Proteins do not act in isolation; they frequently act together in protein complexes to carry out concerted cellular functions. The evolution of complexes is poorly understood, especially in organisms other than yeast, where little experimental data has been available.E. coli and yeast in order to study differences in the evolution of complexes between these two species. We show that substantial differences exist in how complexes have evolved between these organisms. A previously proposed model of complex evolution identified complexes with cores of interacting homologues. We support findings of the relative importance of this mode of evolution in yeast, but find that it is much less common in E. coli. Additionally it is shown that those homologues which do cluster in complexes are involved in eukaryote-specific functions. Furthermore we identify correlated pairs of non-homologous domains which occur in multiple protein complexes. These were identified in both yeast and E. coli and we present evidence that these too may represent complex cores in yeast but not those of E. coli.We generated accurate, high coverage datasets of protein complexes for E. coli and yeast. Whereas some yeast complexes have evolved by recruiting paralogues, this is not apparent in E. coli. Furthermore, such complexes are involved in eukaryotic-specific functions. This implies that the increase in gene family sizes seen in eukaryotes in part reflects multiple family members being used within complexes. However, in general, in both E. coli and yeast, homologous domains are used in different complexes.Our results suggest that there are differences in the way protein complexes have evolved in  Most proteins in cells carry out their function as subunits of protein complexes . These aDezso et al.  have shoIt is currently unclear how well protein complexes are conserved between species. For a particular complex in one species, many species are deficient in some of the subunits . AdditioThe evolutionary conservation of some complexes has been examined in detail. Comparisons of the eukaryotic SWI/SNF and RSC chromatin remodelling complexes have shown that they consist of an evolutionarily conserved core of subunits . Across Many small complexes observed in structural data are homodimers and this arrangement confers several advantages. Firstly, homodimers can evolve stable interactions more parsimoniously than heterodimers . SecondlThere has been much discussion about whether duplicates deriving from Whole Genome Duplication (WGD) adopt different fates than those deriving from individual duplication events. Wapinski et al  showed tE. coli by EcoCyc . Sev. Sevin ses  uses fles [e.g. . Pereiraes [e.g.  uses loces [e.g.  and Restes [e.g.  use a coes [e.g.  has alsoes [e.g.  showed tEscherichia coli and the single-celled eukaryote Saccharomyces cerevisiae. For these species we derived combined PPI datasets using experimentally determined interactions from the Intact [E. coli than for yeast.The evolution of protein complexes is still poorly understood and differences between species have been difficult to study on a global scale. In order to probe the differences in complex evolution between species we created protein complex datasets for a prokaryote and a eukaryote: the gram-negative bacterium e Intact  and MINTe Intact  resourceE. coli differs from yeast with a smaller proportion of complexes containing cores of homologous protein pairs. Furthermore we show that both E. coli and yeast have complexes which share duplicate, non-homologous protein pairs. There is evidence that these pairs may also form cores of yeast complexes, but not those of E. coli.We used the datasets clustered by MCL to examine the distribution of homologues amongst protein complexes showing that they tend to be randomly distributed in both species. Non-randomly distributed homologues tended to be involved in eukaryote-specific complexes such as the spliceosome and proteasome. We show that complex evolution in I. We optimised this parameter on the yeast PIN by determining accuracy against the MIPS dataset of known yeast complexes as was done by Brohee & van Helden [In order to study the evolution of protein complexes we wanted to use accurate datasets with high genome coverage. We used an approach similar to that employed by Brohee & van Helden , Pereiran Helden , using tE. coli.Two sources of PPI data were considered in generating complexes: MINT  and IntAWhere possible each interaction was then weighted using the semantic similarity of the biological process GO terms of the corresponding proteins (see methods). This is a measure of the functional similarity of two proteins in terms of their role in the cell. Figure Figure E. coli MCL-GO complexes in reproducing the known E. coli complexes from EcoCyc. The optimal value of I was also 2.2. Although there is a slight increase in performance at higher inflation parameter values the separation from random is much greater at I = 2.2. We have observed an accuracy for E. coli complexes which is noticeably lower than yeast, although still very much above random. This might be caused by lower coverage of the E. coli genome with PPIs. We therefore go on to test our key hypotheses using several pre-existing datasets.Figure E. coli complexes containing a total of 2210 distinct protein sequences and 855 yeast complexes containing a total of 4740 distinct protein sequences. These complex datasets thus covered roughly 56%, and 85% of E. coli and yeast genomes respectively based on genome sizes of 3952 and 5586 genes (genome sizes were taken from Integr8 [E. coli complexes.The MCL-GO clusters for each species were filtered to remove clusters containing only one protein. This resulted in 574  Integr8 ). FigureE. coli and yeast around half of complexes were completely covered by only one term. The majority of proteins (>50%) could be described by a single functional term in ~75% of E. coli and yeast complexes. These results suggest that the MCL-GO complexes tend to be functionally coherent, with the majority of proteins in the majority of complexes performing the same general function. Furthermore it suggests that in both species, complexes can be reasonably well annotated using the most frequent term applied to its constituent proteins.To determine whether the MCL-GO complex datasets made biological sense we analysed their functions. Although we have used functional terms (Gene Ontology) to generate the complexes, we felt it was important to determine whether they represented tight functional units. We have done this using FunCat terms rather than GO to try to reduce circularity, however we accept that there may be some bias. In any case we believe it is interesting to examine the functional distribution of complexes in our datasets. Figure E. coli had a larger proportion of complexes devoted to metabolism and energy than yeast whereas yeast had a greater proportion of complexes involved in the cell cycle, transcription and cellular transport. Cell cycle and transcriptional processes are more complex in eukaryotes due to DNA packaging in chromosomes and intricate regulation respectively. Thus the MCL-GO complexes for E. coli and yeast appear to reflect the known biology of these species.We annotated each complex using its most common FunCat term. Figure E. coli and yeast MCL-GO complexes to the curated EcoCyc and MIPS complexes. The figures are presented in additional file E. coli and yeast.We also compared the distribution of functions for There has been much debate about the fate of duplicated genes. It has been proposed that newly duplicated gene products initially retain common interactions which subsequently diverge . There hE. coli and yeast MCL-GO complexes, the number of superfamily members against the number of different complexes in which each superfamily is found. There was a strong positive correlation between superfamily size and the number of complexes in which that superfamily was found. For E. coli r2 is 0.99 and for yeast 0.97. This suggests that duplicate domains do not tend to conserve their functional context.Figure E. coli superfamilies and 95% of yeast superfamilies were randomly distributed. The exceptional, non-randomly distributed superfamilies are discussed in the next section.Are there superfamilies which do not follow this trend and tend to conserve their complex membership? For each superfamily we determined how many times two proteins, both containing a member of that superfamily, were found together in a complex. This was compared to the number of co-complex pairs that would be expected if the proteins were distributed randomly amongst complexes (see methods). We found that, for most superfamilies, their members did not co-occur in complexes more than would be expected by chance. 98% of biological process Gene Ontology terms we found that 28% of superfamilies in E. coli and 22% in yeast had members which were involved in more similar biological processes than expected by chance (p < 0.01). While homologous domains tend to become involved in different complexes after duplication, 1/5 to 1/3 of superfamilies appeared to conserve their functional role to some extent. However, when we compared the functional similarity of the proteins with which each superfamily member was directly interacting, there was much less conservation (1% of E. coli and 8% of yeast superfamilies having interactors with conserved function), i.e. if protein A interacts with proteins B, C and D and protein A homologue A' interacts with E, F and G, then B, C and D are not functionally similar to E, F and G. This suggests that those superfamilies which conserve their function to some extent tend to diversify into distinct aspects of similar processes. This has been recognized previously in the work of Baudot et al. [E. coli than in yeast, although the functional data we have used is less reliable in E. coli. Further details of this analysis are presented in additional file We wanted to know whether different members of a superfamily were involved in similar biological processes despite their random distribution amongst complexes. Using t et al. . Here wee p < 0.0. While hA small number of superfamilies were found to be non-randomly distributed amongst MCL-GO complexes in the previous analysis; Table E. coli there was only one non-randomly distributed superfamily identified, the NAD(P)-binding Rossmann-like Domain superfamily. This is a very large, universal  domain superfamily which provides oxidoreductase activity in a wide variety of biological processes. Those complexes containing multiple members of this superfamily tended to be large, with diverse functional roles. It was therefore unclear as to the role of multiple members of this superfamily in individual complexes.In RNA-binding superfamily was found in two complexes relating to the spliceosome. The spliceosome is a complex which removes introns from pre-mRNA and requires functions which include binding a variety of RNAs. Multiple members of the Quinoprotein Amine Dehydrogenase Domain superfamily were found in complexes rich in annotation relating to the spliceosome in one case and rRNA processing in the other. The Ribosomal Protein superfamily was found in a complex rich in annotation for rRNA processing. rRNA processing is known to occur in the nucleolar complex which is involved in the production of ribosomes. However associations related to rRNA processing may represent a bias in some of the experimental data used to generate the complexes. Some high-throughput complex identifications in yeast [In yeast there were six non-randomly distributed superfamilies amongst MCL-GO complexes. These fell into three categories. The first is RNA processing. The in yeast ,46 contain yeast . Indepenin yeast .Glutamine Phosphoribosylpyrophosphate superfamily is involved in Ubiquitin-mediated endopeptidase activity via the proteasome complex and different members of the superfamily are required for different types of protease activity [The second category is the proteasome. The activity .Protein Tyrosine Phosphatase superfamily are found in a complex involved in signal transduction via a MAP kinase pathway controlling pseudohyphal growth. There was no clear role for the Ubiquitin-Like superfamily.The third category is signal transduction. Multiple copies of the It appears that those members of superfamilies which cluster together tend to be involved in eukaryote-specific processes. They are however almost exclusively universal superfamilies , suggesting that these eukaryotic advancements have largely developed from duplication and divergence of pre-existing superfamilies. Here we have described the functions of complexes to better understand the importance of co-complex paralogues. In the spliceosome and proteasome variations on similar functions such as RNA binding and proteolysis are required. Multiple copies of homologous regulatory proteins may represent alternative regulatory subunits of signalling/regulatory complexes e.g. the Myc-Max and Mad-Max basic-helix-loop-helix transcription factor complexes noted by Pereira-Leal et al. . In MCL-E. coli and yeast.We have shown that homologous domains tend to be randomly distributed amongst protein complexes and that duplicates therefore tend to diversify rather than remain involved in the same complex. An alternative analysis by Pereira-Leal et al.  has showE. coli or yeast complex we determined whether there was at least one pair of proteins sharing, in the first case, a homologous domain or, in the second case, their entire multi-domain architecture . In E. coli 7.5% of complexes contained homologues at the domain level; 1.5 times more than expected by chance. There were 516 pairs of homologues co-occurring in E. coli complexes and these pairs were found to interact significantly more often than random pairs of proteins from the same complex (p = 0.001). For yeast the value was much higher and we observed 18.4% of complexes which contained homologues, 3.4 times more than expected. 720 pairs of co-complex homologues were identified in yeast and these were significantly enriched for interactions (p = 0.001). Furthermore these pairs were found to be co-expressed to a greater degree than expected by chance giving further support to their concerted role in complexes  or those sharing entire multi-domain architectures (protein homologues).E. coli was influenced by the way in which the complexes were generated, the frequency of homologues in the PINs of the two species were also examined. This showed the same trend, with a smaller percentage of links between homologues in E. coli than yeast. This suggests that a lower frequency of interacting homologues in E. coli is a feature of interactions in general, not just those within complexes. Details are presented in additional file To determine whether the trend for fewer complexes containing homologues in E. coli complexes showed no significant increase in the number of complexes containing homologous pairs relative to random complexes. However the Gavin and Krogan Exp-TAP yeast complexes showed significant proportions of complexes containing homologous pairs. Near the end of our study two further datasets providing useful comparison were published. Hu et al. [E. coli protein complexes created by clustering high-confidence PPIs. Pu et al. [E. coli, the TAP-MS datasets provide no evidence for homologous pairs having a significant role in E. coli complex evolution, however the dataset of Hu suggests that some homologous pairs may have a small role as was found using the E. coli MCL-GO dataset.We also examined several alternative complex datasets to determine whether they supported our conclusions Figure . Four ofu et al.  publisheu et al.  publisheE. coli than in yeast, and reaffirmed  that hol et al.  in yeastE. coli MCL-GO complexes, involving 156 superfamilies. These pairs occurred in 68 separate complexes (~12%). This is a greater proportion of complexes than that containing paralogous pairs (~8%). In yeast MCL-GO complexes, 183 pairs were identified, involving 186 superfamilies and 83 complexes (~10%). Full details of the superfamily pairs identified are presented in additional file E. coli and yeast there was a significant tendency for interaction (p < 0.001). In both species the pairs were also significantly more functionally similar  than expected by chance (p < 0.001). Furthermore the pairs tended to have more highly correlated expression than expected by chance  in yeast were involved in correlated superfamily pairs. 39 of these superfamilies were involved in correlated pairs in both species, however none of the superfamily pairs were found to be correlated in both species. This suggests that these pairs may not persist over long evolutionary timescales.17% (114) of superfamilies in E. coli represent complex cores or whether there is further evidence against this mode of evolution in this species.Having determined that both species have protein complexes with correlated domain pairs, we wanted to determine the role of these pairs in the complexes. In particular we were interested to see whether these pairs might represent cores of complexes. In order to do this we used the same method as Pereira-Leal et al. who showeE. coli to be present in more distantly related organisms than other proteins , suggesting that they are not associated with evolutionary cores of complexes.Although there is a tendency for orthologues of interacting homologous pairs from ns Table , this trInteracting, homologous proteins in yeast were found to be significantly older than proteins in general (p < 0.01). This supports the result of Pereira-Leal et al.  using moE. coli Exp-TAP datasets support the trends identified in the MCL-GO dataset. Neither proteins with homologous domains nor correlated domain pairs were significantly older than other proteins in the Arifuzzaman [E. coli datasets. The picture is less clear in the yeast Exp-TAP datasets. Although the Krogan dataset supports the finding that correlated domains are older than other proteins, the test for homologous pairs was not quite significant. In the Gavin Exp-TAP [Table fuzzaman  and Butlfuzzaman  Exp-TAP  Exp-TAP  yeast daE. coli and yeast. In order to achieve this, protein complex datasets were generated and shown to accurately reproduce known complexes.We have presented an analysis of the distribution of homologous domains in the protein complexes of E. coli[E. coli and yeast.We found that homologous protein domains tend to be randomly distributed amongst complexes and therefore tend to occupy distinct functional niches. Those exceptional superfamilies whose members were found together more than expected by chance were involved in signalling/regulation or a limited number of eukaryote-specific complexes requiring colocation of similar functions. It has been shown that homologues are rarely found together in small molecule metabolic pathways of E. coli and we hE. coli there were far fewer complexes which could have evolved in this way. It is known that there is less gene copy redundancy in prokaryotes and that their gene families are smaller [Pereira-Leal et al.  proposed smaller  resultin smaller . We showE. coli however. Complexes are known to have duplicated in yeast and these correlated pairs are likely to include parts of duplicated complexes. There has been a whole genome duplication in yeast [E. coli [E. coli complexes have changed relatively little over a large evolutionary timescale, cores would not be detectably older than other parts and they could not be distinguished by their age. This would account for a failure to identify correlated domains as E. coli complex cores.We then identified pairs of correlated domains which occur together in multiple complexes as was done previously by Betel et al. . It was in yeast , but not[E. coli , however[E. coli ,40, but [E. coli ,19. If EE. coli than yeast and thus we can be less certain about our conclusions in this organism. We have however tested our key conclusions using several alternative datasets for both species and found similar results in every case.There is currently less protein-protein interaction information available for Drosophila melanogaster was considered for our analysis; however there was insufficient data to produce a reliable complex dataset.In future studies it would be interesting to examine higher eukaryotes to determine whether these processes of complex evolution are more common than in yeast. E. coli (NCBI taxon id: 562) there were 13941 interactions between 2865 proteins (~72% genome coverage) and for S. cerevisiae (NCBI taxon id: 4932) 38825 interactions covering 5735 proteins (~100% genome coverage).Protein-Protein Interaction (PPI) datasets were generated by taking the union of interactions from the MINT  and IntAE. coli and yeast combined PPI datasets described above were clustered into complex datasets using the MCL algorithm [The lgorithm . It has lgorithm . Complexlgorithm  method dlgorithm  to detergold standard complexes from MIPS in the case of yeast and EcoCyc in the case of E. coli. The comparison was performed in the same way as described by Brohee & van Helden [The inflation parameter, which controls the granularity of the clusters produced, was optimised by comparing predicted complexes (clusters) with curated, n Helden , using ti captured by the predicted cluster j, best reflecting that complex.Sensitivity is the weighted average over all complexes of the proportion of each gold standard complex Ni is the number of proteins in complex i and In the above formula Sni, j for a particular complex, taking the greatest value over all predicted clusters.The complex-wise sensitivity is the maximum sensitivity Ti, j is the number of members of complex i in cluster j. Sensitivity is calculated only considering complexes with more than 1 protein.In the above formula Positive Predictive Value (PPV) is a measure of how pure the predicted clusters are, i.e. for the most common complex type in each cluster, what percentage of labelled proteins in the cluster are from this complex?Tj is the number of members of cluster j with membership of a known complex and Here PPVi, j for a particular cluster over all complexes. PPVi, j is described below.The cluster-wise PPV takes the maximum value of Ti, j is the number of members of cluster j in complex i. The trade-off between sensitivity and PPV was captured by taking the geometric mean of the sensitivity and PPV, referred to as the accuracy (Acc).The accuracy achieved on these datasets was compared to that for randomly generated complexes to show that our procedure was useful, as was done by Brohee & van Helden . This waE. coli MCL-GO complexes, covering 1579 proteins (71%). The yeast MCL-GO complexes had 2666 CATH domains from 630 superfamilies over 2070 proteins (44% of protein in this dataset). Throughout this work, multiple members of the same superfamily within protein chains were ignored.CATH  protein E. coli, coverage with GO terms derived from experimental annotation was very low so Electronically Inferred Annotation (IEA) was included, only negative results  were excluded. This resulted in 3989 biological process terms over 1803 proteins (82% coverage). For yeast MCL-GO datasets, IEA terms were ignored. This resulted in 10622 terms over 3926 proteins for yeast (83% coverage).Functional data in the form of Gene Ontology  annotatiE. coli proteins (71% coverage) and 12385 terms covering 3432 proteins in yeast (72% coverage).FunCat  functionE. coli and from MIPS [Pre-existing complex datasets were used in our analyses. High-quality, curated datasets of known complexes were required in order to determine how accurately PPI datasets could be clustered into complexes. Such datasets were available from EcoCyc  for E. crom MIPS  for yeasE. coli Exp-TAP complex datasets were derived from Butland et al. [http://sunserver.cdfd.org.in:8080/protease/PPI/. Yeast Exp-TAP complexes derived from Gavin et al. [MCL-GO complexes, derived by clustering PPIs from a variety of experimental approaches, were used throughout this work as they had higher coverage of the genomes of each organism than curated datasets or individual experimental approaches such as TAP. The clustering approach used however only allowed each protein to exist in a single complex. In reality some proteins exist in multiple complexes and this discrepancy could bias our results. Therefore we also examined complexes based only on TAP data, which does allow individual proteins to appear in multiple complexes. TAP experiments identify relationships between one 'bait' protein and multiple 'prey', directly inferring complexes without the need for clustering. These are referred to collectively as Exp-TAP datasets. d et al.  and Arifd et al.  and downn et al.  and Krogn et al. were downn et al. . These EExperimental datasets were annotated with GO terms and CATH domains using the same protocols as for the MCL-GO complexes.E. coli, 101 had at least 5 members (62% of domains); of 630 in yeast 113 had at least 5 members (68% of domains). For each superfamily we determined the number of distinct pairs of proteins containing that superfamily which were found in the same complex. This was compared to the number of distinct pairs which were found together in 10000 randomised complex datasets. Complexes were randomised by shuffling members between complexes, retaining the complex size distribution. For each superfamily, p-values were calculated by determining the proportion of these 10000 randomised trials where the observed number of pairs was exceeded. The False Discovery Rate (FDR) correction for multiple hypothesis testing was applied , using Having asked whether superfamilies have members which tend to co-occur in complexes, we asked whether complexes tend to contain co-occurring superfamily members. In examining the proportion of complexes which contain homologues we considered both domain and protein homologues. Two proteins which shared a common CATH superfamily member were considered domain homologues. Two proteins which shared their entire CATH Multi-Domain Architecture (MDA) were considered protein homologues. A MDA is the series of domain annotations from N terminus to C terminus, excluding multiple domain segments, potentially domain-containing gaps and tandem repeats. We wanted to determine whether complexes tended to contain pairs of homologues and so the number of complexes which contained at least one pair of homologous proteins (using either domain or protein homologues) was counted. To determine whether the number of observed complexes was significant, the observed count was compared against the distribution of counts derived from 10000 randomised complex datasets. P-values were calculated empirically. Complex datasets were randomised by shuffling complex membership while retaining the size of complexes. We assume that these, largely interacting, homologues have resulted from homodimer duplication, which has been demonstrated by  to be a A and B such that A occurs in protein p and B occurs in protein q and p and q are present in the same complex. We then determined which of these pairs occurred in more complexes than expected by chance. This procedure was performed previously by Betel et al. [We identified pairs of CATH domain superfamilies (i.e. pairs of non-homologous domains) l et al. . For eacWe wanted to determine whether proteins containing these correlated domain pairs tended to interact directly. We compared the frequency with which these proteins were observed to interact in MINT and IntAct data with the frequencies of interaction of the same number of randomly chosen co-complex protein pairs. Sets of random co-complex pairs were created 10000 times to derive a p-value. We also wanted to know whether correlated pairs represented functional units within complexes. To do this the average GOSS score between these pairs was compared with the average GOSS score between the same number of random co-complex protein pairs. Again this was performed 10000 times to derive a p-value.E. coli and yeast protein amongst 32 species (listed in additional file We wanted to determine whether correlated domain pairs might represent protein complex cores. To do this we assume that proteins in the core of complexes are older than other proteins, i.e. their orthologues are found in more distantly related species. We used the analysis employed by Pereira-Leal et al.  to deterE. coli in this analysis were 'E. coli specific', 'Proteobacteria', 'Proteobacteria/Firmicutes', 'Bacteria', 'Eukaryota+Bacteria', 'Bacteria+Archaea' and 'Universal'. For yeast we used 'Saccharomyces cerevisiae specific', 'Fungi', 'Metazoa/Fungi', 'Eukaryota', 'Eukaryota+Archaea', 'Eukaryota+Bacteria' and 'Universal'. The chi-square test was used to determine whether significant differences existed in the age distribution of different classes of proteins.The age groups defined for AJR designed the study and carried out the analyses. JAGR assisted in study design and helped draft the manuscript. CAO supervised the project and helped draft the manuscript. All authors read and approved the final manuscript.supplementary methods and results. We analyse complex functions in MCL-GO and gold standard datasets showing potential biases in the gold standard datasets. We examine the functional coherence of superfamilies finding that yeast has more superfamilies which are involved in a wider range of biological processes, but are on average less diverse in terms of their catalytic actions or cellular locations. Furthermore we find that different members of the same superfamily carry out their functions in different contexts. We describe methods and detailed results for calculating correlated expression. We examine the frequencies of observed and expected interactions between homologues in protein interaction networks. The details of the phylogenetic profiling work is described.Click here for file


However, the low quality of current PPI data detected from high-throughput screening techniques has diminished the potential usefulness of the data. We need to develop a method to address the high data noise and incompleteness of PPI data, namely, to filter out inaccurate protein interactions  that the protein pairs interact for each biological or computational evidence source. The second step, interaction classification or InterCLASS, integrates the collected Bin-Confidence scores to build classifiers and identify reliable interactions.We performed comprehensive experiments on two benchmark yeast PPI datasets. The experimental results showed that our proposed method can effectively eliminate false positives in detected PPIs and identify false negatives by predicting novel yet reliable PPIs. Our proposed method also performed significantly better than merely using each of individual evidence sources, illustrating the importance of integrating various biological and computational sources of data and evidence. Protein-protein interactions (PPIs) are at the core of the biological machinery of any living cells. PPIs are involved in almost every level of cellular functions, playing key roles in the transport machinery across the various biological membranes, the packaging of chromatin, the network of sub-membrane filaments, the regulatory mechanism of gene expression, and so on , while aIn recent years, many high-throughput experimental techniques, most notably yeast-two-hybrid and anity purification, have been developed to enable comprehensive detection of PPIs. Numerous publicly available databases, such as DIP , BIND 44 and Biofalse positive PPIs that do not occur in the cell. At the same time, the high-throughput methods can also be biased against soluble or membrane proteins and fail to detect certain types of interactions such as weak transient interactions and interactions that require post-translational modification. This results in false negative detection and low experimental coverage of the interactomes [However, the quality of current detected PPI data is far from satisfactory. The experimental conditions in which the detection methods are carried out may cause a bias towards detecting interactions that do not occur under physiological conditions. In other words, the experimental data may contain ractomes ,7.How serious is the PPI data quality situation - specifically, what are the false positive and false negative rates in current PPI data? Many researchers have attempted to answer this question ,8-11. Byfalse positives and false negatives computationally. The computational methods can be categorized into two classes. The first line of bioinformatics research focuses on exploiting the topological properties of the PPI networks to assess the reliability of PPI. For example, Interaction Generality (IG1) [Given the experimental limitations in high throughput PPI screenings, researchers have recently begun to exploit the growing availability of various additional biological and computational resources to address the issues on ty (IG1)  was propty (IG1)  was substy (IG1) , made usty (IG1)  measuredty (IG1) -18 were et al. [et al. [et al. [Another line of the research exploits the increasingly enriched genomic features to identify true positives and predict false negatives. Deane et al.  selected [et al.  employed [et al. ,22. Pati [et al.  also useIn this paper, we aim to exploit the use of both topological properties of PPI networks and the genomic features of proteins to identify reliable detected PPIs and predict putative interactions. We propose a method to integrate the diverse biological and computational resources. Our method consists of two steps: interaction binning (InterBIN) and interaction classification (InterCLASS). Since the diverse biological and computational evidences were individually scored in a different nature, we first group the protein pairs for each class of evidence into bins so that we can more accurately estimate the likelihood (Bin-Confidence score) that the protein pairs interact. Then, we employ machine learning methods such as support vector machines and Bayesian classifier to classify whether a detected or putative PPI is reliable or not using the Bin-Confidence score re-weighted evidences as features. We conduct experiments to show that our proposed method is significantly better than the existing methods, and that integrating the various biological and computational sources of data and evidence can provide better results.In this section, we present our two-step method for integrating diverse sources of biological and computational evidences for reliable PPI detection. First, we introduce each of individual biological and computational evidences which are used to assign original raw scores for protein pairs to indicate their propensity to interact. Then, we describe our InterBIN method to group protein pairs based on their scores from each evidence, and then define the more accurate Bin-Confidence scores for each protein pair. Finally, we describe our InterCLASS method that exploits the integration of the evidences re-weighted using Bin-Confidence scores and infers reliable PPIs using machine learning methods such as Support Vector Machines (SVM) and Bayesian classifier (BC).x, y), we use each of the following biological and computational evidences to evaluate their propensity to interact. Each evidence f assigns a score Sf  to  independently based on the nature by which the evidence was derived.In this study, given a protein pair  -- if x and y interact with some common proteins , then  is assumed to share some physical or biochemistry characteristics that allow them to bind to these common proteins. The more common proteins they interact with, the higher chance they interact.1. athRatio  and IRAPathRatio  assume tathRatio  only conathRatio , on the In this paper, we utilized all above three topological measures and compared their effectiveness to infer reliable protein interactions. In particular, for FS-weight, we used the following simplified variant as follows :Nx and Ny are the set of direct neighbors of the protein x and y respectively.where . Gene expression profiles. The correlation between the similarity of expression patterns for a pair of genes and interaction of the proteins they encode has been demonstrated for various species [2 species . Statistx, y), the proteins' propensity to interact can be measured by using the Pearson Correlation Coefficient between their encoded genes' expression profiles Gx and Gy as follows,Given a protein pair  database and others, have compiled comprehensive information about domains [3.  domains .x, y) can be computed by the number of interacting domains that they contain:In this study, we use domain-domain interactions to evaluate the propensity of a protein pair to interact . The proSPF  = |{di, dj)| di ∈Dx, dj ∈Dy, di interacts with dj}|, Dx and Dy are the set of Pfam domains of protein x and y respectively.Protein sequences and their similarity. One of the most fundamental and successful tools in the field of bioinformatics is sequence alignment. By aligning protein sequences with each another, we can evaluate how similar the protein sequences are. In most cases, close homologs (> 30% sequence identity) physically interact with each other [x and y, the bit-scores from the BLAST results are used to show the sequence similarity between them, denoted as SSS.4. ch other ,28. In pch other . Given tIn summary, the various biological and computational evidences mentioned above are widely-used for assessing the reliability of protein interactions. Another commonly-used biological evidence is functional similarity -- proteins that have the same molecular functions are more likely to interact since they are involved in the same biological processes. In this study, we intentionally exclude the use of functional information to predict reliable protein interactions -- we keep it to evaluate our experimental results eventually.The original raw scores obtained from each evidence,  have been directly exploited to identify reliable interactions previously ,15. HoweIntegrating original raw scores from various evidences is not as straightforward as one may think. Each piece of individual evidence actually provides information of a rather different nature; the original raw scores assigned by each method can differ significantly in scale, range, and distribution. For example, out of the 17262 interactions in DIP data, 9265 interactions  are with FS-weight scores less than 0.1 while 14099 interactions  are with IRAP scores at least 0.9. It is clearly not a good idea to integrate various evidences by directly using their original raw scores.x, y) interacts given the observation that a evidence f assigns a score Sf  to . To do this, we can examine all protein pairs that are assigned with the same score Sf  in the training data and compute the fraction of those protein pairs that interact or occur in positive training set P (how to construct positive and negative training data P and N will be described in next subsection). However, the scores assigned by each evidence f may not be discrete, and there may not be enough protein pairs that are assigned the same score. Thus, it is necessary that we first group the protein pairs with similar scores together.To address this issue, we re-weigh the evidence more robustly by estimating the likelihood that a protein pair  be the original raw score of the protein pair .Let us now introduce how to group interactions and calculate the Bin-Confidences for them using our method InterBIN. For a evidence n protein pairs, they can be divided into different Bins based on their original raw scores in following simple steps.Assuming that there are 1. Sort all protein pairs based on their original raw scores.x1, y1), ..., , first μ protein pairs , ..., ) are inserted into the first group xμ, yμ) will be also inserted into μ + k).2. For the sorted protein pairs,  and repeat Step 2 until m groups are obtained finally. If the last group μ , it is merged into the group 3. Start from the protein pair  in Bin-Confidencef  in equation 4, which is the confidence score of Using the above method, we divide all the protein interactions into different groups such that the interactions within the same group have similar original raw scores and each group also has similar size. We can then estimate the confidence score for each group by the proportion of positive examples within it. Basically, the more positive protein interactions a group P is the positive training set.where For all the protein pairs in a detected PPI network, InterBIN basically transforms their original scores from various evidences into standard Bin-Confidence scores. Next, we show how we incorporate the Bin-Confidence scores to build classifiers to identify reliable interactions.P and N. While experimentally derived protein interaction data are publicly available in a number of databases such as DIP [P and N ) to be with an unknown status -- we will decide if they interact using our machine learning-based classifiers discussed below.In order to use machine learning to build an accurate PPI classifier, first, we need to compile a positive and a negative training set h as DIP  and BioGh as DIP  etc, we h as DIP  by consiP and N, for each protein pair  in P and N, we gather the original score Sf  for each evidence f. Next, we compute the Bin-Confidencef  for all the protein pairs  with respect to f. With them, we are now ready to build the final classifiers that integrate the diverse biological and computational evidence sources. In this study, we apply two different types of classifiers, namely, Support Vector Machines and Bayesian classifier, that are popular classifiers in many bioinformatics applications.Given the positive and negative training sets The SVM  is a staP and N, we can build a feature vector  where fi is a score  from the individual evidences where is the total number of biological evidences. In this way, SVM is able to train a classifier [For each training example in assifier  exploitix, y) and its n features f1, ..., fλ, the probability that it is a negative interaction is denoted as P{false|f1, ..., fλ}. We can assume that the λ features of a protein pair are conditionally independent and then apply Bayesian rule to calculate this probability. In this way, the probability of  to be positive, P{true|f1, ..., fλ}, can be calculated as follows:Given a protein pair  to be positive with respect to the feature fi, P{true|fi}, can be estimated by using either its original raw score or its Bin-Confidence score for fi.where the probability of  have beμ was set as 1% on DIP data and 0.5% on BioGrid data. For example, μ = 1% means that each group has at least n × 1% protein pairs and n is the total number of protein pairs in the training set. We will explain how such a parameter setting came about in the coming subsection.After collecting the positive and negative training examples, we calculated the Area Under ROC Curve (AUC) for each method using both the original raw scores and Bin-Confidence score. The AUC for each method was obtained by performing a 5-fold cross validation. To group protein pairs and calculate their Bin-Confidence score, Table It is interesting to note that the genomic evidences appeared to have much worse performance than the topological evidences in Table μ, which was used to determine the group size, has a direct effect on the Bin-Confidences and on the accuracy for methods using Bin-Confidences. Figure μ varies.The parameter μ generally means a smaller number of groups. Therefore, there will be more interactions in each group having the same Bin-Confidence. As such, the resulting Bin-Confidence assignments can become too general as the large number of interactions within the same group cannot be distinguished. On the other hand, using a smaller μ risks getting less accurate Bin-Confidences due to the small number of samples in each group. Figure A larger μ was around 1%, while InterSVM achieved its highest AUC when μ was set as 0.05%. However, since there are only 8206 positive and negative examples in the DIP dataset, each group would contain only around 4 interactions if μ were to be set as 0.05%, resulting in inaccurate Bin-Confidences for interactions and the over-fitting of the SVM. As such, we recommend that μ is set as 1% for DIP data, and μ is set as 0.5% for BioGrid data.With the DIP dataset, the individual evidences as well as InterBC achieved their decent AUC when P nor in N ). We can apply the classifiers learnt from the training data to classify those with unknown status to address the false positive detection issue.Note that while protein-protein interactions have been detected and stored in the protein interaction databases such as DIP  and BioGMeanwhile, although current PPI data are becoming more and more comprehensive, it is still far from complete due to inherent experimental limitations and biases. Recent paper  concludex, y) in these figures means that top-x interactions (or predicted interactions) have an average similarity y.We have applied our integrative methods which is learned from existing reliable interactions and non-interactions, to predict false negatives and false positives. The quality of the predicted false negatives and false positives is evaluated and validated by the corresponding functional similarity scores between the protein partners. Figure For those interactions in the PPI datasets that we have labeled with an unknown status, we rank them by their decision values from each classifier. For DIP data, the top-ranked interactions by InterBC and InterSVM were shown to have much higher functional similarity than those ranked by individual evidences, including FS-weight, PathRatio and IRAP.  by InterSVM and InterBC respectively and found that they actually have a big overlap--there are 1574 interactions in common. The top 2000 candidates ranked by InterSVM also have 476 interactions that are confirmed to be reliable interactions in BioGrid data, while the top 2000 ranked by InterBC have 462 interactions that are similarly reliable. For example, the interaction between proteins YCR081W and YDR443C in both above two sets is believed to be reliable as it has been detected multiple times by TAP-MS -37. The et al. [i.e., these two interacting proteins have homologs that interact in other species; (2) whether interacting proteins have at least one GO annotation in common and (3) whether interacting proteins have interacting Pfam domains. In our proposed method, we have utilized six commonly-used features to identify reliable interactions. Please note that GO annotations (the second feature) typically are not used for predicting protein interactions since they are reserved to evaluate the performance of computational/experimental methods. In order to compare with Patil's method, we also included two new features in our method, namely, the number of homologous interactions and the semantic similarity of two interacting proteins (measured by the semantic similarity of their GO annotations as show in equation 6).Given an interaction, Patil et al.  proposedTable Meanwhile, InterSVM with either original scores or Bin-Confidence outperforms Patil's method on both datasets. We believe that the reasons why our proposed methods perform better than Patil's method are as follows. First, we integrated more biological and computational features that are indeed beneficial, e.g., FS-weight . Second,Protein-Protein interactions play important roles in cellular processes and provide an invaluable resource for network-based biological knowledge discovery such as protein complex detection , proteinIn this paper, we integrate diverse sources to detect PPI for eliminating the false positives and negatives. We first estimate the Bin-Confidences for interactions by grouping them based on their original raw scores from each source. Using the robust Bin-Confidence scores of protein pairs from the various sources as features, we train machine learning models using SVM and BC to integrate the sources and construct classifiers to detect reliable interactions. Experimental results show that our integrative method outperforms the approaches merely using each of individual topological or genomic evidences. The accuracy of SVM and BC is also significantly improved by using the Bin-Confidences instead of the original raw scores.Since our Bin-based machine learning approach is designed to integrate diverse biological and computational evidences, as future work, we will collect more relevant biological data , as well as computational evidences (such as phylogenetic similarity) to be included in our method. Based on the current results, we are confident that the inclusion of these additional evidences, while diverse in nature, will allow us to effectively filter false positives and predict novel false negatives from the current PPI datasets.We will first introduce the experimental data used in our work. We will also introduce evaluation measures for the accuracy of various prediction methods. The validation for the identified false negatives and false positives using Gene Ontology terms will also be presented.Two publicly available yeast PPI data, DIP  and BioGhttp://www.uniprot.org/).As to the genomic sources, interacting domains were downloaded from following 3 databases, 3DID , iPfam  and DOMIThe Receiver Operating Characteristics (ROC) curve is a graphical plot of the sensitivity vs. 1-specificity for a classifier as the decision threshold varies. The Area Under the ROC Curve (AUC) is a widely-used measure of the accuracy of a specific classifier.In this paper, in addition to our integrative approach (using SVM and BC), each original method for identifying reliable interactions can also be regarded as a classifier. For example, IRAP  can be uSince a protein performs a certain cellular function by interacting with other proteins, the cellular functions of interacting proteins are likely to be similar. Functional similarity between proteins based on the GO terms are often exploited as computational validation of protein-protein interactions ,15. Assux and y annotated by {g11, ..., gm1} and {g21, ..., gn2} respectively, their functional similarity defined in [SGO, is calculated as follows.Given two proteins fined in , SGO is the semantic similarity between GO terms gi1and gj2defined in [where fined in .The authors declare that they have no competing interests.MW, XL and HNC conceptualized and designed the method and drafted the manuscript together. MW was responsible for the implementation. CKK and SKN participated in discussion and conceptualization as well as revising the draft. All authors read and approved the manuscript.


Prediction of function of proteins on the basis of structure and vice versa is a partially solved problem, largely in the domain of biophysics and biochemistry. This underlies the need of computational and bioinformatics approach to solve the problem. Large and organized latent knowledge on protein classification exists in the form of independently created protein classification databases. By creating probabilistic maps between classes of structural classification databases (e.g. SCOP ) and claWe demonstrate that PROSITE and SCOP have significant semantic overlap, in spite of independent classification schemes. By training classifiers of SCOP using classes of PROSITE as attributes and vice versa, accuracy of Support Vector Machine classifiers for both SCOP and PROSITE was improved. Novel attributes, 2-D elastic profiles and Blocks were used to improve time complexity and accuracy. Many relationships were extracted between classes of SCOP and PROSITE using decision trees.We demonstrate that presented approach can discover new probabilistic relationships between classes of different taxonomies and render a more accurate classification. Extensive mappings between existing protein classification databases can be created to link the large amount of organized data. Probabilistic maps were created between classes of SCOP and PROSITE allowing predictions of structure using function, and vice versa. In our experiments, we also found that functions are indeed more strongly related to structure than are structure to functions. Function and 3D structure of the proteins are said to be related to each other . This unsemantic overlap. In other words, different schemes of classification may not be completely independent of each other, and relationships may exist between classes of different taxonomies. For instance, proteins sharing a certain motif may also belong to a common phylogenetic family.Growing proteomics data have motivated the design of many schemes to classify proteins. Proteins can be classified according to a variety of classification schemes based on features like proteins domains , structuProtein classification is performed by either manual annotation , or autocross learn using existing two classifications and identify relationships between the two. It would be, therefore, instructive to look at relationships between the two classifying schemes and assess if there is a semantic overlap between the two independent classification systems.With protein databases being populated at an astonishing pace, it has become essential to consolidate the knowledge latent in the existing and emerging databases. Presently these relationships can only be established by manual annotations using static accession numbers. These numbers (e.g. SwissProt accession number ) link prWe have modified previously reported cross-training algorithm by introducing a hierarchy based approach  to applydomain       accuracy [i] = test classifier CL on testing data TE [i]   end fori = 1 to 10   mean-accuracy = mean of accuracy [i] for    Calculate ss for this set using the t-test.   mean-ss = (mean-ss*(k-1) + ss)/kend whiless was defined asThe value of statistical significance k was found to be 4 on PROSITE dataset. For a given protein pi the count of a k-length subsequence f was defined asOptimal L is the length of the complete protein sequencewhere Count is the approximate number of occurrences of the feature f in a protein pi. To introduce added weightage to the active sites in the protein, the occurrence, Occ, was counted multiple times (c times). SwissProt [c was taken as 10 in our experimentations.wissProt  entries Blocks were defined as features and count was calculated asdistance is the dissimilarity indexwhere with the most conservedcorresponding blockThis definition ensures that more weightage is given to larger blocks, which are assumed to preserve more biological information. Further, weightage is inversely proportional to the block distance (dissimilarity index) with the most conserved block .Previous attempts to use secondary structure as features for protein classification have been mostly limited to utilization secondary structure content ,47, or lp of size L, a secondary structure array was defined asFormally, for a protein Then using this array the 2-D elastic feature was defined asMolecular mass, size, percentage of helices, beta strands in the whole protein etc. were other features used for classification. One column/dimension was maintained for each feature. Value of each feature was either equal to the absolute value (like mass in case of molecular-mass) or it was binary . Equal interval binning was used for many features  to allow generalization.A protein was represented as a vector of all the above features. This representation is based on an assumption that features are orthogonal to each other. This assumption was made for the sake of time efficiency and to reduce the complexity of algorithm.Hierarchical cross training on SVM involves introduction of new artificial dimensions/features to distinguish between the otherwise indistinguishable instances using normal feature sets. So if A-classes are a good predictors of B-classes, classification accuracy of proteins in B may be improved by allocating for each protein in B a set of new columns/features, one for each A-class Figure . Hence, Cmi are also orthogonal to all other features. Since protein classes are taken from a hierarchy, this assumption is not entirely true. This concern was addressed by modifying the algorithm and adapting it for hierarchical biological taxonomies.Here, hierarchy during cross training, the basic idea used was that a protein that belongs to a child class also belongs to the corresponding parent class. To be more specific, let p be a protein, c any class and Anscc be set of all classes ancestor to class c. Two cases arise:Firstly, one-vs-rest SVMs are trained for each class. For training a non-leaf class the positive data used is present within the descendant leaf nodes, while the rest of the data is taken as negative examples. While dealing with a Rule1: p has a high probability to belong to class c: Then p has a high probability to belong to the ancestor classes Anscc too.1. Rule2: p has a low probability to belong to class c: In this particular case, nothing can be said about p's relation with the ancestor classes Anscc.2. ross Train Algorithm:CCA) usingTrain SVMs for A-classes (DA).   proteins from dataset-A (CB)Train SVMs for B-classes (DB).   using proteins from dataset-B (pi in DB is classifiedEach protein CA and the corresponding   using Cmi =    class-membership vector (   (class-membership represents the probability of an   instance belonging to various classes in a   taxonomy) is calculated.   Here pi with SVM   by 'testing' protein jth class.   for the Update-Protein: Using the class-membership Cmi   for every protein in B, the protein features   are updated using protein update rule.Similarly repeat the above steps for proteins in A.CA using the modified proteins from DA.Retrain CB using the modified proteins from DB.Retrain Return to step 3 if there is increase inCA and CB.   classification accuracy of c with "high probability". One simple way of estimation is by calculating the class-membership vector Cmp for any given protein p by testing it with the SVM-classifier for each class. The class with the maximum positive value in Cmp is defined as the only class to which the protein p belongs to with "high probability". This method is, however, naive and would miss the correct class in case more than two classes have high and close positive values. Also, during experimentations it was found that in many instances the entire Cmp vector is negative and hence no single positive value exists. A softer version was therefore developed which can replace the cross training update rule where Cmp was re-scaled and then the above two rules were used to update the membership values of the ancestor classes.The above information is incorporated in the protein update rule. Further, it needs to be established when does a given protein belongs to a particular class ubroutine to update Protein Vector:SI/P : protein p, O/P : updated proteinCmp.Calculate the class-membership vector Rescaling step: Find maximum class-membershipvalmax. Add    value    to each element in the vector. This step will   ensure a positive value for at least one class.Identifying high probability classes: Find allCp for which class-membership   classes    value is positive.for every class c ∈ Cp {c is valc.   Let the class-membership value of Anscc.   Find the ancestor classes Updating ancestor classes: IncreaseAnscc by valc.}   class-membership for each class in end Subroutinefi (classes from taxonomy A) value with a constant value. The comparison decides whether to go to either the left or right subtree. The leaf-nodes are classes to which the instant can belong to (classes from taxonomy B). Hence, if we know the corresponding membership in one taxonomy for a protein, it can be used to find its class in the other taxonomy. The advantage of this approach is that the protein is not required to belong to only a single class and the user can input the strength for each class. A probabilistic weighted score is generated based on the decision tree. We employed the decision tree algorithm to find out the probability of proteins belonging to a class in SCOP to belong to a given class in PROSITE, and vice versa. This created a probability map from SCOP to PROSITE, and vice versa, linking all the classes in either taxonomy to each other with a probabilistic weight. Since PROSITE is a functional classification scheme and SCOP is a structural classification scheme, by corollary, the above probabilistic map can be construed as a probabilistic map between functional and structural properties of proteins.The decision tree  algorithSubroutine to create decision tree:A &B are taxonomies.DA = dataset for A after full cross-training with B.Cmi∀pi ∈ DACalculate class-membership vector B.   using classes in pi in A using Cmi.Represent every protein    Call it DTA using this dataset Train a decision tree DTB.Repeat the above steps for B to get decision tree DTA is a ruleEach path in classes-in-B → class-in-A.   DTB is a ruleEach path in classes-in-A → class-in-B.   end SubroutineKG and VS devised the idea, implemented the project, conducted experiments and drew conclusions. AL supervised the project and drafting of the manuscript.Baseline test for hierarchical cross training. Linear SVM based classifiers were used to train SCOP and PROSITE without using classes of the other taxonomy as features. In a subsequent test, hierarchical cross training algorithm was used to train the classifiers using the same feature sets (detailed in Methods section). An average increase of 5:2% in F-measure was obtained after employing hierarchical cross training. The table lists the old  and new  F-measure obtained for a few illustrative classes.Click here for file


Our results are consistent across the full spectrum of test domains including those from recent CASP experiments and most notably in the ‘twilight’ and ‘midnight’ zones wherein <30% and <10% target-template sequence identity prevails (mean twilight RMSD of 1.69A). We further demonstrate the utility of the PCAIN protocol to derive biological insight into protein structure-function relationships, by modeling the structure of the YopM effector novel E3 ligase (NEL) domain from plague-causative bacterium Yersinia Pestis and discussing its implications for host adaptive and innate immune modulation by the pathogen. Considering the several high-throughput, sequence-identity-independent applications demonstrated in this work, we suggest that the PCAIN is a fundamental fold feature that could be a valuable addition to the arsenal of protein modeling and analysis tools.Vastly divergent sequences populate a majority of protein folds. In the quest to identify features that are conserved within protein domains belonging to the same fold, we set out to examine the entire protein universe on a fold-by-fold basis. We report that the atomic interaction network in the solvent-unexposed core of protein domains are fold-conserved, extraordinary sequence divergence notwithstanding. Further, we find that this feature, termed protein core atomic interaction network (or PCAIN) is significantly distinguishable across different folds, thus appearing to be “signature” of a domain's native fold. As part of this study, we computed the PCAINs for 8698 representative protein domains from families across the 1018 known protein folds to construct our seed database and an automated framework was developed for PCAIN-based characterization of the protein fold universe. A test set of randomly selected domains that are not in the seed database was classified with over 97% accuracy, independent of sequence divergence. As an application of this novel fold signature, a PCAIN-based scoring scheme was developed for comparative (homology-based) structure prediction, with 1–2 angstroms (mean 1.61A) C Figure S1), ostensibly challenging the notion that all information dictating fold mapping of proteins—the protein fold code—is programmed in the sequence Nature employs merely a few thousand protein folds to generate the entire repertoire of the multimillion strong protein universe protein core atomic interaction network or PCAIN , highlighting the PCAIN as “signature” of the native folded state of protein domains. Building on the fold-specific nature of PCAINs, we demonstrate the use of PCAIN-based scoring schemes for effective classification of protein sequences into their native folds and for high-throughput, accurate homology-based (comparative) protein structure prediction. We further highlight the potency of PCAINs for extending the current capabilities of homology modeling into the ‘twilight’ and ‘midnight’ zones Figure S2) had 112,450 protein domains classified into 1,018 folds. We chose 8,698 protein domains from accross the 1,018 folds representing all the different homologous superfamilies in CATH to seed our database. The structure-based multiple sequence alignments for the seeded domains were obtained from DHS Figure S3) using the solvent accessibility parameters from DSSP/CATH-wolf Figure S4) as described in the We used the CATH database Figure S5). The average degree of correlation in PCMs and PCAINs were also computed for each family, providing a quantitative estimate of the degree of fold-conservation for these scores . From this data, it is clear that the PCM provides for no discernable fold-specificity owing to random correlations within  and accross  folds. On the other hand, it is evident that the PCAIN is highly fold-specific with low inter-family correlation coefficient values  and high intra-family correlation coefficient values . In order to better illustrate this point, the PCM and PCAIN scores for several randomly selected fold families from architectures spanning a significant portion of the protein universe is also shown . This analysis suggests that despite the large degree of sequence divergence in a majority of fold families, atomic interactions between amino acids in the solvent-unexposed core of domains (PCAINs) are a highly fold-conserved feature. The poor fold-specificity of the PCM on the other hand, is tell-tale of high “evolutionary tinkering” noise Given that the PCMs and PCAINs are functions of the threshold interaction distance (ρ) and conserved solvent accessibility (ω) parameters, the entire analysis was repeated for various threshold interaction distances ranging from ρ = 3.5–5.0 and conserved solvent accessibility cutoffs ranging from ω = 0–10, to observe consistently higher fold-specificity for PCAINs than PCMs . This analysis confirms the high-throughput accuracy of PCAIN-based structure prediction and showcases it as a valuable addition to the arsenal of structural modeling tools.The reference structure-based sequence alignments were seen to have extremely high correlations to the PCAIN-based anchored alignments with pearson's correlation coefficient of 0.91. It is interesting that atomic interactions are mined from 3-D structural coordinates and 2-D PCAINs are used to identify the fold-conserved set of atomic interactions that are finally mapped to thread 1-D amino acid sequences. This underlines the application of fold-conserved (including in twilight and midnight zones) higher dimensional data from structural (3D) and contact (2D) spaces for effective protein analysis. This also establishes that PCAIN-based anchored alignments closely mimic the actual structure-based sequence alignments, thus confirming the utility of PCAINs vis-a-vis sequence alignment. Furthermore, superposition of the modeled test structures onto the reference crystal structures demonstrated good structure prediction accuracy in the range of 1–2 angstroms, with mean RMSD of 1.61 angstroms  and conserved solvent accessibility (ω), we investigated the effect of modulating these parameters Figure 5pseudo-interactions will add to the noise thus decreasing SNR and PCAIN potency. Too little threshold interaction distance (ρ), on the other hand, is not feasible, since it will be less than inter-atomic Van der Waals distances. The ‘ω’ parameter accounts for the interplay between water molecules and the residues constituting the protein structure and from this perspective the PCAIN may be viewed as essentially the solvent unexposed network (SUN) of interacting residues. Specifically, a higher ‘ω’ value implies accounting for partially solvent exposed (and hence possibly non-conserved) atomic interaction networks, thus adding to the noise factor and decreasing PCAIN effectiveness. A ‘ω’ value close to zero, on the other hand, may be too stringent. Along the lines of this analysis, it is conceivable that fine-tuning of the PCAIN may be required for specific molecular biology applications. Having mapped the effective operable landscape for PCAIN-based methods with the goal of obtaining the maximal PCAIN effectiveness and highest possible SNR, we propose an adaptive framework  landscape may be interpreted as follows. Protein structures are ensembles of backbone bonded dipeptide confirmations that are characterized by the  plot Yersinia pestisY. pestis pathogenesis has been elusive, due to the unsolved structure of the YopM novel E3 ligase (NEL) domain Protein fold recognition and structure prediction have numerous biological applications Salmonella SspH2 Salmonella SlrP Shigella IpaH Yersinia pestis YopM, in addition to high correlation of the PCAINs for these domains . Given that these patches constitute the NEL catalytic site Salmonella NEL domain interaction sites with human leukocyte antigen-DR  class II receptor) and thioredoxin (TRX) Salmonella and Shigella. Such an ubiquitinase activity of YopM NEL has significant implications for modulation of host adaptive and innate immune response to plague . Hence, defining protein cores based on treatment of protein structures as atomic networks characterized by the  plot and denoising of PCMs by recognition of signature network patterns, distinguishes our PCAIN methodology from the previously explored knowledge-based threading potentials. Our finding that the atomic interactions between just 15–20% of residues in native structures of each examined fold are conserved, further suggests that the PCAIN is a minimalistic fold code.It has recently been shown that two specific amino acid sequences with overwhelming identity (∼88%) adopt distinct folds, thus postulating that for the specific protein pair considered, only ∼12% of the amino acid sequence codes for sequence-to-structure mapping Finally, this study provides compelling evidence in support of Anfinsen's dogma Figure S8 for which more than 75% of CATH and PCAIN core residues are distinct). The structure-based multiple sequence alignments were obtained from DHS Figure S3) and the absolute solvent accessibility (ASA) factors from DSSP/CATH-wolf Figure S3). This set of consistently solvent inaccessible columns was mapped back onto the conserved residue positions thus defining the core for all the seeded protein domains from each alignment. This was compiled into a dataset of protein core residues, one corresponding to each protein family and each considered value of parameter ω. The frequency of each amino acid at the core positions was also consolidated into a dataset of family-specific protein core residue propensities. The complete protein core characterization method, right from CATH mining until the construction of the datasets was automated with the implementation of a script in MATLAB 7.6.0 from The MathWorks, Inc. .At the time when this study was performed, the CATH database Figure S4). This was compiled into a database of PCMs on a fold-by-fold basis. The previously identified core residues for each domain of each fold at various ω values was used to identify the rows and columns of interest from PCMs at various ρ values and these were concatenated into the corresponding PCAINs for each domain of each fold at various  values, as depicted pictorially  and accross 15 unselected folds for refined visualization purposes (A MATLAB script was written to automate the computation of protein contact maps (PCMs) for all seeded domains of the 1018 folds at various threshold interaction distance parameter (ρ) values –A general screen of 50,000 randomly selected domains  were considered for testing the fold classification efficacy of PCAIN-based and PCM-based scoring schemes. The effectiveness of the classification approaches were then estimated  and their PCAINs were estimated as detailed above. The computed target PCAIN scores were then correlated with the PCAIN scores (from the seed database) of every representative homologous superfamily member of the identified fold family in order to compute the optimal template, based on similarities at the level of evolutionary origin and function. The corresponding scaffold residues of the target and template sequences are then ‘anchored’ and pairwise sequence fragments between subsequent anchors are aligned using standard functions from the MATLAB bioinformatics toolbox with the BLOSUM62 scoring matrix and default gap penalties. The process involving fold identification, template selection and anchored alignment is maximally automated with the design of a MATLAB-based model. The structure-based sequence alignments are correlated with the PCAIN-based anchored alignments to estimate the efficacy of the PCAIN approach to sequence alignment . Each modeled 3D structure was then superposed onto the actual crystal structure obtained from the PDB using an automated MATLAB function and the root mean square deviations upon superposition were computed  and Salmonella typhimurium SlrP (GenBank: AAD39928.1) were modeled using the described PCAIN methodology with the identified optimal template structure of Shigella type III effector IpaH (PDB ID: 3CKD). All structure-function relationship analysis, including vacuum electrostatics generation for the modeled Yersinia pestis YopM NEL, modeled Salmonella typhimurium SlrP NEL, crystal structures from Shigella IpaH NEL (PDB ID: 3CKD), and salmonella SspH2 NEL (PDB ID: 3G06), were performed with PyMol.The molecular structures of NEL domains from Figure S1Evolutionary sequence divergence of protein families. More than 60% of protein families from the pfam database were found to be significantly divergent in their sequences (High range), around 30% of protein families were found to be moderately divergenct in ther sequences (Medium range) and less than 10% of protein families were found to be well conserved in their sequences (Low range). This shows that evolutionary tinkering and sequence divergence are rampant across the protein universe.(0.14 MB JPG)Click here for additional data file.Figure S2The diversity of protein folds. Representative protein domains from CATH showcasing the fold diversity, classified according to their class (mainly α/mainly β/αβ) and architecture.(0.11 MB JPG)Click here for additional data file.Figure S3Sample sets from fold family alignments highlighting the solvent-unexposed (core) conserved positions (blue columns). (A) Sample proteins from a family of the architecture - Orthogonal bundle. (B) Sample proteins from a family of the architecture - Up-down bundle. (C) Sample proteins from a family of the architecture - Alpha-horseshoe. (D) Sample proteins from a family of the architecture - Alpha-alpha Barrel. (E) Sample proteins from a family of the architecture - Beta-Ribbon. (F) Sample proteins from a family of the architecture - Beta-Barrel. (G) Sample proteins from a family of the architecture - Beta-Trefoil. (H) Sample proteins from a family of the architecture - Beta-Prism. (I) Sample proteins from a family of the architecture - Beta-Sandwich. (J) Sample proteins from a family of architecture - Beta-Propeller. (K) Sample proteins from a family of architecture - αβ Roll. (L) Sample proteins from a family of architecture - αβ Box. (M) Sample proteins from a family of architecture - αβ Complex.(1.50 MB JPG)Click here for additional data file.Figure S4A sample dataset from the protein contact maps (PCM) database. The inter-residue contact maps at 5 angstroms threshold distance are shown for representative domains from a diverse set of topologies/folds spanning all natural architectures in the protein universe.(0.19 MB JPG)Click here for additional data file.Figure S5Protein contact maps (PCMs) versus protein core atomic interaction networks (PCAINs) intra- and inter- fold family correlations reveals striking specificity for PCAIN across the universe of folds. Averaged intra-fold  and inter-fold  correlation coefficients of (a.) PCMs and (b.) PCAINs at 5 angstroms threshold, shows clears that the PCAIN is highly fold-specific whereas the PCM shows no discernible fold specificity.(0.43 MB JPG)Click here for additional data file.Figure S6Flowchart governing PCAIN-based fold recognition of target sequence, template selection, anchored target-template alignment, and homology-based structure prediction. The detailed procedures associated with each step are described in the (0.05 MB JPG)Click here for additional data file.Figure S7Superposition of structures predicted based on PCAIN methodology for CASP  target sequences (a.) TO203 and (b.) TO197, illustrates PCAIN-based structure prediction. PCAIN-based structures predicted (cyan) are superposed onto reference crystal structures (pink) for (a.) TOP203 and (b.) TO197 from CASP-6 with RMSDs of 0.91A (at 29% target-template sequence identity) and 0.87A (at 60% target-template sequence identity) respectively. The corresponding results of structure prediction accuracy from the CASP models shown as tables shows minimum RMSDs of 1.29A and 1.37A respectively.(0.26 MB JPG)Click here for additional data file.Figure S8Defining protein cores and extracting their information with the PCAIN methodology. (A.) Polar and charged residues (yellow) are also part of the core of protein domain as identified by our method, as shown with E.coli thioredoxin (cyan) as an example. (B.) Only 7% identity (shaded green) is present in the sequence of residues that constitute the core of glutaredoxin and thioredoxin that adopt the same fold, whereas 93% of the core residues are different in identity (shaded yellow). However, the PCAINs of these two proteins are seen to have 98% correlation, over the PCMs that have only 41% correlation. This example further illustrates that the identity or hydrophobicity of residues are poor tools for extracting information from protein cores, whereas the PCAIN is optimal for extracting conserved information from protein cores. Similarly, very poor overlap is seen between residues used for CATH alignments (underlined) and the residues that contribute to the PCAIN, thus illustrating the novelty in determination of PCAIN residues.(0.07 MB JPG)Click here for additional data file.


Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks.t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins.Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks. Since the advent of microarray technology, genome-wide expression analysis has become an important tool in biology . Much ofp-values [The integration of PPI data with gene expression data requires an adequate scoring method to measure the discriminative potential of a given subnetwork or a connected subgraph. Several scoring methods have previously been proposed: combining multiple p-values , mutual p-values , and edgp-values ,13. In ap-values -17. In ap-values ,9, and ep-values  have beeThis paper focuses on a scoring method adopting when using a greedy search. It has been reported that while the correlation among the genes in a subnetwork is not conspicuous, it is important in assessing statistical significance . The mulp < 0.05. We performed the GO term enrichment analysis to investigate how well the identified subnetworks represented the functional modules. Under the assumption of a hyper-geometric distribution, we calculated the significance level for the number of proteins in an identified subnetwork that were included in each GO term in the category of "biological process" and selected most significant GO terms for functional annotations of the identified subnetwork. The ten most significant subnetworks are listed in Table A greedy search with our MANOVA-based scoring function identified 86 significant subnetworks with In this example, 123 significant subnetworks were identified by our method. The ten most significant subnetworks are shown in Table We compared our MANOVA-based scoring method with two other methods. Nacu et al.  suggesteNext, we examined the sizes of the significant subnetworks commonly identified by all three methods. Figure Finally, we investigated the distribution of the correlation coefficients within the subnetworks identified as being significant by all three scoring methods to determine the strength of correlation among the proteins in the subnetworks. The correlation coefficient for every protein in the sub-networks with the seed protein was calculated. Figure ρ were assigned to the nodes of the target networks, while expression values with correlation coefficient ρ' were assigned to the other remaining nodes. The expression values with correlation coefficient ρ and ρ' were generated by the following procedure.We performed a series of simulation studies to evaluate the three scoring methods. Specifically, we focused on the ability of each method to identify previously assumed target subnetworks. The simulation study was performed for the PPI network in Figure kth subnetwork, assume that there are pk nodes (genes) among which tk nodes (genes) belong to a given target network. For the ith gene in the phenotype group g, let xig  epresent the target node and yig  the nontarget node. Then, the expression values are generated to obtain a correlation coefficient of ρ for every pair in the target nodes and ρ' for every pair in the nontarget nodes by the following formulas:For the Zs are normally distributed random variables and the c and c' are constants satisfyingwhere the ρ and ρ', we calculated c and c' by setting c0 and α to the target nodes to make an average difference between the target nodes and nontarget nodes. α was assigned a value of 0.01. A greedy search was performed for each scoring method. Permutation tests were omitted in the simulation study because the expression values were generated from the normal distribution. If the identified subnetwork included all of the nodes of the target subnetworks, we considered it to be a successful identification. This procedure was repeated 100 times and the true positive (TP) rate was estimated as the number of successful identifications divided by 100. In order to estimate the false positive (FP) rate, every node (gene) in a subnetwork, regardless of whether it was a target or nontarget node, was assigned expression values of the same correlation coefficient value (ρ = ρ' in the above formulas).For given values of ρ and ρ'  as shown in Table We performed the simulation study with two cases of This article proposes a MANOVA-based scoring method to identify subnetworks in a PPI network. Our method was successfully applied to two human microarray data sets, resulting in biologically significant PPI subnetworks. We also compared the results with those of two other scoring methods using a greedy search algorithm. The characteristics of these scoring methods when using a greedy search are not well known, because applying the different scoring methods to a greedy search algorithm usually generates subnetworks with different proteins and sizes. To our knowledge, our research is the first comparison study to evaluate the performance of various scoring methods in the context of a greedy search. We believe that our results provide a guide to develop and expedite the integration of PPI data with other biological data using a greedy search.In an individual gene-level analysis -4, a lisFurthermore, the MANOVA-based scoring method can deal with the data of any number of groups. Our results show the successful identification of the subnetworks differentiating three groups related to prostate cancer metastasis, as well as two groups related to the wound response of a fibroblast culture. Since it is common for researchers to compare more than two experimental groups defined by multiple experimental factors, our method could provide a useful tool for identifying subnetworks under multiple conditions.p-values. Thus, the violation of this normal assumption is not critical to our method. Second, the correlation among the dependent variables is linear. Note that linear correlation has been successfully applied to the analysis of gene expression data in many previous studies [However, there are some assumptions when using the MANOVA approach . First,  studies ,22. TherOur method adopted the greedy search for identifying PPI subnetworks. In contrast to approaches using a predefined gene set -7, greedFinally, it is worth noting that other types of additional information could be used to improve our method. For example, managing multiple microarray experiments with annotations of the experimental conditions  would prA MANOVA-based scoring method with a greedy search was proposed for combining PPI data with expression data. Our method takes advantage of a characteristic feature of MANOVA: it considers the correlation structure of multiple dependent variables when comparing the mean vectors across different groups. Our method was successfully applied to two human microarray data sets. The results from comparisons with two other scoring methods showed that the MANOVA-based scoring method tends to yield a smaller number of significant subnetworks with larger numbers of highly coregulated proteins. It also performed better in terms of sensitivity than other methods in simulation studies. Therefore, the MANOVA-based scoring method could provide more opportunities to identify novel pathways or complexes, including unknown genes or proteins related to the phenotypes of microarray experiments.Following the idea of Ideker et al. , we matcWe tested our method using two real expression data sets. One was related to the serum effect on fibroblast cultures, and the other involved the progression of prostate cancer in 44 individuals.In vivo cells encounter serum, the soluble fraction of coagulated blood, when they are injured. Serum promotes the biological processes involved in wound healing by fibroblasts. To characterize wound response, Chang et al. [Chang et al.  studied g et al.  investigg et al. .This data set was a collection of cDNA microarray expression measurements from 22 samples of benign epithelium, 32 samples of primary prostate cancer, and 17 samples of metastatic prostate cancer. The progression was benign to prostate cancer (PCA) to metastasis . The datWe exploited the interaction dataset used in Nacu et al.  to test t statistic. Chuang et al. [A subnetwork is defined as a gene set that induces a single connected component in the PPI network . Given ag et al.  proposedΛ statistic as a MANOVA-based score for a given subnetwork. MANOVA is an extension of analysis of variance (ANOVA) that covers cases where there is more than one dependent variable. In our case, we treat each gene of a subnetwork as a dependent variable. Suppose there are K subnetworks and G phenotype groups. For the kth subnetwork, assume that there are pk nodes (genes). For the ith gene in group g, let We propose using Wilks' MANOVA considers the correlation structure of multiple dependent variables when it is used to compare the mean vectors across different groups. Because subnetworks representing complexes or pathways usually consist of multiple coexpressed genes, we propose using the MANOVA model to consider these genes simultaneously.r, which is defined as the difference between the previous and current scores divided by the previous score. Second, the distance from the seed is adopted as another criterion. That is, only proteins within a specified distance d from the seed are added to an expanded subnetwork. Finally, the maximum possible number of proteins in a subnetwork is also used as a termination criterion to avoid the singular matrix conversion in the process of calculating Wilks' Λ statistic from MANOVA. When one of the three criteria is satisfied, the iteration stops. Given that the median distance between any two proteins in the human PPI network is five, we set d to be 2 to provide a sufficient number of neighbors to keep the search local. A value of 0.05 is chosen for the parameter r [Given the scoring functions, a greedy search is performed to identify subnetworks within the PPI network. In the beginning, each candidate subnetwork has a single seed protein. In this study, proteins with more than five interactions were chosen as seed proteins. To expand the subnetwork from a seed protein, we first construct every possible subnetwork consisting of the seed and each of its neighboring proteins. After completing the score calculation for all of the possible subnetworks, we choose the neighboring proteins in the subnetworks with the maximum scores and include them as members of expanded subnetworks. This process is iterated until the termination conditions are met. Three termination criteria are used. First, the search stops when no addition of neighbor proteins increases the score over a specified relative improvement rate ameter r . The maxp-values. The iteration number for this permutation test was set to 1000.We proposed a permutation-based test to derive the significance levels. The permutation-based approach provided a reasonable estimate for the null distribution of the subnetwork scores, and allowed us to compute The authors declare that they have no competing interests.All of the authors participated in the design of the methods. TH implemented the algorithm and analyzed the results. TP coordinated the research. All of the authors have read and approved the manuscript.