Bioinformatics includes a suite of methods, which are cheap, approachable, and many of which are easily accessible without any sort of specialized bioinformatic training. Yet, despite this, bioinformatic tools are under-utilized by immunologists. Herein, we review a representative set of publicly available, easy-to-use bioinformatic tools using our own research on an under-annotated human gene, SCARA3, as an example. SCARA3 shares an evolutionary relationship with the class A scavenger receptors, but preliminary research showed that it was divergent enough that its function remained unclear. In our quest for more information about this gene – did it share gene sequence similarities to other scavenger receptors? Did it contain conserved protein domains? Where was it expressed in the human body? – we discovered the power and informative potential of publicly available bioinformatic tools designed for the novice in mind, which allowed us to hypothesize on the regulation, structure, and function of this protein. We argue that these tools are largely applicable to many facets of immunology research. What is Life?  heterozygosity affects one’s interaction with bacteria  and the uman gut , and whyuman gut . While sThis review uses recent examples of our own quest to seek out information on a potential member of the class A scavenger receptor family, SCARA3, via publicly available bioinformatic tools. The scavenger receptors are a family of proteins required for host defense and phagocytosis of senescent cells and modified proteins . Althougde facto file format for most, if not all, bioinformatic sequence analyses. Simply put, this format is a description of a sequence preceded by a greater-than (“ >”) symbol, followed by the sequence in the standard IUPAC nucleotide or protein code.The FASTA file format was originally described by William R. Pearson as part of his 1990 bioinformatic software package of the same name . Since tAn accurately annotated and appropriately formatted sequence of the gene(s) of interest is a prerequisite of many bioinformatic techniques. Since 2007, the National Center for Biotechnology Information (NCBI) has made the nucleotide sequences of more than 260,000 organisms accessible through its publicly available database, GenBank . GenBankGenomic regions, transcripts, and products” section, which is helpful in visualizing the exonic structure and chromosomal orientation of a gene. The “Bibliography” section summarizes peer-reviewed articles in which the gene is at the forefront. Additionally, a multiple sequence alignment of the gene of interest to known homologs can be generated by choosing the “Homology” section under “General gene information”; this may be of interest to those conducting cross-species or evolutionary studies.One such database is Entrez Gene, which provides gene-centered information . Entrez NCBI Reference Sequences (RefSeq)”  of the T-cell receptor, requires the dual phosphorylation of two of its tyrosine residues [reviewed in Ref. ]. PredicAs an example, previous research has demonstrated that the prototypical member of the class A scavenger receptors, SRAI, has a serine in the cytoplasmic domain of this protein, which, when phosphorylated, is essential for its phagocytic function , 19. HowIn addition to NetPhos, there are many post-translational modification prediction tools publically available which require the sole input of a protein sequence. A representative collection of these tools is summarized in Table Some regions of a gene are more susceptible to the accumulation of mutational change over evolutionary time than others and protection from change is largely due to the biological importance of such a region . Highly pairwise,” involve only two sequences, or “multiple,” involve more than two sequences. Second, they can be “global,” which means the full length of all sequences are aligned, or “local,” indicating that the best alignment is displayed, even if that means only aligning a portion of the inputted sequences to each other  at variable positions in a set of input sequences in order to maximize the number of similar residues per column in the alignment . These ach other . The usech other . These tIn our case, we were interested in the similarities of SCARA3 to the other members of the class A scavenger receptors  that, to date, have been better characterized in terms of biological function and expression. Any similarities between specific regions of SCARA3 and these well-characterized cousins would allow us to hypothesize that these regions perform similar functions in both proteins. As such, we computed a global alignment of the human SCARA3 protein with the other four members of this protein family Figure , 24.Homo sapiens using the ClustalW2 tool  has a set of tools available for both pairwisel Figure A. Clustal Figure C. This al Figure . ConsequAnother approach to the identification of conserved motifs, especially useful when no known homologs exist, are specialized tools that examine an input sequence for known domains. An example of such a tool is NCBI’s Conserved Domain Search (CD-search) which compares a user-provided sequence against an NCBI-curated database of known domains . These tOf course, while clues to a protein’s function can be hidden within its sequence, at the end of the day, it’s the protein’s structure that dictates its function. Because of the ease of DNA and protein sequencing given today’s technologies, there is more sequence data available compared to structural evidence; however, databases with structural information are available. The Protein Data Bank (PDB) is a worldwide collection of macromolecular structures governed by the Research Collaboratory for Structural Bioinformatics (RCSB). This online, searchable databaseA quick search of PDB with the search term “SCARA3” resulted in no hits. This is unsurprising given that little work has been done with this protein. However, since we know from our sequence analyses that there are regions of homology between SCARA3 and the other receptors, it is worth searching for these proteins as well. A search for “MARCO” revealed a structure (PDB ID: 2OY3) of the SRCR domain of the mouse MARCO protein Figure . The PDBUnfortunately for our explorations of SCARA3, our previous sequence analyses indicate that the SRCR domain of MARCO –the only current macromolecular structure of a scavenger receptor – is not a region that is shared between these two receptors and, thus, it does not indicate any new information about our protein of interest. As structural prediction technologies improve, and more experiments are conducted, the size of PDB will grow, but even in its current state it is an excellent resource for structural information.However, even if an experimentally verified protein structure such as those in PDB does not exist for a protein of interest, predictions as to the potential secondary structure of a protein can still be made based on the primary protein sequence. One common method is the reliance on identifying similar motifs in a protein sequence of interest when compared to a well-studied protein with known function . HoweverPsipred is an excellent example of such a predictive tool. Psipred is an online resource, which combines multiple secondary structure prediction methods into one, easy-to-use web-interface . First, The results of inputting the human SCARA3 protein sequence into the online Psipred tool gave us an indication of which segments of the sequence formed α-helices and β-sheets Figure . When weIn addition to these general tools, there are others that focus on predicting specific aspects of different types of proteins. The TMHMM Server, for example, focuses on the prediction of transmembrane domains using a statistical model . Output gene signatures” of disease states or to provide mechanistic insight into disease etiology. Because variability within individuals dictate symptoms and disease progression, it is very rare that changes in expression of a single gene will be sufficiently robust for diagnosis; however, combinatorial changes that indicate a common mode of regulation are more robust and allow for the formation of “gene signatures.” For example, an “interferon signature” of gene expression was discovered in lupus when type I interferon inducible genes were found to be elevated in the peripheral blood mononuclear cells (PBMCs) of patients with lupus compared to healthy controls  using microarrays, RNA sequencing (RNAseq), and other platforms have been a valuable tool for immunologists. Transcriptomics can be used to discover “controls . Other ncontrols , predictcontrols , and stucontrols . As the  sources  the costGene Skyline” and “Modules and Regulators” tools  take a similar approach to characterizing the transcriptional profiles of human leukocyte subsets and include different activation states , which occur, on average, every 1200 base pairs . SNPs caSingle-nucleotide polymorphism analysis of a protein can greatly aid in the understanding of its function as these small alterations can result in substantial changes in the functionality of the protein. For example, a SNP at a receptor’s binding site may alter the original protein such that it would be able to bind a pathogen that it previously was unable to, or, in contrast, may abolish its ability to bind its usual binding partner. In one study, researchers studied differences in SNP frequencies of Mal/TIRAP to explain differences in TLR2 and TLR4 signaling between European and African populations . After cThere are several publicly available online databases for the analysis of SNPs in a protein of interest (summarized in Table Population Diversity” section shows that the SNP is found at higher frequencies in Asian populations, with allele frequencies up to 0.222 while other populations remain close to 0.1 (Figure Multiz Alignment” track shows areas of conservation between multiple vertebrates and suggests that SNP rs17057523 is present within a conserved area of SCARA3. Further testing by cloning the variant can help determine the function of this domain by examining functional differences between the SNP and wildtype allele.When the UCSC Genome Browser is used to search for SCARA3, the resulting SNP track shows all of the reported SNPs within the gene Figure B. Most o1 Figure . AdditioWhat has been covered here represents the basic knowledge upon which most bioinformatic analyses will be conducted. As in any field, there are a plethora of examples of highly specialized bioinformatic tools and software that have been developed for the various sub-fields of immunology. For example, HLA peptide binding predictions can be made using various tools such as that available from the National Institute of Healthin silico approaches to generate and test hypotheses and answer research questions. Of course, like all hypotheses, those generated with in silico approaches must be experimentally tested. Whether in silico approaches are more or less accurate that traditional methods of hypothesis generation are yet to be evaluated. Our inquiry into the properties of SCARA3 indicates that these tools are immensely useful in generating hypotheses that can then be tested bench-side. Although many researchers have decried the lack of trained bioinformaticians and bioinformaticists, perhaps the best way to overcome the current shortage may be for scientists to become conversant in some of the basic techniques of bioinformatics in much the same way that we must be knowledgeable of the statistical tools required to analyze and understand our research.In our opinion, bioinformatics is a methodology that is under-utilized in immunological studies. Far from being inaccessible and complicated, many bioinformatic tools are straightforward and available via online servers, meaning that a researcher can obtain results instantaneously without fear of the often-steep learning curve associated with installable software. Although a strong background in computer science is an asset for more complicated techniques, in order to perform the analyses that we have described here, a passing familiarity with the cut and paste function is all that is required. If the reader is interested in going beyond this, there are excellent, freely available resources such as Software CarpentryThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establish correlations between genotype and phenotype, but may fail to infer the biologically relevant mechanisms. The emerging paradigm of Network-based Association Studies aims to address this problem of statistical analysis. However, a mechanistic understanding of how individual molecular components work together in a system requires knowledge of molecular structures, and their interactions.Drosophila melanogaster. The integrated view of the functional roles of nsSNP at both molecular and network levels allows us to identify driver mutations and their interactions (epistasis) in H, Rad51D, Ulp1, Wnt5, HDAC4, Sol, Dys, GalNAc-T2, and CG33714 genes, all of which are involved in the up-regulation of Notch and Gurken/EGFR signaling pathways. Moreover, we find that a large fraction of the driver mutations are neither located in conserved functional sites, nor responsible for structural stability, but rather regulate protein activity through allosteric transitions, protein-protein interactions, or protein-nucleic acid interactions. This finding should impact future Genome-Wide Association Studies.To address the challenge of understanding the genetic, molecular, and cellular basis of complex phenotypes, we have, for the first time, developed a structural systems biology approach for genome-wide multiscale modeling of nsSNPs - from the atomic details of molecular interactions to the emergent properties of biological networks. We apply our approach to determine the functional roles of nsSNPs associated with hypoxia tolerance in Our studies demonstrate that the consolidation of statistical, structural, and network views of biomolecules and their interactions can provide new insight into the functional role of nsSNPs in Genome-Wide Association Studies, in a way that neither the knowledge of molecular structures nor biological networks alone could achieve. Thus, multiscale modeling of nsSNPs may prove to be a powerful tool for establishing the functional roles of sequence variants in a wide array of applications. Moreover, the complex phenotype is often associated with interactions among multiple causal genes (epistasis), any of which alone is not sufficient to drive phenotypic change. It is challenging for statistical methods to identify epistasis given the large number of possible interactions. Fundamentally, the "causal" relationships inferred from these methods are mathematical correlations. They may not provide biological insight into the underlying molecular and cellular mechanisms that associate genotypes with phenotypes.Recent advances in next generation sequencing have generated abundant genetic variants and "omics" data. Together, these extremely large, multidimensional datasets present an exciting opportunity to identify genes, and to predict pathways likely to be involved in diseases and traits. However, these complex data sources plus the broad spectrum of phenotypes, challenge the quest to uncover the genetic, molecular, and cellular mechanisms that underlie phenotypes -3. A maja priori knowledge of structures, their interactions and dynamics may facilitate the identification of causal mutations and their interactions from noisy data even where statistical techniques fail. In this paper, we have developed an integrated multiscale modeling framework to decipher the impact of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on the information flow from the activity of a single molecular component, to the function of the complete molecular machinery, and ultimately to the emergent properties of the biological network. Conceptually, our approach is rooted in Crick's central dogma of molecular biology and Blois's scalar theory of biomedical information [a priori knowledge of protein structure, evolution and interaction, and cellular signaling and regulatory pathways. In principle, it allows us to identify driver mutations de novo. Second, we show evidence that a large fraction of driver mutations may be involved in perturbation of protein-protein interaction and protein-nucleic acid interactions, and alternation of molecular allosteric regulation; molecular mechanisms that have been paid too little attention in GWAS thus far. Third, we introduce a new method to identify mutation mediated pathway profiles, which can be used to prioritize driver mutations and epistasis, by integrating sequence variances, protein-protein interaction networks, and gene expression profiles.A mechanistic understanding of how individual molecular components work together in a system, and how the system is affected and adapted to individual changes, requires knowledge of molecular structures, their interactions, and their conformational dynamics . Conversormation . The funDrosophila melanogaster (D. melanogaster) has been used as a model system to investigate the mechanisms underlying hypoxia tolerance. Through long-term laboratory selection, Zhou et al. have generated D. melanogaster populations that tolerate severe, normally lethal, levels of hypoxia [To demonstrate the feasibility of our approach, we apply multiscale modeling to reveal the genetic, molecular, and cellular basis of hypoxia, a physiological condition in which the cell is deprived of an adequate oxygen supply. The hypoxia-induced phenotype has been related to multiple pathological conditions including cancer . Cells,  hypoxia . Microar hypoxia . Compari hypoxia . These d hypoxia  and the  hypoxia .D. melanogaster. Although more experiments are needed to validate the direct association of other pathways with hypoxia tolerance and their potential cross-talk with Notch signaling, the mutation could be a driver if it up-regulates Notch signaling. As shown in Table Complex phenotypic changes typically arise from re-regulated cellular signaling and regulatory pathways (core pathways). As there are often multiple genes involved in a core pathway, a large number of combinations of genetic alterations can lead to the up- or down-regulation of a pathway. Our hypothesis is that driver mutations will collectively contribute to the re-regulation of a core pathway, which manifests itself as a change to the genome-wide signature, measured here by differentially expressed genes between hypoxia and normoxia phenotypes. In this way it is possible to identify the pathway involved in genotype-phenotype associations from the interacting gene networks that connect the mutated genes to the differentially expressed genes. The pathway that appears more frequently than by chance is a potential core pathway. If the hypothesized core pathway is validated by experiments, or consistent with prior knowledge, the association with the core pathway can prioritize the driver mutation. Based on this rationale, we developed a knowledge-driven network analysis method Figure . First, To better understand the molecular basis of potential driver mutations, we mapped point mutations to protein structure models. Among the 52 proteins containing nsSNPs, none of them have known structures available in the RCSB Protein Data Bank (PDB) . HomologFor putative mutations in nine genes predicted from network analysis Table , we firsIn addition to allosteric regulation, the putative driver mutation may modify protein-protein interactions. This is the case for Rad51D, as shown in Figure The functional importance of nsSNP is further supported by SNAP , softwarSeveral mutations in CG31220  and cancer cell response to hypoxia [As discussed above, a complex phenotype rises from re-regulated biological pathways that themselves result from the collective effects of multiple genetic mutations (epistasis). Since the down- or up-regulation of core pathways directly impacts the organismal phenotype, the experimental validation of the core pathway would provide strong evidence to support the predicted driver mutations that are responsible for the re-regulation of the core pathway. Indeed, we have experimentally validated that Notch signaling is the core pathway of hypoxia tolerance in  strains . The cri strains . Other enogaster . Dys enc pathway . Further pathway . For oth hypoxia . GalNAc- hypoxia . Wnt5 is hypoxia -42. The  hypoxia . Ulp1 is hypoxia .In this study, none of the driver mutations associated with hypoxia are conserved functional site residues, nor are they responsible for structural stability. The driver mutations are hypothesized to be involved in either protein-protein interactions (in the case of Rad51D), protein-nucleic acid interaction , or allosteric regulation . A recent survey of the structural basis of in-frame mutations in protein-protein interactions has suggested that changes in specific interactions play a critical role in pathogenesis . From a Drosophila genes may provide critical insight in the prioritization of candidate genes in human, which may fail using conventional statistical techniques. Indeed, the majority of driver mutations identified in this study are human orthologs and associated with the hypoxia cellular phenotype, as shown in Table Recently several studies in hypoxia adaptation in humans have been performed on Tibetans ,50, AndeBased upon multiscale modeling, we propose that the up-regulation of Notch and Gurken/EGFR and the down-regulation of Toll and Torso/RTK pathways are responsible for hypoxia tolerance. Using integrated structural and network analysis, we hypothesize that nsSNPs in H, Rad51D, Ulp1, Sol, Wnt5, CG33714, GalNAc-T2, Dys, and HDAC4, may all lead to the functional modification of these genes via allosteric regulation and protein-protein/DNA/RNA interactions and hence are driver mutations defining the hypoxia tolerance phenotype. Our predictions are supported by experimental evidence ,26. MoreA sequence information based method, SNAP  is used D. melanogaster. A subnetwork that connects a mutated gene and up-, and down-regulated genes is identified using a shortest path search of the PPI network. The genes identified in each subnetwork are subject to Gene Set Enrichment Analysis (GSEA). If the genes in the subnetwork are enriched by the essential biological processes/pathways, the mutated gene is a potential driver.The network-based analysis of driver mutation is shown in Figure D. melanogaster genome is performed using the R package [A cDNA microarray analysis of 13,061 known or predicted genes from the  package . K-neare package  in the s package  is used  package . A Bonfe package  false di package ,59 is usA program based on Dijkstra's algorithm  is devels2 is the unbiased estimator of the variance of the sample and n is the number of participants.Where 2) and a background random network (x1). Background random networks are built by randomly selecting one gene as a source node and a set of other genes as destination nodes. A positive t-value means a shorter than average path. The mutations on the genes with statistically significant high t-values are prioritized as driver mutations.Here the t-value is used to measure the difference between the identified subnetwork   is appliSMAP [Homology models of proteins are built using Modeller . SequencSMAP -69. MutaCovariance analysis based on multiple sequence alignments of proteins in the same Pfam family  as the mThe data sets supporting the results of this article are included within the article.Genome-Wide Association Studies (GWAS), Epidermal Growth Factor Receptor (EGFR), Receptor Tyrosine Kinase (RTK), Mutation Seeded Subnetwork (MSSN), Protein-Protein Interaction (PPI).Authors declare that they have no competing interests.Conceived and designed the experiments: Lei Xie. Performed the experiments: Li Xie, Dan Zhou, Clara Ng, Thahmina Ali, Raoul Valencia, Barbara L. Ferreira, Vincent Xue, Maliha Tanweer, Analyzed the data: Li Xie, Lei Xie. Contributed reagents/materials/analysis tools: Li Xie. Dan Zhou, Gabriel G. Haddad, Wrote the paper: Li Xie, Lei Xie, Clara Ng, Philip E. Bourne.Figure S1. Structure models for proteins with mutations that are close to substrate binding sites or active sites.Figure S2. The structural model of CG33714.Figure S3. The structure model of GalNac-T2.Figure S4. The structural model of WW domain of Dys.Table S1 Genes in the first category.Table S2. Genes in the second category.Table S3. Genes in the third category.Table S4. Non-neutral mutations predicted by SNAP.Click here for file


Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein–protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain–domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.Following successful completion of the  Arabidopsis thaliana, large amounts of protein–protein interaction (PPI) data are available from the major PPI databases , nematode worm (Caenorhabditis elegans), fruitfly (Drosophila melanogaster), and human (Homo sapiens), and built an interactome based on orthologs predicted using InParanoid . Here, we have constructed the inferred B. rapa interactome based on A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct and TAIR; and (ii) ortholog-based A. thaliana PPI prediction data  ortholog prediction using InParanoid, (ii) identification of gene duplications in the Plant Genome Duplication Database  algorithm  has occurred, and B. rapa contains 41,174 identified protein-coding genes compared with 33,602 in A. thaliana  genome sequence , which is aimed at identifying factors controlling crossover frequency and distribution in higher plants. This project uses affinity-based techniques to isolate meiotic protein complexes from Brassica oleracea for analysis by mass spectrometry  genome sequence and the predicted interactome presented in this paper have already proved to be valuable resources, facilitating the screening of B. oleracea peptides for protein identification and the identification of possible PPIs. As such, we believe that the predicted interactome is also a useful resource for the wider Brassica research and crop-breeding community.The inferred Usually PPI DBs provide a web-interface, where an individual or list of protein/gene IDs can be used to query the DB. Some DBs can also be downloaded in a customized format for further investigation, e.g., the Database of Interacting Proteins  consists of 16,644 binary interactions from 1,398 published research articles. The total number of proteins involved in D1 is 6,451, which does not include splicing variants. The contributions of the three source DBs to D1 can be seen in Figure The compiled Besides experimentally verified PPIs from the three DBs, predicted PPIs were also used in our study. Geisler-Lee et al.  studied A. thaliana PPI data in order to expand the predicted B. rapa interactome. It is vital that the links between the two species are established correctly. An obvious way of achieving this is to identify orthologs between them. Using InParanoid, a total of 17,859 orthologous clusters were detected, which contain 18,830 and 21,873 proteins for A. thaliana and B. rapa respectively. Note that the number of orthologous clusters is less than the number of proteins for both species. This is a desirable feature as it may be indicative of possible gene duplication events within each species. Thus, in terms of DB implementation, this creates multi-to-multi relationships within the orthologous clusters.The objective of the present research is to use known B. rapa genome sequence, several comparative genomics DBs . These one-to-one data were then added to the previously compiled set to form the final “bridging” DB, denoted as D3. B. rapa proteins not covered by D3 account for approximately 5.93% . This is in agreement with a previous study which found that 95.8% of gene models have a match in at least one of the public protein DBs  can be used to predict possible protein interactions. HMMER . As a result, 3,482 Pfam-A domains were assigned to 27,452 B. rapa proteins. On average, we had 1.43 domains assigned to each B. rapa protein. This is comparable with the TAIR Pfam annotation (1.41 domains/protein).The total number of B. rapa domain assignments. Here we used only known  and high confidence predictions from DOMINE, which accounts for 8,173 unique interacting domain pairs. Known interacting domain data in DOMINE come from iPfam and 3did , takes into account any prior knowledge of the DDIs. Given the above constraints, the goal is to assign 1 s and 0 s to the domain pairs such that the maximum number of constraints is satisfied. For that purpose, under this factor graphical modeling framework, a powerful statistical inference method, belief propagation (BP), is employed to infer the domain–domain interaction probabilities.Given the existence of false positives in PPI data and our hypothesized negative data, the above constraints need to be “softened” to take into account the errors in the interaction map. This error is incorporated via an additional parameter ε, which ranges between 0 and 1 and quantifies our confidence in the PPI data (ε = 0 means the PPI network is 100% reliable). Another parameter, the Belief propagation performs exact inference if the underlying graph is a tree, which corresponds to the global minimum of a function, called Bethe free energy , interologs based interaction (P2), and interacting domain-based interaction (P3). P1 and P2 were obtained using physical and predicted PPI data in D1 and D2, and the “bridging” DB D3. P3 were obtained using B. rapa protein domain assignments and the interacting domain data, which combine both “generic” known/high confidence interacting domain data in D4, and the A. thaliana “specific” interacting domain predictions using the MP algorithm and D1-sub/D5.An overview diagram illustrating data and methods used in the present study is shown in Figure B. rapa sequences using Argot2  two proteins in the pair need to share the same Gene Ontology (GO) cellular component terms in order for the domain-based prediction to take effect; (ii) if not predicted to be interacting in P1 or P2, a protein pair needs to have more than one interacting domain pairs; (iii) if predicted to be interacting in P1 and P2, a protein pair can have only one interacting domain pair. GO terms were assigned to a priori probability, β, and the degree of reliability of the interaction datasets available for the inference, ε . A large proportion of these domain pairs  are the only domain pair in their respective protein pair in the positive training set D1-sub. They were successfully recognized; for example, domain pair PF01627 and PF03962 in protein pair AHP2 (At3g29350) and AtMND1 (At4g29170). (Interactions between AHP2 and AtMND1 were recorded under ID BIOGRID: 337481 and EBI-1555097). These predictions were considered unique contributions of the MP algorithm, and possibly conserved between B. rapa interactome, denoted by P-all). The relationship among the three sets is shown in Figure P1, P2, and P3 contain 77,073, 316,128, and 364,768 predicted interactions respectively; all three datasets gave a total number of 740,565 unique predicted interactions (the predicted The three sets of PPI predictions constitute two levels of confidence of the predicted interactome. The high confidence prediction (Phc) has support from at least two sources of evidence, the low confidence prediction (Plc) has support from only one. Phc and Plc contain 17,255 and 723,310 interactions respectively. Some structural properties depicting P-all and the two different confidence level sub-networks were calculated using R package igraph  were assigned at least one GO term. We then categorized these proteins  and the proteins from P-all  in terms of GO plant slim categories using AgBase  Because of gene duplication/loss, genes of A. thaliana and B. rapa form a multi-to-multi relationship. However, in the interologs based prediction (P1 and P2), it is barely possible to rule out any predicted interactions  InB. rapa interactome came from A. thaliana. Thus it is vital that the relationships between the two genomes were correctly defined. Importantly, consideration must be given to the fact that there has been almost complete triplication of the B. rapa genome relative to A. thaliana, although since formation of the postulated original hexaploid ancestor, substantial gene loss has occurred , recently described by the Birmingham meiosis group  and the start of B. rapa chromosome 5 (BR5). This is in agreement with observations by Wang et al.  and the inference resources. We also listed domain (Pfam) and GO term names for these genes/proteins where available. We can see that quite often the relationships were confirmed by more than one method/resource. Furthermore, most related proteins have a similar domain structure, for example AtMAD2 and its counterparts in B. rapa . For GO terms, as we used stringent criteria, fewer GO terms were assigned to B. rapa proteins. However, assigned terms mostly agree with their counterparts in A. thaliana.Table B. rapa meiotic proteins was extracted from P-all , RAD50 (19 connections), MLH1 (15 connections), SMC1 (14 connections), and MAD2 (13 connections). Interestingly, these hub proteins were identified by the MCL algorithm  meiosis, interactions were found among MLH1, MLH3 (HATPase_c), and PMS1 (DNA mismatch repair and HATPase_c; Argueso et al., A. thaliana. Note that some of the self-interacting domains in Figure Protein domains contained in the putative meiotic network were extracted and their interactions are shown in Figure B. rapa interactome using PPI data available from A. thaliana. These PPI data were either physical interactions verified through experiments, or predictions based on orthology. The relationship between the two genomes was established by studying orthologs/collinearity/sequence similarity. We also utilized domain interactions in our predictions. Both known and predicted interacting domains, as well as protein domain assignments of B. rapa, were used to predict possible interactions.In the present study, we have inferred the B. rapa genome, and its high confidence interaction predictions give a coverage of around 78% for those proteins. As a first effort of establishing a B. rapa interactome, our inferred interactome could be a useful resource for experimental biologists or other researchers using B. rapa as a working plant. The interactome is available at http://www.meiosys.org/dissemination/ as pure text files; other formats e.g., SQL are available upon request.The inferred interactome contains 17,255 predicted interactions at high confidence level, and 723,310 predicted interactions at low confidence level. The interactome covers around 50% of the proteins in the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We also modify the indexing techniques to support θ-based nearest neighbor search, which returns data points less than θ to the query point. The results show that both iDistance and iKernel significantly enhance the searching speed. In top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In θ-based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally similar proteins, thus finding structurally similar proteins accurately and efficiently from a large set of proteins is crucial. A protein structure can be represented as a vector by 3D-Zernike Descriptor (3DZD) which compactly represents the surface shape of the protein tertiary structure. This simplified representation accelerates the searching process. However, computing the similarity of two protein structures is still computationally expensive, thus it is hard to efficiently process many simultaneous requests of structurally similar protein search. This paper proposes indexing techniques which substantially reduce the search time to find structurally similar proteins. In particular, we first exploit two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. After that, we extend the techniques to further improve the search speed for protein structures. The extended indexing techniques build and utilize an reduced index constructed from the first few attributes of 3DZDs of protein structures. To retrieve top- The size of protein structure database such as the Protein Data Bank (PDB) continues to grow. PDB had around 1000 structures in 1992, but now it stores over 150,000 structures. In addition, the number of proteins with unknown functions is increasing due to efforts in structural genomics projects. Knowing the functions of proteins is crucial to many studies of biological processes. Especially, researchers need to know the key proteins that play an important role for severe deseases, and it is directly related to human life. Therefore, assigning functions to novel proteins is one of the most significant problems in proteomic study, and several methods have been developed to assign functions to an unknown protein. Basically, the function of a protein can be identified by searching amino acid sequence database for similar sequences that the functions are already known. However, the 3D structures of proteins are more conserved than the sequences and using the structural information provide more reliable similarity measures.θ-based nearest neighbor search which returns data points less than θ to the query point. The experimental results show that the indexing techniques both decrease the searching speed, and our nearest neighbor search algorithms further speed up the protein structure search. Speicifically, in top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In θ-based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.Many methods have been introduced for pair-wise protein structural search. They align the two structures and compute the Root Mean Square Deviation (RMSD) between the core atomic positions, e.g., alpha carbon coordinates, of the aligned proteins. However, most of methods based on structural alignment cannot be used to search structures against large database, since it has high computational complexity. Sael et al. introduced a new approach for fast protein surface similarity search using 3DZDs . This apThis paper is organized as follows. We briefly introduce related works about protein structure search and top-k query search. Then, we explain iDistance, iKernel and the extended top-k query search method in combination with iDistance and iKernel. Finally, we provide experimental results to verify the efficiency of our approaches, and conclusion with future works.A protein consists of a sequence of amino acid (AA) residues. A sequence of AA residues folds into a 3-dimensional (3D) structure in space and forms a functional protein. A 3D structure of a protein is recorded in a pdb file format as a set of Cartesian coordinates of all the atoms in the protein. The 3D structure contains rich information relating to function and evolution of the protein.Earlier structural similarity measurements were designed for pair-wise analysis where the user only needed to compare handful of protein structures -7. HowevThere are major structure databases such as PDB, CATH , and SCO3D-Surfer is a new and efficient protein structural search system which represents protein structures based on 3D-Zernike Descriptor (3DZD). The major advantage of 3DZD is that it allows a fast k-nearest neighbor (k-nn) search of protein structures. It has been verified that the retrieved k-nn proteins by 3D-Surfer have similar functional and evolutional information in terms of SCOP classification . Some ofO(Nd) complexity where N is the number of data points and d is the dimensionality of the data, and 3D-Surfer also used this approach. For efficient top-k search, there have been various methods via space partitioning including X-tree, TV-tree, and SR-trees [There is a long stream of researches on finding nearest neighbor search problem which is an optimization problem for finding closest points in metric spaces. The simplest method is to compute the distance from the query point to every other point in the database. It has SR-trees -23. iDisIn this section, we first introduce the protein structure dataset and their 3D-Zernike Descriptor (3DZD). Then, the descriptions of iDistance and iKernel methods and the proposed efficient top-k query search method based on the characteristics of 3DZD are provided.3DZDs are compact and rotationally invariant representation of 3D structures. 3DZD has been successfully used for protein  and ligaThe 3DZD descriptors for protein structural dataset of 158781 number of protein chain structures was obtained through 3D-Surfer database. The entire structures in PDB was collected and processed on 2009 . For eacf(x) that contains a surface information of protein is expanded into a series of Zernike-Canterakis bases defined as follows:In 3DZD construction, a given 3D function -l < m < l, 0 ≤ l ≤ n, (n - l) is even, nl Rare radial functions constructed to convert f(x), 3D Zernike moments need to be computed first. They are defined by expanding the orthonormal bases as follows:where nlF, is computed by normalizing Then, the 3DZD, n is the order of 3DZD determining the resolution of the descriptor. Then, the norms allow ratational invariance to the desriptor. For each pair of n and l, 3DZD has a series of invariants, the numbers in the vector of 3DZD, where n is ranged from 0 to the predefined order (20 in this case).where 2kr1-A) and interleukin-4 receptor α-chain (PDB code: 3DVG-A). As you can see, two proteins have different structures overall and their descriptor also shows visible difference , it indi Figure ), it alsiKernel is originally designed for the efficient learning of support vector machine (SVM) . Howeverm clusters and centroids of each clusters are computed in the feature space. Various clustering techniques can be applied. Then, based on those centroids and clusters, we can build an index by assigning data points into a set of rings in clusters as follows.To build an index, given data points are clustered into g number of data points. The paramter g is user adjustable and need to be determined prior to index construction.Note that each ring have q and ring structure i,jC. With this new notion of the MPD, k-nn search works as follows. Given a query point q, we first initialize a priority queue Q with a set of pair ¡i,jC, MPD¿ of each cluster in the ascending order of their MPDs between the q where only the outermost ring is considered first. Then, at each iteration, the top entry of Q is popped. If a ring is popped, the data points in the ring are inserted to Q with the distance from q, and if the popped item is a data point, it is simply added into top-k result since the priority queue ensures that all instances in the queue have larger distances from q and also all rings have larger MPDs between q.To process a k-nn of a query structure, we exploits the index and Minimal Possible Distance (MPD) . MPD is Q, rings or data points are ordered in the distance in ascending order . At the first iteration, C5,4 with the lowest MPD is popped. Then the distance from q to its instances, C5,3, are computed and these instances are inserted back to Q k clusters so that the within-cluster sum of squares is minimized [For both indexing techniques, we require division of data set into clusters by assigning similar number of data points to each cluster. First method randomly selects reference points and assign remaining data points to their closest reference point. K-means clustering, which is one of the most widely used clustering methods, is also tested. The goal of K-means clustering algorithm is to divide a set of points into inimized . K-meansinimized . TherefoFigure k × 10 result using half dimension takes less time than using basic indexing techniques .1 Given a query protein Q, search top-k × 10 results, find exact top-k result.2 Using the top-k × 10 is very small number compared to the size of the database (around 1.6 million).Note that q varies . Therefore, we exploit the indexing techniques in θ-based nn search as well. Using θ, a data point can be nearest neighbor, only if they have shorter distance to the query than θ. To do this, in the linear scan, we need to check whether the distance between each protein structure and query is less than θ or not, we do not need to check the number of nearest neighbor, k. In the iDistance, we set the query range, r as θ. Then the number of visiting clusters and computing the distance between their data points ot query decreases, when the number of nearest neighbor with shorter distance than θ has smaller than k. In the iKernel, the search process is terminated untill the Minimum Possible Ditance (MPD) of the popped instance is smaller than θ. It also reduces the cost of visiting clusters and computing the distance bewteen the query and their data points. Note that, using θ, the extended approaches always return the exact nearest neighbors.The nearest neighbor search can be solved based on two different user parameters of either the number of nearest neighbor, In this section, we verify the effectiveness of indexing techniques on top-k search of protein structures. Sael et al. showed that 3DZD works well on finding similar proteins in terms of functional and evolutionary characteristics based on SCOP classification . The SCOThe experiments were conducted on the machine, Intel Core(TM) i7 CPU (3.40GHz), and 16 GB memory. In overall experiment, we used 100 data points that are randomly selected from data set, and averaged entire processing time and evaluation ratio.There are a few parameters that are needed to be optimized in the iDistance and iKernel methods. In this section, we observe how the result varies as the user parameters varies to select the best. We also observe how the cluster number affects the top-k search. We vary the partition data points using different number of clusters: 121, 242, 498, and 866. 121 is the dimensionality of data set, and 242 is the two times the dimensionality  than iDistance due to the same reason.In this section, we explore the effect of s Figure . Howeverθ-based nearest neighbor search , and about 20% of queries do not have results. We use 2 as θ for next experiments, since the processing time/evaluation ratio is reduced enough compared to the linear search and the nearest neighbors are close to the queries compared to top-k nn search.We also provide the ratio of queries not having nearest neighbors according to given queries, and the average number of nearest neighbors resulted from h Figure . As easiθ-based nearest neighbor search, we observe the distribution of the processing time/evaluation ratio of the proposed methods. As shown before . In the table, the number in bracket is the ratio of actual top-25 result in top-25 result which are approximately obtained by the extended approach  and data points. Using k, iKernel works faster than iDistance which indicates that iKernel is more efficient to find exact number of nearest neighbor with new structure, ring. In constrast, however, using θ, iDistance works faster than iKernel which indicates that iDistance is more appropriate to find the nearest neighbor within the fixed distance, θ.To support our statement more, we simulate the scenario that a number of users enter queries at the same time via multi-threading Figure . Accordik nearest neighbors, using indexing techniques alone, we were able to make the search speed 77% faster compared to the prevoius version of 3D-Surfer that uses linear Euclidian distance scan between the 3DZDs in the database. We also proposed an extended version of the protein structure search based on the key observation that the prior dimension of the descriptor indicates global shape of the protein structure. Using the extended techniques it is improved up to 87.9%. When we retrieve nearest neighbor with shorter distance to the query than θ, using indexing techniques alone, we were able to make the search speed 81% faster compared to the linear scan. Using the extended techniques it is improved up to 96%. For future work, we will improve the nearest neighbor search with indexing techniques by utilizing the characteristics of the query prior to searching. In addition, we will apply indexing techniques for protein binding site similarity search with other data set represented based on 3DZD as well.In this paper, we introduce an efficient indexing for protein structure search where protein structures are represented as vectors by 3D-Zernike Descriptor (3DZD). When we retrieve top-The authors declare that there are no competing interests.Sungchul Kim designed models, conducted data analysis and experiments, and wrote the paper; Sael Lee designed the study, gave technical support, and wrote the paper; Hwanjo Yu gave conceptual advice, designed experiments and wrote the paper; All authors discussed the results and commented on the manuscript at all stages.This work was partially supported by Mid-career Researcher Program through NRF grant funded by the MEST (No. KRF-2011-0016029). This work was also supported by IT Consilience Creative Program of MKE and NIPA (C1515-1121-0003)Proceedings of the ACM Sixth International Workshop on Data and Text Mining in Biomedical Informatics, 2012 © ACM, 2012. http://doi.acm.org/10.1145/2390068.2390078This work is based on an earlier work: “Indexing methods for efficient protein 3D surface search”, in


Stable evolutionary signal has been observed in a yeast protein-protein interaction (PPI) network. These finding suggests more connected regions of a PPI network to be potential mediators of evolutionary information. Because more connected regions of PPI networks contain functional complexes, we are motivated to exploit the orthology relation for identifying complexes that can be clearly attributed to such evolutionary signal.We proposed a computational methodology for detecting the orthology signal present in a PPI network at a functional complex level. Specifically, we examined highly functionally coherent putative protein complexes as detected by a clustering technique in the complete yeast PPI network, in the yeast sub-network which spans only ortholog proteins as determined by a given second organism, and in yeast sub-networks induced by a set of proteins randomly selected. We proposed a filtering technique for extracting orthology-driven clusters with unique functionalities, that is, neither enriched by clusters identified using the complete yeast PPI network nor identified using random sampling. Moreover, we extracted functional categories that can be clearly attributed to the presence of evolutionary signal as described by these clusters.Application of the proposed methodology to the yeast PPI network indicated that evolutionary information at a functional complex level can be retrieved from the structure of the network. In particular, we detected protein complexes whose functionality could be uniquely attributed to the evolutionary signal. Moreover, we identified functions that are over-represented in these complexes due the evolutionary signal. Analysing and mining protein-protein interaction (PPI) networks data using evolutionary information is a central research area in bioinformatics (see e.g. -10). In protein-protein interaction level, using pairwise orthologs with respect to various different species. The authors observed that a high local clustering around protein-protein interactions correlates with evolutionary conservation of the participating proteins. This means that highly connected proteins and protein pairs embedded in a well clustered neighbourhood tend to be evolutionary conserved and therefore retain their evolutionary signal. These findings suggest also that more connected regions of a PPI network are potential mediators of evolutionary information.A recent study performed by Wutchy et al  used sucfunctional complex level, that is, functional complexes that can be clearly attributed to this evolutionary signal. To this aim, we try to characterize functions of those complexes predicted by clustering the sub-graph of a PPI network induced by all proteins having orthologs in another given species, but not predicted  neither when clustering the entire network, nor when clustering sub-graphs of the network induced by random sampling of proteins. We consider the resulting functions as a strong characterization of the underlying evolutionary signal of orthologs at functional complex level, since they are suppressed or not observed when clustering using the entire network and are not outcomes of a stochastic process.Motivated by the above observations, in this paper we focus on the explicit use of orthology for detecting evolutionary signal at a GC , OC (ortholog clusters) and RC (random clusters). Note that the latter class of clusters is the collection of cluster sets produced by the application of clustering to the PPI network induced by a random selection of a set of proteins  repeated for dozens times. For all clusters in each class we infer putative functions by measuring their gene ontology (GO) functional enrichment  =  of G, with set X of nodes and set X Eof those edges of E that join two nodes in X. For a set S, we denote by |S| the number of its elements.We are interested in quantifying the orthology signal by means of a set of functions of those putative protein complexes detected by applying a clustering algorithm to a PPI network. To this end, we directly exploit evolutionary information of proteins as described by the presence of orthologs in another, given species. We call these proteins 'true orthologs'. The following terminology is used in the sequel. A PPI network is represented by means of a graph G =  and a given species s, we propose a methodology for detecting the orthology signal at a functional complex level, consisting of the following steps.Given a PPI network . Retrieve from a database the set O of 'true orthologs' of V with respect to s, with |O| = n.1Generate the following three classes of clusters, using a given clustering algorithm.2. (a) Class 1 clusters (GC). Apply clustering to the whole PPI network G.(b) Class 2 clusters (OC). Apply clustering to the sub-network induced by O.(c) Class 3 clusters (RC). Apply clustering to the sub-network induced by a randomly selected subset of V of size n. Repeat the process a number N of times. Consider all sets of clusters detected across these runs .For each class of clusters,3. (a) Infer putative complexes and identify their functions.(b) For each identified function, compute its retrieval index as the fraction of those proteins in the detected complexes which have been assigned to that function and experimentally verified to have that function.Select the set of those functions derived using putative complexes from class OC and whose fractions are higher than those of the same function derived using putative complexes from class GC and from class RC.4. Output the set of putative complexes from class OC having at least one of the selected functions.5. GC represent results of no selection  bias and the collection of the sets of putative complexes present in the class RC corresponds to the random selection bias. Accordingly, the complexes of class OC represent the orthology selection bias. Thus, the method considers the complexes exhibiting orthology signal as those of the OC class having a function which may not be attributed neither to the global bias nor to the random bias.The set of putative complexes of class Next, we discuss the details of main steps of the proposed methodology.Each class of clusters is produced by applying a clustering technique to the corresponding PPI (sub-)network. In this study we used the MCL clustering. MCL  computesWe want consider putative protein complexes containing more than a single protein-protein interaction. Therefore, after applying the clustering method we retain only clusters of size greater than or equal to 3. In order to infer the putative functions of a cluster, we measured the enrichment of functional annotations of the corresponding protein set, as entailed by the GO annotation , using oThe GO is known to have a hierarchical structure (directed acyclic graph) which can be used to define the level of an annotation. Specifically, the level of an annotation is equal to the length of the furthest path from the root of GO hierarchy to that annotation. This strategy always defines a filial annotation to have a higher level (deeper in the hierarchy) than its all parental annotations and hence no inconsistency on the description of GO hierarchical level (a parent having the same or higher level than its child) is introduced. The GO terms closer to the root of GO give more general description of biological functions while terms closer to the leaves of GO have granular and very specific biological definitions.To measure functional annotation enrichments of proteins present in a cluster we used only experimentally verified annotations as reported in the yeast gene association file of Saccharomyces Genome Database (SGD) , availasignificantly coherent function or functional GO annotation if the following criteria are satisfied:Each detected cluster is a potential protein complex. The quality of a protein cluster is given by the coherence of biological functions of proteins contained in the cluster. If a certain subset of proteins in a cluster has a significantly coherent function, a prediction of that function for all proteins in the cluster can be made. Note that one may obtain more than one protein function prediction if more significantly coherent functions in the cluster are found. We say that proteins of a cluster have a 1. the GO annotation is significantly enriched by the proteins in the cluster .2. more than half of the proteins in the cluster has this significant annotation.3. the annotation is at least at the GO level four from the root of GO hierarchy.putative protein complex and the set of significantly coherent functions defines the set of inferred functions.In such a case the cluster can be used as protein function predictor and the significantly enriched GO annotation of the cluster is used to predict protein function of each of the proteins in that cluster. If a cluster does not satisfy the above conditions, no prediction can be made. Similar criteria were used by, e.g. ,52. The In the last step, for each putative complex we do an additional inference analogous to the protein function annotation procedure as follows. The GO hierarchy defines a parent-child relationship between GO functional terms where each descendant inherits all features of its ancestors. As a consequence, once a protein has a GO term annotation assigned, the protein has implicitly also annotations of all parental terms of the annotated function. Hence, using the same ratio, given the set of inferred functions of a putative protein complex, the complex also inherits all parental GO terms of the inferred functions. Thus, we may distinguish the following two sets of annotations, the most granular, filial, annotations, where no parent-child relationship between corresponding GO terms may be observed, and the set of all annotations which is the union of the filial annotations and all its ancestral annotations in GO hierarchy. Notice, by the definition, for a given complex all filial annotations are significantly coherent functions of the complex while the parental annotations need not to be significantly enriched.Having a set or class of putative protein complexes, one can quantify, at a fine-grained, protein level, a so called retrieval index of functions inferred by the protein complexes and defined as follows.G and let X ⊆ V. Let G[X] =  be the corresponding induced sub-graph of G and X0 ⊂ X be the set of singletons in G[X]; that means there is no edge (interaction) in X Econtaining any of the proteins in X0. We define the set of background proteins as B(X) = {X \ X0} and we denote S(X) the set of all proteins contained in putative complexes discovered in G[X]. Additionally, let C(f) ⊆ V be the set of candidate proteins for function f, that is, the set of all proteins having either experimentally annotated function f or an experimentally annotated function that is a descendant function of f in the GO hierarchy. Then let P = {U∩C(f)} be the set of those proteins which have an experimental evidence for the function f and are present in the set U ⊆ V . We can define the retrieval index of a function f in X as the following fraction:Consider a PPI network f from a protein sample X by the set of putative complexes identified in X. It can be viewed as an index measuring how likely a given function is present in a given set of putative complexes with respect to a given set of proteins.This fraction measures the retrieval of a given function ϱ corresponds to the retrieval index of f for the GC class, ϱ corresponds to the retrieval index of f for the OC class, and ϱ corresponds to the retrieval index of f for the i RC∈ RC class, where i Ris the random protein sample used at the run i when building the RC class.Note that f to be related to the orthology signal if it satisfied two conditions: (a) it has a higher retrieval (at the level of putative protein complexes) in the set of orthologs than in the set of proteins of whole network and (b) it is unlikely to be retrieved when using random sampling. The second condition is formalized by comparing ϱ with the 95th percentile of the set of retrieval indexes of f in the RC class. Specifically, for each function f from the GO hierarchy such that f ∈ B(O), we compute its functional retrieval indexes for GC, OC and the RC classes. Then, the function f is orthology-related iffWe consider a function R95% is a random protein sample i Rsuch that ϱ is the up 95th percentile of the all ϱ,...,ϱ.where OC class has at least one orthology-related function, we consider that complex to be orthology-related.Finally, if a putative complex of the GC: global clusters; GYC: global yeast clusters; OC: ortholog clusters; OYC: ortholog yeast clusters; OYC-E: ortholog yeast clusters with respect to E.coli; OYC-W: ortholog yeast clusters with respect to worm; OYC-F: ortholog yeast clusters with respect to fly; OYC-H: ortholog yeast clusters with respect to human; RC: random clusters; RYC: random yeast clusters; RYC-E: random yeast clusters as given by the number of yeast orthologs with respect to E.coli; RYC-W: random yeast clusters as given by the number of yeast orthologs with respect to worm; RYC-F: random yeast clusters as given by the number of yeast orthologs with respect to fly; RYC-H: random yeast clusters as given by the number of yeast orthologs with respect to human.PPI: protein-protein interaction; SGD: Saccharomyces Genome Database; GO: gene ontology; COGs: clusters of ortholog groups; UBC: ubiquitin-conjugating; ER: endoplasmic reticulum; COPII: coat protein complex II; The authors declare that they have no competing interests.1 conceived and designed the study. EM2 and ECSP performed the biological interpretation of the results. All authors read and approved the final manuscript.PJ carried out the computational analysis. PJ and EM


Explicit comparisons based on the semantic similarity of Gene Ontology terms provide a quantitative way to measure the functional similarity between gene products and are widely applied in large-scale genomic research via integration with other models. Previously, we presented an edge-based method, Relative Specificity Similarity (RSS), which takes the global position of relevant terms into account. However, edge-based semantic similarity metrics are sensitive to the intrinsic structure of GO and simply consider terms at the same level in the ontology to be equally specific nodes, revealing the weaknesses that could be complemented using information content (IC).http://cmb.bnu.edu.cn/hrss.Here, we used the IC-based nodes to improve RSS and proposed a new method, Hybrid Relative Specificity Similarity (HRSS). HRSS outperformed other methods in distinguishing true protein-protein interactions from false. HRSS values were divided into four different levels of confidence for protein interactions. In addition, HRSS was statistically the best at obtaining the highest average functional similarity among human-mouse orthologs. Both HRSS and the groupwise measure, simGIC, are superior in correlation with sequence and Pfam similarities. Because different measures are best suited for different circumstances, we compared two pairwise strategies, the maximum and the best-match average, in the evaluation. The former was more effective at inferring physical protein-protein interactions, and the latter at estimating the functional conservation of orthologs and analyzing the CESSM datasets. In conclusion, HRSS can be applied to different biological problems by quantifying the functional similarity between gene products. The algorithm HRSS was implemented in the C programming language, which is freely available from  GO has been successfully used in protein classification With the advent of high-throughput technologies such as DNA and RNA sequencing and microarray, automatic genome annotation of large sets of genes has been increasingly used. An accessible and systematic scheme is required to handle the large amount of annotations to genes and their products, and to make comparisons of these gene products computationally interpretable on a standard platform. The Gene Ontology (GO) GO consists of three structured ontologies which allow the description of molecular function (MF), biological process (BP), and cellular component (CC). Each ontology is structured as a directed acyclic graph (DAG), which differs from hierarchies in that a ‘child’, a more specialized term, can have many ‘parents’ or ‘ancestors’, a less specialized or more general term. Child terms are instances or components of parent terms. Therefore, GO follows a true path rule where annotation to a given term implies annotation to all of its ancestors by inheritance. The valuable functional knowledge encoded in GO should be useful for developing new predictive systems to compare gene products at the functional level, which may be integrated with other models in large-scale genomic research.The functional relationships between gene products annotated with GO terms are quantified either implicitly using methods for the shared GO terms of gene products distance) along the paths linking the GO terms being considered. The specificity of the most recent common ancestor (MRCA) of two GO terms, measured by the distance between the ancestor and the root term in the GO DAG, was used as the only parameter to quantify the semantic similarity between the term pair Edge-based methods determine semantic similarity based primarily on counting the number of edges  the similarity between two terms as simply the IC of their most informative common ancestor (MICA), indicating its specificity Node-based approaches rely on comparing the properties of the terms involved and their ancestors or descendants. The property of a GO term can be estimated by a variety of means All of these methods quantify the relationship between two gene products by using pairwise strategies, such as the average, maximum and beset-match average, based on measuring every term pair. Other methods, such as simUI IC-based metrics are less sensitive to the problem of variable semantic distances compared with edge-based approaches We designed an edge-based semantic similarity method, RSS iterm and jterm be two terms. We defined i, termj)dist of the two terms is according to the structure of the GO. Component β measures how general iterm and jterm are in the GO. The generality of a term is defined as the minimum distance between the term and the leaf terms descending from it. Component γ measures the local distance between the two terms and their MRCA (Formula 1),For a given GO, let mponents , denotediterm and jterm can be quantified by combining α, β and γ together,GOmaxDepth is the maximum distance from the root term of the GO to the leaf terms. From the definition, the values of RSS are between 0 and 1. See Section S1 in Then, the RSS between the two terms of a given GO, c,p(c) is the probability of occurrence of the term c in a specific corpus, such as the GO annotations of the yeast genome or the UniProt Knowledgebase, and is normally measured by the frequency of the number of genes annotated to c and all its descendants over the total number of genes in the corpus. The more often the term is used for annotation, the lower its semantic value.IC is defined as the negative log likelihood of a term u and v, where u is an ancestor of v, as the difference between their IC values,We defined the IC-based distance between two terms iterm and jterm (i.e. component α in RSS method) is iterm and jterm. Resnik directly uses ICα to score the semantic similarity between the two terms Then, the IC-based specificity of the most recent common ancestor of any two terms, ICdist between the term and the most informative leaf nodes (MIL) descending from it. It is likely that not all of its leaf terms have proteins annotated in a particular corpus. Therefore, only the leaf terms with annotations were considered. A new component β is defined as the average of the generality values of iterm and jterm,iMIL and jMIL are the most informative leaf nodes of iterm and jterm, respectively.The IC-based generality of a term is defined as the γ is similar to that in the RSS method. By replacing the MRCA in Formula 1 with the MICA, we have P and Q be two gene products of interest, and TP and TQ the sets of all the GO terms assigned to proteins P and Q, respectively. The relationship strength between P and Q is defined through two pairwise rules, maximum (MAX) and best-match average (BMA). The MAX strategy takes the maximum semantic similarity value of all term pairs between TP and TQ as gene functional similarity (Formula 9), whereas the BMA strategy finds all the best semantic similarity values for each term in TP and TQ and then calculates the average value as gene functional similarity (Formula 10).An improved RSS method adapting both node- and edge-based concepts, called Hybrid Relative Specificity Similarity (HRSS) , is baseUnless stated otherwise, Gene Ontology data (released in April 2012), and gene annotation datasets (released in April 2012) for yeast and human downloaded from the Gene Ontology database To evaluate the performance of our new metric against other semantic similarity methods on capturing physical protein-protein interactions, following the approach of Jain and Bader The gold standard positive dataset for human involving 2403 high quality physical/direct interactions among 1790 proteins was obtained from a core subset of the Database of Interacting Proteins (DIP) TPR = TP/(TP+FN) and FPR = FP/(FP+TN). The ROC curve for excellent classifiers would rise steeply starting from the bottom left corner, pass close to the top left corner where both the sensitivity and specificity are 1, and finally arrive at the top right corner. In this case, the area under the ROC curve (AUC) is the largest. AUC is calculated using the trapezoidal rule, kX is the FPR and kY is the TPR.The power of a classifier was evaluated by receiver operating characteristic (ROC) curve analysis. The ROC curve presents a trade-off between true positive rate (TPR or sensitivity) and false positive rate (FPR or 1-specificity), which are calculated as http://www.ensembl.org/biomart/martviewOrthologous protein pairs between human and mouse were retrieved through BioMart from Ensembl (release 69) at observedASV) was calculated. Next, an orthologous pair dataset with the same size as the observed dataset was generated by randomly choosing a mouse ortholog for each human protein without replacement, and an randomASV value was calculated on this randomized dataset. This process was repeated 1000 times (on average 1.6 protein pairs in a randomized dataset are true), and a mean and standard deviation (stdev.) of the 1000 randomASV were calculated. Finally, a Z-score value, defined as To make the functional similarity comparable between different semantic similarity measures and ontologies, a Z-score analysis was applied to these one-to-one orthologs for each measure on each ontology. Since the distribution of the orthologous protein pair is unknown, we estimated the significance by randomizing the simulation process. First, an average similarity value for the observed orthologous pairs . HRSS were compared with our edge-based method RSS, four node-based methods TCSS, Resnik, Jiang and Lin using MAX and BMA strategies (TCSS only uses MAX), and two groupwise measures simUI and simGIC  of the Youden index was achieved when the HRSS cutoff value was close to 0.2. We also tested the F1-measure, F =  2(PPV×TPR)/(PPV+TPR) where PPV =  TP/(TP+FP), and achieved a similar threshold , the proportion of predicted negatives that are true) and reasonable coverage on observed negatives . The cutoff for this prediction of high-quality negatives was 0.1, corresponding to a prediction accuracy of 0.859 and specificity of 0.69 , medium-low confidence  and low confidence  , medium-low confidence  and low confidence   chosen above, the HRSS (MAX) values for the BP ontology in yeast, using all GO annotations, were divided into four groups with high confidence , medium-high confidence (MRSS<0.1) . The HRSRSS<0.1) . SimilarRSS<0.1) .In general, the notion that one-to-one orthologs are functionally similar holds well, although there are notable cases of major differences in the functions of orthologs at greater evolutionary distances, particularly across the primary kingdom divides To make the functional similarities comparable between different semantic similarity measures and ontologies, a Z-score analysis was applied to these one-to-one orthologs for each measure in each ontology . When BMA was used, HRSS showed the best performance consistently lines in , followeBMA and MAX were also compared in evaluating the functional similarity between orthologs. As listed in The evaluation of 13,430 protein pairs using UniProt GO annotations in all three ontologies was performed based on the CESSM platform . The BMA was applied to the pairwise methods . Both HRIn summary, BMA was more suitable than MAX for the CESSM platform. Accordingly, HRSS (BMA) and simGIC were best for correlating with sequence similarity and Pfam similarity, whereas each of the seven methods gave similar results with the ECC dataset.We tested the performance of GO-based semantic similarity measures on the correlation with gene expression using microarray data from yeast and human. Both Pearson's and Spearman's correlation coefficients between semantic similarity and gene co-expression were calculated, but poor correlations were obtained  without individual curator supervision. A large portion of gene annotations fall into the electronically inferred class. In our study, IEA annotations comprised 49% of the GO annotations in yeast and human, and the percentage in the UniProt GO annotation database (released in October 2012) was almost 99% of 110,275,783 total annotations. Interestingly, the percentages of proteins that have only IEA annotations vary among different annotation corpora, with zero occurring in yeast, 65% in human and 99% in UniProt. IEA annotations were widely considered unreliable, so they were assigned a lower weight or were disregarded in many studies. A recent study comprehensively evaluated the quality of the IEA annotations based on UniProt GO annotations in terms of reliability, coverage and specificity measures There are two major caveats of our method. First, the IC of a term is normally calculated from the annotation information, which is the number of genes annotated with the term and its descendants in a corpus, and is thus referred to as a ‘corpus-based’ metric. An alternative approach to estimate the IC of GO terms is based on the GO structure itself, such as those in Teng et al. and Seco et al. γ as the average or maximum local distance between the two terms and their DCAs.Second, only a single common ancestor, the MICA, was used in our methods. In fact, GO terms can have several disjunctive common ancestors (DCAs), all of which contribute to various degrees to the semantic similarity of the terms. To effectively explore the multiple inheritance relationships present in GO, Couto et al. designed the GraSM approach, which replaces the IC of the MICA with the average IC of all DCAs α and β) of RSS (ICα), but also comprehensively included the generality of the two terms (component ICβ) and their local similarity relative to the most informative ancestor (converted from component γ) (H), medium-low confidence (ML) and low confidence (L)  of RSS . Thus, lonent γ) . Differeence (L) . AlthougFigure S1ROC curves comparing different methods based on the human protein-protein interaction datasets (including IEA). The evaluation was carried out for the BP and CC ontologies. The (A and B) maximum (MAX) and (C and D) best-match average (BMA) pairwise rules were used in the ROC analysis.(PDF)Click here for additional data file.Figure S2ROC curves comparing different semantic similarity methods based on yeast protein-protein interaction datasets (including IEA). The evaluation was done for the BP and CC ontologies. The (A and B) MAX and (C and D) BMA pairwise rules were applied.(PDF)Click here for additional data file.Figure S3Distributions of the positive and negative interacting protein pairs with various HRSS (MAX) values. HRSS was calculated for (A and B) yeast and (C and D) human protein-protein interactions in the gold standard positive and negative datasets for BP and CC ontologies (including IEA annotations). The  interval of HRSS values is equally divided into 11 categories, which are {[0.1×i, 0.1×(i+1)), i = 0,1,…,9} plus HRSS = 1.(PDF)Click here for additional data file.Figure S4Statistical significance of system for evaluating the functional similarity (MAX) of human-mouse orthologs. The evaluation system was based on BP, CC and MF ontologies (A) including or (B) excluding IEA annotations. The histograms (measured on the left y-axis) indicate the mean and standard error of the functional similarities of observed orthologs, and the lines (measured on the right y-axis) show the Z-score values calculated from the ASVs of observed orthologous pairs and randomized pairs . Error bars indicate one standard error.(PDF)Click here for additional data file.Figure S5Correlation between semantic similarity (BMA) and the CESSM dataset (excluding IEA). CESSM displays the data of (A) sequence, (B) Pfam and (C) ECC similarities.(PDF)Click here for additional data file.Figure S6Correlation between semantic similarity (MAX) and the CESSM dataset (including and excluding IEA). CESSM holds the data of (A and D) sequence, (B and E) Pfam and (C and F) ECC similarities.(PDF)Click here for additional data file.Figure S7Comparison of two pairwise strategies, MAX and BMA, on correlation with CESSM dataset (excluding IEA). The CESSM dataset shows the similarity of (A) sequence, (B) Pfam and (C) ECC for UniProt protein pairs. The difference between the two strategies was measured by the correlation coefficient of the BMA strategy minus that of MAX.(PDF)Click here for additional data file.Figure S8Correlation between semantic similarity and gene expression similarity. The (A and B) BMA and (C and D) MAX pairwise strategies were used. The evaluation based on the BP and MF ontologies (including IEA) was carried out for human and yeast, independently.(PDF)Click here for additional data file.Table S1The sizes of gold standard positive and negative interaction datasets used for ROC analysis in human and yeast.(PDF)Click here for additional data file.Table S2Area under the ROC curves (AUCs) for the yeast PPI dataset.(PDF)Click here for additional data file.Table S3Indexes used for evaluating the performance of HRSS (MAX) on scoring protein-protein interactions in yeast.(PDF)Click here for additional data file.Table S4Indexes used for evaluating the performance of HRSS (MAX) on scoring protein-protein interactions in human.(PDF)Click here for additional data file.Table S5Summary of the functional similarities of observed human-mouse orthologs calculated by various semantic similarity methods (including IEA).(PDF)Click here for additional data file.Table S6Z-score analysis of various semantic similarity methods (BMA) for estimating the functional similarity among human-mouse orthologs.(PDF)Click here for additional data file.Table S7Z-score analysis of various semantic similarity methods (MAX) for estimating the functional similarity among human-mouse orthologs.(PDF)Click here for additional data file.Text S1Describes additional details of the semantic similarity methods used in this study, as well as additional information regarding the correlation between semantic similarities and gene co-expression.(PDF)Click here for additional data file.


In genomics and proteomics, membrane protein analysis have shown that such analyses are very important to support the understanding of complex biological processes. In Genome-wide investigations of membrane proteins a large number of short, distinct sequence motifs has been revealed. Such motifs found so far support the understanding of the folded membrane protein in the membrane environment. They provide important information about functional or stabilizing properties. Recently several integrative approaches have been proposed to extract meaningful information out of the membrane environment. However, many information based approaches deliver results having deficits of visualisation outputs. Outgoing from high-throughput protein data analysis, these outputs play an important role in the evaluation of high-dimensional protein data, to establish a biological relationship and ultimately to provide useful information for research.We have evaluated different resulting graphs generated from statistical analysis of consecutive motifs in helical structures of the membrane environment. Our results show that representative motifs with high occurrence in all investigated protein families are responsible for the general importance in alpha-helical membrane structure formation. Further, motifs which often occur with others in their function as so called “hubs” lead to the assumption, that these motifs constitute as important components in helical structures within the membrane. Otherwise, consecutive motifs and hubs which show a high occurrence in certain families only can be classified as important for family-specific functional characteristics. Summarized, we are able to bridge our graphical results from high-throughput analysis of membrane proteins over networking with databases to a biological context.Our results and the corresponding graphical visualisation support the understanding and interpretation of structure forming and functional motifs of membrane proteins. Our results are useful to interpret and refine results of common developed approaches. At last we show a simple way to visualise high-dimensional protein data in context to biological relevant information. Proteins are the main catalysts, structural elements, signalling messengers, molecular machines of biological tissues and essential for many fundamental biological processes within organisms . FundameIn conjunction with genome-wide investigations, previous works have been engaged in analysing of classified poly-topic membrane protein families. For example the research of Y. Liu, D. M. Engelman and M. Gerstein observed the amino acid distribution of TM helices in their work of computational genomic analysis of membrane protein families . The abuThe unsolved problem how a protein folds and sequence homology are related can be better understood by sequence motif analyses. Thus, the enormous increase of membrane protein data and protein structures requires the handling of such high-dimensional biological data. In this work, our novel statistical approach shows which motifs contribute fundamentally to be involved as structural or functional sequence parts. Useful graph visualisations will fill the lack of high-throughput protein data analysis and evaluation. Here, we will reveal functional and structural relationships of sequence motifs. Summarized, we inspect structural and functional aspects of sequence motifs within the field of membrane proteins, largely from a computational point of view.As first step of our analysis different datasets were obtained. Two of them were derived from the Pfam database . The firThe second dataset (DS2) consists of 11 membrane protein families with 15644 proteins and 160 known structures as listed below.After the datasets have been obtained, non-redundant sequences from DS1 and DS2 were generated. To avoid generating misguiding statistics by including identical or highly similar sequences, CD-HIT  and BlasGenerally, proteins are large biological molecules they fold into a three-dimensional structure, which is determined by the protein sequence (primary structure) which consists of one or more chains of the 20 canonical amino acids. In the current work only ‘TM’ sequence information was used for our analysis. In this context, short sequence motifs have been extracted which contribute to build the membrane protein structure in the ‘TM’ environment. Each extracted motif can be written in a generalized, regular expression-like form of XYn, where X and Y correspond to amino acids separated by n-1 highly variable positions.A naive text search algorithm was applied for motif extraction  was defined. In this work a MA specifies that only four directly consecutive motifs are to be considered in each statistical frame. The number of four consecutive motifs depends on the number of ‘TM’ environment occupied residues and the maximum length of a motif defined for this work. In addition directly consecutive motifs means that a motif is ultimately following the previously Figure  without The high throughput analysis of membrane protein families obtained by previous described steps returns different result graphs. Useful information about frequently occurring consecutive motifs has been ascertained for all investigated membrane protein families of DS1 and DS2. The resulting graphs of both datasets are shown in Figures Further, different coloured Nodes are recognizable. Here, red Nodes were predicted to be part of ‘TM’ region and blue ones to be part of ‘nTM’ by determination of the residue conservation at each variable motif position of the given data sets like described in section “Topology separation and prediction of discriminative motifs”. Based on this, ‘TM’ non-typical motifs have been predicted in both result graphs. Such motifs can indicate, that they may be involved in special functions. On closer inspection of the DS2 result graph Figure , a highlIn summary, we could show that membrane protein families are characterized by individual motifs influenced by their structural and functional properties. Finally, on consideration of all data processing steps including by final visualising and under networking with biological databases, we are able to build a bridge between graph information in conjunction with a biological context.Generally, in this work it could have been shown how to visualize high-dimensional membrane protein data in form of graph structures and how to fill the lack between high-throughput protein data analyses and evaluation. 32 poly-topic membrane protein families with domains of unknown functions and 11 membrane protein families consisting of receptor, transporter and neurotransmitter-gated ion-channel proteins were analysed. Transmembrane and non-transmembrane sequence regions were predicted using the TMHMM method. Possible sequence motifs of variable lengths have been extracted out of predicted ‘TM’ regions, by using a naive text extracting algorithm. Four immediately consecutive sequence motifs were defined as a statistical frame called “motif-architecture”. Subsequently, multiple numbers of motif-architectures have been extracted out of all ‘TM’ regions, followed by information transformation into graph structures. Motifs as representative nodes connected by weighted edges to other nodes form a graph. All result graphs support the understanding and evaluation of high occurring consecutive motifs of the investigated protein families. This high occurrence of architecture-motifs points to the general importance that these motifs within the respective protein structure are significantly relevant for the membrane protein folding. ‘TM’ region atypical motifs have emerged which point to the general importance as being involved in defining a protein’s function. Here in special, motifs which are involved in the consensus pattern of retinal binding sites of Pfam receptor families. Finally, hub-motifs which often occur together with others point out to indispensable motifs in helical regions.Because of the stronger protein structure conservation in evolution than the sequential composition of the folded protein chains, there are individual motifs or characteristic sequence parts which expose a certain biochemical function of proteins. This means that membrane protein families are characterized by structural and functional motifs. Thus, it is possible to compare such families by the inclusion of individual sequence motifs.Conclusive evaluation of our results with biological databases confirms this fact and shows a simple way bridging visualisation of membrane protein data to biological context.The authors declare that they have no competing interests.SG performed research and drafted the manuscript. Both authors read and approved the final manuscript.