Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets.Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results. In recent years high throughput technologies have provided experimental tools to identify protein interactions in large scale, generating tremendous amount of protein interaction data . On the So far, several computational approaches have been proposed to predict protein interactions . These aFalse positive prediction in all computational methods and their limited overlap with experimental results are post-genomic challenges. Computational PPI prediction approaches consider protein-protein interactions in the most general context and often refer to 'functionally interacting proteins', implying that the proteins cooperate to perform a given task without necessarily involving any physical contact. Experimental PPI detection techniques, such as yeast two-hybrid and large-scale affinity purification with mass spectrometry, attempt to discover direct physical interactions between proteins. However, there is a limited overlap between sets of interacting proteins identified by functional and physical relationships . Given tTo enhance the overlap between computational predictions and experimental results, we need to find a systematic way to remove false positives, resulting in an increased true positive fraction of every predicted PPI dataset. In order to achieve this goal a common ground upon which the predicted results can be evaluated is required. Gene Ontology (GO) annotations may serve as the common ground, even though annotation is an ongoing process. Gene Ontology (GO) is the database that contains controlled vocabularies to annotate molecular attributes for different model organisms. Annotations are defined in three structured ontologies which allow the description of molecular function (F), biological process (P), and cellular component (C). Each ontology is structured in child-parent hierarchies in which a 'child' may have many 'parents' and child terms are components of parent terms. Thus, information provided by GO must be useful in further assessment of predicted PPIs and may be integrated with global filtering algorithms to reduce the number of false positives in PPI predicted datasets. Currently, several attempts have been reported in constructing functional association predictors solely based on GO information. In some studies, associations between proteins in a pair are assessed in terms of the similar GO terms , while oTherefore, GO can be utilized as a useful informative resource to either predict or further analyze the predicted PPI datasets. However, ontology annotation is an incomplete process and suffers from inconsistency within and between genomes. In some cases, two confirmed interacting proteins are assigned two different GO annotations which are not equivalent in terms of information content. One protein is assigned a term that represents a broad type of activity, and its interacting partner is assigned a more specific term that represents a subtype of that activity. In other cases, some proteins have not even been assigned all three ontologies which make the interaction assessments more difficult without human intervention. Thus, the molecular functions of GO annotations of related proteins should be harmonized in relation to the information content and compared on a more general level. There are advantages and disadvantages associated with the harmonization of GO terms. The advantage is that the predicted relationship between proteins in a pair can be detected systematically using some keywords and it is not required to be verified manually. The disadvantage is that the integration of GO annotations and predicted PPIs might not be able to reveal the specific functions of the interacting proteins. However, knowing the fact that PPI prediction techniques are merely capable of specifying the general category of relationship between two proteins, this disadvantage is not a great source of concern.S. cerevisiae and C. elegans. Assuming the experimentally confirmed pairs are true, the GO annotations of these interacting proteins were utilized to extract keywords which represent general category functions of the proteins. Then, a set of heuristic rules was established to be satisfied by the predicted interacting protein pairs based on extracted keywords and the fact that two proteins acting in the same cellular components are more likely to interact than those located in different components. Next, four computational methods representing four out of six categories of prediction techniques, mentioned earlier in this section, were selected. Using these methods, four predicted datasets were created for each organism of interest. The heuristic rules were applied to these predicted datasets. When a predicted pair of interacting proteins satisfied the rules it was considered a true positive, otherwise the pair was assumed false positive and removed from the dataset. The results show that the filtered datasets have higher true positive fractions than non-filtered datasets and the improvement is statistically significant.In this study a global framework to refine computationally predicted datasets is developed. First, two experimental PPI datasets with high confidence were prepared for two model organisms Using information deposited in the UNIPROT and GO databases, the experimentally obtained protein pairs for yeast and worm were processed, resulting in 1042 non-redundant GO term information (including 4391 yeast proteins) and 748 non-redundant GO term information (including 3390 worm proteins), respectively. These pieces of term information were further clustered, resulting in 35 and 25 keywords for yeast and worm, respectively . If ni = 1, it indicates that two proteins in pair i are represented by a keyword; and ni = 0, otherwise. Cumulative sensitivity of all keywords was obtained as:where z is the number of keywords. If nij = 1, it shows that two proteins in pair i are represented by the common keyword j; and nij = 0, otherwise. Cumulative sensitivity demonstrates the recovery power of all keywords collectively when they are applied to the source (training set). Specificity of a keyword and cumulative specificity of all keywords are similarly defined and calculated:where y is the total number of pairs in the predicted dataset (the test dataset). Cumulative specificity translates into the recovery power of all keywords when they are applied to a predicted dataset (test set).where Figure In order to further examine the significance of the extracted top-ranking keywords from the training dataset, the cumulative specificities of the keywords applied to four predicted protein-protein interaction datasets were calculated. These four predicted datasets were obtained using computational methods including phylogenetic profiles (PP), gene expression (GE), maximum likelihood estimation (MLE), and chance co-occurrence distribution (CC). The implementation of these methods is described in Additional File Although the eight top-ranking keywords significantly recover the experimental or predicted datasets, the cumulative sensitivity or specificity is not distributed equally as seen in Figures It should be noted that the highest obtainable cumulative sensitivity, in yeast for example, is 64.43% by means of all keywords and 64.21% by means of eight top-ranking keywords. Currently, it is impossible to obtain complete sensitivity (100%), as some experimental pairs do not have consistent annotations. This inconsistency comes from the fact that there are deficiencies in either annotation or experimental techniques. In case of the worm dataset the inconsistency is worse. Only 55% of worm genes are annotated and many annotations are not consistent. It is also notable that the GO molecular function annotations can not be used directly as keywords. When the definitions of the GO molecular functions were considered as keywords, the cumulative sensitivity of the training dataset was only 45.00%, comparing to that of 64.43% when the keyword extraction approach was implemented.Protein interactions take place in either permanent or transient complexes formed in a cell, suggesting that proteins are required to exist in close proximity to interact physically . Hence, (I) Two predicted proteins in the pair should match one of the eight trained function keywords.(II) Two predicted proteins in the pair should be in the same GO cellular components.As many computational protein interaction prediction techniques suffer from mass false positive predictions, satisfying the rules filters the predicted datasets and removes the false interactions to some extent.Based on the algorithm depicted in Figure In order to evaluate the improvement made by applying rules to the predicted PPI datasets, the signal-to-noise ratio (SNR)  was emplstrength (S):SNR was calculated for all four predicted datasets for each of yeast and worm in the following two circumstances: before applying the rules to a dataset (raw dataset), and after applying the rules to a dataset (filtered dataset). The effect of the rules on the reduction of false positive predictions was measured by the S value varies from 2.32 to 19.90 for the yeast datasets, and 1.96 to 3.94 for the worm datasets, implying that the proposed algorithm exerts a stronger influence on improving PPI pairs predicted by the PP method than those predicted by the MLE method. In other words, the MLE approach predicts more robust protein pairs than other three methods used in this study.As seen in Table Given the fact that protein interactions are required for cell functioning and permanent or transient interactions between proteins are regulated under biological circumstances, this statistical analysis can well distinguish between the predicted/filtered datasets and random selection of interaction. The predicted and filtered set of interactions in a dataset is significant when the true positive fraction of the dataset is greater than that in a randomly selected dataset. In other words, the selection of interacting proteins is significant when the strength value is greater than 1.The algorithm proposed here to reduce the number of false positive predicted protein pairs could also be extended to evaluate and compare the effectiveness of PPI prediction among different computational approaches. The algorithm is a post-prediction processing step that is applied to the resulted predicted dataset when a computational method is implemented. Thus, it can be attached to any computational approach for further analysis of predicted results. However, it should be noted that ontology is an ongoing process. With more genes assigned with GO terms, the proposed filtering algorithm is a promising approach to reduce the number of false positive interactions and thus to enhance the accuracy of PPI prediction.UNIPROT and GO employ different strategies to annotate proteins. In cases where experimental information is not available, sequence homology is the strategy that is used to assign biochemical information to proteins. Thus, there is a concern that protein pairs detected by the proposed algorithm as true positives may also be inferred by homology which consequently demonstrates low accuracy of filtered protein pairs. We compared our filtered datasets with interacting proteins reported in KOG database provided by NCBI. This database includes orthologous and paralogous proteins of eukaryotic species. Each group is associated with a conserved and specific function. Our examination shows that, on average, only 1.24% of the protein pairs that satisfies the rules can be predicted through homology. Refer to Additional File Annotation is an ongoing process and there are many proteins: i) to be annotated, ii) erroneously annotated, iii) annotated but do not comply with experimental findings, iv) with unidentified locations and ubiquitous status. These proteins and their pair-wise interactions with other proteins contribute to the intrinsic deviation of the proposed approach to filtering false positive predictions. To confine the bias caused by the above mentioned reasons and decrease the deviation of the resulted filtered datasets, the rules have been applied solely to those protein pairs whose GO annotations are available. Furthermore, protein pairs with GO annotations suffer from inconsistency among annotations. Extracted functional keywords address this deficiency and can recover proteins pairs whose general functions are similar, even though their GO annotations are not in the same level of information content. Nevertheless, the deviation resulted from this inconsistency can be well represented by means of sensitivity and specificity. For example, in Yeast high confidence experimental dataset (training set), there are 16507 protein pairs of which 15748 protein pairs (95%) contain GO annotation. This figure indicates that the coverage of GO annotations for well-studied organisms is satisfactory. Of 15748 protein pairs with annotations, 10146 pairs contain similar function annotations accounting for 64.43% which is equal to the sensitivity of the extracted keywords.et al. [et al. [The intrinsic deviation partially depends on the precision of annotation process. In fact, annotation process has shifted from manual annotation to automated annotation due to growing influx of protein information in the past few years and time-consuming process of manual curation. Shifting from manual annotation to automated procedures decreased the accuracy of annotation process. However, with the aid of powerful text-mining tools the accuracy of annotation is now satisfactory. Based on an investigation in 2002, Incellico Inc. reported that the accuracy of GO annotation is 95% . GOA is et al.  evaluate [et al.  presenteGene ontology annotation was used as a common ground to evaluate protein pairs predicted by four different PPI-predicting methods. Molecular function annotations in the Gene Ontology database were used to extract discriminating keywords, upon which heuristic rules were set. The rules were incorporated into an algorithm by which predicted datasets were filtered and false positive predictions were partially removed from the datasets. When only eight top-ranking keywords were chosen, over 99% of molecular function could be recovered as indicated by the cumulative sensitivity for both experimentally obtained and computationally predicted protein pairs. The effectiveness of the proposed algorithm to filter false positive predicted protein pairs varies from one method to another. The proposed algorithm is unbiased and could be implemented on all computational methods to increase the accuracy of PPI prediction. As more genes are assigned with GO annotations, the proposed filtering algorithm will become even more effective.et al. [The dataset containing experimentally obtained protein pairs was used to extract the functional keywords from the GO annotations. The yeast dataset was compiled from the following three sources: (1) von Mering et al.  reportedet al.  containset al.  containset al., [C. elegans.The worm dataset was constructed from BIND  and Li eet al., . They reFour PPI predicting methods from four out of six categories discussed in the Introduction section were chosen, including phylogenetic profiles (PP), chance co-occurrence distribution coefficient (CC), gene expression profiles (GE), and maximum likelihood estimation (MLE). The criteria of choosing these methods were based on: their genome-wide applicability and competitive results in the category -45. The The GO annotations of proteins were retrieved from the UNIPROT knowledgebase  which isinterpro2go" (retrieved from Mappings to GO on the GO website), all InterPro entries were mapped to GO terms and the GO terms of each protein were searched using AMIGO term search engine. The searched GO term information of each protein was collected and redundant information was removed. The remaining term definition relevant to the molecular function annotation (a part of term information) was compiled and used as a training dataset. The dataset was further manually grouped into different clusters according to their general molecular activities; for instance, GO:0003723 and GO:0000166 were placed in the same cluster because of molecule-binding activities. Refer to Additional File S. cerevisiae and C. elegans.Proteins involved in experimentally verified protein pairs were submitted to UNIPROT. Then GO and InterPro cross-reference assignments of the protein were retrieved. Through "n) of a word in a cluster was counted, and the probability of finding that word in the training dataset was calculated using Poisson distribution:In order to determine a representative keyword in a cluster, the number of occurrences , gene co-expression (GE), chance co-occurrence distribution coefficient (CC), and maximum likelihood estimation (MLE).Click here for file


Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations.The "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations).We explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications. Experimental results related to molecular biology research are published in the form of journal articles and stored in biomedical literature databases such as PubMed . The amoEfficient automatic filtering and information extraction algorithms for biomedical literature are needed to compliment manual information extraction and reduce the human effort needed. Moreover, they could aid in maintaining links from database annotations to article sources. In order to speed up the annotation process, a broad variety of methods drawn from the field of text mining and statistical natural language processing (NLP) have been applied to common biological problems. Some techniques have been applied directly to derive important information for protein annotations -6. Other.We present a first version of our approach to extract function annotation passages from free full text articles. This procedure produced the highest number of correct annotation extractions in the BioCreative contest (task 2.1) . The BioBioCreative task 2.1, was concerned with the automatic extraction of protein annotations. The evaluation was carried out by expert annotation database curators from the EBI GOA team ,18, provIn order to extract the required annotation passages, lists of terms and word types for entities involved in the annotation were compiled. For each list, a semi-heuristic scoring scheme was developed, which was used to score sentences after tagging them with the elements recovered from each list. Those scores were then used by averaging sentence sliding windows to score each sentence taking into account the context of flanking sentences. The highest scoring sentence window was returned as the annotation evidence text passage.In order to analyze the associations of proteins with GO-terms within scientific articles we used the Gene Ontology Annotation database (GOA) . This daIn order to identify functional annotations for proteins, information extraction of text passages relevant to them is crucial. As proteins constituted one of the entities involved in the annotation, we constructed a tool which generates lists of sub-tag sets for a given query protein. Each sub-tag set consisted of a list of word types and names associated with the protein query (see table O00115) [O00115: DNASE2, DNASE 2 and DNASE-2). Often different names for a certain protein, (synonyms) are obtained through cross references to other biological databases.Among the sub-tag sets used for the protein entity class were the original protein name, symbol or identifier, e.g. the SwissProt accession number. In the case of this contest the SwissProt accession number was provided as the protein query, consisting of a unique identifier for each protein entry in the SwissProt database (e.g. O00115) . Within O00115) . Therefodeoxyribonuclease II, lysosomal', 'DNL' and 'DNL2' (from HUGO). The word types forming the gene names also constituted a sub-tag set. For instance, in the case of O00115 the following word types were part of this sub-tag set: 'deoxyribonuclease', 'II', 'lysosomal'. The pragmatic context information was also exploited as a sub-tag set to take into account the meronymic relations (whole-part relations) for proteins. Hence we used terms contained in the Global Open Biology Ontologies (GOBO) dataset. GOBO contains structured sets of terms related to different aspects within the field of molecular biology. We used the mutation event and sequence ontology tables, and exploited them as a separate sub-tag set. The presence of such meronymic terms might aid in disambiguating certain protein symbols using context information. Examples of the "GOBO mutation event sub-tag set" were mutation (0000128) and conformational change (0000116) and of the "GOBO sequence ontology sub-tag set" were EST (SO:0000345) and transcript (SO:0000673).We developed a database which contained cross linked protein entries derived from several sources, including the HUGO, OMIM, SwissProt, UniGene and LocusLink databases. This database allowed us to extract all the possible naming conventions and definitions for a query protein based on its database identifier. All the protein symbols and names obtained through external links were also incorporated into a separate sub-tag set. For the example presented above, the elements contained in this sub-tag set included: 'sensu Animalia). From a linguistic point of view, a significant difference between protein symbols/names and the GO-terms is that the former are proper nouns while the latter can be considered to be adverbial nouns. Therefore, the GO-terms are even more diffcult to identify in free text as they often lack morphological characteristics which are present in proper nouns, such as capital letters or special characters as in the case of gene names (e.g. Greek letters). To recover some of the GO-terms it seemed therefore crucial to process them so that they would resemble their natural language (NL) variants, namely how they might be encountered in free text. We developed for this purpose a rule based system which modifies an input GO-term returning several potential NL-variants. Some of the processing steps performed by this system were minor typographical changes (e.g. lower case conversion) and word token substitutions by corresponding synonyms or adjective into noun conversions , collocation shuffing and preposition insertions  are ER membrane virus budding and endoplasmatic reticulum membrane viral budding and for condensed nuclear chromosome/perocentric region (GO:0000780) one of the NL-variants is pericentric region of condensed nuclear chromosome. Among the resources provided by the Gene Ontology consortium were also a GO-term synonym list and links for GO-terms to external databases such as the MIPS database keywords [regulation of mitotic recombination (GO:0000019), the word types 'regulation', 'mitotic' and 'recombination' were present in the sub-tag set containing GO-term word types. We generated stemmed versions for all the tag sets using the Porter Stemmer [As already mentioned GO-terms are controlled vocabulary items embedded into an ontological structure. To determine if these terms are suitable for NLP tasks, the lexical properties of GO  were anakeywords . These s Stemmer .hi) associated with each sub-tag which depended on the type of relationship it displayed with the original query and its own average occurrence. More specific sub-tags  were given a higher weighting score than more general sub-tags (e.g. the word types forming the GO-term or protein name). The stemmed versions of each sub-tag were scored lower relative to the original (unstemmed) word/s (as stemmed versions may result in ambiguous words). In the case of the word types forming the GO-term, GO-term definitions and composed protein names, an extended stop word filtering was performed. We started using several different heuristic weights for each sub-tag. Then, using a small set of sample articles, we adjusted the weights until the correct text passages (which contained the annotation) were returned as the highest scoring text window.After defining the different sub-tag sets for each entity, we determined their utility for extracting annotation passages. Therefore we analyzed first the average number of occurrences for each sub-tag using GOA abstracts (see figure hi be the heuristic sub-tag score (weight) for each sub-tag i for a given entity, then hi would be calculated by:Let  is the average number of occurrences of elements of sub-tag i in GOA sentences and ei corresponds to the relative heuristic weight used for sub-tag i, based on domain expert estimates, the relation to the query term and finally some adjustments based on a small sample of full text articles containing an annotation passage. Notice that the heuristic sub-tag weights for the GO-term entity were in general higher than for the protein entity.where SGML format. The first step consisted in previous SGML-parsing, low-level processing and junk formatting. All the analyzed documents were subjected to a rule-based sentence splitting algorithm. Section and paragraph information was retained in the form of empty sentences (this is important for the sliding window procedure explained later). After performing the low-level processing, we tagged to the document sentences the lists of word types and terms contained in the sub-tag sets for each entity, using an exact string matching algorithm. Thus only the sub-tag elements which are matched to a sentence are instantiated. Each sentence has thus a set of GO entity and Protein entity sub-tag elements which were encountered within this sentence. There are also sentences with no matches to sub-tag elements, for instance the empty sentences corresponding to sections and paragraphs. In the case of the following sentence the sub-tag it Golgi corresponding to the sub-tag class containing GO-term forming words was machted: Golgi compartment .The test set provided for task 2a consisted in full text articles from the Journal of Biological Chemistry in sliding window spans a broad variety of domains, such as information technology where it has been widely used in signal processing for analysis of frequent items in packet streams [The use of the concept  streams . In bioi streams . Sliding streams . In our wi within the window was determined byLet L be the total number of sentences forming the trapezoid sliding window (window length), then the sentence position weight In this way, we scored the flanking sentences comprising the sliding window lower then the core sentences of the window. This is based on the assumption that the flanking sentences might contain contextual information useful for scoring the sentences relative to the presence of a given entity. was calculated byThe average sliding sentence window entity score, wi is the corresponding sentence position weighting factor and L is the sentence window size. In the case of the entity profiles, L = 5 sentences. Si is the sum of the scores of the matching sub-tags of a given sentence, n being the total number of matched items and hi the associated heuristic sub-tag score.where The "trapezoid sliding windows" generate average entity scores for each sentence within the document. Taking the average sentence scores relative to the sentence number, it is possible to generate a document entity profile for the GO-term as well as for the protein. The higher the average sentence score, the more likely it should be that the corresponding text window contains information relative to the entity or to items associated with the entity. These profile scores were used to determine relevant text passages for each entity. The values of the scores can hint at the types of sub-tags being matched to the window, as high average window scores are associated with high scoring sub-tag matches. In general the average sentence scores for proteins are lower then for GO-terms, this is due to the overall weighting scheme used for the sub-tags.wi used in the case of the entity windows were ignored, meaning the flanking sentences had the same weighting as central sentences.After calculating the average entity score for each sentence we had to combine both resulting entity document profiles into a unique annotation document profile  is the average entity sentence score for the GO-term and (Protein) the average entity sentence score for the protein. The sentence window with the the highest average annotation score A' was returned: A' = arg max  score as the annotation passage.where L = 4 (window size), protein entity profile and a GO-term entity profile when considering the average entity sentence score for each of the window positions. These profiles serve as input for a second averaging procedure which combines two entity-profiles to obtain a single document annotation profile using a sentence sliding window. The highest scoring sentence window was returned as the annotation evidence text.The proposed procedure derives individual sentence scores for each entity or class. It uses the weight scores of matched word tokens and entity terms. Each weight score depends on the type of tag (sub-tag) the matched token belonged to. Every sub-tag set consist of a list of word types or terms with a certain degree of association with respect to the initial query entity. The weight score itself for a given sub-tag is based on its occurrence in Gene Ontology Annotation (GOA)  abstractperfect, refers to correct predictions of the annotation textual passages, the category general refers to predictions that are 'in principle correct' but too general for practical use (i.e. a term belonging to a high level within the ontology) and finally the category low which in effect refers to a wrong prediction.We submitted a total of 1076 text passages as candidate predictions to provide relevant textual information for protein-GO-term annotations. Each segment used for annotation prediction consisted of four consecutive sentences extracted from the corresponding full text article. The curators evaluated 1050 of our submissions on whether they were relevant as traceable annotations (annotations which are based on concrete text segments). The assessment included separate evaluations of the extraction of each individual entity (proteins and GO-terms). Three distinct evaluation categories were proposed by the assessors. The category None.Due to the fact that a vast amount of data had to be evaluated, a minor fraction of submissions remained without assessment and were returned with the label We obtained the highest number of correct predictions of annotations for task 2a, with a total of 303 correct textual evidences  is sufficient to abolish its endosomal association. Its endosomal localization is also sensitive to the phosphatidylinositol 3-kinase inhib itor, wortmannin. Using in vitro liposome binding assays, we demonstrate that Myc-tagged endofin associates preferentially with phosphatidylinositol 3-phosphate, whereas the C753 S point mutant was unable to do so. We also show that endofin co-localizes with SARA but that they are not associated in a common complex because they failed to co-immunoprecipit ate in co-expressing cells.As can be observed from above the example, the protein name and relevant word types for the GO-term are both contained within this textual passage.endofin and the GO-term entity phosphatidylinositol binding.With respect to the evaluation of the individual entity extractions, we obtained a total of 594 correct evidences for the protein entity, which corresponds to about 56% of total submissions , the false positive rate increased.The ranking of correct GO-term extraction and of correct GO annotation extraction displays a shift in the case of the Cellular Component and the Molecular Function groups. The highest number of correct annotations corresponded to the category Molecular Function instead of Cellular Component. As word types forming Cellular Component terms are often used in other contexts  was very noisy, it was diffcult to perform statistically significant analysis using common NLP methods. Among the sources of noise encountered in this dataset were the different annotation conventions depending on the background knowledge of the annotator. Further, major changes in curation conventions over time might have influenced the annotation extraction criteria used for the manual annotations. The most significant problem was the fact that the actual annotations were performed using full length articles. As we only had access to document abstracts, whilst the passage of text relevant for annotation extraction will often be located in other parts of an article (e.g. tables or in the results section), some of the abstracts might even lack the text segments relevant for annotation. Therefore some of the sub-tag weights used did not correlate with their precision.The annotation score may serve to determine the confidence intervals of the distinct predictions  when compared with annotation extraction's (Protein-GO-term relation extraction).This sentence sliding window method is able to score sentence windows of full text articles relative to the given query entities such as proteins and GO terms, as well as annotations based on those entities in cases were a standard training set is missing.MK and MP conceived the initial idea, implemented it and performed the low level processing steps and statistical evaluations and AV supervised and coordinated the project. MK and AV authored the manuscript.


BioPIXIE is a probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web.We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in  Understanding biological networks on a whole-genome scale is a key challenge in modern systems biology. Broad availability of diverse functional genomic data from protein-protein interaction, gene expression, localization, and regulation studies should enable fast and accurate generation of network models through computational prediction and experimental validation. Reliability of experimental results varies among data sets and technologies, however, and these data generally provide only pair-wise evidence for biological relationships between genes or proteins. Most cellular mechanisms, on the other hand, involve groups of genes or gene products that behave in a coordinated way to perform a specific biological process. We will refer to such groups of functionally related genes as process-specific networks. Although a wide variety of functional genomic data is available, and much has been learned from them, we are far from exploiting the full potential of these data for discovering such process-specific networks. There are several reasons for this: lack of accessibility to data and methods to analyze them, barriers to incorporating expert knowledge in the network discovery process, and noise and heterogeneity in high-throughput gene data.The first problem is simply the lack of accessibility of both the data and analysis methods. Even when data are publicly available, results are often buried in large files, and computational methods developed to analyze them are often not available in forms that the typical biologist can use. Thus, experimental researchers are unable to identify interesting results from computational studies that are worth verifying. Instead, most biologists are limited to what the authors of such studies deem important or interesting enough to highlight in the written publication. Our ability to effectively utilize genomic data for process-specific network discovery has thus been hampered by the lack of effective interfaces to both the data and the relevant analysis methods.The second challenge is to allow biology researchers to integrate their biological knowledge in analysis. When biologists inquire about particular biological processes, they bring with them existing knowledge that can and should be used to generate the most sensitive and precise hypotheses possible. Such information is hard to extract automatically, and effectively incorporating biological expert knowledge is of course closely linked to the accessibility challenge noted above. Most previous methods for process-specific network prediction have not allowed biologists to use their previous knowledge in their area of interest to target the analysis process. Biological research demands convenient and accessible systems that leverage existing knowledge to direct and facilitate discovery.The third challenge in constructing accurate process-specific networks from diverse genomic data lies in the heterogeneity and high noise levels in large-scale data sets. High-throughput data by nature are often noisy and simple combinations of results from different types of experiments  are of limited effectiveness because they sacrifice either sensitivity or specificity.Recent applications of probabilistic data integration to the related but simpler problem of predicting protein function from diverse genomic data have demonstrated that integrated analysis of heterogeneous sources provides a substantial increase in prediction accuracy. Much of the work in function prediction focuses on fusing information from multiple heterogeneous sources for pairs of proteins to make more reliable statements about pair-wise functional relationships. Bayesian networks ,2 and vaDespite much investigation into heterogeneous data integration for the purpose of function prediction, there have been only limited attempts to use confidence-weighted linkage maps from integrated data to address the more biologically significant problem of how to group functionally related proteins together into process-specific networks. These network-level questions are distinctly different from function prediction problems and require new methodology for general data integration and network discovery. Previous work in identifying groups of genes involved in specific biological pathways from interaction networks has focused on mainly binary interactions, which are prone to false positives and inadequate coverage when only limited types of genomic evidence are used. For instance, two studies ,10 descrTo our knowledge, the only existing work that leverages expert knowledge in constructing biological networks or protein complexes from integrated data is a network reliability approach to protein complex recovery  and a grTo address these challenges to discovering process-specific networks from functional genomic data, we have created a publicly available system called bioPIXIE . The system allows users to enter a set of proteins and then uses a novel probabilistic graph search algorithm on a protein-protein linkage map derived from diverse genomic data to predict the surrounding process-specific network for the local neighborhood of interest. Most importantly, the system includes a convenient interface for dynamic visualization of the resulting predictions and provides analysis of their functional coherence. We have completed an extensive evaluation of our method against known pathways as well as experimentally verified a subset of predictions made by our system.Our system achieves accurate network prediction by effectively integrating diverse data sets and probabilistically identifying new components of process-specific networks given only one or a few known members. We evaluated the ability of our approach to recover known process-specific networks given initial query sets by using a collection of well-annotated functional groups, including KEGG pathways, sets of biological process GO terms, and MIPS protein complexes. We restricted our evaluation to groups of 15 to 250 total proteins in which at least half of the member proteins had one type of evidence linking them with another member protein. We identified 31 such groups from the set of KEGG pathways, MIPS protein complexes, and GO terms , from each protein to the query set. Although this algorithm uses more information than SEEDY, both in terms of topology and indirect links, we found its performance to scale poorly with increased density of the weighted interaction network. Specifically, as more genomic data are included in the integration, the probabilistic integrated network becomes more populated, resulting in many more possible (probability >0) paths between any one protein and a particular query set. There are so many paths that the fraction of random binary networks with paths to the query set is no longer a discriminative measure, which results in more false positives. Although such a method might be appropriate for sparse data, it does not appear to work well when larger datasets are applied to the problem of query-based complex or pathway recovery.Another factor in the performance of our method is its robustness to the quality and size of the query set. For each of the 31 groups of proteins described earlier, we evaluated the recovery performance for 20 query proteins, of which between 1 and 19 were randomly chosen from the entire proteome and the rest were chosen from the appropriate process or complex. All 31 groups could tolerate 25% query set noise with less than a 10% reduction in the average AUC; 27 of those could tolerate 50% query set noise, and 14 of those could tolerate up to 75% random proteins in the query set . Thus, P value < 10-22). However, when Rad23 is entered as a query with proteasome components Pup1, Pre6, Rpn12, the resulting network . Rad23 has high-confidence relationships with several proteins in both processes, but the recovered network returned by our system is dependent on the context implied by the query. This query-driven context facilitates accurate recovery of network components related to the biological process or pathway of interest.The query-driven nature of the search algorithm is a key factor in the accuracy of our method. The relationships between query proteins selected by the user affect which neighboring proteins are added to the final network. Thus, the network resulting from a query is not simply a sub-section of the complete integrated protein-protein interaction graph rooted at the query proteins; rather, it is probabilistically biased by the network search algorithm toward the specific biological context represented in the query set. Figure k Figure  is insteP value of 5 × 10-9 for YPL017C), which is consistent with the phenotype of mutants known to affect chromosome segregation such as ctf4Δ [bioPIXIE does not simply recapitulate known biology, but it also predicts novel network components based on the diverse types of input data. In fact, the 'false positives' identified by bioPIXIE in the evaluation above may be novel discoveries or known proteins that interact very closely with the biological process in question but are not annotated to it by the current standard. Thus, although the computational evaluation above is an accurate comparative evaluation of the methods, we wanted to experimentally confirm the quality of predictions made by our method. We have done so by using bioPIXIE to generate hypotheses about previously uncharacterized proteins in yeast and experimentally testing these hypotheses. Specifically, for several biological processes of interest, we entered member proteins as queries and identified uncharacterized proteins consistently returned in the predicted networks. One biological process with high-confidence uncharacterized proteins was the process of chromosomal segregation. In yeast strains null for these genes , we observed a significantly increased number of large-budded cells with a single nucleus at the bud neck compared to wild-type populations . This eas ctf4Δ ).We expect that bioPIXIE will be a convenient and effective tool for biologists to explore the growing sets of functional genomic data as well as direct further experimentation in their domains of interest. As an example of this type of exploratory analysis, we used bioPIXIE to examine the Cdc37-Hsp90 complex and found evidence for previously uncharacterized roles in important processes. Hsp90 is a molecular chaperone that participates in the folding of several proteins, including signaling kinases and hormone receptors, which are involved in growth and apoptotic pathways; it has thus been identified as a possible anticancer drug target. Hsp90 is a highly conserved protein found in organisms from bacteria to humans, and there are two Hsp90 homologs in yeast, HSC82 and HSP82 (reviewed in -22).Using bioPIXIE, we were able to identify known and novel targets of Hsp90 and its co-chaperones, in particular Cdc37. Cdc37 and other proteins associated with Hsp90 are thought both to function as chaperones themselves and potentially to determine Hsp90 target specificity. Cdc37 interacts with Hsp90 and is involved in the folding of protein kinases , and previous work has suggested that Cdc37 might be a general kinase chaperone . When CdFurthermore, our algorithm predicts a potential novel role of the Cdc37-Hsp90 complex in DNA replication. Specifically, bioPIXIE identifies connections between components of this complex and Cdc7, a serine/threonine kinase involved in replication origin firing, which is regulated by Dbf4 in a manner analogous to the way that CDKs are regulated by cyclins . Our sysOur approach of combining data integration with a method for process-specific network discovery provides a convenient framework for addressing biological questions at a higher level. Thus, in addition to constructing specific and testable hypotheses about individual biological processes, we can use the system to discover novel interplay, or cross-talk, among biological networks. To investigate possible cross-talk among biological networks, we start with a single functional group as our query set, use bioPIXIE to predict additional network components, and analyze the resulting superset of proteins for statistical enrichment of other functional groups. By repeating this for each process of interest, we can construct a map of cross-talk that represents a variety of high-level biological relationships . We have applied this approach to map functional links among a set of 363 KEGG pathways, GO categories, and co-regulated transcription factor targets. By using this variety of classification systems, we can detect links across different biological relationships - from biological roles (GO process ontology) to cellular locations (GO component ontology) to metabolic pathways (KEGG). Upon mapping cross-talk among these groups, we clustered the results to reveal biologically significant groups of inter-related processes . Du We have developed bioPIXIE, an analysis and visualization system for the discovery of biological process-specific networks. bioPIXIE's public interface allows researchers to use their knowledge to explore novel and previously known components of a variety of biological processes. The system provides detailed information about experimental sources for each prediction, including links to original literature, and can be used to generate testable hypotheses. It is important to note that predictions made by bioPIXIE require further experimental validation; we hope that the public availability of our system and all results presented here will encourage such verification by yeast biology laboratories.A key strength of our system is in addressing network-level behavior as opposed to focusing purely on pair-wise protein relationships. This is critical because many biologically significant questions involve the behavior of groups of proteins in networks or the interplay among networks with different functions. Furthermore, from a computational standpoint, the network-level approach to analysis and modeling of biological data is beneficial because subtle but coordinated group behavior can provide a more accurate picture of biological relationships than can be detected through pair-wise protein linkages. Although we focus on discovering networks, bioPIXIE can also be used for function prediction of individual proteins. Functions of uncharacterized proteins can be predicted either by analyzing uncharacterized components that are returned by the system given a known query set or by using an uncharacterized protein itself as the query, building the local interaction graph around it with our network-discovery algorithm, and analyzing the proteins in the final graph for statistical enrichment for particular functions. Another advantage of bioPIXIE is the probabilistic nature of the method that can easily adapt to new types of data. In the future, bioPIXIE will incorporate additional data sets from sources already modeled by the system as well as data from new approaches such as protein microarrays.Another future direction for our method is to use process-specific neighborhoods generated by the system as a starting point for deciphering more precise details of biological relationships. Our notion of functional relationship is intentionally rather general so a wide variety of biological interactions can be detected. However, developing detailed models of how groups of functionally related proteins specifically relate to each other requires more precise definitions of relationships. We propose our method as a way to pinpoint groups of proteins acting together, after which other methods can be applied to investigate details of relationships between these proteins. This narrowing process will undoubtedly improve downstream computational approaches.Finally, our method may be applicable to higher eukaryotes. Additional challenges for such applications include handling multiple cell types, less comprehensive sets of functional genomics data, and incomplete genome annotation. Our method is general, and by extending the Bayesian network structure to organism-specific data sources and learning the corresponding integration weights from available annotation data, bioPIXIE can enable discovery and accurate modeling of previously uncharacterized process-specific networks in a diverse range of organisms. It is important to stress that the success of applying our method and other related approaches to higher eukaryotes depends on public availability of functional genomics data for these organisms and continued improvement of their annotation data, ideally through expert curation.Saccharomyces cerevisiae, characterized unknown components in these processes, and identified novel cross-talk relationships. We are making bioPIXIE publicly available through the web to ensure that analysis and interpretation of accurate network predictions we generate, as well as the underlying data, are conveniently accessible to biological researchers.We have developed a novel probabilistic methodology for identification of biological process-specific networks based on diverse genomic data and have used this methodology to create a fully functional system for network analysis and visualization. bioPIXIE allows researchers to identify novel pathway components and to study specific interactions among them. Predictions made by our system are specific enough to be tested using common molecular biology techniques. Using this approach, we have accurately modeled multiple known processes in Our method relies on four critical components: Bayesian integration of heterogeneous data; an expert-driven search paradigm; a probabilistic graph search algorithm; and an easily accessible interface for interpretation of the results Figure . In simpThis component uses a Bayesian network to integrate diverse data to derive a probabilistic linkage map among proteins.Saccharomyces cerevisiae Promoter Database (SCPD) [Saccharomyces Genome Database (SGD) [We have collected a diverse set of evidence from over 950 publications from several databases, including complete physical and genetic interaction data from the GRID and BIND databases (downloaded on 6/25/04), which contain both high-throughput interaction data sets and some interactions from individual experiments curated from the literature ,40,41. We (SCPD) , and biose (SGD) . Additiose (SGD) ,34,44-50se (SGD) .Given these diverse data, we can answer questions about pair-wise protein relationships using a Bayesian network that leverages our previous work . A BayesSpecifically, we obtained gold standard protein-protein relationships for learning the network CPTs by propagating each biological process annotation up to its ancestors and counting the number of unique annotations per GO term. Because the biological specificity of each term roughly corresponds to the number of total annotations, we chose two thresholds to define the set of positive  and negative  protein pairs. Protein pairs whose most specific co-annotation occurs in GO terms of 300 total annotations or less were considered positives, while pairs whose most specific co-annotation occurs in GO terms of 1,000 total annotations or more were considered negatives. The resulting set of positive and negative protein pairs can also be downloaded from the online supplement .Given this set of gold standard pairs, we used the expectation-maximization algorithm  to compuA critical aspect of our method is that we make use of existing expert biological knowledge to improve the accuracy of process-specific network prediction by allowing the biologist to drive the search process. Specifically, the user enters a list of proteins (of arbitrary size) he or she either expects to play a role in the same biological process, or wants to test for functional relationships. Our system then queries the surrounding confidence-weighted network derived from integrated data for additional related proteins. The resulting process-specific network is not a simple sub-section of the complete integrated protein-protein interaction graph; rather it is probabilistically biased by the graph search algorithm (described in detail below) toward the biological process represented in the set of query proteins. This paradigm is based on two important observations: first, detailed knowledge of specific biological processes is typically learned in a directed fashion, not by taking a completely unsupervised view of high-throughput data; and second, novel process-specific proteins can be predicted more precisely when we consider their relationship to groups of known proteins simultaneously. This query-driven process results in a view of the integrated genomic data in the context of the specific process being interrogated. Figure n1 direct neighbors with largest connections to the query set. Secondly, find the n2 direct or indirect neighbors with largest connections to the query set, requiring that all indirect paths pass through proteins from step 1. Finally, return n1 + n1 proteins and associated links.Given an initial set of query proteins defined by the user, we wish to find other proteins with significant connectivity back to the starting group. It is unrealistic to expect related proteins to have direct connections to all other proteins in the same biological process due to incomplete data. Thus, we measure connectivity back to the original query set via both direct and indirect relationships. A brief overview of the algorithm follows: Starting with a user-defined query set of related proteins, first, find the eij, in the integrated network:Because we used a Bayesian approach to data integration, weights of edges connecting pairs of proteins are precisely the posterior probability of a functional relationship between the proteins given all observed evidence for the pair, for example, for each edge weight, eij = P .Xij, with probability of success eij. The number of direct relationships protein pi shares with the original query set, Q, can then be found by summing over all pi's connections to proteins in Q. Letting the random variable SQ(pi) denote this sum, we obtain:Given this formulation, the existence of any pairwise biological relationship can be treated as a Bernoulli random variable, pi is:Then, the expected number of direct relationships to the query set for protein N1) after which the maximally connected indirect neighbors adjacent to the first neighborhood and the original query set are added . Letting the random variable  denote the number of two-step indirect connections between protein pi and the query set (Q) through first neighborhood proteins (N1), we obtain:As not all proteins involved in a particular process will have high-probability direct relationships with other members of the same process, we also need to measure indirect connectivity to the query set. However, from a biological standpoint, not all indirect connections are actually meaningful. We expect there are a limited number of high-probability adjacent neighbors of the query set through which indirect connections are meaningful. Thus, our approach relies on a two-step search approach where a pre-defined number of direct neighbors are found  provides the best precision and recall over a wide range of biological processes. This was determined by optimizing the difference of recall and impurity (1-precision) with respect to the first neighborhood size. Representative examples and further details are included in supplemental figure S7 in [n2) reflects a tradeoff between precision and recall as demonstrated in Figure n2 based on the density of the local network and the limits of the user interface . Thus, second neighborhood proteins are added to the graph until the total number of proteins reaches 40 or no neighbors with links exceeding the prior probability of interaction remain.We have empirically determined that a first neighborhood of between 10 and 20 proteins . We conThe Bayesian network used in integrating genomic data was implemented using SMILE, a C++ library developed by the Decision Systems Laboratory at the University of Pittsburgh . The useThe following additional data are available. Additional data file , is recommended for viewing the dsl file.This file contains the structure and final learned conditional probability tables used for integrating multiple heterogeneous sources of functional genomic data. GeNIe, available at Click here for fileThis file contains a list of pathways and protein complexes that were used to evaluate the performance of bioPIXIE. The source of the group and the number of proteins in each is also included.Click here for fileThis file contains a comparison of the performance of bioPIXIE to existing methods for biological network recovery. The area under the precision-recall curve (AUC) is computed and plotted separately for each of the 31 evaluation pathways and complexes.Click here for file


The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins.In the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the protein-protein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new protein-protein interactions that indeed have been reported recently.Eigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions. Systems biology is a new frontier for bioinformatics research, aimed at understanding complex biological systems in cells by integrating interactions between large numbers of constituent components, including genes, proteins, and metabolites. Examples of systems biology research include studies of gene interaction networks -3, regulProteins represent the major category of large functional biomolecules. How proteins interact with one another is a current subject of many high-throughput studies. The number of proteins in an organism can reach tens of thousands. Comprehending the functional, developmental, and regulatory networks comprising these temporal and spatial protein pairs is a formidable task -17 sinceProtein clustering in global interaction networks is important for revealing cellular functionality (for example ). ClusteOur approach in this paper using connectivity matrix and subsequent eigenvalue/eigenvector decomposition is also based on the topological properties of the interaction network as a whole. Although significant proteins (in each eigenvector) form clusters, these clusters differ from those obtained by methods that are based solely on protein properties, because they reflect the organizational patterns of the protein interactions themselves based on topological considerations.In the present study, we show that computational analyses of experimental data on protein networks can lead to discoveries of new, unexpected relationships, which emphasizes the importance of the global view of a protein interaction network.In this paper, we have used spectral analysis of graphs methodology. We earlier applied a similar approach for protein dynamics analysis using elastic network models -29. Specet al.  for the protein network can be written asIt can be shown that the original contact (connectivity) matrix C = UTΛU     (4)Λ is the diagonal matrix containing eigenvalues λ1, λ2, ...λN of C, and U is the matrix formed from eigenvectors of C. Thus, the elements Cij of the contact matrix C can be expressed aswhere uki denotes the ith component of the eigenvector corresponding to the kth eigenvalue. Equation 5 can be viewed as the eigenvalue expansion of the contact matrix. From Eq. 5 it follows:where The eigenvalues with the smallest indices (that correspond to the largest absolute values of λ, as seen in Fig.


It is therefore crucial to understand the physicochemical properties as well as the evolution of interactions between proteins. The Protein Data Bank represents an important source of information for such studies, because more than half of the structures are homo- or heteromeric protein complexes. Here we propose the first hierarchical classification of whole protein complexes of known 3-D structure, based on representing their fundamental structural features as a graph. This classification provides the first overview of all the complexes in the Protein Data Bank and allows nonredundant sets to be derived at different levels of detail. This reveals that between one-half and two-thirds of known structures are multimeric, depending on the level of redundancy accepted. We also analyse the structures in terms of the topological arrangement of their subunits and find that they form a small number of arrangements compared with all theoretically possible ones. This is because most complexes contain four subunits or less, and the large majority are homomeric. In addition, there is a strong tendency for symmetry in complexes, even for heteromeric complexes. Finally, through comparison of Biological Units in the Protein Data Bank with the Protein Quaternary Structure database, we identified many possible errors in quaternary structure assignments. Our classification, available as a database and Web server at  The millions of genes sequenced over the past decade correspond to a much smaller set of protein structural domains, or folds—probably only a few thousand. Since structural data is being accumulated at a fast pace, classifications of domains such as SCOP help significantly in understanding the sequence–structure relationship. More recently, classifications of interacting domain pairs address the relationship between sequence divergence and domain–domain interaction. One level of description that has yet to be investigated is the protein complex level, which is the physiologically relevant state for most proteins within the cell. Here, Levy and colleagues propose a classification scheme for protein complexes, which will allow a better understanding of their structural properties and evolution. Most proteins interact with other proteins and form protein complexes to carry out their function . A recenThe Protein Data Bank (PDB)  makes avTo facilitate understanding of, and access to, the constantly growing body of information available on protein structures, a hierarchical classification of protein complexes is needed in the same way that SCOP  and CATHTo our knowledge, all previous classifications have considered parts of structures rather than whole complexes. For instance, in SCOP  and CATHall known structures from a whole protein complex perspective.Protein complexes, however, often contain more than two domains: they may contain multiple polypeptide chains, and each chain can contain more than one domain. Therefore, properties that depend on the whole protein complex cannot be studied by consideration of interacting domain pairs alone. Such properties of protein complexes are size, symmetry, evolution, and assembly pathway. There have been studies on manually curated subsets that address issues such as evolution of oligomers ,12, biocFor historical, medical, or other scientific reasons, the PDB is highly redundant, and some structures such as the phage T4 lysozyme are present in hundreds of copies. To our knowledge, no method allows the removal of redundancy among protein complexes. Available methods would break them down into nonredundant sets of domains (ASTRAL) , polypepOur structural classification of whole protein complexes  includesWe retrieved all Biological Units from the PDB October 2005), which are the protein complexes in their physiological state, according to the PDB curators. This information is attained by a combination of statements from the authors of the structures, literature curation, and the automatic predictions made by the Protein Quaternary Structure (PQS) server ,18. The 05, whichWe filtered Biological Units according to the following criteria: we only considered the structures present in SCOP 1.69  because After applying these filters, we obtained 21,037 structures, which we use throughout this study.A prerequisite for creating a hierarchical classification of protein complexes is a fast way of comparing complexes with each other. The full atomic representation is not practical, because automatic structural superposition is difficult, if not impossible, for divergent pairs of structures . InsteadWhich subset of features shall we choose? A natural way to break down a complex is into its constituent chains, each of which is a gene product. The pattern of interactions between the chains determines the QS and hence function of the complex. Unlike large-scale proteomic experiments, where complexes consist of a list of constituent subunits, PDB structures provide us with the QS: the exact stoichiometry of the subunits and the pattern of interfaces between them. The QS often plays a role in regulating protein function, and its disruption can be associated with diseases ,23. For To extract the pattern of interfaces from the structures, we calculate the contacts between pairs of atomic groups. We define a protein–protein interface by a threshold of at least ten residues in contact, where the number of residues is the sum of the residues contributed to the interface by both chains. A residue–residue contact is counted if any pair of atomic groups is closer than the sum of their van der Waals radii plus 0.5 Å . We inveAs one of our goals is to compare the evolutionary conservation of protein chains both within and across complexes, we must include information that allows us to relate the chains to each other. To do this, we use structural information, as defined by the SCOP superfamily domains, as well as sequence information. The N- to C-terminal order of SCOP superfamily domains enables us to detect distant relationships, while the sequence similarity allows comparisons at a finer level, e.g., filtering of identical chains.We chose the chain domain architecture, the sequence, and the chain–chain contacts to represent protein complexes because these are universal attributes of complexes. In contrast, other attributes such as the presence of a catalytic site, or the transient or obligate nature of an interface, are neither universal nor always available from the structure. However, these attributes can be easily projected onto our classification scheme to see how they relate among protein complexes sharing evolutionarily related chains.To this core representation we add symmetry information, which refines the description of the subunits' arrangement beyond the interaction pattern. We process the symmetry of each complex using an exhaustive search approach. Briefly, we centre the coordinates of the complex on its centre of mass; we then generate 600 evenly spaced axes passing through the centre of mass. We check whether the complex, rotated at different angles around each of the axes, superposes onto the unrotated complex. From this, we deduce the symmetry type. For a more detailed description, please refer to the A graph is simple and well-suited to store and visualize this information A. The grNote that we also include monomeric proteins in the classification, and we represent them by a single node. Though monomeric proteins are not complexes, their inclusion allows us to compare their frequency and other properties to those of protein complexes.An advantage of the graph representation is that it allows fast and easy comparison using a graph-matching algorithm. As the graphs carry specific attributes about the structure and sequence of the chains, and about the symmetry of the complex, we had to implement a customized version of a graph-matching procedure to take this information into account. For algorithmic details please refer to the Importantly, our graph-matching procedure allows different attributes to be considered, as illustrated in With these five criteria, we elaborate progressively stricter definitions of similarity between complexes as shown in From the second level of the classification downward, we include evolutionary relationship information. The definition of evolutionary relationship that we use at this level is that pairs of matching nodes (polypeptides) between two graphs must have similar 3-D structures, i.e., the same SCOP domain architecture. This means that two matching polypeptide chains sometimes have little or no sequence identity, but have only structural similarity, i.e., they are distantly homologous. The groups of complexes at this level of the classification are called QS families, and we find 3,151 of them in the PDB.4 formed by a single gene, and one containing the hemoglobin α2β2 formed by two homologous genes. We call the groups at this level QSs. Throughout the study, we use this level composed of 3,236 QSs as a reference set of nonredundant protein complexes. Note that our choice for using this level as a nonredundant set is related to our interest in gene duplication events, but other levels can be used, depending on the question asked.At the next level, we require in addition that two matching complexes must have the same number of genes coding for each domain architecture. This is illustrated in From level four downward, we group protein complexes according to the sequence similarity between the matching polypeptides of two complexes, from 20% identity at the fourth level to 100% at the twelfth level. We call these groups QS20/30/40, etc. As the sequence similarity threshold gets stricter, the 3,236 QS groups break down into smaller subgroups, from 4,452 QS20 at the fourth level to 12,231 QS100 at the twelfth level.In addition to the four criteria described above, we can impose the requirement that two complexes must have the same symmetry type to be part of the same group. Because this choice is made for all levels, two classifications are available: one where symmetry is used during the comparison process and one where it is not used. When symmetry is used, we split any group of protein complexes into two or more groups, so that all complexes within a group have the same symmetry. However, only a few groups had to be split according to symmetry, as we show later.The hierarchical classification is illustrated in N can be calculated and increases dramatically with N [. A single QS topology exists for N = 1 or N = 2, while there are 6 QS topologies for N = 4, and 261,080 for N = 9. Comparatively, we observe a low number, 192 QS topologies in total, that account for the 21,037 protein complexes. This low number suggests that some QS topologies are preferred over others in the protein universe. All the QS topologies containing up to nine chains are shown in QS topologies represent the number of subunits (nodes) in a complex and the pattern of interfaces (edges) between them, and is thus a topological level only. In mathematical terms, a QS topology is an unlabelled connected graph. The number of possible graphs for a given number of nodes y with N . A singlThe first, as shown in N chains (N ≥ 3), there are only N-2 topologies with the same number of interfaces per chain. For instance, only seven topologies satisfy this criterion for N = 9, a small number compared with 261,080 possible ones. The second constraint limiting the number of QS topologies lies in the composition of the PDB, which consists mainly of homo-oligomeric complexes, that is, complexes formed by multiple copies of the same protein. As protein–protein interfaces are often hydrophobic ,27, a diThe third constraint limiting the number of QSTs is that 85% of the complexes in the PDB are symmetrical. This trend is captured in So the QS Topologies allow a survey of the organization of the chains in protein complexes. This is best illustrated by the large protein complexes shown in , two of which are shown: Escherichia coli lyase and Homo sapiens hemoglobin.When we consider structural similarity in the form of domain architecture identity between pairs of matching subunits of two complexes, the 192 QSTs break down into 3,151 Quaternary Structure Families (QSFs) (H. sapiens hemoglobin QSF will break down into two QSs: (i) the γ4 hemoglobins (formed by four copies of a single gene), and (ii) the α2β2 hemoglobins (formed by two copies of two homologous genes). However, all structures present in the E. coli lyase QSF consist of one gene only, and, therefore, the QSF contains a single QS.In the next level of the classification, the QS level (level 3), a constraint will be added on the number of genes per domain architecture  contained mixed symmetry types. A manual inspection revealed that 93 of these cases are in fact due to a mix between presence and absence of symmetry in the complexes of each QS. The reason for the absence of symmetry is either biological, for example due to a conformational change (16 cases), or due to an error in the PDB Biological Unit (42 cases). There were also two cases of a false negative result in our symmetry search procedure and 33 ambiguous cases that we were unable to resolve. There are a further 16 QSs with two different symmetry types. Of these, seven are true biological cases, five are errors in the PDB Biological Unit, and three are unresolved. The PDB codes of likely erroneous Biological Units are provided in In addition to mixed symmetries, further criteria to filter for errors can be derived from the representation of a protein complex as a graph. For example, in homomeric complexes, in which all the subunits are identical, all chains are expected to have the same number of interfaces. The graph representation allowed us to identify cases where this requirement is not satisfied.A difference in the number of interfaces of the subunits within a homomeric complex can be biological and is associated with conformational changes in most cases. An example is the hexameric prokaryotic Rho transcription termination factor (PDB 1pv4), which forms an open ring resulting in a linear graph topology in its unbound state . The rinHowever, in some cases, the asymmetrical graph topology of homomeric complexes corresponds to an error in the definition of the PDB Biological Unit. In The PDB and the PQS servers are the only resources that provide information on Biological Units of crystallographic structures. An essential difference between these two resources is that PDB Biological Units are partially manually curated, whereas those from PQS are generated in an entirely automated manner. It is therefore interesting to compare the extent of agreement between the two databases.Mycobacterium tuberculosis (1q51), which consists of a homohexamer [Manual inspection and curation of more than 20,000 Biological Units present in both databases is extremely time-consuming. However, we can capture essential differences by comparing the much smaller number of QSTs. We have seen that the PDB Biological Units correspond to 192 QSTs. The PQS yields a slightly higher number of 218 QSTs. When comparing the two sets, we find 155 QSTs common to the PDB and PQS, implying 37 exclusive to the PDB and 68 exclusive to the PQS. We hand-curated the structures exclusive to each database and found that 19 out of the 40 QSTs exclusive to the PDB, and 42 out of 68 QSTs exclusive to the PQS, are likely errors. The accession codes of these structures are shown in ohexamer . Here, tE. coli proteins in SwissProt.In the section about QSTs, we showed that most complexes in the PDB are small, homomeric, and symmetrical. How general is this result? The PDB is a restricted dataset in which transmembrane proteins, low complexity regions, and disordered regions are underrepresented ,40, and Interestingly, the trend in the PDB is in close agreement with our observations in SwissProt as shown in All five datasets show that homo-oligomerization is very widespread. This could be because it provides simple ways of regulating protein function. It can serve as a sensor of protein concentration or pH at which self-assembly occurs and triggers a function, as in the case of the cell death protease caspase-9 . It can It is interesting to note the apparent contradiction between large-scale proteomics data and structural data. In proteomics data, most proteins are interconnected into a “giant component” , while ihttp://www.3Dcomplex.org. We pre-computed three classifications, each with and without symmetry information. Note, that even without selecting symmetry as a classification criterion, the symmetry information is still displayed, so that one can see whether a group contains one or several symmetries. Two of the pre-computed classifications start with the QS topology and differ in the combination of sequence identity levels. The third pre-computed classification starts at the QS level, so that all QSs can be viewed on the same page.The hierarchical classification of all complexes in the PDB is available as a database on the World Wide Web at It is also possible to select any combination of levels in the classification and browse the result computed on the fly. Combining together different levels in the classification yields different sets of complexes that can be viewed together. For example, when choosing the first and the last level only, QS topologies are linked to all PDB structures. This could be used, for example, to survey all the PDB structures composed of four proteins connected in a particular way.The database can also be searched by PDB accession code, by SCOP superfamily identifier or domain architecture, by keyword, and by symmetry type. An example of an application of the search facility is a query for all the protein complexes in which one particular domain superfamily participates, in order to learn about the evolution of the interactions of that superfamily. One could also search for a combination of terms, such as transferases that have D2 symmetry.Besides automatic search and downloading options, manual inspection of complexes is facilitated by the novel visualization mode of representing complexes as graphs. This allows one to analyse and compare many aspects of complexes at a glance that are much more difficult to extract from the standard representations of 3-D structures. For anyone interested in a particular structure, viewing the structure within the 3D Complex classification allows fast comparison with other complexes. For instance, one can quickly gain an overview of the size and pattern of the interfaces in the complex of interest and related complexes.Most proteins act in concert with other proteins, forming permanent or transient complexes. Understanding these interactions at an atomic level is only possible through analysis of protein structures. Here we have presented a novel method to describe and compare structures of proteins complexes, which we used to derive a hierarchical classification system.This hierarchical classification allows us to answer to the question, “How many different complexes exist in the PDB?” Depending on the level of detail, we find from 192 structures at the top level to 12,231 structures at the bottom level of the hierarchy. Which one of these levels is used in an analysis will depend on the type of question addressed.Considering the top level of the hierarchy, the QSTs, we see a strong bias toward small, homomeric, and symmetrical complexes, and we show that this result can be generalized to SwissProt proteins. We observe that complexes with an even number of subunits are favoured in SwissProt, indicating that dihedral symmetries are more frequent than cyclic symmetries, in the same way as in the PDB. The QS family and QS levels are appropriate nonredundant sets of complexes for many types of analysis. Here we use the QS level for the comparison with SwissProt, and we find that it is in closer agreement than the complete set of complexes.The remaining levels encompass sequence homology between complexes, ranging from a sequence identity threshold of 20% for QS20 to 100% for QS100, at 10% sequence identity intervals. Using these levels, we explore how four types of similarities between complexes  relate to each other. At all four levels, we observe the same trend: many complexes are unique, and a few are highly redundant. By integrating these levels with symmetry information, one can address issues such as the sequence threshold at which symmetry type is conserved or broken. By projecting the levels onto each other, the abundance of homologues at different sequence identity thresholds becomes apparent., 3D-repertoire, http://www.3drepertoire.org) and with the increasing proteomics data on protein complexes [We describe the first global framework for analysis of protein complexes of known 3-D structure. The classification will be a starting point for future work aimed at understanding the structure, evolution, and assembly of protein complexes. It is our hope that it will facilitate a better understanding of protein complex space, in the same way SCOP and CATH have played major roles in our understanding of fold space . This isomplexes .A* algorithm [aG, bG) as input and three tolerance parameters: M, the number of label mismatches; I, the number of node indels (insertions or deletions); and E, the number of edge indels. It returns whether aG matches bG allowing for M, I, and E. An additional parameter, S, is a score threshold above which a pair of nodes is matched.The graph alignment algorithm developed here is a modified implementation of the lgorithm . It takeaiN from aG at random. (ii) Map it to all the nodes bjN from bG. The mappings (aiN–bjN) with a valid cost (costs are explained below) are added to the list of mappings denoted as L. (iii) Extract from L a mapping m with the best (lowest) cost and extend it, i.e., take a node of aG that is not contained in m and that is connected to a node in m; map it to all the nodes of  that have not been mapped yet, and create a new mapping for each. Add the new mappings with a valid cost (see below) to L. (iv) Restart stage 3 either until L is empty, in which case the two graphs could not be matched, or until a mapping m contains all the nodes from aG and b,G in which case the two graphs are matched. Note that the procedure is exhaustive and therefore does not depend on which node is picked first at random.The algorithm can be decomposed into four steps: (i) take a node m, we calculate three costs: (i) M,C the number of pairs (ai– NbjN) that do not have the same domain architecture, or whose sequence similarity is below the threshold S. (ii) CI, the number of pairs containing a gap (aiN–gap). Note that in the current version, gaps cannot be inserted in aG but only in bG. (iii) CE, the number of edge inconsistencies between the mapped nodes.Given a mapping M , CI,C and EC are below or equal to the tolerance parameters M, I, and E, respectively. In the present study, the QST were generated with M = number of nodes in aG and S = 0, because homology between nodes is not considered at the QST level. The highest resolution structure of each QST is taken as a representative of the group. Matched complexes are clustered by single linkage to create the groups of complexes that constitute this level of the hierarchy. QS families and QSs were generated with M = 0, as the domain architectures have to match perfectly between two complexes, but the sequence identity parameter S = 0. The consistency in the attribute “number of genes per domain architecture” was checked prior to the graph comparison. All the other levels (from QS20 to QS100) were generated with M = 0 and S ranging from 20 to 100. The number of node and edge mismatches tolerated was 0 throughout all levels (I = E = 0), though this could be loosened in future work.The mapping is only valid if the costs http://www.3Dcomplex.org were generated using GraphViz [The graph images on the Web site at GraphViz .no possible symmetry (NPS) category, and these complexes are not used in the following steps.The process of finding symmetries is performed in three main steps. First, we check whether symmetry can exist in a complex based on its composition in terms of groups of identical or homologous chains. If each group of identical or homologous chains contains an even or odd number of chains (different from one), then symmetry can exist, and the complex is labelled either with the name of the symmetry type found or with NS if no symmetry is found. If we see that no symmetry can exist, e.g., in the case of a heterodimer of nonhomologous subunits, the complex is classified in the N equivalent residues for each group. N is limited to 50 but must be larger or equal to 15. Structurally equivalent residues are found using a FASTA sequence alignment [For the next two steps, let's take as an example a complex with two groups of identical chains AB and CD . We first extract the α-carbon of n to (180 + (360/n)) degrees, by steps of 360/n, where n is the number of subunits. After each rotation, a distance d is measured and is equal to the mean of the Euclidian distances of each atom with its closest structurally equivalent atom. The distance d reflects the quality of the superposition associated with each axis and angle. We select the top 2n axes and refine each of them to minimize d. We retain all axes and angles for which d < 7 Å, and we group those separated by less than 25 degrees. Thus, for each structure, we obtain a set of axes and angles of symmetry from which we deduce the symmetry type.Next we search for axes and angles of symmetry. First, we centre the coordinates of the structure on its centre of mass  Within each complex, identical or homologous chains  are grouped. A set of 50  structurally equivalent residues is selected for the chains within each group and represented using the alpha-carbons. The protein shown here is a homohexamer, so there is a single set of equivalent residues.(B) The coordinates of the set are transformed so that the origin is the centre of mass, shown as a green point.N) degrees, where N is the number of subunits, are applied around each axis. An RMS deviation is calculated after each rotation. If it is lower than 7 Å, the axis and the angles are retained. The symmetry of the complex is deduced based on the set of retained axes and angles of symmetry.(C) 600 axes are generated, connecting the green origin to each of the 600 blue points. Rotations of (360/(63 KB PDF)Click here for additional data file.Protocol S1(29 KB DOC)Click here for additional data file.Table S1The percentage overlap in the table indicates the proportion of structures that stay in the same QS class when varying the threshold for a chain–chain contact definition. The threshold value corresponds to the number of residues contributed by both chains. The high overlap shows that our methodology is robust.(28 KB DOC)Click here for additional data file.Table S2PDB accession codes in parentheses are redundant complexes for which the same error was found. For each PDB structure, the number of chains in the current Biological Unit is given, as well as our own suggestion for correction of the prediction. In cases where the current and suggested number of chains is the same, we believe the Biological Unit should contain different interfaces with the same number of chains.(A) Errors were found upon manual inspection of groups of protein complexes in the same QS that contained some complexes for which no symmetry was detected and other complexes with symmetry. The number of likely errors in this table is 68.(B) Errors were found upon manual inspection of protein complexes in the same QS that had different symmetry types. The number of likely errors in this table is 13.(C) Errors were found by looking manually at peculiar QS Topologies, where identical subunits did not have the same number of contacts. The groups correspond to the four different topologies shown in (D) List of possible errors found during searches described for the three tables above, but for which support from sources such as literature was not available. The number of possible errors is 62.(E) Errors were found during the comparison process between topologies found in PDB and PQS. The following list comes after curation of the topologies that are exclusive to PDB.(F) Errors were found during the comparison process between topologies found in PDB and PQS. The following list comes after curation of the topologies that are exclusive to PQS.(63 KB DOC)Click here for additional data file.


The number of protein structures from structural genomics centers dramatically increases in the Protein Data Bank (PDB). Many of these structures are functionally unannotated because they have no sequence similarity to proteins of known function. However, it is possible to successfully infer function using only structural similarity.Here we present the PDB-UF database, a web-accessible collection of predictions of enzymatic properties using structure-function relationship. The assignments were conducted for three-dimensional protein structures of unknown function that come from structural genomics initiatives. We show that 4 hypothetical proteins , for which standard BLAST tools such as PSI-BLAST or RPS-BLAST failed to assign any function, are probably methyltransferase enzymes.We suggest that the structure-based prediction of an EC number should be conducted having the different similarity score cutoff for different protein folds. Moreover, performing the annotation using two different algorithms can reduce the rate of false positive assignments. We believe, that the presented web-based repository will help to decrease the number of protein structures that have functions marked as "unknown" in the PDB file. and  One cha database. The map database, which i database is anoth database-9 for auBefore predicting the enzyme function based on structural relationship, we checked if it was possible to assign the EC number to the protein using only sequence similarity information. George et al. found that even for homologues detected by a third iteration PSI-BLAST profile there is a 50:50 chance of assigning a fairly specific three-digit EC number . This woTwo different strategies were applied to annotate the proteins with EC numbers: namely 3D-Hit and 3D-Fun. The first method simply scans using the 3D-Hit program  a sequenAll structural similarity scores are used for annotation in the 3D-Fun strategy. First, the query structure and all sequentially non-redundant proteins are hierarchically clustered (grouped) by structural similarity using complete-link algorithm,14. NextWe used both presented algorithms to infer the EC number for the 499 proteins from structural genomics that are currently available and have unknown functions. In order to avoid over-annotation due to partial EC numbers we used Green and Karp recommendation . If 3D-HIn the Figures In spite of the fact that the 3D-Hit and 3D-Fun algorithms used fold-specific cutoffs of similarity score, both of them gave conflicting predictions for some of the 499 proteins selected from structural genomics. For example, the EC number was correctly assigned to 1RVK and 1K77 structures only by one program, 3D-Hit or 3D-Fun, respectively. Figure Structural genomics initiatives tend to target structures that are less typical of the PDB as a whole and so the cutoffs derived from the whole PDB may not be entirely applicable. Therefore, we analyzed 58 structures with predicted EC numbers, which were recently published and functionally annotated since this may give a truer indication of the accuracy. We found only one additional (except described above) incorrect prediction: 1VGY had beenA. Aureus, E. Coli, T. Maritime, and B. Subtilis), which came from different structural genomics consortia, were chosen to demonstrate the utility of the algorithm. The EC numbers of these bacterial proteins have not been assigned in PDBSprotEC and SCOPEC databases. Moreover, standard sequence comparison tools such as PSI-BLAST run against the NCBI non-redundant protein sequence database or RPS-BLAST applied using the Conserved Domain Database[Four three-dimensional structures of hypothetical proteins from various species ( Database failed t Database and 1UAJ Database structur Database. A fold The PDB-UF database is a collection of assigned EC numbers to protein structures of unknown function, which come from the structural genomics centers. Structure-based prediction of the EC number was conducted having different cutoff values for a different protein folds. In order to reduce the number of false positives the annotation was performed using the Meta-strategy. The web-based repository will be updated automatically when new protein structures are released. and at The database is available at MvG developed the PDB-UF database and the 3D-Fun method. DP and LR designed the 3D-Hit algorithm. KG and ES provided thoughtful insights and helped in testing. All authors have read and approved the manuscript.