematically. Then, when data were collected from multiple resources, eliminating the heterogeneity among multiple kinds of datasets is very valuable. In the future, a comprehensive interaction databases should store more information about an interaction entry, such as a binding region/motif, structure, detection interaction method, and so on. Furthermore, the function to predict potential RNA–Protein interaction based on the sequence and structure motif in an interaction database is necessary and valuable. Given this, a user-friendly interface that provides access to databases is favorable.In short, the complexity and diversity of cell biology can be achieved by the combinatorial possibilities offered by RNA–Protein interactome. Hence, RNA–Protein interaction databases are a necessary tool for cell biology in the future.


Trypanosoma brucei, an important human and animal pathogen, are hypothetical proteins of unknown function. This paper describes a protein correlation profiling mass spectrometry approach, using two size exclusion and one ion exchange chromatography systems, to derive sets of predicted protein complexes in this organism by hierarchical clustering and machine learning methods. These hypothesis-generating proteomic data are provided in an open access online data visualization environment (http://134.36.66.166:8083/complex_explorer). The data can be searched conveniently via a user friendly, custom graphical interface. We provide examples of both potential new subunits of known protein complexes and of novel trypanosome complexes of suggested function, contributing to improving the functional annotation of the trypanosome proteome. Data are available via ProteomeXchange with identifier PXD005968.A disproportionate number of predicted proteins from the genome sequence of the protozoan parasite  T.brucei was reported, with ∼9,100 genes identified, thereby providing a valuable resource for the trypansomatid research community 1114T.brucei mitochondrial and cytoplasmic lysates, in single biological replicates , in up to four biological replicates. We identified and quantified elution profiles for 6004 protein groups. Computational analysis allowed us to predict 234 protein complexes. This data set includes many well-characterized complexes in T. brucei, supports the existence of other complexes that were predicted based on homology to known complexes from other organisms and additionally provides evidence for the existence of novel, previously uncharacterized complexes. By incorporating these data into a freely accessible, online database, we provide a useful resource for both trypanosome biologists and for all groups studying protein-protein interactions and complexes in other organisms.During the course of the investigation described in this paper, Gazestani and colleagues reported using glycerol gradient centrifugation and ion-exchange chromatography to identify protein complexes in procyclic form plicates . Taking m, final concentrations, respectively. The antibiotics, G418 and hygromycin, were used at final concentrations of 15 μg/ml and 50 μg/ml respectively.Powdered SDM-79 media was hydrated with 5 L of Milli-Q water, and supplemented with hemein to 7.5 mg/L and 2 g/L of sodium bicarbonate. The pH was adjusted to 7.3 with NaOH, and sterile filtered using Stericups 500. Under sterile conditions, heat inactivated and nondialyzed fetal bovine serum (PAA) was added to 15% (v/v) and Glutamax I to 2 m2, in fully capped culture flasks.Procyclic trypanosomes (clone 29.13.6) were cultured in SDM-79 media at 28 °C, without CO9 cells/replicate) were washed three times in 50 ml of phosphate-buffered saline (PBS) and lysed using a Bioruptor Pico  water bath sonicator for 10 cycles of 30 s on/off, in 0.75 ml of PBS containing 0.1 μm 1–5-chloro-3-tosylamido-7-amino-2-heptone (TLCK), 1 mm phenyl-methyl sulfonyl fluoride (PMSF), 1 μg/ml leupeptin, 1 μg/ml pepstatin and 5 mm ethylenediametetraacetic acid (EDTA). Lysates were centrifuged at 17,000 × g for 10 min and the supernatant filtered through a 0.45 μm filter unit, all at 4 °C. Bradford assays were performed on the filtrates for protein quantitation.Procyclic trypanosomes  were injected onto either a BioBasic SEC 300, or a BioBasic SEC 1000 column , using a Dionex Ultimate 3000 uHPLC system and collected in 48 fractions of 120 μl. Columns were equilibrated with PBS and eluted at a flow rate of 0.3 ml/min at 4 °C.Filtered lysates , 1 m urea and 5 mm dithiothreitol and incubated for 2 h at 37 °C, followed by addition of iodoacetamide at a final concentration of 25 mm at room temperature for 1 h. Trypsin and LysC were added, each at a ratio of 1:100  and incubated overnight at 37 °C Each fraction was made up of 1% (v/v) trifluoroacetic acid and desalted using Sep-Pak tC18 plates, with peptides eluted in 50% acetonitrile, 0.1% trifluoroacetic acid. Peptides were dried using a GeneVac evaporator and resuspended in 5% formic acid and quantified using a 3-(4-carboxybenzoyl) quinoline-2-carboxaldehyde assay. Five biological replicates were performed in total for each SEC chromatography column used.Each fraction was made up to 0.1 m sodium phosphate (pH 6.0) and 100 mm NaCl, sonicated and filtered as above, followed by separation in a running buffer containing 0.2% SDS on a BioBasic SEC300 column.For a highly denaturing lysis, cells were lysed in 4% sodium dodecyl sulfate (SDS), 10 mm tris(2-carboxyethyl)phosphine (TCEP), heated to 95 °C for 10 min and resolved on 4–12% SDS-PAGE. Gels were run in MES buffer for 45 min at 200 V, then stained for total protein, using SYPRO Ruby, as per manufacturer's protocol.Aliquots of fractions from SEC runs were pooled into groups of three, made up to 1× lithium dodecyl sulfate sample loading buffer and 25 mm ethanolamine (pH 9.0) containing 0.1 μm TLCK, 1 mm PMSF, 1 μg/ml leupeptin, 1 μg/ml pepstatin and 5 mm EDTA. Lysates were centrifuged and filtered as described previously. Filtered lysate was injected onto a Protein-Pak Hi Res Q, 5 μm, 4.6 × 100 mm, column , equilibrated in 20 mm ethanolamine (pH 9.0). Proteins were resolved over a gradient of 0–100% 0.5 m NaCl in 20 mm ethanolamine (pH 9.0), over the course of 26 min, at a flow rate of 0.3 ml/min at 5 °C. Ninety-six 105 μl fractions were collected from 1.5 to 35 min.Procyclic trypanosome cells were prepared in a similar manner as described for SEC analysis, with lysis in 1 ml 20 mm TCEP, then heated to 65 °C for 30 min. Once samples had cooled to room temperature, N-ethylmaleimide was added to a final concentration of 50 mm and incubated for 1 h. The denatured, reduced and alkylated proteins in each fraction were prepared for digestion using a Kingfisher Flex Purification System  in combination with magnetic SP3 beads. Twenty microliters of a 1:1 mixture of hydrophobic and hydrophilic, carboxylate modified, Sera-Mag SpeedBead magnetic particles  was added to each fraction, followed by the addition of 500 μl of acetonitrile and 15 μl of 10% formic acid. In a 96-well plate format, the Kingfisher Flex System was then used to wash the magnetic beads (protein bound) for each collected fraction, twice in 1 ml 70% ethanol, once in 1 ml 100% acetonitrile and then released into a precooled plate, containing 50 μl 0.1 m Tris-HCl (pH 8.0), 0.1% SDS, 1 mm CaCl2 and trypsin and LysC at a 1:100 ratio of protease to estimated protein per fraction. The plate was incubated overnight at 37 °C at 500 rpm in a ThermoMixer . Following overnight digestion, the 96-well plate was thoroughly vortexed to ensure resuspension of SeraMag beads and 950 μl of acetonitrile added. Peptides bound to the magnetic beads were washed in 1 ml of acetonitrile, eluted in 40 μl of 2% dimethyl sulfoxide (DMSO) and beads removed from the sample again on the Kingfisher System. Formic acid was added to each sample to a final concentration of 5% and peptide concentration determined using a 3-(4-carboxybenzoyl) quinoline-2-carboxaldehyde assay.Collected fractions were made up to 4% SDS and 25 mFor each biological replicate of either 48 SEC, or 96 SAX fractions, 1 μg of peptide was injected from the most concentrated fraction and the equivalent volume injected for the remaining fractions. Peptides in 5% formic acid were injected onto a C18 nano-trap column using an Ultimate 3000 nanoHPLC system (ThermoFisher Scientific). Peptides were washed with 2% acetonitrile, 0.1% formic acid and resolved on a 150 mm × 75 μm C18 reverse phase analytical column over a gradient from 2–28% acetonitrile over 120 min at a flow rate of 200 nL/min. Peptides were ionized by nano-electrospray ionization at 2.5 kV. Tandem mass spectrometry analysis was carried out on a QExactive+ mass spectrometer (ThermoFisher Scientific), using HCD fragmentation of precursor peptides. A data-dependent method was used, acquiring MS/MS spectra for the top 15 most abundant precursor ions.T. brucei brucei 927 annotated protein database from TriTrypsDB release 8.1, containing 11,567 entries. The mass tolerance was set to 4.5 ppm for precursor ions and MS/MS mass tolerance was set at 20 ppm. The enzyme was set to trypsin and endopeptidase LysC, allowing up to 2 missed cleavages. Carbamidomethyl on cysteine was set as a fixed modification. Acetylation of protein N termini, deamidation of asparagine and glutamine, pyro-glutamate , oxidation of methionine and phosphorylation of serine, threonine and tyrosine, were set as variable modifications. Match between runs was enabled, allowing transfer of peptide identifications of sequenced peptides from one LC-MS run to nonsequenced ions, with the same mass and retention time, in another run. A 20-min time window was set for alignment of separate LC-MS runs and a 30-s time window for matching of identifications. The false-discovery rate for protein and peptide level identifications was set at 1%, using a target-decoy based strategy. Each individual SEC fraction was set as an individual experiment in MaxQuant parameters, to output IBAQ data for protein groups in every fraction. Only unique peptides were used for quantitation.SEC RAW data files were analyzed using MaxQuant version 1.5.1.3, with the in-built Andromeda search engine , 21, supT. brucei brucei 927 annotated protein database from TriTrypDB release 26.0, also containing 11,567 entries. All other settings were identical, apart from the fixed modification on cysteine, which was set to N-ethylmaleimide. The results can be viewed from the MS-Viewer website  intensities, normalized to the maximum intensity detected across all fractions, using the mean of either four, or three, biological replicates from SEC300 and SEC1000 experiments, respectively. From SEC experiments, proteins were required to be detected with at least one unique peptide found in two biological replicates and with Pearson correlation coefficients among elution profiles >0.6. A detailed report on the protein identified with only one unique peptide is provided in Five biological replicates were performed for both the SEC300 and SEC1000 experiments, based on the variance detected in previous experiments using SEC based analysis , 23. Fron were evaluated, in which n was the cutting distance producing only two clusters. GO term enrichment p values were computed with a Fisher test. The Bonferroni correction was applied and only the GO-terms with a p value <0.05 were accepted. The cutting distance producing the highest number of enriched GO terms was taken to produce the final clusters for each experiment type.The mean LFQ profiles for each protein were hierarchically clustered, separately for each experiment type , using the Euclidean distance measurement and Ward's agglomeration method. The Gene Ontology (GO) term enrichment was computed for each cluster obtained by cutting the dendrogram tree at predetermined distances. Cutting distances from 0 to A pipeline similar to that applied previously for protein correlation profiling (PCP) analysis  was usedThe coapex score  is based1–2CC was computed. Then the maximum self-cross-correlation of the first protein profile (P1CC) and the maximum self-cross-correlation of the second protein profile (P2CC) was determined. The NCC was finally derived as P1–2CC/max. The PCC was computed as the Pearson correlation score between the two elution profiles.The NCC was derived in two steps. First, the maximum cross correlation between the two protein profile pairs PThe PCC and NCC were calculated for the SEC300, SEC1000 and SAX experiments described here, and for experiments produced in . This insupplemental Table S1 and S21). A negative data set was extracted by random sampling of the 290 proteins annotated in different complexes . As it would be possible to introduce false negative interactions in this step, the random sampling was repeated 100 times. Finally, using these true positive and true negative test pairs, 100 Random Forest classifiers were assembled based on the same true positive pairs, but with each using a different negative set. Two predictor sets were developed, one set of 100 predictors based on the features derived only from experiments performed in this paper  and a second set of predictors that implemented all the available features .For the machine learning analysis, a data set of “gold standard” true positive peak pairs (GD) was manually assembled. Thirty-one known protein complexes were derived from data deposited in CORUM , togetheAll the classifiers were inspected to determine the area under the curve values of the receiver operator curves in 10-fold cross validation. The median values of the probability score outputs of the two 100 classifier sets were used as the final interaction prediction score for the protein pairs.i.e. a 1% false positive rate). The protein pairs from the two predictor sets were separately fed to the ClusterONE algorithm  retrieved by the machine learning pipeline, or by the clustering analysis of the SEC300, SEC1000, or SAX data sets were identified. For each experimentally identified gold standard complex, the number of proteins in common with the predicted group  was calculated (COMMON) and divided by the total number of proteins in the predicted gold standard complex to compute the precision/specificity of predictions from each method. The sensitivity/recall was calculated by dividing the COMMON group by the number of proteins in the experimentally predicted complex. A mean was calculated for both precision/specificity and sensitivity/recall from all the gold standard complexes identified in each of the hierarchical clustering or machine learning analyses.To compare the performance of protein complex prediction between the hierarchical clustering and machine learning methods used in this study, the gold standard complexes , or 20 mm ethanolamine (for SAX), containing protease inhibitors, followed by sonication lysis. The resulting lysates were centrifuged, filtered, and fractionated, either using BioBasic SEC300, or SEC1000 columns, separating protein complexes based on their size and shape, or a Protein-Pak HiRes SAX column, separating protein complexes based on their charge. The proteins in the fractions from each type of chromatography were reduced, S-alkylated and digested to peptides with trypsin and endopeptidase LysC. After desalting, the resulting peptides were analyzed by LC-MS/MS . The retention times of each standard on the SEC300 column were used to generate a linear regression model, allowing the calculation of apparent molecular weights for the proteins and protein complexes found in our data set . A separate set of protein standards were used to characterize the resolution and separation of the SAX column .Protein molecular weight standards were used to characterize the separation ranges of the BioBasic SEC300 and SEC1000 columns, indicating that the SEC300 column has an effective separation range from 8 kDa to 1.2 MDa, whereas the SEC1000 column separates material above 1.2 MDa .To assess the monomeric molecular weights of proteins eluting across the SEC300 fractionation range, fractions were pooled in groups of three, run on SDS-PAGE under reducing conditions and stained for total protein. Most proteins eluted at a higher apparent molecular weight by native SEC than expected from their monomeric, denatured and reduced molecular weights indicated by SDS-PAGE. This is consistent with many of the individual proteins participating as components of larger complexes. In contrast, when cells were lysed in a highly denaturing buffer, containing 4% SDS and the SEC was carried out in the presence of 0.2% SDS, there was a direct correlation between SEC and SDS-PAGE apparent molecular weights . Most biological replicates showed high reproducibility, with median Pearson correlation coefficients for each fraction typically >0.75. However, one and two replicates from the five SEC300 and SEC1000 data sets, respectively, deviated significantly and inspection of individual protein elution profiles indicated compromised chromatography . The data from these replicates were therefore excluded from further analyses, emphasizing the importance of checking inter-replicate reproducibility before combining data for downstream analysis.The reproducibility of individual biological replicates was assessed for both the SEC300 and SEC1000 experiments ; by cutting the resulting dendrograms, it was possible to define groups of proteins that have similar elution profiles and hence potentially interact. A range of cutting distances was simulated, and the within-cluster Gene Ontology (GO) term enrichment and the mean Pearson correlation coefficients of elution profiles were observed (D–2F). As the number of clusters was reduced, a sharp increase in the number of enriched GO terms was observed in all data sets, as functionally associated proteins were grouped together within a cluster. As clusters became larger and unrelated proteins were grouped together, the number of enriched GO terms decreased. The cutting distance producing the highest GO term enrichment within clusters across the data set for each form of fractionation was selected. Thus, for the SEC300, SEC1000, and SAX experiments, cutting distances of 1.53, 1.28, and 1.92 were chosen, producing, respectively, 440, 365, and 529 clusters of proteins. As a control, the order of proteins within the dendrograms of each data set was shuffled randomly and then the same analyses performed; under these conditions, a similar increase in GO term enrichment across cutting distance was not apparent (D–2F).Several individual protein elution profiles produced two peaks by SEC300 chromatography. The generally lower intensity lower molecular weight peaks likely correspond to protein degradation products or protein monomers/dimers that are in equilibrium with the higher molecular weight complex in which they appear. Because the hierarchical clustering algorithm selects for the most intense peak in a protein profile, these lower molecular weight peaks generally do not feature in the clustering analysis. Hierarchical clustering of all protein elution profiles was performed for each data set separately A–2C; by observed D–2F. As apparent D–2F.T. brucei literature or by analogy with highly conserved complexes in other organisms) were inspected (A and 3B), consistent both with the detection of stable complexes via cochromatography and with previous reports showing that the proteasome core and caps dissociate from each other in T. brucei lysates .To validate the assumption that protein cochromatography correlates with protein association in the data sets, the elution profiles of proteins expected to be present as stable complexes  the protein complexes were less stable when subjected to SAX chromatography than to SEC (B and 3C). This was not unexpected as SAX chromatography was performed at pH 9.0 and involves elution with a salt gradient, whereas SEC was performed at a more physiological pH and ionic strength. These observations were, therefore, interpreted as being consistent with the dissociation of a subset of native protein complexes when exposed to high salt and high pH during SAX chromatography.In some instances, , and 100 sets of randomly selected true negative interaction pairs . At an interaction prediction score >0.75 there was a false positive rate <1% , hence this was used as the threshold for positive interaction across the whole data set. Receiver Operator Characteristic curves also demonstrate the high performance of the machine learning method .To predict the likelihood of binary interactions among all pairs of coeluting proteins detected in our data sets, a scoring methodology was designed to quantify the similarity of elution patterns. Two random forest predictors were implemented. The first predictor was trained with features extracted from data produced in this project. The second predictor was trained by combining the first set of features with features extracted from a recently published interactome study in . brucei . We also. brucei , with th. brucei . Both prDsupplemental Fig. S4). The outputs of the two random forest predictors were fed to ClusterOne . We were able to rediscover again 28 out of the 32 gold standard protein complexes  and we have ascribed either a putative function, or name, to the complexes when they contain proteins of either known or suggested biological function in the TriTrypDB genome database   are described in the Discussion.Analysis of how often the random forest predictors used each feature to classify positive and negative interactions showed that the SEC300 coapex score, cross-correlation and Pearson correlation and SEC1000 coapex features had the highest predictive power  to  and S3) . Althougsupplemental Fig. S8 and supplemental Table S1). Protein complex predictions derived from the machine learning method clearly outperform hierarchical clustering of the SEC300, SEC1000, and SAX data sets in terms of both precision  and sensitivity . Hierarchical clustering of the SEC300 data set performs similarly to machine learning in terms of mean sensitivity (0.79), but has a much lower mean precision (0.46), indicating an ability to exclude false positive interactions, but missing out on many true positive interactions. Hierarchical clustering of SEC1000 or SAX data sets appear to produce similarly low mean values of precision (0.42 and 0.36 respectively), and perform worse than SEC300 in regard to sensitivity (0.58 each).To compare the output from the hierarchical clustering and machine learning methods of predicting protein complexes presented in this study, the precision and sensitivity of each method in predicting the 31 gold standard complexes was calculated . A further 90 protein complexes predicted in TbCF-HC net are not corroborated in our data set, and 190 are predicted in our data set and not corroborated in TbCF-HC net. For complexes uniquely predicted in TbCF-HC net, the mean Pearson correlation coefficient of elution profiles of constituent proteins within complexes, is below 0.5 when computed from the SEC300, SEC1000, and SAX data sets, and is below 0.7 when computed from IEX-cyto, IEX-mito, GG-WCL, and GG-mito . Focusing on complexes predicted in common between both data sets, or complexes unique to the data presented here, the SEC300 and SEC1000 experiments outperform all others, with the highest mean Pearson correlation of elution profiles within protein complexes .The comparison to TbCF-HC net, published in , revealssupplemental Fig. S9). This indicates that many elution profiles in the IEX-cyto experiment will have a similar shape, and hence appear to coelute, just by chance, negatively impacting the quality of the detected protein complexes. We think that this effect is related to the smaller number of elution fractions analyzed by Gazestani et al. (19 fractions) in comparison to our SAX experiments (96 fractions).Furthermore, the SAX experiment also outperforms the IEX experiments performed in , in regahttp://134.36.66.166:8083/complex_explorer). Thanks to a user-friendly graphical interface, researchers can conveniently explore and display all the predicted complexes and elution profiles reported in this study. There are three distinct applications with which to browse the data:All of the processed MS and chromatography data and predictions have been made freely available via a custom, searchable database. The data can be browsed on a web server at . Each cluster is displayed as a network of interconnected nodes (protein groups), with the lines among the nodes indicating evidence of pairwise associations. The stringency (cutting threshold), for the associations can be varied with a slider below the browser and the cluster browser can be queried to highlight individual protein groups and cluster numbers. Mousing over the nodes brings up their GO-terms within a word cloud of the GO-terms for the other nodes in the complex, which can give a general impression of possible cluster function. The dynamic browser can be adjusted through “settings” to highlight any or all of the following:Nodes with human homologsNodes identified as essential in cell culture Nodes that were used as “gold-standards” for the machine-learningClusters which agree with homologous associations in the STRING database Clusters that are inter-related by the same STRING associationsClusters predicted by those STRING associations aloneNodes which appear in more than one clusterThe “Complex Explorer”  is a dynx- (time) axis and display either raw or normalized LFQ intensity data on the y axis. Further, the color-coded elution profiles of individual protein groups can be switched on or off by double-clicking on the gene IDs below the chromatograms. Below the dynamic cluster browser is a table of all the nodes in the browser, which can be searched in various ways and downloaded by the user for other applications.Clicking on any node in a cluster brings up a table of the protein group components of that cluster, alongside the SEC300, SEC1000, and SAX chromatograms for those protein groups. The chromatograms are dynamic and can be expanded in the supplemental Fig. S10) allows exploration of any potential protein-protein association of the user's choosing. The application allows the input of a list of up to twenty TriTrypsDB gene IDs and outputs the associated fractionation data in any of the SEC300, SEC1000 and SAX data sets, as well as in the density gradient and ion exchange fractionations recently published in  application allows exploration of putative protein-protein associations based on hierarchical clustering of the protein elution profiles from SEC300, SEC1000, and SAX chromatography . Therefore, the following discussion focuses on the protein complex predictions from the machine learning analysis. However, we believe that the individual protein elution profiles and hierarchically clustered data sets are of use to the wider T. brucei research community, providing wider groupings of proteins which are coeluting across one of the three fractionation methods used, that may still provide evidence for protein-protein interaction. To this end, these data sets and the predicted clusters are available for researchers to look at on our searchable online database, together with the machine learning predictions.In this study, protein complex predictions are produced using two distinct methodologies; hierarchical clustering of independent fractionation experiments, or machine learning, using scoring features derived from all of our fractionation experiments, together with orthogonal data from STRING and previously published ata sets . The com1F0 ATP synthase (complex 130), and several others . Although these are all conserved eukaryotic protein complexes whose presence may, therefore, be expected in trypanosomes, the underlying complex protein group compositions also suggests novel components. For example, although most of the proteins detected in the proteasome complex  are annotated as proteasome alpha and beta subunits, one, at the time of this analysis, was annotated as “unspecified product” (Tb927.9.11310). A bespoke BLASTp search subsequently revealed that this gene product has 100% homology with the 20S proteasome beta subunit of T. brucei gambiense (XP_011776865.1). Thus, the cochromatography of Tb927.9.11310 with the proteasome core complex provided the impetus to re-evaluate its identity.Novel insights into subunits of trypanosome protein complexes identified here include the proteasome core (complex 31) and regulatory unit (complex 30), the prefoldin complex (complex 29), the chaperonin T-complex (complex 129), AMPK (complex 3), vacuolar ATP synthase (complex 20), the exosome (complex 28), subcomponents of the spliceosome (complexes 86 and 180), the nucleosome (complex 87), ARP2/3 (complex 112), FSupplemental Table 5). This contains all the characterized subunits  of the F1 domain of the F0F1-ATP synthase complex, as well as ribonucleoprotein p1 (the b subunit of the F0 domain)  comprises three hypothetical proteins, two of which have been annotated by (0F1-ATP synthase.Another example is provided by complex 130 ( domain) , but alstated by  as trypaT. brucei identified 133 proteins, 77 of which were classed as large-subunit and 56 as small-subunit associated proteins , plus one hypothetical protein (Tb927.7.3030) in complex 134, suggesting this may be a novel mitochondrial ribosome subunit.Previous affinity purification-MS analyses of the mitochondrial ribosome of proteins . Twenty-supplemental Table S10)  of the AMP-dependent protein kinase (AMPK) complex (ble S10) . It alsosupplemental Table S11; Asupplemental Fig. S6). PUF proteins are known to bind to mRNA and the hypothetical protein has also been predicted to bind mRNA through capture on oligo(dT) beads  across SEC300, SEC1000, and SAX fractionation in complex 72 (T) beads . Taken tsupplemental Table S12; Bsupplemental Fig. S6)  and subunit 10 of the CCR4-NOT complex (ig. S6B) . The hypig. S6B)  and suggig. S6B) . Thus, tsupplemental Table S13; Csupplemental Fig. S6). Research with yeast has shown that Pwp2 is known to associate with four other proteins  in the U3 ribonucleoprotein assembly machinery, forming a complex necessary for pre-18S rRNA processing . Cbf5 is the enzymatic component of the H/ACA ribonucleoprotein complex, which pseudouridylates target RNAs. In other eukaryotes, there are many H/ACA ribonucleoprotein complexes, formed through the interaction of different RNAs with the same four core proteins  , Tb927.4.470 , and Tb927.4.750  (d Nop10) . The coeT. brucei . We also observed the association of these proteins with a putative HSP70 protein (Tb927.9.9860). HSP90 chaperones function through their association with HSP70 proteins, which recruit and transfer substrate proteins to HSP90 (Previously published studies have demonstrated the constitutive interaction of a heat-shock protein 90 (HSP90), with protein phosphatase 5 (PP5) in . brucei . Complexto HSP90 . These asupplemental Table S16; Bsupplemental Fig. S6). Two proteins have been experimentally verified as members of the spliced leader RNA cap methyltransferase  is dominated by nucleolar associated proteins but also contains two arginine-N-methyltransferases (TbPRMT1 and TbPRMT3). The latter two enzymes belong to the same complex and are mutually dependent on each other for stability  the presence of two subunits of the GPI transamidase, together with signal peptidase, suggests an association between GPI transamidase and the translocon complex in the endoplasmic reticulum (ER) (T. brucei (In complex 164 (lum (ER) . The sug. brucei . InteresIn summary, the trypanosome PCP-MS data presented here provide a valuable new resource for the research community to find and characterize either novel components of known complexes and/or to assess whether individual proteins of interest appear in larger complexes. The open access availability of the data via an interactive, online database should facilitate such searches. We also refer the interested reader to the recently published TrypsNetDB .https://www.ebi.ac.uk/pride/archive/. Processed data and data exploration tools can be found at http://134.36.66.166:8083/complex_explorer. Annotated spectra can be viewed from the MS-Viewer website (http://msviewer.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msviewer) by entering the following search keys: SAX: czyi4m7zoe; SEC300: esvc3krys1; SEC1000: 5gt8lsrrv7.All mass spectrometry data have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD005968,


We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed.Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G . We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms.We present a new fast algorithm for clustering large sparse networks: Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks.Our algorithm The online version of this article (doi:10.1186/s12859-016-1191-6) contains supplementary material, which is available to authorized users. However, extracting useful knowledge from such networks is not straightforward. Therefore sophisticated PPI network analysis algorithms have been devised in the last decade for several goals such as: the prediction of protein-complexes  and Tandem Affinity Purification coupled with Mass Spectrometry (TAP-MS), it is now possible to compile large maps of protein interactions, which are usually denoted as mplexes , the prH and Tanmplexes , the prmplexes , 6), the, theprotmplexes , the elmplexes , and thmplexes , to nammplexes  designed for clustering large PPIN and we apply it to the problem of predicting protein complexes in PPIN. The complexes we seek have just very basic properties, they should appear within the PPIN as ego-networks of high density and thus we can model them as maximal quasi-cliques. These features are not particularly new, but we show in Section ‘core decomposition of a graph, which for each vertex (protein) in a graph provides a tight upper bound to the size of the largest quasi-clique that includes that vertex. We use this upper bound to trim locally the subgraphs of interest in order to isolate the sought quasi-clique, and proceed then to the final peeling out of loosely connected vertices. Our approach has some superficial similarity with that of CMC -clique. CFinder might produce too many low density clusters if the user choosees k too small, or miss interesting complexes if k is too large. Core&Peel avoids both pitfalls since we have a more adaptive control over cluster overlaps. Our algorithm is empirically very fast: all instances in this paper run in less than 2 minutes on common hardware. The asymptotic analysis m+n) for a graph G of n nodes and m arcs, where a(G) is the arboricity of G . The output quality is assessed by comparative measures of the ability to predict known complexes and of the ability to produce biologically homogeneous clusters, against 10 state-of-the art methods. In both quality assessments Core&Peel leads or ties in most tests vs all other methods, often by a large margin  are good predictors of the presence of a protein complex, provided the PPIN is large. Our own measurents on one medium size graphs (≈20K PPI) and four large graphs (≈130K/220K PPI) in Section ‘The estimation of the number of erroneous PPI calls  in PPI networks is highly dependent on the technology and the experimental protocols used. Yu et al.  report aCore&Peel algorithm targeting communities in social graphs is described in , a γ-quasi clique is a graph G= such that: NG(v)={u∈V|∈E} is the set of immediate neighbors of v in G. Note that a γ-quasi clique has density D(G)≥γ. In general, however, for a dense graph with density D(G) we cannot infer a bound on the value of γ for which there exists a quasi-clique in G =1 that implies γ=1, and those cases covered by Turán’s theorem -core. A core decomposition of a graph is the partition of the vertices of a graph induced by their core numbers . If wenumbers .partial dense cover of a graph. We aim at collecting efficiently only high quality candidate dense sets that cover the dense regions of the input graph. A Partial Dense Cover PDC is defined as the range of the function f:V→2V that associates to any vertex v∈V a subset of V with these properties: f(v)≠∅ then v∈f(v), (the set f(v) contains the seed v or it is empty).if f(v)⊆Nr(v)∪{v}, (the set f(v) is a subset of the r-neighborhood of v, i.e. all its vertices are at distance at most r from v. f(v) is the largest set having size at least q, density at least δ, satisfying (a) and (b), or otherwise it is the empty set.In this section we formalize our problem as that of computing a f(v) that, for a given v, satisfies (a), (b) and (c). If this is the case, we pick arbitrarily one such set as the value of f(v). We drop G and r from the notation when they are clear from the context. Since the PDC is the range of the function f, by definition, it contains no duplicate sets, though its elements can be highly overlapping. One way to imagine this structure is as a relaxation of a minimum clique cover of a graph that is the problem of determining the minimum value k such that the vertices of a graph can be partitioned into k cliques. We relax this problem by (1) relaxing the disjointness condition  (2) allowing also a covering with graphs of density smaller than 1.0 . Computing a clique cover of minimum size k is a well known NP-complete problem . If this density is too small (i.e. δ(v)≤δlow) for a threshold δlow, which we specify later, we do not process this node any more (filter (f=0)). In the second filter (f=1) we check if there are at least q nodes with degree at least (q−1)δ. The third filter (f=2) is a combination of the previous two filters. Nodes that pass the chosen filter are processed in Phase III.Phase III. In this phase we take v and the induced subgraph G[NC(v)(v)] and we apply a variant of the peeling procedure described in  must contain at least q nodes of degree at least (q−1)δ). Finally, we define the filter policy f=2, that is the union of the previous two filters.f Turán  that guDetails on Phase III. The peeling procedure we use is similar to the one described in p∈P we coethod of  which isBasic measures on the PPINs and protein complexes data sets are reported in Table We report the comparative evaluation of our algorithm vs several other algorithms, among those considered state-of-the-art. We used for these experiments an Intel core i7 processor (4 cores) at 2.6 GHz, with 16 Gb RAM memory, and with Mac OS X 10.8.5.We have selected 10 algorithms, namely: MCL, Coach, MCODE, CMC, MCL-CAW, ProRank+, SPICi, ClusterOne, RNSC, and Cfinder among those in literature. A brief description of each is in Additional file Each method has its own pool of parameters to be set. For the quality score shown in Section Core&Peel has the best value in 12 cases, CMC in 2 cases, and ClusterOne in 1 case. The Aggregated Score, which balances strong and weak points of the four basic measures, indicates that Core&Peel, CMC and ClusterOne have about the same performance for the medium-size PPI newtwork DIP. But for Biogrid data and even more for String data Core&Peel takes the lead, even with a wide margin.Figures BG-hs-UBC. We tested also the other PPI network used in our study and this is the only case in which removing a node of maximum degree changes significantly the outcome of the prediction. Out of 15 measures, Core&Peel has the best value in all 15 cases. Good performance is obtained on some measures by CMC and Spici.Figures Core&Peel, Spici and ClusterOne improve their AS value, while RNSC and COACH have a reduced AS value. The improvement in absence of UBC can be easily explained by the fact that UBC appears only in a few complexes of the golden standard, thus the evaluation phase is made more precise by its removal from the network and thus from the predicted clusters. The better results attained by RNSC and COACH on the graph with UBC may be a hint that, for these two approaches, the presence of UBC helps in homing in more quickly on the true complexes hidden in the graph.It is interesting to notice how the algorithms perform differently on the BG-hs with and without UBC. On Biogrid data without UBC, We include as a sanity check also three random predictions . The purpose of this check is to assess how well the measure we are using are able to discriminate the predictions on real data sets from those generated randomly by generators allowed to access some partial knowledge about the structure of the golden standard.The method Rand1 is given the size distribution of the sets in the golden standard and produce a random collection of sets out of the vertices of the PPI with the same size distribution. The method Rand2 is as Rand1 except that the random sets are generated starting from the subset of all vertices in the PPI that belong to some complex in the golden standard. The method Rand3 is obtained by taking the golden standard and applying to it a random permutation of the nodes of the PPI. Note that this approach besides preserving the size distribution preserves also the distribution of the size of the intersections of any number of sets of the golden standard.In terms of performance, Rand1 behaves almost like Rand3, while Rand2 (having stronger hints) attains better results. The semantic similarity measure is the one that has better discrimination power vs all the three random test cases.Core&Peel has better SS performance on all the 6 PPIN tested than the 10 competing methods. Semantic similarity is the only measure that explicitly places a premium in correctly identifying the proteins that simultaneously belong to multiple complexes, thus we can infer that Core&Peel successfully uncovers the overlapping structure of the the known protein complexes.The second index is the number of predicted clusters with an associated functional annotation  of Gene Onontology (GO)) below a given False Discovery Rate (FDR) threshold. Note that here we use a non-normalized measure (absolute count) since we want to favor algorithms with a rich high quality output. We are safeguarded against rewarding unduly methods that inflate their output since we operate each algorithm with the parameters that optimize the  F-measure. Moreover, even though none of the methods we use incorporates GO as part of its model, it is relatively safe to assume that, in most cases of interest, GO annotations are indeed available and may be used for a post-processing re-ranking or filtering of the predictions.−2 to 10−7 on the q-value.The biological function enrichment measure (using the BP annotation in GO) is shown in Figs. Core&Peel has a larger or equal absolute number of high quality predictions below q-value 10−3 than the competing methods on five data sets out of six. For the BG-hs-UBC dataset Core&Peel leads below q-value 10−4. The overall trend is fairly consistent for all the six data sets tested.p-value, and the corresponding GO class. The top 10 predicted clusters we identify have p-value for their prevalent GO-annotation  in the range 10−191−10−72. For a comparison, the top 10 functional modules detected by the recent method ADM (Adaptive Density Modularity) of Shen et al. We have tested our method for its robustness against injection of random noise in the input network. Starting with the the Biogrid HS network we have added randomly additional (noise) edges for a number of additions ranging from 5 to 25 % of the initial number of edges in steps of 5 %. We have generated 10 networks for each class and taken the mean value of the 4 basic quality indices of Section ‘Core&Peel comes second in speed in all the data set (except for DIP where it is third).Figures Core&Peel that involve a known complex and one or two additional proteins, for which there is evidence of a biological function in the literature. We take the clusters detected by Core&Peel in the Biogrid and String homo sapiens network, we rank them by the highest value of the Jaccard correlation coefficient (jacc) and the semantic similarity (SS) with a matching known complex, we analyze the discrepancies (i.e. proteins non listed in the known complex but with a large number of PPI connections to it) and we highlight the literature supporting the functional relevance of the interaction. We chose here the parameter setting maximizing the f-measure.In the long run the effectiveness of a protein-complex prediction method hinges upon its capability to uncover interesting and unexpected new phenomena of biological relevance. As an intermediate step we report on predictions made by Case 1  This predicted cluster matches almost perfectly with the 20S proteasome complex (Corum-id 191). Moreover the predicted cluster includes two additional proteins: UBC (Ubiquitin C) and IQCB1 (IQ motif containing B1). The hitpredict database (http://hintdb.hgc.jp/htp/) also predicts high quality interactions between IQCB1 and six PSA plus three PSB proteins. The functional connection of UBC and the proteasome complex within the protein degradation pathway is also well known (see e.g. . It is reported in [gag open reading frame that controls translation initiation directly recruits the 40S subunit and eIF3”.orted in  that “A Case 8  This predicted cluster matches almost perfectly with SAP complex (Sin3-associated protein complex) (corum id 591). Moreover the predicted cluster includes an additional protein: ING2 . It is reported in [SAP30, Sds3, SAP180/RBP1, SAP130, and ING1b/ING2, whose precise roles at the molecular level are poorly understood but most likely involve targeting the complex to specific genomic loci via one or more interaction surfaces".orted in  that “BeCase 9  This predicted cluster matches almost perfectly with the Ribosome complex (corum-id 306). Moreover the predicted cluster includes an additional protein: SIRT7 (Sirtuin 7). Tsai et al. [Sirtuin 7 in the Ribosome biogenesis and protein synthesis.i et al.  investigCase 10  This predicted cluster matches almost perfectly with the Exosome (11 prot) complex (corum-id 789). Moreover the predicted cluster includes an additional protein: XRN1 (5’-3’ exoribonuclease 1). Li et al. [XRN1 and the Exosome complex in Hepatitis C-Virus RNA decay.i et al.  describeCore&Peel is remarkably consistent in finding known complexes across 1 medium and 5 large data sets, ranking first in aggregated score against ten state-of-the-art methods in all 6 cases .The experimental results reported in Section ‘Core&Peel also leads in the ability to produce cluster predictions that are highly consistent with GO-BP annotations. The specific complex-protein interaction predictions listed in Section ‘Core&Peel algorithm exploits properties of complexes embedded in PPINs  that are more evident the larger the PPINs become, and it does not suffer from phenomena of combinatorial explosion . Thus we believe that Core&Peel can become a method of choice when even larger PPINs are built and analyzed, such as those arising in multi-species PPIN studies (see [The ies (see ) and thoies (see ).Core&Peel is fast and easy to use, requiring the setting of very few natural parameters relative to the minimum size, density and separation of the target complexes. Indeed, having a small sample of the type of complexes to be sought, these parameters can be extracted directly form the sample.Core&Peel uses very little biological information except that embedded in the PPIN topology. Thus we believe further gains can be achieved by augmenting our scheme with the ability to handle PPINs endowed with edge weights modeling, for example, PPI quality, or other types of a priori knowledge), or by incorporating GO annotations-based filters within the basic algorithmic framework . We do not aim at suggesting that a particular type of PPIN repository should be preferred over others, and we do not even aim at implying that one should always use large PPIN in place of smaller ones  so to gain confidence in the robustness of the main thesis (i.e. the suitability of see e.g. ). Both qsee e.g. , thus thAdditional file 1Core&Peel Method- Supplementary Materials. Description of the parameters and settings used in testing the competing sw for PC detection in Large PPIN. Description of data sets features distributions. Analysis of Asymptotic Complexity for Core&Peel. (PDF 1284 kb)Detailed Experimental settings. Protein complex prediction for large protein protein interaction networks with the