M. tuberculosis is a formidable bacterial pathogen. There is thus an increasing demand on understanding the function and relationship of proteins in various strains of M. tuberculosis. Protein-protein interactions (PPIs) data are crucial for this kind of knowledge. However, the quality of the main available M. tuberculosis PPI datasets is unclear. This hampers the effectiveness of research works that rely on these PPI datasets. Here, we analyze the two main available M. tuberculosis H37Rv PPI datasets. The first dataset is the high-throughput B2H PPI dataset from Wang et al’s recent paper in Journal of Proteome Research. The second dataset is from STRING database, version 8.3, comprising entirely of H37Rv PPIs predicted using various methods. We find that these two datasets have a surprisingly low level of agreement. We postulate the following causes for this low level of agreement: (i) the H37Rv B2H PPI dataset is of low quality; (ii) the H37Rv STRING PPI dataset is of low quality; and/or (iii) the H37Rv STRING PPIs are predictions of other forms of functional associations rather than direct physical interactions.M. tuberculosis experimental PPIs are much more similar to the H37Rv STRING functional associations dataset (with score ≥ 770) than the H37Rv B2H PPI dataset. H37Rv predicted physical interologs from IntAct also show extremely low similarity with the H37Rv B2H PPI dataset; and this similarity level is much lower than that between the S. aureus MRSA252 predicted physical interologs from IntAct and S. aureus MRSA252 pull-down PPIs. Comparative analysis with several representative two-hybrid PPI datasets in other species further confirms that the H37Rv B2H PPI dataset is of low quality. Next, to test the possibility that the H37Rv STRING PPIs are not purely direct physical interactions, we compare M. tuberculosis H37Rv protein pairs that catalyze adjacent steps in enzymatic reactions to B2H PPIs and predicted PPIs in STRING, which shows it has much lower similarities with the B2H PPIs than with STRING PPIs. This result strongly suggests that the H37Rv STRING PPIs more likely correspond to indirect relationships between protein pairs than to B2H PPIs. For more precise support, we turn to S. cerevisiae for its comprehensively studied interactome. We compare S. cerevisiae predicted PPIs in STRING to three independent protein relationship datasets which respectively comprise PPIs reported in Y2H assays, protein pairs reported to be in the same protein complexes, and protein pairs that catalyze successive reaction steps in enzymatic reactions. Our analysis reveals that S. cerevisiae predicted STRING PPIs have much higher similarity to the latter two types of protein pairs than to two-hybrid PPIs. As H37Rv STRING PPIs are predicted using similar methods as S. cerevisiae predicted STRING PPIs, this suggests that these H37Rv STRING PPIs are more likely to correspond to the latter two types of protein pairs rather than to two-hybrid PPIs as well.To test the quality of these two datasets, we evaluate them based on correlated gene expression profiles, coherent informative GO term annotations, and conservation in other organisms. We observe a significantly greater portion of PPIs in the H37Rv STRING PPI dataset (with score ≥ 770) having correlated gene expression profiles and coherent informative GO term annotations in both interaction partners than that in the H37Rv B2H PPI dataset. Predicted H37Rv interologs derived from non-M. tuberculosis H37Rv PPI datasets in subsequent studies for a wide range of purposes.The H37Rv B2H PPI dataset has low quality. It should not be used as the gold standard to assess the quality of other (possibly predicted) H37Rv PPI datasets. The H37Rv STRING PPI dataset also has low quality; nevertheless, a subset consisting of STRING PPIs with score ≥770 has satisfactory quality. However, these STRING “PPIs” should be interpreted as functional associations, which include a substantial portion of indirect protein interactions, rather than direct physical interactions. These two factors cause the strikingly low similarity between these two main H37Rv PPI datasets. The results and conclusions from this comparative analysis provide valuable guidance in using these  M. tuberculosis[M. tuberculosis H37Rv is one of first fully sequenced M. tuberculosis strains [M. tuberculosis H37Rv protein-protein interaction (PPI) data has become an important source of protein function and relationship information for relevant studies in microbiology, molecular biology, computational biology and medicine. However, M. tuberculosis H37Rv PPI data is far from complete and accurate. Hitherto predicted M. tuberculosis H37Rv PPIs in the STRING database  [M. tuberculosis will be based on both of these datasets.Each year millions of tuberculosis patients perish, and fully one-third of the world’s population is infected with the causative agent of this disease, erculosis. M. tube strains . With thThere is an extremely low overlap of just 276 protein-protein interactions shared between the 8,042 H37Rv PPIs in the B2H dataset and the 248,574 predicted H37Rv PPIs in STRING. It is the objective of this work to investigate the cause of this unexpectedly low overlap. We hypothesize that this low overlap between the two datasets may be due to (i) the B2H dataset is poor in quality, (ii) the STRING dataset is poor in quality, and/or (iii) the STRING dataset does not correspond to direct physical protein-protein interactions.M. tuberculosis H37Rv PPI datasets, we evaluate them based on correlated gene expression profiles, coherent informative GO term annotations, and conservation in other organisms. Two proteins that interact are expected to be expressed at the same time and space; thus their underlying genes are likely to exhibit correlated expression profiles. Two proteins interact to effect a biological process or molecular function; thus they are expected to be annotated to some GO terms in common or GO terms that are closely related. Many protein-protein interactions are expected to be conserved across several organisms that have common ancestry; thus real protein interactions are likely to coincide with interologs from such organisms.In order to test the quality of these two These assessments indicate that H37Rv B2H PPIs agree less well with correlated gene expression profiles, coherent informative GO term annotations, and conservation in other organisms than H37Rv STRING PPIs (with score ≥ 770) . This suggests that PPIs in the H37Rv B2H dataset may contain a high level of noise .S. aureus is a bacterial pathogen with available high-throughput experimental physical PPI data; and it is close to M. tuberculosis. Thus interologs conserved in S. aureus should be very likely conserved in M. tuberculosis. However, H37Rv predicted physical interologs from IntAct show quite low similarity with the H37Rv B2H PPI dataset; and this similarity level is lower than that between the S. aureus MRSA252 predicted physical interologs from IntAct and S. aureus MRSA252 pull-down PPIs. This suggests the H37Rv B2H dataset may also be missing many real PPIs .M. tuberculosis H37Rv B2H dataset with representative two-hybrid PPI datasets in other species  [M. tuberculosis H37Rv B2H PPI dataset is poorer than other two-hybrid PPI datasets.To further confirm the suspected low quality of the H37Rv B2H PPI dataset, we compare this evisiae) -10. ThesM. tuberculosis, we turn to the model organism S. cerevisiae where more comprehensive information is available. We extract from STRING an unbiased representative S. cerevisiae PPI subset  that are predicted using similar methods as the H37Rv STRING PPI dataset. For the three different types of protein pairs, we use the following gold standard: (i) the S. cerevisiae two-hybrid PPI dataset from Yu et al [S. cerevisiae protein complexes from Wodak Lab [As mentioned earlier, the H37Rv STRING PPI dataset (with score ≥ 770) , comprising entirely of PPIs predicted using a variety of methods, shows good agreement with correlated gene expression profiles, coherent informative GO term annotations, and conservation in other organisms. However, protein pairs that are functionally linked are also expected to agree well with correlated gene expression profiles, coherent informative GO term annotations, and conservation in other organisms, even though many functionally-linked protein pairs do not have direct physical interactions. In order to test whether the predicted PPIs in STRING correspond to direct physical protein-protein interactions, we should analyze the similarity between these predicted PPIs with several distinct types of protein pairs such as experimental PPIs obtained from two-hybrid assays, protein pairs that belong to the same protein complexes, and protein pairs that catalyze adjacent steps in enzymatic pathways. As these types of additional information are not available for Yu et al , (ii) alodak Lab , and  are proteins that are functionally linked.This analysis indicates that the predicted M. tuberculosis H37Rv PPI datasets. The details of our analyses are presented in the sections below.This work thus provides an important guidance to the researchers who might base their works on the two M. tuberculosis and S. cerevisiae.This section can be divided into four parts: (i) Discover the low similarity between the two main H37Rv PPI datasets. (ii) Evaluate the quality of the two H37Rv PPI datasets in the same organism. (iii) Assess the quality of the H37Rv B2H PPI dataset across organisms. (iv) Analyse characteristics of the STRING PPIs in M. tuberculosis H37Rv. It is clear from Figure The H37Rv B2H PPI dataset is used as benchmark and different subsets of H37Rv STRING PPIs are tested against it. We consider all H37Rv STRING PPIs as well as STRING H37Rv PPIs based on specific methods  Precision: 0.00574 Recall: 0.00896.The extremely low agreement between the H37Rv PPIs in the STRING and B2H PPI datasets is rather unexpected. We hypothesize that it may be a result of one or more of the following situations. First, it may be that the H37Rv B2H PPI dataset contains an unusually high level of noise. Second, it may be that the H37Rv STRING PPI dataset and subsets thereof contain an unusually high level of noise. Third, it may be that the predicted PPIs in STRING are not direct physical interactions; rather, they may primarily be other types of functional associations such as protein pairs in the same protein complexes and enzyme pairs catalyzing successive reaction steps.C. jejuni[Synechocystis [S. cerevisiae[The results above reveal the surprisingly low coverage between the two H37Rv PPI datasets. However, as shown in Figure C. jejuni, Synechohocystis  and S. cerevisiae. This suWe next use PPI functional intensity matrix to visualize the functional relationship between the two interacting proteins in PPIs. Figure The functional intensity matrix visualisation in Figure M. tuberculosis H37Rv GO term annotation and informative GO term identification are summarized in Table M. tuberculosis H37Rv B2H PPI dataset has the lowest quality among all the PPI datasets been evaluated. The H37Rv predicted functional association dataset (without thresholding at score ≥ 770) also has a low percentage of PPIs with coherent informative GO terms and is thus of low quality. However, most PPI datasets from STRING (with score ≥ 770) show a much higher percentage of PPIs having coherent informative GO terms than the H37Rv B2H PPI dataset, suggesting that a higher percentage of PPIs in these datasets may have better reliability than those of the B2H PPI dataset and of the STRING PPI dataset as a whole.The results of M. tuberculosis H37Rv. Given that M. tuberculosis H37Rv gene expression data is readily available in public repositories , this lAs PPIs are often conserved by related organisms, homology transfer is an often-used technique to predict PPIs—the so-called interologs. On the one hand, a higher level of agreement that a PPI dataset has with interologs gives us confidence that the PPIs are more reliable—i.e., fewer false positives—as they are conserved in other organisms. On the other hand, a lower level of agreement signals that PPIs conserved in related organisms are potentially missing—i.e., more false negatives—in the PPI dataset.M. tuberculosis H37Rv from other (non-M. tuberculosis) organisms that have experimental PPIs in STRING. We then compare this set of interologs to both the H37Rv STRING predicted functional associations dataset (with score ≥ 770) and H37Rv B2H PPI dataset. We use this subset of STRING PPIs  to avoid possible confounding effects because the full set of H37Rv STRING PPIs contains some PPIs that actually originate from interologs predicted from experimental PPIs.We use different homology transfer methods to build a set of predicted interologs in the target organism In order to find better source PPIs for predicting interologs, a more reliable subset of the experimental PPIs in STRING has been chosen in this experiment. This subset consists of the experimental PPIs in STRING that also have STRING prediction supports.M. tuberculosis H37Rv interologs from other (non-M. tuberculosis) organisms’ experimental PPIs in STRING are in much lower agreement with the H37Rv B2H PPI dataset than with the H37Rv STRING predicted functional associations dataset (with score ≥ 770). In particular, depending on the method used for constructing interologs, just 4-5 interologs are found in the H37Rv B2H PPI dataset, while 524-830 interologs are found in the H37Rv STRING predicted functional associations dataset (with score ≥ 770); see Table Table M. tuberculosis. To control for these potential confounding factors, we conduct another set of experiments using interologs predicted from the following two sources that—in theory—are closer to the characteristics of H37Rv B2H PPIs. The first source is the latest pull-down PPI dataset in S. aureus MRSA252 [Rickettsia sibirica 246, Escherichia coli K12, Campylobacter jejuni, Treponema pallidum, Synechocystis sp. PCC 6803, Mycoplasma pneumonia, Myxococcus xanthus DK 1622, and Streptococcus pneumoniae. We group the physical interactions of these eight bacteria from IntAct [The results discussed above are already persuasive but not precise enough to make sufficient judgment. In particular, the experimental PPIs in STRING are a mixture of both experimental physical interactions and experimental functional associations . This may introduce some bias towards the larger agreement observed with the STRING predicted functional associations dataset. Furthermore, some source experimental PPIs in the dataset are not from organisms close to  MRSA252 . This da MRSA252 . In partm IntAct  into theM. tuberculosis H37Rv  and to S. aureus MRSA252 . We also transfer interologs from the S. aureus MRSA252 high-throughput pull-down PPI dataset to M. tuberculosis H37Rv (named “H37Rv pull-down interologs dataset”). All the homolog identifications of this experiment use the condition PIDE >30, Coverage >0.2, E-VALUE< 1*e-10.We transfer interologs from the “selected IntAct prokaryotic physical interactions dataset” to S. aureus MRSA252 physical interologs dataset and pull-down PPI dataset is also much higher than that between H37Rv physical interologs dataset and H37Rv B2H PPI dataset. These results further strengthen the conclusion that the H37Rv B2H PPI dataset has poor quality.The comparative analyses results are given in Table M. tuberculosis H37Rv B2H PPI dataset is low in quality. However, it is generally believed that two-hybrid PPI datasets do not have high reliability because of their high false positives and false negative rates. It is thus of interest to determine whether this H37Rv B2H PPI dataset is even worse than other two-hybrid PPI datasets in terms of quality. So we compare the M. tuberculosis H37Rv B2H PPI dataset to several representative two-hybrid PPI datasets in other organisms—viz., C. jejuni, Synechocystis and S. cerevisiae[—by calculating their percentage of PPIs having coherent informative GO terms. The results are shown in Figure M. tuberculosis, C. jejuni, Synechocystis and S. cerevisiae are respectively 1.20, 1.28, 3.03, and 5.73. These results sufficiently demonstrate that, compared to other representative two-hybrid PPI datasets, the M. tuberculosis H37Rv B2H PPI dataset has distinctly lower percentage of coherently annotated PPIs.In the discussions above, we have concluded that the quality of the erevisiae-10—by caWe have also computed for each two-hybrid PPI dataset its similarity to the corresponding predicted functional associations dataset from STRING; see Figure From the results presented earlier, it seems that many H37Rv STRING PPIs may not be direct physical interactions. In order to understand what these PPIs may better correspond to, we collect pair-wise protein/gene relationships  and PPrel (916 pairs), other type of relationships are very few. We calculate the Jaccard coefficient, precision, and recall of each of the M. tuberculosis H37Rv PPI datasets discussed earlier using ECrel  that appear in the same protein complexes using the protein complexes dataset from Wodak Lab [S. cerevisiae Complex PPI dataset” may contain a lot of indirect PPIs, like relationships between two non-directly-binding proteins in protein complexes. A representative S. cerevisiae two-hybrid PPI dataset [We similarly obtain the integrated pathway gene pair relationships (mainly ECrel and PPrel) for rom KEGG  for moreodak Lab . It is o dataset  is also S. cerevisiae predicted functional associations dataset from STRING database as the testing dataset in this analysis. The overlapping number of PPIs, Jaccard coefficient, precision and recall are calculated, and the results are given in Figure S. cerevisiae two-hybrid PPI dataset has the lowest similarity to the S. cerevisiae STRING predicted functional associations dataset, whereas the complex PPI dataset and ECrel datasets (both from KEGG and from integrated pathway gene pair relationships) reveal good similarity to the S. cerevisiae STRING predicted functional associations dataset. This result is in accordance with the results on M. tuberculosis H37Rv, clearly demonstrating that the STRING database PPIs include a substantial amount of PPIs that are indirect protein relationships, including protein pairs in the same protein complexes and protein pairs catalyzing successive enzymatic reaction steps.To avoid a biased comparison, as the full STRING PPI dataset may include many PPIs from the datasets above, we use the M. tuberculosis H37Rv B2H PPI dataset has low quality. In the process, we find four subsets of the B2H PPI dataset that may be more reliable than the rest of this dataset. The first subset consists of PPIs where both interaction partners have coherent informative GO terms—viz., B2H PPIs sharing functional homogeneity or localization coherence. This subset contains 115 PPIs and is named “B2HsameGO dataset”. The second contains overlapping PPIs between the H37Rv B2H and STRING PPI datasets, which can be considered as B2H PPIs supported by STRING prediction approaches. This subset consists of 276 PPIs and is named “B2HSTRING dataset”. The third subset contains those H37Rv B2H PPIs that have been verified by different experiments [M. tuberculosis H37Rv B2H PPI datasets.We have shown that the E. coli). Given the data available in this study, we are not able to clearly identify which erroneous B2H PPIs are caused by which inherent limitations of the two-hybrid system. We leave this interesting and difficult challenge to a future project.There are several inherent limitations of PPI data generated by two-hybrid approaches (both B2H and Y2H), including PPIs that are detected between over-expressed proteins, between fusion proteins, and in a different host ; “predicted physical interactions” ; “experimental functional associations” ; “predicted functional associations” . Differences among PPI datasets from the four categories are inevitable, and they all have some portion of real PPIs and some noise. However, a high noise level often overwhelms the agreement between the datasets from these four categories. Real PPIs are both functional associations and physical interactions (intersect dataset), because if two proteins truly interact with each other in normal environments, the two proteins must have functional relationships. The four subsets of reliable H37Rv B2H PPIs identified by us only contain a small number of PPIs and are not enough to illuminate the whole direct physical interactome in M. tuberculosis H37Rv, See Additional file Based on the approach used in generating the PPIs, each of the two major categories can be further divided into two parts, “The H37Rv STRING PPI dataset (with score ≥ 770) has its merits, especially with respect to functional associations as described earlier. However, some obvious limitations of STRING still need to be overcome in order to maximize its benefits to the community.Firstly, STRING version 8.3 does not make the source of individual PPIs completely transparent to the public. Neither the web interface nor the downloadable “protein.link.detail.v8.3.txt” file provides a clear indication of a PPI’s exact source, especially for users who lack a license for accessing the “protein.link.full.v8.3.txt” file. For example, they can only know that a PPI comes from “co-expression” but it can actually either come from “transferred co-expression” in other organisms through homology transfer or from “co-expression” of this organism. When looking at the “protein.link.detail.v8.3.txt” file, the H37Rv STRING PPI dataset contains 4,133 PPIs from experiments, whereas only 4 of which actually come from experiments in H37Rv and the rest are from “transferred experiments”. When users have the license to access all the information in this database, they still do not have a clear sense of what type of PPIs are included in STRING. It claimed to consist of “known and predicted protein-protein interactions” on its website , though S. aureus MRSA252 pull-down PPI dataset [M. tuberculosis H37Rv B2H PPI dataset [Second, each version of the STRING database release time is quite long, making the STRING PPI datasets incomplete and often obsolete for many organisms. Although some PPI datasets have been published months ago, like the  dataset  and the M. tuberculosis H37Rv. Hence, we have resorted to the rather indirect evidence presented in this paper.Ideally, we should have used confirmed PPI data as the benchmark in evaluating the quality of the H37Rv B2H and STRING datasets. Unfortunately, such confirmed PPI data is not available in a sufficiently large scale for M. tuberculosis H37Rv B2H PPI dataset with that of other B2H PPI datasets; but such large-scale reference B2H PPI datasets are currently not available in public databases. However, by comparing the quality of the B2H PPI dataset with the quality of other representative two-hybrid PPI datasets, we can still conclude that, besides inherent limitations of two-hybrid approaches, the H37Rv B2H PPI dataset has lower quality than other two-hybrid PPI datasets. This suggests that the noise introduced by individual experiments in this H37Rv B2H PPI dataset may be higher than other datasets compared.Due to inherent limitations of two-hybrid approaches, both B2H- and Y2H-generated PPI datasets usually have low quality. It is more appropriate to compare the quality of the M. tuberculosis H37Rv gold standard PPI data will be confirmed. We will then be in more suitable position to address this concern in a more direct and effective way.Hopefully, in the near future, more high quality B2H PPI data will be reported and more M. tuberculosis H37Rv B2H and STRING PPI datasets. We have demonstrated the two main causes of this low level of agreement. The first reason is the low quality of the B2H PPI dataset, which seems to contain a significant amount of false positives as well as false negatives. The same is true of the H37Rv STRING PPI dataset as a whole, though a subset comprising PPIs with score ≥ 700 seems more reliable. The second reason is that the STRING PPI dataset contains a substantial amount of predicted PPIs that are not direct interactions, which seem more likely to correspond to protein pairs that are in the same protein complexes or protein pairs that are catalyzing adjacent reaction steps in enzymatic pathways.In this work, we have observed the strikingly low agreement between M. tuberculosis PPI datasets, predicted or otherwise. Researchers who need to use this dataset should do so with great caution. Yet, as the only available large-scale physical interaction dataset of M. tuberculosis H37Rv at the moment, even though it suffers from high noise and low quality, the direct protein physical interaction information in this dataset should not be ignored. We have identified four subsets of this B2H PPI dataset that are more reliable, which can be combined into a single dataset, which can serve as a suitable reference H37Rv physical interaction dataset for many applications.Because of the low quality of the H37Rv B2H PPI dataset, it should not be used as a gold standard to evaluate the quality of other M. tuberculosis H37Rv.STRING score is useful for indicating which STRING PPIs have higher quality. We suggest a STRING score threshold set around 770. Nevertheless, the H37Rv STRING PPI dataset (with score ≥ 770) may contain a lot of indirect protein relationships attributable to protein pairs in the same protein complexes or protein pairs forming successive reaction steps in the same biological pathways. Therefore, this dataset can be a good source as a functional associations reference, but it may not be the ideal choice for the purpose of studying physical interactions in STRING database uses a combination of prediction approaches and an integration of other information . The details of PPIs generated by each of the approaches are listed in Table STRING PPIs come from a mix of experimental data; PPIs copied from public databases (e.g. KEGG and BioGRID) and predicted PPIs. So we derive from STRING a subset of predicted PPIs and name this unbiased STRING subset “predicted functional associations dataset”. This dataset is derived only from the following prediction approaches: neighborhood, transferred neighborhood, gene fusion, co-occurrence, transferred co-expression, text mining, and transferred text mining.We use Jaccard coefficient, recall, and precision to measure the agreement between a benchmark PPI dataset and a testing PPI dataset. Jaccard coefficient is defined as the size of the intersection of the two datasets divided by the size of the union of the two datasets. Recall is the proportion of benchmark PPIs that are in the testing dataset. Precision is the proportion of testing PPIs that are in the benchmark dataset. Thus,Jaccard coefficient = TP / (TP+FP +FN);Precision = TP / (TP+FP);Recall = TP / (TP + FN).Here, TP (true positives) represents the number of PPIs in the testing dataset that overlap with the benchmark dataset; FN  represents the number of PPIs in the benchmark dataset that are not in the testing dataset; TN (true negatives) represents the number of all possible PPIs that appear in neither the testing dataset nor the benchmark dataset; and FP  represents the number of PPIs in testing dataset but are not in the benchmark dataset. The Jaccard coefficient, recall, and precision of the benchmark and testing datasets considered in this work are given in Figures 1, 7, 8, 9, and 10.M. tuberculosis H37Rv B2H PPI dataset were calculated and plotted in Figure In order to find which STRING score region has a higher percentage of overlapping PPIs with the B2H PPI dataset, STRING score distribution of “overlap PPI number ratio” between the STRING predicted functional associations dataset and the COG function annotations are used in the analysis. We calculate the percentage of PPIs in each cell in the matrix. In the matrix, each cell is a combination of the COG functional category between a pair of proteins. The percentage (“intensity”) of PPIs in each cell is the number of PPIs in that cell divided by the total number of PPIs in the dataset. The number of PPIs in a certain cell means the number of PPIs fit the functional categories combination; that is, one partner has been annotated with the COG category on the x axis, the other partner has been annotated with the COG category on the y axis. If “m” PPIs have “J-K” annotations in a dataset that has “n” PPIs—and “J-K” annotations means one partner form J COG functional category, the other partner from “K” COG functional category—then the PPI functional intensity in the “J-K” cell is “m/n”. This is slightly different from the conventional approach of calculating the PPI functional intensity matrix; but it is good enough to convey broad information on the functional distribution of the PPI datasets tested. The PPI functional intensity matrices generated in this work are displayed in Figure M. tuberculosis H37Rv, C. jejuni NCTC11168 and Synechocystis sp. PCC6803 proteins are annotated with GO terms using InterProScan [rProScan . GO termA pair of proteins come into contact with each other and interacts to perform a function. If the GO term annotations of the proteins in an organism are complete, we can expect such a pair of interacting proteins to have at least one informative GO term annotation in common. Therefore, a predicted or reported PPI is more likely to be a false positive when the two proteins in the PPI do not have an informative GO term annotated to them in common. We can therefore gauge the quality of a PPI dataset by calculating the percentage of PPIs in the dataset (where both proteins in the PPI have informative GO term annotations) that has “coherent” informative GO term annotations. A PPI is said to have coherent informative GO term annotation if the two proteins in the PPI have an informative GO term annotation in common.However, the percentage of PPIs in a dataset that have coherent informative GO terms can be affected by the number of informative GO terms and by biases in the distribution of proteins these informative GO terms are annotated to. For example, if only one informative GO term was available in the organism, then 100% of the annotated PPIs would be coherent. Thus, to better assess the quality of a PPI dataset by coherence of informative GO term annotations, we need to compare the percentage of coherently annotated PPIs in the dataset to appropriately generate random PPI datasets. In particular, a high ratio (named “Info GO ratio”) of the percentage of coherently annotated PPIs in the PPI dataset compared to that of the random PPI dataset suggests that PPI dataset is likely to be of high quality. We generate random PPI network using the Random Network Plug-in  in Cytosx, Gy) of the two underlying genes are calculated by Pearson correlation coefficient as follows[A pair of proteins needs to be expressed in the same location and at the same time in order to interact. Thus the highly correlated gene expression profiles of two genes suggest the likely interaction of their two gene products. Genome-wide gene expression profiles under metabolic inhibitory and control conditions  are useds follows,i is the ith expression value of the gene corresponding to protein x in the gene expression profiles Gx; and x.Here, n is the number of conditions for the expression profiles; xThe distributions of Pearson correlation values for PPIs in the datasets considered here are plotted in Figure M. tuberculosis H37Rv and the other 629 organisms in STRING. In the first method, we use Inparanoid [An interolog is a conserved interaction between a pair of proteins which have interacting homologs in another organism. We use two different methods to identify homologs between paranoid  with HVAHere, the HVAL  is the pwhere PIDE is the percentage identities of the BLASTP hit and L is the length of the alignment.In order to better understand what the predicted PPIs in STRING really correspond to, we use the pair-wise relationships of proteins/genes from the XML files of each of the pathways in KEGG , WikiPatThe software packages and program tools used in this study are:(a) Protein Sequence Alignment software package: BLASTP ,33.(b) Orthologues Identification software package:Inparanoid stand-alone package, Version 4.1 .(c) BioPAX pathway file processing software package:Paxtools .(d) Random PPI network generation:Random Network Plug-in  for Cyto(e) Protein Signature annotation program:InterProScan .The datasets used in this study are:M. tuberculosis H37Rv Gene Expression Datasets [(a) Datasets .(b) Pathways Datasets:KEGG .WikiPathways .BioCyc .(c) PPI datasets:M. tuberculosis H37Rv high-throughput B2H PPI dataset [M. tuberculosis H37Rv in STRING [S. cerevisiae protein complexes dataset obtained from Wodak Lab [odak Lab .S. cerevisiae two-hybrid PPI dataset [ dataset .C. jejuni two-hybrid PPI dataset [ dataset .Synechocystis two-hybrid PPI dataset [ dataset .M. tuberculosis H37Rv proteins function annotations:(d) COG functional categories annotation .S. cerevisiae GO annotations downloaded from Gene Ontology website [(e)  website  dated 6/The authors declare that they have no competing interests.This work was jointly conceived, planned, analyzed, and written up by LW and HZ. The analytical experiments were performed by HZ.M. tuberculosis H37Rv B2H PPI datasetsReliable . Description: the reliable M. tuberculosis H37Rv B2H PPI datasets, list in four text files, tab delimited.Click here for file


Minimotifs are short contiguous peptide sequences in proteins that have known functions. At its simplest level, the minimotif sequence is present in a source protein and has an activity relationship with a target, most of which are proteins. While many scientists routinely investigate new minimotif functions in proteins, the major web-based discovery tools have a high rate of false-positive prediction. Any new approach that reduces false-positives will be of great help to biologists.We have built three filters that use genetic interactions to reduce false-positive minimotif predictions. The basic filter identifies those minimotifs where the source/target protein pairs have a known genetic interaction. The HomoloGene genetic interaction filter extends these predictions to predicted genetic interactions of orthologous proteins and the node-based filter identifies those minimotifs where proteins that have a genetic interaction with the source or target have a genetic interaction. Each filter was evaluated with a test data set containing thousands of true and false-positives. Based on sensitivity and selectivity performance metrics, the basic filter had the best discrimination for true positives, whereas the node-based filter had the highest sensitivity. We have implemented these genetic interaction filters on the Minimotif Miner 2.3 website. The genetic interaction filter is particularly useful for improving predictions of posttranslational modifications such as phosphorylation and proteolytic cleavage sites.Genetic interaction data sets can be used to reduce false-positive minimotif predictions. Minimotif prediction in known genetic interactions can help to refine the mechanisms behind the functional connection between genes revealed by genetic experimentation and screens. Minimotifs are short contiguous peptide sequences in proteins that are associated with known biological functions. Minimotifs are generally of less than 15 residues in length and confined to a single secondary structure element. Functions encoded by minimotifs include direct covalent modification of the minimotif, binding determinants for other molecules, and protein trafficking tags.th position). PSSMs are matrices that indicate the probability of the 20 amino acids at each position of the minimotif.Minimotifs are defined by a common set of attributes for their sequence and function Consensus sequences and PSSMs can be used to predict new minimotifs, and thus new functional elements in protein queries. However, because the minimotifs are relatively short when compared to the more complex sequence definitions for protein domains, there is most often a high probability that minimotifs occur in a protein by random chance. Thus false-positive predictions are a general problem in minimotif prediction by websites such as Minimotif Miner, Eukaryotic Linear Motif Server, and ScanSite We and other groups have used other types of data to reduce false-positive predictions including protein-protein interactions, molecular and cellular protein functions, evolutionary conservation, protein disorder, protein structure, protein surface prediction, and protein localization In this paper, we assessed whether genetic interaction (GI) data can be used to reduce false-positive minimotif predictions, and implemented several filters as a part of the Minimotif Miner web system One potential caveat of this approach is that several papers indicate that only a portion of GIs map to physical protein-protein interactions Saccharomyces genome database (SGD) contain information about GIs GIs were derived from several sources as shown in The Minimotif Miner 2.3 data model has information about ∼5300 verified minimotifs along with the source protein where the motif was found, and the target protein that imparts the biological function to the minimotif. This data was used to evaluate the efficiency of several GI-based filtering algorithms in reducing false-positive predictions.We have installed the GI filters on the Minimotif Miner 2.3 website along with existing filters, such as the protein-protein interaction filter, molecular and cell function filter, etc. This enables the users to build custom filtering methodologies based on their requirements or interest. As with the other filters, we also provide users with the exclude option, to examine the motifs that do not have known GIs.We intended to develop a set of algorithms that uses GI data to refine the predictions of minimotifs in MnM. We devised three variations of GI-based filtering algorithms for evaluation. Since many GIs are conserved among related and diverse species, these interactions can be used to identify those minimotifs that have a previously known genetic relationship. The first basic algorithm (designated as “Genetic Interaction (GI) Filter”) is as follows: Let P be any putative motif, let S be its source protein, and let T be the target protein associated with the motif P. Let S′ be the gene that encodes protein S, and T′ be the gene that encodes protein T. All the databases containing the GIs are searched for any direct interaction between S′ and T′. If an interaction exists, the motif P will be retained by this basic GI filter, otherwise the motif P will be removed by this filter. This is repeated for all of the T proteins that are predicted for a particular S query. Protein and Gene alias names are taken into account while searching the databases in order to enforce a thorough search in the database.A set of GIs can be used to build GI networks that contain nodes that represent genes and edges that represent interactions. This structure enables us to explore higher order interactions in the network that are not direct. These second-order, third-order, etc., GIs between nodes may be useful for minimotif filtering and our “GI-node Filter” algorithm is based on this concept. Given a putative motif P along with its source protein S and it target protein T, the genes of proteins S and T are located from different sources. Let the genes of S and T be S′ and T′, respectively. The following steps are repeated for N number of times, N being the node count. All the genes interacting with S′ and T′ are identified. Let S″ be the new set of genes identified to be interacting with S′ and T″ be the set of genes identified to be interacting with T′, respectively. Now, the GI databases are searched for the interaction between any genes in the set S″ with a gene in the set T″, and for the interaction between any genes in the set of T″ with a gene in the set S″. If an interaction is found, the motif P is retained by the filter. Otherwise, it proceeds to find the interacting genes of S″ and T″ iteratively based on the node count. If there is no interaction even after Nth iteration, the motif P gets filtered out by the algorithm. The size of the interaction network for a gene grows exponentially as N increases. When we tested this filter with the node count of 2, 3 and 4, the results on nodes 3 and 4 produced very poor selectivity. So, we limited our experiments to a node count of 2.Minimotifs are often conserved across species and taxa ROC (Receiver Operating Characteristic) curves for comparing GI filters were generated using R software package We wanted to evaluate which filters performed best by yielding a clear separation between true positives and false-positives. The effectiveness was tested by comparing metrics of a set of verified motifs to a set of known negatives. Minimotif Miner database 2 was used as the source of data for verified motifs, as it has a total of ∼5300 minimotifs annotated from the literature and has supporting experimental evidence. Each minimotif has associated information such as source protein, which contains the minimotif and a target protein that engages the minimotif, respectively, and is associated with an activity such as binds, modifies, or traffics. About 3000 minimotifs have both source and target accession numbers that can be cross-referenced to GI data. Therefore, the MnM2 database was used as the source for validated motifs for the true dataset.As there is no direct access to a true-negative dataset of minimotifs, we generated a negative dataset comprised of protein pairs that are not likely to have a minimotif relationship or a genetic interaction. We randomly paired genes for this purpose, since the number of known GIs relative to the total number of possible GIs is negligible. For instance, 25,000 genes have ∼312 million possible pair-wise interactions, but the number of known GIs is small and should not impact our conclusions. We generated ∼27,000 such pairs of source-target genes, and used this as our negative dataset in the process of validating the filters against false-positives.Measures such as sensitivity and selectivity were employed to validate our algorithms. Our sensitivity analysis measures if a putative motif that is retained by the filter is indeed a part of the true dataset. It is the percentage of true positives that are retained by the filter. Our selectivity metric was based on a computation of the percentage of true-negatives that are accepted by the filter. Thus, algorithms with a higher sensitivity and a lower selectivity are desirable. Hence, the discrimination ratio (DR), the ratio of sensitivity to selectivity, with values more than 1 is favorable. The higher the ratio, the more favorable the filter is in discriminating true minimotifs from incorrect predictions.The results comparing metrics for the three GI filters are shown in We wanted to know whether the GI filters were providing any additional information for reducing false positive minimotif predictions when compared to other existing minimotif filters. The frequency filter is based on the minimotif complexity and likelihood of occurrence of a minimotif −08) for the GI filter as compared with 0.72 (p = 0.08) for both the frequency score and cellular function filter  respectively  were 0.93 (p = 2.9*10ectively Table 3 −8 vs. 1.1*10−6 −1.5*10−6). It was also seen that the novel motif prediction rate for GI filter when compared against frequency score filter is 24% and that with the cellular function filter is 56%. Similar results were observed when the GI-HomoloGene filter was used in this analysis (data not shown). Although the pairwise filter results analysis are not as striking, collectively the filter comparisons show that the GI filters contain additional informational content with regard to eliminating false positive minimotif predictions.We next investigated if using the GI filter in pairwise combinations with the frequency score or cellular function filter produced better filtering results. The area under the ROC curve was modestly better for these filter combinations (0.95–0.96 when compared with the GI filter (0.93), but the p-values were not as high for the pairwise filter combinations .Most minimotifs in the MnM database are for binding or posttranslational modifications. When analyzed separately 56% of the posttranslational modification minimotifs have a known genetic interaction, while only 19% of binding motifs had a known GI. Statistical analysis of this stratification using ROC plots shows that the GI filter for both the binds and modifies minimotifs groups are significant (p<0.01)Table 4, ). We also note that the HomoloGene-GI filter (data not shown) had similar performance to the GI filter. An ROC curve analysis using minimotif length as a variable was performed, but did not produce any discernable pattern (data not shown). Collectively, these analyses suggest that a high percentage of some types of minimotifs have GIs and supports the approach of using GIs as filters for eliminating false positive minimotifs.The most common posttranslational modification annotations in the MnM 2 database are phosphorylation sites (n>100) and protease sites (n = 20); 49% and 80% of these motifs had known GIs, whereas 7% and 0% had GIs when the random dataset was analyzed as a control. ROC curve analysis shows that the GI filter for the phosphorylation, as well as the all minimotif and bind minimotif groups are good minimotif filters (p<0.01)  could be used to improve minimotif predictions. GI networks are composed of pairs of genes in complementary pathways or are involved in the same pathway Beyond prediction of minimotifs, the GI filter provides a tool to examine GIs at a finer level of granularity. Identification of a GI infers that two genes have a related function in a complementary pathway or in the same pathway. However, genetics does not identify how the two genes are related. Protein-protein interaction networks can help to identify the relationships, but only a fraction of GIs have known protein-protein interactions. This could be due to the fact that different protein-protein interaction databases do not yet have extensive redundancy, suggesting that there are many protein-protein interactions yet to be discovered. However, if protein-protein interactions are transient, such as in an enzyme/substrate relationship or in a highly regulated signaling complex, these interactions are not likely to be identified in a protein-protein interactions screen, but may be detected in a GI screen. In support of this contention, 56% of minimotifs with a posttranslational modification activity/substrate relationship had a known GI, whereas only 19% of minimotifs with a binding activity had a GI. This is the particular case where our new GI tool will help to identify binding, trafficking and enzymatic functions for known GIs. The user, simply enters the query source protein, identify s of a pair of genetically interacting proteins and looks for a relationship with the partner protein. Furthermore, this tool is also likely to assist in construction of pathways in a similar manner.


This dataset holds great promise as a comprehensive resource for de novo identification of protein-protein interactions or to underpin and prioritize candidate protein interactions from other studies. To demonstrate the potential use of our dataset we focussed on the mitochondrial translation machinery. Our results show that mitoribosomal complexes can be analyzed by blue native gel electrophoresis, as at least four distinct complexes. Analysis of these complexes confirmed that 24 proteins that had previously been reported to co-purify with mitoribosomes indeed co-migrated with subunits of the mitochondrial ribosome. Co-migration of several proteins involved in biogenesis of inner mitochondrial membrane complexes together with mitoribosomal complexes suggested the possibility of co-translational assembly in human cells. Our data also highlighted a putative ribonucleotide complex that potentially contains MRPL10, MRPL12 and MRPL53 together with LRPPRC and SLIRP.Complexome profiling is a novel technique which uses shotgun proteomics to establish protein migration profiles from fractionated blue native electrophoresis gels. Here we present a dataset of blue native electrophoresis migration profiles for 953 proteins by complexome profiling. By analysis of mitochondrial ribosomal complexes we demonstrate its potential to verify putative protein-protein interactions identified by affinity purification – mass spectrometry studies. Protein complexes were extracted in their native state from a HEK293 mitochondrial fraction and separated by blue native gel electrophoresis. Gel lanes were cut into gel slices of even size and analyzed by shotgun proteomics. Subsequently, the acquired protein migration profiles were analyzed for co-migration  Perturbed protein-protein interactions can have strong negative effects on cell viability, which in turn may have devastating effects in an organism. This is exemplified by the severe clinical syndromes that are associated with assembly defects of the mitochondrial oxidative phosphorylation (OXPHOS) complexes via its proteolytic peptides by mass spectrometry in-silico protein migration profiles. The outline of this method is schematically shown in et alBlue native gel electrophoresis (BNE) has been developed to study native protein complexes de novo protein-protein interactions or drive validation of predicted protein-protein interactions from e.g. affinity purification – mass spectrometry (AP-MS) experiments. The latter application is of particular interest as the mass spectrometric identification of co-purified proteins on itself does not provide physicochemical information about the actual interactions themselves or the size of the complexes One of the advantages of complexome profiling over the classical 2D BN SDS-PAGE method is that the electrophoretic profiles are stored in a data matrix of proteins (rows) with gel slices (columns) where each cell contains the respective relative abundance. This allows the unambiguous analysis of protein co-migration by computational methods such as protein correlation profiling via different quantitation methods was previously used to deliver the proof-of-principle for the approach In this paper we present the first complexome profiling dataset from human cells which can be used by researchers to support alleged protein-protein interactions, identify novel protein-protein interactions, or to prioritize candidate protein interactors. A small subset of the protein profiles generated Four of the HCL clusters were of particular interest as they contained proteins of the 28 S and 39 S mitochondrial ribosome (mitoribosome) which has not been analyzed by BNE thus far. Recent publications reported interactions between many different proteins and the mitoribosome based on AP-MS results n-dodecyl b-d-maltoside(approximately 1 gr/gr protein). The suspension was incubated for 10 minutes on ice to solubilize proteins and subsequently centrifuged at 10 000×g for 25 minutes at 4°C. The concentration of solublilized proteins in the supernatant was determined using the MicroBCA protein assay kit (Pierce) prior to addition of BN sample buffer. BN gels (4–12% and 5–15% acryl amide gradient gels) were cast using Bio-Rad mini PROTEAN® II systems in combination with 1.5 mm spacers. Both gels were loaded with 80 µg of protein per lane and run till the dye front reached the end of each respective gel.HEK293 cells grown in DMEM (Biowhitaker) supplemented with 10% (v/v) FCS and 1% (v/v) penicillin/streptomycin were harvested at ∼80% confluency using PBS. Cells were disrupted by pottering in isotonic buffer and a mitochondrial enriched fraction was obtained by centrifugation according to Vogel in-gel tryptic digestion essentially according to Heide in vacuo centrifugation to remove acetonitrile. Subsequently, stop and go elution (STAGE) tips The resulting gel lanes were cut into 24 gel slices at even distance and each gel slice was subjected to in-house at 100 bar with ReproSil-Pur C18AQ 3 µm 140 Å resin (Dr. Maisch) resuspended in methanol. Proteolytical peptides were loaded directly onto the analytical column using 0.5% (v/v) acetic acid at a flow rate of 600 nl/min. A linear gradient of 12–46% acetonitrile (ACN) with 0.5% (v/v) acetic acid as ion pair reagent was used to gradually elute peptides from the column at a flow rate of 300 nl/min. Following each analysis, the column was washed for 10 minutes with 80% ACN at 600 nl/min and conditioned using 0.5% acetic acid for 10 minutes at 600 nl/min. Intermittent blank injections were performed to minimize carry-over effects between samples.Duplicate measurements for each gel slice were performed by nanoflow reversed-phase C18 liquid chromatography (Agilent 1100 series) coupled online to a 7T linear ion trap Fourier-Transform ion cyclotron resonance mass spectrometer  m/z 524) at 300 nl/min and calibrated using caffeine (m/z 195), MRFA (m/z 524) and Ultramark 1621  standards from the MSCAL5 ProteoMass™ LTQ/FT-Hybrid ESI Positive Mode CalMix kit (Sigma Aldrich). The ICR cell precursor scans were performed using a single microscan at 50 000 resolving power (FWHM at m/z 400) on 1E6 ions or 500 ms maximum injection time (whichever came first). Data dependent acquisition of MS/MS spectra by the linear ion trap used a single microscan at normal scan speed. The automatic gain control was set to 1E4 ions for MS/MS with 400 ms maximum injection time. Collision induced dissociation of precursor ions was performed at 27% normalized collision energy for 30 msec and activation Q = 0.250. An isolation width of 3 Th was used to trap and isolate precursor ions for MS/MS experiments. Dynamic exclusion was enabled to minimize re-analysis of precursor ions .The mass spectrometer was operated in positive ion mode and programmed to acquire a single full MS survey scan in the ICR cell with up to four data dependent collision induced dissociation (CID) fragmentation spectra in parallel by the linear ion trap. The mass spectrometer was tuned on MRFA peptide (in-house developed Perl script. Mascot database searches were performed against the Homo sapiens RefSeq (release 44) database Raw mass spectrometry data was converted into Mascot search engine compatible peak lists by ExtractMsn (Thermo Fisher Scientific) and an in-house developed Perl script was used to generate protein abundance profiles from IDEAL-Q output data. p>0.9) and remaining peptide abundance profiles were ranked by descending similarity score. Finally, the protein abundance profiles were generated as the average profile from the top 5 peptide abundance profiles with highest similarity scores. Protein abundance profiles from duplicate gel slice measurements were averaged for each acryl amide gradient. Finally, a core dataset of 953 protein abundance profiles was compiled that consists of proteins that were detected in both acryl amide gradients.Quantitative information was extracted for valid protein identifications as integrated ion current chromatograms of respective peptides by IDEAL-Q software via DAVID Protein identifiers, gene ontology information, and official gene symbols were obtain Hierarchical clustering for all mitochondrial protein abundance profiles was performed by the multiple experiment viewer (MEV) software Co-migration of mitoribosomal proteins with previously reported interactors from affinity purification – mass spectrometry experiments were analyzed by hierarchical clustering. Proteins reported by Rorbach r = 0.991 and r = 0.994, respectively). However, the hydrophobicity distribution of proteins in our dataset shows a slight shift towards more hydrophobic proteins. This might be explained by the relative high abundance of mitochondrial membrane proteins in combination with the protein extraction and electrophoresis methods that were optimized for the analysis of membrane proteins. This is supported by a significant enrichment for proteins with one or more transmembrane domains with respect to the RefSeq Hs database . The distribution of transmembrane domains for proteins in the dataset as well as the RefSeq Hs database is shown in r = 0.993 versus r = 0.927, respectively).A combined total of 118760 MS/MS spectra were mapped to 3981 proteins or 1766 protein identification groups. Proteins were identified at ≤1% FDR with an average of 9 non-redundant peptides and an average summed Mascot ion score of 417 . Minimum Mascot ion score thresholds to achieve 1% protein FDR or better were determined to be 50, 27, 21, 21, 19 and 16 for proteins with coverage of 1, 2, 3, 4, 5 and ≥6 peptides, respectively. Peptides were identified with an average Mascot ion score of 41  and an average precursor ion mass error of 4.85±3.03 ppm. Analysis of the HEK293 mitochondrial fraction by complexome profiling using two acrylamide gradients resulted in a dataset of 953 proteins where migration profiles could be constructed for both the 4–12% and 5–15% acrylamide (AA) gels. Here, the use of two AA gradient gels allows for cross-validation of protein co-migration and helps to distinguish between protein complexes of similar size. Valid protein and peptide identification data is available from p-value  = 5.65E–18).Because a mitochondria enriched fraction was used in this study, the dataset not only contains mitochondrial proteins but also provides migration profiles of proteins that reside in other cellular organelles. Unfortunately, the mitochondrial localization of many proteins is still unknown, and we therefore included information from the MITOP2 database The protein profiles dataset is provided as Out of the 953 proteins, 209 proteins could be assigned to 24 known heteromeric complexes based on protein description or online information resources Some of the mitochondrial proteins in the dataset were expected to form yet unknown protein complexes. Since the protein profiles were stored in a data matrix, the dataset allowed for the use of computer algorithms in the analysis of protein co-migration profiles. We applied hierarchical cluster analysis (HCL) to categorize the dataset for ease of use, similar to the approach of Heide To investigate the rate at which proteins cluster together by mere chance we constructed 10 randomized matrices in which the abundance values were shuffled for each respective AA gradient gel. For each randomized matrix we performed the same hierarchical cluster analysis that was used for the “real” dataset. To determine the rate at which proteins cluster together we focussed on the 16 known mitochondrial complexes described in Four clusters from the HCL analysis were of particular interest as they contained multiple 28 S and 39 S subunits of the mitoribosome which are shown combined in Results from the HCL analysis for mitoribosomal proteins prompted us to look at these complexes in more detail with respect to previously reported interacting proteins. All mitochondrial proteins that were previously reported to interact with the mitoribosome based on affinity purification studies by Rorbach via the HCL analysis of all mitochondrial protein profiles: ERLIN2 and DLST  and in addition amenable to computational analysis. This allows simple comparisons of migration profiles between proteins of interest or the use of computer assisted analyses to identify proteins with similar migration profiles. This approach provides independent supportive evidence for protein-protein interactions suggested by other methods (e.g. bioinformatic or co-purification studies) and may thus be used for validation purposes and to prioritize candidate protein-protein interactions for functional validation studies. In addition, complexome profiling data can be used to identify putative protein-protein interactions from an independent and untargeted experiment. However, depending on the research question and setup of the complexome profiling experiment, additional experiments are likely required to prove protein-protein interactions since co-migration of non-interacting proteins may occur.Here, we used our dataset to analyze co-migration profiles of mitoribosomal proteins and previously described interactors from co-purification studies. By now, several hundred proteins have been suggested to interact with the mitoribosome based on AP-MS studies. These are likely present in multiple complexes which probably include secondary interactions and false positives. As a first step to elucidate these interactions we looked for co-migrating proteins to support alleged interactions. As expected, our analysis showed that the majority of 28 S and 39 S mitoribosomal subunits co-localize in high molecular mass complexes together with previously reported (putative) interactors from AP-MS studies.bona fide interactors or components of the mitoribosome et al reported that both proteins contribute to mitochondrial translation For the 28 S and 39 S complexes, eight and three previously reported interactors from AP-MS studies were found to co-migrate, respectively. Of these, two proteins  are known The 300 kDa ribosomal complex is rather difficult to put into context. First of all, several proteins were found to co-migrate which might be part of other complexes of similar size. Both VDAC proteins as well as PYCR1 and PYCR2 are known to form homomeric complexes of about 300 kDa. Similarly, both HADHA and HADHB are components of the hydroxacyl-CoA dehydrogenase complex for which the determined size corresponds with the α2β2 configuration of the enzyme. Interaction of the CCDC109A protein product with the mitoribosome seems unlikely from a biochemical point of view as it encodes the mitochondrial calcium uniporter c oxidase (COX) subunits. Recent studies demonstrated that LRPPRC and SLIRP interact in a ribonucleoprotein complex involved in posttranscriptional gene expression regulation in mitochondria et al. showed that knockdown of LRPPRC results in an isolated COX deficiency whereas further knockdown of LRPPRC induces a generalized defect of OXPHOS complexes Our analyses highlighted a putative 200 kDa ribonucleotide complex that appears to contain three MRPL subunits together with LRPPRC, SLIRP and cytochrome Our dataset provides the first available analysis of protein complexes in human cells by complexome profiling. The complexome profiling dataset presented in this paper is not exclusively limited to the analysis of mitochondrial protein-protein interactions since a mitochondrial enriched fraction was used. Data for several hundred non-mitochondrial proteins is also available from our dataset that can be used to analyze protein complexes in other cellular compartments. Although this dataset contains a lot of information, future complexome profiling experiments need to be performed in different cell types, tissues, and differential conditions to completely elucidate the composition of mitochondrial protein complexes in humans. Ideally, future complexome profiling datasets should be merged with existing datasets in order to fully understand the complex dynamics of protein-protein interactions. To this end, the fact that protein electrophoretic migration profiles are stored within a data matrix readily facilitates the combination of present and future datasets.via detection of at least four distinct complexes that are composed of mitoribosomal subunits together with potentially novel and previously reported interactors. In addition, co-migration of multiple proteins associated with the biogenesis of inner membrane complexes together with the 39 S and 28 S mitoribosomal complexes is supportive of co-translational assembly in human cells. Our analysis also exposed a putative 200 kDa ribonucleoprotein complex that potentially contains LRPPRC and SLIRP together with three MRPL subunits. Finally, our data highlights TMEM126A as a putative complex I assembly factor based on co-migration with two known CI assembly chaperones and its sequence similarity with the CI assembly factor TMEM126B which is known to interact with the same two CI assembly factors in other cell types.We have generated a collection of BNE profiles for 953 proteins that can be used to identify novel protein-protein interaction and to underpin or prioritize candidate protein-protein interactions. As an example we have used our dataset to analyze mitoribosomal complexes. Our analysis showed for the first time that mitoribosomal complexes can be analyzed by blue native electrophoresis as demonstrated File S1Protein and peptide identification data.(XLSX)Click here for additional data file.File S2Distributions of the physicochemical protein properties of the complexome profiling dataset and RefSeq Hs database.(PDF)Click here for additional data file.File S3Complexome profiling dataset.(XLSX)Click here for additional data file.File S4Overview of complexome profiling data for known complexes in the dataset.(PDF)Click here for additional data file.File S5Hierarchical cluster analysis of protein profiles.(XLSX)Click here for additional data file.File S6MEV hierarchical cluster analysis file.(ZIP)Click here for additional data file.File S7Hierarchical cluster analysis results for the complexome profiling dataset and shuffled matrices.(PDF)Click here for additional data file.


Trypanosoma brucei causes African Trypanosomiasis or sleeping sickness in humans, which can be lethal if untreated. Most available pharmacological treatments for the disease have severe side-effects. The purpose of this analysis was to detect novel protein-protein interactions (PPIs), vital for the parasite, which could lead to the development of drugs against this disease to block the specific interactions. In this work, the Domain Fusion Analysis (Rosetta Stone method) was used to identify novel PPIs, by comparing T. brucei to 19 organisms covering all major lineages of the tree of life. Overall, 49 possible protein-protein interactions were detected, and classified based on (a) statistical significance , (b) their involvement in crucial metabolic pathways, and (c) their evolutionary history, particularly focusing on whether a protein pair is split in T. brucei and fused in the human host. We also evaluated fusion events including hypothetical proteins, and suggest a possible molecular function or involvement in a certain biological process. This work has produced valuable results which could be further studied through structural biology or other experimental approaches so as to validate the protein-protein interactions proposed here. The evolutionary analysis of the proteins involved showed that, gene fusion or gene fission events can happen in all organisms, while some protein domains are more prone to fusion and fission events and present complex evolutionary patterns.The protozoan  Trypanosoma brucei and is transmitted by the tsetse fly, through a bite into the bloodstream. The infection spreads throughout the body and, if untreated, can be lethal. The symptoms are sometimes ignored or at least underestimated, as they include fever and other common symptoms, and behavioral changes, such as anxiety or sleep disorders. Unfortunately, the tests used to verify the infection nowadays include painful and complicated procedures such as lumbar puncture, and lymph node aspiration. The available drugs, as effective as they may be, are outdated and can cause severe and often deadly side-effects African trypanosomiasis is one of the neglected parasitic diseases which infects both humans and animals in regions of sub-Saharan Africa which cover about 37 countries; more than 60 million people are at risk even today Sustained infection is caused by the unique ability of the Trypanosomes to deceive the host’s immune system through the antigenic variation of its surface proteins T. brucei proteome to the proteomes of a number of other protists. Here we have extended this analysis to include another 19 fully sequenced organisms, covering the full range of evolutionary diversity in eukaryotes and prokaryotes. For all proteins involved in the PPIs identified by this method, we examine their involvement in crucial metabolic pathways and their evolutionary history.Another approach to this problem is the possible identification of protein-protein interactions (PPIs) which, if inhibited pharmacologically, can cause lethality to the parasite through the blockage of a necessary biological pathway which cannot be bypassed in any other way. A preliminary study In order to detect PPIs, we used the domain fusion analysis  to identify potential protein pairs that possibly interact, and evaluate these targets considering their importance for survival of the pathogen.T. brucei in order to have more results that can be approached pharmacologically.The domain fusion analysis has already been successfully applied to the specific pathogen Homo sapiens). Theoretically, inhibiting PPIs that are unique to the parasite and not shared by the host, allows us to make a significant step towards the absence of severe side effects, if a future drug is produced to block the specific interaction.Following the identification of potential interacting protein pairs, we used phylogenetic trees to determine the evolutionary fate of each protein pair associated with a putative PPI, in order to focus on protein pair candidates that are fused in the host organism . If a protein pair is separated in T. brucei but fused in the host, and if the predicted protein-protein interaction is crucial to the parasite’s survival, designing an inhibitor for the protein-protein interaction would specifically target the parasite and not the host protein; this marks the identified interaction as a promising drug candidate.To study the evolutionary history of the protein pairs, the identified fused protein was used as a BLAST query to search for homologs against the major organism lineages in order to observe the evolutionary pattern of each fusion event . Within each organism family group, we not only checked the BLAST hit with the highest identity value and the lowest E-value threshold  T. brucei, the more fusion events were identified  annotation. As some of these were found multiple times, the common events were reduced to one before the GO analysis. For these 39 unique protein pairs, the Gene Ontology search did not show any significant bias for the biological process of the proteins involved in the fusion events in general, nor for their respective molecular function . Also, tDetected in Plasmodium falciparum (GI: 23497583). The bifunctional nature of the G6PD-6PGL enzyme in Plasmodium falciparum was elucidated some years ago and is known for its unique structural and functional characteristics which are restricted to this genus T. brucei is a complex of two separate proteins . In this fusion we detected an NAD(P) oxidoreductase (accession number: XP_823399.1) fused with another oxidoreductase (accession number: XP_823179.1)  . The fir sapiens .Detected in Staphylothermus hellenicus (GI: 297255006). In this fusion we detected a centromere binding protein (accession number: XP_822277.1) fused with a nucleolar protein (accession number: XP_844534.1) (44534.1) . The cenDetected in Bacillus anthracis (GI: 49182202) and Methanobrevibacter smithii (GI: 148551659). In this fusion we detected a peptide methionine sulfoxide reductase (PMSR) protein (accession number: XP_846892.1) fused with a hypothetical protein (accession number: XP_829255.1) (29255.1) . The PMSDetected in O. sativa (GI: 113611229). In this fusion we detected a Casein kinase II regulatory subunit (accession number: XP_829146.1) fused with a hypothetical protein (accession number: XP_844119.1)  . The Cas. brucei , so it hDetected in D. rerio (Uniprot Accession No: Q6DBR7), R. oryzae (Gene number: RO3T_16834), A. fumigatus (Gene number: Afua_1g11540). In this fusion we detected a phosphatase-like protein (accession number: XP_001219018.1) fused with a hypothetical protein (accession number: XP_001219015.1) . The hypT. brucei, three in excavates, and two in eukaryotes . We found that almost 43% of the verified results (21 out of 49) are found fused in the Homo sapiens genome while they are encoded by separate genes in T. brucei Click here for additional data file.Table S2Fusion events detected in this study, for which functional annotation is available. This table includes all the protein pairs that were found to participate in fusion events through the automated analysis using the SAFE software and verified by backward BLAST, and for which functional annotation is available for both proteins. The results are grouped by organism (first column) and the common fusion events between the organisms are marked with a distinct color  in the second column. A description of each protein that is involved in the fusion event is also shown, along with the ORF numbers, and the Protein IDs. The table also contains information from the BLAST analysis, displaying the percentage of identities (common amino acid residues in the sequences compared), and the e-value of each result. In the Description column, there is a short description of each event. The description is highlighted in yellow when the two proteins have been previously reported to interact or co-exist in a protein complex, with the respective references shown; the symbol [p] designates participation in the same biological pathway. Finally, the last column displays information about the fate of the protein pair in Homo sapiens: f: the protein pair is fused, s: the protein pair is separate (two different proteins), a/b: only one part of the fused protein is conserved in humans, either the first (a) or the second (b), f/s: the protein pair is found in both fused and separate configurations.(PDF)Click here for additional data file.Table S3Fusion events detected in this study, for which no functional annotation is available. This table includes all the protein pairs that were found to participate in fusion events through the automated analysis using the SAFE software and verified by backward BLAST, and for which no functional annotation is available for either protein, i.e. both are designated as “hypothetical”. Data are shown/marked as described in the legend for (PDF)Click here for additional data file.Table S4Fusion events detected in this study, for which only partial functional annotation is available. This table includes all the protein pairs that were found to participate in fusion events through the automated analysis using the SAFE software and verified by backward BLAST, and for which functional annotation is only available for one of the two proteins, the other being designated as “hypothetical”. Such results identify novel interactions, and a protein function can be attributed to the hypothetical proteins through the careful in-depth research of each fusion event. Data are shown/marked as described in the legend for (PDF)Click here for additional data file.Table S5Gene ontology (GO) annotation of the fusion events identified. The Uniprot gene ontology (GO) annotations , as well as the Conserved Domains Database (CDD) annotations, are shown for the 49 results that passed the backward BLAST verification; as some of the events were found multiple times, the common events were reduced to one before the GO analysis, resulting in 39 unique protein pairs. No significant bias is apparent for the biological process of the proteins involved in the fusion events in general, nor for their respective molecular function. Based on the GO annotations, approximately 40% of our results had unknown biological process (compared to ∼71% for the whole genome), 9% of our results had unknown molecular function (compared to ∼39% for the whole genome), and 59% of our results had unknown cellular component (compared to ∼47% for the whole genome).(XLSX)Click here for additional data file.Table S6Phylogenetic trees on which the evolutionary categorization of the fusion events was based. These trees show the evolution of each protein pair throughout the tree of life. The highlighted oval shape indicates the species in which the fusion protein was identified. The colored dots along the tree branches represent the state of the protein in each lineage, based on BLAST analysis. Red: the protein pair is separate (two different proteins), Green: the protein pair is fused, Blue: only one part of the fused protein is conserved, either the first or the second member of the protein pair, Grey: Absence of both proteins, or not enough data to conclude the presence of the protein pair.(PDF)Click here for additional data file.Table S7Available structural information for homologs of the domains which participate in the gene fusion events identified. For each of the 49 fusion events verified by reverse BLAST, the accession numbers of the corresponding protein pair in T. brucei is given. To identify available structural information, protein BLAST was used to compare each protein sequence against the protein sequences extracted from the PDB three-dimensional structure records. For each protein, the accession number for the top matching PDB record is given, as well as details of the percent identity and the residue range for the match. The last column gives the residue range for the part of each protein that participates in the fusion event, as identified initially by the SAFE software, which shows that in most cases, the PDB hit largely overlaps with the fusion domain. The available structural information for the proteins that participate in fusions events can be used in molecular modelling studies to further explore the potential protein-protein interactions, and to design specific inhibitors which block such interactions, as potential drugs to combat trypanosomiasis.(PDF)Click here for additional data file.


Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful.Mycoplasma genitalium.In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network.To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the  Sequence similarity has proven to be useful for many years in attempting to annotate genomes ,2. A simThe development of high throughput technologies has resulted in large amounts of predicted Protein-Protein Interaction networks (PPI) for different genomes and, subsequently, methods using PPI data for functional inference ,9-12 havRecently, other methods using PPI to predict functions for individual genes or proteins have been developed by considering modularisation in biological networks  These meMycoplasma.However, most of these approaches use PPI and, while very useful, they have limitations. The obtained PPI data result in a rich, but quite noisy and still incomplete, source of information. Also, PPIs are only available for a reduced group of organisms  due to tMycoplasma genitalium is one of the most studied species, as it is the smallest organism having a small genome size [This last case implies a significant restriction since ome size ,24 and home size . Due to In many ways, Protein-Protein Association Networks (PPAN) are a more informative way of describing proteins and their mutual interactions . In contMycoplasmas: Functional module identification, orthology exploitation and Gene Ontology (GO) Semantic similarity-based measures [Mycoplasma species.In this work, a new protocol is described for predicting new gene annotations involving different approaches using PPAN for measures . We haveMycoplasma PPAN (species A and B); second, assign Gene Ontology (GO) terms to each protein inside the species B modules (proteins without GO terms remain unannotated); third, calculate the orthology value between each potential pair of orthologs (A and B), and if the value exceeds a threshold, transfer GO terms specifically from Species B ortholog proteins to the Species A unannotated protein; Finally, the transferred GO terms list of unannotated Species A protein was analysed, comparing them with the annotated proteins GO terms inside the Species A module that they belong to, using GO semantic similarity measures. If the similarity value is above a threshold, then the Species A unannotated protein is associated with these assigned GO terms. A schematic view of the algorithm is depicted in Figure 1The procedure is as follows: First, identify functional modules between two Mycoplasma species proteomes. One of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially if one wishes to work with Mollicutes. The entries in STRING [Mycoplasmas. A protein-protein functional association can mean either a direct physical binding or an indirect interaction, such as participation in the same metabolic pathway or cellular process. The associations are derived from high-throughput experimental data, from the mining of databases and literature and from predictions based on genome context analysis. STRING carefully assesses and integrates all of these data in order to obtain a single confidence score for all protein interactions, taking a more generalised perspective on proteins and their associations than other databases whose main purpose is to collect and curate direct experimental evidence about protein-protein physical interactions.We have performed this study on seven n STRING  correspoMycoplasma pneumoniae.Moreover, the improvement of the use of STRING in Protein Function network procedures has been indicated , and in Mycoplasmas being extracted and then a Protein-Protein network for each of the species being constructed, resulting in undirected PPAN. The networks were built using high-associations (weighted score> 0.7). See Additional File Mycoplasma PPANs used in this study. These PPAN have been used for the following calculations. The following procedure is depicted on Figure The PPAN for each species were obtained from the STRING database (2008 release), the information for M. genitalium and every other Mycoplasma species using NetworkBLAST [We first identified the functional modules shared between orkBLAST ,34,35 asMycoplasma species from the GenBank repository dated October 2010 ftp://ftp.ncbi.nih.gov/genbank/, and for every two species analyzed, we performed a bi-directional BLAST [M. genitalium was performed over the other Mycoplasma's species 2 and vice versa. The algorithm was fed with the BLAST results and PPAN s of each of the species analyzed. NetworkBLAST outputs a set of modules that are evolutionarily conserved across the two PPAN. First, the proteomes in FASTA format were obtained for each al BLAST  search oMycoplasma proteome in the GO database http://www.geneontology.org.   was usedM. genitalium protein.The orthology value calculated between each potential pair of orthologs between conserved modules using ORTHOMCL . If the M. genitalium proteins was analyzed, comparing them with the GO annotations from annotated proteins inside the module that they belong to. The Information theory-based semantic similarity (ITTS) for predicting function through interacting proteins was followed [The transferred GO annotations list of unannotated followed ,14:• To calculate Semantic Similarity between the transferred GO terms from unannotated protein and GO terms from interacting neighbours inside the same functional module.• To consider GO terms with a similarity value above a threshold, then associate unannotated protein with these GO termsHowever, we can accept the predictions if they are similar to the annotations in the module, i.e., have similar GO ontology annotations, then a "manual curation" procedure is needed. A schematic view of the ITTS procedure is depicted in Figure M. genitalium was randomly divided into five parts. Four parts for model learning (training), and the remaining part for validation (testing). Known GO annotations were removed from the test set and it was tried to predict the terms of the proteins in the test set using the rest of the sets (training sets).The performance of the method was measured as an average value in a five-fold cross-validation analysis, where the GOA dataset for Finally, the predicted terms were compared with original annotations to determine the amount of correctly predicted annotations. Effectiveness is validated using standard information retrieval measures: recall and precision. Several terms have defined:A: set of annotated GO functions (in test set)P: set of predicted GO functionsF: GO functions in train setSo, we can establish:then, define:Mycoplasma genomes  to predict the M. genitalium annotations for two ontologies: Biological Process and Molecular Function. These genomes vary greatly in the availability of annotations and interaction data, which provides a good setup to study the strengths and limitations of our procedure. As shown in Table M. pneumoniae which reaches 98% and, on the other hand, M. pulmonis only reaches 81.5%) and the average of precision is 65%. On the other hand, the average of precision is 30% and the average of recall is 80% in the Biological Process ontology. As expected, recall is higher in the Molecular Function ontology due to, in previous studies, researchers having found that sequence similarity is strongly correlated with semantic similarity based on the Molecular Function aspect of GO [The variance of the reconstructed annotation was computed in order to see if it is affected by the random split choice. The performance of the method was measured as an average value in a five-fold cross-validation analysis using each of the ct of GO . This asThe high recall in both ontology cases indicates that a large number of GO terms is recovered by our method from all of the GO terms that are relevant to the search. The high precision in the Molecular Function ontology case indicates that a large proportion of the GO terms are relevant to the search among all of the GO terms recovered by our method.M. genitalium were used in this study. The GOA2005 in this article is dated March 2005, and was obtained directly from NCBI. GOA2005 contains 443 distinct gene-GO entries. The second GO annotation file, referred to as GOA2010, is dated October 2010 and was obtained from NCBI also. Table Two GOA files for Mycoplasma genomes to predict the M. genitalium annotations), but now using the older GO2005 file and then validating the newly predicted annotations using the newer GOA2010 association file as a second evaluation. Using a similar procedure as Tao's [M. genitalium predictions using M. pneumoniae results are presented here. However, the rest of the results can be obtained from the authors upon request , the same procedure as above was followed , while the higher semantic similarity values for the other genomes oscillate between 0.7 and 0.85.We found that our procedure performed well, for the Molecular Function Ontology, and high precision scores (more than 80%) and recall values of nearly 60% were obtained. In the Biological Process Ontology, precision scores obtained were also high (more than 90%) and recall values of nearly 85% were obtained for those GO terms that were associated with two or more genes. However, it can be seen that in both experiments  poor values were obtained compared with the other genome predictions in the case of We also, wish to determine if our method can be compared with other similar method in terms of precision and recall when trying to predict informative GO terms. Informative GO terms were defined as those terms that are annotated to at least n proteins (n = 10) and has at least a level-4 or higher. Defining:t: Relevant/Informative GO termsn: Retrieved GO termsand define now Precision and Recall as:Here, how well our procedure detects informative GO terms, as compared to other similar approximations is studied. FS-Weight is an algorithm which predicts the function of a protein based on the idea that the interaction between indirect neighbours inside a PPI is likely to share common functions . These iMycoplasma species. Due to space constrictions, only M. genitalium and M. pneumoniae results are presented here, however, the rest of the results can be obtained from the authors upon request. The precision versus recall graphs for the prediction of informative GO terms are depicted in Figure We wish to study the prediction performance of our procedure for detecting conserved modules among Different semantic similarity measures were used to obtain scores between the GO terms included inside of each predicted module and the GO terms obtained using our procedure. For PPI predictions, the modules obtained were assigned using NetworkBLAST and then calculated the semantic similarity between the GO terms included and the GO terms predicted. For BYPASS predictions, the GO terms were assigned to the predictions using the Gene Ontology database directly. The semantic similarity values were then used as variable thresholds. .M. genitalium) and, then, the predicted GO terms have lower similarity values than do our predictions. It can be seen, however, that our results have the same problem as do the PPI aproximations [It can be seen in Figure imations . Due to M. genitalium unnanotated genes with function predictions, with respect to GO annotations, can be found in Table M. genitalium, M. pneumoniae, M. hyopneumoniae and M. penetrans.Finally, the number of It has been shown herein, that our procedure out-performs other similar algorithms to predict GO-based annotations using Protein-Protein networks, with equal or higher overall precision from a significantly broader range of GO terms. The incorporation of other approximations such as functional module detection, conserved between species and orthology exploitation, predict function with higher precision and recall in two ontologies of the GO database. As compared to other GO search engines, our algorithm is capable of finding GO terms with high semantic similarity values due to using orthology information between proteins predicted inside functional modules conserved between species, and it has been also shown to recapitulate "known" future GO annotations artificially removed from the dataset using five-fold cross-over validation, with high precision and recall.Mycoplasma genomes could help to improve protein function in other Mycoplasma less-annotated genomes in a more effective way than ab initio classic annotation methods. However, caution must be exercised when using this technique. We have shown how critical neighbour genomes with good GO annotation are: The performance of this procedure is limited because it needs Mycoplasma genomes with a high GO annotation degree and a high number of predicted conserved modules. If genomes were used with low predicted modules and a low number of GO terms annotated within those modules, a reliable number of predictions could not be achieved (data not shown). Future work will cover this by using the latest development of the GO database, and which evolutionary distances between Mollicute genomes are still allowable in order to predict a reliable number of conserved functional modules between them.We believe that our combined approach could be applied in future as a high-precision Mollicute genome annotation procedure: Moderately well GO-annotated Mycoplasma proteome in the GO database http://www.geneontology.org. The version of GO used in this study dated from October, 2008, and was parsed for each of the species, thus avoiding several problems:The standardised Gene Ontology vocabulary (GO) was used  as a staMycoplasma such as: GO:0000004. .• Excluding terms annotated as obsolete and terms that no have relation with Mycoplasma proteomes are annotated in GOA files mainly with GO terms with evidence code IEA (Inferred from Electronic Annotation). To be retained, IEA annotations must be manually reviewed in order to be assigned an upgraded Evidence Code such as ISS . All the of IEA GO terms that do not have an associated ISS code were removed.• Nearly all • IEA annotations are generalised to apply to a diverse range of species and usually only represent very broad functions such as 'Protein binding' and 'Enzyme binding'. In effect, this means that as functional genomics data are modelled using GO annotation, a large proportion of the remaining data describes only very broad biological concepts . GO termThe semantic similarity between two concepts has been used for investigating the relationships between GO annotations and gene sequences ,39 as weThe information content of each GO term for each protein inside the detected modules was calculated and then three measures were applied to estimate the semantic similarity between GO terms assigned using orthology exploitation to un-annotated proteins and the GO terms assigned to the annotated proteins inside the same functional module. Information content is defined as the frequency of each term which occurs in the GO corpus.go1 and a GO terms set GO = {go1,go2...kgo} is dfined as:The semantic similarity of one GO term p(go), where there is more than one shared parent. ms pis defined as;As GO allows for multiple parents for each concept, two GO terms can share parents by multiple paths. We take the minimum S is the set of parent terms shared by go1 and go2.where Different similarity calculations were then followed depending upon the GO ontology. For the Molecular Function ontology, Lord's procedure  was follFor the Biological Process ontology, we followed Couto  and Lin and also, Jiang  measure The sheets are tabulated in two columns . The STRING scores are not described in this file but can be available from authors upon request.Click here for fileM. genitalium predictions using M. pneumoniae PPAN. The file is tabulated in four columns: M. genitalium ORF, Number of functional module conserved between M. genitalium and M. pneumoniae, Resnik GO Similarity Measure between GO terms list assigned from M. pneumoniae ortholog and GO terms from neighbor M. genitalium protein inside its functional module, List of GO terms assigned to the protein.Click here for file