A protein annotation database, such as the Universal Protein Resource knowledge base (UniProtKb), is a valuable resource for the validation and interpretation of predicted 3D structure patterns in proteins. Existing studies have focussed on point mutation extraction methods from biomedical literature which can be used to support the time consuming work of manual database curation. However, these methods were limited to point mutation extraction and do not extract features for the annotation of proteins at the residue level.This work introduces a system that identifies protein residues in MEDLINE abstracts and annotates them with features extracted from the context written in the surrounding text. MEDLINE abstract texts have been processed to identify protein mentions in combination with taxonomic species and protein residues (F1-measure 0.52). The identified protein-species-residue triplets have been validated and benchmarked against reference data resources . Then, contextual features were extracted through shallow and deep parsing and the features have been classified into predefined categories (F1-measure ranges from 0.15 to 0.67). Furthermore, the feature sets have been aligned with annotation types in UniProtKb to assess the relevance of the annotations for ongoing curation projects. Altogether, the annotations have been assessed automatically and manually against reference data resources.This work proposes a solution for the automatic extraction of functional annotation for protein residues from biomedical articles. The presented approach is an extension to other existing systems in that a wider range of residue entities are considered and that features of residues are extracted as annotations. The understanding of the biological function of proteins remains to be a central challenge in biology. In protein science, sequence analysis of amino acids or studies of their spatial distribution have led to predictions and discoveries of a number of biological significant patterns and motifs, e.g. metal-binding sites, catalytic triads, and ligand binding sites -7. ComplWorks have been published in the biomedical text mining community that focused on the extraction of point mutations, which is one type of a residue mention but others have to be considered as well -19. The All of the listed text mining systems are dedicated to the extraction of point mutations, but do not extract contextual features from text as functional annotations of protein residues. A recent publication  proposesThe contribution of this work is the automatic extraction of protein residue annotation from biomedical articles. Contextual information are exploited to identify features of residues that correspond to one of six chosen target categories. As a result, proteins can be selected with residues clustered by annotation types, which can lead to, for example, the identification of active site residues.The extraction of functional annotations for protein residues from literature consists of two parts: protein residue identification, and contextual feature extraction. Figure Named entity recognition for proteins was based on a combination of dictionary lookup with fuzzy matching and basic disambiguation -25. All The extraction of residue mentions follows approaches of previous publications by reusing a selection of regular expression patterns for point mutation ,19. The The identification of the entity triplet organism, protein, and residue, is based on the algorithm described by  with somIn the first step proteins were associated with their hosting organisms. All pairs of protein-species were determined for each protein in a text and ranked by the smallest word distance between the entities in the pair. The identification of organism-protein began with the pair with the smallest word distance measure. A valid association was found, if the relation was specified in UniProtKb. If an association was validated then the search was terminated, and the protein was annotated with the corresponding Uniprot identifier, otherwise the next entity pair from the list was tested. If no match between protein and species was found, then the search was relaxed to genus matching. This relaxed matching is the expansion to the  algorithThe second step of entity triplet association was the association of residues with their source proteins. The procedure of selecting and ranking the residue-protein pairs was similar to the protein-organism association identification. For each pair that was to be tested the annotated Uniprot identifier of the protein was used to retrieve the protein sequence from the database. Three cases can be distinguished: (1) the residue correctly matches the protein sequence; (2) several alternative sequences are matching from a list of proteins; and (3) no match can be found for the residue with the available protein sequences. If a match was not found, then the protein-residue pair was rejected, and the search continued with the next pair from the ranked list. Notice that protein-residue association is purely based on the identification of a residue in a protein sequence. A grounding step was not utilised as the identification of the correct sequence position of a residue would require either the simultaneous alignment of residues, or the use of a commonly agreed and standardised sequence indexing scheme.The extraction of contextual features of protein residue mentions is based on a syntactical analysis of natural language sentences. Two approaches were developed in this work and compared in the performance evaluation study: shallow parser based relation extraction, and full parser based relation extraction.The first approach was to develop a shallow parser, which aims to find the boundaries of major constituents in a sentence, such as noun phrases. The design is based on heuristics and the idea of finding general relations between closed-class English words . All preInitially, an abstract text was split into sentences, and then annotated with part-of-speech (POS) tags using the CISTAGGER , which wThe second approach in contextual feature extraction utilises the full parser ENJU   is the probability of a category associated with a term. The categorisation of a multi-word term into one of the categories, amounts to the identification of the category c* that maximises the association A:where UniProtKb is a comprehensive protein knowledge base that contains manually curated functional annotations for proteins, their sequences, and their residues. It also contributes citation references (PMIDs) for relevant articles. However, the precise association of a citation and a protein residue in context of functional annotation is generally not available.The test dataset for the developed functional annotation extraction is based on the citation references from UniProtKb. A Uniprot corpus was generated by retrieving abstract texts from MEDLINE, that are indexed by the knowledge base. From the 136,566 citations listed in UniProtKb, an almost complete set of 136,559 abstract texts was retrieved from MEDLINE. Although not all information presented in the UniProtKb are necessarily available in the Uniprot corpus, the Uniprot corpus is a starting point for the evaluation of the developed text mining modules. Two derived test corpora were generated from the Uniprot corpus: the gold standard corpus with manual annotation and the cross-validation corpus with automatically annotated information derived from UniProtKb. Table From this set of 100 abstract texts, manual analysis results in four types of annotations. The first type is the annotation of the biological entities of organism, protein, and residue, while the second is the annotation of entity triplet associations, i.e. organism-protein-residue. In addition, text segments of sentences with a residue entity were annotated, if they represent keywords for functional annotation. Finally, the association of a keyword and a residue was also annotated in GC.For the automatic evaluation of extracted data, a cross-validation corpus (XC) was derived from Uniprot corpus. This test set was used to analyse the performance of protein-organism (XC1) and residue-protein (XC2) associations. The test set was annotated automatically, i.e. the biological entities were detected with the same entity recognition systems. The documents in the Uniprot corpus were scanned for tri-occurrences of organism, protein, and residue in text and analysed, if the combinations of the four identifiers, (UID+TID+RID+PMID), can be found in the database. UID is the Uniprot ID, TID is the NCBI Taxonomy ID, RID is a residue identifier, which consists of a residue name + sequence position, and PMID is the PubMed identifier. If at least a single match was found, then the document was selected. For the non-matching combinations the corresponding annotations were removed from text. From the retained and validated associations of the identifiers, two sets of three identifier combinations were determined: UTP = (UID+TID+PMID), and URP = (UIP+RID+PMID). 70,401 UTPs from 55,998 abstract texts were determined for XC1, and correspondingly 68,008 URPs were derived from 5,253 MEDLINE articles in XC2.The performance of each process of the developed protein residue identification was scored against a manually annotated gold standard corpus. Proteins, where the protein entity recognition system and manual curation assigned the same entity were considered as true positives (TP). The same rule also applied for counting TP of residue and organism entity detections.The evaluation of the entity triplet association detection considered only associations as TP, if both pair relations organism-protein and protein-residue were determined correctly. If one of the relations was incorrect, a found association was counted as false positive (FP).In contrast, the automatic evaluations of the entity recognition and entity association detection systems were performed on XC. A true positive of an annotated entity within an abstract text was identified, if UniProtKb lists the same entity in context of the given PMID. For example, if organism X in text Y is also indexed in UniProtKb as a combination of TID+PMID, then a true positive was counted.A correct protein-organism association was detected, if the determined identifier combination UTP was found in XC. Similarly, a correct residue-protein association was found, if the derived identifier combination URP was found in the test corpus. Notice that within this evaluation setup a UTP or URP was counted as FP if the information was not stored in UniProtKb albeit the correct entity association. The effectiveness of the entity recognition and the association detection systems was measured in terms of precision, recall and the balanced F-measure (F1).The extraction of contextual features of residues results in a set of syntactical relations, which are represented as PAS. The performance of this extraction module was evaluated by comparing the returned PAS data with manual annotations in GC. A TP was counted, if the syntactical relations in a PAS were correct, and if the arguments in the PAS contained the annotated residue entity and the marked keyword(s) in the test corpus.The performance of the developed classification method was evaluated by a 100 times 5-fold cross-validation. For each iteration, terms in the reference set were shuffled, and partitioned into a test set (1/5 of the data) and a training set (4/5 of the data). The average precision, recall and F1-measure were calculated for each classifier from the determined confusion matrix.The following sections assess first the extraction system and then the extracted data.All presented results have been evaluated against the manually curated test set, i.e. the gold standard corpus (GC). Entity recognition for the residue mention yields to 92% precision and 98% recall (95% F1 measure). This performance is slightly higher than the one of previously reported solutions of point mutation identification ,16,18, iThe performance of the organism entity recognition system shows better performance than the protein entity recognition solution .The identification of entity triplets consisting of one of each entity type, i.e. the protein, organism and residue mention (POR), is based on association rules. The identification resulted to 82% precision and 38% recall, where our precision is similar to other reported solutions. Our recall is lower than the one reported in MutationGraB and MuteXt and could be explained by the fact that both systems are focused on protein family specific full-text articles. According to our manual inspection the main cause for low recall is because the association of the protein to the organism is often not stated explicitly.The POR detection still requires improvements but is sufficiently precise (82%) to use it for the annotation of protein residues with contextual features. Furthermore, the identification of the correct organism from the literature is helpful for the alignment of the finding with the correct database entry, but it is also an option to relax the organism identification and to rely more on the database content for the final resolution of the protein residue.The POR triplets were now cross-validated against the content of UniProtKb which is the reference database for protein annotations using the cross-validation corpus (XC). We assessed the association of the protein to the organism independently from the residue-protein association. The comparison yielded 82% precision and a recall of 88% for the protein-organism. In the performance assessment of the residue-protein association detection we measured 83% precision and 13% recall. The low recall is less surprising in this case since the annotation of residues is not as mandatory as the species annotation in UniProtKb.The cross-validation of both associations resulted to 54% F1 measure with 83% precision which is similar to our analysis against the GC. By contrast, the average recall in the cross-validation is higher than the one reported from analysis based on the gold standard. The reference database provides reliable information for comparison but the time-consuming curation process is one reason for the lack of content in comparison to the scientific literature.Finally, we filtered all MEDLINE abstracts for mentions of POR triplets and compared the retrieved citations against the references in the citation sets in UniProtKb/PDB. In total 40,750 MEDLINE abstracts make a reference to POR triplets. In total 9,354 Uniprot proteins are covered and 2,884 of these proteins have hyperlinks to 14,007 PDB entries. These 2,884 shared protein mentions are linked to 18,427 MEDLINE abstracts, whereas UniProtKb/PDB makes only reference to 4,652 PMIDs out of which 657 PMIDs are shared between both resources. This demonstrates again that the MEDLINE abstracts do not necessarily cover the information that curators have deemed relevant  and ENJU produced 31% F1 . Manual inspection of the false positive results indicates that mistakes in the part-of-speech tagging are the main source of the error rate. For example, in the sentence"Conversely, K382Q displays a highly altered responsiveness to the activator, suggesting that Lys(382) is involved in both activator binding and allosteric transition mechanism." (PMID:10751408),both parsers identified "altered" as a verb in past tense, although the correct POS is a noun modifier. The performance of the POS tagger is critical for the detection of phrase boundaries.Altogether, the developed shallow parser identifies predicate-argument structure from the context of residue entities. The extracted information is used as functional annotation.The identification of functional annotations in the scientific literature is a challenging task. It requires the identification of textual features that can be linked to predefined classes. The categories of interest are listed in table The classifiers have been trained on the proposed feature sets and the performance has been measured using a cross-validation scheme. For MAN, the best performing classifiers produced F1 measures of 62%, 57%, and 57% for the categories STR_COMP, CHEM_MOD and BINDING  is 172. This represents the target for protein residue identification.Corresponding to these PORs is the set of functional annotations (PAS data). For 109 out of 172 PORs, keywords were co-mentioned in verbal relations. The number of PAS associated with the 109 PORs is 117. This represents the target of functional annotation extraction.Figure In comparison, if the system would have identified all protein residues correctly, the performance of the whole extraction would have yielded in a precision of 0.68 and a recall of 0.48. This shows that functional annotation can be extracted with a reasonable confidence. The recall can be explained by the performance of the contextual feature extraction module.The result indicates that the extracted functional annotations have a reasonable precision, but are low in coverage. This can be explained by the sum of the performances of each text mining module. On one hand, an incorrectly determined protein residue leads to a false positive of PAS. On the other hand, failed entity recognition contributes to the false negative rate. In addition, language complexity, and incorrectly parsed sentences are the other reasons for the false positive and false negative rate of functional annotation extraction.Despite the low coverage of the functional annotation extraction system, the extracted information is correct and reusable for the annotation of protein residues. A comparison with UniProtKb shows, that 5 out of 16 are rediscovered knowledge .The following information was extracted and semantic categories were assigned to the arguments of the PASpred = inhibitedarg1 = Specific bindingCHEM_MODarg1-of = [the WT and mutant receptors CYS14ALA and CYS199ALA]/arg2-in = the presencearg2-of = the disulfide bond reducing agent.Although, one part of the information in the example has been correctly assigned with the label CHEM_MOD, the entire text phrase should be labeled with BINDING. A solution to this problem is not trivial and requires several levels of linguistic analysis.The aim of this work was to compile protein residue features from MEDLINE texts as annotation for UniProtKb proteins by combining a series of previously studied text mining methods. Although the performances of each module may not be at optimal level, the generated data output indicates that the strategy is able to deliver biological meaningful results. Cross-validation with UniProtKb analysis indicates that the extraction contains novel information that can complement and update the knowledge in UniProtKb and consequently provide annotations for PDB protein structures. However, as with high performing biological entity recognition and relation extraction systems become more available, this conceptual strategy in protein residue annotation extraction may yield optimal results for the biological community.It is important to note that the extraction was done only on abstract texts from MEDLINE. The benefits lie in the broad coverage of biological topics, and the open access to a vast amount of scientific articles, which are advantageous for evaluating the biological significance of residues in proteins from various biological context. However, we acknowledge that the goal in this specialised information extraction task is to be able to find relevant data from full text articles. Considering that full text documents contain more information than abstract texts, and that this setup is more advantageous for previously reported point mutation identification systems, it is interesting to see how the proposed functional annotation extraction system performs on full text articles.The authors declare that they have no competing interests.Kevin Nagel carried out the experiments, developed and implemented the methods, assessed the annotations, and drafted the manuscript. Antonio Jimeno-Yepes participated in the development of the methods and drafted the manuscript. Dietrich Rebholz-Schuhmann participated in design of the experiments, assessed the annotation and drafted the manuscript. All authors read and approved the final manuscript.Labeled termlist according to the MAN scheme. The listed terms were found according to a substring analysis of noun phrases with identified protein residues with the whole string of other coocurring noun phrases . The terms were manually assigned to categories from the categorisation scheme MAN.Click here for fileComparison of extracted functional annotations from GC with UniProtKb. Comparison of extracted protein residue annotations from GC with UniProtKb. Mined functional annotations are listed as PAS, while relevant information from UniProtKb are reproduced from the feature table entry line.Click here for file


Protein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several  experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible.OpenPPI_predictor) that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism.Here we report the development of a simple open source program module (Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans.Results from our predictions can be visualized using the OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/.The  STRING [MINT [BioGRID [DIP [POINT [IntAct [The cell is the structural and functional unit of living organisms. Cells carry out numerous functions, from DNA replication, cell replication, protein synthesis, and energy production to molecule transport, to various inter- and intra-cellular signaling. Many of these fundamental processes require cascades of biochemical reactions that are catalyzed by interacting protein enzymes. Other interacting proteins provide structural support for the cells, form scaffolds for intracellular localization, and serve as chaperones or as transporters. The large-scale study of all cellular proteins is known as proteomics ,2. SinceSTRING , MINT [1NG [MINT , BioGRID[BioGRID , DIP [18P [POINT  and IntA [IntAct .OpenPPI_predictor) for such intermediate role. The tool predicts the complete protein-protein interaction network for target genomes, using interactome data from related organisms (i.e. reference genomes). For further analysis, the generated putative interactome can be visualized using the Cytoscape software [B. malayi. The predictions rely on the available interactome data of the close relative nematode C. elegans. The predicted number of interactions, and types of interacting partners, the distinguishing features from the human interactome guide our wet lab researchers in the selection of protein targets which seem essential for the parasite, so blocking them would disrupt its cell cycle, yet the intervention would not interfere with human protein complexes.Most of these reviewed prediction methods are implemented as web servers, which are convenient for the in-depth analysis of selected nodes and features, but offer little flexibility when the prediction of a complete cellular interactome is an intermediate goal, embedded in an involved discovery scheme. In this paper, we report the development of a simple open source tool  Ortholog (diverged from the same immediate ancestor) protein identifier, and (b) Protein - Protein interaction predictor. The tool requires four kinds of inputs:i. Sequences of proteins from the reference genome,ii. Interactome for the reference genome ,iii. Sequences of proteins from the genome of interest.iv. Protein ortholog assignments between organism of interest and reference organism.The ortholog protein identifier extracts information from an ortholog database, and makes connections across the reference genome and the genome of interest. The output from this module is a list of connections between proteins in the genome of interest and their corresponding orthologous relatives in the reference genome.The protein - protein interaction predictor module uses the already known interactome of the reference genome. Interactions in the reference set are projected back to the corresponding orthologous proteins of the genome of interest.More formally, the workflow of our method is as follows: assume we have two query proteins Q1 and Q2, with corresponding orthologous proteins R1 and R2 in the reference genome. If R1 and R2 interact in the reference organism, then the prediction is made, that Q1 and Q2 also interact. Knowledge about the relationship of R1 and R2 are transferred to a predicted relationship between Q1 and Q2.OpenPPI_predictor tool. The algorithm used in this pipeline is divided into following steps:Figure OrthoMCL-DB.Step 1: Create the ortholog connections between reference genome and genome of interest sequence, using ortholog identifier component from Cytoscape software.Step 2: Use protein-protein interaction predictor to predict interactome for genome of interest from interaction data in the reference genome, and the ortholog connections created in Step 1. The predicted interactome is in format that is compatible to Cytoscape software to visualize and analyze the predicted interactome generated in Step 2.Step 3: Use OpenPPI_predictor is implemented in AWK and C shell language and installed on Linux. The source code can be downloaded from http://tools.neb.com/~posfai/. The program has been tested by generating predictions for pairs of yeast and mammalian model organisms, using several resources listed in the Introduction.OpenPPI_predictor to predict the B. malayi protein - protein interaction network. Genome and proteome data was fetched from NCBI (http://ncbi.nlm.nih.gov) ortholog assignments from OrthoMCL-DB [C. elegans interactome data was downloaded from Worm Interactome Database [We have used hoMCL-DB , while rDatabase .B. malayi is a human parasite. It causes elephantiasis, a wide spread and devastating disease, characterized by swelling of the lower limbs. Other filarial parasites, Wuchereria bancrofti and Brugia timori are also widespread, and cause serious diseases. Though these latter organisms differ from B. malayi morphologically, symptomatically, and in geographical extent [C. elegans is a free living nematode, and one of the most studied organisms, with available experimental genome, proteome, and interactome data. C. elegans interactome is used here to predict the B. malayi interactome, because of the high level of genomic conservation between these species [C. elegans interactome is composed of 178151 interactions, from the 20100 proteins encoded in the genome. The interactions have been established through large scale projects using different methods.The filarial nematode l extent , our tar species . The C. http://blast.wustl.edu), followed by some form of clustering .Orthology resources typically employ all-versus-all BLASTP analysis . Some t[ProGMap , Berkeleley PHOG , TdrTargrTargets , BLASTO  [BLASTO , use adds , and th [P-POD, ), but seatabase  carries less uncertainty, if a closely related reference organism can be found. Data from multiple related reference organisms would increase the signal to noise ratio, and improve the value of predictions. Experimentally verified test tube interactions may not be projected unconditionally to in vivo conditions: OpenPPI_predictor tool. The tool predicts the protein interactome for a genome of interest, using the interactome data from a closely related organism, and protein orthology information between the two species. The tool is designed for genome wide interactome predictions, and provides a simple, flexible and easy to use platform for proteomic research.Here we report the development of the Making predictions about possible protein-protein interactions is only an intermediate step in understanding protein function or in the search of drug targets. Upstream and downstream steps, biochemical and physiological considerations (many listed in earlier paragraphs) in finding applicable datasets, in filtering input data, in interpreting results and in drawing inferences make only the predictions relevant.In the future, we plan to enhance both the utility and the coverage of our predictions using data from multiple related organisms, taking into account the phylogenetic distances between the interrogated pairs. We plan on ranking, or categorizing the predicted interactions according the consistency with which the predictions appear in the pair-wise predictions.The authors declare that they have no competing interests.openPPI_predictor. CSP and JP wrote the manuscript. All authors have read and approved the final manuscript.CSP wrote source code for


In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteins and the rapid advancement of this technique, in combination with other proteomics methods, results in an increasing amount of proteome data. This data must be archived and analysed using specialized bioinformatics tools.We herein describe "PARPs database," a data analysis and management pipeline for liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics. PARPs database is a web-based tool whose features include experiment annotation, protein database searching, protein sequence management, as well as data-mining of the peptides and proteins identified.Using this pipeline, we have successfully identified several interactions of biological significance between PARP-1 and other proteins, namely RFC-1, 2, 3, 4 and 5. Proteomics aims to identify, characterize and quantify all of the proteins expressed by a given living organism, tissue or cell line. Typicalin silico biology and empirical methods. Thus for protein interaction studies, there is clearly a need to develop a systematic and stepwise in silico approach to predict potential interactors. This approach will most likely improve our understanding of how complex biological systems work.One of the most effective approaches to elucidate the biological function of a protein is the identification of its interaction partners. We are only now beginning to appreciate the nature and complexity of networks of interacting proteins. The unravelling of any such network using traditional biochemical approaches remains a significant challenge. Recently, however the application of high-throughput technologies, such as large-scale yeast two-hybrid analysis and mass spectrometry coupled to immuno- or affinity-based capture has made possible the rapid generation of huge protein interaction datasets -10. As cThe need to develop a Laboratory Information Management System (LIMS) for researchers in the field of proteomics that would allow to track, archive and aid in a greater understanding of how biological systems work has been recognized. In 2002, Cho and co-workers developed an original LIMS for proteome research (YPRC-PDB), construThe focus of our laboratory is the study of the action of poly(ADP-ribose) polymerases (PARPs) and their role in the cell. Poly(ADP-ribosylation) is a post-translational protein modification consisting of long chains of poly(ADP-ribose) (pADPr) synthesized by PARPs at the expenses of NAD+. Poly(ADP-ribose) chains are short-lived owing to the activity of the poly(ADP-ribose) glycohydrolase enzyme, which catabolizes the pADPr within minutes of its synthesis. The PARWe herein describe the architecture and major features of a web-based utility called "PARPs database" (PARPs-DB), which is designed to rationally organize the protein and peptide data generated by the LC-MS/MS analysis of tryptic digest of proteins that co-immunoprecipitate with PARPs proteins into reports meaningful to biological researchers. PARPs-DB offers a LIMS work environment to annotate and study protein-protein interactions and its easy-to-use relational data management system can rapidly supply information pertaining to the biological characteristics of a majority of proteins in a proteomic dataset. The major features of our LIMS are flexibility, compactness, and connectivity to public databases.Also presented is a list of previously unidentified PARP-1 interactors that were found via affinity co-immunoprecipitation, as well as the analysis of these new PARP-1 interactions generated through PARPs-DB.in silico approach that can predict or prioritize potential interactors, it seems reasonable to propose that PARPs-DB will become an essential tool for initially evaluating novel hypotheses and will offer improved rationale for the prioritization of potential interactors. In effect, by facilitating the processing of protein-protein interactions and the selection of the most promising interactors  PARPs-DB should lower the cost and shorten the time necessary to discover the most biologically significant interactions between PARPs and other proteins.Given the advantages provided by an We don't have infrastructure to access on-line but the PARPs database source codes and user documentation are available for the scientific community . Tools fPARPs Database consists of a core system of services that provide underlying system functionality. Modules, which provide most data handling and analytical support (such as LC-MS/MS data mining), plug into the core. This design means the platform is easily expandable: the architecture allows new analytical modules to be added and integrated without having to modify the core system. PARPs database was designed to be platform-independent and easy to maintain, and is implemented in Solaris Sun Operating system 10 . It requires access to an Oracle 10 g relational database , with which it communicates through an abstraction layer that isolates the core system from subtle differences between Oracle database builds. The user interface supports the use of the Apache server for exteDialects for PostgreSQL and MySQL servers were implemented, and support for Microsoft SQL is under development.Figure The sample origin (shown in red) holds basic information, such as the specific biological material used, which subcellular fraction was studied, and the experimental conditions to which the organism was subjected. Sample origin has also two offsprings: 'organism' holds the name of the species/strain used and a list of the relevant gene/mutations carried; and 'tagging process' describes the labelling of the parts of a combined sample for differential expression studies, such as isotope-coded affinity tag (ICAT) mass spectrometry[ctrometry.The sample (shown in purple) simply holds an identification code (laboratory-specific), the production date, and the name of the responsible person.The next classes represent sample's processing step before moving on to run a mass spectrometry experiment. For example, running a two-dimensional gel with sample, then putting a spot from that gel through two-dimensional liquid chromatography. The class HPLC describes the equipment's origin, its dimensions, the stationary phase, the pore size in the beads, the total injection volume, and the flow rate. The class gel capture the description of the gel, the image analysis software used, and whatever images of the gel are available, referred to by URIs . There are also several parameters describing the gel itself .Mass spectrometry (shown in orange). Details about the makeup of the mass spectrometry machine is stored in seven classes. Source is an abstract class that will, in practice, be either MALDI or Electrospray, each of which has its own set of fields . Instrument represents the mass analysing and fragmentation section of the mass spectrometer .MS results analysis (shown in green). To perform a protein identification, a particular Peak list would be submitted to an identification tool, such as Mascot, Sequest and X!Tandem. The classes 'DBSearch Parameters' capture information about who processed data, when they did it, what program they used, what database was used, what errors were taken into account when searching, what potential modifications were allowed on proteins from the sample that generated the peak list.The protein tables (shown in blue) store identifiers (accession numbers) that point to external web-based information sources. Short text annotations such as Gene Ontology [Ontology  descriptThe database was designed to contain a minimal amount of information but still sufficient data to allow effective Structured Query Language (SQL) queries. These queries enable ready access to any information stored in the database as well as in the XML files generated by the data analysis server. With the tables and XML files serving as the primary data storage objects, the relational dataset is relatively easy to build, maintain and query.A key design element of PARPs database is the ability to generate analytic modules that plug into and use the core of PARPs system. Three pivotal LC-MS/MS tools integrated in PARPs-DB are the peptide-spectrum matching programs: Sequest, X!Tandem and Mascot. We have also included PeptideProphet to validate peptides assigned to MS/MS spectra  and ProtThe underlying protein knowledge base used by PARPs-DB was extracted from multiple online resources, based on cross-references. Five human gene and protein data sources were integrated within PARPs-DB : protein databases maintained by IPI  and UniPFinally, in order to visualize protein-protein interaction networks, we have developed a protein-protein interaction viewer, in Java language (Java JDK 1.4.2_05 and Netbeans 3.6). This viewer uses three libraries: Xerces Java Parser 2.6.2, Piccolo Java 1.1, and JDOM 1.0 this last library being used to manipulate and parse the XML files. The central organization of the protein-protein interaction viewer is a network graph with molecular species represented as nodes and intermolecular interactions represented as links, that is, edges between nodes. This application provides basic functionality for integrating data on the graph, a visual representation of the graph and integrated data. Data are integrated with the graph model using attributes. Graphical browsers allow the user to examine all attributes on the currently selected nodes and edges.One of the most fundamental tools for interpreting molecular interaction data is visualization of nodes and edge as two dimensional network. It utilizes a relaxation layout algorithm which attempts to prevent overlapping of nodes. This viewer is small, stable, multi-platform and simple to use. It can function as a stand-alone applet or be integrated into a web application.2 at 37°C. All the above-mentioned reagents were purchased from Invitrogen .Human cervical carcinoma HeLa cells obtained from ATCC  were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin in an humidified atmosphere of 5% CO4, pH 8.0, 150 mM NaCl, 1% NP-40, 1 mM DTT, 0.5 mM PMSF and Complete™ protease-inhibitor cocktail (according to Roche diagnostics instructions)) was added to the cells. Cells were harvested using a cell scraper. Lysed cells coming from three dishes were pooled then gently mixed by inversion for 1 hour at 4°C and centrifuged 10 minutes at 6000 g at 4°C to remove insoluble cellular debris. The cellular extract was mixed with 180 μl of magnetic beads coupled to protein G  and 8 μl of monoclonal antibody to PARP-1(F1-23)or 8 μl of normal mouse IgG as control and incubated during 2 hours at 4°C with rotation. The beads had been previously blocked during 1 hour with 1% BSA and washed with lysis buffer. At the end of the incubation period, the beads were washed 3 times with lysis buffer. 180 μl of 2× Laemmli SDS sample buffer containing 5% (v/v) β-mercaptoethanol was added to the beads and they were placed in a boiling bath for 5 minutes to elute the immunoprecipitated proteins.Cells grown in 150 mm culture dishes were washed with ice-cold phosphate-buffered saline (PBS). 400 μl/dish of ice-cold lysis buffer (175 mM KPOImmunoprecipitated proteins were separated by SDS 8% PAGE. The gel was fixed for 30 min with 10% (v/v) methanol and 7% (v/v) acetic acid solution, then stained with SYPRO Ruby fluorescent protein stain  according to the manufacturer's instruction. The entire protein profile of the immunoprecipitated proteins was sliced from the gel into 50 bands using a gel excision Lanepicker™ (The Gel Company) and placed into a 96-well plate. In-gel protein digests were performed on a MassPrep™ liquid handling station (Waters) using sequencing-grade modified trypsin (Promega) according to the manufacturer's instructions. Peptide extracts were evaporated to dryness using a SpeedVac™ and resuspended in 10 μl of 0.1% formic acid in water.18 PicoFRIT™capillary column . Elution of peptides was performed using a linear acetonitrile gradient (0–60%) over 20 min at a flow rate of approximately 200 nl/min . MS, including collision-induced dissociation, was performed in an automated fashion using the dynamic exclusion option.Final extracts were analysed by LC-MS/MS using an LCQ-DECA XP mass spectrometer equipped with a nanospray ESI (electrospray ionization) source and a Surveyor autosampler and HPLC system (Thermo Electron). A 5 μl volume of extract was first focused on a Peptide CapTrap™ (Michrom Bioresources) and then loaded on a Biobasic CPeptides were assigned MS/MS spectra by searching using Sequest (version 2.0 SR2), Mascot (version 2.1) and X!Tandem (2006.04.01.2) and the assignments were also validated with Scaffold software . MS/MS spectra were searched against the IPI human protein database (version 3.01) to whichTotal protein extracts and proteins eluted from the immunoprecipitations were separated by 8% SDS-PAGE and then transferred onto a 0.45 μm pore-size PVDF membrane . After incubating 1 hour with the blocking solution (PBS with 0.1% (v/v) Tween-20 (PBS-T) containing 5% non-fat milk), the membrane was probed with primary antibodies to PARP1  or RFC1   overnight at room temperature with shaking. After washing with PBS-T, species-specific horseradish peroxidase-conjugated secondary antibody was added for 1 hour at room temperature. The signals were finally detected with the Western Lightning™ Chemiluminescence reagent plus kit .The workflow of the PARPs protein interaction research is illustrated in Figure The sample preparation  into mzXML/mzData. PARPs-DB is very flexible. Additional mass spectrometers and converters to XML files can be easily included. mzXML and mzData are designed to encompass all of the information required by the peptide-spectrum matching software such as Sequest and Mascot. Moreover these data representations, developed respectively by Seattle Institute for Systems Biology (ISB) and the European Bioinformatics Institute (EBI), provide an OS and architecture-independent standardized file format and remove the burden of having to support multiple native formats. The Sashimi project currently provides converters from native binary files to mzXML . Unfortunately, there is no program available to convert proprietary binary format to mzData. Therefore, the easiest way to publish a peak list data in mzData today is to convert mzXML into mzData using any XML parser.For the purpose of n Figure , differeBy converting all native binary data to mzXML/mzData and using these standards at the start of our analysis pipeline, the downstream software tools, specifically the database search module and raw spectral viewer, can be used in a uniform manner regardless of the instrument used to acquire the MS/MS spectra.the protein identification section  spectra, n Figure  is used PARPs database  was developed. The GUI guides the user through all steps of the experiment to enter information such as immunoprecipitation methods, gel images, mass spectra, search engine results, etc. Figure , which eLightweight Directory Access Protocol (LDAP) provider such as institution's name server. Experimental data and other materials are stored in projects and their sub-folders, much like a file system. Each project has one or more groups of users associated with it, and each group can have a distinct set of permissions  to each of the project's folder. When users log in, the authorization system determines what data they have permission to view, edit, and/or delete and provides access accordingly.To access the LIMS server using a web-based client, the user must first login with an authorized username and a given password. PARPs-DB users are authenticated against a sample origin," "mass spectrometry" and "sample results" corresponding to different steps of a proteomic experiment. These sections allow users to store experimental parameters, results and annotations. Each section has a distinct set of permissions. For example, a molecular biologist cannot access the mass spectrometry section, and conversely, mass spectrometer users cannot access the molecular biology section. In each section, we have developed tools to help the user reduce the time needed to analyse data.Inside the PARPs-DB, there are three main sections, "sample origin section enables the user to enter experimental parameters by selecting a number of options. Experimental information includes cell type and cellular conditions, method of gene transfer (when applicable) and gene sequence, and details of the immunoprecipitation method such as lysis buffer composition, antibodies, cell lysis. The user can print experimental details entered in the database  or according to the identification probabilities  and external protein-protein interactions  in the database. Information about protein-protein interactions beyond the target protein is shown in the interaction network to visually characterize the protein network. Proteins of interest can be searched by either accession number or keywords. When users input the accession number of a protein, the protein interaction network is shown as nodes (proteins) and edges (interactions). The interaction network can also be displayed with the annotations for the proteins in the nodes. Each node is linked to the protein annotation. Data and results can be exported to other formats including PSI-MI, Excel and DTA (Sequest files) for additional analysis using other tools. This method was created to exchange data easily between different laboratories.A major obstacle to uniform proteomic analysis has been the great heterogeneity of data formats at three distinct levels: different mass spectrometers output their raw spectral data in different proprietary formats, methods that assign peptides to MS/MS spectra output their results in a variety of formats, and different methods to infer protein identifications from lists of peptides output their results in different formats. The proteomics community has recognized this problem and is tackling it through the formation of working groups  concerned with the development of standards for the capture and sharing of proteomics data. The PARPs database was developed in agreement with the HUPO-PSI (Human Proteome Organization-Proteomics Standard Initiative), which includes PSI-MI (Molecular Interactions), MS (Mass spectrometry) and GPS . The GPS development of standard ways to represent proteomics data and an agreed minimum required level of detail are both urgently required to facilitate the analysis, dissemination and exchange of proteomics data. Minimal Information about Proteomic Experiment (MIAPE) is a proposed standard format for proteomics covering 2-DE and MS. The PARPs database contains classes derived from PEDRo. As mentWe have developed PARPs-DB as an in-house, flexible Laboratory Information Management System (LIMS) which integrates all aspects of the study of protein-protein interactions by mass spectrometry-based proteomics, from sample processing information to protein interactions visualization. PARPs-DB allows easy network access to public databases, manages the output of different search engines  and supports HUPO's Proteomic Standard Initiative formats. Although several LIMS have been made freely available during last few years, none of these existed when work on the PARPs database was initiated in 2002. Advantages of PARPs-DB over these free LIMS includes: integration of all data related to the mass spectrometry-based study of protein-protein interactions; multiple search engine support; and user-friendliness.It is indicative of the poor availability of appropriate commercial systems that development of in-house LIMS such as PARPs-DB started at around the same time in different laboratories across the world, in order to fill the urgent need to automate proteomic data storage and analysis. Commercial LIMS now available remain however prohibitively expensive for small MS facilities and more rigid than an in-house system. These systems often include rigorously defined user roles and access privileges, as well as extensive auditing of data file changes. Although essential to pharmaceutical companies filing drug applications which must comply to regulatory standards (e.g. 21 CFR part 11), these features can hamper academic research because files can not easily be modified, maintained, and updated. Additionally, integration of the output of a new mass spectrometer can be more difficult and costly than with an in-house, flexible system. Finally, available LIMS from mass spectrometer vendors present many limitations: there is notably no integration of the data generated by other vendors' instruments, nor of the data not directly to the mass spectrometer; for instance, they do not handle pre-MS sample processing information and protein interaction data.To illustrate the use of PARPs-DB for discovery of protein-protein interactions, we describe here an experiment for PARP-1 co-immunoprecipitation with interacting proteins. This co-immunoprecipitation is part of experiments aiming at identifying PARP-1 interactors which will be published elsewhere . Construction of this interaction network involved three bioinformatics steps and the predicted interactions were then verified using standard biochemical techniques.The first step in the generation of PARP-1 interaction model is an extensive search of the literature in order to collect published experimental data on PARP-1 interactions. The keyword used in the PARPs-DB search for literature was PARP-1. However, a functional network is not only limited to physical protein-protein interactions but also includes genetic and biochemical interactions Figure .The interacting molecules are summarized in Figure One important point in the analysis of data from public protein-protein interaction databases is the quality of the results. Indeed, Deng et al compared the data from all the large-scale yeast interactions screens present in the public protein interaction databases. They developed a maximum likelihood estimation method to access the reliability of the interaction data, and found that the Uetzdata were more reliable than the Ito data, anThe next step was to group protein-protein interactions by molecular function via the Gene Ontology  controllin silico is vital to provide a confident interaction network useful for further study. With the exception of PCNA, which has been characterized, the prioritized candidates were next tested experimentally to confirm the predicted protein-protein interactions. This was carried out using co-immunoprecipitation of PARP-1 followed by mass spectrometry to identify the interacting proteins.Verifying the interactions from molecules identified Scaffold (version Scaffold-01_03_02) was used to group and validate MS/MS based peptide and protein identifications from Sequest, Mascot and X!Tandem. This software is based on the PeptideProphet algorithm which provides an empirical statistical model which estimates the accuracy of peptide identifications made by database search engines. For each tandem mass spectrum, PeptideProphet determines the probability that the spectrum is correctly assigned to a peptide. Scaffold system was used to group the assigned peptides according to corresponding protein and to compute probability of a correct protein assignment for each protein. Peptide identifications with Scaffold software were accepted if they could be established at greater than 80.0% probability as specified by the PeptideProphet algorithm. For the co-immunoprecipitation eluate, protein identifications were accepted if they could be established at greater than 95% probability and contained at least 2 identified peptides. Table The co-immunoprecipitation assay with RFC1 antibody performed in HeLa cells suggests that Replication Factor C subunit RFC1, 2, 3, 4, 5)  : Unix, Linux, Oracle and MySQL;Programming Language: Perl, JAVA, SQL;Licence: GNU GPL;PARPs-DB is distributed under the GNU GPL licence and available from the website SQL: Standard Query Language; NCBI: National Center for Biotechnology Information; MS: Mass spectrometry; PARP: Poly(ADP-Ribose) Polymerase; ISB: Institute for Systems Biology; EBI: European Bioinformatics Institute; LDAP: Lightweight Directory Access Protocol; IPI: International Protein Index; HPRD: Human Protein Reference Database; BIND: Biology Interaction Database.AD implemented, designed the database for the mass spectrometry. MR, CE and APC performed molecular biology approach and helped revise the manuscript. GGP participated and supervised the project. All authors read and approved the final manuscript.How to install PARPs-DB. This document gives some help to install Oracle and PARPs-DBClick here for file


Cluster Co-Conservation (CCC) has been extended to a method for developing protein interaction networks based on co-conservation between protein pairs across multiple species, Cross-Species Cluster Co-Conservation (CS-CCC). Co-conservation (phylogenetic profiles) is a well-established method for predicting functional relationships between proteins. Several publicly available databases use this method and additional clustering strategies to develop networks of protein interactions (cluster co-conservation (CCC)). CCC has previously been limited to interactions within a single target species. We have extended CCC to develop protein interaction networks based on co-conservation between protein pairs across multiple species, cross-species cluster co-conservation. At present, about 400 microbial genomes are fully sequenced. The prediction of protein function from sequence is a critical issue in genome annotation efforts. Currently, the best established method for function prediction is based on sequence similarity to proteins of known function. Unfortunately, homoogy-based prediction is of limited use due to the large number of homologous protein families with no known function for any member. An alternative method for predicting protein function is the phylogenetic profiles approach, also known as the co-conservation (CC) method first introduced by Pellegrini et al. . Co-conset al. -8. CC meet al. [et al. [et al. [Even though all CC methods rely on the premise that functionally related proteins are gained or lost together over the course of evolution, several different strategies for performing CC studies have been reported. For example, Date et al.  used reaet al.  construc [et al.  construc [et al.  applied  [et al. .Currently, several web-based databases that compile predictions of protein-protein interactions are available, for example, PLEX , String Escherichia coli K12 protein pairs (confidence level of 0.6) consisted of proteins within the same COG functional group, 19% of protein pairs were in different functional categories, and 25% had at least one unclassified member due to limited experimental data. As the number of species is expanded, these percentages range from 54-62%, 30-45%, and 0-10%, respectively. At the highest confidence level (0.8), the inclusion of 6 species resulted in almost 80% of the predicted interactions involving proteins from the same functional category. These results suggest that expanding the number of species included in the analysis, as provided for by CS-CCC, not only predicts interactions that are not predicted at different confidence levels used in CCC analysis, but also that the nature of such predicted interactions is fundamentally different. One explanation for such observations is that CS-CCC has improved capabilities for extending the protein interaction network to include the various functions required in complex biological processes . As an example of this possibility, in the CS-CCC analysis using all 6 bacterial species at confidence level 0.8 . This point is exemplified via the color-coded, species specific arcs in Figure CS-CCC is based on the use of CC methods simultaneously across several species. As such, the reliability of the CS-CCC method is directly linked to the reliability of existing CC methods, which has been extensively documented -8. SpeciE. coli and Pseudomonas aeruginosa ArgT networks revealed connections with the FliY protein . These data suggest that chemotaxis, flagella, and pili proteins may be co-conserved because they all contribute to biofilm formation. Moreover, the inclusion of P. aerginosa in the CS-CCC analysis brought pili proteins into the biofilm cluster, suggesting that in some bacteria, all of these processes co-evolved. Thus, CS-CCC can identify co-conserved networks of proteins that function in biochemically distinct pathways but that contribute to complex biological phenomenon.Figure ormation ,14 and aormation . Twitchi54 or SigL) clustered with RpoN-regulated proteins and flagella proteins are clustered with type III secretion system proteins  are activated by RpoN [In some of the bacteria studied, RpoN  or EAL (Glu-Ala-Leu) domains, which have been implicated in expression of biofilm phenotypes [P. aeruginosa proteins with GGDEF or EAL domains [E. coli K12 has a similar cluster of GGDEF and EAL domains . Thus, CS-CCC analyses revealed previously unknown protein interactions that provided sufficient justification to test a specific biological hypothesis suggested by these interactions.Examination of small protein clusters revealed that most pairs or triplets contain proteins that function in the same processes. To further test this observation, we experimentally examined the triplet containing YcgB, YeaH, and YeaG, which cluster together across different bacteria Figure . Becausehimurium , we deteIn this study, we have shown that CS-CCC of proteins provides important information. Both the presence and the absence of clustered co-conservation for any given protein are informative. There are at least two reasons why proteins that function together are not co-conserved in a species: first, a protein is found only in certain organisms or a protein function is performed by different proteins in different organisms; and second, a result is a false negative.S. typhimurium LT2 and 40 known effectors in P. aeruginosa and found that none of these proteins are co-conserved. In contrast, some of the known translocon T3SS proteins, which form the secretion apparatus, are highly co-conserved , and some of the axle-like proteins FliE, FlgB, FlgF, FlgL, and FliD. FliE has been shown to physically interact with FlgB . The staLarge volumes of data make computational methods feasible, exciting, and preferable to gene-by-gene homology searches. We have shown that use of CS-CCC expands protein interaction networks to include proteins with distinct functions that are involved in coherent biological processes, offers insight into the function of uncharacterized proteins, reveals unique information about each genome examined, and gives insight into the process of evolution.Protein co-conservation can be a result of many factors, including vertical inheritance or functional selection. Thus, we have examined patterns of CCC within and across several bacteria using CS-CCC. Our analysis showed that this computational approach provides us with more information than the traditional homology approaches or CCC. Homology approaches to protein function are based on similarity to other proteins with known functions and are limited by the fact that many proteins have unknown functions. While homology-based methods can be effective for predicting the functions of remote homologs, these methods perform poorly as the evolutionary distance between homologous proteins increases. Even a sophisticated homology-based method fails to successfully assign functions to most of the proteins for a particular organism. CCC, on the other hand, is not strictly based on homology but is limited by its ability to analyze only a single species at a time. In contrast, CS-CCC examines each cluster across multiple species and reveals interactions that both homology-based methods and CCC fail to identify. Use of CS-CCC allows researchers to extend the protein interaction network to better understand pathways involving multiple proteins with multiple functions. Therefore, the CS-CCC method is a significant advance and will be useful for researches in many different fields of biology.Prediction by CS-CCC provided us with global views of six complete bacterial genomes. Identification by CS-CCC of proteins that cluster together enabled more accurate predictions of the biological roles that proteins with previously unstudied functions may play. For instance, proteins that function in distinct but coordinated processes can be co-conserved across species even though not all processes occur in all bacteria Figure . In addiFor any given method, there are advantages and disadvantages. The number of false positives and false negatives is a key measurement of accuracy. In our case, the number of false negatives is not possible to estimate without performing many additional laboratory experiments. However, our evaluation of CS-CCC showed that the number of false positives was low. Since this method was evaluated based on our selected bacteria, there may be some bias toward overestimation of accuracy when applied to other organisms, and this remains to be tested. In addition, we have shown that our results can be sensitive to the number of bacteria included in our analysis. Finally, there may be some aspects of the bacteria we chose that are not representative of other bacteria, further reducing the generality of these results. Thus, while the report here represents a compelling demonstration of the value of performing CCC across multiple species, future efforts should be focused on developing better understanding of which and how many organisms to include in CS-CCC studies.B. subtilis) were used to evaluate the CCC method. Substantial experimental data exist for all six bacteria. The gammaproteobacteria included E. coli (K12 and O157-O157:H7 EDL933), S. flexneri (2a str. 2457T), S. typhimurium (LT2), and P. aeruginosa (PAO1). E. coli (K12) is the most intensively studied Gram-negative bacteria and is the closest studied relative of P. aeruginosa, and S. typhimurimum LT2. E. coli (O157-O157:H7 EDL933) is a clinical isolate from raw hamburger meat implicated in hemorrhagic colitis outbreak, and S. typhimurium LT2 causes enteritis in humans. P. aeruginosa is an opportunistic pathogen and is the major cause of morbidity and mortality in patients with cystic fibrosis; P. aeruginosa PAO1 was isolated from a wound [P. aeruginosa is a versatile Gram-negative bacterium that also thrives in soil, marshes and coastal marine habitats, and on plant tissues [E. coli K12 diverged 4.5 million years ago (MYA) from O157, an estimated 100 MYA from Salmonella, 200 MYA from Pseudomonas, and 1,200 MYA from Bacillus. Thus, we examined a combination of pathogenic and non-pathogenic organisms that range from closely to distantly related.We chose to focus on the Gamma subgroup of proteobacteria because members of this subgroup are among the best characterized, including whole genome sequences and curated datasets of protein functions and interactions. The genomes of five closely related Gamma Gram-negative and one low G+C bacteria , we used BFS to build a network (source network) for a set of target proteins from the source genome. We then built networks for each additional organism that contained proteins with the same name as at least one of the proteins from the source networks. This process identifies proteins and protein interactions that are consistently identified across multiple species  bacterial strain. 1, E. coli K12; 2, E. coli K12 and E. coli O157; 3, E. coli K12, E. coli O157 and S. flexneri; 4, E. coli K12, E. coli O157, S. flexneri, and S. typhimurium LT2; 5, E. coli K12, E. coli O157, S. flexneri, S. typhimurium LT2, and P. aeruginosa; 6, E. coli K12, E. coli O157, S. flexneri, S. typhimurium LT2, P. aeruginosa, and B. subtilis. Same functional category (blue); different functional category (green); at least one protein is unclassified (yellow).Comparison of TIGR functional categories of predicted pairs at three different confidence levels. The first method (1) used only Click here for file


Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available.in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum.An The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties. Plasmodium falciparum accounts for 80% of clinical cases and 90% of deaths from malaria [P. falciparum is becoming increasingly resistant to current anti-malarial drugs and the development of a vaccine is an extremely time-consuming, ongoing process, the death rate is expected to double in the next 20 years [Even after a century of attempts to control and eradicate malaria, up to half a billion clinical cases are reported each year  with an  malaria . Malaria20 years . Since mPlasmodium falciparum Interactome Database [A major limitation in malaria research is the availability of effective approaches to mine post-genomic data for new putative drug targets and lead compounds. The selection criteria for promising drug targets are therefore of extreme importance to researchers involved in malaria target screening projects and lead design ,6. CurreDatabase .P. falciparum, but the main aim of the project is to build metabolic pathways using protein-protein interactions. A brief summary of key features of these resources is provided in Table Although PlasmoDB is a comprehensive annotation database, the focus of the search environment is not specifically directed towards the drug target and lead discovery process, and protein-ligand interactions are not provided. The MDT database does provide some information on potential drug targets and their inhibitors. PlasmoMap allows protein-protein interaction data from predictions to be downloaded, a single PlasmoDB ID may also be queried for predicted interactions, but no advanced searches against the data are offered. The TDR Targets database serves as an information repository as well as a tool for the prioritization of targets in whole genomes against neglected tropical diseases including malaria. PlasmoID is a developing project that provides protein interactions for No tool exists to perform advanced interactive mining of protein together with ligand properties in a species-comparative environment. The Discovery project attempts to provide an interface where researchers may efficiently perform the selection of putative drug targets and lead compounds based on relevant properties. Relationships were constructed between the proteins, protein features and chemical compounds in a way that allows powerful searching and efficient navigation between molecules.P. falciparum, Plasmodium vivax, Plasmodium chabaudi, Plasmodium berghei and Plasmodium yoelii were obtained from PlasmoDB and for Homo sapiens and Anopheles gambiae from Ensembl [-6. InterPro [P. falciparum and human proteins were obtained from ModBase [Discovery was developed in Python using the TurboGears web development framework  with an  Ensembl . Data fo Ensembl , KEGG [1 Ensembl  and PDB- Ensembl  and prot Ensembl  and DIP  Ensembl . CurrentInterPro  analysisInterPro  from the ModBase . Custom  ModBase . An outl ModBase . Jmol [2 ModBase  was used ModBase  to displ ModBase  alignmenThe power of the Discovery system is in the rich relations between the different data types in the system. Relations to protein features were created based on existing annotations, or analyses performed in-house. Inter-and intra-species protein-protein interaction predictions were based on orthology to interacting partners in DIP and MINT. Predicted protein-ligand interactions were based on KEGG annotations and on BLAST searches against PDB and DrugBank.The Discovery resource was designed to provide an effective interface where researchers could view annotations for malaria proteins that are relevant to the drug design process, in an environment where comparisons could efficiently be made between organisms. Users may embark on a search from either a protein or ligand perspective.When performing a simple search from a protein perspective, users may enter a PlasmoDB/Ensembl ID or a keyword. The user is then presented with either the specified protein, or a list of proteins matching the keyword by description or by one of its relationships. This list is separated by species. The user is then able to further explore the hits of interest. More powerful is the advanced search function, where the user may enter search terms in several categories, and select for the presence or absence of specific relations Figure . The resWhen approaching the search from the ligand perspective, the user may search by ligand keyword, or by drawing a chemical structure using the Marvin applet. Different methods are then available to search with the structure against ligands from KEGG, PDB and DrugBank using the ChemAxon JChem Base functionality. Results are displayed as a JChem table, and the user may select a compound to view further information. A view is then provided of the compound's structure, ADMET properties as pre-calculated in JChem Base and possible interacting proteins based on KEGG annotations, and BLAST results against PDB and DrugBank Figure . The queP. falciparum DHFR include antifolate such as pyrimethamine, proguanil, aminopterin [P. falciparum and the human DHFR, and although these drugs cannot be used to treat malaria because of the possible toxicity of these particular drugs to the human host, precursors thereof may be used to treat malaria [P. falciparum DHFR. WR99210 is different from the resistance-compromized pyrimethamine in that it has a flexible side-chain that binds tightly with not only the wild-type enzyme, but also the mutant P. falciparum DHFR, while still retaining anti-malarial activity [Dihydrofolate reductase-thymidylate synthase (DHFR-TS) was selected to demonstrate the data displayed in the system, as it is well annotated and an established drug target. DHFR-TS occurs as a bifunctional protein in malaria, with both the DHFR and TS proteins fused together as a single polypeptide. Dihydrofolate reductase (DHFR with PlasmoDB id PFD0830w) is inhibited by the class II antifolates, including drugs such as pyrimethamine and proguanil, a prodrug of cycloguanil. All the molecules in the antifolate drug family currently in use were discovered during the Second World War in the 1940's, and since then no new anti-malarial antifolates have been developed that reached phase I/II clinical trials . One of nopterin , methotrnopterin , 2-aminonopterin . Aminopt malaria . A thirdactivity .Plasmodium falciparum DHFR-TS was selected in the Discovery system by searching with the keyword dihydrofolate, and selecting the entry 'PFD0830w bifunctional dihydrofolate reductase-thymidylate synthase'. On the summary page the EC annotation shows the two enzyme domains in the protein, EC:1.5.1.3, corresponding to the dihydrofolate reductase enzymes and EC:2.1.1.45 corresponding to the thymidylate synthases, and links to KEGG are provided. The GO annotations include GO:0006231 (dTMP biosynthetic process), GO:0006545(glycine biosynthetic process), GO:0006730(one-carbon compound metabolic process), GO:0009165 (nucleotide biosynthetic process), GO:0004146 (dihydrofolate reductase activity) and GO:0004799 (thymidylate synthase activity). Orthologs, based on OrthoMCL analysis, are indicated as: A. gambiae AGAP010457-PA with no description, three human enzymes, namely ENSP00000314727, ENSP00000314902 and ENSP00000315644, all three of which are thymidylate synthases, a P. berghei protein PB000415.02.0 and a P. chabaudi protein PC000873.02.0, both putative bifunctional dihydrofolate reductase thymidylate synthases. The P. yoelii protein PY04370, a putative thymidylate synthase, was also returned. It should be noted here that the ortholog prediction encounters a special case with bifunctional proteins, as a protein may only occur in one OrthoMCL cluster, which in effect groups bifunctional proteins according to only one of the domains. In this case, the human and mosquito orthologs were for the TS domain of the bifunctional enzyme. The T-Coffee alignment of these proteins is shown in the JalView applet, enabling further manipulations of the sequences.When considering the summary of the InterPro results, the first featured annotation common to all species is a BLAST hit (TS) against ProDom, a database of protein domain families automatically generated from the UniProt Knowledge Database. Gene3D G3DSA:3.40.430.10 is present in all the Plasmodium species, but not in human or mosquito. HMMPfam returned two hits as to be expected for the bifunctional sequence, namely PF00186, described as DHFR 1, and PF00303 described as Thymidylat synt.P. chabaudi that does not show the DHFR domain (probably due to the shorter sequence). Only the TS domain is shown in human and mosquito. ProfileScan returns ID PS51330, identifying the Plasmodium proteins as DHFR 2. Superfamily identifies both parts of the bifunctional protein in the Plasmodium species as SSF55831 (TS) and SSF53597 (DHFR). HMMTigr TIGR03284 (TS) and Gene3D G3DSA:3.30.572.10 (TS) are present in all species included. This comparison highlights possible areas that may be investigated to identify parasite-specific features in the protein.In human and mosquito, only the TS domain is associated with this ortholog group, as DHFR and TS occur separately in these species. HMMPIR hit PIRSF000389 identifies the protein as a bifunctional DHFR-TS in all the Plasmodium species. PatternScan returns two hits, PS00075 and PS00091, identifying DHFR 1 and thymidylate synthase in the Plasmodium species, except for Leishmania major (PDB template 1qzf), P. falciparum (PDB template 1j3q) and TS from Pneumocystis carinii (PDB template 1f28). A range of high-scoring PDB BLAST hits over both DHFR and TS are returned. PFD0830w is predicted through KEGG annotations and BLAST searches to potentially bind with more than a hundred ligands, including the well known inhibitors of DHFR. When searches were performed using common chemical compound names or structures such as pyrimethamine or proguanil, the bifunctional DHFR-TS from P. falciparum was listed as a possible interacting protein. No protein-protein or host-pathogen interactions were predicted for this protein.Three ModBase models are reported for this protein, based on DHFR-TS from This sample investigation effectively highlights the main features of DHFR-TS as described in the Discovery system, including possible orthologs, protein motifs and domains anda set of ligands possibly interacting with this protein.The Discovery system currently provides basic and advanced interfaces to search protein properties in the resource, as well as a chemical search interface for ligands. Results include orthology, protein features, metabolic information, structural information, and interaction information. Of specific interest is the availability of relationships between proteins and potential interacting ligands, together with ADMET-related properties for the ligands. The system currently has some shortcomings such as the lack of statistical measures for predicted protein-ligand and protein-protein interactions. This could be improved by incorporating sequence conservation across similar sequences for scoring purposes. Also, the prediction of host-pathogen protein-protein interaction does not yet take similarity, sub-cellular localization and properties such as PEXEL motifs and transmembrane regions into account. Additionally, in the special case of bifunctional proteins, these are currently clustered only with the ortholog of the best-matching domain by OrthoMCL, and alternative approaches need to be investigated for bifunctional matching. These and other shortcomings will be addressed in future releases, yielding futher improvements to the system.It is anticipated that the Discovery system will offer an effective starting point where users interested in drug discovery may obtain relevant information about a pre-selected malaria protein, and discover other proteins of interest by searching according to a selection of protein and ligand properties. The system's further development will serve to make it a valuable tool in the malaria drug discovery process.The authors declare that they have no competing interests.The orthology view functionality was developed by FJ and TAPDB, the function, metabolism and structure views by FJ, RJK and TAPDB, the protein-protein interactions by CMH, the protein-ligand interaction by TAPDB, CMH, FJ, the host-pathogen interaction view by CJO and the chemical searches by FJ and TAPDB. All authors contributed to the writing of the manuscript.


The study of evolutionary relationships among protein sequences was one of the first applications of bioinformatics. Since then, and accompanying the wealth of biological data produced by genome sequencing and other high-throughput techniques, the use of bioinformatics in general and phylogenetics in particular has been gaining ground in the study of protein and proteome evolution. Nowadays, the use of phylogenetics is instrumental not only to infer the evolutionary relationships among species and their genome sequences, but also to reconstruct ancestral states of proteins and proteomes and hence trace the paths followed by evolution. Here I survey recent progress in the elucidation of mechanisms of protein and proteome evolution in which phylogenetics has played a determinant role. During the 1960s, a decade before DNA sequencing become feasible, biochemists and molecular biologists were increasingly attracted by questions regarding the evolution of proteins. By that time, protein sequencing techniques were producing a growing number of sequences , and sooThe relationship between bioinformatics and the study of protein evolution was further strengthened with the advent of large-scale sequencing projects. The growing number of sequences stored in the databases, including those of complete genomes, provided a completely new dimension to the study of protein evolution: that of the evolution of complete proteomes. New bioinformatics tools were developed that allowed the comparison of complete genomes, the efficient detection of orthology relationships and the reconstruction of the evolution of complete proteomes. Almost four decades after the first computer-aided evolutionary analysis of proteins, there is a vast scientific literature reporting bioinformatics analyses that focus on protein or proteome evolution. The aim of this review is to provide a general overview of recent bioinformatics approaches to the study of the evolution of proteins and proteomes that involve the use of phylogenetics. Due to space limitations, I will focus on the evolution of proteins at the sequence level and of proteomes in terms of their protein content. Therefore, I purposely excluded considerations regarding evolution of protein structures, protein interaction or regulatory networks. I start providing an overview of the different bioinformatics applications that can be used to reconstruct the phylogenetic relationships of a protein family to then describe its applications. These include the reconstruction of ancestral protein sequences, the determination of orthology and paralogy relationships and the use of phylogenetic profiles and co-evolution to predict protein function. Subsequently, the evolution of proteins is considered within the context of the pathways and the complete proteomes in which they function. To illustrate the different sections, some specific examples from the literature have been selected.The phylogenetic analysis of a protein  starts wBy applying a specific evolutionary model to explain the amino acid substitutions observed in the multiple sequence alignment, the evolutionary distances between all pairs of proteins can be computed. This evolutionary distance, which reflects the expected mean number of changes per site that have occurred since two sequences diverged from their common ancestor, is used by the so-called distance-methods for phylogenetic inference. One such method is Neighbor Joining (NJ). NJ constitutes a good and fast heuristic algorithm that estimates the “minimal evolution” tree, a phylogenetic tree which minimizes the sum of the lengths (evolutionary distances) of all its branches . NJ and A different approach for phylogenetic inference is that of Maximum Likelihood (ML) . Here, tYet another type of phylogenetic inference is that of Maximum Parsimony (MP) , which sPerhaps, the development of algorithms for phylogenetic reconstruction, database search and multiple sequence alignment represents the most visible contribution of bioinformatics to the study of protein evolution. This is not surprising because the applications of phylogenetic trees are many and they are used in diverse fields. Besides their traditional taxonomic use for inferring the evolutionary relationship between organisms, phylogenetic trees can be used to establish orthology and paralogy relationships among proteins , to deteOrthology and paralogy are key concepts in the field of protein and proteome evolution. The use of these terms has been extended as comparative and evolutionary genomics have penetrated other fields. However, there is still some confusion about their exact meanings. Many researchers believe that orthologous proteins are simply proteins with the same function in different organisms, whereas paralogs are simply homologs within one organism. These definitions do not agree with the original given by Walter The detection of orthology relationships is ideally performed by detecting speciation and duplication events through phylogenetic analysis . AlternaWe have seen how phylogenetic reconstruction can ascertain the evolutionary relationships within members of a protein family, which evolved independently after speciation and duplication events. A complication may arise when different protein families combine through recombination events. In this case, the evolutionary unit is not the full protein anymore but a smaller, discrete molecular entity called protein domain .One of the first multi-domain proteins to be studied in detail was the tissue plasminogen activator (TPA), which contains four different domains that are also present in other protein families . The facMulti-domain proteins are created through gene duplication and recombination events. Since the role of protein domains is dependent on the context in which they are found, the emergence of new domain combinations may involve the creation of completely new functions. Not surprisingly, this mechanism of domain shuffling has been extensively exploited during evolution. This constitutes another example of how completely new functions can emerge from the tinkering of preexisting components.Several large-scale studies have recently focused on the evolution of proteomes in terms of the domain combinations that they contain. One of such studies shows that the repertoire of domain combinations observed in nature is just a small fraction of all possible combinations . This suIn the phylogenetic tree, the internal nodes represent ancestral sequences from which proteins at the leaves have evolved. The properties of these sequences are often relevant to ascertain how modern functions come about or what mutations were crucial in the development of functional specificities within a protein family. Infer the properties of such ancestral sequences or even to reconstruct them is the aim of the emerging field of “ancestral sequence reconstruction”. Ancestral sequence reconstruction uses extant sequences and the phylogenetic relationships among them to infer the most plausible ancestral sequences . AttendiOnce the sequence of interest is computationally reconstructed, it is possible to infer its functional properties based on the presence of specific residues at key positions, e.g. the active site. Additionally, one can take one step further and synthesize the ancestral protein to directly test its biochemical properties, an experiment that is often referred to as “ancestral sequence resurrection” . An inteBut how reliable are these reconstructions and therefore the functional inferences?This is a hotly debated issue and some researchers remain sceptic over conclusions on extinct sequences. In general, ancestral sequence reconstruction suffers from the same weaknesses as other evolutionary methods: its correctness depends on the quality of the data and the adequateness of the model. Moreover, these methods are based on probabilistic approaches and thus the reconstructed sequences are not free of ambiguities. With no real ancestral sequences at hand it is hard to judge the correctness of the reconstructions. Recently, some analyses have suggested that ancestral reconstruction may indeed have a sequence-compositional bias that can affect inference of ancestral function . A reasoOne of the most powerful techniques for assigning a biological function to a protein sequence is the detection of homologous sequences with known function in a sequence-similarity search. When performing this transfer of knowledge between similar sequences we are using an evolutionary approach: we assume that the sequences share a common ancestor and that their function has been maintained during evolution. However, homology-search is not the only way in which the evolutionary analysis of a protein can serve to infer its functional role.In recent years, as part of the so-called genome-context analyses , severalE. coli by detecting significantly correlated positions between the distance matrices used to build the phylogenetic trees. Others (Another variant of the use of co-evolution to predict protein function uses the evolutionary information that is contained in the sequences . For spe. Others  used a s. Others . These p. Others . This meProteins do not work as isolated entities. Instead, they perform their function through interactions with other proteins, as part of pathways, complexes and other types of functional modules. Therefore, to fully understand the evolution of a protein it is necessary to consider it in the context of the evolution of its functional partners. Recently, the availability of fully sequenced genomes has enabled the comparison across species of the composition of pathways, protein complexes and other functional modules . Such coIn the case of pathways, the evolutionary analyses of glycolysis , citric To overcome such conceptual issues, Snel and Huynen  performeIn the genomic era it has been possible to move from the evolutionary analysis of single protein families to that of complete genomes and proteomes. Large-scale comparative genomics analyses have shown that, during evolution, the protein repertoire encoded in a species genome is continuously shaped by processes such as gene loss, gene gain and gene duplication. These processes can be studied and quantified by reconstructing ancestral proteomic states along the species tree . MoreoveOne of the first such scenarios to be studied with the help of comparative genomics was the origin of the first cell and the properties of the so-called Last Universal Common Ancestor . The recAnother evolutionary scenario that has been investigated through ancestral proteome reconstruction is that of the origin of mitochondria. Mitochondria are eukaryotic organelles that originated from the endosymbiosis of an αproteobacterium and a proto-eukaryotic cell . SeveralThe advent of bioinformatics, in combination with the availability of data obtained at a genome scale, has radically changed the way in which we study protein and proteome evolution. First, sophisticated tools for the comparison of protein sequences and the reconstruction of phylogenetic trees have allowed a better understanding of protein evolution at the molecular level. Moreover, the large variation observed in the composition of functional modules and proteomes from different species shows the great plasticity of living systems to adapt to different environments. Finally, through reconstructions of ancestral states it is now possible to trace the series of events that have shaped proteins and proteomes. Even the resurrection of extinct molecules is now a possibility. Will we see one day the experimental resurrection of an extinct cellular organism based on an ancestral proteome reconstruction? We will probably have to wait a long while. What is certain, however, is that future developments in bioinformatics will continue to shed light on the underlying mechanisms that govern the evolution of proteins and proteomes.


Proteins perform many of their biological roles through protein–protein, protein–DNA or protein–ligand interfaces. The identification of the amino acids comprisingthese interfaces often enhances our understanding of the biological function ofthe proteins. Many methods for the detection of functional interfaces have been developed,and large-scale analyses have provided assessments of their accuracy. Amongthem are those that consider the size of the protein interface, its amino acid compositionand its physicochemical and geometrical properties. Other methods to thiseffect use statistical potential functions of pairwise interactions, and evolutionaryinformation. The rationale of the evolutionary approach is that functional and structuralconstraints impose selective pressure; hence, biologically important interfacesoften evolve at a slower pace than do other external regions of the protein. Recently,an algorithm, Rate4Site, and a web-server, ConSurf (http://consurf.tau.ac.il/), forthe identification of functional interfaces based on the evolutionary relations amonghomologous proteins as reflected in phylogenetic trees, were developed in our laboratory.The explicit use of the tree topology and branch lengths makes the methodremarkably accurate and sensitive. Here we demonstrate its potency in the identificationof the functional interfaces of a hypothetical protein, the structure of which wasdetermined as part of the international structural genomics effort. Finally, we proposeto combine complementary procedures, in order to enhance the overall performanceof methods for the identification of functional interfaces in proteins.