Drosophila melanogaster based on insight into domain interactions.Proteins control and mediate many biological activities of cells by interacting with other protein partners. This work presents a statistical model to predict protein interaction networks of Three high-throughput yeast two-hybrid experiments and the collection in FlyBase were used as our starting datasets. The co-occurrences of domains in these interactive events are converted into a probability score of domain-domain interaction. These scores are used to infer putative interaction among all available open reading frames (ORFs) of fruit fly. Additionally, the likelihood function is used to estimate all potential protein-protein interactions.All parameters are successfully iterated and MLE is obtained for each pair of domains. Additionally, the maximized likelihood reaches its converged criteria and maintains the probability stable. The hybrid model achieves a high specificity with a loss of sensitivity, suggesting that the model may possess major features of protein-protein interactions. Several putative interactions predicted by the proposed hybrid model are supported by literatures, while experimental data with a low probability score indicate an uncertain reliability and require further proof of interaction..Fly-DPI is the online database used to present this work. It is an integrated proteomics tool with comprehensive protein annotation information from major databases as well as an effective means of predicting protein-protein interactions. As a novel search strategy, the ping-pong search is a naïve path map between two chosen proteins based on pre-computed shortest paths. Adopting effective filtering strategies will facilitate researchers in depicting the bird's eye view of the network of interest. Fly-DPI can be accessed at This work provides two reference systems, statistical and biological, to evaluate the reliability of protein interaction. First, the hybrid model statistically estimates both experimental and predicted protein interaction relationships. Second, the biological information for filtering and annotation itself is a strong indicator for the reliability of protein-protein interaction. The space-temporal or stage-specific expression patterns of genes are also critical for identifying proteins involved in a particular situation. In most cases, proteins are the way that genes exert their function. These macromolecules mediate their functions by forming complicated and interconnected networks that are flexible and dynamic. For instance, more than 200 cell types are identified in the human body. These cells use the same genome content, but different scenarios for their performance. In another case, living organisms have developed various survival tactics protein interactions against nearly all kinds of stresses to persist and to flourish in a changing world. Clarifying the protein-protein interaction network is essential to understanding cellular processes, explaining its prominence as a major field in the post-genomic era. Elucidating protein interacting partnerships may help annotate unknown proteins and provide further insight into biological networks.et al. [Various experimental strategies are available for identifying protein interactions . Among wet al.  predict The protein-protein interaction network is naturally complex. Visualization tools are the most effective means of obtaining a global view of a protein network. Several analytical approaches and visualization systems can depict the interaction map. BIND  incorporDrosophila melanogaster (the fruit fly) has long been a highly useful model organism. By taking advantage of the large body of publicly available biological information, this study constructed an on-line database, Fly-DPI, to present the protein-protein interaction network of flies. Like previously published computational works on protein-protein interaction predictions [dictions ,21-26, odictions , KEGG [2dictions , GenBankdictions , and Unidictions  provide D. melanogaster at specific spatiotemporal stages, composed of experimental and inferred interaction data. The arrangement of nodes and edges in an interacting network is optimized to reduce overlaps and crossover. Patterns and colors of connecting edges are used to indicate various strengths of interactions based on the association measures and different centering node they originate from. Starting from a query result table, proteins of interest can be explored through its interacting partners in the map, and the interaction neighborhood can be extended up to three levels. A new approach, ping-pong search, opens up new avenues for easily acquiring additional putative path maps as segments of a biological pathway. In summary, the Fly-DPI is a system for predicting protein-protein interactions based on known protein-protein interaction data, integrated with complete and useful protein annotation information from major databases. Fly-DPI is designed to explore in-depth potential protein interaction relationships, and provides reliability estimates for protein-protein interactions for both experimental and inferred interaction, which can be used to build a protein interaction network and predict novel interactions. This system offers an important method of studying fly and other species to expand our knowledge in biological and medical studies.Fly-DPI serves as a user-friendly interface integrated with graphical networking maps based on our previous work . With itinteractomes for D. melanogaster (fly-DPI) was constructed with the LAMP system  and the GD library was used for dynamic image creation.To introduce a user-friendly graphical interface, a database of protein et al. [et al. [et al. [The general search interface of Fly-DPI allows for a search by ORF name, gene loci, RNA transcripts, FlyBase gene index, GO IDs, KEGG metabolism, EC number, or full-text searches with keywords. Two sets of data pools can be searched. The first is the high-confidence network based on the interactions defined by Giot et al. , Stanyon [et al. , and For [et al. ; putativThe output table of a query is returned to a new window based on the parameters set in the query form. By selecting an appropriate probability threshold, the interaction map of the selected protein is immediately generated in another new window, or by selecting "exp" to view interactions in the original experimental datasets only. A higher threshold will show those higher confident interactions in the map based on our statistical model. Connecting edge (interaction) color and pattern are used to track back to the starting node (protein) of these interactions when exploring the network and the strength of the interaction using the estimated statistical model. For example, a blue line indicates that the interaction exists in yeast two-hybrid experiments, while other colored lines are based on the node from which these edges have been sent out in previous action. Additionally, a solid line indicates the probability that the interaction approximately equals one, while a dotted line indicates that the interaction has a probability score below 1. In the example case , the ping-pong network will recalculate in the background and provides an e-mail notification to users upon their completion. Following the link provided in the email, users can access their query network for one month after the e-mail is sent.Fly-DPI provides a innovative search for finding possible connections between two proteins, the lly Fig. . The annet al. [The hybrid model presented here uses legitimate initial association measures to perform heuristic computations for the MLE. A key achievement of the computation with this model is the successful iteration of all parameters  and the fact that the obtaining of the MLE for each pair of domains helped the maximized likelihood to reach its converged criteria and maintained the stability of the probability. The proposed method is demonstrated to outperform those methods based on non-informative priors. One model assumption is treating the domain-domain interaction as an independent event. Although most domain-based methods for predicting interaction between proteins also presume that individual domain-domain interactions are independent of the formation of other domain-domain interactions, Han et al.  proposedD. melanogaster protein interactome. To testify the robustness of the proposed model, the dataset is randomly divided into training and test sets using a ratio of 9:1. To apply Jackknife method to validate the proposed approach, the test set is re-sampled for each iteration of modeling and the average sensitivity and specificity calculated. The probabilities of domain-domain interaction Pr(Dmn = 1) are dichotomized according to different thresholds from 0 to 1, as a predictor of protein-protein interactions. The figure showing the supplemental data S1 in A hybrid model is applied to the experimental data  available from The specificity is higher than for the previous maximum likelihood approach ,12. HoweComparing the putative interactions inferred from the high confidence set with those interactions only presented in the literature found that several putative interactions predicted by our hybrid model (Pr = 1) are supported by interaction collected in BIND. The interaction network of Gro protein is used here as an example. As shown in Fig. We also compared our predictions with other Y2H data as well as small-scaled protein-protein interaction data presented in literatures (collected by BIND) using different lab technologies, including immunoprecipitation, protein 3D structure and affinity-chromatography. The number of Y2H interactions collected in BIND is 23,088. There are only 591 interactions not included in our original dataset. As expectancy, flyDPI hits more interactions than others when we compared the overlaps amid our predictions and other experimental methods with those interactions in Y2H from BIND. Meanwhile, the overlaps between specific experimental method and flyDPI are more than those ones between specific experiment method and Y2H from BIND , GenBank, InterPro, FlyBase, KEGG, and Unigene. This biological information is useful for assigning possible functions to an unknown protein. For example, if a protein is identified as interacting with proteins involved in a particular pathway, this strongly indicates that it plays a role in that particular pathway, or may even perform the same biological functions with its partners. The EST expression data from Unigene provides insights regarding the location and the time span expressed by a gene, which can then be used to identify the protein it encodes. Proteins that are not expressed simultaneously have little chance of interacting with each other. Consequently, information on gene expression itself serves as a strong biological filter for the reliability of protein-protein interaction. The space-temporal or stage-specific expression patterns of genes are also important keys for identifying proteins involved in particular situations. The use of adequate filtering strategies can help researchers to obtain a bird's eye view of the network of interest.The identification of potential pathways is an important objective in systems biology. The innovative strategy of ping-pong search is a naïve path map retrieval tool for fly interactome based on the shortest paths between any two proteins collected in Fly-DPI. This method localizes putative pathways including both proteins and provides further insight regarding their interaction with their partners. For instance, if two membrane receptors are chosen as the query seeds, the common signaling effector of the two signal pathways may be identified as one node in the searching result. The smart interactive interface can enable users to identify the paths between any two target proteins with options to refine the paths between them. This study also analyzes the network properties of fly interactome. This investigation found that the average shortest path lengths between any two proteins are 3.78  and 4.06 (interactions with high confidence), respectively  and reduce wastage of laboratory resources .First, to deal with the false-positives resulting from high-throughput Y2H experiments, the Fly-DPI offers two referencing systems, statistical and biological, to assess data reliability. The hybrid model provides statistical estimation of the probability of putative protein interaction relationships from the domain-domain interactions decomposed from the experimental data. The biological annotation helps researchers assign functions to unknown proteins based on their interacting partners. Furthermore, the ping-pong search tool provides a naïve path map between two chosen proteins via pre-computed shortest paths from the Fly-DPI. The ping-pong search behaves more like an intuitive graphic tool to help discover potential paths within a pathway or in the convergence of different pathways. Fly-DPI is maintained as an updated interactome database by routinely renewing the annotation databases and incorporating newly published interactions into the system. Other sophisticated and advanced statistical models, such a weighting system, are evaluated to improve prediction accuracy and provide a rewired database integrated with experimental and predicted interactome within a systems biology perspective.D. melanogaster proteomics used in this study were obtained from three recently published high-throughput yeast two-hybrid experiments [Protein-protein interaction data of eriments ,36,37 aneriments . The totD. melanogaster as described below.Domains are recognized as functional blocks of compact protein structures, which are frequently lineated in a cassette-like fashion. They are usually evolutionarily conserved and contribute to versatile functions of a protein . TherefoAnnotations for each protein and domain in the Fly-DPI were obtained from GO , FlyBase (release 4.2), Unigene (build 38), UniPort Knowledgebase  , Integr8Pi, Pj) interact (Pij = 1) means at least one pair of their domains interacts to each other. The probability of each pair of domains having an interaction, Prob(Dmn = 1), is then estimated, where Dmn = 1 if domain m (Dm) interacts with domain n (Dn) while Dmn = 0 if these two domains does not interacts with each other.The statistical model of the protein interaction network is built by extracting the chance of the co-current of two domains in an interaction from the protein interaction dataset. Two proteins  have interaction (Pij = 1) if at least one pair of their domains interacts; (2) any pair of domains having interaction is presumed to be an independent event from others. The first assumption is obvious under current knowledge to protein-protein interactions. The second assumption may be obscure if several domains are found to have similar occurrence patterns from the same protein-protein interaction data. However, it is biologically reasonable to assume the independence of domain interaction.Our Hybrid Model assumes that (1) two proteins  = Pr(Pij = 1)(1 - fn) + (1 - Pr(Pij = 1))fpPr(n is near 0.80 and fp is estimated to be <3.6 × 10–4 [Pij = 1) in the likelihood function. The maximum likelihood estimates are obtained to maximize the likelihood function L. However, the high dimensionality of the parameters introduces the computational difficulty. In addition, a calculation starting from different initial values of Dmn may encounter the local maximal peak value which could be far from a good estimate. It is crucial to choose adequate initial values to ensure our result is biological relevance. Therefore, we applied association measure [Based on the previous study, f6 × 10–4 . A gener6 × 10–4  is utili measure  as the iThe association measure is the ratio between the frequency of a domain pair found in the observed protein-protein interactions and that of a whole network. A high association measure indicates the domain pair occurs in many interacting protein pairs. The explicit form of the association measure is given asImn: the number of interacting protein pairs containing the domain pair .Nmn: the total number of protein pairs containing Hybrid model provides an estimated probability of each domain pair to interact. By selecting an appropriate threshold, all estimated probabilities are dichotomized as predictors of protein interactions. A higher threshold will restrict the prediction with higher confidence of the predicted protein interactions.Specificity and Sensitivity. Sensitivity is calculated by dividing the number of true positives (TP) through the number of all positives, which equals the sum of the true positives and the false negatives (FN); specificity is calculated by dividing the number of true negatives (TN) through the number of all negatives, which equals the sum of the true negatives and the false positives (FP). Because of the interdependency of parameters, the complexity in searching for global optimization increases the computational difficulty. There is no complete computation reported in related publications yet, while only stepwise interactions have been approached.The accuracy of the prediction is estimated by the All authors have contributed together towards this goal. CL conceived of this study, integrated all the information, sketched the succinct web design and drafted the manuscript. SC participated in manuscript preparation, discussions and provided many useful suggestions for web interface and presentation. PC carried out a major part of the work including developing of statistical model and manuscript preparation. CC (Chi-Shiang Cho) devoted a lot of time in implement whole infrastructure including the design on schema of database, web GUI for dynamic network, robustness and security of Linux -Apache- Mysql -PHP platform. FL helped in revising the website, updating of database with annotations and implement of hybrid model for prediction. CL (Chieh -Hwa Lin) and CC (Chia-Ling Chen) helped in data collections from various sources, compared the predictions with experimental datasets and update of databases. CL (Chen-Zen Lo) participated in processing information from EST, KEGG and GO into structural way for web presentation and biologic filters. CAH supervised and directed the development process of the whole project and guided in statistical analysis, conforming to ethical principles, critical examination and manuscript preparation. All authors have read and approved the final manuscript.To apply Jack-knife method in the validation, in each time test set is re-sampled and the average sensitivity and specificity are calculated. The ratio between the size of the training set and test set is set to be 9:1.Click here for fileComparison with all dataset existed in BIND with Fly-DPI .Click here for fileComparison with all dataset existed in BIND with Fly-DPI (high confidence).Click here for fileDistribution of the shortest path between pairs of proteins in Fly-DPI. On average, any two proteins in the network are connected via 3.78  and 4.06 (interactions with high confidence) in our data, respectively.Click here for file


As originally outlined by Jonathan Eisen [Phylogenomic inference of protein (or gene) function attempts to address the question, an Eisen –3, phyloan Eisen . One of an Eisen , based oan Eisen ,6. Our ean Eisen —and the In practice, phylogenomic inference of gene function is not often used. Far from it. The majority of novel sequences are assigned a putative function through the use of annotation transfer from the top hits in a database search. In our analysis of over 300,000 proteins in the UniProt database, only 3% of proteins with informative annotations  had experimental support for their annotations; 97% were annotated using electronic evidence alone. These annotations are uploaded to GenBank, where they persist even if they are eventually determined to be in error.The systematic errors associated with this annotation protocol have been pointed out by numerous investigators over the years –10. The Gene duplication. This enables protein superfamilies to innovate novel functions on the same structural template, so that the top database hit may have a function distinct from the query.Domain shuffling. Domain fusion and fission events add an additional layer of complexity, as a query and database hit may share only a local region of homology and thus have entirely different molecular functions and structures.Propagation of existing errors in database annotations. This is particularly pernicious, as existing annotation errors are seldom detected and, even if detected, are not necessarily corrected.Evolutionary distance. Two proteins can share a common ancestor and domain structure, yet have very different functions simply due to their presence in very divergently related species.Phylogenomic analysis, properly applied, avoids these errors and provides a mechanism for detecting existing database annotation errors ,7. Why tThis may be about to change. A variety of software tools and algorithms enabling phylogenomic inference have been developed in recent years see . Some ofIt is worth examining the assumptions underlying these phylogenomic resources, and phylogenomic inference as a whole.Phylogenomic inference is based on a fundamental assumption: the phylogenetic tree topology used as the basis of functional inference is correct. This assumption must be questioned, particularly when highly divergent sequences  are included in a tree.Protein superfamilies provide distinct challenges to phylogenetic reconstruction. Following gene duplication, proteins can undergo significant structural and functional changes associated with neofunctionalization, resulting in loop regions and other parts of protein structures not being strictly homologous across all members of a multigene family see . Even amAssessing the expected accuracy of phylogenetic methods for protein superfamily reconstruction is a challenge in itself. Unlike phylogenetic reconstruction of species trees, where fossil evidence exists to help investigators assess tree accuracy, we have no fossil record for protein superfamilies. Simulation studies have tended to assume models of molecular evolution that are appropriate to single orthologous DNA sequences , but do An additional complication in phylogenetic reconstruction of protein families is the almost universal dependence on an accurate MSA as input. Studies of alignment accuracy for pairs of proteins at different levels of evolutionary and structural divergence show dramatic increases in alignment errors with sequence divergence . SeveralAnother barrier to the use of phylogenomic inference methods is their computational complexity. Owing to the large size of protein superfamilies (with hundreds or thousands of taxa), many applications of phylogenomic inference employ fast distance-based methods instead of character-based approaches or forego even simple models of evolution in favor of faster hierarchical clustering algorithms . WithouIn practice, assessing the likely accuracy of a particular tree is typically accomplished through bootstrap analysis or comparison of trees constructed using different phylogenetic reconstruction methods. Analysis of multiple trees for a given family often shows regions of agreement as well as significant differences of opinion: closely related subtrees are often found consistently across different methods, with primary differences between trees being at the coarse branching order between these conserved subtrees. Functional inferences can then be based on subtrees with high bootstrap support or on those subtrees that are found in the strict or majority consensus of several tree methods. However, these methods of analysis are quite time consuming and impractical for large datasets or for high-throughput application.evidence codes, documenting the origin of the annotation  [experimental support is available. Our analysis of more than 300,000 proteins in the UniProt database shows only 3% of proteins with functional annotations have experimental support. We suspect that many more proteins than these have been experimentally pursued, but that the results of these experiments are not being propagated efficiently  to the sequence databases or to the GO Annotation project [Genome Research and the PLoS journals). We would go further and recommend that sequence databases specifically encourage ontology annotation during sequence submission. We expect that advances in text-mining software will also help correct the imbalance, although the field is not yet at a point to contribute on a large scale [Any system of functional inference depends on the accuracy of the characterized members. The Gene Ontology Consortium has provided a mechanism whereby sequence annotations have associated atement) . We beli project . One reage scale . FinallyOrthologs—genes or proteins related by speciation—are generally assumed to have greater functional similarity than paralogs, which are related by gene duplication. However, inference accuracy also depends on evolutionary distance and the particular functional attribute under consideration. Some attributes of protein families, such as the three-dimensional structure, persist across large evolutionary distances. Other attributes, such as substrate specificity, can be modified based on a handful of amino acid substitutions in critical positions. The persistence of certain traits may be more limited in some families and more expansive in others. The assumption that orthology implies a functional similarity must therefore be tempered by an assessment of evolutionary distance ,43.Moreover, determining orthology is not always straightforward. RIO and Orthostrapper take the approach of using phylogenetic trees to assess orthology between homologs ,15. ThisFinally, the dearth of experimental evidence supporting functional annotations, together with ambiguous tree topology reconstruction, often limits the number of proteins that can be annotated effectively based strictly on orthology. Because of the limitations in restricting functional annotations to orthologs, methods have been developed to allow functional inference to extend beyond the strict confines of orthology. The SIFTER algorithm enables annotations to be propagated over a phylogenetic tree, using GO annotations and priors over existing annotations . We beliWe have focused in this paper on the use of phylogenomic inference of protein function. However, phylogenomic inference can be applied to a wide array of protein family attributes. Selection of templates for comparative model construction can be performed in a phylogenomic context, e.g., picking the template that has the smallest tree distance to a target of unknown structure. Phylogenomic inference of pathway involvement may also be possible under some circumstances, for instance, in cases in which a subtree contains orthologs in closely related species.Looking to the future of phylogenomic analysis, we believe that the greatest improvement to this field will take place when investigators have access to rigorously validated biological data through which phylogenomic methods can be assessed for accuracy. The Structure Function Linkage Database , which l


The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level.Through performing rigorous alignments of the Affymetrix probe sequences against a comprehensive pool of currently available transcript sequences, and further linking the probesets to the International Protein Index, we generated transcript-level or protein-level annotation tables for two popular Affymetrix expression arrays, Mouse Genome 430A 2.0 Array and Human Genome U133A Array. Application of our new annotations in re-examining existing expression data sets shows increased expression consistency among synonymous probesets and strengthened expression correlation between interacting proteins.By refining the standard Affymetrix annotation of microarray probesets from the gene level to the transcript level and protein level, one can achieve a more reliable interpretation of their experimental data, which may lead to discovery of more profound regulatory mechanism. Microarray technology was invented to rapidly profile the quantities of mRNA transcripts in a particular cellular context -3. Its aIt has been well established that alternative splicing increases mRNA diversity, and over 60% of human genes are involved in this mechanism . In addiAs a matter of fact, Affymetrix suffixes their probeset ID according to the probeset's specificity. For example, probesets that recognize unique transcript variants are suffixed with _at, and probesets that recognize multiple alternative transcripts from a single gene are suffixed with _a_at or _s_at, and so on. This suffix system gives a hint on the varied specificities of the probesets, and could be considered as an endeavor trying to do away with customers' worry about the gene-level data analysis strategy. However, the correctness of the suffix system has been in doubt ,18. TherIn this paper, we linked the probesets of two widely used Affymetrix arrays with the International Protein Indexes (IPIs) ,21 throuTwo Affymetrix arrays, MOE430A_2 and HG-U133A, were chosen for this study, in which 21,097 and 16,213 putative protein-coding probesets for the two arrays were subject to the alignment investigation  ,25, and These results support the argument that it is more reliable to interpret microarray data at transcript level than at gene level. The inferiority of the Affy-protein level annotation to our annotation could be attributed to the technical details in their alignment and association procedures . First, A similar comparison was conducted for HG-U133A array, involving another transcript-level annotation by Harbig et al. . Across According to our data, the puzzling fact that probesets for one gene may show variable expression profiles can be clarified when viewing the data at the protein level. Such genes are likely to involve alternative transcript variants which are constitutively different in expression levels. We looked at two genes as examples. Shown in Figure Shown in Figure In recent years, considerable efforts have been devoted to identifying and characterizing protein-protein interaction (PPI). Besides investigations on the molecular events involved in PPI, functional annotation of an unclassified protein according to its interacting partners is also an important topic . Since iWe noticed that in these genome-scale studies PPI information was always first converted to gene pairs, after which the Pearson correlations of the probeset pairs corresponding to the gene pairs were evaluated. That is, the analysis targets were expanded from real interacting protein pairs to all possible cross-gene protein pairs for which interaction may not always exist. As illustrated in Figure In order to validate our proposition, we investigated the relationship between protein interactions and expression correlation at both gene-level and protein-level perspectives, using 1,037 interacting protein pairs from the HPRD  and 28 mFor all datasets, we observed strengthened expression correlations between interacting proteins under the PPI schema relative to the GGI schema. Taking the GDS987 dataset including 41 arrays as an example Figure , while iIn Figure Similar observations were made over all datasets : one-to-null probesets that do not recognize any transcript, one-to-many probesets that hybridize to multiple alternative transcript variants of the intended gene, and one-to-one probesets that hybridize to unique alternative transcript variants of the intended gene. We discriminate the intended alternative transcript variants of Affymetrix probesets based on the NetAffx's gene-level annotation for the first time. Given the fact that existing solutions are accompanied with imperfections and no consensus has been reached on an overwhelming strategy, our alternative solution to the problematic standard annotation points out a new way to improve the interpretation and exploitation of Affymetrix microarray data.et al. found that a number of probesets did not detect any transcript and attributed this phenomenon to the elimination of the target sequence in the process of sequence update [Although the transcript collections were not identical and the reannotation strategies differed more or less, we made out some similar discoveries to previous reports. For example, Harbig e update . In our e update , specifipriori knowledge of the possible exon content of a genomic sequence, the genomic tiling array [et al. proposed a primitive analysis method to explore alternative splicing with Affymetrix 3' gene expression arrays, though they regretted that only alternative splicing biased toward the 3'end of the gene can be detected in their way [et al. remains as a qualitative analysis method aiming at detecting novel alternative splicing events, our work gives explicitly the relationship between the probesets and the currently known alternative transcript variants, which can be immediately exploited to facilitate quantitative analysis of alternative variants. As our mapping relationships are defined for the standard probesets of the traditional gene expression arrays, they can be conveniently exploited as the standard NetAffx annotation information, without any ad hoc influence on the widely applied experiment protocols or the routine data processing algorithms. In the demonstrative implementations of the novel annotation tables, we actually observed several examples of negatively correlated alternative variants  and 'HG-U133A_annot_csv.zip' (annotated on 2006-04-11), were downloaded from Affymetrix website . Also doth, 2006. For mouse, this dataset comprises 1,582,211 sequences, with 1,521,234 from DDBJ, 6,506 from EMBL and 54,471 from GenBank. For human, there are totally 201,206 sequences, with 41,128 from DDBJ, 62,701 from EMBL and 97,377 from GenBank. File 'gene2accession' (updated on 2006-03-28), downloaded from Entrez Gene [GenBank: mRNA sequences from CoreNucleotide division of NCBI Nucleotide database were obtained via the Entrez Nucleotide search  on AprilRefSeq: 55,832 mouse mRNA sequences and 40,530 human mRNA sequences were obtained from the RefSeq database [Ensembl transcripts: 37,854 mouse transcript sequences were obtained from the Ensembl database (release 38) . MappingIPI entries and their mappings to external protein accession numbers were acquired from the International Protein Index (IPI) database  , and protein-level ones according to IPI ID (protein-level). Additionally, probesets corresponding to a single protein according to NetAffx probeset-protein mapping tables  are grouped as 'Affy-protein' level synonymous probesets. In the case of the HG-U133A array, we included a fourth level, the 'Harbig-protein level', for comparison. The Harbig-protein level reflects the probeset-protein association transformed from a recent alternative annotation of the Affymetrix U133 plus 2.0 array , also pret al. [30 and 28 expression datasets were selected from GEO respectively for MOE430A_2 and HG-U133A, and the original intensity data within each GEO dataset (GDS) were transformed to log 2 base and normalized to a constant median across all samples. For a synonymous group, if the expression values of all probesets in all samples were no larger than the constant median value, the probesets in this group were regarded not moderately expressed, and their expression profiles not informative enough. Therefore, we only kept the synonymous groups with at least one expression value above the constant median value, similar to the filtering procedure used by Tian et al. . For theFinally, the mean of the expression coherences of all synonymous groups over a dataset was calculated. In this way we obtained an evaluation of expression consistencies within synonymous probesets for a microarray dataset, and may compare the expression consistencies at the three levels over different microarray datasets.Given protein-protein interaction data from HPRD or IntAct, we first transformed the binary relations of protein accessions to IPI-IPI pairs, and also got the corresponding Gene-Gene pairs. For each PPI, we assembled the PPI probeset pairs and the GGI probeset pairs as illustrated in Figure n  pairs of probesets, where each two probeset pairs formed a group. The two PCCs of each group were firstly averaged into a group-level PCC, and the distribution was calculated over the n group-level PCCs. The group-level averaging was devised to mimic the counterpart operation in PPI PCC or GGI PCC calculation.The distributions of the PPI PCCs and the GGI PCCs were plotted in a same figure to show the contrast Figure . In addiBLAST: The Basic Local Alignment Search ToolGEO: Gene Expression OmnibusGGI: gene-gene interactionHG-U133A: Human Genome U133A ArrayHPRD: Human Protein Reference DatabaseIPI: International Protein IndexMOE430A_2: Mouse Genome 430A 2.0 ArrayPCC: Pearson correlation coefficientPPI: Protein-protein interactionYYL, KT, and YXL conceived and designed the experiments. HY, FW, KT performed the experiments. HY, YYL, FW, and LX analyzed data. YYL, HY, and LX wrote the manuscript. All authors read and approved the final manuscript.Novel annotation tables. Novel annotation files at transcript level, for MOE430A_2 and HG-U133A arrays.Click here for fileExpression correlations of synonymous probesets. Expression consistency within synonymous probeset groups for 30 (assayed with MOE430A_2) and 28 (assayed with HG-U133A array) GEO datasets, with synonymity defined at different levels: gene level, Affy-protein level, and our protein level. For HG-U133A array, a recent related work  was also adopted for comparison.Click here for filePearson correlation coefficients (PCCs) calculated on dataset GDS987. Pearson correlation coefficients (PCCs) of PPI pairs and corresponding GGI pairs. Calculated for 1037 HPRD protein-protein interactions using the GEO dataset GDS987.Click here for fileExpression correlation of PPI and GGI pairs from HPRD. PPI and GGI Pearson correlation coefficients for 1037 HPRD PPIs.Click here for fileSummary of expression correlations of 1037 HPRD PPI and GGI pairs. 1) Distribution plots for PPI and GGI Pearson correlation coefficients (PCCs), one for a GDS dataset. PCCs of random pairs form the background distribution. 2) Scatter plots of PPI and GGI PCCs.Click here for fileExpression correlation of PPI and GGI pairs from IntAct. PPI and GGI Pearson correlation coefficients for 274 IntAct PPIs.Click here for fileSummary of expression correlations of 274 IntAct PPI and GGI pairs. 1) Distribution plots for PPI and GGI Pearson correlation coefficients (PCCs), one for a GDS dataset. PCCs of random pairs form the background distribution. 2) Scatter plots of PPI and GGI PCCs.Click here for fileInformation on used GDS datasets. Accession numbers of GEO datasets used in the analysis.Click here for file


Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence.Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of CWe predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either "contacted" or "non-contacted", the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds.The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary protein sequence and higher order consecutive protein structural and functional properties. Prediction of protein three-dimensional structure from primary sequence is the central problem in structural bioinformatics. One approach is to use known structur∈homolog proteins as templates to determine the tertiary structures of novel proteins of unknown structure. Approaches include comparative modelling, threading and fold recognition methods. One protein structural feature is of particular interest here, namely, residue contact number (CN) which can be used to enhance protein fold recognition . This meβ (or Cα) atoms in other residues within a sphere around the Cβ (or Cα) atom of that given residue. Previous approaches to the prediction of protein contact number fall into two categories: classification and regression. In the classification approach, residue contact numbers were first classified into two populations allowing a subsequent use of machine learning methods such as recurrent neural networks to perform predictions }.     (2)rd  with discrete and consecutive definitions and thus have 8 combinations all of which will be used in our SVR approach.We have tried four values of rd, we calculate the mean (SD). So, the normalized contact number Nnorm is determined by the following formula:The distributions of contact numbers can be approximated by normal distributions, as shown in Fig. rd thresholds. At the second step, we recover the absolute contact numbers from their predicted normalized values using this equation.At the first step, we predict the normalized contact number because 1) it is easy to handle the data, and 2) it is easy to compare the results for different We predict contact number from protein local sequence. For a given residue, the local sequence contains its N-terminal and C-terminal seven nearest-neighbour residues. Thus, the local sequence makes a window of fifteen amino acids. We code each residue in the window using the PSI-BLAST position-specific scoring matrix . The matTo find the function between protein local sequence and normalized contact number, we use ∈-insensitive support vector regression (∈-SVR) ,15. The f(Xi) = 〈W, Φ(Xi)〉 + b,     (4)W is the weight and b is the bias. Φ(Xi) is a non-linear function mapping a data point from the input space to the feature space, so consequently, SVR is able to perform non-linear regression. The goal of the regression is to find the optimal W and b using some optimisation criteria. In ε-SVR, errors greater than ε are penalized, where two positive variables ξ and ξ* are used to measure the deviation of samples outside the ε-insensitive tube. The optimisation problem can be expressed aswhere C is the regularization constant that determines the trad∈off between the norm and the error penalty.where The solution of the above problem was given by the authors of ∈SVR ,15 as foαi and Xi), Φ(X)〉, the inner product of Φ(Xi) and Φ(X), by a kernel function K, if K = 〈Φ(Xi), Φ(X)〉. The radial basis function are used in our study, as given bywhere K = exp(-γ||Xi - X||2),     (7)γ is a parameter to be tuned by the user.where γ as 0.01 and C as 5.0, because this set of parameters yielded the best performance in our previous work [We constantly set ε as 0.01, ous work ,8. A numous work .To test our approach, we selected 945 unique protein chains, which were previously used for prediction of protein ASA, and were prepared by PDB-REPRDB . The strThe proteins are randomly divided into three groups with each group having 315 chains. Each group is in turn used for training with the remaining two groups used for testing. Therefore, each group is tested twice by the two functions derived from the other groups, and as a result we have six groups of examination results.Pearson's correlation coefficients and root mean square errors are calculated with respects to all residues and individual proteins. In addition, the absolute errors are calculated for the residues with different contact numbers. In order to compare with previous classification methods, we use different thresholds to classify contact numbers as "contacted" or "non-contacted" and compute the overall accuracy. The accuracy is defined as the ratio between the number of correctly predicted residues and the total number.The names of 945 protein chains. The first four characters are their PDB names. The fifth is the chain name and "_" means single chain.Click here for file


In order to consolidate the known human proteins interactions two tests were developed to measure the relative accuracy of the available interaction data. In addition, 6,580 interactions among 3,737 human proteins were recovered from Medline abstracts and combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing data sets. Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins.We established two tests of the accuracy of human protein interaction datasets and measured the relative accuracy of the available data. We then developed and applied natural language processing and literature-mining algorithms to recover from Medline abstracts 6,580 interactions among 3,737 human proteins. A three-part algorithm was used: first, human protein names were identified in Medline abstracts using a discriminator based on conditional random fields, then interactions were identified by the co-occurrence of protein names across the set of Medline abstracts, filtering the interactions with a Bayesian classifier to enrich for legitimate physical interactions. These mined interactions were combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing datasets.These interactions and the accuracy benchmarks will aid interpretation of current functional genomics data and provide a basis for determining the quality of future large-scale human protein interaction assays. Projecting from the approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies more than 375,000 interactions in the complete human protein interaction network. This set therefore represents no more than 10% of the complete network. The past few years have seen a tremendous development of functional genomics technologies. In particular, the yeast proteome has been the subject of considerable effort, including genome-wide protein interaction assays using yeast two-hybrid technology ,2, affinOnly minimal progress has been made with respect to the human proteome. The existing protein interaction data are largely composed of small-scale experiments collected in the BIND  and DIP One lesson from the yeast interactome research is clear: it is critical that such upcoming interaction assays be accompanied by measured error rates, without which the utility and interpretability of the data is jeopardized. To establish a basis for future interaction mapping we sought to consolidate existing human protein interaction data and to establish quantitative tests of data accuracy. We also sought to use data-mining approaches to extract additional known interactions from Medline abstracts to add to the existing interactions.Most of the current biological knowledge can be retrieved from the Medline database, which now has records from more than 4,800 journals accounting for around 15 million articles. These citations contain thousands of experimentally recorded protein interactions. However, retrieving these data manually is made difficult by the large number of articles, all lacking formal structure. Automated extraction of information would be preferable, and therefore, mining data from Medline abstracts is a growing field -29.In this paper, we present two quantitative tests (benchmarks) of the accuracy of large-scale human protein interaction assays, test the existing sets of interaction data for their relative accuracy, then apply these benchmarks in order to recover protein interactions from the approximately 750,000 Medline abstracts that concern human biology, resulting in a set of 6,580 interactions between 3,737 proteins of accuracy comparable to manual extraction. Combination of the interaction data creates a consolidated set of 31,609 interactions between 7,748 human proteins. On the basis of this initial set of interactions, we estimate the scale of the human interactome.We first gathered the existing human protein interaction datasets (summarized in Table Although the interactions from these datasets are in principle derived from the same source (Medline), the sets are quite disjoint Figure , implyinTo measure the relative accuracy of each protein interaction dataset, we established two benchmarks of interaction accuracy, one based on shared protein function and the other based on previously known interactions. First, we constructed a benchmark in which we tested the extent to which interaction partners in a dataset shared annotation, a measure previously shown to correlate with the accuracy of functional genomics datasets ,14,21. WFor both benchmarks, the scoring scheme for measuring interaction set accuracy is in the form of a log odds ratio of gene pairs either sharing annotations or physically interacting. To evaluate a dataset, we calculate a log likelihood ratio (LLR) as:where P(D|I) and P(D|~I) are the probability of observing the data (D) conditioned on the genes sharing benchmark associations (I) and not sharing benchmark associations (~I). By Bayes theorem, this equation can be rewritten as:where P(I|D) and P(~I|D) are the frequencies of interactions observed in the given dataset (D) between annotated genes sharing benchmark associations (I) and not sharing associations (~I), respectively, while P(I) and P(~I) represent the prior expectations . This latter version of the equation is simpler to compute. A score of zero indicates interaction partners in the dataset being tested are no more likely than random to belong to the same pathway or to interact; higher scores indicate a more accurate dataset.Among the literature-derived interactions , a total of 17,098 unique interactions occur in the public datasets. Testing the existing protein interaction data on the function benchmark reveals that Reactome has the highest accuracy (LLR = 3.8), followed by BIND (LLR = 2.9), HPRD (LLR = 2.1), core orthology-inferred interactions (LLR = 2.1) and the non-core orthology-inferred interaction (LLR = 1.1). The two most accurate datasets, Reactome and BIND, form the basis of the protein interaction-based benchmark. Testing the remaining datasets on this benchmark  reveals a similar ranking in the remaining data. Core orthology-inferred interactions are the most accurate (LLR = 5.0), followed by HPRD (LLR = 3.7) and non-core orthology inferred interactions (LLR = 3.7).Escherichia coli, the human genes have no standardized naming convention, and thus present one of the hardest sets of gene/protein names to extract. For example, human proteins may be named with typical English words, such as 'light', 'map', 'complement', and 'Sonic Hedgehog'. Names may be alphanumeric, may include Greek or Roman letters, may be case sensitive, and may be composed of multiple words. Names are frequently sub-strings of each other, such as 'epidermal growth factor' and 'epidermal growth factor receptor', which refer to two distinct proteins. It is therefore necessary that an information-extraction algorithm be specifically trained to extract gene and protein names accurately.To expand the list of human interactions, we turned to literature mining. We adopted the strategy of separately identifying the protein names in the abstracts and then matching up the interacting protein partners. This process was made difficult by the fact that unlike other organisms, such as yeast or We developed an algorithm capable of distinguishing human protein names from similar words on the basis of their context in the sentence. Building on our previous work in this area , we deveIn order to establish which interactions occurred between the proteins identified in the Medline abstracts, we used a two-step strategy: measure co-citation of protein names, then enrich these pairs for physical interactions using a Bayesian filter. First, we counted the number of abstracts citing a pair of proteins, and then calculated the probability of co-citation under a random model. Figure The co-citation algorithm is remarkably robust to variations in the minimal accuracy with which the protein names are identified by the CRF algorithm Figure . This roHowever, it is clear that proteins are co-cited for many reasons other than physical interactions. We therefore tried to enrich specifically for physical interactions by applying a secondary filter: We applied a Bayesian classifier to measure the likelihood of the abstracts citing the protein pairs to discuss physical protein-protein interactions. The classifier  scores eTaking as a minimally acceptable level of accuracy the interactions hand-entered from Medline (HPRD), our co-citation/Bayesian classifier analysis yields 6,580 interactions between 3,737 proteins. By combining these interactions with the 26,280 interactions from other sources, we obtained a final set of 31,609 interactions between 7,748 human proteins. In this, we have chosen not to include the complete set of orthology-derived interactions due to their lower performance on the annotation benchmark, although these will ultimately be quite useful when supported by future data. Table We have incorporated the results of this analysis into a web-based server , which cIn order to study the features of the network, we visualized the complete network of protein interactions in Figure -3 ± 3 × 10-5) shows the clustering in the human protein interaction network is considerably above that expected at random, in spite of the incompleteness of the network. The 'degree' of the network is defined as the average number of links per protein and captures the connectivity of the network. Except for Reactome, each of the datasets indicated in Table A quantitative analysis of the network clustering and connectivity distribution (reviewed in Barabasi and Oltvai ) is presA good accuracy measure is of tremendous importance, impacting on the reliability of all downstream analysis. The log likelihood analysis eases comparison and assessment of diverse datasets. The score indicates the probability that the identified interactions are correct based on enrichment of positive interactions over background expectations. Note that this approach is distinct from simply measuring the intersection with the benchmark associations - because enrichment of positive to negative associations is measured, rather than just recovery of positive associations, even datasets with small intersections to the benchmark set can be evaluated for accuracy. Note also that the benchmarks themselves are not likely to be 100% correct - protein annotations are subjectively assigned, many proteins belong to multiple pathways, and even hand-curated protein interaction data can be mis-entered. Nonetheless, the log likelihood framework is tolerant of errors and merely requires that the benchmark data are generally correct among true interaction partners. Figure From our previous work , we realCrucial to this process was the creation of a high-quality dictionary of human protein names and synonyms with mappings back to database entries. We therefore decided to start by creating a set of unambiguous gene names along with their synonyms that could all be mapped to a single unified gene identifier . The dictionary had to have very few spurious entries to ensure minimal false positives. The resulting ID-Serve database captures the various identifiers for a given gene and creates a repository for the retrieval of these genes along with their mined interactions. Building on this dictionary, the CRF algorithm then analyzed the context in which likely protein names appeared in order to identify the protein names more accurately. In the approach we describe, protein interaction partners are identified from among these protein names by a filtered version of co-citation.The co-citation approach ,26,40 caIn conclusion, to prepare for attempts to map the set of human protein interactions we sought to consolidate known interactions and to establish measures of accuracy that are useful for the evaluation and integration of upcoming datasets. We established two benchmarks for assessing the quality of large-scale human protein interaction datasets, providing quantitative measures useful for the testing and integration of interaction data. Using these benchmarks, along with available and mined interactions, we assembled an integrated dataset of 31,609 interactions between 7,748 human proteins, forming a framework for the interpretation of human functional genomics data. These data are collected in the ID-Serve database , which cThe training datasets used for the literature mining are as in . The dicWe have previously described  effectivIn both training and testing the CRF protein-name tagger, the corresponding Medline abstracts were processed as follows: text was tokenized using white space as delimiters and treating all punctuation marks as separate tokens. The text was segmented into sentences, and part-of-speech tags were assigned to each token using Brill's tagger . For eacThe model assigns each candidate phrase a probability of being a protein name. We selected all names scoring higher than a given threshold (testing thresholds between 40% and 95%), retaining the proteins' LocusLink identifiers along with the PubMed identifiers (PMID) of the associated abstracts. The significance of co-citation of two protein names across a set of Medline abstracts was calculated from the hypergeometric distribution ,26 as:,where:N equals the total number of abstracts, n of which cite the first protein, m cite the second protein, and l cite both.and The top-scoring 15,000 co-cited protein pairs were then re-ranked according to the tendency of the co-citing abstracts to discuss protein-protein interactions. Specifically, the likelihood of a co-citing abstract to discuss physical protein interactions was evaluated using the naive Bayesian classifier as described in , which sN genes as:We evaluated the clustering of genes in an interaction network  by calcuCi is the clustering coefficient of gene i, evaluated over the set of genes with at least two interactions and measured as the number of links, n, among the gene's k neighbors, divided by the number of maximum possible linkages, k(k -1)/2.where The specific GO and KEGG annotations for the functional benchmarks were downloaded from the Gene Ontology database  and the ID-Serve is a relational mySQL database of human proteins created to simplify comparison of datasets with differing protein identifiers. The database maps 42,232 LocusLink (now EntrezGene) identifiers to their corresponding Genecard, Swiss-Prot, Ensembl, OMIM, Unigene, NCBI GI codes and Accession numbers and to the GO and KEGG pathway annotations. Protein interaction data can be retrieved from ID-Serve, with co-citation derived interactions hyperlinked to the supporting Medline abstracts.The following additional data relevant to the analysis, training and testing carried out in this work are available with the online version of this paper and can also be obtained from the ID-Serve database . AdditioTraining set of 200 Medline abstracts with all occurrences of protein names taggedClick here for fileTraining set of 750 Medline abstracts with all occurrences of protein names taggedClick here for fileDictionary of human protein names and synonyms indexed to LocusLink identifiersClick here for fileFinal set of 31,609 protein interactions between 7,748 proteins derived from this analysisClick here for fileFinal set of co-citation/Bayesian classifier-derived interactions with the PubMed identifiers of co-citing abstractsClick here for fileBenchmark training set of functional annotationsClick here for fileBenchmark test set of functional annotationsClick here for fileBenchmark set of physical interactionsClick here for fileDiscriminating word list used by the Bayesian classifier to estimate the likelihood of Medline abstracts to discuss protein interactionsClick here for file


Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks.We present a comprehensive framework for protein network searches using pathway queries. Given a linear query pathway and a network of interest, our algorithm, QPath, efficiently searches the network for homologous pathways, allowing both insertions and deletions of proteins in the identified pathways. Matched pathways are automatically scored according to their variation from the query pathway in terms of the protein insertions and deletions they employ, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We applied QPath to systematically infer protein pathways in fly using an extensive collection of 271 putative pathways from yeast. QPath identified 69 conserved pathways whose members were both functionally enriched and coherently expressed. The resulting pathways tended to preserve the function of the original query pathways, allowing us to derive a first annotated map of conserved protein pathways in fly.Pathway homology searches using QPath provide a powerful approach for identifying biologically significant pathways and inferring their function. The growing amounts of protein interactions in public databases underscore the importance of our network querying framework for mining protein network data. Sequence homology searches have been the workhorse of bioinformatics for the past 30 years, providing the means to study the function and evolution of genes and proteins. Recent technological advances in large-scale measurements of protein-protein interactions (PPIs) such as yeast two-hybrid screens ,2 and prIn contrast to the vast research on gene and protein homology detection, there are only a few studies on homology detection at the network level, including studies on PPI networks -8, metabHere we give a novel comprehensive framework for querying linear pathways within a given network. Our algorithm, QPath, searches for matching pathways composed of distinct proteins that are similar to the query proteins in their sequence and interaction patterns. The matched pathways are scored according to their level of variation from the query pathway in terms of protein insertions and deletions, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We provide a computational method for estimating the weight of each of these terms in the overall score, so as to maximize the fraction of the functionally significant matching pathways identified.S. cerevisiae, the fly D. melanogaster, and human, aiming to address two coupled, fundamental questions motivated from sequence analysis: (i) Can pathway homology be used to identify functionally significant pathways? (ii) Can one infer the function of a pathway based on pathway homology information? We provide positive answers to both questions. Notably, our finding that matched pathways in fly tend to preserve the function of their corresponding query pathways in yeast, has enabled us to derive a first annotated map of protein pathways in fly that are conserved from yeast.We applied QPath to analyze the PPI networks of the yeast sequence score, which measures their sequence similarity to the query pathway; an interaction score, which measures the reliability of their constituent interactions; and the number of protein insertions and deletions they employ. The top-scoring pathways are identified using a dynamic programming based algorithm that guarantees that matched pathways will be comprised of distinct proteins. The output of the algorithm is a set of non-redundant, significant matching pathways. The QPath program is available upon request.We developed a novel algorithm for querying a given protein network with a linear pathway of interest. The algorithm searches for matching pathways that are similar to the query in their sequence and interaction patterns. It relies on efficient graph-theoretic techniques, allowing it to process long pathways (up to 10 proteins) in minutes . The search was limited to pathways consisting of 6 proteins to achieve reasonable running times when applying QPath to query those pathways while allowing for (up to 3) insertions and deletions. We identified a set of 271 non-redundant pathways whose scores exceeded those of 99% of randomly chosen pathways (see Methods). The full list of identified pathways appears on the supplemental website .p < 1e-300). The significant functional enrichment and expression coherency of the identified pathways suggest that these pathways are biologically significant. In agreement with the expected lower quality of the fly network, we observed lower rates of functional enrichment and expression coherency when analyzing analogously-computed high-scoring pathways in fly . Only few of the queries had matching pathways with no insertions or deletions, implying that the algorithm's support for insertions and deletions was essential for identifying matching pathways . Furthermore, the indel category of a pathway was also found to be correlated with its functional enrichment: as expected, fly pathways exhibiting fewer protein insertions and deletions  tended to be more functionally enriched than more distant pathway matches . For each yeast query we refer to the matched pathway with the highest obtained score and, hence, most likely to be functionally enriched, as the p < 1e-4). The expression coherency of the best-match pathways was also significantly higher than that of randomly chosen pathways . Interestingly, the pathway-based conservation of function was also much higher than the function conservation level among yeast-fly best sequence match proteins, which is estimated at 40% [Next, we investigated whether pathway similarity may be used to infer the function of a matched pathway based on the known function of the corresponding query pathway. Overall, out of the 171 yeast query pathways with an identified fly best-match pathway, 69 were functionally enriched and had a functionally enriched fly best-match pathway. Moreover, for 64% of these queries, the fly best-match pathways preserved one or more functions of the corresponding yeast query pathways. In contrast, when randomly shuffling the matches between fly pathways and yeast queries, only 31% of the fly pathways exhibited conservation of function (p < 0.05)). To evaluate the statistical significance of these predicted annotations, we computed for each best-match pathway the prevalence of the predicted annotation among its proteins (using a hypergeometric score), and compared these statistics with results obtained after randomizing the matches between yeast and fly pathways. The predicted annotations were found to be significantly more prevalent (p < 1e-04).We used the observed function conservation to derive a functional annotation of all fly best-match pathways, based on the enriched functions of their corresponding queries in yeast. Figure To demonstrate the use of our algorithm in a BLAST-like manner to query known protein pathways, we applied it to search the fly network for matches to queries consisting of known signaling pathways from yeast and human. As a first example, we used a ubiquitin-ligation pathway in yeast to query the fly network Figure . We idenAs a second example, we used two signaling pathways in human as queries to the fly network: a MAPK cascade and a Hedgehog signaling pathway. The top-scoring pathway in each case agreed well with the known functional annotations in fly. The MAPK query and its best-match are shown in Figure Figure We have presented a novel framework for querying linear pathways in PPI networks, allowing both deletions of proteins from the query pathway and insertions of proteins to the matched pathway. Matched pathways are assigned with scores reflecting their tendency to be functionally enriched, based on their variation from the query pathway, the sequence similarity of their proteins to the query proteins, and the reliability of their constituent interactions.The effectiveness of the algorithm was demonstrated in querying the fly PPI network using protein pathways from yeast and human. When applying the algorithm to search for yeast pathway queries in fly, the matching pathways were significantly more functionally enriched compared to arbitrary pathways in the fly network. The resulting pathways tended to preserve the function of the original query pathways, demonstrating the applicability of our tool for predicting pathway function much in the same way as gene and protein functions are predicted using BLAST.p < 1e-300).As with any PPI network study, it is important to deal with the vast amounts of noise present in the protein interaction data -28. To hp-value of 3.9e-7. To test whether merging genetic and physical interactions contributes to the identification of functionally significant pathways, we applied QPath to re-query the human MAPK pathway in the merged network of fly  and small scale experiments (denoted as exp:s class in DIP). For fly, due to the smaller number of interaction screens available, we used each of three available large-scale screens [Protein-protein interaction data for yeast and fly were downloaded from DIP  representing interaction reliabilities. Given a pathway query 1,...,qk)Q =  denote a sequence similarity score between query node qi and vertex j ∈ V. An alignment of Q in G is defined as a pair , where 1,...,pk) P = , between query node qiand vertex j∈ V was set to logarithm of the BLAST E-value between the corresponding proteins, normalized by the maximum score over all pairs.We represent a PPI network using an undirected weighted graph k in a graph. In color coding, one assigns a randomly chosen color from {1,...,k} to every vertex in the graph, transforming the problem of finding a simple length-k path to that of finding a path of length k that spans distinct colors. Since any particular path may be assigned non-distinct colors and, hence, fail to be discovered, many random coloring trials are executed. Below, we describe one iteration of color coding tailored to the query case.The goal of the algorithm is to identify a matched pathway with distinct vertices yielding an optimal alignment to the query. To this end, we adapt the color coding technique of Alon et al.  , which v ∈ V a color c(v) drawn uniformly at random from the set C ={1,...,k + Nins}. For a given coloring, we use dynamic programming to find an optimal matching pathway. We let W denote the maximum weight of an alignment for the first i nodes in the query that ends at vertex j∈ V, induces θdel deletions, and visits a vertex of each color in S. W is computed recursively as follows:Our algorithm starts by assigning every vertex W , and the corresponding alignment is obtained through standard dynamic programming backtracking. In fact, the algorithm outputs not only the optimal match but a set of high scoring matches for each combination of number of insertions and deletions employed. The running time of each trial depends on the length of the query, the size of the network and the number of insertions and deletions allowed, and is 2O(k+Nins)mNdel. The probability that any given path is assigned k distinct colors is at least ek-Nins-. Thus, for any ε ∈ , the running time of the algorithm for obtaining the optimal match with probability at least 1-ε is ln(n/ε)2O(k+Nins)mNdel. We used ε = 0.01 for all runs of the algorithm, yielding a practical time of a few minutes per query  proteins that are annotated with term t (or with a more specific term). Let p(t) be the hypergeometric probability for observing n(t) or more proteins annotated with term t in a protein subset of size |P|. Having found a term t0 with minimal probability p(t0), the score was set to the p-value of the enrichment under term t0, computed by comparing p(t0) with the analogous probabilities for 10,000 random sets of proteins of size |P|.Functional enrichments of protein pathways were computed based on GO process annotations  for theiExpression coherency of a pathway was measured as the mean absolute value of the pairwise Pearson correlations between the expression patterns of the genes that code for the pathway's proteins. To assess the significance of the expression coherency of a set of pathways, we compared it to the expression coherency distribution of a random set of pathways with the same size distribution. Gene expression measurements were obtained from Stanford microarray database  and inclTS performed the computational analysis. DS performed the biological analysis. All authors participated in designing the study and preparing the manuscript.supplementary figures and tables. The file contains supplementary figures 6 and 7, and tables 2 and 3.Click here for file


Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently.. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website.We introduce a large-scale protein domain interaction interface database called InterPare InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces. Proteins are the most important class of molecules in a cell. Most proteins function by interacting with other molecules, especially other proteins. The interactions among proteins are highly regulated and tightly conserved throughout evolution, ,2 mainlyet al. [Since the 1970s, there has been much effort to determine the principles of protein-protein recognition. Pioneers in the field of protein-protein interaction, such as Chothia and Janin [et al. ,11 focuset al.  studied et al. ,14. Joneet al.  introducet al. ,17 invesRecently, there has been a new trend in the study of protein interfaces. Several groups have introduced computational geometric and topology methods for the study of protein interfaces. Most importantly, the Voronoi diagram ,19,23 haDespite all the efforts to unveil the underlying principles of protein-protein interaction for over 30 years, there has not been much progress at the fundamental level since the research by Chothia and Janin . The int or ). InterPare presents interfaces between protein domains identified by three methods. First, the interface is detected by calculating the geometric distance between subunits of multidomain proteins or protein complexes in the PDB [Here, we introduce a large-scale protein interaction interface database called InterPare  Calculating the geometric distance between atoms in different domains (PSIMAP method).2) Detecting the differences of Accessible Surface Area (ASA) from all the residues in two states: the detached individual subunit state and the multimeric state.3) Calculating Voronoi diagrams.1. The geometric distance method checks the distance between atoms in two interacting domains.Two domains are assumed to interact with each other if there are at least 5 residue pairs whose atomic distance falls within 5 Angstrom distance (5-5 rule), according to the PSIMAP algorithm -34. In t2. The Accessible Surface Area (ASA) method detects protein regions that are buried and hence excluded from a solvent when forming a multimer or a complex.2 solvent accessible surface area (ASA) upon aggregation or complexation [If two or more subunits form a protein complex or aggregate, they have to lose a portion of area that was accessible by a solvent . With the ASA method, we define interface residues as residues that have lost more than 1 Ålexation ,38,39. I) in a SCOP domain and their corresponding residues  in a PDB entry,  and  can be either an interface residue  = 1) or a non-interface residue  = 0) based on the difference of ASA in that residue. The threshold (1 Å2 in our case) can be selected by the user on the InterPare website (from 1 Å2 to 5 Å2). As the threshold gets higher, the number of interface residues gets smaller. An interface region, in a domain, that consists of at least 10 interface residues is acceptable, and those having less than 10 residues are considered as artifacts. InterPare only serves domain interaction interfaces having at least 10 interface residues. We calculated the ASA of protein molecules using a program called NACCESS [For all residues  (n is number of atoms in the protein) [First of all, a three dimensional power-diagram s Figure . Power-dprotein) ,47 whereP . The default van der Waals radii of atoms were taken from Chothia . PolygonP Figure , if two  provides files that are required to view molecules with InterFacer. Atom coordinate files of three different regions are available to download. In addition, 1) the size of the interface and surface area, and 2) amino acid compositions on the surface, interior, and interface regions are provided on the results page.InterPare contains protein surface, interior, and interface information from PDB entries. There are three query interfaces to access the information in InterPare. Queries can be 1) keywords, 2) PDB or SCOP IDs, or 3) protein sequences in FASTA format. In the case of a protein sequence, InterPare provides a structural domain assignment module using PDB-ISL  and PSI-The protein interfaceome can be defined as the whole set of protein interaction interfaces found in cells. There can be many methods to define such an interface data set. We use the concept of the hierarchical classification of protein domains from SCOP. We extend the SCOP classification to molecular interfaces. The advantage of this approach is that each interface can be classified in the context of domain evolution. SCOP Superfamily is the level of classification where protein structures are clearly known to be related within the classification group. The protein Family level in SCOP is a more functionally relevant class, where each member of the Family is related and functionally similar. Below Family, there are individual domains. We applied three algorithms to find interfaces associated with SCOP. Any protein domain classification system, such as FSSP  and CATHWe expect that hierarchically similar clusters in the interfaceome will have highly conserved interfaces to maintain their interaction partners. This can provide a new level of functional prediction capability for the designing of novel molecules that can interface with proteins and hence control protein activities.InterPare is an open and public database server for protein interaction interface information. It contains large-scale interface data for proteins whose 3D-structures are known. We identified 31,620 inter-chain interfaces and 12,758 intra-chain interfaces. At this moment, there are 10,583, 10,431, and 11,010 PDB entries whose domain interaction interfaces have been identified according to geometric distance, ASA, and Voronoi diagram methods, respectively. These interfaces are based on protein domains which are from the SCOP database. By using SCOP, InterPare is tightly associated with the domain classification hierarchy, making the search and lookup convenient.. InterPare is jointly maintained by the National Genome Information Center (NGIC) of Korea, Object Interaction Technologies, Inc., Daejeon, Korea, and the BiO Center . It is free to any user.InterPare is available through PSIMAP: Protein Structural Interactome mapPDB: Protein Data BankSCOP: Structural Classification Of Protein structureFSSP: Fold classification based on Structure-Structure alignment of ProteinsSSG worked on the ASA part, drafted the manuscript, and managed this project. CBP implemented a program regarding the Voronoi diagram. JSK developed the InterPare webpage. ISJ and DMB identified protein interfaces using the PSIMAP algorithm. HSC implemented molecular viewer named InterFacer. JSL made C version of PSIMAP program. DSK supervised the development of Voronoi diagram method. DHO and JB supervised this project and revised the manuscript. All authors have read and accepted the final manuscript.


Proteins interact with each other in a highly specific manner, and protein interactions play a key role in many cellular processes; in particular, the distortion of protein interfaces may lead to the development of many diseases. To understand the mechanisms of protein recognition at the molecular level and to unravel the global picture of protein interactions in the cell, different experimental techniques have been developed. Some methods characterize individual protein interactions while others are advanced for screening interactions on a genome-wide scale. In this review we describe different experimental techniques of protein interaction identification together with various databases which attempt to classify the large array of experimental data. We discuss the main promises and pitfalls of different methods and present several approaches to verify and validate the diverse experimental data produced by high-throughput techniques.2 (a “standard size” patch) [It is now becoming clear that protein interactions determine the outcome of most cellular processes –4. There” patch) . It has ” patch) ,7,9,10. ” patch) –13. Hot ” patch) , and the” patch) –18.In many cellular processes, proteins recognize specific targets and bind them in a highly regular manner. The specificity of interactions in these cases is determined by structural and physico–chemical properties of two interacting proteins. As a result, there should be a certain degree of conservation in the interaction patterns between similar proteins and domains. Indeed, it has been found that close homologs almost always interact in the same way and protein–protein interactions place certain evolutionary constraints on protein sequence and structural divergence –24. ReceIn this review and its companion review in the April issue , we atteProtein interactions can be analyzed by different genetic, biochemical, and physical methods, which are listed in The development of the Y2H technique has considerably accelerated the screening of protein interactions in vivo. Y2H is based on the fact that many eukaryotic transcription activators have at least two distinct domains, one that directs binding to a promoter DNA sequence (BD) and another that activates transcription (AD) A. It wasFor screening entire genomes, the Y2H method has been advanced into two main approaches –46: matrmatrix approach, a matrix of prey clones is created where each clone expresses a particular prey protein in one well of a plate. Then each bait strain is mated with an array of prey strains and those diploids where two chimeric proteins interact are selected based on the expression of a reporter gene and the position on a plate.In the library approach, each bait is screened against an undefined prey library containing random cDNA fragments or open reading frames (ORFs). Diploid positives are selected based on their ability to grow on specific substrates; and interacting proteins are determined by DNA sequencing. The first two genome-wide analyses of the yeast “interactome” revealed 692 and 841 putative interactions, respectively [In the ectively ,48. The ectively ,50., different in vitro techniques can be used.The small overlap between Y2H experiments can be explained by different factors, among them: differences in protein interaction sampling, Y2H bias towards nonspecific interactions , and limMS is a powerful method of studying macromolecular interactions in vitro. The principle of the MS method is to produce ions which can be detected based on their mass-to-charge ratios, thereby allowing the identification of polypeptide sequences ,52,53 FB. The prStaphylococcus protein A and a calmodulin binding peptide separated by the tobacco etch virus protease cleavage site [A TAP tag consists of two IgG binding domains of age site ,62   and the Expression profile reliability method (EPR) [od (EPR)  is basedParalogous verification method (PVM) [od (PVM)  is basedProtein localization method (PLM) [od (PLM)  defines od (PLM) .A large variety of databases exists to study binary protein interactions and the higher order interactions in protein complexes. A summary of some available databases is given in In spite of the interaction data diversity, there exist considerable overlaps in the datasets contained in the databases, making it difficult to recommend a single resource for a particular type of information. In one effort to deal with this redundancy, the International Molecular Exchange Consortium (IMEx) has been formed in which databases agree to share their data in a consistent and timely fashion . In addiThe Database of Interacting Proteins (DIP) contains experimentally determined protein interactions and includes a core subset of interactions that have passed a quality assessment . InteracThe Biomolecular Interaction Network Database (BIND) includes high-throughput experimental datasets and protein complexes from PDB ,106. It MPact is a resource to access MIPS, which contains a manually curated yeast protein interaction dataset  collectePIBASE is a database of domain interactions from the protein structure data . It uses3did allows one to explore the details of domain interactions from protein structure data  . For eacThe Conserved Binding Mode (CBM) database is a collection of domain interactions from the structure data where domains are defined by the Conserved Domain Database . Unlike Domain Interaction Map (DIMA) database is a domain interaction map derived from phylogenetic profiling Pfam domains . InsteadIn this paper we have reviewed a wide spectrum of experimental techniques for identifying and characterizing protein interactions; each technique can provide a piece in the puzzle of mechanisms of protein recognition . Despite


The relationship between divergence of amino-acid sequence and divergence of function among homologous proteins is complex. The assumption that homologs share function – the basis of transfer of annotations in databases – must therefore be regarded with caution. Here, we present a quantitative study of sequence and function divergence, based on the Gene Ontology classification of function. We determined the relationship between sequence divergence and function divergence in 6828 protein families from the PFAM database. Within families there is a broad range of sequence similarity from very closely related proteins – for instance, orthologs in different mammals – to very distantly-related proteins at the limit of reliable recognition of homology.We correlated the divergence in sequences determined from pairwise alignments, and the divergence in function determined by path lengths in the Gene Ontology graph, taking into account the fact that many proteins have multiple functions. Our results show that, among homologous proteins, the proportion of divergent functions decreases dramatically above a threshold of sequence similarity at about 50% residue identity. For proteins with more than 50% residue identity, transfer of annotation between homologs will lead to an erroneous attribution with a totally dissimilar function in fewer than 6% of cases. This means that for very similar proteins  the chance of completely incorrect annotation is low; however, because of the phenomenon of recruitment, it is still non-zero.Our results describe general features of the evolution of protein function, and serve as a guide to the reliability of annotation transfer, based on the closeness of the relationship between a new protein and its nearest annotated relative. Assignment of function to gene products in the absence of direct experimental information is an important challenge of computational molecular biology -3. In anIn this study we quantitatively assess the relationship between the divergence of protein function and the divergence of amino acid sequence in families of homologous proteins. In addition to illuminating the process by which proteins evolve altered and novel functions, the results provide guidance about the expected accuracy of transfer of functional annotation among homologous proteins in databases.et al.  hydrolase activity), and 0000036 (acyl carrier activity) , and the distal GO ID(s) (from PIR). For each pair of proteins in each family, we measured the similarity of the sequence and function.We aligned amino acid sequences by the standard dynamic programming algorithm using the BLOSUM 62 matrix . We usedindividual GO IDs as the number of edges in a minimal-length path between the two nodes in the GO DAG that passes through the lowest common ancestor of the two nodes. Based on this, we needed to define a measure of distance between sets of distal GO IDs, as might appear in the annotation of a protein with multiple functions.We represented the functional divergence of the two proteins by the distance between their sets of annotations; that is, between the sets of distal GO IDs assigned to each protein. The measure of the distance between sets of annotations was based on a measure of the distance between individual GO IDs. We defined the distance between two There are several possible ways to assign a metric to the GO DAG. Because there is no natural metric, we have chosen one that appeared suitable after being guided by consideration of specific simple cases  is the simplest: each protein has one annotation and the minimum path length between them has length 4. The distance function should report this value.In case (b), each protein has two annotated functions, appearing on two branches of the graph. However, the two proteins show similarity of both functions: for each X there is an O at distance 2. Although the distance between the leftmost X and the rightmost O is 4, it does not seem reasonable to report a functional distance of 4 between these two proteins. Therefore it would not be suitable to define a distance function as the set of minimal path lengths between every X, O pair.In case (c), one protein has two annotated functions but the other has only one. The proteins share a similar function: the rightmost X and the O have distance 2. However, in this case, compared to (b), it is relevant that the distance from the leftmost node labeled X to the closest node labeled O is 4. This is a genuine difference between the annotated functions of the two proteins. It may be that protein X was recruited for a novel function not similar to the function of protein O. It is also possible that protein O shares the other function with protein X but is not so annotated. In any event, the distance function should report both 4 and 2. This implies that it would not be suitable to define a distance function as the minimum X-O distance for all X-O pairs.We therefore adopted the following definition of the difference in functional annotations between two proteins, X and O. For each distal GO ID X, we determine the minimum distance to all the distal GO IDs O, and for each distal GO ID O, we determine the minimum distance to all the distal GO IDs X. This set of values represents the distance between the annotation sets X and O. In the cases shown in Figure Any classification scheme may vary in the fineness with which it distinguishes different regions of its domain. Because for protein function (unlike for sequence or structure) there is no natural metric, there is no direct way to calibrate distances between nodes in either the EC or GO classifications. The problem is somewhat more acute for the GO classification because of the variable depths of the DAG. We explored the possibility of "normalizing" the GO distances according to the local depth of the DAG, but were unable to do this in a consistent way, largely because of the non-uniqueness of the lengths of the paths from any node up to the root.In order to demonstrate that the problem will not seriously affect the results in at least most cases, we did the following calculation: For all distal nodes in the GO DAG  we determined the minimal-length path from the root to the distal node. The result was that for 85% of the distal nodes, the minimal path lengths to the root were between 4 and 6. For these cases any reasonable "normalization factor" will vary only between 0.8 and 1.2. Nevertheless, for the particular application: "Given a novel sequence, what is the likelihood of error in transferring annotation between homologues?" we explicitly recommend a homologous-family-by-homologous family approach, in which one would in most cases be comparing functions in similar sections of the GO DAG. For these, the fineness of the distinctions between related functions would be comparable, and the differences in overall depths of different nodes would be a controlled quantity.All authors together conceived and designed the experiments, analyzed the data, and wrote the paper, with VS taking the leading role. VS and AML wrote required software and ran the computations.Comparison of sequence identity between the pair-wise 'Muscle' alignments and the multiple sequence alignments provided by PFAM. The data provides a comparison of sequence identity between the pair-wise 'Muscle' alignments and PFAM alignments.Click here for file