We present a network-based method, namely quantitative proteomic signature profiling (qPSP) that improves the biological content of proteomic data by converting protein expressions into hit-rates in protein complexes.We demonstrate, using two clinical proteomics datasets, that qPSP produces robust discrimination between phenotype classes  and uncovers phenotype-relevant protein complexes. Regardless of acquisition paradigm, comparisons of qPSP against conventional methods (e.g. t-test or hypergeometric test) demonstrate that it produces more stable and consistent predictions, even at small sample size. We show that qPSP is theoretically robust to noise, and that this robustness to noise is also observable in practice. Comparative analysis of hit-rates and protein expressions in significant complexes reveals that hit-rates are a useful means of summarizing differential behavior in a complex-specific manner.Given qPSP’s ability to discriminate phenotype classes even at small sample sizes, high robustness to noise, and better summary statistics, it can be deployed towards analysis of highly heterogeneous clinical proteomics data.This article was reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.Reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.The online version of this article (doi:10.1186/s13062-015-0098-x) contains supplementary material, which is available to authorized users. Schizosaccharomyces pombe (fission yeast) [Saccharomyces cerevisiae (budding yeast) [Leprospira interrogans [Escherichia coli [Mycobacterium tuberculosis [Drosophila melanogaster [Caenorhabditis elegans [Arabidopsis thaliana [Mass spectrometry (MS)-based proteomics has become a central technology for life sciences research. With extensive fractionation of samples and data-dependent acquisition (DDA) MS or so-called shotgun/discovery proteomics, near-complete proteome coverage is now achievable for a number of prokaryotic and eukaryotic species, including n yeast) , 2, Saccg yeast) , Leprosperrogans , Escherihia coli , Mycobacrculosis , Drosophnogaster , Caenorh elegans , Arabidothaliana –9, humanthaliana , 11 and thaliana , 13.However, to obtain functional understanding of dynamic biological systems, proteins have to be quantified consistently across different states or samples. In data-dependent acquisition (DDA) MS or shotgun proteomics, peptides eluting from the column are first surveyed and selected for fragmentation based on intensity using a quasi-stochastic heuristic. Different peptides are selected for fragmentation in different runs, leading to stochastic and inconsistent quantitative information. When applied to relatively large sample cohorts, many proteins could not be consistently quantified across samples. This inconsistency was not due to these proteins being absent or below the limit of detection; rather, these proteins were simply not analyzed.Recent technological advancements in proteomics , 15 haveNetwork-based methods can mitigate noise, consistency and coverage issues in proteomics data. Proteome coverage can be expanded, alongside recovery of undetected proteins by identifying proteins that are closely associated with already-identified proteins –22. ConsTraditional DDA or shotgun proteomics produces sparse data that is highly inconsistent between samples, even from the same phenotype class. The lack of a complete data matrix means that most statistical methods cannot be effectively used. PSP resolves this problem by transforming the detected proteins into a vector of hit-rates by comparing against protein complexes , 23. RecThe colorectal cancer (CR) study of Zhang et al.  using hiProteins are quantified via spectral count, which is the total number of MS/MS spectra acquired for peptides from a given protein. In this study, we excluded label-based quantitation such as iTRAQ and TMT  due to set al. in their study of clear cell renal carcinoma or renal cancer [DIA methods are relatively new in the field, and large applicable datasets are not easy to obtain. As an instance of DIA proteomics, we used the SWATH datasets from Guo l cancer . The firAlthough technical duplicates were present in each phenotype class, we opted to analyze them together: If technical duplicates gave rise to high random variation, then the technical replicates would not group together; this would indicate that the data-acquisition platform was unstable. Moreover, if technical variability is present, it would impede the ability of less-robust algorithms to detect biological signal. Since we are interested to know the performance of the qPSP in the presence of noise  in the data, it should not matter so much that the technical replicates were grouped together in the study.All SWATH maps were analyzed using OpenSWATH  against For RC-C, 2331 proteins were quantified across the 12 SWATH maps of the first data set with a peptide and protein false-discovery rate (FDR) lower than 1 %. In the original RC, 2375 proteins were quantified across all samples with a precursor FDR below 0.1 % and 32 % sparsity. The high level of sparseness was likely due to higher noise and poorer alignments between features. To improve consistency in the data, we relaxed the retention time (RT) alignment criteria using TRansition of Identification Confidence (TRIC) (version r238); specifically, we allowed a wider maximal RT difference (max_RT = 30) but increased the precursor FDR to 0.001 to limit false matches. The two most intense peptides were used to quantify proteins. Finally, protein FDR was set to 0.01 with 3123 proteins reported.We define “alpha” as the value used to subset a ranked protein list based on abundance. The alpha stability is a measure of alpha-protein rank consistency between samples in the same phenotype class  ---i.e., whether the alpha proteins are similar across same-class members. Evaluation of the alpha stability (i.e. how consistent protein rankings are) provides insight into inter-sample variability as well as noise levels.We used Alpha-Stability Analysis (ASA) to evaluate protein rank stability across samples to obtain insights into technical and biological variability. ASA first sorts the proteins in each sample by protein abundance so that the most highly expressed proteins are ranked first in that sample. The top n% proteins in the sorted list of each sample constitute the set of alpha proteins of that sample. ASA then cross-compares the sets of alpha proteins between all samples using the Jaccard distance (i.e. the intersection of two sets of proteins over the union of proteins in the same two sets).Although subnets or clusters can be predicted from large biological networks, it is found that real biological complexes are biologically more coherent . Thus, wWe assume that proteins with higher abundance are measured with higher accuracy in the mass spectrometer. qPSP first sorts proteins in each sample based on their measured abundance, and selects the most abundant proteins above a certain percentile . A second percentile value  is set to extend the list of proteins for improving sensitivity while maintaining precision . To penaA) and a vector of complexes of length n. For each complex Ci in the complex vector, the hit-rate is the intersection of proteins in SA and Ci , modulated by the proteins’ weights , over the total number of proteins in Ci. We let the hit-rate for SA in Ci be H. Therefore, the vector of hit-rates for sample SA is HSA = <H…, H>. This vector of hit-rates is the sample’s subnet signature profile.By default, we set alpha1 as top 10 %, and alpha2 as top 10-20 %. Rank-based weighting was achieved via discretization of the continuous range from top 10–20 % into four bins: 10–12.5 % (weight 0.8), 12.5 % to 15 % (0.6), 15–17.5 % (0.4), 17.5 to 20 % (0.2). All other proteins outside of alpha2 (viz. remaining proteins) have a weight of 0 (and are thus ignored). Alpha1 proteins are assigned the full weight of 1. For convenience, proteins with non-zero weight are referred to as alpha proteins. For each sample, a vector of hit-rates is produced by considering the overlaps of the alpha proteins against a vector of complexes. To see how this works, let’s say we have a sample from class A ,…, H > and HB = <H,…, H>, where A and B are phenotype classes of sizes m and n respectively. The t-statistic between HA and HB is computed by the standard formula:To perform feature selection (where a feature is a protein complex in this instance), for each complex C in the complex vector, we compare two lists against each other, HA = <H, then C is differentially expressed between classes A and B.There are several adjustable parameters that determine the endpoint measure, which we examined in the following, including the values of alpha1 and alpha2, and the directionality of alpha values (i.e. top n% or bottom n%).P ≤0.05). To do this, a t-statistic (Tp) is calculated for each protein p by comparing the z-normalized expression scores between phenotype classes C1 and C2, with the assumption of unequal variance between the two classes [p, sj is the standard deviation and nj is the sample size, in class Cj. The Tp is compared against the nominal t-distribution to calculate the corresponding p-value. A feature is deemed significant if p-value ≤ threshold (where threshold is set to 0.05).As a match against a commonly used form of network-enrichment approach, we compared qPSP against the standard hypergeometric enrichment method (hyp_geo). The hyp_geo comprises 2 parts. In the first part, differential proteins are first selected using the unpaired two-sided t-test , the probability P that b or more proteins from the test set are associated by chance with the complex is given by:In the second part of hyp_geo, the significant proteins from the first part are compared against CORUM complexes using the hypergeometric test. Given a total number of proteins X ≥ b) ≤ threshold (where threshold is set to 0.05).The complex is deemed significant if P  expected of a complex C in phenotype-A and phenotype-B patients. Suppose there are k proteins in C. Next, let x and y be the number of false detections in typical phenotype-A and phenotype-B patients. Then r = x/(x + u*k) = y/(y + v *k).Let the false-detection rate be r, where 0 < r < 1. Let u and v  respectively be the actual hit rate  of C expected in phenotype-A patients is u, the total actual hits in the complex expected in phenotype-A patients is u*k. Since r = x/(x + u*k), we have x = r*u*k/(1-r). Thus, the expected value of the observed hit rate of C in phenotype-A patients is HA = total actual hits + false positives = u*k + r*u*k/(1-r) = u*k/(1-r). Similarly, the expected value of the observed hit rate of C in phenotype-B patients is HB = v*k/(1-r).HA,HB between A and B:The t-score generated by comparing phenotype-A and -B patients is independent of r. To show this, first consider the estimator of the common standard variation SThe t-score is given as:t-score is independent of the false-detection rate, r. The empirical p-values are based on the distribution of t-scores. Since t-score is independent of r, it follows that the p-value is also independent of it.Hence, the t-score is independent of the noise levels so long as the false-detection rate is the same between the phenotype classes. Under this assumption, qPSP is expected to be robust to noise and thus useful for analyzing data with higher noise levels but greater proteome coverage (lower FDR cut-offs). This can be useful for improving sensitivity while maintaining reasonable signal-to-noise levels.Therefore, the Of course, the proof above makes also the assumptions that m and n are about the same size, and the standard deviation for each population is more or less the same. While this assumption may not always hold true, using CR data, we find that qPSP is robust to noise in practice (see section “qPSP is immune to noise in SWATH data”). Moreover, an analogous result can be obtained  when t-score with unequal variance is used instead.We evaluate whether qPSP allows robust distinction of phenotype classes  using both the renal cancer (RC) derived from DIA proteomics Fig.  and the Using qPSP, the strong discrimination between normal and cancer classes is also clearly demonstrated in the PVCLUST-generated HCL tree  where bootstrap resampling confidently separated them into two distinct groups  in RC (since we have the raw spectra for RC but not CR) to 1, 5, 10 and 15 % levels, and repeated qPSP analysis on each of these FDR-adjusted RC datasets. Figure To understand the stability of the reported significant complexes, we cross-compared the set of reported significant complexes at 1, 5, 10 and 15 % FDR. It is expected that since the t-score is independent of the noise levels, the set of significant complexes should remain stable.P < = 0.05) are given in Additional file P < 0.05). Most of the variability was found at the 15 % FDR level. Of the remaining complexes, although there were mild fluctuations of the p-values across different FDR levels, there appeared to be no evidence of the p-values becoming less significant as the FDR levels increased . If we pick a small sample size, we would only observe a subset of the 5 causes. If only one sample is picked, then only one cause would be observed. The more samples that were picked, the more likely better coverage was obtained (to pick up more causes), and hence the complexes associated with these causes. The increase is expected to taper off once sufficient samples to observe all complexes associated with all underlying causes are obtained.The stability of the significant complexes in qPSP and hyp_geo differed greatly as well. Figures To examine complex instability from a complex-wise perspective, we summed each column of the binary matrix  and kept columns with sum greater than 0 (meaning that a simulation has reported this complex to be significant at least once), and divided the value by 1000 as a normalized value Figs.  and 6c sThe frequency distribution of the column sums (in histogram form) provided a simple but effective visual representation for discerning whether the majority of significant complexes were stable or unstable. Figures A valid concern is that qPSP is biased towards complexes that are enriched with high-abundance proteins. However, we argue that the benefit of having a reasonable number of consistent significant complexes is preferable to the downside of unstable and low number of significant complexes. Instability prevents generalizable interpretation of biological mechanism , while low numbers of significant but true complexes provides too little information to make any kind of biological inference .Given how widely the hypergeometric test (hyp_geo) is used, its poor performance was surprising. Hence it is necessary to understand why it failed. Since RC appears to be less complex than CR, we investigate further using RC.To obtain a deeper understanding of feature-selection stability in hyp_geo, we plotted the stability histograms for complexes, their constituent proteins, and the stand-alone two-sample t-test Fig. . In thesIt is interesting to note that the stability distribution of hyp_geo complexes mirrors closely that of their constituent proteins . In turn, the stability distribution of these complex proteins resembles closely those proteins determined to be significant by the two-sample t-test. Given these resampling tests, there is no enrichment for highly stable t-test proteins within the hypergeometric-selected complexes. This means that the hypergeometric test is very sensitive to stability issues by the upstream t-test.Since the hypergeometric test only makes a small number of statistically significant selections (approximately 20–30 complexes) compared to qPSP, it is of value to see if qPSP’s apparent feature-selection stability would be diminished if we restricted the selected features to the top 30 most significant instead of just those that met the p-value cutoff. Additional file As a second fairness check, we restrict proteins to the top 20 % (in a manner similar to the top 20 % in qPSP) before running the hypergeometric test, Additional file In Fig. HCL  between normal, and severe and less severe cancers showed the overall distribution of hit-rates for normal, severe and non-severe classes Fig. . We reduThe relationship between these complexes and the severe/less severe cancers are shown in Fig. There is some evidence in literature linking these complexes to renal cancer: For example, the 20S proteasome is a potential biomarker for non-metastasized breast cancer . A recenDetails on the 15 differential complexes, which include their annotated functionalities, are provided in Table qPSP is a powerful network contextualization approach for proteomics data. Using examples from DDA and DIA data, we demonstrated that qPSP produces strong yet stable class discrimination. It is robust against noise and makes reproducible feature (complex) selections, unlike subnet-enrichment analysis based on the hypergeometric test. Using the renal cancer dataset, we demonstrated that the qPSP hit-rate is a succinct summarization of contributing signals across multiple complex protein constituents. Given these qualities, qPSP is a powerful approach for analysis of clinical samples and selecting features for biomarker and drug target discovery.1. Formulation of the qPSP methodologyIn its current form, qPSP basically utilizes the complex information (from the CORUM database) to form protein groups and then uses the protein abundance for the derivation of a new measure - hit rate per protein complex for any proteome sample. The hit rate measure is then used for subsequent statistic testing to elucidate enriched protein complexes between samples.The current formulation did not consider the effect of protein complex stoichometry. To emphasize, high abundance of some proteins in a complex grouping does not necessarily imply that the same abundance of the complex. Hit rate/complex calculated from such cases can be grossly overestimated in complex abundance and skewed statistical significance. In contrast, the original PSP, which only uses presence or absence of the protein, has less bias in this regard. As a separate note, abundance of proteins is also independent of whether the actual protein interactions took place to form the complexes. Often, proteins are post-translationally modified so that the interactions do not take place though they are present in the cell. It is worrisome that the term “high abundance complexes” on the basis of protein abundance alone were being used in the text.Taken together, the preceding assumptions made by qPSP are not trivial. At best, the hit rate is a predictive measure, but not a sufficient surrogate measure of protein complex formation and abundance. Its subsequent usage in statistical tests to identify enriched protein complexes creates the issue of nested predictions (predictions based on predictions); the proper usage of hypothesis testing is meant for testing differences in observed measures, not predictive measures.2. Robustness of qPSP against noise: effects of t-score or pre-filtering?From the theoretical argument, the authors presented a proof to show that t-score is independent of false-discovery rate (FDR = FP/TP + FP). Here, noise implicitly refers to the number of FP . The proof seems incomplete since there was no discussion about effects of the pooled standard deviation term Su.v and the sample sizes n and m on the actual level of FP.With respect to the two sample t-test, FP is equivalent to type I error (α). When the variances are unequal, the probability of type I error tends to be greater than the stated α. When n ≠ m, this probability will be greater than the stated α if the smaller sample is associated with the larger variance. In both cases, the level of FP (or noise) will be underestimated and the robustness of the t-test will be compromised as well. As such, qPSP does not seem impervious to FP arising from both unequal variances and sample sizes.In actual applications, the robustness of qPSP to noise seems to stem from the pre-filtering step  rather than from the more elaborated hit rate/complex measure and FDR-independent t-statistics. There are two observations to note. First, the authors have shown that qPSP suffers in performance if the bottom hits were included in their RC-C study. Second, in the RC study, repeated qPSP analysis on various FDR-adjusted dataset has little impact on the classification tree results. The latter seems more likely a consequence of the pre-filtering step to create similarly truncated datasets  regardless of the initial FDR adjustments on the data.3. Evaluation of the qPSP methodologyThe evaluation of qPSP’s robustness reveals another issue. If the pre-filtering step alone in qPSP is sufficient to build similar clustering plots and hierarchical trees as shown throughout the article, then the inclusion of protein complex information and t-statistics adds little impact to the overall qPSP methodology.Specifically to the RC dataset analysis, although the authors were able to show clear discrimination between the phenotypes in the HCL trees  as a component of FDR (=FP/TP + FP).3. It would be more readable to write the hit rate/complex computations as a formulation of vectors and matrix.Section: implementation of qPSP:Variable n has been used to twice. One instance is to describe a vector of complexes of length n, another use is as the size of samples n of phenotype B. The naming of the two variables should be distinct.1/Arguments on not considering protein complex stoichiometry in qPSPa) “not consider the effect of protein complex stoichometry…We acknowledge and agree that this is a potential shortfall. However, whether the complex can or cannot be formed is something that cannot be directly addressed at the level of the proteomics screen. At the same time, we also agree that the simultaneous consideration of possible PTMs can be useful as additional information for refining the qPSP technique. In a typical proteomics screen however, increasing the screen for each PTMs (whether deterministic or stochastic), will greatly increase the library search space. Because there are a lot less expected matches relative to expected, it will drive up the false positive rates. This also means that we will have to increase the critical level accordingly but at the cost of further driving down proteome coverage. These analytical issues are interesting areas for further refinement and improvements but are upstream of qPSP.For now, we are simply making the assumption that when the constituents of a complex are highly enriched/present in the proteomics screen, it is more likely that the complex can be formed. Currently, we are in fact looking into alternative assays that allow predictions of the strength of formation of various complexes. This can be potentially used as a weight for subsequent iterations of qPSP .b) “hit rate is a predictive measure, but not a sufficient surrogate measure of protein complex formation and abundance”The reviewers are correct to say that the hit-rate can be considered as a probabilistic indicator of protein complex formation and abundance. And in this regard, it is a form of predictive measure. But this is dependent on context and the manner in which these hit-rates are being used.For now, we are just saying that each sample can be represented by a series of hit-rates against a feature vector of subnets. And that differential signal can be detected in this manner. We did not emphasize that the hit-rates themselves are predictive of the complex being formed or quantified. Moreover, in the last section of the manuscript, we considered the relation between hit-rates and the constituent protein quantitations. They correlated quite well in spite of the hit-rate being a summary measure.c) “It is worrisome that the term “high abundance complexes” on the basis of protein abundance alone were being used in the text”.We appreciate that the presence of “high abundance constituents” does not necessarily imply the presence of the “high abundance complex”. This term has only been used in one paragraph and was meant to make known a potential shortfall of the qPSP approach, namely, its tendency to pick complexes that is enriched for higher abundance proteins. We cannot observe all constituents in these complexes given that proteomics picks only about 3000 proteins in general, so for pithiness, we simply made the assumption that the complexes are high abundance. We will change this to the clearer term: “complexes enriched for high-abundance proteins” instead.2/Robustness of qPSP against noisea) “The proof seems incomplete since there was no discussion about effects of the pooled standard deviation term Su.v and the sample sizes n and m on the actual level of FP”.u,v and m and n. Hence the unbiased s.d. Su,v can be inferred based on k, m, n and the observed s.d. SHA,HB. Incidentally, the proof can be adapted to show the analogous result for t-score based on unequal variance.The reviewer is correct that we made very specific assumptions wrt to the proof and it does not cover all potential scenarios as highlighted by the reviewer. Generally, we are saying that the actual level of FP comes from the false discovery rate for the proteomics data, which is estimated independently of SWe added some discussion on this issue in the main text of the manuscript. But briefly, in the unequal variance scenario,This can be plugged back into the t-expression:And will still converge towards the same result as the proof in the original manuscript as k/1-r can still be factored out and cancelled.b) “With respect to the two sample t-test, FP is equivalent to type I error (α). When the variances are unequal, the probability of type I error tends to be greater than the stated α. When n ≠ m, this probability will be greater than the stated α if the smaller sample is associated with the larger variance. In both cases, the level of FP (or noise) will be underestimated and the robustness of the t-test will be compromised as well. As such, qPSP does not seem impervious to FP arising from both unequal variances and sample sizes.”This is indeed true. But maybe we can clarify this better. We are saying that qPSP is independent of r, which is the false discovery rate or uncertainty arising from the identification of proteins from proteomics. In typical proteomics, noise is typically very high, which normally requires imposing a very stringent false discovery rate  to restrict false matches (between 0.01 % to 1 % FDR). But this in turn, results in very low proteome coverage. Approaches that are able to circumvent this issue can be potentially very valuable.The FP the reviewer is talking about here is regarding feature-selection based on the t-test, and not what we were discussing about above. The type I error was discussed in the earlier section “qPSP improves signal in proteomics data” where we used control data to test how many false positive complexes were predicted using resampling statistics. And yes, we agree, feature-selection instability where s.d. is very high and sample sizes very low is not something qPSP is immune against.b) “the robustness of qPSP to noise seems to stem from the pre-filtering step  rather than from the more elaborated hit rate/complex measure and FDR-independent t-statistics. There are two observations to note. First, the authors have shown that qPSP suffers in performance if the bottom hits were included in their RC-C study. Second, in the RC study, repeated qPSP analysis on various FDR-adjusted dataset has little impact on the classification tree results. The latter seems more likely a consequence of the pre-filtering step to create similarly truncated datasets  regardless of the initial FDR adjustments on the data.”qPSP is based on rank shifts amongst the extreme ranked proteins away from a defined alpha cutoff. As all samples have the same proteins reported, if we don’t use cutoffs, then there is no difference in the hit-rates. So the alpha cut-off is necessary.We’ve shown that using the top 20 % of proteins seems to be a pretty good compromise. If alpha is set to too high e.g. top 1 %, then we have very few proteins to observe). If alpha is set too low, then we lose discriminatory power. Bottom alphas include low abundance proteins for which the rank shifts are more unstable, and less reliable. Their rank shifts are likely more due to chance than to phenotype-specific changes. In earlier work on genomics, we’ve also shown that in methods e.g. PFSNET and FSNET, using the top 20 % with complexes/subnets is an optimal alpha cutoff.The FDR adjusted analyses reflect stability of analysis against low to high FDR levels given the same qPSP analysis procedure . This level was shown to be useful earlier in the paper, so we kept to it. We can definitely change the alpha levels but as we mentioned earlier, lowering the alpha will simply just increase the number of proteins, making sensitivity higher but also making it harder to find discriminatory signal amongst complexes. But this is not the aim of the section, which is to show that the results will hold constant in spite of changing the proteomics FDR levels.3/Evaluation of the qPSP methodologya) “ The evaluation of qPSP’s robustness reveals another issue. If the pre-filtering step alone in qPSP is sufficient to build similar clustering plots and hierarchical trees as shown throughout the article, then the inclusion of protein complex information and t-statistics adds little impact to the overall qPSP methodology.Specifically to the RC dataset analysis, although the authors were able to show clear discrimination between the phenotypes in the HCL trees  is shown to be highly unstable and unreliable while the hypergeometric enrichment approach in itself also suffers from reproducibility issue. qPSP’s contextualization of protein expressions into complex hit-rates preserves the stability of feature-selection and reproducibility better than these two other commonly used approaches.In Fig. in silico and downstream biological validation is beyond the scope of this paper.But we are limited by our biological knowledge (none of us are cancer biologists), and so we provide the list of differential complexes for real cancer experts to examine Table . But prob) Lastly, since qPSP is extended from PSP, the latter should be included in the performance comparison to demonstrate an improved performance”.PSP cannot work on data which has a complete matrix. This was one of the main reasons why we adapted qPSP using the fuzzified rank approach.4/Terminologiesa) “False-discovery rate is a more common terminology than false-detection rate”.We have made the changes.b) “It should be made clear that noise is referring to FP  as a component of FDR (=FP/TP + FP)”.We have made the changes.c) “It would be more readable to write the hit rate/complex computations as a formulation of vectors and matrix”.We feel that this would have the effect of isolating non-computer science/non-math readers instead.d) “Variable n has been used to twice. One instance is to describe a vector of complexes of length n, another use is as the size of samples n of phenotype B. The naming of the two variables should be distinct“.We have made the changes.This work is an extension of network contextualization methods for proteomic signature profiling by restricting the analysis to the top 20 % most abundant proteins and their complexes. Through reanalysis of 2 different cancer sets, the authors propose that the number of significantly detected complexes is higher than with classical hypergeometric network enrichment tests without increasing false positives and that the complex detections are robust to noise and small sample size.The comparison does not seem very balanced given that all proteins (including low abundance ones that would increase noise) are considered for the hypergeometric tests (see major suggestion 1).The network contextualization approach intuitively makes sense as by requiring multiple proteins from a protein complex to be identified before the complex and its proteins are considered present should give a more conservative picture of true protein identifications. This manuscript is dedicated to the addition of the criterion to only consider the top 20 % most abundant proteins (top 10 % weighted highest and staggered weights for the top 10-20 %) through which the conservatism is enhanced even further with some measurable advantages as mentioned above but the question is how useful is such a conservative view biologically when only restricting to most abundant protein complexes?The first thing that comes to mind is that this approach will find and possibly be restricted to ribosomes and there-like which provides a rather limited chance of getting novel mechanistic insights. Unfortunately, many or most diseases’ causative mechanisms are expected to be more subtle and come from a variety of genes/proteins and pathways. Although the disease state could still correlate with ribosome abundance levels etc. it would likely be an indirect effect. This means that the ribosome levels may be used as biomarkers of disease states but much less so to provide a deeper understanding of disease mechanisms which could be targeted for drug interventions.Indeed, 2 of 3 examples where they discuss biology of hits include ribosome complexes. However, the list of significant complexes in Additional file Major suggestion 1) Make a clean comparison of the different approaches to allow judging contributions of abundance criterion vs network contextualization. E.g. PSP vs. qPSP , qPSP vs hyp_geo_top20%abundant , qPSP vs hyp_geo_all . Parts of it are included already but should be presented more clearly.Major suggestion 2) Show tables of at least the top 10 identified complexes unique to phenotype groups studied here in the main manuscript and longer lists in supplementary. Instead of complex identifier numbers use names (and list major protein constituents and basic known function). Also list, in a separate table, complexes uniquely identified by the qPSP approach compared to other methods tested here and discuss briefly any possibly interesting biological insights.5/Major suggestion 1) Make a clean comparison of the different approaches to allow judging contributions of abundance criterion vs network contextualization. E.g. PSP vs. qPSP , qPSP vs hyp_geo_top20%abundant , qPSP vs hyp_geo_all . Parts of it are included already but should be presented more clearly.PSP cannot be used here. It was originally meant for data with highly inconsistent protein identifications between samples and doesn’t work on data with a complete matrix.For most papers, the default selection criteria for the hypergeometric test is to simply test proteins pre-selected by the t-test regardless of abundance. The suggestion to use hypergeometric test with the abundance criterion is an interesting one but not a setting that is typically used in application.To satisfy the reviewer’s request, we have included this analysis under Additional file 6/Major suggestion 2) Show tables of at least the top 10 identified complexes unique to phenotype groups studied here in the main manuscript and longer lists in supplementary. Instead of complex identifier numbers use names (and list major protein constituents and basic known function). Also list, in a separate table, complexes uniquely identified by the qPSP approach compared to other methods tested here and discuss briefly any possibly interesting biological insights.Thanks for the suggestion. There aren’t many interesting complexes so we just put them in Table As for interesting features found by the other methods --- we found that they are hardly stable anyway. A comparative functional analysis of qPSP against the features these other methods selected seems to have very limited utility.


Furthermore, there are over 300 additional ‘prion-like’ yeast proteins that have similar amino-acid composition to prions (primarily a bias for asparagines and glutamines). Here, we examine the protein functional and interaction networks that involve prion, prionogenic and prion-like proteins. Set against a marked overall preference for N/Q-rich prion-like proteins not to interact with each other, we observe a significant tendency of prion/prionogenic proteins to interact with other, N/Q-rich prion-like proteins. This tendency is mostly due to a small number of networks involving the proteins NUP100p, LSM4p and PUB1p. In general, different data analyses of functional and interaction networks converge to indicate a strong linkage of prionogenic and prion-like proteins, to stress-granule assembly and related biological processes. These results further elucidate how prions may impact gene regulation, and reveal a broader horizon for the functional relevance of N/Q-rich prion-like domains.Prions are transmissible, propagating alternative states of proteins. Prions in budding yeast propagate heritable phenotypes and can function in large-scale gene regulation, or in some cases occur as diseases of yeast. Other ‘prionogenic’ proteins are likely prions that have been determined experimentally to form amyloid  Yeast prions are propagating altered states of proteins that can be transmitted sustainably into yeasts cell during budding, mating or laboratory infection protocols. The first well-characterized yeast prions, that underlie the [PSI+] and [URE3] prion states, are propagating amyloid forms of the proteins Sup35p and Ure2p. [PSI+] arises from the propagation of an amyloid form of Sup35p, part of the translation termination complex. Thus, formation of [PSI+] prions reduces the efficiency of translation termination and increases levels of nonsense-codon readthrough i.e., domains with a pronounced bias for N and/or Q Hemiascomycota, with different parts of it demonstrating purifying selection and frequent N to Q bias switching between clades A common compositional feature of almost all of the well-characterized yeast prions is a tract with a bias for asparagine (N) or glutamine (Q) residues. Bioinformatic surveys of the yeast proteome have demonstrated the existence of hundreds of proteins with ‘prion-like’ domains, in vitro and in vivo amyloid formation were combined with a Sup35 prion assay, wherein predicted prion-forming domains were fused to the C-terminal part of the Sup35p protein (the protein that underlies the [PSI+] prion), and these constructs tested for the ability to produce [PSI+]-like states in yeast cells i.e., proteins that failed to make amyloid and a [PSI+]-like state in the prion assay) from this survey have been recently used to train an algorithm, PrionScan, that predicts prion domains bioinformatically The compositional pecularities of known prion domains were used to train a hidden Markov model to derive a protein target list for testing for prion-forming ability Saccharomyces cerevisiae, focusing on their functional and interaction networks. We observe strikingly significant enrichments of prion-like proteins, in the interactor lists of prion/prionogenic proteins. After dissecting this data in detail, we find two specific gene-regulatory networks of proteins that are significantly enriched in prion, prionogenic and prion-like proteins, and discuss their implications for prion biology. Overall, several data analyses converge to indicate a complex networked role for prion/prionogenic and prion-like proteins in stress granule assembly and related processes.Here, we perform a bioinformatic analysis of sets of prion, prionogenic and prion-like proteins of et al. et al. Data sets of prion, prionogenic and prion-like sequences were derived from the PrionHome database Known Prions (denoted KPs);Experimental Prionogenic Domains (EPDs);Experimental Prion Negatives (EPNs);Bioinformatic N/Q-rich Prion-like Proteins (NQPs).A schematic representation of the relationships between these four data sets is shown .Known Prions (KP) list comprises proteins that have been relatively well-characterized as amyloid-based prions. These are (UniProt IDs and standard names): P05453, Sup35p; P07884, Mod5p; P09547, Swi1p; P14922, Cyc8p; P23202, Ure2p; P25367, Rnq1p; P32432, Sfp1p; Q08972, New1p; P54785, Mot3p; Q02629, NUP100p).Specifically, the Only one of these KP proteins is non-N/Q-rich (Mod5p). Also, one of the proteins that we include as a KP could be placed in the EPD set (New1p), since only constructs containing a large prion-determinant New1p subsequence have been shown to propagate prions; however, we included it in the KP set since, like some other KP cases, it has been shown to propagate through the cytoduction technique Experimental Prionogenic Domains (denoted EPDs) comprises the Known Prions plus a set of yeast sequences from the analysis of Alberti, et al. in vivo amyloid formation by the full-length proteins from the other assays). In the SUP35C prion assay, predicted prion-forming domains are fused to the C-terminal part of the Sup35p protein (the protein that underlies the [PSI+] prion), and these constructs are tested for the ability to produce [PSI+]-like states in yeast cells. The candidate prion-forming domains were predicted to be prionogenic using a Hidden Markov Model algorithm trained on known prion domain cases The set of Experimental Prion Negatives (EPNs), which are proteins that failed to provide positive results for all of the assays for prion activity in the analysis of Alberti, et alFrom the same experiments, we made a list of et al. et al. et al. -10). Binomial probability thresholds were derived from analyzing KP determinant domains. These prion-like domains are biased purely for N and/or Q residues, or for N and/or Q residues with a subsidiary compositional bias for Y, S or G , and do not have contributing biases from charged residues  or major hydrophobic residues , with P-value <1×10−4.The data set of Bioinformatic N/Q-rich Prion-like Proteins (NQPs), are a set of 354 proteins that have N/Q-rich prion-like domains in them, either predicted using the compositional bias criteria in Harrison, The proteins predicted to be candidate prions by the Alberti et al. HMM et al. SUP35C prion assay, when tested The NQPs are a list of proteins into which we can probe deeper to analyze bioinformatically the functional characteristics of such proteins and their protein interactions. We have noted that five of the ten relatively well-characterized prions of the KP set do not  did not produce positive results for the Alberti, K is the total number of occurrences of a GO term in the background population (the whole yeast proteome), N is total number of yeast proteins, k is the number of occurrences of a GO term in a sample data set , and n is the size of this data set.We examined the Gene Ontology (GO) membership from the EPD, EPN and NQP sets, we ranked significantly enriched categories, according to how many yeast proteins annotated with the GO category appear in the protein sets examined. These enrichments were then collapsed to the most specific GO category, if multiple enrichments were due to the same list of proteins, and if the most specific GO category yielded a lower P-value . This procedure helps us to find GO biological process categories which have a relatively small number of annotations in budding yeast, but which may be especially relevant to prion biology, or to the function of N/Q-rich domains.So that we can home in on GO biological process categories that have high  P-value . We looki.e., if protein A interacts with both protein B and protein C, and B and C are both assigned to GO term X, then we consider it as one interaction between protein A and GO term X). For this calculation, the terms in the hypergeometric probability equation are:In addition, we examined for significant enrichments of GO biological process terms in the protein interactions of the KP, EPD, EPN and NQP sample data sets . The intN =  number of interactions between any protein and GO term for the whole yeast proteome;K =  number of interactions between any protein and a specific GO term for the whole yeast proteome;n =  total number of interactions between any protein and GO term for the sample data set;k =  number of interactions between any protein and a specific GO term for the sample data set.As above, we applied a Holm-Bonferroni correction for multiple hypotheses.−10 were considered in this analysis, since known yeast prion proteins (with the exception of the non-N/Q-rich Mod5) contain regions of bias for glutamine and asparagine of at most this P-value.We used the DISOPRED2 program Binary protein interaction data was downloaded from the IntAct database N =  total number of protein interactions for the yeast proteome;K =  total number of interactions involving an enrichment/depletion set protein;n =  total number of interactions for the sample set of yeast proteins;k =  total number of interactions for the sample set of yeast proteins, involving an enrichment/depletion set protein.We performed statistical tests using hypergeometric probability and an initial P-value threshold of 0.05, corrected for multiple hypothesis testing using the Holm-Bonferroni method, over all the tests performed .We have defined sets of known prions (KP), experimental prionogenic proteins proteins that are either KPs or that contain a domain that has been shown experimentally to make a likely prion (EPDs), experimental prion negatives (EPNs) that have been shown experimentally to be unlikely to form prions, and bioinformatically derived N/Q-rich prion-like proteins (NQPs) . All of Our main objective is to thoroughly analyze the protein interaction networks of these sets of prion, prionogenic and prion-like proteins. Do these proteins tend to interact preferentially with each other, or with other groups of proteins? If this is the case, can we extract protein networks and protein network features that may be important to prion biology? Before that, however, we overview the functional associations of the data sets, and briefly examine the protein disorder content of these protein sets, since this is information that is important for interpreting the protein interaction data.To assess which biological processes the KP, EPD, EPN and NQP data sets function in, we examined for enrichments of Gene Ontology biological process terms. The EPDs and EPNs do not have any biological process enrichments that are significant after multiple hypothesis correction  transcriptional regulation, with specific enrichments in response to stress . Most ininteractions of the prion, prionogenic and N/Q-rich prion-like proteins (NQPs) (−11). The general processes of ‘regulation of mRNA stability’ and ‘negative regulation of translation’ (which are associated with stress granule assembly) also provide significant interaction enrichments.In addition, we examined for biological process enrichments in the s (NQPs) . The NQPIntrinsic disorder occurs where a protein or protein domain remains natively unfolded, for at least part of its functional biological role. Intrinsic disorder is an important factor in the interaction behaviour of a protein. Certain types of intrinsically disordered proteins tend to interact with each other, and in some biological processes they are important interaction hubs We investigated whether there is any significant evidence for preferential interaction of prion (KP), prionogenic (EPD), prion negative (EPN) and N/Q-rich prion-like (NQP) proteins in their lists of protein interactors. The networks of protein interactors for the KP and EPD data sets comprise many linkages between KP, EPD and NQP data set members . These are:−6);U6 snRNA-associated Sm-like protein LSM4 (P40070), with 14/31 interactors being NQPs ;nuclear and cytoplasmic polyadenylated-RNA-binding protein PUB1 (P32588), with 13/20 interactors being NQPs .nucleoporin NUP100 (Q02629) with 10/28 interactors being NQPs  are for these hubs, we separated the EPD data into hub and non-hub interactions to check whether the EPD←→NQP enrichment is due mostly to these hubs. This seems to be the case .Because of the significant overall depletion of NQP←→NQP interactions in the yeast interactome, we examined enrichments in the KP, EPD and EPN sets involving the NQP proteins, using the NQP interactome as a background population, instead of the whole yeast proteome . With thet al., prion domain predictions et al. et al., list contains a small number of non-NQP prion domain predictions. We examined using both the whole proteome, and the NQPs as a background population. The results indicate that the NQP enrichments observed, particularly for EPDs, are robust to the criteria for NQP data set definition. Particularly notable is that the EPD non-hubs have significant enrichments of NQP interactors for the Harrison, et al. and ‘intersection’ data sets  may be causing the EPD←→NQP interaction preference . These pSp1 are involved in interaction with other nuclear proteins and in self-interaction Zranb2 in zebrafish, interacts via a Q-rich domain with Smad1, a protein important in the regulation of bone morphogenetic proteins Gal11, a component of the Mediator complex required for transcriptional activation of many genes, is necessary for interaction with glucocorticoid receptor tau 1 These data indicate that some yeast prion/prionogenic proteins are part of a definable interaction sub-network, involving other proteins that have N/Q-rich prion-like domains. These preferred interactions are not due to either binding to other very highly disordered proteins, or to the presence of protein-binding domains in the sequences. The interactions with NQPs may be mediated, at least in part, by the prion/prion-like N/Q-rich domains.  There are many examples of N/Q-rich domains mediating protein interaction in the literature. For example, Q-rich domains in the transcription factor et al. The three major KP/EPD hubs are NUP100p, PUB1p and LSM4p . NUP100 LSM4 is an experimentally-determined prionogenic protein The nuclear and cytoplasmic polyadenylated RNA-binding protein PUB1 was determined to be prionogenic experimentally Do any biological processes arise multiple times in the bioinformatic analyses above? We performed enrichment analysis to find biological process GO categories that have high membership of NQPs , that arStress granules are aggregations of protein and RNA that form inside cells when they are in stressful environments. Formation of stress granules is likely a protective mechanism for RNAs and their associated proteins. In mammalian cell lines, TIA-1 promotes stress granule assembly through prion-like aggregation in response to stress e.g., random interactions like seed precursors for amyloids, or some other sort of interaction ‘noise’), the specific network structures discovered indicate very non-random network topologies, particularly considering the general significant depletion in NQP←→NQP interactions. Alternatively, rather than being a part of direct protein interaction, the N/Q-rich domains may serve some unknown general function in the GO categories to which the three hubs are linked, e.g., a mechanistic role in channel operation (in the case of NUP100), or in the positioning of proteins relative to other protein complexes (in the case of LSM4 and PUB1).Generally, the prion/prionogenic protein interactions with NQPs observed in these analyses may be mediated (at least in part) by the N/Q-rich prion/prion-like domains. Although some of the binary interactions detected may be non-functional Click here for additional data file.Table S2Checking the enrichments of various other protein sets containing NQPs.(DOC)Click here for additional data file.Text S1Lists of NQP proteins and other data sets analyzed.(DOC)Click here for additional data file.Text S2Enrichments in Gene Ontology (GO) biological process categories for the EPD, EPN and NQP data sets. The format of each line is as follows : $1 =  data set . $2 =  GO category. $3 =  description of the GO category in words. $4 =  hypergeometric probability of the enrichment. For the NQP set, those GO biological process categories that are significantly enriched after a Holm-Bonferroni correction for multiple hypotheses, are listed. For the other data sets, none of the categories are significantly enriched after the Holm-Bonferroni correction. In these cases, any with P-value < = 1E−4 are listed.(DOC)Click here for additional data file.Text S3Gene Ontology biological process categories that have high membership, for the EPD and NQP data sets. The format of each line is as follows : $1 =  Number of yeast proteins in the GO category. $2 =  Number of proteins from the set (EPD or NQP) in the GO category. $3 =  the fraction of the yeast proteins in the GO category that are in the set (EPD or NQP). $4 =  GO biological process category. $5 =  description of the GO category in words. $6 =  hypergeometric probability of the enrichment. $7 =  YES or NO for whether the enrichment passes the Holm-Bonferroni correction for multiple hypotheses. All enrichments with raw hypergeometric P-values < = 1E−4 are listed, if also most (> = 0.5) of the yeast proteins in thatGO biological process are included in the data sets. For the NQP set, those that are are significant after a Holm-Bonferroni correction are listed first.(DOC)Click here for additional data file.Text S4Enrichments in Gene Ontology (GO) biological process categories for the protein interactions of the EPD, EPN and NQP data sets. The format of each line is as follows : $1 =  Number of yeast proteins in the GO category. $2 =  Number of proteins in the GO category that are interacted with by the set. $3 =  GO category. $4 =  description of the GO category in words. $5 =  hypergeometric probability of the enrichment. $6 =  YES or NO for whether the enrichment passes the Holm Bonferroni correction for multiple hypotheses. For the NQP set, those GO biological process categories that are significantly enriched after a Holm-Bonferroni correction for multiple hypotheses, are listed. For the other data sets, none of the categories are significantly enriched after the Holm-Bonferroni correction. In these cases, any with P-value < = 1E−4 are listed.(DOC)Click here for additional data file.Text S5Enriched Gene Ontology (GO) biological process categories for the NQP interactors of the three hubs LSM4, PUB1 and NUP100. The format of each line is as follows : $1 =  Number of yeast proteins in the GO category. $2 =  Number of proteins in the GO category that are interactors of the specific KP/EPD protein examined. $3 =  GO category. $4 =  description of the GO category in words. $5 =  hypergeometric probability of the enrichment. The enrichments that are significant after multiple-hypothesis correction with the Holm-Bonferroni method are listed.(DOC)Click here for additional data file.


In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer from  Automated annotation of protein functions is challenging in the postgenomic era. With the rapid growth of the number of sequenced genomes, the overwhelming majority of protein products can only be annotated by computational approaches . Nature During the past several years, many Multilabel Multi-Instance learning algorithms have been developed , 10–12. Although the underlying nature of predicting protein functions with incomplete annotation matches well with the Multi-Instance Multilabel with weak-label learning framework, till now there is no attempt that has been made under this learning framework. Jiang had proposed a multilabel semisupervised learning algorithm, PfunBG, to predict protein functions, employing a birelational graph (BG) of proteins and function annotations . Yu et a Geobacter sulfurreducens, Shewanella loihica PV-4) which have been widely used in the MFCs researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting functions of proteins in the electricigens genomes with incomplete annotation. In addition, it is worth mentioning that our approach is a general method for predicting protein functions with incomplete annotation.Microbial fuel cells (MFCs) are devices that can use bacterial metabolism to produce an electrical current from a wide range of organic substrates . Due to Nature often assembles multiple domains together to form multidomain and multifunctional proteins with high possibility, and each domain may implement its own function independently or in a cooperated manner with its neighbors. We can regard each domain as an input instance and take each biological function as an output label. Labels of the training examples are known; however, labels of instances are unknown. In our previous work, we disclose that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks . PreviouWe study the Multi-Instance Multilabel weak-label learning framework for protein function prediction with incomplete annotation for two tasks as illustrated in Xi, Yi  } the training dataset with m examples. Xi is the ith protein in the training dataset, and Xi is a bag with ni instances {xi,1, xi,2,…, xi,ni}. Yi denotes the Gene Ontology terms which are assigned to Xi, and Yi =  ∈ {0,1}L is a label vector with L labels, where yi,l = +1 if the lth label is positive for Xi, and 0 otherwise. Note that the labels of instances xi,j's    are untagged. In the MIML weak-label setting, Y is unknown and instead we are just given a partial label matrix Xi, a label vector  lth label is assigned for Xi, and 0 otherwise. Different from the full label matrix, Formally, we represent by {een bags . Geobacter sulfurreducens, a bacterial strain capable of high current production  ∈ {0,1}L is a label vector with L labels, where yi,l = +1 if the lth label is positive for Xi, and 0 otherwise. Notice that the labels of instances xi,j's  are unknown. In the MIML weak-label setting, however, only a subset of labels are tagged. Specifically, for Xi, a label vector lth label is assigned for Xi, and 0 otherwise. The goal is to predict all the positive labels for unseen bags T and wlT is the transpose of wl. To make use of label relationship, a label relation matrix R ∈ L×L is considered, where W =  and an output matrix V is a loss function for each label, |·|1 represents the l1-norm, ϵ controls the sparsity of η trades off the empirical risk and model complexity.For simplicity,  Hamming loss (HL), macro-F1 (maF1), and micro-F1 (miF1) [ Hamming loss assesses how many times on average a bag label pair is wrongly predicted. The smaller the value of hamming loss, the better the performance. Macro-F1 computes F1 measure on each class label at first and then averages over all class labels. Macro-F1 is more influenced by the performance of the classes owning fewer examples. The larger the value of macro-F1, the better the performance. Micro-F1 globally calculates the F1 measure on the predictors over all bags and all class labels. Micro-F1 is more affected by the performance of the classes involving more examples. The larger the value of micro-F1, the better the performance. The definition of these criteria can be found in [In this paper, we adopt three popular multilabel learning evaluation criteria, that is,1 (miF1) –30. Hamm Geobacter sulfurreducens and Shewanella loihica PV-4 datasets. For each evaluation criterion, ↑(↓) indicates the larger , the better the performance; the best results on each evaluation criterion are highlighted in boldface. As indicated in In our experiments we consider four weak-label ratios (W.L.R.) , definedμ and the fraction parameter  α.  HL, maF1, and miF1. Here,  μ varies from 0.2 to 1.0 with an interval of 0.2 when  α is fixed to 0.1, and  α increases from 0.02 to 0.1 with an interval of 0.02 with the fixed  μ equal to 1.0. It is indicated that the performance of the MIMLwel algorithms achieves the perk in most cases by setting the scaling factor  μ to 1.0 and the fraction parameter  α to 0.1. In this paper, the MIMLwel algorithm is implemented by setting the scaling factor  μ to 1.0 and the fraction parameter  α to 0.1.The MIMLwel approach  involves Geobacter sulfurreducens dataset  learning tasks. Automated protein function prediction was typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has additional functions is unknown. It is evident that the protein function prediction tasks suffer from weak-label problems, and we disclose that prediction of protein functions with incomplete annotation matches well with the MIML with weak-label learning framework in this paper. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein function in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results show that MIMLwel is superior to most state-of-the-art MIML algorithms, which validates the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.


Bacterial microcompartments (MCPs) are protein-bound organelles that carry out diverse metabolic pathways in a wide range of bacteria. These supramolecular assemblies consist of a thin outer protein shell, reminiscent of a viral capsid, which encapsulates sequentially acting enzymes. The most complex MCP elucidated so far is the propanediol utilizing (Pdu) microcompartment. It contains the reactions for degrading 1,2-propanediol. While several experimental studies on the Pdu system have provided hints about its organization, a clear picture of how all the individual components interact has not emerged yet. Here we use co-evolution-based methods, involving pairwise comparisons of protein phylogenetic trees, to predict the protein-protein interaction (PPI) network governing the assembly of the Pdu MCP. We propose a model of the Pdu interactome, from which selected PPIs are further inspected via computational docking simulations. We find that shell protein PduA is able to serve as a “universal hub” for targeting an array of enzymes presenting special N-terminal extensions, namely PduC, D, E, L and P. The varied N-terminal peptides are predicted to bind in the same cleft on the presumptive luminal face of the PduA hexamer. We also propose that PduV, a protein of unknown function with remote homology to the Ras-like GTPase superfamily, is likely to localize outside the MCP, interacting with the protruding β-barrel of the hexameric PduU shell protein. Preliminary experiments involving a bacterial two-hybrid assay are presented that corroborate the existence of a PduU-PduV interaction. This first systematic computational study aimed at characterizing the interactome of a bacterial microcompartment provides fresh insight into the organization of the Pdu MCP. Many bacteria produce giant proteinaceous structures within their cells, which they use to carry out special metabolic reactions in their interior. Much has been learned recently about the individual components—shell proteins and encapsulated enzymes—that assemble together, thousands of subunits in all, to make these bacterial microcompartments or MCPs. However, in order to carry out their biological functions, these systems must be highly organized through specific protein-protein interactions, and such a higher level understanding of organization in MCP systems is lacking. In this study, we use genomic data and phylogenetic analysis to predict the network of interactions between the approximately 20 different kinds of proteins and enzymes present in the Pdu MCP. Then, we use computational docking to examine a subset of those that are predicted to involve enzymes bound to the interior surface of the shell proteins, and show that the results are consistent with recent experimental data. We further provide new experimental evidence for one of the predicted protein-protein interactions. This study expands our understanding of a complex system of proteins serving as a metabolic organelle in bacterial cells, and provides a foundation for further experimental investigations. In the ed above . DespiteSince it was demonstrated that the targeting of PduP is mediated mostly by its terminal peptide segment , we souget al. [Results of the peptide docking simulations are overlaid in et al. , with a Various other docking calculations served as computational controls. In contrast to the results obtained for docking to the concave surface of the shell protein, docking of the peptides on the other (flat) side of PduA showed no consistent or compelling modes of binding. Those peptide models are instead scattered over the hexamer surface . Moreoveet al., where the first 42 amino acids from PduV were demonstrated to play a crucial role in PduV targeting to the MCP [As an initial step in modeling a possible interaction between PduU and PduV, which was predicted by the coevolution analysis, a homology model had to be constructed for PduV. The PduV model was then docked into the crystal structure of the PduU hexamer using RosettaDock  see Met. As a co the MCP . As a fiHIS3, an essential gene for histidine biosynthesis [HIS3 product to levels that are sufficient to allow growth on a selective medium lacking histidine and to overcome the effect of 3-amino-1,2,4-triazole (3-AT), a competitive inhibitor of the His3 enzyme. If a large number of colonies are obtained following co-transformation, an interaction between the target and bait proteins is indicated. When PduU and PduV were tested, the number of colonies obtained following co-transformation was comparable to that of a positive control with bait and prey proteins  that are known to strongly interact  part of our work, while the docking calculations reveal a plausible mode of binding between those proteins.Preliminary experimental assays were carried out on the PduU-PduV pair in parallel with our computational analysis. The BacterioMatch II two-hybrid system was used to test for interactions between these two proteins. In this system, a reporter strain is co-transformed with appropriate target and bait fusion genes. A protein-protein interaction between the target and bait activates the transcription of ynthesis , therebyinteract . Resultsinteract . Negativinteract . The posE.coli [a contrario, miss true interactions. Additionally, these methods cannot always distinguish direct  and indirect  correlations, a recurrent problem in coevolution studies that is illustrated here by the integration of PduF in our network. In order to mitigate the deficiencies of the computational methods we employed, a conservative approach was taken by considering only those predicted interactions that had the highest probability (p≥0.7). These cases were largely consistent with existing experimental data, where they were available. An example of a positive result is the agreement between our predictions and structural data relating to the reactivation mechanism of the diol dehydratase [Proteins rarely carry out biological processes on their own. Instead, they typically participate with other proteins in the context of larger interaction networks. This is especially true for MCPs, where encapsulated pathways require coordination and spatial organization of their numerous components, from shell proteins to enzymes. Though structural studies of individual MCP components have paved the way to a better understanding of their assembly mechanism, a full comprehension of such metabolic systems requires investigation of their PPI networks. Unfortunately, experimental data for MCP protein complexes are still sparse, leading us to turn to predictive methods. Here, we used coevolution calculations and a binary classifier to predict pairwise PPIs in the Pdu MCP, and proposed a model of its interactome. Approaches using binary classifiers for coevolution-based PPI predictions have been developed by others. Comparable approaches have been successfully applied to E.coli , and to E.coli . Interprydratase .et al., which demonstrated the necessity of the PduA C-terminal helix in PduP binding and the role of hydrophobic residues in that interaction [+ recycling by PduQ [Extending on our predicted interactome model, we focused further analyses on PPIs emanating from the PduA shell protein node and involving Pdu enzymes . Of theseraction . An exceeraction  and has  by PduQ et al. on the PduA C-terminal helix support that assignment [The results of our docking studies are of particular significance for the issue of sidedness of the MCP shell—i.e. which side of the shell proteins faces inward vs. outward. Previous arguments have suggested that the concave side of the shell protein faces into the MCP lumen ,37,38. Msignment . In our pduK, pduT or pduU do not affect the formation of the MCP, pduA mutations produce disrupted or enlarged shells [PduA and PduJ, two highly similar paralogs of the BMC shell protein, are the two most abundant shell proteins after PduBB’ in the Pdu system. As a consequence, they are suspected to play a critical structural role . Indeed,d shells ,91. Pulld shells . Here weAttributing a special functional role to PduA (or PduJ) is consistent with the view that, though the multiple paralogous shell proteins in the MCP share a canonical BMC structure, each shell protein variant fulfills a specific task. For instance, tandem BMC proteins such as EutL are proposed to regulate the transport of metabolites via conformational changes and a gated pore ,40,92,93In this portrait of the Pdu family, the role of PduU remains to be elucidated. Here, we aimed to bring new clues by investigating the intriguing PduU-PduV case. Indeed, PduV is also poorly characterized compared to other Pdu components. Furthermore, from our predictions, PduV was the only enzyme exclusively interacting with a shell protein. The diverse docking simulations involving PduU and PduV all agreed with the existence of such a PPI, and predicted the N-terminal region of PduV binds directly to the PduU beta-barrel, consistent with recent experimental data on the importance of the N-terminus of PduV . These pTo conclude, the present study brings further insights into the organization of the Pdu MCP, and constitutes the first systematic computational effort to describe an MCP interaction network. The basis of this work is predictive, but we have investigated one of the predicted interactions experimentally as part of this investigation, with a positive result. Further experimental studies will be required to more fully evaluate the interactome model developed here. Application of the same approach to other types of characterized MCPs might be of equal interest and could reveal similar insights.pduABCDEFGHJKLMNOPQSTUVWX (Suppl. Data). Incomplete or erroneous annotations of the Pdu gene products were corrected after sequence comparison with the Pdu operon from Salmonella enterica LT2, the best-characterized strain in terms of Pdu MCP.Protein orthologs were collected from 34 bacterial genomes in the KEGG database  and collFor each ortholog group, its corresponding protein sequences were aligned with MUSCLE . The mulmirrortree approach, while three others reflect topological similarities  between two trees is arising by chance. Along the same idea, topological similarities were computed between tree A and ToL, tree B and ToL, and finally A and B .Topological descriptors are derived from the pos for an interacting protein group pair and neg for those not interacting. Each of the ortholog group pairs sees its input vector of seven coevolution descriptors evaluated by the RF classifier. To classify a pair, its input vector is run through each decision tree of the forest and sees its mean probability attributed. The mean probability threshold for distinguishing the pos from the neg cases was set to 0.5, where a probability ≥ 0.5 will classify the pair as pos.We implemented a Random Forests (RF) classifier  from theThe dataset used for training the RF classifier—the “gold standard”—was derived from experimental data found in the literature on the Pdu MCP. Manual mining of this data led to a total of 40 pairs of Pdu proteins whose physical interactions (or lack of interaction in many cases) could be verified experimentally via binding assays ,100,101,The interactome was pictured as an undirected graph with the igraph library in R . Nodes aab initio the first 18 residues of each enzyme with the PEP-FOLD server [While the structures of PduA and PduU are available in the PDB , structuD server .PduV was not presumed or predicted to bind by way of a terminal extension, so a model of that intact enzyme fold was required for docking analysis. The structure of PduV is presently unknown. Therefore, to enable computational docking, we built a homology model with I-TASSER  by threaab initio FlexPepDock run, where the Vina model is used as an input coordinates file. Vina has been designed for small molecule docking, which allows a ligand flexibility up to 32 rotatable bonds only, a limit not existing in FlexPepDock. However, it can still be used efficiently when medium-sized ligands like peptides are treated as semi-rigid for predicting an approximate binding region. File preparation for AutoDock Vina included a configuration file specifying an exhaustiveness of 10 and a 27000 Å3 grid box encompassing the surface of the hexamer and centered on the pore. Coordinate files in PDBQT format were generated from the PduA crystal structure and the PEP-FOLD models of each peptide. For the peptides, rotatable bonds were defined for the side chains while Kollman United Atom charges were assigned to both the hexamer and the peptides. The pose computed by Vina with the lowest energy score was subsequently considered as the starting point for FlexPepDock. In this second stage, we ran 10000 simulations where the peptide was completely refolded and docked into the PduA hexamer. After ranking the 10 000 poses by lowest Rosetta energy, the top 500 poses were collapsed into clusters for which the internal RMSD was less than 2.5 Å. Finally, we picked the definitive model as the one with the lowest energy among the two most populated clusters.For protein-peptide docking, our approach relied mainly on the Rosetta-based protocol FlexPepDock . Its abiFor the PduU-PduV case, we used a standard RosettaDock protocol where the input included coordinates of both partners in their unbound state, typically those from the PduU hexamer and and the PduV homology model. The number of simulations, the ranking, clustering and selecting methods were identical to the FlexPepDock procedure, while the allowed flexibility in this case is limited to the side chains.pduU and pduV DNA sequences were amplified by PCR and then restricted and ligated into pBT for expression as fusions with the λcI protein, and into pTRG for expression as fusions with the RNAPα protein. Ligation reactions were used to transform E. coli XL1-Blue MRF’. Plasmid DNA was purified using a Qiagen mini prep kit, and all clones were verified by DNA sequencing. Self-activation by each recombinant bait and prey was tested before the two-hybrid interaction assays to determine if the bait or prey was capable of activating the reporter cassette on its own. Determination of protein-protein interaction was carried out by co-transforming BacterioMatch II validation reporter competent cells using recombinant bait and target.To test for interactions between PduU and PduV, the BacterioMatch II two-hybrid system (Agilent technologies) was used according to the manufacturer’s instructions with the following modification: co-transformation was carried out by using 30 ng each of the bait and prey vector. To construct the needed plasmids, S1 FigThe quality of the RF classifier was assessed for three different combinations of coevolution descriptors: One combines the descriptors based on direct relationships between two proteins (A and B in (TIF)Click here for additional data file.S2 FigAUC values were calculated after running the RF classifier with different incremental combinations of the descriptors, starting from the most accurate and adding the next best one at a time. Here again the classifier yields the best performance when combining all the descriptors.(TIF)Click here for additional data file.S1 DatasetProtein-Protein interactions predicted by the RF classifier along with their respective mean probabilities (PPI in bold had P >0.7 and were used for the construction of the Pdu interaction network pictured in (DOCX)Click here for additional data file.S2 Dataset(DOCX)Click here for additional data file.


Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate.The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from  Protein interactions are central for many cellular processes. Physical characterization of these interactions is essential for understanding of life processes and applications in biology and medicine. Because of the inherent limitations of experimental techniques and rapid development of computational power and methodology, computer modeling is a tool of choice in many studies. Publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for modeling of proteins and protein complexes. A major paradigm shift in modeling of protein complexes is emerging due to the rapidly expanding amount of such information, which can be used as modeling constraints. Text mining has been widely used in recreating networks of protein interactions, as well as in detecting small molecule binding sites on proteins. Combining and expanding these two well-developed areas of research, we applied the text mining to physical modeling of protein complexes (protein docking). Our procedure retrieves published abstracts on a protein-protein interaction and extracts the relevant information. The results show that correct information on binding can be obtained for about half of protein complexes. The extracted constraints were incorporated in a modeling procedure, significantly improving its performance. This is a PLOS Computational Biology Methods paper.The rapidly growing amount of publicly available information from biomedical research is a modern day phenomena that is likely to continue and accelerate in the future. Most of this information is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data revolutionized structure prediction of proteins in the 80’s  and, recIn our opinion, the next stage of this revolution is brewing due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction . In thisProtein-protein interactions (PPI) are central for many cellular processes. Structural characterization of PPI is essential for fundamental understanding of life processes and applications in biology and medicine. Because of the inherent limitations of experimental techniques and rapid development of computational power and methodology, protein docking is a tool of choice in many studies. One of the main problems in protein docking  is identGiven the inherent uncertainties of the global-search docking predictions, such independent information on the binding modes is extremely valuable . Such inhttp://www.ncbi.nlm.nih.gov/pubmed), and are a growing resource for automated mining of the PP binding mode. Many applications  utilizing TM techniques have been developed to improve access to the published knowledge  are larger than the corresponding bars for the AND-queries). For example, for SH2D1A-p59Fyn complex (1m27) AND-query did not retrieve any abstracts, whereas OR-query retrieved 6 abstracts, from which 3 interface and 1 non-interface residues were extracted , which resulted in 1,619 identified residues in 2,028 abstracts for 142 complexes, for AND- queries; and 6,735 identified residues in 20,040 abstracts for 342 complexes, for OR- queries. All identified residues were correct for 25 and 31 complexes, and no interface residues were identified for 24 and 59 complexes for AND- and OR-queries, respectively. Analysis of these results  and filters out considerable number of the abstracts . Results for the AFxx sets and other kernels do not show significant change in the performance of SVM with the margin as well  and RBF γ = 1 kernels without the margin. Hereafter, we will abbreviate SVM models as AB, where A stands for the feature set . Then, the performance of our SVM models can be quantified by a single measure, Matthew correlation coefficient, MCC  and the xx sets with xx < 15 , for which residues were found in the abstracts retrieved by the OR-queries, using three optimal SVM models , trained and validated on the 1,305 abstracts retrieved by the AND-queries (see above). In this approach, an abstract is classified either as positive (publication in the PPI context) or negative (not PPI-relevant context) and then only positive abstracts are retained for the culation .PTM decreases) constitute only ~ 1/3 of the initial set . Thus, a significantly larger number of residues per complex were identified  and the i-RMSD ≤ 5 Å in top 10 predictions) and relaxed  success criteria.To single out the role of the TM constraints, they were applied to re-rank unrefined and otherwise unscored docking models output directly from the GRAMM scan . For comparison, the re-ranking was also done separately with the correct interface residues as constraints see . We usedThe TM scoring significantly increased docking success rates, by 71%  on protein structures. Combining and expanding these two well-developed research areas, we applied TM to structural modeling of protein-protein complexes (protein docking). Abstracts of publications on 579 protein complexes from Dockground were retrieved from PubMed, using AND- and OR-queries . The AND-queries identified more correct residues than the OR-queries, but retrieved abstracts with residues for significantly less complexes. SVM was used to improve the performance of OR-queries. The SVM models generated using simple bag-of-words representation of the text, removed irrelevant information extracted by the OR-queries, albeit not enough for an accurate discrimination of non-interface from the interface residues, as shown by the inconsistent performance of different SVM models. Whereas human expertise can consistently distinguish relevant from non-relevant to the interface information , a reliable and accurate automated procedure requires greater sophistication than the basic one used in our study.The basic TM was used to generate constraints for docking, and tested on the protein-protein unbound docking benchmark set. TM significantly increased the docking success rates. Contextual analysis by deep parsing on sentence/residue level (an on-going study in our group) should improve the detection of the interface residues, and further increase the docking success rates. The preliminary results in this proof-of-concept study showed that TM is a promising approach to protein docking, with its utility increasing along with the rapidly growing amount of publicly available information on protein complexes.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.S12 Fig(PDF)Click here for additional data file.S13 Fig(PDF)Click here for additional data file.S14 Fig(PDF)Click here for additional data file.