We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger . The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.DOI:http://dx.doi.org/10.7554/eLife.18715.001 Characterizing protein-protein interaction (PPI) networks on a genomic scale is a goal of unquestioned value  but it aComputational approaches offer another means of inferring PPIs . AutomatWe recently developed a PPI prediction method , PrePPI,Here, we describe an updated version of PrePPI for the human proteome that incorporates additional interaction evidence. As illustrated in To determine whether two given proteins, A and B, are involved in a PPI, we calculate a set of scores  reflectiStructural Modeling (SM). A set of coarse interaction models for the direct interaction of A and B is created by superposing models or structures of A and B on experimentally determined structures of the physical interaction between proteins A’ and B’. These models are scored based on the structural similarity of A to A’ and of B to B’ and the properties of the modeled interface between A and B.Phylogenetic Profile (PP). This score indicates the extent to which orthologs of A and B are present in the same species .Gene Ontology (GO). This signifies whether A and B have similar functions based on their proximity in the GO hierarchy .Additional sources of evidence incorporated into the current version of PrePPI are as follows .Orthology (OR). This measures the likelihood that A and B interact if their orthologs A’ and B’ in other species interact. To provide the broadest coverage of the human proteome, we use a novel approach to combine information from multiple orthology databases. Although orthology is also used in the phylogenetic profile (PP) term, we note that the new OR term directly reflects the fact that orthologs of A and B may interact in another species whereas this is not the case for PP. The two sources of evidence are largely complementary (Pearson correlation coefficient=0.003).Expression Profile (EP). We developed a new EP score, which is designed to reflect similarities in the expression patterns of two human proteins A and B. The previous version of PrePPI also used EP evidence, but only for human proteins. In contrast, we now consider not only similarities in expression patterns of A and B, but of orthologs of A and B in a set of 11 model organisms. Again, this term does not correlate with the OR or PP term (correlation coefficients of 0.02 and 0.1 respectively).1, P2, … Pn, that are structurally similar to A, the likelihood that B also interacts with A increases with n. We also include a score based on the number of times a protein structurally similar to A interacts with a protein structurally similar to B, where the structurally similar proteins can be obtained from any species.Partner Redundancy (PR). This score is based on the assumption  that if Protein Peptide (PrP). This score reflects the likelihood of an interaction between A and B if A contains a structured domain from a given domain family and B contains a short sequence motif similar to one known to interact with that type of structured domain .A Bayesian approach combines the above scores into a single likelihood ratio (LR) reflecting the probability of a PPI between A and B (The receiver operating characteristic (ROC) curves in The current PrePPI database contains about 1.35 million reliable PPI predictions, a significant increase over the 300,000 PPIs in the previous version. Some of this increase is due to the availability of new structural information; about 400,000 reliable predictions are made in the current implementation if we include the new structural data but not the new sources of evidence. However, most of the increase is due to the new sources of evidence now integrated into PrePPI. The second column in removing individual sources of evidence. Here again, SM is found to be the most important contributor to the total number of predictions but clearly each source is required to achieve 1.35 million predictions. This is not surprising given the fact that the total LR is a product of six individual terms . Moreover, only about 25% of the 127,000 predictions derived from structure alone are based on paralogs.A serious concern however is the question of whether the PrePPI scoring function is able to distinguish among paralogs since they all have similar structures and thus, for example, two families of paralogs might all be expected to interact with one another. For example, can PrePPI specifically associate small GTPases with their corresponding GEFs or GAPs? In our previous paper  we carriWe compared the set of reliable predictions made in the previous version of PrePPI to the current set of reliable predictions. Overall, about 63% of the interactions assigned an LR>600 in the previous version are also assigned an LR>600 in the current version. Of course any interactions with an LR>600 in the previous version of the database will have some LR value in the current one, but for 37% of the cases the value is now <600. It is possible then that some true interactions are lost in this way. However, of the ~313,000 reliable predictions in the previous version, 3683 are in the human HC reference set and nearly all (3456) are assigned an LR>600 in the current database. Thus, although some true interactions are lost, overall the new evidence and new data incorporated into PrePPI appear to have the desired effect of improving prediction performance. This is due to additional human data, a larger yeast training set, and new sources of evidence introduced in the current version of PrePPI. These results highlight the importance of regular updating of the database and we currently have plans to update annually.GO in We calculated the percentage of reliably predicted PPIs for which two interacting proteins are annotated with the same GO 'biological process' (BP). We considered GO BP terms at the second level . After removing the Gene Ontology (GO) term from the PrePPI LR LRGO in , the numWe tested the ability of PrePPI to identify indirect interactions by determining the extent to which members of complexes compiled in CORUM, a database of ~1800 multi-subunit complexes , are reca posteriori for members of a complex does not imply the ability to predict its subunit composition. As a step towards developing a predictive algorithm, we randomly chose two members of each of the 1258 CORUM complexes that contain more than 2 subunits. We then asked whether all members of a given complex could be identified from PrePPI predicted interaction partners of the randomly chosen pair with LR > 600. This was possible for 865 of 1258 complexes that have three or more subunits. All members of a complex were identified within the top 10 partners for 305 of 892 complexes with three to six subunits, and for 601 of these complexes, all members were identified within the top 100 partners. Moreover, all members could be identified in the top 100 predicted interaction partners for 43 of 339 complexes with seven to 22 subunits. The results of this and the preceding paragraph suggest that the PrePPI database in conjunction with graph and network algorithms should make it possible to predict members of multi-protein complexes.The ability to account ClinVar aggregates information about genomic variation and its relationship to human health through a database of 2700 proteins with non-synonymous single nucleotide polymorphisms (SNPs) annotated as associated with specific diseases . From thWe examined the ability of other databases to predict pairs of disease-similar proteins. The Y2H and BioPlex sets, respectively, identify 5 and 63 pairs of the ClinVar positive set while the union of four computationally derived databases‒STRING, PIP , I2D Br and HumaThe added dimension of protein structure inherent in PrePPI makes it possible to go a step further than just identifying PPIs. Specifically, if a disease-associated single nucleotide polymorphism (SNP) is present in a PPI interface, disruption of the relevant interaction is potentially responsible for the disease phenotype. If this occurs frequently, it would be expected that disease-associated SNPs would be enriched in interfaces. To test this, we compiled disease-associated SNPs from ClinVar and The 1000 Genomes Project and found that the odds ratio for such SNPs to be in PrePPI predicted interfaces was 1.6. This is statistically significant, calculated as in  and simiGO is excluded from the full PrePPI LR in Gene set enrichment is a method to identify genes/proteins associated with a given property  that are overrepresented in a given collection of genes/proteins. A type of GSEA , originaWe applied the procedure outlined in FAM69 consists of five members: one of the annotated functions   and fourIn a second example, the procedure provides a novel annotation for the protein FAM178B (Q8IXR5). FAM178A, now known as SLF2 , was recently shown to be involved in DNA repair . ConsistAs delineated in A second observation derived from A criticism that has been leveled at many interaction databases is that their contents are biased towards well-studied proteins. SM, LRPrP)*LRPR) > 600 is the most reliable criterion for a direct interaction, structural information contributes to many other reliable predictions and, indeed, ~571,000 predictions have LRPrePPI > 600 with the additional criterion that Max*LRPR > 100, i.e. the LR based on SM, PrP, and PR evidence alone, results in an LR >100. Thus, there may be a much broader pool of predicted direct interactions than obtained from the stricter criterion of structural LR > 600, possibly on the order of 500,000.As discussed above, PrePPI makes about reliable 127,000 predictions based only on evidence that indicates a direct interaction . While structural LR ; filters based on different types of functional information such as GO annotation, either taken from UniProt or using the wide range of GSEA predicted annotations available; a filter based on sub-cellular localization; atomic coordinates of structural models which provide testable hypotheses about predicted physical interactions and which provide functional and mechanistic insights not available from binary measures of PPIs.It should be understood, however, that models used in PrePPI are only approximate, in part because they are created in a coarse-grained and high-throughput manner. They may contain nonphysical features such as steric clashes, and there may be isolated cases where a prediction is made using an incorrect model. In general, however, based on previously described experiments where interactions were disrupted by mutating residues in predicted interfaces  and, as HC, where TP is the number true positive predictions in the human HC set with an LR above a given cutoff and nHC is the total number of interactions in the human HC reference set; FPR=FP/nN, where FP is the number of false positive predictions in the N set with an LR above a given cutoff and nN is the total number of pairs of proteins in the N set.We compiled several reference sets of PPIs. They are named with one of three classifications: (1) 'HC,' for 'high confidence,' to indicate these interactions are supported by at least two publications; (2) 'experimental' to indicate they were obtained from databases of experimentally determined interactions: DIP , MINT C BioGrid The original version of PrePPI has been fully described elsewhere . The set–12. Models were built with MODELLER  are eitMODELLER  based onStructures of protein complexes used as templates for interaction models are defined either by (1) the PDB 'biounit' files, which are expected to represent the biologically relevant quaternary structure for interacting proteins, (2) the PISA database  of compun bins b1, b2,…, bn. In the Bayesian network, a likelihood ratio ib divided by the percentage of protein pairs in the yeast N set with a score for X in the same bin ib. Details regarding the scores and associated LRs based on structural modeling (SM)  , proteining (SM) , gene oning (SM)  and phyling (SM)  evidenceSM and LRPrP is used since the SM- and PrP-derived evidence is based on mutually exclusive interaction models, i.e. two structured domains versus a structured domain and a peptide, respectively. The final LR is thusThe LRs for the different types of evidence (except for SM and PrP) are integrated with a naïve Bayes approach, i.e. by taking their product. The maximum of LRThe orthology component of the Bayesian network is based on the assumption that if two proteins are known to interact in one species, their orthologs are likely to interact in another . DetectiOR. The Bayesian network based on information from all the databases outperforms the Bayesian networks based on any individual database  Gopher, (2) OMA, (3) KEGG, or (4) the union of EggNOG, Hogenom, OrthoDB, Ensmbl Compara, and InParanoid. For example, the vector  denotes that orthologs of A and B defined by Gopher interact in one species, orthologs of A and B defined by OMA interact in two or more species, no orthologs of A and B defined by KEGG interact, and no orthologs of A and B taken from the union of the remaining databases interact. Each vector represents a bin for the orthology term. There was insufficient data in the yeast HC reference set to calculate an LR for all possible 81 four-component bins. Therefore, lower-dimensional vectors were also determined by considering a smaller subset of databases. When making a PPI prediction for a given pair of proteins, the highest dimensional vector supported by sufficient data is used to obtain LRbase see . Bins anam proteins n of these proteins are found to interact with B, then the likelihood that A interacts with B increases with n. We thus define bins based on the number n, and on another property of B, the total number of proteins, d, which interact with B in the 'human experimental' set. A bin here is thus a vector of ranges. For example, the bin n is between two and five, and d is between 0 and 100.Our approach is a modification of that developed by de Chassey et al  in whichm, that any PR is taken from the first Bayesian network if possible and, otherwise, from the second one. Note that both A and B were excluded from {PrePPI. Bins and associated LRs for partner redundancy are shown in A drawback to this approach is that 'known interactions' are defined by membership in the 'human experimental' set and will necessarily involve only human proteins, thus limiting its statistical power. We therefore extended the analysis. When an experimentally determined structure or model of B is available, the following analysis is performed: Bins are defined based on the number of times, Preprocessed co-expression datasets including pre-calculated pairwise Pearson’s correlation coefficients for 11 species  were collected from COXPRESdb  and ArraTable 4.j and Bj in non-human species j as defined by Gopher because these eleven species are well-represented in this orthology database. From the expression databases listed in jc, for the correlated expression of Aj and Bj, are used to define the scores Spos and Sneg when the correlation coefficients are positive and negative (anti-correlated), respectively, as follows:Given two human proteins A and B, we find their orthologs Aandj and Bj have positive correlation (large posn), the larger posS will be, and vice versa for negS. The net effect of these formulae is to return a number with absolute value less than 1 but greater than the absolute value of any of the original j. Sorthcis then defined as:These scores reward consistency between different data sources, and similar approaches have been used previously to combine biological evidence in protein function annotation . The morhumanS, for proteins Aj and Bj is calculated in a similar way using the correlation coefficients j cfrom the human datasets listed above. We then define a cross-species correlation scoreSince there are multiple expression datasets for human  a set of 73,552 SNPs in ~3,800 proteins (from ClinVar and the 1000 Genomes Project) that were annotated as disease-associated using data from the Human Gene Mutation Database  and (2)  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Naama Barkai as the Senior Editor and three reviewers, one of whom, Nir Ben-Tal (Reviewer #1), is a member of our Board of Reviewing Editors.Thank you for submitting your article "A computational interactome and functional annotation for the human proteome" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:eLife if the following points can be addressed.The Honig lab has been developing the PrePPI methodology and web-server/database for predicting protein-protein interactions at genome-wide scale. PrePPI infers interactions based on known protein-protein complexes, as well as direct and indirect experimental techniques, such as phylogenetic profiles, gene ontology, expression profiles, etc. Here they present a revised PrePPI which is shown to be much superior to the previous version in terms of coverage and accuracy. Improvement is based on data accumulation, which is trivial (but very important!), but also on the addition of novel characteristics of protein-protein interactions, such as information from orthologues, expression profiles, partner redundancy and protein/peptide interactions. PrePPI now covers more than 1.35M interactions in the human proteome, 4 times more than the original version. The paper additionally reports the use of this information for function annotation, gene set enrichment analysis (GSEA), and an analysis of SNPs at interfaces. This is a substantial piece of research that merits publication in Essential revisions:A) The algorithm1) Features used for scoring the interaction likelihood: How is "5- Orthology" different from "2- Phylogenetic-Profile"?2) Features used for scoring the interaction likelihood: How is Expression Profile used differently in the new vs. old PrePPI?3) How specific are the predicted interactions? Considering known interaction pairs of orthologs from two protein families A and B, can PrePPI correctly pair A1 to B1, A2 to B2 and so on?4) Is it the case that the vast majority of predicted interactions are among paralogous proteins, where missing interactions are inferred based on concrete data for a pair of interacting proteins? It would be helpful to report the fraction of these.B) Evaluation of coverage and accuracy5) Training on yeast and testing with human: What is the cross-sets similarity between the proteins in both sets in terms of sequence and structure? Because some of the descriptors are based also on orthologues, how similar are the proteins in the sets in terms of their representations?6) The authors include in the definition of PPI proteins that are functionally related. This is a very generous interpretation of a PPI which is helpful for function annotation and GSEA. The Abstract should clearly state the definition of PPI and note that there are about 50,000 reliable predictions based on structural evidence alone which is indicative of a direct interaction .7) To get an estimate of the expected accuracy, it could be insightful to compare the new and old PrePPI. The manuscript gives an overall comparison in terms of counts of interactions etc., but comparison of individual interactions would complete the picture. For example, is the difference between the old and new PrePPI only in better coverage? What fraction of the interactions that have been assigned high confidence in the old prePPI are dismissed in the new prePPI? For these (if found), what are the reasons? Changes in the available structural or non-structural data? Or is it the result of smarter data processing?8) When querying the web site with human trypsin type 1 (P07477), one of the interactors was P35030 (human trypsin type 3). Strangely the structural model for the interaction showed the two trypsin structures were superposed. What does this mean? It clearly is not biologically sensible. This raised the question of whether the website and the entire approach have been stress tested by detailed analysis of specific examples rather than global metrics (ROC curves etc.). The authors should generate a random list of a few Uniprot IDs and input them to the server and then carefully check the answers. This requires work by a researcher with extensive biological expertise – so odd predictions can be identified. The paper could report the set of tested Uniprot IDs.9) Related to (8) above, please also address the realism of the predicted physical interactions. The original paper acknowledged that the structural models are not refined and are often non-physical. Indeed, often the interacting partners in PrePPI models suffer from major steric clashes. On the other hand, some models are based on non-interacting chains, although they might have high prediction scores.C) Comparison to other algorithms10) PrePPI should be compared to other methods. For clear comparison it is essential to clarify the cross-validation since the PrePPI LR-score combines experimental and computational evidence. It is not clear from the paper if experimental data used for training were excluded in testing . This is11) When compared with the human data from Y2H from Rolland et al., 12) The point in (5) is also relevant for the comparison of the third  set from Vidal to the two other sets.13) Given the broad definition of a PPI (see 6 above), when PrePPI is compared to the coverage of other databases it would be important to state that these other databases might be using a far more restricted definition of a PPI.14) The paper describes (subsection “Shared GO annotation”) the number of predictions obtained after removing the GO terms. If it is tractable, it would be interesting to have a table showing the effect of removing each of the terms in turn.D) Update15) At present, PrePPI is presumably up-to-date in terms of usage of all experimental data (structure-based or otherwise), and its enhanced performance in comparison to other resources is, in part, related to that. It would be great if PrePPI will be kept up-to-date automatically. It should be possible with the appropriate scripts. Anyway, please indicate in the main text how frequently you plan to update PrePPI.E) Data availabilityeLife policy all the data should be made easily accessible to the public. Please take care of that.16) The web site works for a single sequence input. It is not clear how much global data is available to the community. Can one download a list of all predicted PPIs with the scores? Can one obtain predicted protein-protein structures? This needs to be stated in the paper. According to  […] Essential revisions:A) The algorithm1) Features used for scoring the interaction likelihood: How is "5- Orthology" different from "2- Phylogenetic-Profile"?For proteins A and B, the “orthology” score directly reflects whether the orthologs of A and B are thought to interact in some other species. “Phylogenetic profile” only reflects whether orthologs of A and B are in a similar set of species without explicitly using any information regarding whether they interact. The difference has been clarified in the manuscript . In response to this point as well as point 2 below, we have also added a new figure (2) Features used for scoring the interaction likelihood: How is Expression Profile used differently in the new vs. old PrePPI?In the old version of “Expression Profile”, we only consider correlated expression of human proteins A and B. In the new version we also incorporate expression information for orthologs of A and B in 11 model species. We have modified the manuscript to highlight this difference .3) How specific are the predicted interactions? Considering known interaction pairs of orthologs from two protein families A and B, can PrePPI correctly pair A1 to B1, A2 to B2 and so on?We now devote a separate section to this comment and the next one (“b) The role of paralogs and related issues of specificity”). Briefly, we show that the structural LR contains some degree of specificity which is then enhanced by non-structural information. We gave specific examples of this in supplemental figures in our previous paper but here we have presented a more general analysis. Perhaps surprisingly, although now rationalized in the manuscript, PrePPI predicts a very wide range of LRs for pairs of proteins that are annotated as paralogs of proteins in the template used for prediction, with higher LRs corresponding to a higher probability of being a known interaction (as determined by current databases).4) Is it the case that the vast majority of predicted interactions are among paralogous proteins, where missing interactions are inferred based on concrete data for a pair of interacting proteins? It would be helpful to report the fraction of these.This is now discussed in a new section on paralogs (“b) The role of paralogs and related issues of specificity”). In short, about 25% of our reliable structure-based predictions involve paralogs and even among these, there is a considerable degree of specificity (and see response to point 3).B) Evaluation of coverage and accuracy5) Training on yeast and testing with human: What is the cross-sets similarity between the proteins in both sets in terms of sequence and structure? Because some of the descriptors are based also on orthologues, how similar are the proteins in the sets in terms of their representations?We have now devoted a small section to this question (“a) Independence of training and test sets”). There are 2,671 interactions in the yeast training set where the two proteins also have orthologs  that interact in the human testing set. To see if this was artificially inflating PrePPI performance, we recalculated ROC plots where LRs were calculated excluding the evidence from orthology for these 2,671 interactions and found essentially identical performance.6) The authors include in the definition of PPI proteins that are functionally related. This is a very generous interpretation of a PPI which is helpful for function annotation and GSEA. The Abstract should clearly state the definition of PPI and note that there are about 50,000 reliable predictions based on structural evidence alone which is indicative of a direct interaction .The Abstract has been modified according to the reviewer’s suggestion where we now say that between 127,000 and 500,000 interaction predictions may be direct. This is based on a reevaluation the number of direct interactions with the major change in estimate due to the addition of partner redundancy information in the current draft and to our highlighting the possibility of there being a much larger number of direct interactions which results from the use of a less stringent criterion. A section in the Discussion is now devoted to this general issue.We also point out that other large computationally derived databases also use a generous definition of a PPI . The reviewers clearly appreciated that this definition is very useful in the use of GSEA and we have now added a comment about this point in the Discussion that did not appear in the previous version (see aforementioned paragraph).7) To get an estimate of the expected accuracy, it could be insightful to compare the new and old PrePPI. The manuscript gives an overall comparison in terms of counts of interactions etc., but comparison of individual interactions would complete the picture. For example, is the difference between the old and new PrePPI only in better coverage? What fraction of the interactions that have been assigned high confidence in the old prePPI are dismissed in the new prePPI? For these (if found), what are the reasons? Changes in the available structural or non-structural data? Or is it the result of smarter data processing?We have now devoted a new section (“c) Overlap between the current and previous versions of PrePPI”) to a comparison between new and old PrePPI. One major difference is that we trained the new Bayesian network on a yeast reference set of interactions that is 4 times larger than the one previously used. This resulted in a global change in the LRs so it would be difficult to point to a single systematic cause of differences in the predictions, although we did find that the LR’s based on structural modeling (SM) were systematically lower. Overall, 37% of reliable interactions in the previous version of PrePPI no longer appear as reliable in the current version. However, this very likely reflects improved prediction accuracy in the new version. This is suggested, as now discussed in the paper, by the fact that nearly all true interactions (in the HC set) that were previously found with LR>600 are assigned an LR>600 in the current version. We also note that ~ 400,000 reliable predictions can be made based on the old evidence in PrePPI, so while better coverage may be contributing to improved performance, so is better data processing in the form of both better training and new sources of evidence.8) When querying the web site with human trypsin type 1 (P07477), one of the interactors was P35030 (human trypsin type 3). Strangely the structural model for the interaction showed the two trypsin structures were superposed. What does this mean? It clearly is not biologically sensible. This raised the question of whether the website and the entire approach have been stress tested by detailed analysis of specific examples rather than global metrics (ROC curves etc.). The authors should generate a random list of a few Uniprot IDs and input them to the server and then carefully check the answers. This requires work by a researcher with extensive biological expertise – so odd predictions can be identified. The paper could report the set of tested Uniprot IDs.The trypsin prediction is, of course, nonsensical and is an unfortunate drawback to the coarse modeling used in PrePPI. It occurs because trypsin is cleaved and in the PDB (PDB code 1aks), the N and C terminal fragments are labeled as chain A and chain B. PrePPI naturally treats this as two interacting chains and thus a suitable template to model an interaction between trypsin 1 and trypsin 3.We randomly chose 6 proteins  and included, in addition, 3 isoforms of RAS  which we are examining in detail in other projects. We looked at the top interaction models based on the SM score and found no pathological interaction models like the trypsin example above. We now alert the user to the possibility of encountering incorrect models in a new section “PrePPI availability and usage”. It is difficult, if not impossible, to eliminate a priori any templates that lead to predictions such as the one for trypsin above, and applying filters based on steric clashes would be too computationally cumbersome given the number of models we make. However, we will implement a system where certain PDB files can be excluded forever as templates if strange predictions are reported to us.If the reviewers agree, we prefer not to add specific examples of results based on our stress testing to the paper since, even though six were chosen randomly, they would almost certainly raise questions about “cherry picking”. But we have now included a few of them as working examples on the server and, in addition, the server will contain appropriate warnings.9) Related to (8) above, please also address the realism of the predicted physical interactions. The original paper acknowledged that the structural models are not refined and are often non-physical. Indeed, often the interacting partners in PrePPI models suffer from major steric clashes. On the other hand, some models are based on non-interacting chains, although they might have high prediction scores.As discussed above with regard to point 8, the issue of interaction models with steric clashes is now discussed in the section “PrePPI availability and usage”. We did find a few examples of predictions between non-interacting chains in the template in the 9 examples we looked at. In these cases, there was a problem with the way we constructed the models. As it is currently implemented, we always use the PDB file from RCSB as the template to reconstruct the interaction model, but the actual template used to calculate the score may have a different quaternary structure . We are working to fix this, but in the meantime, these instances are not displayed in the interaction model window; rather, a message appears stating that a model could not be built.C) Comparison to other algorithms10) PrePPI should be compared to other methods. For clear comparison it is essential to clarify the cross-validation since the PrePPI LR-score combines experimental and computational evidence. It is not clear from the paper if experimental data used for training were excluded in testing . This isA comparison to other databases is difficult since the widely used databases typically also serve as repositories for experimentally determined interactions, so their “predictions” will always be in our HC reference set. Note that experimental evidence is not used for any of the analyses in the manuscript. As now pointed out, this data is included on the web site to make PrePPI as comprehensive as possible. We now say this explicitly  but we feel that given these issues, the comparison to experimental approaches we provide in the manuscript is sufficient to validate PrePPI performance.We now devote a separate section to homology models which supplements the analysis in our previous manuscript. We show using ROC curves  that mod11) When compared with the human data from Y2H from Rolland et al., PrePPI performs poorly in a precision recall curve since it makes so many untested predictions which appear as false positives. For this reason, there is little meaning to the definition of precision. We will of course add these curves to the manuscript if the reviewers disagree. We believe that the ROC curves for the PRS/RRS data sets are meaningful and have changed the text to indicate that PrePPI performs comparably to other databases as measured in this way but has greater coverage. Moreover, we now say explicitly in the Discussion that we expect PrePPI predictions to be less reliable than HT experiments. We have also removed any comparison from the abstract so that the reader will have access to the basis of the comparison in the body of the text.We have extended our analysis by recalculating the ROC plots using a new N set that was defined based on the criterion that the protein pair are annotated by GO as being in different cellular compartments . This was necessary since our previous N set specifically excluded all interactions found by high-throughput experiments, artificially giving the Rolland Y2H and BioPlex a false positive rate of zero. The size of the new N set is 1,632,716  and is now reported in the manuscript. The new N set is smaller and thus permits greater balance than the N set used in the previous submission while yielding similar results. The results shown in the new 12) The point in (5) is also relevant for the comparison of the third  set from Vidal to the two other sets.As in our response to point 5 above, excluding evidence from orthology does not change the ROC plots, so orthology is not artificially inflating PrePPI performance with respect to recovery of the Vidal PRS set.13) Given the broad definition of a PPI (see 6 above), when PrePPI is compared to the coverage of other databases it would be important to state that these other databases might be using a far more restricted definition of a PPI.We actually explicitly say that the large size of PrePPI may be due to this general definition of a PPI . We also note that the same definition is implicitly used in other databases.14) The paper describes (subsection “Shared GO annotation”) the number of predictions obtained after removing the GO terms. If it is tractable, it would be interesting to have a table showing the effect of removing each of the terms in turn.This is now included in D) Update15) At present, PrePPI is presumably up-to-date in terms of usage of all experimental data (structure-based or otherwise), and its enhanced performance in comparison to other resources is, in part, related to that. It would be great if PrePPI will be kept up-to-date automatically. It should be possible with the appropriate scripts. Anyway, please indicate in the main text how frequently you plan to update PrePPI.This is now included in a new section of the Discussion called “Assessing PrePPI testing and training”..E) Data availability16) The web site works for a single sequence input. It is not clear how much global data is available to the community. Can one download a list of all predicted PPIs with the scores? Can one obtain predicted protein-protein structures? This needs to be stated in the paper. According to eLife policy all the data should be made easily accessible to the public. Please take care of that.This is now included in a new section of the Discussion called “PrePPI availability and usage”.


Microarrays constitute a new platform which allows the discovery and characterization of proteins. According to different features, such as content, surface or detection system, there are many types of protein microarrays which can be applied for the identification of disease biomarkers and the characterization of protein expression patterns. However, the analysis and interpretation of the amount of information generated by microarrays remain a challenge. Further data analysis strategies are essential to obtain representative and reproducible results. Therefore, the experimental design is key, since the number of samples and dyes, among others aspects, would define the appropriate analysis method to be used. In this sense, several algorithms have been proposed so far to overcome analytical difficulties derived from fluorescence overlapping and/or background noise. Each kind of microarray is developed to fulfill a specific purpose. Therefore, the selection of appropriate analytical and data analysis strategies is crucial to achieve successful biological conclusions. In the present review, we focus on current algorithms and main strategies for data interpretation.  The human proteome comprises ~23,000 protein-coding genes leading to >100,000 protein species mainly derived after alternative splicing and post-translational modifications . Thus, the overwhelming size and complexity of human proteome requires the development of high-throughput techniques enabling the detection of multiple proteins in a single analysis. Despite recent advances in proteomics technologies, only a small portion of human proteomes has been unraveled at the biochemical level. After the age of genomics, proteomics might seem to be a promising start to look deeper into the mechanisms of disease progression by generating individual protein expression profiles for every patient . DespiteThe idea of using microspots of antibodies printed on solid supports to develop more sensitive and quantitative assays was initially proposed by Roger Ekins in the late 1980s . HoweverDuring the last few years protein microarrays have provided the possibility to study protein-protein interactions and, to identify several biomarkers for different diseases ,9 includAccording to their applications, the planar protein microarrays have been classified in three main categories: analytical microarrays, reverse phase arrays (RPA) and functional microarrays  7,14,21,2114,21.A range of capture agents, differing in composition, origin and, thus, differing in their affinity properties may be attached on microarrays . Both, aIn this case, cellular or tissue lysate or even serum samples are immobilized on the microarray surface and the detection is completed through an antibody against the target proteins. To achieve a higher fluorescent signal to be detected, a secondary antibody conjugated with a fluorochrome is added to the first one. This ensures the signal intensity is directly related with the specificity, the binding affinity and the sterical accessibility of the antibody against the target protein ,9,14. ThFunctional microarrays are composed of full length functional proteins or protein domains and study the biochemical characteristics and functions of native proteins, as well as peptides or domains highly purified through cell-based methods or by cell-free expression on the microarray ,11,21,22In situ expressed microarrays, one of the subtypes of functional microarrays, are based on cell-free expression systems such as Escherichia coli 30 s, rabbit reticulocyte lysates (RRL) and wheat germ extracts . Em. Em12]. On the other hand, highly purified recombinant proteins are needed to satisfy the huge demand for capture agents. Because of that, new high-throughput techniques have to be developed, as well as, improving the existence ones. Moreover, these specific proteins allow generating microarrays which are analyzed faster, with high affinity bindings, permitting more efficient screening and also, avoiding or, at least decreasing, the cross-reactivity ,17.The slides used to immobilize the capture agent are usually made of glass, but can also be made of plastic, metal or polymer membranes ,41 SinceThe immobilization of proteins is usually completed using non-covalent protein surface interactions with hydrophobic  or positively charged  surfaces . This kiFinally, it is worth mentioning that thanks to flow cytometry, a new type of array has been developed, called the color-coded microsphere array. Each microsphere is covered with different antibodies, and each antibody can be detected through different fluorochromes; thereby, monitoring the binding antibody-antigen. The latter is attained from the sample of interest, can be fast, accurate, low cost and highly-sensitive. A new system of magnetic microspheres has been designed recently with high-reproducibility and sensibility, low background noise and price, and having a wide dynamic range ,11.i) High level of multiplexing. (ii) A sensitive, accurate and wide dynamic range signal readout. (iii) Information about protein-protein interactions. (iv) Flexibility in array composition. (v) Automatic preprocessing  in a short time. (vi) Clinical applications. All due to the integration of flow cytometry, antibody array detection of size-resolved lysates and computer-assisted data processing [In summary, a color-coded suspension microsphere array presents the following advantages over current antibody arrays:  which is based on the enzymatic extension of a primer-antibody conjugate followed by hybridization of labeled probes [et al. have employed chemiluminescence in order to achieve the required purpose [Achieving a more powerful signal is possible using indirect labeling, which is the most common system with serum samples, antibodies and in sandwich assays. However, cross-reactivity can be caused; hence the assortment of antibodies able to be employed is limited . Anyway,d probes . Also, td probes . On the  purpose . HoweverThe wealth of information generated by protein microarrays may provide solid evidence concerning protein functions, their interactions and even their involvement in signaling pathways. Interestingly, these data can also be applicable as a tool for clinical diagnostics. Nevertheless, the translation of data into meaningful information requires automated data processing and handling. As depicted in Spot intensity determination: microarray image analysis starts with the fixing of spot intensity. Generally, for this task, GenePix Pro software  is used. First of all, a grid of circles must be placed over the protein spots. Their position and size have to be adjusted in order to get reliable intensity data. Finally, an output file is created by the program.• Z-score analysis: the Z-score equation, , where Zs is the Z-score for the sth spot, Ss is the signal for that spot, µ is the mean signal across all spots and σ is the standard deviation across all spots, is an interesting tool to determine which signals are significantly different from the expected value and which are not.• Concentration-dependent analysis (CDA): due to the quantity of spotted proteins on the slide, absolute signals are affected by protein concentration. As a means to solve this issue, a different Z-score, , can be calculated to remove outliers. This novel Z-score is calculated using an iteration process that is repeated until every spot signal measured is in accordance with the mean value. In the equation above, Zs is the Z-score for the sth spot, Ss is the signal for that spot, µw is the mean signal for the spots within the window and σw is the standard deviation for spots within the window.• Antibodies immobilized on the microarray surface can be detected through direct labeling, requiring only a single capture antibody specific for each target protein. Alternatively, a sandwich approach can be carried out, which consists of two sets of antibodies, the first one is specific for the target protein, and the second one for the first antibody . Then, tSingle-color assays: Olle et al. [* e et al.  developeDual-color assays: Data pre-processing protocols are usually applied to prevent undesired technical artifacts. These protocols frequently include the following steps (adapted from [* ted from ):• Filtering, in order to remove failed and low-quality spots.• Background correction, to avoid fluorescence signal due to non-specific binding.• Data normalization, aimed to reduce variations between the two samples co-hybridized on each array and also between arrays.i) house-keeping probes, (ii) inclusion of spike-in controls and (iii) use of algorithms to define sets of probes. In the following, these methods are described [All the steps mentioned above are commonly used in protein arrays due to the difficulty of quantifying protein expression in a multiplexed manner. Multiple causes may lead to the occurrence of such artifacts, including the effect of electric charges, hydrophobic interaction of proteins, artifacts due to differences in protein sizes and antibody/antigen binding kinetics. For all these reasons, microarray data frequently require normalization. Several methods can be used for data normalization, such as: , whereas the single reference sample is labeled with a different fluorescent fluorochrome . In this type of design, it is necessary to calculate the log ratio of dyes intensities.• Balanced-block design: two samples which are hybridized, bearing two different fluorochromes (Cy3 and Cy5). Then, samples are balanced with respect to dyes. In this case, the microarray is considered as a block.• Incomplete-block design: more than two samples are co-hybridized on the microarray, whereas only two fluorochromes are used (Cy3 and Cy5). Despite this, samples are balanced.• Loop design: each sample is hybridized in a different array using a different fluorochrome. This supposes a great disadvantage because the number of arrays is duplicated.• Balanced-block and loop designs allow correct dye effect normalization, which are required for the correct analysis of two-dye systems. The first one is used for comparison studies, whereas the loop design is useful for discovery studies . The tabet al. [This algorithm is especially valuable in those cases where house-keeping controls are not available. Tseng et al.  suggestep is considered to be rank-invariant on an array, if the difference of the ranked Cy5 and Cy3 intensities is less than a threshold d and the average of the ranked intensities is not among the highest or lowest l ranks. For each array j, the set of rank-invariant proteins  is determined by the following expression [A protein pression :jp) and r(Cy3jp) are the ranks of the intensities and G is the number of spotted proteins.where r  depictedThe In-vMod algorithm corrects the intensity values through the extrapolation of the curve to the lower and upper intensity limits. jg) to get a global normalization [This next algorithm is applied to the whole probes on the array (wlization .ig = |r(Cy5jg) − r(Cy3jg)| is the absolute difference of the ranked intensities of protein g on array j. max refers to the maximum value.where Δi) global loess normalization (GL); (ii) variance stabilizing normalization (VSN) and (iii) generalized procrustes analysis (GPA). GL fits a non-linear loess curve in which equal weight is assigned to all probes. VSN is based on the stabilizing of the variance of the transformed intensities to be approximately independent of the mean intensities. Finally, GPA scales and aligns matrices with the same dimension as a mean to normalize data [On the other hand, several standard normalization methods are used in order to correct the background of the slides including:  is a new tool that permits obtaining information about protein sizes, protein complexes and protein profile changes through flow cytometry detection. The statistical method used was based on modifications of Partitioning Around Medoids (PAM). Previously, they have tried to resolve individual color-coded microsphere types by using k-means clustering, model-based clustering, minimum spanning tree clustering, and hierarchical clustering. But, finally, PAM was seen as the best method. In this way, they adopted the standard approach of sequential two-dimensional gating. Kernel density was used to analyze the distribution of fluorescent signal .Automated gating of color-coded microspheres: the automated tool is responsible for the specific identification and differentiation among microsphere types and the consequent allocation of the code to each one.• Analysis of size-MAP data: quantification of antibody-bound proteins amounts was determined with the medians of the fluorescence label signals. Next, these data will be processed through quality control (QC), normalization and analysis.• QC): first of all, the number of microspheres of each population is checked. Next, the density function of the signal is also determined.○ Quality control , is subtracted from the signal of the microsphere population of interest.○ Analysis: each protein entity has to be established and, for this purpose, fractions constituting specific protein entities must be defined. Then, signals for each fraction are summed up, representing the final result, which is the relative amount of a particular protein entity.Initially, in order to analyze antibody microarrays, strategies from cDNA arrays were implemented. Fluorescent dyes  were used to label each sample and, then, log ratio of the intensities was calculated for every feature on the array. Nevertheless, this design was not the optimal one . TherefoThe normalization is based on an internally normalized ratio (INR). A reference sample is labeled with fluorescent dye , whereas the sample of interest is labeled with another dye . In other arrays, the same samples are labeled with the opposite dyes (Cy5 for the sample of interest and Cy3 for the reference sample). Then, the ratio of both fluorescent dyes is calculated for each array. The INR is the geometric mean of the two ratios . Also, ARPA analysis is based on the construction of a serial dilution curve, which is characterized by two main advantages : first, Serial dilution curve: the monotonic s-shaped response curve is described by Sips model:• a corresponds to the background noise; b is the response rate in the linear range; M is the maximum or saturation level and x is the concentration of the protein. Also, the equation can be modified in order to avoid data about protein concentration. In this way, y can vary. Generally, y≠ 1 applies to condition in which there is some heterogeneity in the solute molecules or the surface receptors. y approaches to 1 when the range of the free energy of binding shrinks to a singular point. In this last case, the equation is equivalent to the conventional Langmuir model.In the algorithm displayed above, Parameterization of the serial dilution curve: a non-linear regression model is used to find the optimal parameters.• Estimating protein concentrations: first of all it is necessary to check if protein concentration is saturated. This occurs if the M/r ratio is lower than signals measured. Then, the minimum and maximum of x  are estimated with these formulas:• M/r is a threshold value in which M is the saturation level and r should be >1. K refers to the Kth dilution step.where In summary, the Sips model presents physically meaningful parameters and has the optimal conditions for RPA experiments .Microarray analysis includes four main steps which must be followed, such as design , data preprocessing, inference, classification and validation. All these variables may significantly differ depending on the kind of microarray used. Therefore, it is important to bear in mind that different data analysis methods are also required . Array design is crucial and may drastically affect data analysis. For that reason, careful design of microarrays is required, since it may significantly influence data analysis and final interpretation. It is recommendable (if not compulsory) that biological replicates are included in the microarrays, providing greater statistical confidence. Nevertheless, the introduction of replicates necessarily introduces more data to be analyzed, adding more complexity to the evaluation of the results [ results . Data preprocessing  is the second step. Image analysis is made using image-processing algorithms that distinguish foreground from background intensities. To date, it is not known which method is the best for this purpose [ purpose . Inference is based on statistical strategies, which also incorporate variability in the analysis [analysis .Classification and validation: Not only the large amount of data that are generated, but also the wide variety of results obtained which can be generated according to the type of array, are the reasons that explain why different data analyses are required. Since antibody microarrays and phage display arrays are different, the results obtained also differ [o differ . This fao differ .Despite the differences among data processing methods applicable to microarray analysis, several general recommendations need to be considered  as follo


With the emergence of high-throughput “omics” data, network theory is being increasingly used to analyze biomolecular systems. In particular, proteins rarely act alone, which prompted the arrival of proteomics. “Network proteomics” is just defined as the research area that introduces network theory to investigate biological networks ranging from protein structure networks to protein-protein interaction networks. In addition, the application of network proteomics in biomedical fields has increased significantly. This special issue has collected contributions, not only focusing on the state of the art of methodology in “network proteomics” itself but also focusing on the current status and future direction of their applications in translational medical informatics. Aspergillus oryzae, which would enhance its metabolic network reconstruction. By using the molecular dynamics simulation and principle component analysis, H. Wan et al. investigated the interaction between the last half repeat in TAL effectors and its binding DNA. This work would give a deeper understanding of the recognition mechanism of protein-DNA interactions.This issue starts with discussing the role of protein structure that participates in interactions. N. Raethong et al. developed a strategy to annotateProtein complexes offer detailed structural characteristics of protein-protein interactions. H. Zhang et al. proposed a method by combining the high frequency modes of Gaussian network model and Gaussian Naive Bayes to identify hot spots, which are residues that contribute largely to protein-protein interaction energy. G. Hu et al. performed a comparative study of elastic network model and protein contact network for protein complexes in case of hemoglobin.Protein-protein interactions not only are limited to binary associations but also employ special topological structures to perform their biological functions. N. Bernabò et al. approached the comparison of two networks models obtained from two different text mining tools to suggest that actin dynamics affect spermatozoa postejaculatory life. J. Xu et al. also compared the topologies of network modes of drug-kinase interactions between established targets and those of clinical trial ones.Both protein structures and protein-protein interactions are involved in the subject of a growing number of pharmacological studies. F. Ye et al. identified that compounds DC_C11 and DC_C66 are two small inhibitors against protein-protein interactions between CARM1 and its substrates, while Y. Wang et al. identified that CID_70128824, CID_70127147, and CID_70126881 are three potential inhibitors for targeting the androgen receptor to treat prostate cancer. Finally, a review paper completes the issue. X. Li et al. introduced recent advances in the development of network models of identifying synergistic drug combinations.Altogether, we wish this issue has given a wider development of structural biology and systems biology with the advantage of biological network analysis as well as prospecting the future of this area towards translational bioinformatics and systems pharmacology.Guang HuLuisa Di PaolaFilippo PullaraZhongjie LiangIntawat Nookaew


Circular dichroism (CD) spectroscopy is extensively utilized for determining the percentages of secondary structure content present in proteins. However, although a large contributor, secondary structure is not the only factor that influences the shape and magnitude of the CD spectrum produced. Other structural features can make contributions so an entire protein structural conformation can give rise to a CD spectrum. There is a need for an application capable of generating protein CD spectra from atomic coordinates. However, no empirically derived method to do this currently exists.PDB2CD has been created as an empirical-based approach to the generation of protein CD spectra from atomic coordinates. The method utilizes a combination of structural features within the conformation of a protein; not only its percentage secondary structure content, but also the juxtaposition of these structural components relative to one another, and the overall structure similarity of the query protein to proteins in our dataset, the SP175 dataset, the ‘gold standard’ set obtained from the Protein Circular Dichroism Data Bank (PCDDB). A significant number of the CD spectra associated with the 71 proteins in this dataset have been produced with excellent accuracy using a leave-one-out cross-validation process. The method also creates spectra in good agreement with those of a test set of 14 proteins from the PCDDB. The PDB2CD package provides a web-based, user friendly approach to enable researchers to produce CD spectra from protein atomic coordinates.http://pdb2cd.cryst.bbk.ac.ukBioinformatics online. Circular dichroism (CD) spectroscopy is a research technique used extensively worldwide. It is invaluable for providing structural information about proteins, particularly the secondary structure content. It is used for assessing whether a protein is correctly folded, is effective for monitoring structural changes induced through interactions either with other proteins or with ligands, and for determining protein stability through the application of temperature or pH changes. With the development and utilization of the technique, it was evident that a protein’s secondary structure content was the major contributor to its CD spectrum and many empirical methods were, and are still being created, to obtain this information from the spectrum  , the localized topological features of these secondary structure components (TOP), and the overall structural similarity between the query structure and the proteins within the basis dataset used (ZSC). The results from all three levels define proteins in the dataset with similar characteristics to the query protein. Finally the algorithm uses a refinement method to remove proteins from those selected whose CD spectra could be considered as outliers relative to the rest of the spectra, thereby leaving a set of proteins with CD spectra that can be used to create the resulting CD spectrum. The SP175 dataset , plus a The way that the structural neighbors for a given query protein are selected is based on the three different descriptors.ABO• For the ABO method the secondary structure content as defined by DSSP was calculated for each protein . The perTOP• The TOP method utilizes vectors defined for each of the alpha and beta secondary structure components comprised of three or more consecutive residues found in proteins . The angZSC• N pairs of equivalent Cα atoms. The second is the number of residues that are aligned in the process. These terms have been combined to obtain the ZSC calculated value between the query protein and all the proteins of the SP175 dataset.The ZSC is a measure of how similar the fold of two proteins is and is based on two components obtained from using the combinatorial extension (CE) protein–protein structural alignment method . The firIn order to identify the closest-matching structural neighbors of each query protein, all three descriptors are utilized.• ABOq is the query protein and i the ith protein in the basis set, and α, β and ο are the percentages of alpha helix, beta strand and other components, respectively. The three proteins with the closest scores are selected that satisfy the rule in i and αp represent the values for two of the three top matches of the ABO scores . Application of this rule ensures that the query protein data are encompassed by the basis set data to limit potential bias to the generated final resulting spectrum. In cases where no match to For the ABO score  the Euclidean distance between the query protein and the rest of the proteins in the basis set is defined as:• TOPq,d) is again calculated using the Euclidean distance between the query protein and the rest of the proteins in the basis set as:q is the query protein, d is one of the proteins in the basis set, and iq and id represent the TOP descriptors as identified above. The top three matches are retained for production of the final spectrum.The TOP score  Calculate a mean spectrum CDn is the number of spectra remaining, and CDA(1.3), CDT(1.3) and CDZ(1.3) represent any of the top three CD spectra for the ABO, TOP and ZSC methods, respectively.where (2) Calculate the standard deviation σ, at each wavelength point l, between the mean and the remaining spectra.(3) For each of the remaining spectra, determine the number Pi of wavelength positions where the following is true:(4) If max(P) for any remaining protein is more than one sixth of the total number of wavelengths, then that protein is identified as significantly different from the rest and is removed.(5) This process is repeated if a protein is removed.The remaining proteins are then considered to have spectra that can be used to create the final CD spectrum.F is the generated CD spectrum, nA, nT and nZ are the numbers of remaining proteins from the ABO, TOP and ZSC matches, respectively, after refinement. Ai, Ti and Zi are the ABO, TOP and ZSC scores between the query and the retained protein i, respectively, and CDi is the CD spectrum from the ith protein of the basis set.The remaining proteins with the native scores from the three different approaches are used to produce the final CD spectrum as:le is the generated and lo the experimentally observed magnitude at wavelength l, respectively, and M is the square root of the sum of squares at the wavelength for the maximum observed difference between spectra in the basis set. This is similar to the NRMSD term used in the DichroWeb secondary structure analysis server  the magnitude of gap between the vectors associated with the alpha helices, the beta strands and alpha and beta components was varied during method development across a 10–99 Å range in 1 Ångstrom steps. It was identified that 13 Å was the optimum for defining this characteristic (data not shown).For all optimization strategies, the measure of improvement (or not) was determined by obtaining the overall NRMSD values together with the numbers of ‘good’ and ‘poor’ spectra identified in each case. Here, NRMSD values less than or equal to 0.1 were defined as ‘good’, while those greater than or equal to 0.2 were defined as ‘poor’. For the ABO descriptor it was established that adding in 3Three to ten closest matches for each of the three structure descriptors were tested for retention during optimization of the refinement strategies. The results clearly demonstrated that, for the significant majority of the SP175 validation dataset, any number greater than three retained for each descriptor raised the overall NRMSD value and introduced noise into the derived spectrum, shifting it away from the optimal answer. Three was therefore chosen as the retention value.Although the PCDDB has just over 500 CD entries many of these are either thermal scans, membrane proteins  method, DichroCalc, for generating CD spectra from PDB-formatted protein structure files. However, some note should be made regarding cases where the method does not accurately match known experimental CD spectra, and the potential reasons why. The simplest reason is that some of these instances result from a lack of structures in the data base set with similar features to that of the query protein, which can restrict the accuracy of the comparisons used within the PDB2CD method. However there are other more fundamental possible reasons for poor matches: First, a crystal structure reflects the conformation of a protein in a constrained ‘solid state’, which may differ from its structure in solution. For many structures these two states may be very similar, if not the same, but for others, they differ considerably. Second, some proteins are very flexible, being able to adopt a multitude of different conformational states within the time frame of a CD measurement. The recorded CD spectrum will be the average of these states, and this average may well differ from that of a single crystal structure, which is a ‘snapshot’ view that may or may not represent an energy minimum conformation of that protein. Furthermore, in some crystals some areas may be so flexible that they are not observable in the electron density map, and hence do not appear in the coordinate file. They would thus not contribute to the calculated spectrum, although the measured spectrum will have contributions from all of the conformations that exist in a solution. Interestingly, where there are differences between the calculated CD spectrum and the measured spectrum, this may be a flag of interest as an indication of the conformational flexibility of the protein. Third, comparisons of PDB files of the same protein prepared under different crystallization conditions or in different crystal forms indicate that whilst many proteins retain similar structures in different environments, there are also many examples of proteins where crystal structures of the same protein are very different. This, too, could be an explanation for some of the ‘poor’ fits; hence the user might be advised to test the similarity of PDB2CD-calculated spectra from different PDB files of the same protein, as an indication of this variability.In general it can be seen from the results presented here that the PDB2CD methodology offers a significantly more accurate and improved approach to the current  which can arise from the side chains of specific residues, most notably aromatic residues  and with a very low percentage of ‘poor’ cases (∼6%). PDB2CD is publicly available (http://pdb2cd.cryst.bbk.ac.uk) without requirement for user login.In conclusion, the PDB2CD method provides a novel empirical means of generating a CD spectrum based on a protein's atomic coordinate file. The PDB2CD approach has been cross-validated with one of the most complete and well known datasets of CD spectra with related crystal structures, SP175, and tested on an additional 14 proteins. It has been found to do consistently better than the existing Supplementary DataClick here for additional data file.


Streptomyces are Gram-positive bacteria that are used as important cell factories to produce secondary metabolites and secrete heterologous proteins. They possess some of the largest bacterial genomes and thus proteomes. Understanding their complex proteomes and metabolic regulation will improve any genetic engineering approach.Members of the genus Streptomyces lividans TK24 and developed the Subcellular Topology of Polypeptides in Streptomyces database (SToPSdb) to make this information widely accessible. We first introduced a uniform, improved nomenclature that re-annotated the names of ~ 4000 proteins based on functional and structural information. Then protein localization was assigned de novo using prediction tools and edited by manual curation for 7494 proteins, including information for 183 proteins that resulted from a recent genome re-annotation and are not available in current databases. The S. lividans proteome was also linked with those of other model bacterial strains including Streptomyces coelicolor A3(2) and Escherichia coli K-12, based on protein homology, and can be accessed through an open web interface. Finally, experimental data derived from proteomics experiments have been incorporated and provide validation for protein existence or topology for 579 proteins. Proteomics also reveals proteins released from vesicles that bleb off the membrane. All export systems known in S. lividans are also presented and exported proteins assigned export routes, where known.Here, we performed a comprehensive annotation of the subcellular localization of the proteome of S. lividans and other streptomycetes. It forms the basis for future linking to databases containing experimental data of proteomics, genomics and metabolomics studies for this organism.SToPSdb provides an updated and comprehensive protein localization annotation resource for The online version of this article (10.1186/s12934-018-0892-0) contains supplementary material, which is available to authorized users. Streptomyces are soil-dwelling Gram-positive bacteria, belonging to the phylum Actinobacteria with significant industrial and academic interest ). Part of the annotation available in Uniprot, such as protein existence of structural domains are retrieved from Uniprot and retained in SToPSdb. Additional annotation, including the output of the protein prediction tools, annotation notes, references and experimental evidence are added to each protein and are available through SToPSdb. Annotation for protein function was added upon manual curation, based on the existence of known structural domains for each protein, as it is predicted in the InterPro , Pfam 2 and STARS. lividans TK24 (NCBI accession number: NZ_CP009124) and S. coelicolor A3(2) (NCBI Accession Number: NC_003888) by EDGAR 2.0 [Comparative genome analyses was performed for genomes of DGAR 2.0  to identDGAR 2.0  hits witDGAR 2.0 .http://www.hkvstore.com/phpmaker/]. Visual interventions have been implemented with the jQuery library. IM topology graphs are plotted using the JpGraph Object-Oriented chart library (http://jpgraph.net/) and subcellular location distributions under the cell cartoon are drawn using the “Highcharts”. The architecture of SToPSdb is identical to the E. coli database, STEPdb, as previously described . In the second part of the name we include a description of the protein function (e.g. “Esterase”). For example, protein A0A076MHP6 (Uniprot accession) was re-annotated from “uncharacterized protein” to “DNA alkylation repair enzyme” because of its structural similarity with the proteins of this family, as it is described by InterPro, and protein A0A076M7D5 was re-annotated from “secreted protein” to “Secreted protein (Sec)—Solute-binding family 1 protein”.S. coelicolor A3(2) contains two genes encoding Streptomyces subtilisin inhibitor-like (SSI) proteins  [S. lividans TK24, the homologue of the second of these two genes was labeled in Uniprot as “uncharacterized protein”, however we assigned the re-annotated protein name based on structural similarity search in InterPro and matching with the homologous proteins of S. coelicolor. To help the user, all the re-annotated protein names/descriptions are presented in a downloadable table and online (see below) next to the current Uniprot name and a link to the relevant Uniprot WWW page. Additional representative examples or protein description re-annotation are included in Additional file In total, more than 4000 protein descriptions (~ 53% of the proteome) were re-annotated and are included in the database. For example, it has been reported that SCO4010) . For S. icolor A3 containsE. coli K-12 proteome annotation  and SodF1 (D6ECW7) [Overall, SToPSdb aims to reduce the number of proteins with unknown topology, based on prediction tools, homologies and/or literature. A defined list of subcellular topologies for each protein is used, following the nomenclature previously presented in STEPdb , for theLOCATION  and refiLOCATION   , 41. ProS. lividans proteome, 2153 proteins (29%) had been assigned GO (gene ontology) annotations for Cellular Compartment (CC) via automated proteome annotation programs. This number of proteins corresponds to 29% of the total proteome leaving the remaining 71% with undefined topologies.Initially, we evaluated the subcellular topology annotation available in Uniprot for the TK24 proteome. This was based on the genomic analysis and subsequent automated annotation  added 183 new protein-encoding ORFs, and this updated information has been included in SToPSdb. This brings up the total number of proteins in the S. lividans TK24 proteome to 7505 of which we provide topological annotation for the complete proteome  are found to comprise the exportome and use the Sec, TAT or Type VII secretion pathways either for their insertion into the membrane or their complete translocation across it , a main model of secondary metabolite production [S. lividans following a proteome-wide BLAST analysis. The two proteomes are very similar and share 94% average nucleotide identity (ANI) for the matching sequence at the genome level and 93% of proteins at the protein sequence level, although S. coelicolor has a larger proteome. For the 1064 proteins in S. coelicolor that do not have S. lividans homologues, the localization prediction tools and manual curation were applied as above. Only 2571 out of 8038 proteins of S. coelicolor had available subcellular topology annotations in Uniprot. Following our re-annotation process 8029 proteins of S. coelicolor  have now been assigned subcellular topologies.Having a completely annotated proteome for oduction . This waS. coelicolor, 374 out of the 8038 proteins annotated in LocateP are in disagreement with SToPSdb and 254 are not directly comparable due to the different labeling of subcellular localization between the two databases (4.7 and 3.2% respectively)  and D6EWK6.Many of the components of the Sec secretion machinery of elicolor , 45. Funelicolor . Two copStreptomyces strains [E.coli K-12 . The fourth protein, tyrosinase MelC2, was experimentally shown to be exported in complex with its chaperone MelC1 using the TAT system [S. coelicolor [The TAT secretion system secretes folded proteins across the plasma membrane using their own characteristic signal peptides , 47. 157 strains –50. Four strains . Three oelicolor , 47   have a bproteome . Of thesOver-secretion of Sec substrates, could lead to detrimental accumulation of misfolded proteins in the membrane . The CssS. lividans, 6 previously unannotated BON domain proteins, were annotated as Peripheral plasma membrane proteins facing the cytoplasm.Structural motifs can target proteins to the membrane. The conserved bacterial OsmY and nodulation (BON) domain, possibly targets proteins to membranes, through recognition of the phospholipid surface . In S. lhttp://www.stopsdb.eu. On the top-right corner of the webpage there is a search box, allowing quick queries of specific proteins or advanced search options using gene name, Uniprot accession number, or protein name . By clicking on the “S. lividans TK24” link, a table of proteins will appear on the right, containing the basic identifiers and the protein topology for each protein in this strain. Each protein is linked directly to Uniprot by clicking on the protein accession number. A “more info” button will open a tab with information about the manual curation process , the prediction tools results, the identified protein family domains, and results of any applied biophysical property prediction tools. Different subsets of the secretory proteins can be selected from the Strains navigation menu. Each of these pages contains a table with the respective proteins and their characterization as described above. In addition, a comparative table between the S. lividans TK24 and S. coelicolor protein IDs and a table containing the S. coelicolor protein topology, as it is extrapolated from that of S. lividans based on homology, are also included.The web interface of SToPSdb can be accessed via the URL see also ). On theThe Downloads and Tools menu lists a series of links with supporting information and tools. The “SToPStoGO” page contains the protein topology nomenclature used in the SToPSdb and the corresponding GO terms. “IDMapping” is a tool that reads a list of protein accession numbers returning the corresponding topology. The full content of the database can be found in the “Downloads” page and is available to any user with no restriction. Each protein contains a Uniprot accession number that links SToPSdb with Uniprot, allowing the direct comparison of the SToPSdb entries with the reference proteome database. In addition, the SToPSdb subcellular localization labeling is translated to the corresponding GO terms for cellular compartment, retaining the commonly recognized rules for protein annotation , 68. InfStreptomyces is of core interest and has attracted a significant effort in SToPSdb. Given the importance of experimentally detected/validated proteins in the annotation process, SToPSdb incorporates publicly available experimental data for proteins detected in the exportome (“Proteomics” panel divided in “exportome” and “secretome”). This information can also be downloaded from the “Downloads” page, and the corresponding reference for each study is listed for more detailed datasets. Experimentally detected proteins are listed in one table, and the experimentally detected proteins are marked in the column corresponding to the relevant study. Because the culture medium composition can considerably change the profile of the secreted proteome and has been a focus of many studies [S. lividans TK24 growing in MM–CAS medium (296 proteins) [S. lividans and S. coelicolor growing under low O2 conditions, detected 1832 proteins from which 1486 are cytoplasmic and 346 exported proteins [The exportome of  studies , 69, 70, studies , 49, pro studies , 41 or iroteins) , have beproteins .Streptomyces, using as a model organism S. lividans TK24 [http://www.stepdb.eu), a database for the comprehensive topological annotation of the Gram-negative model E. coli K-12 [We developed SToPSdb to provide information on the protein topology annotation of ans TK24 . SToPSdboli K-12 . DevelopAn example of the beneficial outcome of such curation is the annotation of proteins involved in the secretory pathway. The YidC insertase, with distinct functions and low sequence identity, has been reported only in some Gram-positive bacteria , 74. TheStreptomyces is provided through the is LocateP database [S. coelicolor showed conflicting topological annotation mainly for secreted proteins. The disagreement between these two databases can be attributed to the use of different prediction tools or versions of the same tool. In addition, automated prediction tools will allow a proportion of misclassified proteins, in order to avoid overfitting and generalize well. On the other hand, manual curation can correct more misclassified proteins, but is limited to a small number of organisms that can be annotated.Currently, automated annotation for database . CompariS. lividans TK24 annotation with that of other model strains including S. coelicolor and E. coli K-12, based on their homologous proteins. One interesting observation here is that despite the significant phylogenetic differences between Streptomyces and E. coli, their differences in proteome size, transcription regulation diversity and developmental capacities, the percentage of each proteome devoted to cytoplasmic (~ 69%) or exportome (~ 31%) proteins remains rather similar (Fig. E. coli proteins identified in the peripherome may reflect the dearth of available experimental data in Streptomyces for this biochemically unusual class, rather than a true numerical difference.Emphasis was also given in SToPSdb on the connection of ortome ~ 1% proteiS. lividans, in particular, and streptomycetes in general. We plan to link SToPSdb with integrated omic databases and reconstructed metabolic and protein network models that are currently being developed. This will enhance a comprehensive analysis of the functional processes of S. lividans and can assist in further genetic engineering and biotechnology efforts that can be rationally applied. Towards this direction, we have integrated in this first iteration of the database experimental data derived from proteomics analysis of the exportome. These were derived from multiple proteomics studies that focused on various modes of protein export [We expect SToPSdb to become a reference resource for researchers working with n export , 49, 71.Streptomyces is an important step towards understanding the complexity of this genus of bacteria and its rational exploitation. SToPSdb provides a proteome annotation database focusing on protein topology. Although we use S. lividans TK24, as the model strain the proteome annotation is linked also to S. coelicolor and E. coli K-12, based on homologous proteins. This effort lays the foundation for future developments that will include the connection of SToPSdb with other resources containing experimental information at various physiological conditions, providing a reference resource for this organism. SToPSdb can be easily accessed through a web interface at http://stopsdb.eu.Improvement of the annotation of Additional file 1: Table S1. Re-annotated protein names and Identifiers for the proteins included in SToPSdb.Additional file 2: Table S2. Additional examples of protein description re-annotation in SToPSdb.Additional file 3: Table S3. Protein topology nomenclature.Additional file 4: Table S4. Conflicts in protein topology between Uniprot and SToPSdb.Additional file 5: Table S5. Comparison of SToPSdb and LocateP topological annotation.Additional file 6: Table S6. Secretion system components in S. lividans TK24.


Protein–protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi—a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations.PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a https://github.com/ima23/maxent-ppiBioinformatics online. Despite their structural diversity, proteins only achieve full potential by direct interaction in multi-protein complexes involved in fundamental biological processes such as gene expression, cell differentiation and cell–cell communication  and affinity purification coupled with mass spectrometric identification (AP-MS) . These mComputational methods to identify and filter false discovery from empirical output represent an alternative to assiduous and time-consuming experimental validation or use of simple subtraction of proteins from datasets based on their likelihood to be co-contaminants   used similarity scores on all GO term parents up to the lowest common ancestor (ULCA), including both relationships (‘is_a’ and ‘part_of’) in a single random forest classifier. This approach was observed to perform better than (i) similarity scores applied to the most specific GO terms, (ii) similarity scores applied only to the lowest common ancestors (OLCA) or (iii) similarity scores applied to parent terms up to the lowest common ancestor (ULCA) excluding the lowest common ancestor itself  as implemented in the GOSim package  (Boyanova Methods to compute similarity scores between GO annotations have been grouped into node-based (GO terms), edge-based (GO term relationships) and hybrid methods, each with their own limitations .Co-evolution of proteins, the presence/absence of protein pairs across taxa, has been used in several similarity scores and made available through STRING .The combination of protein phylogenetic profiling, phyloprof . All GO Maximum Entropy modelling is considered to be among the simplest predictive models, as its only constraint is to train a model that maximizes expected disorder in the system as quantified by the entropy. Information theory and Maximum Entropy were successfully used by Here, we introduce a novel approach that uses Maximum Entropy to capture and take advantage of the entire ontology structure—all relationships and ancestor terms—that offers an efficient method for protein co-complex evaluation with insight into the individual weights for each annotation. We apply a Maximum Entropy model, GIS-MaxEnt .Conflict of Interest: Since direct involvement in this work, MWBT has become an employee of Celgene Research SL, part of Celgene Corporation and declares no conflict of interest.Supplementary DataClick here for additional data file.


It contains several important parameters, such as dissociation constant (dK), Gibbs free energy change (ΔΔG), experimental conditions and structural parameters of mutant residues. In addition, the database provides an extended dataset of 282 single alanine mutations with only qualitative data (or descriptive effects) of thermodynamic information.Protein–nucleic acid interactions play essential roles in various biological activities such as gene regulation, transcription, DNA repair and DNA packaging. Understanding the effects of amino acid substitutions on protein–nucleic acid binding affinities can help elucidate the molecular mechanism of protein–nucleic acid recognition. Until now, no comprehensive and updated database of quantitative binding data on alanine mutagenic effects for protein–nucleic acid interactions is publicly accessible. Thus, we developed a new Database URL: http://zhulab.ahu.edu.cn/dbAMEPNI Protein–nucleic acid interactions play essential roles in cellular activities, and they dictate the development of complex multicellular organisms. Although the 3D structures of many protein–nucleic acid complexes have been solved, the general principles governing protein–nucleic acid interactions are not yet fully understood. Alanine scanning mutagenic experiments , 2, whicet al. (database of Alanine Mutagenic Effects for Protein-Nucleic Acids Interaction (http://zhulab.ahu.edu.cn/dbAMEPNI). dbAMEPNI provides alanine mutagenic effects for over 859 mutations in 217 protein–nucleic acid complexes. In addition, dbAMEPNI provides useful structural information such as the solvent accessible surface areas (SASA), secondary structure and hydrogen bonds. We believe that our database will benefit for the study of protein–nucleic acid interactions and provide a useful benchmark dataset for training and testing computational methods to predict hotspots on protein-nucleic acid interfaces.With the increasing availability of protein–nucleic acid complex structures , it is uet al. , 11 builThe alanine mutagenic data were collected from two different sources: the first one is the database ProNIT, containing thermodynamics data for protein–nucleic acid interactions which was published before 2012; the second source is literature published between January 2011 and October 2017. All data in our database have corresponding 3D structures of the protein–nucleic acid complexes available in the Protein Data Bank.http://www.bioinf.man.ac.uk/naccess/). Both features are based on holo protein–nucleic acids complexes. The secondary structure to each mutation residue was assigned by DSSP  The nucleic acids used for measuring the binding affinity were different. For this situation, we selected the value corresponding to the nucleic acid in the 3D complex structure. (ii) The methods for measuring the binding affinity were different. In this case, we considered the data measured by isothermal titration calorimetry method, which is more accurate than other methods. (iii) The temperatures were different. For this situation, we selected the data obtained near 25°C. (iv) The ion concentrations were different. For this situation, we selected the data obtained near 155 mM. For other complicated situations , we calculated the average value of the duplicates. In all, we collected 185 unique mutants from ProNIT. The corresponding PDBIDs are listed in From ProNIT, we considered a total of 345 single mutation residues by alanine scanning taken from the interfaces of protein–nucleic acid complexes with known 3D structures. Among these data, some were duplicated mutants with different In addition, we examined 655 articles that reported the 3D complex structures of protein and nucleic acids in PDB database. The search conditions satisfied the following three requirements: the release date was between 2011-01-01 and 2017-10-01, the macromolecule type was chosen as Protein–DNA or Protein–RNA and the X-ray resolution was <2.5 Å. Thus, we obtained a set of PDBIDs, by which we got the corresponding 655 articles. We looked through the articles, by using the following four schemes to identify the alanine mutagenic information: (i) Read the ‘Method’ section of these articles to check if any mutagenic analysis experiments were conducted in the work; (ii) check the tables and figures in both the main text and the In all, we found 577 alanine mutants with quantitatively characterized thermodynamic effects, along with 282 alanine mutants with qualitatively characterized effects. The flowchart of this process is shown in t-test. The probability (not the frequency) histograms of the five features were shown in Y-axis scales of both hot and non-hot spot residues are in the same range. In the figure, the Y-axis is the probability of residues in certain feature value/value bin, the X-axis is the values of the five features mentioned above. The t-test results indicate that the distributions of SASA in bound state and the buried SASA were significantly different between hotspots and non-hotspots, with P-values of 0.01 and 0.01, respectively. The average values of SASA in bound state for hot spot residues and non-hotspot residues are 30.93 and 46.79 Å2, respectively, which indicates that the hotspot residues on protein–NA interfaces are more inclined to have smaller SASA in bound state than non-hotspot residues. The average values of the buried SASA (ΔSASA) for hot spot residues and non-hotspot residues are 51.19 and 31.19 Å2, respectively, which indicates that the hotspot residues on protein–NA interfaces are more inclined to have larger ΔSASA than non-hotspot residues. This observation is in line with our intuition that the more buried the residue is, the larger probability it is a hotspot residue.Four structural features were calculated for each mutated residue in the database, including SASAs in both bound and unbound states, secondary structure and hydrogen bond between target residue and nucleic acids. The differences of the four features, along with the buried SASA (ΔSASA), between hotspot residues and non-hot spot residues were analyzed. The buried SASA is the difference between SASAs in unbound and bound states, which was calculated as the following equation:ΔΔG are in a specified range. By the ‘Download’ page, users can download all the data freely on our website.dbAMEPNI provides a variety of web-based interfaces and graphical visualizations to facilitate the search and analysis of residues in the database. Users can browse, search and download the data. By the ‘Browse’ web page, users can explore all the entries. On the ‘Browse’ interface, a quick search window can be used to retrieve the entries of interest. By the ‘Search’ web page, users can do some advanced search, for example, users can search entries for which the The website also have a ‘Submit’ web page. We encourage users to submit novel data to the database. Users can submit their data in two ways. First, users can submit a file that contains the new entries, or users can submit their data via a web form. The novel data will be forwarded to the developer and be added to our database after a manual check and confirmation.The ‘Document’ web page of the website provide a simple tutorial to show how to use the website. It explains the abbreviations in the tables of our database. It also provides a statistical analysis of data along with the years.Different interfaces of the database website are shown in et al. (et al. (et al. (Prior to this alanine mutagenic effect database, there are several studies using the data from ProNIT to analyze the interactions between protein and nucleic acids. Pires et al. , 17 used (et al.  analyzed (et al.  develope (et al.  developeThis is the first release of dbAMEPNI database, it contains abundant data of alanine mutagenic effect, which are useful for biochemists and bioinformaticians. In the future, our database will be updated annually based on newly published experimental data. As more data become available, we may divide the database into two parts for protein–DNA and protein–RNA complexes. We will also develop and integrate computational methods for prediction of protein–nucleic acid hotspots into dbAMEPNI.Database Online.Supplementary TableClick here for additional data file.