With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched.Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology.In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions. Escherichia coli [Helicobacter pylori [Saccharomyces cerevisiae [Caenorhabditis elegans [Drosophila melanogaster [Homo sapiens [The concerted interactions of thousands of proteins in cells form the basis of most of the biological processes. Genome-wide identification of protein-protein interactions is important to understand the underlying mechanisms of many biological phenomena e.g. cell cycles, apoptosis, signal transduction, and pathogenesis of diseases. Recently, high-throughput experimental methodologies have been developed to screen the protein-protein interactions (PPIs) in a genome-wide way, e.g. yeast two-hybrid systems , mass sphia coli , Helicobr pylori , Saccharrevisiae ,8,9, Cae elegans , Drosophnogaster , and Hom sapiens ,13. Thes sapiens . More imTo construct a universal predictor, we need to extract protein attributes that are crucial to PPIs predictions. Among the various attributes of proteins, the primary sequences are the most basic and the easiest to obtain because of the rapid development of genomic sequencing technologies. In addition, the primary sequences of proteins virtually specify their structures that provide the molecular basis for PPIs. So protein primary sequences hold the promise to contain virtually sufficient information to construct the most universal predicting method .We know that almost all proteins are composed of twenty amino acids but different proteins have various lengths. Here the first challenge to construct a universal PPI predictor is that how to represent the various lengths of proteins by numerical vectors with the same dimension if vector-based computational methods are used. Even if the methods are not based on vectors, what features of the protein sequences are important to PPIs should be addressed first. So far, many methods have been proposed -20. HoweEscherichia coli and Saccharomyces cerevisiae suggest that our new vector representation for protein sequences improves the prediction accuracy significantly while reducing greatly the computational complexity. The new vector representation of the protein pairs further improves the prediction accuracy and has excellent theoretical properties, i.e., symmetry, reversibility, and unbiasedness.Based on the key ingredients revealed by the optimization model, we suggest new coding methods for both protein sequences and protein pairs. Strict evaluations on datasets of We consider two theoretical aspects to evaluate various vector representations of protein sequences. One is to what extent the protein sequence information is extracted by the vectors. This can be evaluated by checking whether and how protein sequences can be constructed conversely from the vectors. The other is how the vector dimension increases as the information extracted. Because of the curse of dimensionality, representations with low vector dimension are appreciated in real applications. These criteria can be summarized as the following optimization model:s.t.S is a set of protein sequences, V is the vector representation of S generated by the mapping f and g is the inverse function of f. dim(V) means the dimension of V .where k-mer based (denoted by K) [k-mer based representation counts the number of each k-mer appearing in protein sequences, so the vector dimension is 20k, increasing exponentially as k. When k is large enough (often much larger than three), protein sequences can be re-constructed uniquely from the corresponding vectors by seeking an Eulerian trail in a network constructed by the relationships of k-mers. Segmentation-based methods divide a protein sequence into p pieces and then count the number of each amino acid appearing in each piece. So the resultant vector dimension is 20*p. When p is equal to the length of protein sequence, the protein sequence can be reconstructed easily by filling amino acids in each segment because there is only one amino acid in each segment. When p is less than the length of protein sequence, some sequence information is lost and the protein sequence cannot be reconstructed exactly.Based on the evaluation model, we compared the available ed by K) ,16,20 aned by K)  vector rq quantile positions of each type of amino acids. A toy example is illustrated in Figure q, increasing linearly as q. Because position information is complementary to the amino acid or k-mer counts, super representation schemes, for example, QP and KQP, can be constructed. For instance, QP divides a protein sequence to p pieces and then counts the number and records the q quantile positions of each type of amino acids in each piece, resulting a 20*(1+q)*p vector. KQP divides a protein sequence to p pieces and then counts the number and records the q quantile positions of each k-mer in each piece, resulting a 20k*(1+q)*p vector. A detailed comparison of these representing methods is summarized in Table Inspired by the reversibility and low-dimension requirements of the evaluation model and the fact that protein sequences are "sequences", we propose a new vector representation scheme by recording the positions (denoted by Q). Q treats the positions of each type of amino acids as a distribution and records the ing a 20*+q*p vectνA-νB) [νA) and Protein B (νB), we construct two new vectors: one is νA+νB and the other is νA*νB, in which * means the corresponding elements multiplication. Then the two symmetric vectors are concatenated into one vector. For AG, the arithmetical average of νA and νB (denoted by νAM) and the geometric average of νA and νB (denoted by νGM) are calculated. That is, the i-th dimensional element of νAM and νGM are given by the following formulations:To predict PPIs, we need further encode protein pairs into a single vector. The reversibility requirement also applies to the vector representation of protein pairs. Here, symmetry is the first condition that must be satisfied. Protein-protein interaction is widely believed to be symmetric interaction in biology , i.e., pνA-νB) , or workνA-νB) ,24, but νAM and νGM are calculated, the symmetric representation of protein pair  will be the concatenation of νAM and νGM. AG has three important properties: 1) The resultant vector is symmetric regarding to protein pairs  and  because of the commutative laws of addition and multiplication; 2)For each dimension i, νA and νB because of the average operations, without artificial noise introduced. These three properties facilitate the extraction of information in the protein vectors and are beneficial to learning the rules underlying PPIs (see results for more detailed discussions).When k-mer-based method proposed by Shen et al. [Escherichia coli and Saccharomyces cerevisiae with two types of negative samples . AG-CTF reaches 0.996 and SM-CTF reaches 0.988. On the other three comparisons, AG-CTF always reaches the highest AUC values. And AG-CTF is better than SM-CTF because it solves the scale problem. Regarding the other indices, e.g. accuracy, sensitivity, specificity, and precision, AG-CTF also outperforms the other solutions. AG-CTF considers adequately the converse problem and solves the scale question, so its good performance is expected. Because it is based on the vector level, it is easy to track the physical meanings and the computation is efficient. The extremely high AUC values on the benchmark negative data sets are due to the bias incorporated during the construction of negative samples, which has been pointed out previously [The comparison results are illustrated in Table eviously .p = 5[q = 17, denoted by AG-Q340; and 4) the combination of segmentation and position with p = 3, q = 5, denoted by AG-QP360. q is set to seventeen for AG-Q340 because the resultant vectors have the almost same dimension compared to AG-CTF and AG-QP360. p = 3 and q = 5 for AG-QP360 is because of the same reason. We choose p = 5 for AG-P100 because it is a representative of this class of methods and reaches the best AUC value in cross-validation.The above comparison reveals that the symmetry solution based on the arithmetical and geometric averages perform best. In this subsection, we choose to fix this strategy in the comparison of various vector representation schemes of protein sequences. In this way we can eliminate the differences introduced by different symmetric representations and make results rigorous. In total, four vector representation schemes of protein sequences are compared. They are: 1) conjoint triad features proposed by Shen et al. , denotedp = 5, denotedEscherichia coli and Saccharomyces cerevisiae data sets. On the Escherichia coli benchmark negative data set, AUC of AG-CTF reaches the highest 0.996. AG-QP360 and AG-P100 reach 0.994, which are a little bit smaller. AG-Q340 has the least AUC 0.989. On the yeast benchmark negative data set, AG-QP360 has the highest AUC 0.993 while AG-CTF, AG-P100, and AG-Q340 have AUCs 0.991, 0.991, and 0.989, respectively. Regarding the other indices including accuracy, sensitivity, specificity, and precision, AG-QP360 outperforms the other methods.The comparison is illustrated in Table Escherichia coli random negative data set, AG-QP360 gets the highest AUC, 0.899, which is higher than that of AGP100 by one percent. AG-CTF has the third highest AUC (0.886) and AUC of AG-Q340 is the least (0.854). AG-QP360 also has the highest accuracy, sensitivity, specificity, and precision. On the Saccharomyces cerevisiae random negative data set, AG-QP360 still shows outperforming performances.Because of the bias in the benchmark negative data sets, each method can achieve very high AUC values but may limit its discriminating capacity. The negative samples sampled randomly from the complementary graphs are assumed to be unbiased so they may provide more discrimination power . On the k-mers with the true sequence [We also compared the performances of AG-QP360 and AG-CTF on the third type of negative samples to highlight the benefits of linearly scalable vector representations including segmentation based, position-based, and their combination Table . Given asequence . These asequence . Here weComparisons on human PPIs data were also implemented strictly  on  in refs . Other luShuffle . Yu et aive PPIs . They prive PPIs  that werEscherichia coli and 6,635 positive interactions are included in the cross-validation experiments for Saccharomyces cerevisiae. The numbers of negative samples are the same as the number of positive samples for balance. Human PPIs were downloaded from the Human Protein Reference Database (HPRD) on Dec. 21st, 2009 [The protein sequences are from the RefSeq database of NCBI. PPIs involving proteins whose sequences are not available are filtered. Finally, 6,962 positive interactions are included in the cross-validation experiments for st, 2009 .k-mer based method with k = 3. The details can be found in [νA and νB, dist generates the symmetric vector by abs (νA-νB). Sker calculates the kernel matrix according to the S kernel defined in [νA+νB and νA*νB in which * means the multiplication of the corresponding elements. AG gets the symmetric representation according to (4) and (5). libsvm 2.88 [Protein sequences are converted into vectors by four schemes . CTF classifies the twenty amino acids into seven classes and then applies found in . P100 difined in . SM creasvm 2.88  is used XR proposed the idea for this work. XR and YCW designed the predictive methods and the experiments, prepared the experiments and wrote the paper. YW analyzed the results and revised the paper. XSZ and NYD participated in developing the methods and revised the article. All authors read and approved the final manuscript.


Protein ubiquitination is one of the important post-translational modifications by attaching ubiquitin to specific lysine (K) residues in target proteins, and plays important regulatory roles in many cell processes. Recent studies indicated that abnormal protein ubiquitination have been implicated in many diseases by degradation of many key regulatory proteins including tumor suppressor, oncoprotein, and cell cycle regulator. The detailed information of protein ubiquitination sites is useful for scientists to investigate the mechanism of many cell activities and related diseases.http://reprod.njmu.edu.cn/mUbiSiDa.In this study we established mUbiSida for mammalian Ubiquitination Site Database, which provides a scientific community with a comprehensive, freely and high-quality accessible resource of mammalian protein ubiquitination sites. In mUbiSida, we deposited about 35,494 experimentally validated ubiquitinated proteins with 110,976 ubiquitination sites from five species. The mUbiSiDa can also provide blast function to predict novel protein ubiquitination sites in other species by blast the query sequence in the deposit sequences in mUbiSiDa. The mUbiSiDa was designed to be a widely used tool for biologists and biomedical researchers with a user-friendly interface, and facilitate the further research of protein ubiquitination, biological networks and functional proteomics. The mUbiSiDa database is freely available at  Protein ubiquitination, known as the important protein post-translational modification of targeting proteins by ubiquitins for their subsequent degradation in the ATP-dependent ubiquitin proteasome system (UPS), plays an important role in cell activity Protein ubiquitination is implemented by ubiquitin binding to the lysine site of a target protein. The location, numbers, and distribution of ubiquitination site are important information for scientists to investigate the mechanism of UPS and relevant diseases Consequently, in this study we constructed a user-friendly database, mUbiSida, which meets the above requirements. The dataset in mUbiSiDa are mainly collected from published papers. In total, we searched and obtained 104 references containing experimentally validated 35,494 mammalian ubiquitinated proteins from 5 species. Over 95% of these sites are from human and mouse. This comprehensive database enables not only the information retrieval of protein ubiquitination sites, but also the study of cross-regulation between post-translational modification In conclusion, the aim of the mUbiSiDa database is to provide a user-friendly web interface to browse, search, retrieve and update information on mammalian protein ubiquitinated sites, and to promote the further research of protein ubiquitination, biological networks and functional proteomics.Our data are collected from two main resources: Firstly, published literatures from PubMed. Through keyword search results in PubMed, we have obtained a mass of related literatures, after which experimentally identified mammalian ubiquitylation sites were downloaded from these literatures Several main references were listed in the section of References mUbiSiDa was constructed and configured upon a typical LAMP (Linux + Apache + MySQL + PHP) platform. Apache5.0.51b was firstly used to build up a webserver. All dataset were stored in MySQL 5.0, and web interface was achieved by PHP scripts (PHP version 5.2) on Linux, powered by an Apache server. WebPages were designed with html and JavaSript techniques. Website and database were connected and all kinds of function were achieved by PHP techniques.All functions of mUbiSiDa were shown in Home page . As an iIn the text field of the Search page, the users can input the query strings, such as protein ID, protein name, or others, and then the obtained result pages will be the list of protein entries that matched the query strings. Those keywords matching the query strings in the result pages will be yellow highlighted. The users can view the detailed information about their interested protein by clicking the protein ID on the left column. For further retrieval, combined search is also available, but the most two query strings combined by ‘+’ are only supported in the current version homepage of database. In addition, on each result page, mUbiSiDa also provides customized function to reorganize the feature list of protein entries according to user's interests.There are three options in Advanced Retrieval: (1) Advanced Search (2) Protein Name Search, (3) Sequence Blast, which is efficient access for the users to obtain their interested information. The users can obtain the specific protein ubiquitination information by entering more restricted words in six text fields combined with ‘AND’, ‘OR’ and ‘BUT’ in “Advanced Search” query page. We specifically designed “Protein Name Search” query page for the users conveniently to obtain their interested protein when knowing their names. There are four text fields for users to input query strings, part or all of the text fields can be used for combined search. When the ID text field is empty, the other three text fields were designed to be fuzzy query. When the ID text field is not empty, it was set to be precise search. The ID is supposed to be UniProt ID.“Blast search” was also specifically designed for the users to predict the potential ubiquitination site information of new protein in other mammalian species, which is orthologs or paralogs deposited in the database. As a tool for comparing primary biological sequence information, Sequence Blast enables a user to compare a query sequence with all sequences of mUbiSiDa, and identify sequences of mUbiSiDa that resemble the query sequence above a certain threshold. By means of this method, one or few results which sequence is most similar to the input sequence will be obtained after blast process. The Sequence Blast is a high efficient tool for retrieving possible ubiquitinated lysine sites of proteins input by users. Not only will the protein ID, but also related references are posted after using blast. Users can view detailed information of the result by clicking the proteins ID. This tool extends the function of the mUbiSiDa to be used to predict protein ubiquitination sites for most mammalian species.The dataset in mUbiSiDa can be browsed in four options: (1) Browse data by organism, (2) Browse data by biological Process, (3) Browse data by cellular component, (4) Browse data by molecular function.In “Browse Data by Organism” page, the total dataset in mUbiSiDa are grouped by organism, which is listed in a table with 5 kinds of organisms collected by mUbiSiDa. The corresponding entry number of each organism is also listed. The users can view all associated entries for one organism by clicking on right numbers. This function is quite useful for users to view all the relevant information in particular species.The total dataset in mUbiSiDa can also be viewed by Gene Ontology (GO). In the result pages, the gene ontology IDs classified as biological process, cellular component, and molecular function for each protein deposited in mUbiSiDa will be listed. The gene ontology terms, and the corresponding protein entries numbers are also listed. The user can select and click the gene ontology ID to view all proteins of the selected gene ontology ID. The matched results can help the users with a convenient way to view ubiquitinated proteins grouped by GO terms. The user can benefit from this function to do cell research on regulation of protein's function or biological processes.All the dataset in mUbiSiDa can be downloaded at “Resources” web page.In order to maintain an up-to-date and comprehensive resource, we designed the submission page for the users to submit their own data to mUbiSiDa. The submission page requires that the users should offer the following submission items: protein name, ID, organism, ubiquitination site(s), PubMed ID, GO ID, sequence, and other information.Users are allowed to have an access to the detailed information of each entry by clicking the ID on the left column in the result page. All information was carefully checked manually to ensure the accuracy of each entry. The each entry page is divided into six sections. The first section  provide mUbiSiDa, the comprehensive mammalian ubiquitination site database, aiming to systematically aggregate all experimentally verified ubiquitinated protein, and provide biologists for analyzing protein stability and mechanism of protein degradation. Most data in this database are collected from human and mouse. It needs emphasizing that potential ubiquitinated lysine site from other organisms can be predicted according to the results of BLAST SEARCH or Multi-species Alignment Analysis. As a tool designed to be used by biologists and researchers, mUbiSiDa will be continually improved and updated to ensure the convenience and utility of the service, accuracy of the information and innovative in style and function.To obtain an overview of the biological features of human and mouse ubiquitinated proteins, we firstly classified these proteins according to Gene Ontology See . For humIn the future work, efficiency in search process and new style of search need to be improved to satisfy different needs. The function of mammalian ubiquitination sites prediction will be subsequently added to mUbiSiDa in the future version. With continuous improvement, mUbiSiDa is expected to make a contribution to the researches on regulation of protein's function or biological processes.File S1Gene Ontology classification and pathway enrichment analysis results for human and mouse ubiquitinated proteins.(XLSX)Click here for additional data file.


More than 100 mutations in the SOD1 gene have been associated with fALS, altering the geometry of the active site, protein folding and the interaction between monomers. We performed a functional analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in 124 fALS SOD1 mutants. Eleven different algorithms were used to estimate the functional impact of the replacement of one amino acid on protein structure: SNPs&GO, PolyPhen-2, SNAP, PMUT, Sift, PhD-SNP, nsSNPAnalyzer, TANGO, WALTZ, LIMBO and FoldX. For the structural analysis, theoretical models of 124 SNPs of SOD1 were created by comparative modeling using the MHOLline workflow, which includes Modeller and Procheck. Models were aligned with the native protein by the TM-align algorithm. A human-curated database was developed using the server side include in Java, JMOL. The results of this functional analysis indicate that the majority of the 124 natural mutants are harmful to the protein structure and thus corroborate the correlation between the reported mutations and fALS. In the structural analysis, all models showed conformational changes when compared to wild-type SOD1, and the degree of structural alignment varied between them. The SOD1 database converge structural and functional analyses of SOD1; it is a vast resource for the molecular analysis of amyotrophic lateral sclerosis, which allows the user to expand his knowledge on the molecular basis of the disease. The SOD1 database is available at  Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the adult onset of progressive dysfunction and loss of upper motor neurons in the motor cortex and lower motor neurons in the brainstem, spinal cord, and their associated tracts. ALS has a worldwide annual incidence of approximately 1.5 to 2 cases/100,000 and a prevalence of 6-8 cases/100,000 , SN, SNin silyPhen-2  and nsSNlyPhen-2  were utilyPhen-2 , which ilyPhen-2  and ProclyPhen-2 . Models lyPhen-2 .Despite progress in protein structure prediction, comparative modeling remains the only method that can reliably predict the 3D structure of a protein with an accuracy that is comparable to that of a low-resolution experimentally determined structure . A modelhttp://bioinfogroup.com/database/ and will be regularly updated.As a result, a human-curated database was developed using the server side include in Java, JMOL, for biologists and clinicians to explore SOD1 nsSNPs and the resulting changes in structure and function. This database is freely available at The sequence of the human SOD1 [UNIPROT: P00441] were retrieved from the UNIPROT database .The 124 natural variants of the human SOD1 that are associated with ALS were obtained from the OMIM database , UNIPROTThe mutant proteins were functionally analyzed using eleven different algorithms: SIFT, nsSNPAnalyzer, SNPs&GO, Polyphen 2, PMUT, PhD-SNP, SNAP, TANGO, WATLZ, LIMBO, and FoldX .ConSurf (ConSurf v3.0), a bioinformatics tool based on the phylogenetic relations between homologous sequences, was used to evaluate the conservation at each amino acid position of the SOD1 protein ,33. ThisThe mutant models were built using the MHOLline workflow  with thehttp://bioinfogroup.com/database contains the results obtained from this work. The data contained on the site were individually and manually obtained in each algorithm. A human-curated database was developed using the server side include in Java, JMOL. The database at The motivation for choosing SOD1 is the intimate relation of its variants with the development of ALS and the increasing number of studies that have been performed for greater understanding of this disease and of SNP predictive algorithms. in silico analyses per mutation, revealing the lack of a more updated and complete database about SOD1. The SOD1 database, http://bioinfogroup.com/database, was created to supplement these demands and form a database of the results found during the execution of this project and from others in the future, allowing researchers and clinicians to explore SOD1 and its variants. Our database, which contains all the currently described mutations, allows the user to evaluate the effects of SOD1 on ALS in a more embracing manner than that of the other existing databases. In addition, the user can know and analyze the algorithms that are most frequently used to predict SNPs, starting from the access to the individual results of the application for each of the 124 existing mutations. With its interactive interface, the SOD1 database allows dynamic utilization by enabling users to select only the results of the mutations and algorithms that are most important to them.In this work, 124 natural variants of SOD1 obtained through three different databases were analyzed. Until now, no single database contained the complete set of The information derived from the application of algorithms that predict the pathogenicity of mutations is quantitatively represented in SIFT: This method takes a query sequence and uses multiple alignment information to predict tolerated and deleterious substitutions for every position of the query sequence. This is a multistep procedure that, given a protein sequence, (1) searches for similar sequences, (2) chooses closely related sequences that may share similar function, (3) obtains the multiple alignment of these chosen sequences, and (4) calculates normalized probabilities for all possible substitutions at each position from the alignment . SeventynsSNPAnalyzer: A web-based software, nsSNPAnalyzer extracts the structural and evolutionary information from a query nsSNP and uses a machine learning method known as random forest to predict the phenotypic effect of the nsSNP . Nearly SNPs&GO: A SVM-based method that, starting from the protein sequence, uses different pieces of information, including that derived from the GO annotation of the protein, to predict whether a given mutation can be classified disease related . One hunPolyPhen-2: This method uses eight sequence-based and three structure-based predictive features that were selected automatically by an iterative greedy algorithm. The majority of these features involve a comparison between a property of the wild-type allele and the corresponding property of the mutant allele. The functional importance of an allele replacement is predicted from its individual features by a naive Bayes classifier . ApproxiPMUT: This method allows the prediction of the pathological character of single-point amino acid mutation based on the use of neural networks. This method also allows the fast scanning of mutational hot spots, which are obtained using three procedures: (1) alanine scanning, (2) massive mutation and (3) genetically accessible mutations. A graphical interface for Protein Data Bank (PDB) structures, when available, and a database containing hot spot profiles for all non-redundant PDB structures are also accessible from the PMUT server . Nearly PhD-SNP: This method is based on support vector machines (SVMs) that starts from the protein sequence information and can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans, using a dataset of 21185 single point mutations . Nearly SNAP: This method utilizes various biophysical characteristics of the substitution, as well as evolutionary information, some predicted  structural features, and possibly annotations, to predict whether a mutation is likely to alter protein function (in either direction: gain or loss) . Nearly SNPeffect: This method uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. SNPeffect integrates aggregation prediction (TANGO) , amyloidTANGO: Six mutations were classified as causing an increased tendency for protein aggregation, and 5 were classified as a decrease of that tendency. The other 115 mutations were classified as not changing the protein aggregation from that of the wild-type SOD1.WALTZ: Only two mutations were classified as responsible for increasing the amyloid propensity and two for its decrease. The remaining approximately ninety-seven percent was classified as having no changes concerning this parameter.LIMBO: None of the mutations were classified as causing changes in chaperone binding compared with the wild-type SOD1.FoldX: Approximately eighty-six percent of the mutations were classified as responsible for the decrease of protein stability, 4 mutations as causing an increase thereof, and only 13 mutations were found to be unaffected compared with the non-mutated SOD1.Three histidine residues  and one glycine residue (G85R) that formed the ligation site between metal and SOD1 were affected by ALS mutations. All of these mutations were considered pathological according to the seven algorithms for predicting pathogenicity. Furthermore, these mutant proteins did not exhibit the tendency to either aggregate or form amyloids and did not alter chaperone binding. However, their protein stability was considerably reduced in comparison to that of the wild-type SOD1. These results are not consistent with those of other experimental studies, which have shown that H46R and G85R are among the most stable ALS-related mutations .Starting from the SNP analysis from using the seven algorithms individually, only 43 natural variants (34.7%) were classified as pathological by all of the algorithms. Of the remaining variants, 25.8% were classified as neutral, 23.4% had two classifications, and the other 16.1% had between three and six . No mutaInterestingly, all the mutations that occurred on the binding site to metal of the SOD1 were considered pathological by the 7 algorithms that were used. Therefore, we can hypothesize that these mutations have the functional localization on the protein as a common parameter, which is very reasonable considering that cellular functions are performed by 3D well-folded protein structures and protein-protein and protein-ligand interactions  and thatRegarding the analysis performed by SNPeffect, the vast majority of the mutations were classified as having no significant changes in relation to the protein aggregation tendency, amyloid propensity tendency or changes in the binding of chaperones. Conversely, according to the protein stability analysis parameter, most of the mutations (86.3%) were classified as being less stable than the wild-type SOD1. These results are extremely important in understanding the probable pathological change whereby mutations lead to the development of ALS. In particular, this analysis indicated that the mutation A4V exhibited not only a reduced protein stability but also an increased tendency to aggregate, corroborating experimental data that highlighted the greater severity of this mutation over those of other mutations, consistent with experimental results indicating that the mutant A4V more easily aggregates in the presence of cupric ions under copper-mediated oxidative conditions than does wild-type SOD1 . In contThe results generated by the ConSurf tool consist of a structural representation of the protein  and a muEquus caballus and Morrodelphis domestica; while the second is because of the Mus musculus and Morrodelphis domestica species. Evolutionary information is of fundamental importance for detecting mutations that affect human health . ConSurfWhen comparing the results obtained from the SNP and ConSurf analysis algorithms, all mutations that were classified as benign  by at lehttp://bioinfogroup.com/database). RMSD values greater than 0.15 were considered significant structural perturbations that could have functional implications for the protein [The natural variants were substituted into the wild-type sequence for comparative modelling. These sequences were submitted to the MHOLline workflow . An alig protein .http://bioinfogroup.com/database. The database interface  are estimated to be between 50% and 80% ,47. In tThe resulting database allows biologists and clinicians to explore SOD1 nsSNPs and their functional inferences. It is a vast resource for the molecular analysis of amyotrophic lateral sclerosis, which allows a user to expand his knowledge of the molecular basis of the disease.Table S1In Silico Analysis of Missense Substitutions.Tools for (PDF)Click here for additional data file.Table S2Functional Analysis of SOD1 in fALS.(PDF)Click here for additional data file.


As a member of the Open Biomedical Ontologies (OBO) foundry, the Protein Ontology (PRO) provides an ontological representation of protein forms and complexes and their relationships. Annotations in PRO can be assigned to individual protein forms and complexes, each distinguishable down to the level of post-translational modification, thereby allowing for a more precise depiction of protein function than is possible with annotations to the gene as a whole. Moreover, PRO is fully interoperable with other OBO ontologies and integrates knowledge from other protein-centric resources such as UniProt and Reactome. Here we demonstrate the value of the PRO framework in the investigation of the spindle checkpoint, a highly conserved biological process that relies extensively on protein modification and protein complex formation. The spindle checkpoint maintains genomic integrity by monitoring the attachment of chromosomes to spindle microtubules and delaying cell cycle progression until the spindle is fully assembled. Using PRO in conjunction with other bioinformatics tools, we explored the cross-species conservation of spindle checkpoint proteins, including phosphorylated forms and complexes; studied the impact of phosphorylation on spindle checkpoint function; and examined the interactions of spindle checkpoint proteins with the kinetochore, the site of checkpoint activation. Our approach can be generalized to any biological process of interest. Understanding the meaning of data is essential for accurate scientific analysis and interpretation. Ontologies formalize the meaning of terms using a defined vocabulary that facilitates the integration of data and knowledge  family: protein products of a distinct gene family arising from a common ancestor; (ii) gene: the protein products of a distinct gene; (iii) sequence: protein products that have a distinct sequence upon initial translation; and (iv) modification: protein products derived from a single mRNA species that differ because of some change (or lack thereof) that occurs after the initiation of translation , a top-level formal foundational ontology in the biomedical domain. BFO represents, in consistent fashion, the upper level categories common to ontologies developed in different domains and at different levels of granularity. It adopts a view of reality as comprising (1) continuants: entities that continue or persist through time , and (2) occurrents: the events or happenings in which continuants participateMoreover, PRO interoperates seamlessly with other OBO ontologies by reusing terms whenever the classes needed already exist in other ontologies. This is the case for the protein complex terms found in the Cellular Component branch of the Gene Ontology  What proteins and complexes are involved in a particular process? (2) What proteins and complexes are conserved in a given set of species? and (3) What function(s) is associated with a given protein form or complex?To be able to answer the questions described in the previous section, PRO has to provide an adequate coverage of terms and annotations that pertain to the biological questions being asked. The ultimate goal in PRO is the representation of protein-related terms for the 12 GO Reference Genomes and human protein complexes from Reactome. Release 32.0 contains 35,196 PRO terms from which about 25,000 are ProEvo terms (family and gene-level classes), 9,500 are ProForm terms (isoforms and modified forms), and 393 are ProComp terms. In terms of annotations, there are 2,941 GO annotations derived from 1,242 publications. The distribution filesIn this article we use the features of PRO, including a graphical representation of the PRO hierarchy, to explore the spindle checkpoint. The spindle checkpoint monitors interactions between kinetochores and spindle microtubules during mitosis and meiosis and inhibits the onset of anaphase until all kinetochores have made correct attachments to the spindle  are confirmed protein kinases and all seven are phosphoproteins . Because of our interest in phosphorylation of checkpoint proteins, we focused our curation efforts on the subset of articles that were flagged by the text mining tool Rule-based LIterature Mining System for Protein Phosphorylation (RLIMS-P) as containing mentions of phosphorylation in the abstract . All terms and annotations generated in this study can be found in PRO release 32.Once data was entered into the PRO framework, it was analyzed and visualized using the search and graphical display tools in the PRO website. The search functionality allows all parts of a PRO entry, including definition and annotation, to be searched. Query terms can be words or phrases or unique identifiers from other resources such as Pfam or GO. Searches can be restricted to a particular field of a PRO entry; for example, searching for the term “9606” in the Taxon ID field will retrieve all human protein terms. The search terms “NOT NULL” and “NULL” can be used to identify PRO entries that do or do not contain information in a selected field. Multiple search terms can be joined with the Boolean terms “AND,” “OR,” and “NOT” to carry out more complex searches. In addition, searches can be restricted to particular categories of PRO entries such as modified forms, disease-related forms, or complexes using the “Quick Links” menu provided on the PRO search page. Finally, the search result table can be customized to include/remove information and can be downloaded in tab-delimited format.The PRO hierarchy can be visualized using a built-in tool based on Cytoscape Web  network was displayed using locally installed Cytoscape, version 2.8 . The search returned two results: the MAD3 gene-level term (PR:000035499) and the BUB1/BUB1B family level term (PR:000035665).. As expected both groups are annotated as containing the MAD3/Bub1 homology domain I (PFAM:PF08311), and the definition of the BUB1/BUB1B family states in part that: “Members of this class are related to MAD3.” However, the BUB1/BUB1B proteins contain a second conserved domain, the C-terminal protein kinase domain (PFAM:PF00069) that is absent in the MAD3 class.To reveal the common and divergent attributes of these protein classes, the result table was customized, via the Display Option functionality, to display the corresponding annotations and allow their direct comparison Figure A. As expThe combined Cytoscape web view for BUB1/BUB1B and MAD3 terms is shown in Figure Phosphorylation is a major mechanism of regulation in the spindle checkpoint pathway and the interplay among the checkpoint-related phosphorylation events is complex  is shown in Figure BUB1B/Phos:1 PR:000035361), defined in PRO as a BUB1B form that has been phosphorylated on a site analogous to Thr-620 of human BUB1B, is found in humans (PR:000035362) and frogs (PR:000035426). The frog form is phosphorylated on Thr-605, which is considered to be analogous to human Thr-620 because it aligns with human Thr-620 in a multiple sequence alignment  is conserved in the mouse protein  contains the same CDK1 phosphorylation site (Thr-620 in humans) as BUB1B/Phos:1 and is additionally phosphorylated on several sites by PLK1/PLX1. Because experimental evidence indicates that PLK1 phosphorylation of BUB1B is low in the absence of prior CDK1 phosphorylation, PRO does not have a term for BUB1B phosphorylated by PLK1 alone  documents two neighboring sites – Ser-676 and Thr-680 – that have been verified 5428 docuin vivo are conserved in the frog and mouse proteins  is phosphorylated on Thr-608 in humans (PR:000035432) and on the equivalent site, Thr-593 in frog (PR:000035433) . All three sites are conserved in mouse BUB1B and two of the three (mouse Ser-535 and Ser-1033) have been shown to be phosphorylated in vivo, strongly suggesting that BUB1B/Phos:4 exists in mouse . The BUB1:BUB3 complex is highly conserved, occurring in human, fission yeast, and budding yeast (orange squares). The BUB1 proteins from all three organisms have a common parent , indicating that they are orthologous; similarly, the BUB3 proteins have the species-independent BUB3 term (PR:000004856) as a common parent. Given that orthologous BUB1:BUB3 complexes exist in distantly related organisms (humans and yeast) we expect that more examples of this complex will be added to PRO in the future as more of the spindle checkpoint literature is curated. The BUB1:BUB3:MAD1L1 complex, so far observed only in humans, forms at kinetochores during the process of checkpoint activation  and Ontology ID GO:0000776 (kinetochore). Although the kinetochore and centromere are distinct structures, the terms are sometimes used interchangeably in the literature; therefore, we also retrieved human centromere localized proteins by searching for human proteins (Taxon ID 9606) annotated with the GO term GO:0000779 . The searches returned 34 results, including 28 kinetochore-localized protein forms, 5 centromere localized forms, and one term – AURKB (PR:000035358) that is annotated with both kinetochore and centromere localization terms. These terms are annotated with PPI data mined from the literature and from several PPI databases. We downloaded the OBO stanzas and PAF for these proteins from PRO and used the information therein to build a network with Cytoscape ]. BUB1B binding to CENPE may stimulate its auto-phosphorylation activity [see BUB1B/Phos:3 (PR:000035432)].Several spindle checkpoint proteins – BUB1, BUB1B, BUB3, and MAD2 – interact with APC and the checkpoint kinase TTK interacts with DVL2. APC and DVL2, which interact with each other, both participate in spindle assembly [see PRO annotation for APC (PR:000030190) and DVL2 (PR:000035487)]. The significance of these interactions is unclear, but it could reflect a role for APC and DVL2 in checkpoint signaling or a role for the checkpoint proteins in spindle assembly.A protein kinase, PLK1, and a protein phosphatase, PP2A, associate with checkpoint proteins and other kinetochore proteins, positioning them to regulate critical kinetochore substrates. PLK1 interacts with the checkpoint proteins BUB1 and BUB1B as well as SGOL1 and DVL2. PLK1 association with BUB1 depends upon the prior phosphorylation of BUB1  to the corresponding GO term and inclusion of a reference form. Thus, we searched for human proteins (Taxon ID 9606) localized to the kinetochore (Ontology ID GO:0000776) with at least one line of functional annotation that included a modifier (Modifer NOT NULL); to limit the results to phosphorylated proteins, we selected “Phosphorylated forms” from the Quick Links menu. For the reasons described above, we repeated the search substituting GO:0000779  in the Ontology ID field. All eight of the kinetochore/centromere localized proteins appeared in our search results, indicating that all of these proteins had at least one attribute that was affected by phosphorylation. We examined the annotation for each protein and summarized the affected attributes in Table Even though phosphorylation is often used as a mechanism to regulate protein localization, none of the phosphorylated proteins in this group was annotated to indicate increased or decreased localization to the kinetochore/centromere relative the unphosphorylated form. In fact, the unphosphorylated forms of several of these proteins – BUB1B, CDC20, and AURKB – have been shown to localize to kinetochores with similar affinity as the phosphorylated forms see PRO annotation for BUB1B/PhosRes-(PR:000035373), CDC20/PhosRes-(PR:000035369), and AURKB/PhosRes-(PR:000035661). Intriguingly, the kinases for CDC20/Phos:1 (kinase is BUB1), BUB1B/Phos:2 (kinase is PLK1), ATM/Phos:2 (kinase is AURKB), and HHTA1/Phos:2 (kinase is BUB1), are themselves kinetochore/centromere localized proteins  showed an increased ability to recruit other proteins to the kinetochore/centromere relative to their respective unphosphorylated forms. Phosphorylation of Histone H2A on Ser-121 (HHTA1/Phos:2) creates a binding site for SGOL1. Phosphorylation of Histone H3 on Thr-3 (H3T3ph) creates a binding site for BIRC5, which in turn recruits the rest of the CPC . Finally, BUB1B/Phos:3 is required for the kinetochore recruitment of MAD1 and MAD2.Phosphorylation of BUB1B  and AURKB (AURKB/Phos:1) is important for the ability of these proteins to regulate microtubule/kinetochore attachments as the phosphorylated forms show increased participation in attachment of spindle microtubules to kinetochores, metaphase plate congression, and/or chromosome segregation. Formation of stable, bipolar microtubule-kinetochore attachments requires a balance of kinase and phosphatase activity. AURKB destabilizes incorrect attachments by phosphorylating kinetochore components such as NDC80; the phosphatase PP2A counterbalances AURKB activity by dephosphorylating NDC80, thereby stabilizing attachments  shows decreased ubiquitin ligase activity relative to unphosphorylated CDC20, which presumably leads to its increased checkpoint activity. Thus, the spindle checkpoint acts through CDC20 in two independent ways to inhibit the APC/C: through formation of the MCC , which binds and inhibits the APC/C, and by phosphorylation of CDC20, which inhibits its ubiquitin ligase activity. Both AURKB/Phos:1 and ATM/Phos:2 have increased protein kinase activity relative to the unphosphorylated forms, which may be important in their increased ability to participate in the checkpoint response, although this possibility has not been directly tested. BUB1B/Phos:3 may participate in the checkpoint through its recruitment of MAD1L1 and MAD2L1 to kinetochores.The structural framework and features of PRO enable the investigation of many aspects of proteins and complexes, particularly analyses of cross-species relationships and relationships between modified proteins forms and functions. Our spindle checkpoint use case outlines a number of strategies that can be generalized to other cellular processes or pathways of interest.in vivo is usually a combinatorial process where proteins are subject to multiple types of modifications on multiple sites. In this regard, PRO offers a more realistic view of protein modification through its representation of protein forms that carry the combinations of modifications that are observed in vivo. The representation of protein complexes in PRO also takes into account the modification state of the complex components. Moreover, modified forms and complexes in PRO can be individually annotated with functional information, making it possible to discern the contribution of each to a biological process.In this study we showed how the PRO framework could be used to investigate the role of different protein forms that participate in a biological process of interest. We focused on PTM protein forms, as PTM is a central mechanism for the regulation of protein function in cells. Most PTM resources specialize in a single type of modification  and are organized around individual modification sites. However, protein modification  and species-independent terms that share a common domain structure are further grouped under a family level terms. In our analysis, we found that human and yeast BUB1 are 1:1 orthologs and thus share the same species-independent parent terms. However, human BUB1B lies on a separate branch of the PRO hierarchy from its closest yeast relative, MAD3 Figure . Thus, aOften, it is possible to gain insight into the function of proteins in a common pathway by examining their PPIs. PRO facilitates the construction of PPI networks for groups of proteins that are related by some common attribute. Using the built-in PRO search function, it is possible retrieve all PRO terms that share an attribute . The PAF for these terms, which contains PPI information in machine-readable format, can then be downloaded and used to build a PPI network with Cytoscape. Because PRO annotation can show interactions that are dependent on protein modification, PPI networks constructed with PRO have an added dimension that is absent from other PPI network building resources. For example, our PPI network of kinetochore-localized proteins shows that PP2A-B56-alpha interacts specifically with BUB1B/Phos:2 and that PLK1 fails to interact with the unphosphorylated form of BUB1 (Figure As we have shown with this use case, PRO is a valuable tool for the study of a complex biological process. Interoperating with other ontologies and resources, PRO provides a structural framework that organizes current knowledge about protein forms, complexes, and cross-species relationships among proteins. While we focused on the spindle checkpoint, the PRO search, display, and analysis strategies we demonstrated here can be applied to any process. PRO-based analysis is particularly valuable for processes where modified protein forms play a prominent role. While PRO coverage is limited for modified forms, we rely on the user community to help in populating the ontology. The web-based RACE-PRO interface provides one means for the user to contribute to PRO. As PRO grows, it will become an increasingly useful resource that can provide insight into biological processes and stimulate the generation of experimentally testable hypotheses.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.


Deciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary.We integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level.http://pcode.kaist.ac.kr/iddi/.IDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships. IDDI is freely available at  Protein interactions, including binary PPIs and co-complexes, regulate biological process and biochemical reactions. Discovering protein interactions provides detailed interpretation of cellular mechanism of biological functions. Therefore, the identification of protein interaction is a critical issue for biology researchers. Recently, massive amount of protein interaction data is available due to the advancement of large-scale screening techniques such as yeast two-hybrid, affinity purification followed by mass spectrometry. Lots of protein interaction data verified from different experimental methods is publically available. However, although the increased data can give a landscape of the protein interactome, they are not much informative in detailed binding mechanisms and high false positive rate of the data is a big hurdle to interpret the interactome .Investigating protein interactions in domain level can complement these limitations. Proteins consist of one or multiple domains thought as functional units of protein. In most cases, domain-domain interactions (DDIs) are crucial clues of protein interactions. Therefore, DDIs can be key supporting evidences for protein interaction mechanisms.DDIs first have been identified based on 3-dimensional (3D) structures of protein complexes from Protein Data Bank . 3DID 3 and PInSUp to now, two combined DDI databases, DOMINE  and UniDIn this paper, we proposed an integrated analysis system for DDIs and their related protein interactions, called IDDI. We first combined three 3D structure-based DDI datasets and twenty predicted DDI datasets. To estimate the reliability of predicted DDIs, we developed a novel scoring scheme considering the individual accuracy of each datasets, independency among the datasets and the internal prediction scores of the DDIs measured by each method. Total amount of DDIs is increased significantly compared to previous comprehensive DDI databases, and the novel reliability scoring scheme achieved outstanding performance on sorting highly reliable DDIs. Furthermore, we joined our new DDI database with comprehensive PPI database, ComBiCom , and conhttp://www.yeastgenome.org and the InterPro domains to Pfam using a mapping table in InterPro website http://www.ebi.ac.uk/interpro. Although other datasets used Pfam identifier, they are consisted with different versions of Pfam and it is susceptible to display same interactions differently since some domains were changed or eliminated as Pfam is updated. We therefore unified Pfam version of all datasets into the same release of Pfam-A 24.0 version. All domains which are not available at Pfam 24.0 were discarded. All combined DDI datasets with their number of DDIs and domains are listed in Table To construct a new comprehensive DDI database, we merged three 3D structure-based DDI datasets and twenty predicted DDI datasets based on the Pfam identifier. Since the datasets, including P-value, HiMAP, DomainGA and Top-down, use SCOP and InterPro identifier, we converted the SCOP domains to Pfam using SGD Each predicted DDI in our new database is evaluated by a reliability score. We considered three factors that affect reliabilities i) a confidence level of the each predicted dataset, ii) an independency of the dataset and iii) a local prediction score of the DDIs measured by each dataset.Each predicted dataset has different confidence level. Predicted DDIs are more reliable when they were found in more accurate datasets.a and b, the Wo score is defined as:To estimate the confidence levels, we used a weighted overlap method which measured a similarity between two datasets . WeighteIa→b is a subset of a Iwhich interacting domains belong to both dataset a and b and, likewise, Ib→a is a subset of Ib which interacting domains are found in both datasets.where I is a set of DDIs, C of the predicted dataset d is defined as:The GSP set were generated using 3D structure-based DDIs extracted from 3DID, iPfam and PInS. This GSP set contains total 6,768 verified DDIs. With the GSP set, the confidence score Table Figure d that contain DDI i, the independence score ID is defined as:We, therefore, considered how well the datasets that predict the same DDI are independent from each other for estimating reliabilities. For every dataset e is the all datasets that predict i except d. For example, a dataset whose DDI is not overlapped with other datasets will receive an independence score of one.where Local prediction scores of DDIs measured by each predicted dataset are also important key evidence for inferring reliabilities. Although DDIs were found in a same dataset, reliabilities of these DDIs are discrete depending on prediction scores. We scaled different ranges of original prediction score of each dataset from 0 to 1 by using an ordinal scaling method. Six of the datasets including HiMAP, KGIDDI, LLZ, RDFF, P-value and TW don`t provide own prediction scores. DDIs predicted in these datasets receive an average prediction score of the DDIs found in the same number of the datasets.i, a reliability score R is defined as:Using confidence scores, independence scores and prediction scores, we calculated a reliability score for each predicted DDI. For a predicted DDI d is the all datasets that predicted i and d, i Pis a prediction score of i measured by the dataset d.where We constructed a web-based domain-domain interaction analysis interface to provide comprehensive exploration of DDI within protein interaction network. IDDI was constructed on Linux environment and tested for cross-browsing. This system is serviced on tomcat server with Oracle database and web pages were implemented in JAVA and JAVA Server Pages (JSP). Figure IDDI doesn`t include our new integrated DDI database only but also protein interactions from ComBiCom  to graspIDDI provides four kinds of searching services: protein search, domain search, PPI search, and DDI search. This searching system is based on PFAM ID and Uniprot accession number for domain and protein classifier, respectively. PPI relationship was searched from ComBiCom, and protein function information is annotated from Gene Ontology. To provide comprehensive searching system, we need to map proteins with their contained domains and SwissPfam was used to map proteins with their corresponding domains. Using this mapping data, IDDI could provide possible DDIs for protein search or possible PPIs for DDI search.Our new DDI database currently contains 204,705 unique DDIs between 7,351 distinct Pfam domains. Among DDIs, 6,768 interactions were combined from 3D structure-based datasets and 202,914 interactions were extracted from predicted datasets. It is superior to currently available comprehensive DDI databases such as DOMINE and UniDomInt in a number of both interactions and domains (Figure Unlike DOMINE, both IDDI and UniDomInt have linear scoring schemes. Although they considered the confidence of the predicted datasets in common, IDDI reflect the independency of the datasets and the prediction scores of the DDIs additionally. We tried to evaluate the performance of datasets and scoring schemes used in IDDI and UniDomInt with ROC curves Figure . For theFigure Figure We tested the average accuracy for reliability score cut-off in IDDI. The result reveals that the cut-off of 0.329 has the highest accuracy, 0.98. For reference, cut-off value that shows 0.90 of accuracy was 0.102 and 21027 DDIs were included within the cut-off value. End user can determine the cut-off value for research purpose and those DDIs which have cut-off value for high accuracy may show more reliable results.We tried to compare the PPI coverage rate of 3D structure-based DDIs, DOMINE, UniDomInt and IDDI by using binary PPIs in ComBiCom. We defined that the PPI is covered when at least one DDI are found between interacting proteins.Table IDDI was constructed to provide comprehensive search on protein or domain to give an insight on detailed interaction. We lay emphasis on easy information access among related proteins, domains, and complexes. The system includes protein, domain, PPI and DDI search system (see Figure IDDI provides comprehensive searching service to explore the relationship of proteins and domains. It can be used for gene selection for study by prioritization of list of proteins with using filtering function. In this section, we provide an example of p53 interacting target analysis. Figure We proposed a new unified interaction analysis system, IDDI, which enables the comprehensive analysis of protein and domain interactions with their interconnectivity. Large increase of total DDIs enables high interconnectivity of DDIs and PPIs and an advanced scoring scheme enhances the reliability of integrated DDIs in a substantial amount. Furthermore, IDDI provides a convenient interface to investigate the protein interaction with detail domain interaction. IDDI will be a valuable resource for the in-depth study of interaction mechanism and thereby to derive the functional implication of interacting proteins.The authors declare that they have no competing interests.YK integrated all DDI datasets, analyzed the datasets. BM constructed the web-based analysis system. GSY conceived and supervised this study. YK, BM and GSY wrote the manuscript. All authors read and approved the final manuscript.


Protein structure comparison is a fundamental task in structural biology. While the number of known protein structures has grown rapidly over the last decade, searching a large database of protein structures is still relatively slow using existing methods. There is a need for new techniques which can rapidly compare protein structures, whilst maintaining high matching accuracy.tableau representation to compare protein tertiary structures. IR tableau compares tableaux using information retrieval style feature indexing techniques. Experimental analysis on the ASTRAL SCOP protein structural domain database demonstrates that IR Tableau achieves two orders of magnitude speedup over the search times of existing methods, while producing search results of comparable accuracy.We have developed IR Tableau, a fast protein comparison algorithm, which leverages the We show that it is possible to obtain very significant speedups for the protein structure comparison problem, by employing an information retrieval style approach for indexing proteins. The comparison accuracy achieved is also strong, thus opening the way for large scale processing of very large protein structure databases. Cα - Cα distance profiles of a fixed range of residues. SARST [Protein structure comparison is crucial for understanding protein evolution, architecture and function . The Pros. SARST -10 utilis. SARST  and SHEBs. SARST  also coms. SARST -22. ProSs. SARST  and Tabls. SARST  both trys. SARST ,14,23 ustableau as a concise representation of protein folding patterns. The tableau encodes the geometry of interactions between pairs of secondary structural elements that are in contact [Lesk  proposed contact ,25. Kona contact  proposed contact  recentlyfeatures, allowing the application of several well-known similarity measures to efficiently compare these feature vectors. IR Tableau achieves excellent search efficiency , while providing accuracy comparable to the existing methods.Most of the existing protein comparison techniques share a major limitation. They are computationally expensive, requiring hours or even days to search a large protein structure database. This has motivated us to develop a new and rapid protein comparison algorithm, IR Tableau, based on feature indexing techniques from Information Retrieval (IR). Our method transforms the robust tableau representation of a protein fold into a vector of This section describes our IR Tableau method, which utilizes IR techniques to index and search protein structures efficiently.Briefly, tableau encodes the geometry of pairs of secondary structural elements (SSEs) - that is, helices and strands of sheet . The relThere are 8 possible combinations of two characters. For example, Table A typical IR system aims to retrieve documents that are relevant to keywords (terms) in a user-query. Each document is represented as a vector of weights, where each weight denotes the importance of a given term. Terms are usually the words used by a document and each weight may correspond to the frequency of occurrence of some terms in the document. The collection of all term weights for a document effectively describes the contents of that document. This is known as the 'bag-of-words' model. Different documents can then be compared by comparing their weight vectors. If they use similar weights for each term, then they are likely to be related. Since only vector comparison is used, similarity matching of vectors can be performed extremely fast.In our protein context, we analogously translate each protein tableau into a vector of weights, where each weight describes the importance of some feature of the protein. Protein structure comparison is then performed by similarity matching of protein vectors. We next describe our technique for creating the vector for a protein structure.β 1 and SSE β 2. To turn this tableau into a 1D vector, we summarise the distribution of angle frequencies for each possible pair of SSE types.We generate a vector of features for a given protein based on its tableau representation. So effectively, we translate a two dimensional (2D) tableau into a one dimensional 1D) vector. Each cell in a tableau describes the angle between a pair of SSEs in the protein. For example, in Table D vector.αα, αβ, βα and ββ . Hence each protein can thus be described by 4 × 8 = 32 features.Each feature of our vector will describe a pair of SSE types in one of eight possible orientations: PE, PD, RD, RT, OT, OS, LS, and LE. The value of each feature corresponds to the frequency at which that configuration occurs in the protein. There are also four possible pairs of SSE types: ββ OT in the tableau of 1UBI, which appear at <row 1, column 2> and <row 4, column 6> in the matrix. Therefore the value for the feature ββ OT is 2. The full feature vector for this protein is given in Table Again, in Table Choice of an appropriate similarity function is important for accurate comparison. In IR Tableau, there are a number of possible similarity functions which can be applied for comparing the protein vectors.Cosine similarity [N dimensional space. A higher score implies a smaller angle between the two vectors. If the value is 1, it means that the two vectors have the same direction.milarity  simply cJaccard index [The rd index  is anothA and B are sets.where Tanimoto coefficient [The fficient  is a genEuclidean distance is another means of measuring similarity of proteins. Unlike the similarity functions described above, the value for Euclidean distance is not normalized to be between 0 and 1.ai is the ith element of vector A, bi is the ith element of vector B.where Unless stated otherwise, our results in the rest of the paper assume the use of the cosine similarity function.base method), we have also explored the value of associating further, additional features with each protein vector. In general, there is a trade-off between adding extra features which can help discriminate between classes of proteins, versus adding too many features which overwhelm accurate similarity calculation.In addition to the method we have described for generating 32 features for each protein . In this triplet approach, there there are 23 = 8 SSE types and 83 = 512 angles, giving a space of 8 × 512 = 4096 possible features. This idea can be further extended to the use quadruplets, quintuplets and so on of SSEs to generate a larger feature space. However, as we can clearly see, the size of the protein vector grows exponentially with the increase in the number of SSEs in each combination. Another possibility is to disregard the ordering information between SSEs, which may be useful for non-linear matching of sub-structures. In this case, there are only three possible orderings, between two SSEs, rather than four: all αα, ββ and αβ . A final possibility is to only consider consecutive SSEs for generating features from a tableau [In our base method, ordering information was only used for pairs of SSE types. This description loses some information about the position of each SSE. By instead preserving positional information about SSEs, we can hope to build a more accurate profile in each protein vector. Incorporating such relationships may be carried out as described in the following example. Protein 1UBI, whose tableau is shown in Table  tableau . This caN SSEs. The sequence of SSEs can be partitioned into two halves along the chain. All the SSEs in the first-half part will be given a position marker P1. SSEs in the second-half will be marked as P2. Then, when comparing each pair of SSEs, position markers can be used to provide additional position information. In protein 1UBI, the first SSE compared to the last SSE will be β P1 β P2 OT. The number of features generated by using this strategy will then be 4 SSE types ×8 angles ×22 positions = 128 features. If a protein SSE sequence is partitioned into n parts, the number of features will be 4 SSE types × 8 angles × n2positions.Using the exact order of SSEs as the basis for forming a protein vector can cause the vector to be very large, for complex combinations. To handle this, we have investigated a strategy which uses approximate positions for each SSE, rather than exact positions. Suppose we have a protein with DatasetsFor our experimental evaluation, we use:1. the entire ASTRAL 1.73  protein 2. the ASTRAL 1.73 95% sequence-identity non-redundant data set. We generate our index database from the tableau data set published by Stivala et al.  containiWe also use the query data set of Stivala et al.'s  containiAll experiments were conducted using a Intel Core 2 Duo 2.4 GHz processor running Ubuntu 9.10 Linux system. IR Tableau was implemented in Java. SCOP  fold claPq which belongs to the SCOP fold Fq, let us consider the top k proteins returned by the search as hits and the remainder as misses. For an ith protein Fq and i ≤ k then the protein true positive (TP). On the other hand, if Fq and i ≤ k then false positive (FP). If Fq and i >k then true negative (TN). Otherwise, false negative (FN). Using the above statistics, we can then compute the true positive rate , false positive rate (FPR) and positive predictive value (PPV or precision) using the following formulae:Given a query protein TPRk, FRPk and PPVk are calculated for all 1 ≤ k ≤ n, where n is the size of the data set. The ROC defines a curve of points with FPRk as the abscissa and TPRk as the ordinate. Precision-Recall defines a curve with TPRk and PPVk as abscissa and ordinate respectively. For a good search system, the ROC curve is closer to the top-left corner and the Precision-Recall is closer to the top-right corner of the plot.Using the above formulae K positive results is computed by varying k over all possible values. The mean of these precisions gives the MAP.The area under the ROC curve (AUC) is a single-figure measurement for the quality of an ROC curve. The averaged AUC over our experiment with queries can thus be used to evaluate the method's performance. Mean average precision (MAP) is another useful single-figure accuracy measurement. Here, the average precision over the top In this section we first compare our IR Tableau against several popular methods for protein structure comparison. ProSMoS, OPAAS, SARST and some other web-server based programs are not tested, as results are not comparable. Later, we assess the sensitivity and accuracy of IR Tableau using different types of features defined in this work.Table Comparing the running times in Table More elaborately, on the full ASTRAL 1.73 data set, IR Tableau takes 0.627 seconds per each query. (We note that the comparison time is constant for each pairwise comparison between the query and a database protein.) On the other hand, TableauSearch takes a variable amount of time to complete a query depending on the size of the query. For example it takes 13.38 seconds (CPU time) to search the whole database using SCOP domain d1ubia, a protein which contains only 6 SSEs. But searching on the SCOP domain d1f6dc_ containing 26 SSEs takes 118 seconds. For the same query of d1ubia_, Yakusa takes 26 minutes to search the full ASTRAL 1.73 data set.In Table Yakusa has the highest TPR when the FPR is less than 0.35. After this point, IR Tableau becomes slightly better than the other two. TableauSearch is always worse than Yakusa, but better than IR Tableau when the TPR less than 0.3. So in terms of ROC performance, IR Tableau is as good as Yakusa, but over three hundreds times faster.The Precision-Recall (PR) curves for IR Tableau, TableauSearch and Yakusa are shown in Figure k results when k is small. In this circumstance, a small number of positive and a large number of negative results are returned. Therefore a difference in the absolute number of FPs only results in a small change in FPR (as seen in the ROC curves). On the other hand, the same difference in FP results in a large change of precision (as seen in PR curves). In other words, for small k, Yakusa and TableauSearch have an advantage in accuracy over IR Tableau, but as k becomes larger, all three are very similar.The difference between the behaviour in ROC-space compared to PR-space can be explained based on the imbalance between the classes formed from the top-This suggests that IR Tableau may be very useful to use as a hybrid technique in conjunction with one of these more computationally expensive algorithms. Under this strategy, one would first search the protein database using IR Tableau to return a relatively large set of matches and then pass these results to a second algorithm for deeper, more computationally demanding analysis and reranking of matches.β-grasp protein, the AUC of IR Tableau is 0.978 compared with 0.89 in QP tableau, 0.906 in TableauSearch and 0.887 in Yakusa. This is also better than the version of QP tableau with added SSE distance information, which is 0.95 [We also conducted experiments on searching for commonly occurring protein folds. For SCOP domain d1ae6h1, a  is 0.95 . The ROCk , but becomes comparatively better for higher k . The mean average precision is 0.775 for IR Tableau, 0.777 for TableauSearch and 0.703 for Yakusa.IR Tableau achieves a superior TPR across almost all the regions in the ROC curve. For the precision-recall curve, the performance of IR Tableau is comparatively not as good for low In some cases, search results with higher scores are more important than ones with lower scores. Superposition of these returned protein structures is then a very good demonstration of the quality of the top ranked proteins. For protein d1ubia_, the graph in Figure Table Results of IR Tableau when partitioning proteins into N parts are shown in Table So it is therefore natural that partitioning the SSE chain into 5 to 7 parts works well, since it means that we are effectively trying to match the position of each SSE exactly.The behaviour for different similarity functions is shown in Table We have introduced IR Tableau, a new algorithm for protein structure comparison. A key advantage is that it is highly scalable, being faster than existing methods, by over a factor of 100. This speed up factor also increases for longer proteins. Moreover, it is able to achieve good quality of search results, obtaining comparable AUC scores to existing algorithms and slightly lower MAP scores. Highly efficient search algorithms will be very important for protein structure databases of the future, which may contain millions of proteins. We believe that our IR Tableau approach is very promising for such a scenario. In particular, it may be used as part of a hybrid filter approach. The user can run IR Tableau for a high throughput scan of the database for approximate matches. The search results would then be passed to a second algorithm, for deeper  comparative analysis. Conducting experiments along these lines is an interesting avenue for future work.The authors declare that they have no competing interests.All authors contributed to this paper equally.


A good scoring function is essential for molecular docking computations. In conventional scoring functions, energy terms modeling pairwise interactions are cumulatively summed, and the best docking solution is selected. Here, we propose to transform protein-ligand interactions into three-dimensional geometric networks, from which recurring network substructures, or network motifs, are selected and used to provide probability-ranked interaction templates with which to score docking solutions.MotifScore, was developed. It is non-energy-based, and docking is, instead, scored by counting the occurrences of motifs of protein-ligand interaction networks constructed using structures of protein-ligand complexes. MotifScore has been tested on a benchmark set established by others to assess its ability to identify near-native complex conformations among a set of decoys. In this benchmark test, 84% of the highest-scored docking conformations had root-mean-square deviations (rmsds) below 2.0 Å from the native conformation, which is comparable with the best of several energy-based docking scoring functions. Many of the top motifs, which comprise a multitude of chemical groups that interact simultaneously and make a highly significant contribution to MotifScore, capture recurrent interacting patterns beyond pairwise interactions.A novel scoring function for protein-ligand docking, MotifScore is quite different from conventional energy-based functions. MotifScore thus represents a new, network-based approach for exploring problems associated with molecular docking.While providing quite good docking scores,  Drug design and discovery play a pivotal role in driving research in computational chemistry and biology -6. In coScoring functions for molecular docking are traditionally either physics-based or knowledge-based ,9 and diA common thread in these scoring functions is that, with few exceptions, they all operate on the assumption that the total interaction will be faithfully represented by an additive summation over a series of pairwise interactions between interaction centers, which are either real  or imaginary (such as the geometric center of a group of atoms). Despite fundamental fallacies in this assumption, it has been adopted for many years, as otherwise the problem would be intractable or too computationally expensive to handle practically ,22.MotifScore, and the evaluation of its ability to distinguish between good and bad docking solutions. Our results using a benchmark dataset showed that MotifScore performed well against a number of scoring functions used in existing docking programs.In this study, we investigated a new idea for developing a scoring function for molecular docking. The novelty of the idea lies in the use of a network approach to identify frequently occurring patterns of protein-ligand interactions not in the form of pairwise interactions, but in the form of network motifs, where "network" refers to a collection of pairwise interactions. The motifs, which are assemblies of multiple pairwise interactions between proteins and ligands that are frequently observed in a database of known protein-ligand complex structures, thus represent energetically favorable ways of positioning a ligand molecule in the active site of a protein. Such a motif-based approach offers a new line of exploration for research on molecular docking. As a first step in this line of research, we present the construction of a motif-based scoring function, called In constructing the protein-ligand interaction network, the three-dimensional (3D) coordinates of a set of protein-ligand complexes were each transformed into an atom-atom interaction network. There are two types of nodes in the network, namely atoms (interacting centers) from the protein and the ligand. Two nodes (one from the protein and the other from the ligand) are connected by an edge if their interaction is deemed significant by a distance threshold determined from a statistical analysis of a dataset of protein-ligand complexes (see below). Interactions between atoms within the protein or within the ligand are not considered. These networks were then broken down into many interaction motifs representing simple units of the network with specific protein-ligand interaction patterns.To establish distance thresholds for constructing the protein-ligand interaction network, we performed a large-scale statistical analysis on a diverse set of known structures of protein-ligand complexes. The available protein-ligand complex structures as of January 10, 2006 in the Protein Data Bank (PDB)  were scrThis set of PDB protein-ligand complex structures is not entirely non-redundant since a single protein or its mutants may be co-crystallized with many similar compounds, and, conversely, the same compound may be co-crystallized with multiple homologous proteins. However, unless both the protein and the ligand are completely identical, part of the resulting interaction networks can still be distinct, and as defining non-redundant networks is not a straightforward task, we chose to include as many network connectivities (i.e. protein-ligand interactions) as possible while conducting normalizations to minimize the potential bias of using a redundant dataset. Furthermore, analysis on a reduced set  by excluding those that share >25% protein sequence identity and identical ligand showed that the resulting network motifs and their grades (defined below) had a Pearson's correlation coefficient of 0.95 with those of the set of 6,276 structures (data not shown), suggesting that including homologous proteins and identical ligand compounds would not significantly affect the outcome of our analysis.MotifScore. There are more than 200 complexes, each with a set of various numbers of decoys, in the LPDB, but only 113 of these were used in this work because we found that the ligand names, atom names, and the ordering of atoms of many of the decoys in LPDB were not the same as in the original data files in the PDB, making it difficult for us to correctly assign their designated atom types (see below). For testing and evaluating MotifScore, the data set of Wang et al. . Some low frequency triangles appear to be concentrated at ligand-binding sites. For example, the NRE-NRE-NRE triangles, which are triangles that connect 2 or 3 different histidines (there were few NREs from tryptophan in our motifs), were not commonly observed, but, when they were, they tended to be at ligand-binding sites (their b Fwas 17.3). The uneven distribution of b F; the correlation between the two was significant, with the Spearman correlation coefficient being 0.78.The binding site enrichment factor ribution , y~x-γ of AutoDock among the various scoring functions evaluated by Wang et al. [MotifScore could easily distinguish reasonably good docking solutions (rmsd < 2 Å) from bad ones. Intriguingly, there appears to be a very narrow funnel leading to the native state formed by very good docking solutions. This is quite distinct from that of a conventional energy-based scoring function where the funnel leading to the energy minimum, which should be reasonably close to the native state, is usually much smoother [MotifScore would not be ideal. On the other hand, the protein-ligand interaction motifs derived in this work, in their three dimensional arrangements of interacting atoms, capture the spatial arrangements of reasonably good docking solutions, so it is quite possible to develop a scheme to look up the table of interaction network motifs . Wor. WorMotiMotifScore is a novel interaction-motif-based scoring function for protein-ligand docking. Despite the absence of mathematical models to mimic the force field of molecular interactions, MotifScore performed well in distinguishing between good and bad docking solutions in a benchmark test set. Furthermore, owing to the network approach, MotifScore is intrinsically more able than conventional docking scoring functions to capture interactions involving more than two interacting sites, the π-π stacking of two aromatic rings being a prime example. The ligand-binding site-enriched interaction motifs identified are in accord with existing knowledge on protein-ligand binding and may prove useful for binding site predictions. Finally, the three-dimensional protein-ligand interacting motifs could provide very good templates for placing ligand molecules in fast, though coarse, protein docking computations.MotifScore is available for download at: https://sourceforge.net/projects/msdock/. The package contains a set of Perl scripts for computing the scoring function and for creating a Perl database file that stores the names of ligands and their atom types. It also offers some demonstration examples of how to obtain the final docking scores. These scripts work on a Unix or Linux platform and their download is free for academic users.The source code of ZRX carried out the research and drafted the manuscript. MJH conceived the project, guided the research, and revised the manuscript. Both authors read and approved the final manuscript.Supplementary data for details of motif related parameters. This file contains 2 tables and 3 figures. Table S1 lists the lower and upper distance thresholds for each of the 280 atom type pairs. Table S2 lists the binding site enrichment factor, Fb, of each of the 455 non-metal protein atom type triangles. Figs. S1 and S2 display the optimization results of two parameters, Penalty weight W and the raising percentage of lower thresholds, on the training dataset LPDB. Fig. S3 shows a schematic presentation for some of the atom types defined in Table Click here for file