As numerous experimental factors drive the acquisition, identification, and interpretation of protein-protein interactions (PPIs), aggregated assemblies of human PPI data invariably contain experiment-dependent noise. Ascertaining the reliability of PPIs collected from these diverse studies and scoring them to infer high-confidence networks is a non-trivial task. Moreover, a large number of PPIs share the same number of reported occurrences, making it impossible to distinguish the reliability of these PPIs and rank-order them. For example, for the data analyzed here, we found that the majority (>83%) of currently available human PPIs have been reported only once.In this work, we proposed an unsupervised statistical approach to score a set of diverse, experimentally identified PPIs from nine primary databases to create subsets of high-confidence human PPI networks. We evaluated this ranking method by comparing it with other methods and assessing their ability to retrieve protein associations from a number of diverse and independent reference sets. These reference sets contain known biological data that are either directly or indirectly linked to interactions between proteins. We quantified the average effect of using ranked protein interaction data to retrieve this information and showed that, when compared to randomly ranked interaction data sets, the proposed method created a larger enrichment (~134%) than either ranking based on the hypergeometric test (~109%) or occurrence ranking (~46%).From our evaluations, it was clear that ranked interactions were always of value because higher-ranked PPIs had a higher likelihood of retrieving high-confidence experimental data. Reducing the noise inherent in aggregated experimental PPIs via our ranking scheme further increased the accuracy and enrichment of PPIs derived from a number of biologically relevant data sets. These results suggest that using our high-confidence protein interactions at different levels of confidence will help clarify the topological and biological properties associated with human protein networks. Although this latter set is still numerically dominant , examples of the first two types of investigations are expanding rapidly.The development of high-throughput techniques during the last decade has led to an unprecedented increase in the volume of identified human protein-protein interactions (PPIs). The currently available individual PPI data sets can be roughly categorized into three sets: ble PPIs , 2) semiproteins ,5, and 3per se . Herein, we address the question of how to extract high-confidence PPIs while relying only on the aggregated interaction data themselves.Given this extensive resource of known human PPIs and their continuous accelerated growth, how to globally analyze and aggregate the data remain a challenge. Statistical methods for inferring confidence of protein interactions can be broadly divided into two groups -8: scoriThe most intuitive approach to infer high-confidence PPIs is to score PPIs based on the number of times an interaction has been reported -11. HoweHerein, we propose an unsupervised statistical approach to score and rank a set of diverse, experimentally identified PPIs. We applied this methodology to human PPIs  aggregated from nine publicly available primary databases that exclusively contain experimental data  , and, intopology . To the We evaluated the improvement in using IDBOS-ranked PPI data by comparing it with other methods and assessing their ability to retrieve biological associations from a number of diverse and independent reference sets. These reference sets contain known biological data that are either directly  or indirectly  linked to interactions between proteins. The hypothesis we tested was that sets of highly ranked PPIs are enriched in biological associations as determined from the diverse reference sets. We quantified the average effect of using ranked protein interaction data to retrieve this information and showed that, when compared to randomly ranked interaction data sets, IDBOS created a larger enrichment (~134%) than either ranking based on the hypergeometric test (~109%) or occurrence ranking (~46%).From our evaluations, it was clear that ranked interactions were always of value because higher-ranked PPIs had a higher likelihood of retrieving biologically relevant data. Statistically removing the biasing factors inherent in aggregated PPI data via the IDBOS-ranking scheme further increased the accuracy and enrichment of biological information associated with PPIs.We used the collection of human experimental and physical PPIs to create a set of 116,134 reported interactions, containing 80,980 unique physical associations between 13,369 distinct proteins . Out of the unique PPIs, 13,554 interactions were observed in more than one experiment. The number of times a PPI has been reported in the literature is an important metric for inferring high-confidence interactions ,27, but Note that the reported number of interactions involving a protein refers to the total number of observed PPIs in the literature involving that specific protein. This is different from a protein’s degree, which is defined as the number of unique interacting protein partners. Thus, while TP53 is associated with 1,097 observed PPIs, its degree is reduced to 478 due to the multiple observations of many of the involved interactions.i and j, how many times it occurred Oij. Randomized versions of the original PPI list were then created under the conditions that: 1) the protein identifiers and the number of times they occur are preserved and 2) no interactions are allowed between proteins of the same identifier, i.e., self-interactions are not allowed. We generated M = 106 randomized PPI lists and calculated the average number of times each PPI from the original list occurred < Rij > and its standard deviation σR. The corresponding Z-score was then calculated as,Our calculations followed our previously described procedures for generating Z-scores from sets of interacting protein pairs . Brieflyij of observing an interaction between proteins i and j in the original data set. Briefly, for each unique pair in the original PPI list, we calculated the number of times it occurred in each of the n = 1, … ,M random realizations and created the normalized probability density function PDFij, i.e., the probability of finding We further used the randomized data to estimate the p-value pij and pij) are legitimate metrics for ranking an observed PPI, although they are associated with different numerical properties and uncertainties because of the limitations imposed on using a finite number of randomizations.Z-scores and p-values  as outlined in the Materials and methods , and, hence, we aggregated these two metrics by converting the value of each metric into a rank and generating the average rank from both metrics for each interaction. Interactions with the same p-value (or Z-score) were assigned the same rank. If interactions had the same rank in the final averaged-rank list, these interactions were considered equivalent and analyzed together. The ranked data are provided in the Supplementary materials  far-Western blotting, 2) isothermal titration calorimetry, 3) nuclear magnetic resonance, 4) surface plasmon resonance, 5) direct interactions from protein complex structures from the PDB, and 6) homologous human PPIs derived from actual mouse PPI data. These benchmark sets tested the ability of the ranked data to retrieve known interactions derived from a variety of experimentally determined PPI data sets.To assess different PPI ranking schemes, we constructed six benchmark reference sets derived from high-quality experimental studies as detailed in Materials and methods. These independent data sets comprise PPIs detected using Using these six benchmark reference sets, we compared the IDBOS-ranked data set with a set ranked by PPI frequency of occurrence, a set ranked using the hypergeometric test  see Mat, and twocoverage and the fraction of true interactions among those judgeable interactions as accuracy.To make a fair comparison within each benchmark reference set, for each set we defined a protein pair from the aggregated data set as a judgeable interaction if both proteins appeared in the reference set; otherwise, we assumed that the pair could not be judged by the reference set. If a judgeable interaction was observed in the reference set, we termed it a true interaction. A good scheme would rank true interactions before other judgeable interactions. To quantify the ability to retrieve true interactions in each scored set, we extracted the judgeable subset and, using each corresponding rank as a threshold, we counted the numbers of judgeable and true interactions with scores above the threshold. We defined the number of true interactions as Figure accuracy as a function of coverage plots shown in Figure accuracyr as,IDBOS, hypergeometric, and frequency of occurrence ranking were each associated with enhanced accuracy at higher ranks, thus indicating that true interactions ranked before other judgeable interactions. We assessed the overall performance of the ranking methods by calculating relative improvement compared to using no ranking, which is equivalent to assigning random ranks to the existing data. For the n(r) is the number of true PPIs at rank r and N is the sum of all n(r)’s. The gain of using IDBOS, hypergeometric, or occurrence as a ranking method was estimated by comparing the average accuracywhere Table accuracy of the randomly ranked data were 258%, 253%, 217%, and 216%, respectively; using the mouse homologous benchmarking set, the gains were 222%, 211%, 198%, and 155%, respectively. Thus, the IDBOS combined rankings consistently outperformed all attempted scoring schemes.The independent scores  were typically better than, or equivalent to, the hypergeometric method: Z-score ranking was close to the IDBOS, and better than the hypergeometric, whereas the p-value ranking was not as good as the hypergeometric. For example, using the PDB benchmarking set, the gains of the IDBOS, Z-score, hypergeometric, and p-value methods relative to the average AA, pAB, pAC, etc., was the same in both schemes. However, the constraint that pAA was zero in IDBOS and pAA was non-zero in the hypergeometric test, re-distributed the probabilities such that any interaction probability pAB between protein A and another protein B could be different in the two schemes. This strongly influenced the probability of detecting proteins that occurred with high frequency in the data set.The observed differences in Table 3 top-ranked IDBOS PPIs showed low overlap (~0.10) with the frequency of occurrence ranked PPIs, but higher similarity (~0.36) to the ranked PPIs identified using the hypergeometric test. Thus, the high overlap between occurrence and hypergeometric rankings suggested that the inadvertent biases introduced by frequent investigations of popular proteins could not be completely disentangled by the hypergeometric-ranking scheme. Furthermore, we verified that consensus ranking schemes that included the average rank of all three ranking methods, or combining IDBOS and hypergeometric test rankings only, did not increase the accuracy beyond using IDBOS rankings (data not shown). Instead, we contend that the corresponding probability density distribution functions generated by IDBOS are the most appropriate to gauge unbiased PPIs in this data set.The effect of including or excluding self-interacting protein pairs was magnified in the evaluation of interactions involving popular proteins. In the hypergeometric test, the likelihood of randomly generating self-interacting protein pairs is roughly proportional to the square of the number of times the protein appears in the data set. In practice, this leads to an underestimate of the occurrence of non-identity pairs in the random data, and this increases the significance attached to the observed non-identity PPIs involving popular proteins. The effect on ranking PPIs was considerable, e.g., among the top 20 occurrence-ranked interactions listed in Table The hypothesis that PPIs are mediated by a smaller set of specific domain-domain interactions (DDIs) that are repeatedly used, can be exploited by inferring DDIs from known PPIs and then predicting novel PPIs from the inferred DDI set ,41. A hiUp until now, the different scoring schemes were used to evaluate reference data sets that can be considered to be directly related to interacting proteins. We next evaluated the improvement that could be gained by using the differently ranked data sets to rank-order the interactions in reference data sets that are presumed to be enriched with interacting proteins, i.e., in sets of proteins that share the same Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, are implicated in the same disease, share Gene Ontology (GO) function, or tissue mRNA expression levels.Genes encoding interacting proteins are more likely to be part of the same pathway. Hence, the fraction of PPIs that are annotated to belong to the same pathway should be larger in sets containing higher-ranked PPIs. We used KEGG pathway data  to definGenomic variations may underlie different susceptibilities to disease, e.g., individuals with specific single nucleotide polymorphisms (SNPs) can develop, or be pre-disposed to, a particular disease phenotype. Furthermore, genes encoding interacting proteins are more likely to occur within the same disease classification . Using tFigure Finally, we examined the occurrence of interacting protein pairs in a large data set that maps out the global mRNA expression levels for ~20,000 human genes in 32 normal tissues . For detData sets of protein interactions are commonly represented as graphs or networks of protein interactions, and applying network topology metrics to characterize the biological role of PPIs has attracted wide attention ,10,22,48Selecting highly ranked subsets of PPIs, using either IDBOS or occurrence, had the effect of reducing the number of high-degree proteins (hubs) in the overall network. This effect was most pronounced in the top-ranked IDBOS networks, which effectively down-weights interactions of popular proteins. The resulting top-ranked IDBOS networks were not dominated by hub interactions, but show a more distributed interaction network, with distinct topological and scaling properties.Single experiments designed for large-scale detection of PPIs judge multiple observations of the same PPI as evidence for the confidence of the interaction. Multiple observations, across many different experiments, of a particular PPI also lend confidence that this interaction occurs in nature. One would then conclude that if one aggregated all known PPI experiments, the more times a particular PPI occurred, the more confident one would be of the interaction. We found a somewhat counterintuitive result in that ranking PPIs in order of the number of times a PPI has been observed was actually not the optimal way of assessing the importance of frequently reported interactions. Instead, we used the IDBOS method, which ranks interacting protein pairs in the observed PPI data sets as compared to random occurrences derived from an empirically reconstructed probability density distribution function specific to each interaction. We then used these ranks to order the PPI data and showed that this approach consistently identified more known protein associations in numerous benchmark reference sets than using either hypergeometric test rankings or simply frequency of occurrence. The improvement of IDBOS over the hypergeometric test results stems from the differences in the underlying random distributions in the two schemes. While the hypergeometric test does not impose any limitations on self-interacting proteins, by construction, the IDBOS procedure sets their probability of occurrence to zero even for the random case. The input data contain no self-interacting proteins; therefore, the corresponding random probability density distribution functions generated by IDBOS seem to be the most appropriate to gauge unbiased PPIs in this data set.We evaluated the improvement in using ranked PPI data to identify known protein relationships in eleven independent reference sets, including direct and indirect readouts of protein interactions. We used a large number of different reference sets to perform as comprehensive an analysis as possible, as each reference set only covers a specific subset of protein interactions. It was clear that ranking interactions was of value, because higher-ranked PPIs had better success at retrieving protein associations. Across all sets investigated, occurrence ranking improved the performance by 46% compared to using non-ranked data, hypergeometric ranking achieved a 109% increase, and the IDBOS ranking scheme achieved a 134% increase. This conformed to the assumption that aggregating and creating high-confidence subsets of data creates added value, albeit at a cost of reducing the overall number of PPIs that can be considered.We have developed a statistical approach to infer subsets of high-confidence human PPIs, and showed that ranked data can consistently enrich the accuracy of the retrieved PPI data in these sets. Our IDBOS method was more successful in ranking interactions than using either the number of times an interaction has been observed across experiments or rankings based on the hypergeometric test. Furthermore, using either IDBOS or hypergeometric scoring schemes generates unique ranks for almost all interactions, as opposed to the frequency of occurrence method, in which many interactions have the same integer score corresponding to the number of observed occurrences. The IDBOS ranking increased accuracy and enrichment of protein interaction data associated with PPIs by more than two-fold compared to simply ranking interactions based on observed occurrences. We achieved this improvement by comparing the observed interaction data with a probability density distribution function that does not inflate the statistical importance of interactions associated with frequently studied proteins. These results suggest that using our high-confidence protein interactions at different levels of confidence could help clarify the dependence on confidence on topological and biological properties associated with human protein networks.6 random realizations of the PPI data set and compute the corresponding p-values and Z-scores took ~2,000 minutes on a dual core Xeon Irwindale 3.6 GHz 64-bit Linux server equipped with 4 GB of RAM. We used fractional rankings, i.e., PPIs that had the same score received the same ranking number, which is the mean of what they would have received under ordinal rankings. The Supplementary material provides the scored and ranked PPI data set, with equivalently ranked interactions tabulated in arbitrary order.We downloaded the collection of PPIs in October 2011 from the nine databases that covers the bulk of all known experimentally determined PPIs. Databases of non-primary nature, i.e., containing aggregated data and/or predicted and inferred interactions, were excluded. From this collection, we extracted the subset of human physical PPIs, consisting of PPIs from both large-, semi-large-, and small-scale experimental studies. Because Reactome has no standardized annotations describing physical associations or direct interactions, instead we extracted PPIs annotated as “direct complexes.” We treated each interaction reported by each study (identified by a unique publication ID) as a unique record by deleting redundant copies arising from the overlaps among the nine databases. In total, we analyzed 13,369 proteins, 80,980 PPIs, and 116,134 records  for these two proteins to have this or a larger number of interactions by chance was approximated using a hypergeometric distribution as follows:To rank interactions based on an alternative statistical method compared to IDBOS and frequency of occurrence, we included ranks based on a hypergeometric test . For twoNi (Nj) is the number of times protein i (j) was observed in the aggregated data set, and N is the total number of interactions in the aggregated data set of PPIs. This formulation is correct in the limit of N > > NiNj, which was satisfied in this data set. Finally, we ranked the PPI data based on the calculated probabilities.where 2, as a reference set for known human PPIs derived from structural data  to the human protein sequences stored in the UniProtKB/Swiss-Prot database (downloaded in June 2008) and obtained 649 protein complexes that contained at least two different human proteins. We then calculated the contact area of each intra-complex human protein pair using the program EMPIRE , to detehttp://cbdm.mdc-berlin.de/tools/hippie/ information.php, January 2012), each of which consists of PPIs detected by a top-scored experiment technique  term ID, and number of PPIs >100). The extracted data set contained PPIs derived from 1) far-Western blotting, 2) isothermal titration calorimetry, 3) nuclear magnetic resonance, and 4) surface plasmon resonance experiments.We extracted four reference sets from the Human Integrated Protein-Protein Interaction rEferenc (HIPPIE) data set . We used this homologous human PPI set  as a reference data set reflective of direct protein interactions.We extracted the subset of mouse physical PPIs from the above collection and constructed a homologous human PPI set according to the sequence homology between mouse and human proteins defined by the National Center for Biotechnology Information (NCBI) homology mapping scheme  to identify a set of functionally related gene pairs as a reference set. For each gene, we expanded its GO annotation list by including all ancestors of each member in the list. For two genes, g and h, given that they had ngh GO annotations in common, the probability (pgh) for these two genes to have this or larger annotation overlap by chance was estimated from the hypergeometric distribution as follows:We downloaded the GO annotation data (Ng (Nh) is the number of GO annotations of gene g (h) and T is the total number of unique GO annotations. We used –log(pgh) as the score and chose the top 1% of all gene pairs as a reference set, resulting in 1,502,420 co-function pairs for this reference set.where ftp://ftp.genome.jp/pub/kegg/pathway/, June 2010), which lists the pathways that each annotated gene participates in. Similar to the co-function score described above, the co-pathway score was also computed using the hypergeometric distribution method. We chose the top 1% as the reference set of significant co-pathway gene pairs .The KEGG pathway data file (hsa_gene_map.tab) was downloaded from its Web site , indicating that the tags were able to distinguish the transcribed orientation from the non-transcribed orientation in the genome. Assigning the tags that hit a gene region and orientation to the corresponding gene, we obtained a set of tags for each gene, resulting in a total of 14,516 genes having non-empty tag sets. We summed up the abundance tissue profiles of the tags of a gene to create its raw expression profile.We used a global expression data set containing mRNA expression levels for ~20,000 human genes in 32 normal tissues derived from massively parallel signature sequencing . The rawet al.[ek(g) be the expression level of gene g in tissue k. The total expression of gene g in all 32 tissues is,Furthermore, we computed the statistical significance of a gene being preferentially expressed in a tissue (termed “tissue specificity”) using an approach similar to that of Yu et al., which ik is,and the expected total expression of all genes in tissue E(g) darts into 32 areas of sizes Ek, k = 1, 2, …32, and each dart has a probability,If we randomly throw k, we would expect to see E(g) qk darts in this area with variance E(g)qk(1-qk). Similarly, if gene g were equally expressed across all tissues, the expected expression level in tissue i would be E(g)qk with variance E(g)qk(1-qk). Thus, we used the corresponding Z-score,of hitting area g in tissue k. Accordingly, we defined the tissue co-expression score of genes g and h as,as the tissue specificity of gene We chose the top 1% of these scored gene pairs as the reference set of co-expressed gene pairs to evaluate the PPI scoring approaches. There were 1,053,529 co-expression pairs in this set.The authors declare that they have no competing interests.XY, AW, and JR conceived of the study and participated in its design and coordination. XY collected the data, developed the algorithms, and performed the calculations. AW and XY drafted the original manuscript, which was edited by JR. All authors read and approved the final manuscript.Aggregated human physical PPI data. This tab-delimited text file contains the aggregated PPI data based on publications collected from nine databases: BIND, BioGRID, DIP, HPRD, IntAct, MINT, MIPS, PDZBase, and Reactome. Click here for fileDirect interactions supported by known complexes in PDB. This tab-delimited file contains the protein pairs that have direct contact in at least one protein complex in PDB. Click here for fileIDBOS Ranked PPI data. This tab-delimited file contains the aggregated PPI data set with the corresponding IDBOS ranking. Click here for fileOccurrence Ranked PPI data. This tab-delimited file contains the aggregated PPI data set with the corresponding occurrence ranking. Click here for fileHypergeometric Ranked PPI data. This tab-delimited file contains the aggregated PPI data set with the corresponding hypergeometric ranking. Click here for file


Information on protein subcellular localization is important to understand the cellular functions of proteins. Currently, such information is manually curated from the literature, obtained from high-throughput microscopy-based screens and predicted from primary sequence. To get a comprehensive view of the localization of a protein, it is thus necessary to consult multiple databases and prediction tools. To address this, we present the COMPARTMENTS resource, which integrates all sources listed above as well as the results of automatic text mining. The resource is automatically kept up to date with source databases, and all localization evidence is mapped onto common protein identifiers and Gene Ontology terms. We further assign confidence scores to the localization evidence to facilitate comparison of different types and sources of evidence. To further improve the comparability, we assign confidence scores based on the type and source of the localization evidence. Finally, we visualize the unified localization evidence for a protein on a schematic cell to provide a simple overview.Database URL:http://compartments.jensenlab.org Determining the subcellular localization of a protein is a key step toward understanding the cellular function of a protein. Therefore, knowledge on protein subcellular localization is manually curated by UniProtKB  and modeDespite the huge efforts by curators working for the databases mentioned above, it is impossible to fully keep up with the ever-growing literature. Thus automatic text-mining methods can complement human curators. Several text-mining methods have been developed to automatically extract localization information from the biomedical abstracts .Even if one combines curated knowledge, primary experimental data and text mining, there will still be many proteins with little or no information on their localization. Fortunately, the protein sequence itself contains clues to where the protein is localized, such as protein sorting signals, the amino acid composition and sequence homology . ExampleAs these different types and sources of information are complementary, it is important to take them all into account. However, this is not trivial. The databases and experimental data sets come in various file formats and use different identifiers/names for the same proteins and cellular compartments. The sequence-based prediction methods have different web interfaces, the prediction outputs consist of scores that are not directly comparable and local installation of the software is generally required for genome-wide analyses. It is thus difficult and time-intensive to collect and evaluate the evidence pertaining to the subcellular localization of a protein of interest, not to mention for a large number of proteins.Arabidopsis thaliana proteins. Whereas these resources are, or were, collecting evidence from a variety of sources in a single database, they generally do not address the challenge of putting the different types of evidence on a common confidence scale. An exception is the A. thaliana resource SUBA3, which assigns an overall confidence score; however, it is difficult for the user to trace these scores back to their origin.Several databases have attempted to address this data integration challenge. An early effort was DBSubLoc , which iWe have developed an automatically updated web resource to be able to provide up-to-date information on the subcellular localization of proteins from the major eukaryotic model organisms. In addition to integrating manually curated annotations, experimental data and predictions, we use automatic text mining to extract associations from the biomedical literature. Unlike earlier resources, we address the challenge of making evidence comparable across types and sources by introducing a unified confidence scoring scheme. To further shield users from the heterogeneity of the many evidence sources, we map all localization evidence onto Gene Ontology (GO) terms and visualize the combined results on an interactive schematic of a cell. All data are freely available for download to facilitate large-scale analyses.Caenorhabditis elegans. When querying the database for a protein of interest, the user is presented with an interactive schematic of a cell. These figures are color coded according to the confidence of the evidence supporting each of the 11 (12 in case of plants) labeled compartments  database  to five stars (highest confidence). The way that confidence scores are assigned varies between evidence channels as explained in the next section. The confidence scores are also the basis for the color coding of the figures : the higknowledge, is based on annotations from UniProtKB knowledgeniProtKB , FlyBaseniProtKB  and WormniProtKB . We assiniProtKB , 35, whiexperiments channel and currently contain information on 9306 human proteins. The confidence scores of this channel are based on the antibody validation scores provided by HPA . This shows that the method works well on the majority of the compartments . The excTo illustrate the usefulness of COMPARTMENTS for large-scale studies, we identified pairs of compartments that share a statistically significant number of human proteins , medium (three stars), low (two stars) and very low (one star). When only a single antibody has been used for staining, we instead make use of the validation score provided by HPA. This scale has three levels: supportive (three stars), uncertain (one star) and non-supportive (not imported).We also imported subcellular localization data from the Human Protein Atlas (HPA) , 50, whiWe used the protein dictionary from STRING 9.1  and crean is the number of abstracts, P and L in the same sentence will give a score contribution of We matched these dictionaries against all Medline abstracts using an efficient named entity recognition engine described elsewhere . To scorpathways .The text-mining score depends on number of pairs identified in Medline abstracts, which changes as Medline grows. We, therefore, convert the scores into z-scores [We constructed a high-quality benchmark set based on the knowledge channel. The positive examples are pairs of proteins and compartments supported by five-star evidence. The negative examples are pairs of proteins and compartments for which there is no evidence suggesting that the protein is in the compartment and five-star evidence for the protein being in a different compartment. The compartments considered for the benchmark set are the 11 subcellular localizations used in the overview figure, and all evidence for more specific localizations have been backtracked to this level. The benchmark set is available for download from the COMPARTMENTS web resource.D. melanogaster, C. elegans, S. cerevisiae and A. thaliana in STRING 9.1. We converted all scores to stars to make them comparable with other evidence types; the maximum number of stars that can be assigned to a sequence-based prediction is three. This ensures that prediction scores cannot exceed the scores of reliable manual annotations, experiments or text mining.The WoLF PSORT and YLoc-HighRes methods were selected for prediction of subcellular localization. We precomputed predictions for the entire set of protein sequences for human, mouse, rat, PSORT  predictsYLocs is the stars derived from a YLoc prediction, P is the prediction probability that the protein is localized in the given compartment. (YLoc  is a naïP-value for each pair of compartments as the fraction of random data sets resulting in a count greater than or equal to the observed count. Finally, we defined the statistically significant compartment pairs by imposing a false discovery rate of 0.1% using the Benjamini–Hochberg method (From the unified data set, we extracted localization information on human proteins with more than two stars to disregard weak text-mining and prediction evidence. The retrieved data set comprised 18 692 unique human proteins with 29 493 links to compartments: 20 021 were supported by curated knowledge, 4841 by high-throughput experimental evidence, 1468 by text mining and 15 788 by sequence-based predictions. We counted the number of proteins shared between any two compartments. To assess if this is higher than expected, we compared the counts to a null model that assumed no correlation between any compartments. To this end, we generated 1 000 000 random data sets in which links between proteins and compartments were permuted, thereby preserving the number of links per protein and per compartment. We computed a g method .


Construction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database.The function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein.The PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction. Proteins are basic functional components in any biological process. Discovery of the functions of an individual protein is therefore a critical step towards understanding biological processes and the complete biological system. Besides experimental studies, computational prediction plays an important role in current protein function investigation . The genn[Graph theoretical analysis can be used for predicting the function of uncharacterized proteins -11. Brien, considen, transfen-19, wereUnlike function inference methods, construction of a reliable network remains the bottleneck for network-based function prediction. High-throughput experimental methods such as two-hybrid techniques , affinitThe so-called genomic context (GC) methods, namely, gene fusion , gene or2 method or support vector machine (SVM) learning [To a less extent, domain co-occurrence networks (DCN) were investigated for the prediction of protein and domain functions . A protelearning .As a counterpart to DCN, the protein overlap network (PON) provides another way for function prediction. In a PON, each node represents a protein and two nodes are connected with an edge if the proteins share a common domain. While a few efforts have been made to study the properties of DCNs ,34-36, tS. cerevisiae), fruit fly (D. melanogaster), worm (C. elegans), and human (H. sapiens) in April 2012. Every protein was annotated with domains retrieved from the Pfam database [The protein annotations were downloaded from InterPro , a databdatabase , and thedatabase . The foudatabase  and Pfamhttp://www.ebi.ac.uk/GOA/ for yeast, fly, worm, and human in April 2012.We also used UniProt-GOA (gene ontology annotation) database  for predIn our constructed PONs, each node represents one protein. Two nodes are connected by an edge if the corresponding proteins share a common domain defined by Pfam. The proteins and the domains acquired above were used to construct a PON for yeast, fly, worm, and human, respectively.i is defined asWe investigated three main network properties for the PONs: degree and degree distribution, shortest path length, and clustering coefficient. The degree of a node is defined as the number of its immediate neighbors. The shortest path length between any two nodes is calculated with Dijsktra algorithm  using B-ni is the number of connected node pairs (edges) among nodes directly connected to the central node n and ki is the number of neighboring nodes of the central node n.where We used the neighbor-counting method to retrieve GO terms associated with all neighbors of a query protein in the PON and ranked them by their occurrence frequencies. Instead of simply counting the number of protein nodes associated with a GO term, the occurrence frequency was calculated as the number of domains that were associated with the GO term and presented in the neighboring nodes. Naturally, GO terms associated with the common domains shared by a query protein and its neighbors should be considered more favorable than those associated with domains observed only in the neighbors. For the purpose of testing the prediction method, we assumed that the function was unknown for all domains in the query protein. Thus, unless specifically indicated, we made predictions using only domains that were not observed in the query protein, i.e., excluding the common domains. In cases where a GO term was associated with both the common domain and other domains in neighbors, the GO term was considered in prediction but the common domain was not counted in the occurrence frequency. The result was compared with the combination of annotated GO terms of all domains in the query protein. As a control to evaluate the prediction performance, we randomly switched GO terms belonging to two proteins and the corresponding domains throughout the whole genome.Annotated GO terms obtained from the GOA database were statistically analyzed for neighboring nodes of the query protein. Those with a high occurrence frequency, i.e., associated with a large number of proteins, were selected and compared with the annotations of the query in the GOA database.We used the success rate of prediction as the primary evaluation method. The success rate was defined as the ratio of correctly predicted GO terms to the total predictions. For the purpose of evaluation, we limited the number of predicted GO terms to be the same as that of the annotated GO terms in the query protein. Frequently, several GO terms had the same occurrence frequency around the cut-off and only a part of them were randomly selected to meet the criteria. We might also have a smaller number of GO terms associated with the neighboring proteins than the total annotations of the query protein and thus all of the analyzed GO terms constituted the prediction. In addition to the success rate, we also evaluated top 3 ranked GO terms, which means that the prediction was considered correct only if one of the three GO terms matched any annotation of the query protein. Similarly, the GO term with the highest occurrence frequency was evaluated for the top 1 GO term accuracy. A node was considered predictable if any of its neighboring nodes was annotated with one or more GO terms. Predictable nodes with observed GO term annotations were subject for evaluation and the coverage was calculated as the percentage of the evaluated nodes out of all nodes in the network.We built PONs with proteins from the whole genomes of yeast, fly, worm, and human, respectively. The network usually contained many disconnected sub-graphs with one main sub-graph consisting of 50% of proteins except for the relatively small yeast PON, in which the main sub graph contained 16% of the total proteins and 24% of proteins were not connected to any other proteins  despite of the different genome size. The nodes in a PON sub-graph frequently have a common GO term annotation. About 30% of the nodes share the most popular GO term, We predicted the function of a protein by statistically analyzing GO terms associated with domains in its neighboring nodes, i.e. the domain-based method. Since the annotated GO terms in InterPro database were exactly the same for identical domains among various proteins, the mean success rate was extremely high (>99%) if the GO terms associated with the common domains of the query protein and neighboring proteins were favorably considered. The success rate was not 100% only because in some cases the query protein contained a few domains observed in none of neighboring protein. In real predictions, we might not know the functions of the query domains, and so in our prediction, only the GO terms associated with the other “not shared” domains were statistically analyzed and those GO terms with a high occurrence frequency were compared with the combination of annotated GO terms from all domains of the query protein. The success rates predicted with the single-genome PON for yeast, fly, worm, and human were 30.2%, 34.2%, 32.6%, and 40.0% . For the yeast PON, it is unlikely to identify GO terms associated with a large number of domains due to the small network size and the low degree value as seen from Table One likely factor contributing to the high success rate for human as compared with yeast is the size difference between the two networks. For a query protein with domains m Figure . In factWe constructed the composite PON by combining different genomes because the success rate of the domain-based method is positively correlated with the size of the network and the GO term annotation of a domain is exactly the same for various genomes according to InterPro database. The composite PON from the four species  containing 74178 nodes and 5079 domains was constructed to investigate whether the prediction could be improved with a larger PON. The accuracies obtained for each of the four species using the composite PON were compared with those obtained using their individual single-genome PONs. For the three species with a smaller-sized genome, yeast, fly, and worm, the success rate was slightly improved by up to 4% and the coverage was also increased by 7%-12%. For the human genome, the coverage slightly increased by 1.2% though the success rate decreased by 1.6% Table .There are two advantages using the composite PON for protein function predictions: one is to improve the accuracy for those nodes that already can be predicted with the corresponding small-sized single-genome PON, and the other is to improve the coverage by adding new predictions, albeit at lower accuracy. With the composite PON, the success rate significantly increased by 8.7%, 2.6%, and 4.6% for those annotated and originally predicable nodes for yeast, fly, and worm, respectively. On the other hand, the new predictions that were unpredictable with a single-genome PON showed lower success rates (<20%) except for worm (32%), partly due to the lack of annotated domain types at the neighboring nodes. For example, the average occurrence frequencies of the most popular GO terms were 1.3 and 1.8 for the new predictions in the composite network compared with 6.3 and 6.2 for the originally predictable nodes for fly and worm, respectively. In future studies, more proteins and genomes may be used in the combination to improve the prediction accuracy and coverage even for the human genome.AB (A and B are domain or domain combination of the protein), the direct neighbor BC, and a second layer node CD, we made the prediction based on frequency counting of the GO terms associated with the domain D and all its peers. Here, for a clear comparison, we limited the domain D such that D is not a component domain for any immediate neighbors, i.e., D does not combine with A to form any protein. Usually, the accuracy is lower than that predicted from direct neighbors  were considered, an impressive success rate (>30%) and coverage (>30%) could also be obtained. For small genomes, it should be noted that the composite network is essential for the high performance due to the limited number of domains and domain connections in the single-genome networks.Frequently, only a few or no annotated domains were observed at direct neighboring nodes. To increase the coverage, we thus made predictions with the domain-based method considering the second layer nodes, i.e., the radius 2 neighbors of the query protein. For a query protein rs Table . NonetheProteins may acquire different functions in evolution while maintaining the same domain composition. There are inherent limitations of domain-based function prediction methods, where the function of a domain is fixed despite their existence in various proteins. With a PON, we are able to predict the function of a protein by statistically analyzing manually annotated GO terms for each neighboring protein. We obtained the GO term annotations from the gene ontology annotation (GOA) database for both prediction and evaluation. Using a single-genome PON, the mean success rate (60.8%) of yeast, fly, worm and human Table , is muchMoreover, the GOA database used for prediction is a comprehensive GO annotation dataset and the coverage is very high for the four species predicted with the composite PON of other genomes, respectively, achieving a mean value of 79.2%. As a comparison, the domain-based method yield a mean coverage of 71.0%, which is also high compared with those in Table In general, the GOA database method shows a better success rate, coverage of query proteins, and recall rate of observed GO terms than the domain-based method, especially in prediction with a single-genome PON. However, there are only 13 species with genome-wide annotations in the GOA database until the end of 2012, therefore the applicability is rather limited. It is also not clear to what extent the high success rate predicted with the GOA database is related to the sequence similarity between common domains of the query protein and neighbors. On the other hand, we optimized the domain-based method without considering the common domains. The unknown function of a domain can be predicted from other functionally characterized domains, which is especially helpful for biological experimentalists.B and C, combine with the domain A to form two proteins: AB and AC, it is possible that the domain B and C share the same GO terms. Actually, for all connected pairs, e.g., AB and AC, in the human PON, a GO term associated with domain B is also associated with domain C in 24.4% of cases compared with 4.7% in the random control procedure. In fact, this indirect functional association was also observed at protein level in early studies. Chua et al. found proteins interacting with the same partners had a greater likelihood of sharing similar physical or biochemical characteristics [A is also the annotated GO term of B in 35.4% cases, i.e., domains within a protein may share the same GO term annotations, which is the basis for domain function prediction with a DCN [B and C are not due only to the fact that B shares GO terms with A and A shares GO terms with C (35.4% × 35.4% <24.4%).We built PONs for protein function prediction. Our function prediction algorithm is based on two observations. Firstly, if two domains (or domain combinations), eristics . Secondlth a DCN . It shouWang et al. made protein function predictions by integrating the GO terms predicted for each of its domains using a single-genome DCN . If all GO term comparison is a yes-or-no binary criterion in this study even though two different GO terms may share functional similarity. Nevertheless, the mean value of success rates calculated with the binary criterion strongly correlates with that of the similarity scores in large-scale calculations like those presented here. For example, the mean success rates predicted and evaluated with the GOA database were 48.3, 41.1, and 35.5% respectively, for yeast proteins using the single-genome PON, the composite PON of four species, and the composite PON excluding yeast. When the semantic score between the predicted GO term set and the annotated set of each protein was calculated with the online method G-sesame , the meaThe currently proposed methods can be used for the prediction of new functions of a protein in addition to the annotated GO terms, or a protein with unknown function. In fact, there are 3.2% yeast proteins that are not annotated but predictable with the domain based method using the single-genome PON . Using a PON, protein functions can be predicted with the domain-based method or gene ontology annotation database. The GOA database method usually shows a better performance than the domain-based method. The composite PON of multiple genomes can be used to enhance the performance for the domain-based method, especially in prediction for small genomes. But the GOA database method achieves the best accuracy for a protein when the single-genome PON of the same species is used. The success rate of the domain-based method is close to that of the GOA method in prediction for proteins without genome-wide sequence information and domain annotations. Moreover, the domain-based method can be optimized to predict the functions for an uncharacterized domain from the annotated GO terms of other domains.PON: Protein overlap network; GO: Gene ontology; PPI: Protein-protein interaction; GC: Genomic context; DCN: Domain co-occurrence networks; SVM: Support vector machine; GOA: Gene ontology Annotation.The authors declare that they do not have any competing interests.SL designed the study, implemented the algorithm and drafted the manuscript. DZ, HG, DMS, and CZ helped prepare the data and draft the manuscript. All authors read and approved the final manuscript.


A proteome of the bio-entity, including cell, tissue, organ, and organism, consists of proteins of diverse abundance. The principle that determines the abundance of different proteins in a proteome is of fundamental significance for an understanding of the building blocks of the bio-entity. Here, we report three regular patterns in the proteome-wide distribution of protein abundance across species such as human, mouse, fly, worm, yeast, and bacteria: in most cases, protein abundance is positively correlated with the protein's origination time or sequence conservation during evolution; it is negatively correlated with the protein's domain number and positively correlated with domain coverage in protein structure, and the correlations became stronger during the course of evolution; protein abundance can be further stratified by the function of the protein, whereby proteins that act on material conversion and transportation (mass category) are more abundant than those that act on information modulation (information category). Thus, protein abundance is intrinsically related to the protein's inherent characters of evolution, structure, and function. As the endpoint of the central dogma of biology, proteins are central players of most biological processes, and their abundances are regulated precisely to meet with dynamic functional requirement M. musculusD. melanogasterC. elegansS. cerevisiaeE. coliTo this end, it is necessary to identify and quantify proteins at proteome-wide. With the fast advancement of quantitative proteomics based on mass spectrometry, protein abundance can be measured at the whole proteome level, making biological rule discovery possible. The Human Liver Proteome Project (HLPP) SCI), an adjusted SC in accounting for the theoretic tryptic-peptide number of protein, was calculated following the reported methods SCI values from different experimental batches were normalized, and this normalized SCI value was termed the spectral count index normalized (SCIN) D. melanogasterC. elegansS. cerevisiaeE. coliNSAF) value and copies per cell value from original papers, respectively.The quantification of protein abundance in the human liver was estimated using the spectral counts (SC) method. The spectral count index  were categorized into given function and subcellular classes by Gene Ontology (GO) annotations All the gene products of seven datasets  according to the “Yeast GO Slim” standard.To obtain some specialized functional categories such as “meiosis” and “cytokinesis”, functional and subcellular categories of yeast gene products were assigned automatically by the GO Slim Mapper system in the Fig. S1A), and then was simplified to 7 significant diversity time nodes . The protein sequences of the seven proteome datasets were extracted, and sequence conservation was determined using the bi-BLAST method with BioEdit software.Protein origin time was estimated by species diversity time D. melanogaster and the E. coli datasets, WBGene ID (a type of ID in WormBase) from the C. elegans dataset, and the SGD (Saccharomyces Genome Database) ID from the S. cerevisiae dataset. Medium classification stringency and 12 items were chosen for enrichment calculation: COG_ONTOLOGY, SP_PIR_KEYWORDS, UP_SEQ_FEATURE, GOTERM_BP_FAT, GOTERM_MF_FAT, KEGG_PATHWAY, BIOCARTA, BBID, INTERPRO, PIR_SUPERFAMILY, SMART, and OMIM_DISEASE. To avoid high enrichment score disturbance of cellular component categories, GOTERM_CC_FAT was neglected on purpose.The analysis was carried out using the web-accessible DAVID  tool E-value DN) in one protein was calculated including the domain repeats in one protein. Domain coverage (DC) refers the percentage of the entire length of a protein occupied by domains. DC was divided by its corresponding DN to get the DC/DN value. Protein-protein interaction (PPI) domains were defined through the InterDom database PPI_DN) refers the number of PPI domains in one protein, and PPI domain coverage (PPI_DC) means the percentage of the entire length of a protein occupied by PPI domains. PPI_DC/PPI_DN was calculated by dividing PPI_DC by PPI_DN.Protein domains were defined essentially as described previously All difference-tests in our analysis were carried out by using the Wilcoxon rank sum test, a non-parametric statistical hypothesis test for assessing whether two independent samples of observations have equally large values. All the correlations were defined on the non-parametric Spearman rank correlation, which assesses how well the relationship between two variables can be described using a monotonic function. We perform Wilcoxon rank sum test and spearman rank correlation using MATLAB 7.11.0. Predictive power means the probability that any two random pairs of proteins meet with the general trend. The predictive power value of each correlation was calculated based on pairs of randomly sampled proteins in each proteome dataset, and is represented by the percentage of the true predictions over all the protein pairs selected. We selected various subsets of protein pairs with a minimum rank difference percentage from 0 to 95%, and obtained the minimum and maximum predictive power values for each correlation Table S1). That is, the earlier a protein is born, the higher its abundance tends to be. This tendency exists in all the six species examined, indicating that it might be a general rule of organism phylogeny. One hypothesis is that the proteins that appeared first are likely to carry out essential housekeeping roles and are, therefore, required in higher abundance, whereas proteins that evolved later tend to act in specialized functions that do not need to be in high abundance. Enrichment analysis of the functional categories in proteins supports the hypothesis that proteins of housekeeping function or regulatory functions are clustered with distinct origination time in all the six species (Table S2).An important evolutionary property of a protein is its origination time, which reflects its evolutionary span. Taking a proteome as a whole, we found that the abundance of proteins is positively correlated with the protein's origination time, though sometimes the correlations are not so strong (C. elegans and D. Melanogastervs. mouse) and protein abundance is not significant  and protein abundance  The function of a protein is determined by its structure, which is mostly embodied in its domain architecture. We investigated the relationship between protein abundance and protein domain characteristics, including DN or PPI_DN of a protein is negatively correlated with its abundance, especially in higher eukaryotes . Notably, the correlations are not strong in the proteome data of S. cerevisiae and E. coli. This exception may be partly due to the fact that the proportions of multi-domain proteins in these two single-cellular organisms are much lower than other multi-cellular organisms, which is revealed by our analysis based on the data used in this study  and previous related studies DN and protein abundance . In contrast, the DC or PPI_DC of proteins positively correlated with their abundance in the proteomes of all the six species .We found that the DN can be regarded as the simplest parameter representing the complexity of a protein structure PPI_DN represents the complexity of the protein structure related to PPI. The smaller the DN or PPI_DN, the simpler the protein is. It can be inferred that proteins with simpler structures tend to be of higher abundance. Similarly, DC is an index of the structural compactness of a protein, and PPI_DC is an indicator of the structural compactness of PPI DC or PPI_DC reveal that the more compact proteins are of higher abundance. To represent the integrated structural character, including complexity and compactness, we introduced a combined parameter, DC/DN (or PPI_DC/PPI_DN). We found that the DC/DN or PPI_DC/PPI_DN of proteins positively correlated with their abundance in the proteomes of all the six species . Thus, simple and compact proteins tend to exhibit high abundance, which does have some predictive power especially when proteins with vastly different domain characters are compared (Table S3).Fig. S2), which may be related to the increasing of biological complexity. The abundance of proteins in the higher multi-cellular organisms may need more precise regulation, to meet with the complex functional requirement. This may lead to the stronger correlation between protein abundance and its complexity or compactness.Interestingly, the strength of correlation coefficient increases through the course of evolution . For example, meiosis and cytokinesis proteins, which participate in a typical information process of S. cerevisiae that instantly decide the fate of a cell, have low abundance distributions  follows the trend of protein abundance up-regulation correlated with top/down information flow, in which proteins participating in DNA replication are of the lowest abundance, those in RNA transcription are of median abundance, and those in protein translation are of the highest abundance across species (Table S6). Also as predicted, along the biological information flow of “signalling→transcription→translation→metabolism”, downstream systems with higher abundance are of an earlier origination tendency than upstream ones .The distinction of mass category and information category in protein abundance correlation is predicted by the evolutionary and structural rules. For example, human proteins in the mass category are highly enriched in proteins originating earlier than 4 Gya, and tend to be with lower Genome sequencing has enabled us to acquire a vast amount of data, and this has accelerated the discovery of biological rules at the genome level Protein abundance is one of important phenotypic variables of proteins, and is controlled precisely. Exploration for the proteome-wide relationship between the intrinsic properties  and this phenotypic variable will benefit to discovering the essential rules related to proteins. Here, we described three correlations of protein abundance with the protein's intrinsic properties of evolution, structure, and function, observed to be consistent from bacteria to yeast, worm, fly, mouse, and human: (1) There is a positive correlation with both the protein's origination time and sequence conservation during phylogeny, confirming the conclusions of previous studies at a wider range of species Fig. S5). The discovery of those regularities strongly demonstrated that the quantitative and comprehensive proteomic datasets could provide a rich ground for the exploration and discovery of the fundamental rules in nature on the proteome scale.Taken together, protein abundance distribution across the whole proteome displays prominent regularities, even though there are large discrepancies in protein composition and abundance among various proteomes of diverse bio-entities. Such regularities seem to be maintained in different organs as analysis of the mouse kidney dataset yielded identical patterns with the analysis of the mouse liver datasets  Detailed diversity time tree that was constructed according to the reference (Hedges 2002). (B) Simplified diversity time tree with 7 significant time nodes to keep proper time scale distances  for analysis. The 6 species analyzed were in red text.(PNG)Click here for additional data file.Figure S2Proteome-wide correlation between protein abundance and its protein-protein interacting (PPI) domain parameters. Three Parameters including PPI domain number (PPI_DN) (A), PPI domain coverage (PPI_DC) (B) and PPI_DC/PPI_DN (C) were employed for the analyses. R represents Spearman rank correlation coefficient and P represents the P-value of the spearman rank correlation analysis. Medians are indicated as black dots (A), or crosses , and whiskers encompass the range from 25% to 75% of values.(TIF)Click here for additional data file.Figure S3The proportion of multi-domain proteins in each dataset (A) and its relationship with the correlation coefficient values between domain number and protein abundance (B).E. coli (Ec), S. cerevisiae (Sc), C. elegans (Ce), D. melanogaster (Dm), M. musculus cortex of kidney (MmC), M. musculus liver (MmL),and H. sapiens (Hs). R represents the pearson correlation coefficient, and P represent the P-value.(PNG)Click here for additional data file.Figure S4Comparison of mass categorial proteins with information categorial ones on distributions of origin time and domain character among six species. Origin time distributions of proteins in H. sapiens (a1), M. musculus (a2), D. melanogaster (a3), C. elegans (a4), S. cerevisiae (a5) and E. coli (a6). Domain character distributions of proteins in the same species: DN (b1–6), DC (c1–6) and PPI_DC (d1–6).(PNG)Click here for additional data file.Figure S5Consistency of the three rules across tissues. When replacing mouse kidney data by mouse liver data in analyses, all rules were maintained. These analyses include: correlations of protein abundance with origin time (A), sequence conservation (B) domain characters (C), functional categories (D) and biogenesis machines of three bio-molecules (E).(TIF)Click here for additional data file.Table S1Rank sum test p-values between origin time categorized proteins' abundance datasets.(DOC)Click here for additional data file.Table S2The functional enrichment results of different origin time protein categories in 6 species by DAVID.(DOC)Click here for additional data file.Table S3Predictive power of each domain character parameter correlating with protein abundance.(DOC)Click here for additional data file.Table S4Rank sum test p-values between various functional categorized proteins' abundance datasets across six species.(DOC)Click here for additional data file.Table S5Rank sum test p-values between various functional categorized proteins' abundance datasets in H. sapiens and S. cerevisiae.(DOC)Click here for additional data file.Table S6Rank sum test p-values between proteins' abundance datasets of three bio-molecules biogenesis machines across six species.(DOC)Click here for additional data file.


Drosophila.Domain or gene fusion analysis is a bioinformatics method for detecting gene fusions in one organism by comparing its genome to that of other organisms. The occurrence of gene fusions suggests that the two original genes that participated in the fusion are functionally linked, i.e. their gene products interact either as part of a multi-subunit protein complex, or in a metabolic pathway. Gene fusion analysis has been used to identify protein functional links in prokaryotes as well as in eukaryotic model organisms, such as yeast and Trypanosoma brucei, the causative agent of African sleeping sickness. We have also examined the evolution of the gene fusion events identified, to determine whether they can be attributed to fusion or fission, by looking at the conservation of the fused genes and of the individual component genes across the major eukaryotic and prokaryotic lineages. We find relatively limited occurrence of gene fusions/fissions within the protist lineages examined. Our results point to two trypanosome-specific gene fissions, which have recently been experimentally confirmed, one fusion involving proteins involved in the same metabolic pathway, as well as two novel putative functional links between fusion-linked protein pairs.In this study we have extended this approach to include a number of recently sequenced protists, four of which are pathogenic, to identify fusion linked proteins in T. brucei identified by gene fusion analysis. We have used strict thresholds and only discuss results which are highly likely to be genuine and which either have already been or can be experimentally verified. We discuss the possible impact of the identification of these novel putative protein-protein interactions, to the development of new trypanosome therapeutic drugs.This is the first study of protein functional links in  This fusion analysis  takes advantage of the study of genomic structures and sequence similarity to detect putative interacting protein pairs, which, importantly might not have been suspected based on current biochemical knowledge. Briefly, if a pair of non-homologous proteins which are found in different genomic regions in organism A, are found fused into a single ORF in organism B, this suggests that the two independent proteins in organism A may interact. These protein-protein interactions may be transient or more long-lived, either within a metabolic pathway, or as part of a multi-subunit protein complex.Proteins exert their diverse functions usually through interactions with other molecules, and indeed often through interactions with other proteins. Protein-protein interactions are currently the focus of intensive study, as they allow functional characterisation of proteins and help interpret the role of uncharacterised gene products in organisms with fully sequenced genomes. Apart from direct biochemical analysis to detect protein-protein interactions, an Trypanosoma brucei. Trypanosome brucei, a flagellated parasitic protist, is responsible for African trypanosomiasis, a neglected disease known as sleeping sickness in humans. The protozoan uses the tsetse fly as an insect vector, and the sickness is fatal if untreated. All the available treatment drugs are generally unsatisfactory, due to dangerous side effects, high cost of production, and difficulty in distribution  iron-sulfur subunit, mitochondrial", with AAX79657 aligning to the first half and EAN76651 aligning with the second half. Interestingly, this protein binds two iron-sulfur clusters, one at 34-114 aa, and one at 155-185 aa, so in T. brucei these two domains are split between the two proteins. Succinate dehydrogenase  is part of the mitochondrial electron transport chain for respiration/oxidative phosphorylation, which is generally thought to be inherited from the ancestor of mitochondria, i.e. from bacteria. So, in bacteria and most eukaryotes this protein is fused, but in trypanosomes it is split into two  was originally reported as not found in the T. brucei genome publication [T. cruzi, and which reported that SDH2 is encoded by two different proteins  [• The t Figure . Indeed,o Figure . This spctively) .in silico method used to detect such protein fusions is called domain fusion analysis and the composite polypeptide AB, is referred to as a Rosetta Stone protein, as it gives information about a functional link between domains A and B. The method was introduced by Marcotte et al. [et al [Two polypeptides A and B in one organism, are likely to interact if their homologues are expressed as a single polypeptide AB in another. The e et al.  and Enri. [et al , and is . [et al ,12. In tTrypanosoma brucei against seven divergent protists. The automatic analysis, based on the BLASTp algorithm with appropriate thresholds for E-value, alignment length, protein coverage, and excluding homologous sequences, produced 81 putative composites, i.e. 3-38 fusion events detected in each species , is a generally applicable verification method that can be useful for other domain fusion analysis studies.We analysed the proteome of es Table . After v in each , reverse in each , and chet et al. . AlthougT. brucei, namely the two fusion events identified in Chlamydomonas resulting in a split topoisomerase IB, and a split succinate dehydrogenase in T. brucei [in silico analysis is finding functionally relevant pairs of genes. Both cases appear to result from a unique fission event in the kinetoplastida. The reason for this is unknown , but the fact that these heterodimeric proteins are unique to the kinetoplastids, marks them as potential drug targets for new therapeutic strategies [et al. [Of the five results analysed in detail, two have previously been independently identified and experimentally verified in . brucei -36. Thesrategies ,36. Anal [et al. , that thMonosiga, resulting in a combined "glutamine hydrolysing (not ammonia-dependent) carbamoyl phosphate synthase" (EC 6.3.5.5) and "aspartate carbamoyltransferase" (EC 2.1.3.2), appears to be common among fungi, metazoa and some bacteria, and concerns enzymes that participate in subsequent steps of the pyrimidine biosynthesis pathway. Heterotrimeric versions also exist in the bacteria, archaea, and archaeplastida, suggesting multiple independent fusion events between these domains. This is in agreement with previous domain fusion analyses that have identified many composites consisting of pairs of functionally linked proteins that participate in the same metabolic pathway [The fusion event identified in se" EC 2..3.2, app" EC 6.3..5 and "a pathway ,8.Entamoeba of a TBC GTPase activating domain with an N-terminal Ankyrin domain. This suggests that the two interact, and some support for this is lent by a recent study of GTPase activating proteins, that reported that TBC proteins do not directly interact with Rab proteins, but require another protein-interaction domain, confirmed by truncation mutant analysis for the case of a C-terminal Ankyrin domain [Phytophthora of a hydroxymethylglutaryl-CoAlyase with a SNF7 domain protein. This fusion, which is also shared by diatoms and brown algae, may suggest a role for ubiquitylation or the ESCRT system in the sorting of hydroxymethylglutaryl-CoAlyase. Again, this is a testable hypothesis.Two of the results reported here are truly novel. One is the fusion in n domain . Therefon domain , disruptThe functional links identified through domain fusion analysis in this study agree with the general principles for gene fusion and fission events established by previous studies. For example, gene fissions are relatively rare events compared to fusions , and in Trypanosoma brucei TREU927. As our primary interest was the analysis of the proteome sequences, we chose organisms with completely sequenced and annotated genomes. Based on these criteria, we chose the following species: Unikonts-Candida albicans, Monosiga brevicollis, Dictyostelium discoideum, Entamoeba histolytica; Plantae-Chlamydomonas reinhardtii; Stramenopiles-Phytopthora infestans; Excavates-Giardia intestinalis. The complete proteomes of these parasites were retrieved form the NCBI genetic sequence databases and UniProt Knowledgebase [In the present study we compared seven protists, representing all eukaryotic lineages Table , againstedgebase ,41.T. brucei proteins, which appear fusion-linked in other species, i.e. when queried against the proteome of another organism, both match the same protein as their best BLAST hit. We analysed the entire proteome of Trypanosoma brucei TREU927 against the entire proteome of each target organism. To parse the results and to avoid inaccuracies, we used the following settings for the automatic data analysis program:We used a home-built software tool  based onsameorganism, was deleted from the proteome. On average, this removed about 7% of each proteome.• The software algorithm was developed to exclude any homologues which are 85% identical over the whole sequence length. Hence, a protein with a similarity value of 85% or higher to a larger protein of the • A fusion-linked protein was taken into consideration only if each component had a minimum DOMAIN length of 70 amino acids, along with a minimum of 27% identity with the composite protein, based on the BLAST alignment per domain.-3, as not statistically significant.• We automatically excluded all results with an E-value higher than the threshold of 10• Two proteins were considered to be fusion-linked only if each of them aligned with a minimum PROTEIN coverage of 70% to the same reference sequence (based on the protein annotation of the target organism).These parameters were chosen based on previous publications ,13 and aTo estimate the accuracy of the fusion links, we developed a manual verification pipeline:Trypanosoma brucei TREU927 as their best BLAST hits .Trypanosoma brucei TREU927 and Leishmania major, another parasitic kinetoplastid. Both organisms have similar genomes and their proteomes feature a high percentage similarity [T. brucei fusion-linked proteins are homologous to each other, we repeated the BLASTp procedure using the Leishmania major proteome as target. If the T. brucei proteins matched the same protein in L. major they were considered homologous and excluded from further analysis.2. To exclude misleading annotations, we looked for fusion links between milarity . To exam3. We identified the domain architecture of the composite form of the fusion-linked proteins in the target organisms and checked for orthologous domains in their component (split) proteins.Based on these three quality controls, the results were categorised into three groups:• One group consists of composite proteins that have likely undergone fusion events, since (a) the split protein pair consists of two non-homologous proteins, and (b) the annotated interactions between the domains of the composite protein give a functionality equivalent to that of the domains of the split protein pair.• Another group consists of proteins with domains of poorly characterised function, such as "unknown" or "general function"; fusion analysis can be used to predict the function of an unknown protein if it is fusion-linked to a partner of known function.Trypanosome brucei consists of homologous proteins. The links between homologous multi-domain proteins were not analysed further.• The third group contains results where the split protein pair in Smithsonian Institution ([T. brucei as fusion-linked (see above) and searched for homologues against all the available sequences in the NCBI database of all organism family groups shown in the tree. Within each organism family group, we selected the BLAST hit with the highest identity value and the lowest E-value threshold, as a single representative orthologue. The generated results were classified into 5 classes, based on whether the T. brucei fusion linked protein pair retrieved:The tree of life is an ever-evolving depiction of life's common ancestry. We used a tree of life, previously designed by the itution ( with perT. brucei proteins (fused)• a single protein composed of two regions corresponding to the T. brucei proteins (heterodimeric)• two proteins, each one corresponding to one of the T. brucei proteins, and the other two corresponding to the second T. brucei protein (heterotrimeric)• three proteins, one corresponding to one of the T. brucei proteins . This may be because the whole proteome of each family group within the tree of life might not be complete or available through the NCBI or UniProt databases.• proteins with low sequence identity In addition, we attempted to elucidate the biological function and the metabolic pathways of the fusion linked proteins. We were able to claim that the evolutionary scenario of fused and fission domain architecture is valid, only in cases where the characteristics or the function of the fusion and fission events were similar to those of the reference protein.The authors declare that they have no competing interests.SK conceived and supervised the project and provided funding. DD and PT carried out the analysis, and VLK helped with data interpretation. VLK, DD and PT drafted the manuscript and figures and SK helped with critical revision; all authors read and approved the final manuscript.et alComparison of our results with those of Enright . [et al. [et al. [E. coli, H. influenzae and M. jannaschii, detected on the basis of composite proteins in these three genomes plus the genome of S. cerevisiae. Columns COMPONENT list the component gene/protein names (or identifiers); column COMPOSITE lists the composite (fusion) gene/protein name (or identifier); columns with species names list the gene identifiers from the corresponding species; N lists the maximum number of possible pairwise interactions; COMMENT includes various comments for specific cases. White cells in the species columns contain component proteins and blue cells contain composite proteins, on the basis of which interactions are predicted. The sort order follows the three species against the composite-protein sequence-identifiers for the yeast genome, and then the other three species in succession. Genes are named when different names are used; where no name is available, the sequence-identifier is used instead. '#" indicates the absence of a component from a multiple fusion event (case 8). Interacting pairs are separated by "/", while paralogues are separated by commas." A column next to each species name shows the results from the present analysis, where "-" refers to the events not identified by our software  but which might be genuine, "c" refers to the events not identified by our software which are probably artefactual in the original analysis , "p" refers to paralogues (5.7%), "m" to matches i.e. domains that participate in multiple interactions (34%), "d" to doubles i.e. domains that are involved in two separate fusions events , and "u" to unique results that conform to all the selection criteria . The percentages (categories) of results from our analysis are shown at the end of the table along with comments concerning each fusion event and the reasons for any difference in the results. In total, we can confirm 87,5% of the 88 fusion events reported by Enright et al. In addition, our software identified a further 27 events not reported by Enright et al. (Worksheet "UNIQUE").t et al. . To test [et al. . The tab [et al. , showingClick here for fileT. bruceiAlignments of the fused proteins identified in different species, with the corresponding split protein pairs in . A: BLAST output alignment of the P. infestans EEY58132 composite protein with the T. brucei proteins AAX79027 and AAX70704. B: BLAST output alignment of the M. brevicollis EDQ88211 composite protein with the T. brucei proteins AAX70833 and AAX70835. C: BLAST output alignment of the E. histolytica EAL47672 composite protein with the T. brucei proteins EAN76787 and EAN78273. D: BLAST output alignment of the C. reinhardtii EDP05938 composite protein with the T. brucei proteins AAX79872 and EAN76725. E: BLAST output alignment of the C. reinhardtii EDP08267composite protein with the T. brucei proteins AAX79657 and EAN76651.Click here for file


With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem. With the rapid growth of genome sequences and gene expression profiles, there is increasing concern about using computational methods to determine the linkages between protein sequences and their biological functions et al described two main classes of the network-based methods for predicting protein functions: direct methods such as neighborhood counting based method The most established approaches A total of 24 functional categories are adopted in this paper. Each protein is predicted as belonging to some of these 24 functional categories. Here, we are concerned about not only the first-order predicted function - the most likely function predicted by the prediction model, but also the lower-order functions sorted by the prediction criteria. As a result, the accuracies of the first-order function prediction for the overall training set and test set were 69.1% and 70.2%, respectively.ftp://ftpmips.gsf.de/MfunGD/). There are a total of 24 function categories from FunCat The dataset for studying the functions of proteins in mouse here was downloaded from MfunGD http://string.embl.de/), which is a large database of known and predicted protein interactions. These interactions contain direct  and indirect  interactions, derived from numerous sources such as experimental repositories, computational prediction methods. In the network, each edge is marked with a score as the edge weight to quantify the interaction confidence, i.e., the likelihood that an interaction occurs.The interaction network takes proteins as its nodes, with an edge between two proteins if they interact with each other. The initial weighted PPI network was retrieved from STRING Then the obtained 14,732 proteins were separated into two subsets: (A) 10,194 proteins in the above PPI network for training and testing the network-based method (see the following section); (B) 4,538 proteins not in the PPI network for training and testing hybrid-property based method (see the following section). For subset A, 1,076 proteins were randomly selected as the independent test set denoted by Firstly the proteins in the MfunGD The protein IDs in MfunGD were different from those, the ensemble protein IDs in STRING. To convert MfunGD IDs to ensemble IDs, the ID , transition (T), and distribution (D). C describes the global percent composition of each of the groups in the letter sequence; T, the percent frequencies with which the letter changes to another along the entire length of the letter sequence; and D, the distribution pattern of the letters along the sequence, measuring the percentage of the sequence length within which the first, 25%, 50%, 75%, and 100% of the amino acids of each letter is located.Take the 50-length letter sequence described above as an example. It is composed of 10 Hs, 16Ns, 24Ps. The first feature C is For the solvent accessibility, there are only two local feature groups, and hence resulting in seven features rather than 21 as illustrated above. The amino acid compositions have 20 features, each of which represents the percentage or occurrence frequency of the constituent amino acids in a protein sample After each protein was coded, two criteria were applied to the vectors set. (1) Excluded proteins with the same coding vectors, but the different functional categories. (2) Keep one of the proteins that share common coding vectors and functional categories.et al. to process microarray data Maximum Relevance, Minimum Redundancy (mRMR) Method was originally developed by Peng where Let f in And redundancy To obtain the feature For a feature set with Nearest Neighbor Algorithm (NNA) is a simple machine learning method that arranges the object to the class of its nearest neighbor sample. It has been widely used for predicting protein subcellular localization , the obtained The overall prediction combines the results of predictions from both network-based and hybrid-property based methods. The accuracies of the first-order function prediction for the overall training set  in the network-based method as described above. According to STRING, a more reliable PPI will be assigned a higher edge weight. From the principle (Eq. (1) – Eq. (6)), the network-based method is robust with respect to false PPIs. Overall, the predicted PPIs should be used very cautiously.In this study, network-based method and hybrid-property based method were developed to predict the functions of protein in mouse. In order to compare the performance between them, we also trained and tested the hybrid-property based method using the As a sequence-based method, the hybrid-property based method should be compared to other sequence-based method. We selected the method based on the motif information to predict functions of proteins, which has been proved to effective for the predicting functions of proteins in yeast It is shown in In this study, we propose a novel multi-target model, in which a sample may belong to several classes, for predicting protein functions. Two kinds of multi-target predictors are implemented: one is for proteins with PPI information and the other for those without PPI information. The average number of functions that a protein possesses is 3.14. There are 24 protein functional categories, meaning that in average a random guess of a protein function will have a success chance of 13.1% Click here for additional data file.Table S2Test set for network-based method. The Mfun ID and Functional number see  of prote(0.63 MB DOC)Click here for additional data file.Table S3Training set for hybrid-property based method. The Mfun ID and Functional number see  of prote(1.65 MB DOC)Click here for additional data file.Table S4Test set for hybrid-property based method. The Mfun ID and Functional number see  of prote(0.16 MB DOC)Click here for additional data file.Table S5The 90 optimized hybrid features generated by IFS procedure from a total of 132 features.(0.11 MB DOC)Click here for additional data file.