Text mining for metabolic pathways, signaling cascades, and protein networks.

PubMed

Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

2005-05-10

The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

MIPS: a database for protein sequences and complete genomes.

PubMed Central

Mewes, H W; Hani, J; Pfeiffer, F; Frishman, D

1998-01-01

The MIPS group [Munich Information Center for Protein Sequences of the German National Center for Environment and Health (GSF)] at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, is involved in a number of data collection activities, including a comprehensive database of the yeast genome, a database reflecting the progress in sequencing the Arabidopsis thaliana genome, the systematic analysis of other small genomes and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume). Through its WWW server (http://www.mips.biochem.mpg.de ) MIPS provides access to a variety of generic databases, including a database of protein families as well as automatically generated data by the systematic application of sequence analysis algorithms. The yeast genome sequence and its related information was also compiled on CD-ROM to provide dynamic interactive access to the 16 chromosomes of the first eukaryotic genome unraveled. PMID:9399795

Integrating In Silico Resources to Map a Signaling Network

PubMed Central

Liu, Hanqing; Beck, Tim N.; Golemis, Erica A.; Serebriiskii, Ilya G.

2013-01-01

The abundance of publicly available life science databases offer a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol to building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature. PMID:24233784

MIPS: analysis and annotation of proteins from whole genomes in 2005

PubMed Central

Mewes, H. W.; Frishman, D.; Mayer, K. F. X.; Münsterkötter, M.; Noubibou, O.; Pagel, P.; Rattei, T.; Oesterheld, M.; Ruepp, A.; Stümpflen, V.

2006-01-01

The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein–protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server (). PMID:16381839

ATtRACT-a database of RNA-binding proteins and associated motifs.

PubMed

Giudice, Girolamo; Sánchez-Cabo, Fátima; Torroja, Carlos; Lara-Pezzi, Enrique

2016-01-01

RNA-binding proteins (RBPs) play a crucial role in key cellular processes, including RNA transport, splicing, polyadenylation and stability. Understanding the interaction between RBPs and RNA is key to improve our knowledge of RNA processing, localization and regulation in a global manner. Despite advances in recent years, a unified non-redundant resource that includes information on experimentally validated motifs, RBPs and integrated tools to exploit this information is lacking. Here, we developed a database named ATtRACT (available athttp://attract.cnic.es) that compiles information on 370 RBPs and 1583 RBP consensus binding motifs, 192 of which are not present in any other database. To populate ATtRACT we (i) extracted and hand-curated experimentally validated data from CISBP-RNA, SpliceAid-F, RBPDB databases, (ii) integrated and updated the unavailable ASD database and (iii) extracted information from Protein-RNA complexes present in Protein Data Bank database through computational analyses. ATtRACT provides also efficient algorithms to search a specific motif and scan one or more RNA sequences at a time. It also allows discoveringde novomotifs enriched in a set of related sequences and compare them with the motifs included in the database.Database URL:http:// attract. cnic. es. © The Author(s) 2016. Published by Oxford University Press.

SInCRe—structural interactome computational resource for Mycobacterium tuberculosis

PubMed Central

Metri, Rahul; Hariharaputran, Sridhar; Ramakrishnan, Gayatri; Anand, Praveen; Raghavender, Upadhyayula S.; Ochoa-Montaño, Bernardo; Higueruelo, Alicia P.; Sowdhamini, Ramanathan; Chandra, Nagasuma R.; Blundell, Tom L.; Srinivasan, Narayanaswamy

2015-01-01

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding. Database URL: http://proline.biochem.iisc.ernet.in/sincre PMID:26130660

Database resources of the National Center for Biotechnology Information

PubMed Central

2015-01-01

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (Bookshelf, PubMed Central (PMC) and PubReader); medical genetics (ClinVar, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen); genes and genomics (BioProject, BioSample, dbSNP, dbVar, Epigenomics, Gene, Gene Expression Omnibus (GEO), Genome, HomoloGene, the Map Viewer, Nucleotide, PopSet, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser, Trace Archive and UniGene); and proteins and chemicals (Biosystems, COBALT, the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB), Protein Clusters, Protein and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for many of these databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov. PMID:25398906

Database resources of the National Center for Biotechnology Information

PubMed Central

2016-01-01

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (PubMed Central (PMC), Bookshelf and PubReader), health (ClinVar, dbGaP, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen), genomes (BioProject, Assembly, Genome, BioSample, dbSNP, dbVar, Epigenomics, the Map Viewer, Nucleotide, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser and the Trace Archive), genes (Gene, Gene Expression Omnibus (GEO), HomoloGene, PopSet and UniGene), proteins (Protein, the Conserved Domain Database (CDD), COBALT, Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB) and Protein Clusters) and chemicals (Biosystems and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for most of these databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. PMID:26615191

MIPS: a database for protein sequences, homology data and yeast genome information.

PubMed Central

Mewes, H W; Albermann, K; Heumann, K; Liebl, S; Pfeiffer, F

1997-01-01

The MIPS group (Martinsried Institute for Protein Sequences) at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, collects, processes and distributes protein sequence data within the framework of the tripartite association of the PIR-International Protein Sequence Database (,). MIPS contributes nearly 50% of the data input to the PIR-International Protein Sequence Database. The database is distributed on CD-ROM together with PATCHX, an exhaustive supplement of unique, unverified protein sequences from external sources compiled by MIPS. Through its WWW server (http://www.mips.biochem.mpg.de/ ) MIPS permits internet access to sequence databases, homology data and to yeast genome information. (i) Sequence similarity results from the FASTA program () are stored in the FASTA database for all proteins from PIR-International and PATCHX. The database is dynamically maintained and permits instant access to FASTA results. (ii) Starting with FASTA database queries, proteins have been classified into families and superfamilies (PROT-FAM). (iii) The HPT (hashed position tree) data structure () developed at MIPS is a new approach for rapid sequence and pattern searching. (iv) MIPS provides access to the sequence and annotation of the complete yeast genome (), the functional classification of yeast genes (FunCat) and its graphical display, the 'Genome Browser' (). A CD-ROM based on the JAVA programming language providing dynamic interactive access to the yeast genome and the related protein sequences has been compiled and is available on request. PMID:9016498

MitoNuc: a database of nuclear genes coding for mitochondrial proteins. Update 2002.

PubMed

Attimonelli, Marcella; Catalano, Domenico; Gissi, Carmela; Grillo, Giorgio; Licciulli, Flavio; Liuni, Sabino; Santamaria, Monica; Pesole, Graziano; Saccone, Cecilia

2002-01-01

Mitochondria, besides their central role in energy metabolism, have recently been found to be involved in a number of basic processes of cell life and to contribute to the pathogenesis of many degenerative diseases. All functions of mitochondria depend on the interaction of nuclear and organelle genomes. Mitochondrial genomes have been extensively sequenced and analysed and data have been collected in several specialised databases. In order to collect information on nuclear coded mitochondrial proteins we developed MitoNuc, a database containing detailed information on sequenced nuclear genes coding for mitochondrial proteins in Metazoa. The MitoNuc database can be retrieved through SRS and is available via the web site http://bighost.area.ba.cnr.it/mitochondriome where other mitochondrial databases developed by our group, the complete list of the sequenced mitochondrial genomes, links to other mitochondrial sites and related information, are available. The MitoAln database, related to MitoNuc in the previous release, reporting the multiple alignments of the relevant homologous protein coding regions, is no longer supported in the present release. In order to keep the links among entries in MitoNuc from homologous proteins, a new field in the database has been defined: the cluster identifier, an alpha numeric code used to identify each cluster of homologous proteins. A comment field derived from the corresponding SWISS-PROT entry has been introduced; this reports clinical data related to dysfunction of the protein. The logic scheme of MitoNuc database has been implemented in the ORACLE DBMS. This will allow the end-users to retrieve data through a friendly interface that will be soon implemented.

PaperBLAST: Text Mining Papers for Information about Homologs.

PubMed

Price, Morgan N; Arkin, Adam P

2017-01-01

Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST's database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/. IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins' functions.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events.

PubMed

Korla, Praveen Kumar; Cheng, Jack; Huang, Chien-Hung; Tsai, Jeffrey J P; Liu, Yu-Hsuan; Kurubanjerdjit, Nilubon; Hsieh, Wen-Tsong; Chen, Huey-Yi; Ng, Ka-Lok

2015-01-01

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain-domain interactions, protein-protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist's mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop 'novel' therapeutic approaches. Database URL: http://ppi.bioinfo.asia.edu.tw/FARE-CAFE. © The Author(s) 2015. Published by Oxford University Press.

Sys-BodyFluid: a systematical database for human body fluid proteome research

PubMed Central

Li, Su-Jun; Peng, Mao; Li, Hong; Liu, Bo-Shu; Wang, Chuan; Wu, Jia-Rui; Li, Yi-Xue; Zeng, Rong

2009-01-01

Recently, body fluids have widely become an important target for proteomic research and proteomic study has produced more and more body fluid related protein data. A database is needed to collect and analyze these proteome data. Thus, we developed this web-based body fluid proteome database Sys-BodyFluid. It contains eleven kinds of body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk and amniotic fluid. Over 10 000 proteins are presented in the Sys-BodyFluid. Sys-BodyFluid provides the detailed protein annotations, including protein description, Gene Ontology, domain information, protein sequence and involved pathways. These proteome data can be retrieved by using protein name, protein accession number and sequence similarity. In addition, users can query between these different body fluids to get the different proteins identification information. Sys-BodyFluid database can facilitate the body fluid proteomics and disease proteomics research as a reference database. It is available at http://www.biosino.org/bodyfluid/. PMID:18978022

Sys-BodyFluid: a systematical database for human body fluid proteome research.

PubMed

Li, Su-Jun; Peng, Mao; Li, Hong; Liu, Bo-Shu; Wang, Chuan; Wu, Jia-Rui; Li, Yi-Xue; Zeng, Rong

2009-01-01

Recently, body fluids have widely become an important target for proteomic research and proteomic study has produced more and more body fluid related protein data. A database is needed to collect and analyze these proteome data. Thus, we developed this web-based body fluid proteome database Sys-BodyFluid. It contains eleven kinds of body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk and amniotic fluid. Over 10,000 proteins are presented in the Sys-BodyFluid. Sys-BodyFluid provides the detailed protein annotations, including protein description, Gene Ontology, domain information, protein sequence and involved pathways. These proteome data can be retrieved by using protein name, protein accession number and sequence similarity. In addition, users can query between these different body fluids to get the different proteins identification information. Sys-BodyFluid database can facilitate the body fluid proteomics and disease proteomics research as a reference database. It is available at http://www.biosino.org/bodyfluid/.

PaperBLAST: Text Mining Papers for Information about Homologs

DOE PAGES

Price, Morgan N.; Arkin, Adam P.

2017-08-15

Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST’s database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quicklymore » finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions.« less

PaperBLAST: Text Mining Papers for Information about Homologs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Price, Morgan N.; Arkin, Adam P.

Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST’s database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quicklymore » finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions.« less

PaperBLAST: Text Mining Papers for Information about Homologs

PubMed Central

Arkin, Adam P.

2017-01-01

ABSTRACT Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST’s database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/. IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions. PMID:28845458

Extraction, integration and analysis of alternative splicing and protein structure distributed information

PubMed Central

D'Antonio, Matteo; Masseroli, Marco

2009-01-01

Background Alternative splicing has been demonstrated to affect most of human genes; different isoforms from the same gene encode for proteins which differ for a limited number of residues, thus yielding similar structures. This suggests possible correlations between alternative splicing and protein structure. In order to support the investigation of such relationships, we have developed the Alternative Splicing and Protein Structure Scrutinizer (PASS), a Web application to automatically extract, integrate and analyze human alternative splicing and protein structure data sparsely available in the Alternative Splicing Database, Ensembl databank and Protein Data Bank. Primary data from these databases have been integrated and analyzed using the Protein Identifier Cross-Reference, BLAST, CLUSTALW and FeatureMap3D software tools. Results A database has been developed to store the considered primary data and the results from their analysis; a system of Perl scripts has been implemented to automatically create and update the database and analyze the integrated data; a Web interface has been implemented to make the analyses easily accessible; a database has been created to manage user accesses to the PASS Web application and store user's data and searches. Conclusion PASS automatically integrates data from the Alternative Splicing Database with protein structure data from the Protein Data Bank. Additionally, it comprehensively analyzes the integrated data with publicly available well-known bioinformatics tools in order to generate structural information of isoform pairs. Further analysis of such valuable information might reveal interesting relationships between alternative splicing and protein structure differences, which may be significantly associated with different functions. PMID:19828075

SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803.

PubMed

Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

2008-01-01

Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date. We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactions as well as their protein-level interactions using the model cyanobacterium, Synechocystis sp. PCC 6803. It predicts the protein-protein interactions using public interaction databases that contain mutually complementary and redundant data. Furthermore, SynechoNET provides information on transmembrane topology, signal peptide, and domain structure in order to support the analysis of regulatory membrane proteins. Such biological information can be queried and visualized in user-friendly web interfaces that include the interactive network viewer and search pages by keyword and functional category. SynechoNET is an integrated protein-protein interaction database designed to analyze regulatory membrane proteins in cyanobacteria. It provides a platform for biologists to extend the genomic data of cyanobacteria by predicting interaction partners, membrane association, and membrane topology of Synechocystis proteins. SynechoNET is freely available at http://synechocystis.org/ or directly at http://bioportal.kobic.kr/SynechoNET/.

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

PubMed Central

Gioutlakis, Aris; Klapa, Maria I.

2017-01-01

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes. PMID:29023571

KnotProt: a database of proteins with knots and slipknots

PubMed Central

Jamroz, Michal; Niemyska, Wanda; Rawdon, Eric J.; Stasiak, Andrzej; Millett, Kenneth C.; Sułkowski, Piotr; Sulkowska, Joanna I.

2015-01-01

The protein topology database KnotProt, http://knotprot.cent.uw.edu.pl/, collects information about protein structures with open polypeptide chains forming knots or slipknots. The knotting complexity of the cataloged proteins is presented in the form of a matrix diagram that shows users the knot type of the entire polypeptide chain and of each of its subchains. The pattern visible in the matrix gives the knotting fingerprint of a given protein and permits users to determine, for example, the minimal length of the knotted regions (knot's core size) or the depth of a knot, i.e. how many amino acids can be removed from either end of the cataloged protein structure before converting it from a knot to a different type of knot. In addition, the database presents extensive information about the biological functions, families and fold types of proteins with non-trivial knotting. As an additional feature, the KnotProt database enables users to submit protein or polymer chains and generate their knotting fingerprints. PMID:25361973

ARCPHdb: A comprehensive protein database for SF1 and SF2 helicase from archaea.

PubMed

Moukhtar, Mirna; Chaar, Wafi; Abdel-Razzak, Ziad; Khalil, Mohamad; Taha, Samir; Chamieh, Hala

2017-01-01

Superfamily 1 and Superfamily 2 helicases, two of the largest helicase protein families, play vital roles in many biological processes including replication, transcription and translation. Study of helicase proteins in the model microorganisms of archaea have largely contributed to the understanding of their function, architecture and assembly. Based on a large phylogenomics approach, we have identified and classified all SF1 and SF2 protein families in ninety five sequenced archaea genomes. Here we developed an online webserver linked to a specialized protein database named ARCPHdb to provide access for SF1 and SF2 helicase families from archaea. ARCPHdb was implemented using MySQL relational database. Web interfaces were developed using Netbeans. Data were stored according to UniProt accession numbers, NCBI Ref Seq ID, PDB IDs and Entrez Databases. A user-friendly interactive web interface has been developed to browse, search and download archaeal helicase protein sequences, their available 3D structure models, and related documentation available in the literature provided by ARCPHdb. The database provides direct links to matching external databases. The ARCPHdb is the first online database to compile all protein information on SF1 and SF2 helicase from archaea in one platform. This database provides essential resource information for all researchers interested in the field. Copyright © 2016 Elsevier Ltd. All rights reserved.

AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies.

PubMed

Major, Sylvia M; Nishizuka, Satoshi; Morita, Daisaku; Rowland, Rick; Sunshine, Margot; Shankavaram, Uma; Washburn, Frank; Asin, Daniel; Kouros-Mehr, Hosein; Kane, David; Weinstein, John N

2006-04-06

Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either in vitro or in vivo. We, for example, have used them for quantitation of proteins on "reverse-phase" protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java. AbMiner is a user-friendly, web-based relational database of information on > 600 commercially available antibodies that we validated by Western blot for protein microarray studies. It includes many types of information on the antibody, the immunogen, the vendor, the antigen, and the antigen's gene. Multiple gene and protein identifier types provide links to corresponding entries in a variety of other public databases, including resources for phosphorylation-specific antibodies. AbMiner also includes our quality-control data against a pool of 60 diverse cancer cell types (the NCI-60) and also protein expression levels for the NCI-60 cells measured using our high-density "reverse-phase" protein lysate microarrays for a selection of the listed antibodies. Some other available database resources give information on antibody specificity for one or a couple of cell types. In contrast, the data in AbMiner indicate specificity with respect to the antigens in a pool of 60 diverse cell types from nine different tissues of origin. AbMiner is a relational database that provides extensive information from our own laboratory and other sources on more than 600 available antibodies and the genes that encode the antibodies' antigens. The data will be made freely available at http://discover.nci.nih.gov/abminer.

The Protein-DNA Interface database

PubMed Central

2010-01-01

The Protein-DNA Interface database (PDIdb) is a repository containing relevant structural information of Protein-DNA complexes solved by X-ray crystallography and available at the Protein Data Bank. The database includes a simple functional classification of the protein-DNA complexes that consists of three hierarchical levels: Class, Type and Subtype. This classification has been defined and manually curated by humans based on the information gathered from several sources that include PDB, PubMed, CATH, SCOP and COPS. The current version of the database contains only structures with resolution of 2.5 Å or higher, accounting for a total of 922 entries. The major aim of this database is to contribute to the understanding of the main rules that underlie the molecular recognition process between DNA and proteins. To this end, the database is focused on each specific atomic interface rather than on the separated binding partners. Therefore, each entry in this database consists of a single and independent protein-DNA interface. We hope that PDIdb will be useful to many researchers working in fields such as the prediction of transcription factor binding sites in DNA, the study of specificity determinants that mediate enzyme recognition events, engineering and design of new DNA binding proteins with distinct binding specificity and affinity, among others. Finally, due to its friendly and easy-to-use web interface, we hope that PDIdb will also serve educational and teaching purposes. PMID:20482798

The Protein-DNA Interface database.

PubMed

Norambuena, Tomás; Melo, Francisco

2010-05-18

The Protein-DNA Interface database (PDIdb) is a repository containing relevant structural information of Protein-DNA complexes solved by X-ray crystallography and available at the Protein Data Bank. The database includes a simple functional classification of the protein-DNA complexes that consists of three hierarchical levels: Class, Type and Subtype. This classification has been defined and manually curated by humans based on the information gathered from several sources that include PDB, PubMed, CATH, SCOP and COPS. The current version of the database contains only structures with resolution of 2.5 A or higher, accounting for a total of 922 entries. The major aim of this database is to contribute to the understanding of the main rules that underlie the molecular recognition process between DNA and proteins. To this end, the database is focused on each specific atomic interface rather than on the separated binding partners. Therefore, each entry in this database consists of a single and independent protein-DNA interface.We hope that PDIdb will be useful to many researchers working in fields such as the prediction of transcription factor binding sites in DNA, the study of specificity determinants that mediate enzyme recognition events, engineering and design of new DNA binding proteins with distinct binding specificity and affinity, among others. Finally, due to its friendly and easy-to-use web interface, we hope that PDIdb will also serve educational and teaching purposes.

The Universal Protein Resource (UniProt): an expanding universe of protein information.

PubMed

Wu, Cathy H; Apweiler, Rolf; Bairoch, Amos; Natale, Darren A; Barker, Winona C; Boeckmann, Brigitte; Ferro, Serenella; Gasteiger, Elisabeth; Huang, Hongzhan; Lopez, Rodrigo; Magrane, Michele; Martin, Maria J; Mazumder, Raja; O'Donovan, Claire; Redaschi, Nicole; Suzek, Baris

2006-01-01

The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at http://www.uniprot.org or downloaded at ftp://ftp.uniprot.org/pub/databases/.

Protein Bioinformatics Databases and Resources

PubMed Central

Chen, Chuming; Huang, Hongzhan; Wu, Cathy H.

2017-01-01

Many publicly available data repositories and resources have been developed to support protein related information management, data-driven hypothesis generation and biological knowledge discovery. To help researchers quickly find the appropriate protein related informatics resources, we present a comprehensive review (with categorization and description) of major protein bioinformatics databases in this chapter. We also discuss the challenges and opportunities for developing next-generation protein bioinformatics databases and resources to support data integration and data analytics in the Big Data era. PMID:28150231

NPIDB: Nucleic acid-Protein Interaction DataBase.

PubMed

Kirsanov, Dmitry D; Zanegina, Olga N; Aksianov, Evgeniy A; Spirin, Sergei A; Karyagina, Anna S; Alexeevski, Andrei V

2013-01-01

The Nucleic acid-Protein Interaction DataBase (http://npidb.belozersky.msu.ru/) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from the Protein Data Bank (3846 complexes in October 2012). It provides a web interface and a set of tools for extracting biologically meaningful characteristics of nucleoprotein complexes. The content of the database is updated weekly. The current version of the Nucleic acid-Protein Interaction DataBase is an upgrade of the version published in 2007. The improvements include a new web interface, new tools for calculation of intermolecular interactions, a classification of SCOP families that contains DNA-binding protein domains and data on conserved water molecules on the DNA-protein interface.

Role for protein–protein interaction databases in human genetics

PubMed Central

Pattin, Kristine A; Moore, Jason H

2010-01-01

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies. PMID:19929610

PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics.

PubMed

Jemimah, Sherlyn; Yugandhar, K; Michael Gromiha, M

2017-09-01

We have developed PROXiMATE, a database of thermodynamic data for more than 6000 missense mutations in 174 heterodimeric protein-protein complexes, supplemented with interaction network data from STRING database, solvent accessibility, sequence, structural and functional information, experimental conditions and literature information. Additional features include complex structure visualization, search and display options, download options and a provision for users to upload their data. The database is freely available at http://www.iitm.ac.in/bioinfo/PROXiMATE/ . The website is implemented in Python, and supports recent versions of major browsers such as IE10, Firefox, Chrome and Opera. gromiha@iitm.ac.in. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

PubMed Central

Pruitt, Kim D.; Tatusova, Tatiana; Maglott, Donna R.

2005-01-01

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) provides a non-redundant collection of sequences representing genomic data, transcripts and proteins. Although the goal is to provide a comprehensive dataset representing the complete sequence information for any given species, the database pragmatically includes sequence data that are currently publicly available in the archival databases. The database incorporates data from over 2400 organisms and includes over one million proteins representing significant taxonomic diversity spanning prokaryotes, eukaryotes and viruses. Nucleotide and protein sequences are explicitly linked, and the sequences are linked to other resources including the NCBI Map Viewer and Gene. Sequences are annotated to include coding regions, conserved domains, variation, references, names, database cross-references, and other features using a combined approach of collaboration and other input from the scientific community, automated annotation, propagation from GenBank and curation by NCBI staff. PMID:15608248

PDB_TM: selection and membrane localization of transmembrane proteins in the protein data bank.

PubMed

Tusnády, Gábor E; Dosztányi, Zsuzsanna; Simon, István

2005-01-01

PDB_TM is a database for transmembrane proteins with known structures. It aims to collect all transmembrane proteins that are deposited in the protein structure database (PDB) and to determine their membrane-spanning regions. These assignments are based on the TMDET algorithm, which uses only structural information to locate the most likely position of the lipid bilayer and to distinguish between transmembrane and globular proteins. This algorithm was applied to all PDB entries and the results were collected in the PDB_TM database. By using TMDET algorithm, the PDB_TM database can be automatically updated every week, keeping it synchronized with the latest PDB updates. The PDB_TM database is available at http://www.enzim.hu/PDB_TM.

Database resources of the National Center for Biotechnology Information.

PubMed

2016-01-04

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank(®) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (PubMed Central (PMC), Bookshelf and PubReader), health (ClinVar, dbGaP, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen), genomes (BioProject, Assembly, Genome, BioSample, dbSNP, dbVar, Epigenomics, the Map Viewer, Nucleotide, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser and the Trace Archive), genes (Gene, Gene Expression Omnibus (GEO), HomoloGene, PopSet and UniGene), proteins (Protein, the Conserved Domain Database (CDD), COBALT, Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB) and Protein Clusters) and chemicals (Biosystems and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for most of these databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. Published by Oxford University Press on behalf of Nucleic Acids Research 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Database resources of the National Center for Biotechnology Information.

PubMed

2015-01-01

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank(®) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (Bookshelf, PubMed Central (PMC) and PubReader); medical genetics (ClinVar, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen); genes and genomics (BioProject, BioSample, dbSNP, dbVar, Epigenomics, Gene, Gene Expression Omnibus (GEO), Genome, HomoloGene, the Map Viewer, Nucleotide, PopSet, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser, Trace Archive and UniGene); and proteins and chemicals (Biosystems, COBALT, the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB), Protein Clusters, Protein and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for many of these databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov. Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

A Circular Dichroism Reference Database for Membrane Proteins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wallace,B.; Wien, F.; Stone, T.

2006-01-01

Membrane proteins are a major product of most genomes and the target of a large number of current pharmaceuticals, yet little information exists on their structures because of the difficulty of crystallising them; hence for the most part they have been excluded from structural genomics programme targets. Furthermore, even methods such as circular dichroism (CD) spectroscopy which seek to define secondary structure have not been fully exploited because of technical limitations to their interpretation for membrane embedded proteins. Empirical analyses of circular dichroism (CD) spectra are valuable for providing information on secondary structures of proteins. However, the accuracy of themore » results depends on the appropriateness of the reference databases used in the analyses. Membrane proteins have different spectral characteristics than do soluble proteins as a result of the low dielectric constants of membrane bilayers relative to those of aqueous solutions (Chen & Wallace (1997) Biophys. Chem. 65:65-74). To date, no CD reference database exists exclusively for the analysis of membrane proteins, and hence empirical analyses based on current reference databases derived from soluble proteins are not adequate for accurate analyses of membrane protein secondary structures (Wallace et al (2003) Prot. Sci. 12:875-884). We have therefore created a new reference database of CD spectra of integral membrane proteins whose crystal structures have been determined. To date it contains more than 20 proteins, and spans the range of secondary structures from mostly helical to mostly sheet proteins. This reference database should enable more accurate secondary structure determinations of membrane embedded proteins and will become one of the reference database options in the CD calculation server DICHROWEB (Whitmore & Wallace (2004) NAR 32:W668-673).« less

[Establishment of a comprehensive database for laryngeal cancer related genes and the miRNAs].

PubMed

Li, Mengjiao; E, Qimin; Liu, Jialin; Huang, Tingting; Liang, Chuanyu

2015-09-01

By collecting and analyzing the laryngeal cancer related genes and the miRNAs, to build a comprehensive laryngeal cancer-related gene database, which differs from the current biological information database with complex and clumsy structure and focuses on the theme of gene and miRNA, and it could make the research and teaching more convenient and efficient. Based on the B/S architecture, using Apache as a Web server, MySQL as coding language of database design and PHP as coding language of web design, a comprehensive database for laryngeal cancer-related genes was established, providing with the gene tables, protein tables, miRNA tables and clinical information tables of the patients with laryngeal cancer. The established database containsed 207 laryngeal cancer related genes, 243 proteins, 26 miRNAs, and their particular information such as mutations, methylations, diversified expressions, and the empirical references of laryngeal cancer relevant molecules. The database could be accessed and operated via the Internet, by which browsing and retrieval of the information were performed. The database were maintained and updated regularly. The database for laryngeal cancer related genes is resource-integrated and user-friendly, providing a genetic information query tool for the study of laryngeal cancer.

UniPROBE, update 2011: expanded content and search tools in the online database of protein-binding microarray data on protein-DNA interactions.

PubMed

Robasky, Kimberly; Bulyk, Martha L

2011-01-01

The Universal PBM Resource for Oligonucleotide-Binding Evaluation (UniPROBE) database is a centralized repository of information on the DNA-binding preferences of proteins as determined by universal protein-binding microarray (PBM) technology. Each entry for a protein (or protein complex) in UniPROBE provides the quantitative preferences for all possible nucleotide sequence variants ('words') of length k ('k-mers'), as well as position weight matrix (PWM) and graphical sequence logo representations of the k-mer data. In this update, we describe >130% expansion of the database content, incorporation of a protein BLAST (blastp) tool for finding protein sequence matches in UniPROBE, the introduction of UniPROBE accession numbers and additional database enhancements. The UniPROBE database is available at http://uniprobe.org.

MIPS: a database for genomes and protein sequences.

PubMed Central

Mewes, H W; Heumann, K; Kaps, A; Mayer, K; Pfeiffer, F; Stocker, S; Frishman, D

1999-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF), Martinsried near Munich, Germany, develops and maintains genome oriented databases. It is commonplace that the amount of sequence data available increases rapidly, but not the capacity of qualified manual annotation at the sequence databases. Therefore, our strategy aims to cope with the data stream by the comprehensive application of analysis tools to sequences of complete genomes, the systematic classification of protein sequences and the active support of sequence analysis and functional genomics projects. This report describes the systematic and up-to-date analysis of genomes (PEDANT), a comprehensive database of the yeast genome (MYGD), a database reflecting the progress in sequencing the Arabidopsis thaliana genome (MATD), the database of assembled, annotated human EST clusters (MEST), and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume). MIPS provides access through its WWW server (http://www.mips.biochem.mpg.de) to a spectrum of generic databases, including the above mentioned as well as a database of protein families (PROTFAM), the MITOP database, and the all-against-all FASTA database. PMID:9847138

KnotProt: a database of proteins with knots and slipknots.

PubMed

Jamroz, Michal; Niemyska, Wanda; Rawdon, Eric J; Stasiak, Andrzej; Millett, Kenneth C; Sułkowski, Piotr; Sulkowska, Joanna I

2015-01-01

The protein topology database KnotProt, http://knotprot.cent.uw.edu.pl/, collects information about protein structures with open polypeptide chains forming knots or slipknots. The knotting complexity of the cataloged proteins is presented in the form of a matrix diagram that shows users the knot type of the entire polypeptide chain and of each of its subchains. The pattern visible in the matrix gives the knotting fingerprint of a given protein and permits users to determine, for example, the minimal length of the knotted regions (knot's core size) or the depth of a knot, i.e. how many amino acids can be removed from either end of the cataloged protein structure before converting it from a knot to a different type of knot. In addition, the database presents extensive information about the biological functions, families and fold types of proteins with non-trivial knotting. As an additional feature, the KnotProt database enables users to submit protein or polymer chains and generate their knotting fingerprints. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

MiCroKit 3.0: an integrated database of midbody, centrosome and kinetochore.

PubMed

Ren, Jian; Liu, Zexian; Gao, Xinjiao; Jin, Changjiang; Ye, Mingliang; Zou, Hanfa; Wen, Longping; Zhang, Zhaolei; Xue, Yu; Yao, Xuebiao

2010-01-01

During cell division/mitosis, a specific subset of proteins is spatially and temporally assembled into protein super complexes in three distinct regions, i.e. centrosome/spindle pole, kinetochore/centromere and midbody/cleavage furrow/phragmoplast/bud neck, and modulates cell division process faithfully. Although many experimental efforts have been carried out to investigate the characteristics of these proteins, no integrated database was available. Here, we present the MiCroKit database (http://microkit.biocuckoo.org) of proteins that localize in midbody, centrosome and/or kinetochore. We collected into the MiCroKit database experimentally verified microkit proteins from the scientific literature that have unambiguous supportive evidence for subcellular localization under fluorescent microscope. The current version of MiCroKit 3.0 provides detailed information for 1489 microkit proteins from seven model organisms, including Saccharomyces cerevisiae, Schizasaccharomyces pombe, Caenorhabditis elegans, Drosophila melanogaster, Xenopus laevis, Mus musculus and Homo sapiens. Moreover, the orthologous information was provided for these microkit proteins, and could be a useful resource for further experimental identification. The online service of MiCroKit database was implemented in PHP + MySQL + JavaScript, while the local packages were developed in JAVA 1.5 (J2SE 5.0).

MiCroKit 3.0: an integrated database of midbody, centrosome and kinetochore

PubMed Central

Liu, Zexian; Gao, Xinjiao; Jin, Changjiang; Ye, Mingliang; Zou, Hanfa; Wen, Longping; Zhang, Zhaolei; Xue, Yu; Yao, Xuebiao

2010-01-01

During cell division/mitosis, a specific subset of proteins is spatially and temporally assembled into protein super complexes in three distinct regions, i.e. centrosome/spindle pole, kinetochore/centromere and midbody/cleavage furrow/phragmoplast/bud neck, and modulates cell division process faithfully. Although many experimental efforts have been carried out to investigate the characteristics of these proteins, no integrated database was available. Here, we present the MiCroKit database (http://microkit.biocuckoo.org) of proteins that localize in midbody, centrosome and/or kinetochore. We collected into the MiCroKit database experimentally verified microkit proteins from the scientific literature that have unambiguous supportive evidence for subcellular localization under fluorescent microscope. The current version of MiCroKit 3.0 provides detailed information for 1489 microkit proteins from seven model organisms, including Saccharomyces cerevisiae, Schizasaccharomyces pombe, Caenorhabditis elegans, Drosophila melanogaster, Xenopus laevis, Mus musculus and Homo sapiens. Moreover, the orthologous information was provided for these microkit proteins, and could be a useful resource for further experimental identification. The online service of MiCroKit database was implemented in PHP + MySQL + JavaScript, while the local packages were developed in JAVA 1.5 (J2SE 5.0). PMID:19783819

Integration of gel-based and gel-free proteomic data for functional analysis of proteins through Soybean Proteome Database.

PubMed

Komatsu, Setsuko; Wang, Xin; Yin, Xiaojian; Nanjo, Yohei; Ohyanagi, Hajime; Sakata, Katsumi

2017-06-23

The Soybean Proteome Database (SPD) stores data on soybean proteins obtained with gel-based and gel-free proteomic techniques. The database was constructed to provide information on proteins for functional analyses. The majority of the data is focused on soybean (Glycine max 'Enrei'). The growth and yield of soybean are strongly affected by environmental stresses such as flooding. The database was originally constructed using data on soybean proteins separated by two-dimensional polyacrylamide gel electrophoresis, which is a gel-based proteomic technique. Since 2015, the database has been expanded to incorporate data obtained by label-free mass spectrometry-based quantitative proteomics, which is a gel-free proteomic technique. Here, the portions of the database consisting of gel-free proteomic data are described. The gel-free proteomic database contains 39,212 proteins identified in 63 sample sets, such as temporal and organ-specific samples of soybean plants grown under flooding stress or non-stressed conditions. In addition, data on organellar proteins identified in mitochondria, nuclei, and endoplasmic reticulum are stored. Furthermore, the database integrates multiple omics data such as genomics, transcriptomics, metabolomics, and proteomics. The SPD database is accessible at http://proteome.dc.affrc.go.jp/Soybean/. The Soybean Proteome Database stores data obtained from both gel-based and gel-free proteomic techniques. The gel-free proteomic database comprises 39,212 proteins identified in 63 sample sets, such as different organs of soybean plants grown under flooding stress or non-stressed conditions in a time-dependent manner. In addition, organellar proteins identified in mitochondria, nuclei, and endoplasmic reticulum are stored in the gel-free proteomics database. A total of 44,704 proteins, including 5490 proteins identified using a gel-based proteomic technique, are stored in the SPD. It accounts for approximately 80% of all predicted proteins from genome sequences, though there are over lapped proteins. Based on the demonstrated application of data stored in the database for functional analyses, it is suggested that these data will be useful for analyses of biological mechanisms in soybean. Furthermore, coupled with recent advances in information and communication technology, the usefulness of this database would increase in the analyses of biological mechanisms. Copyright © 2017 Elsevier B.V. All rights reserved.

GenomewidePDB 2.0: A Newly Upgraded Versatile Proteogenomic Database for the Chromosome-Centric Human Proteome Project.

PubMed

Jeong, Seul-Ki; Hancock, William S; Paik, Young-Ki

2015-09-04

Since the launch of the Chromosome-centric Human Proteome Project (C-HPP) in 2012, the number of "missing" proteins has fallen to 2932, down from ∼5932 since the number was first counted in 2011. We compared the characteristics of missing proteins with those of already annotated proteins with respect to transcriptional expression pattern and the time periods in which newly identified proteins were annotated. We learned that missing proteins commonly exhibit lower levels of transcriptional expression and less tissue-specific expression compared with already annotated proteins. This makes it more difficult to identify missing proteins as time goes on. One of the C-HPP goals is to identify alternative spliced product of proteins (ASPs), which are usually difficult to find by shot-gun proteomic methods due to their sequence similarities with the representative proteins. To resolve this problem, it may be necessary to use a targeted proteomics approach (e.g., selected and multiple reaction monitoring [S/MRM] assays) and an innovative bioinformatics platform that enables the selection of target peptides for rarely expressed missing proteins or ASPs. Given that the success of efforts to identify missing proteins may rely on more informative public databases, it was necessary to upgrade the available integrative databases. To this end, we attempted to improve the features and utility of GenomewidePDB by integrating transcriptomic information (e.g., alternatively spliced transcripts), annotated peptide information, and an advanced search interface that can find proteins of interest when applying a targeted proteomics strategy. This upgraded version of the database, GenomewidePDB 2.0, may not only expedite identification of the remaining missing proteins but also enhance the exchange of information among the proteome community. GenomewidePDB 2.0 is available publicly at http://genomewidepdb.proteomix.org/.

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

PubMed

Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

2015-04-01

The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events

PubMed Central

Korla, Praveen Kumar; Cheng, Jack; Huang, Chien-Hung; Tsai, Jeffrey J. P.; Liu, Yu-Hsuan; Kurubanjerdjit, Nilubon; Hsieh, Wen-Tsong; Chen, Huey-Yi; Ng, Ka-Lok

2015-01-01

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain–domain interactions, protein–protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist’s mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop ‘novel’ therapeutic approaches. Database URL: http://ppi.bioinfo.asia.edu.tw/FARE-CAFE PMID:26384373

Overcoming Species Boundaries in Peptide Identification with Bayesian Information Criterion-driven Error-tolerant Peptide Search (BICEPS)*

PubMed Central

Renard, Bernhard Y.; Xu, Buote; Kirchner, Marc; Zickmann, Franziska; Winter, Dominic; Korten, Simone; Brattig, Norbert W.; Tzur, Amit; Hamprecht, Fred A.; Steen, Hanno

2012-01-01

Currently, the reliable identification of peptides and proteins is only feasible when thoroughly annotated sequence databases are available. Although sequencing capacities continue to grow, many organisms remain without reliable, fully annotated reference genomes required for proteomic analyses. Standard database search algorithms fail to identify peptides that are not exactly contained in a protein database. De novo searches are generally hindered by their restricted reliability, and current error-tolerant search strategies are limited by global, heuristic tradeoffs between database and spectral information. We propose a Bayesian information criterion-driven error-tolerant peptide search (BICEPS) and offer an open source implementation based on this statistical criterion to automatically balance the information of each single spectrum and the database, while limiting the run time. We show that BICEPS performs as well as current database search algorithms when such algorithms are applied to sequenced organisms, whereas BICEPS only uses a remotely related organism database. For instance, we use a chicken instead of a human database corresponding to an evolutionary distance of more than 300 million years (International Chicken Genome Sequencing Consortium (2004) Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432, 695–716). We demonstrate the successful application to cross-species proteomics with a 33% increase in the number of identified proteins for a filarial nematode sample of Litomosoides sigmodontis. PMID:22493179

DDRprot: a database of DNA damage response-related proteins.

PubMed

Andrés-León, Eduardo; Cases, Ildefonso; Arcas, Aida; Rojas, Ana M

2016-01-01

The DNA Damage Response (DDR) signalling network is an essential system that protects the genome's integrity. The DDRprot database presented here is a resource that integrates manually curated information on the human DDR network and its sub-pathways. For each particular DDR protein, we present detailed information about its function. If involved in post-translational modifications (PTMs) with each other, we depict the position of the modified residue/s in the three-dimensional structures, when resolved structures are available for the proteins. All this information is linked to the original publication from where it was obtained. Phylogenetic information is also shown, including time of emergence and conservation across 47 selected species, family trees and sequence alignments of homologues. The DDRprot database can be queried by different criteria: pathways, species, evolutionary age or involvement in (PTM). Sequence searches using hidden Markov models can be also used.Database URL: http://ddr.cbbio.es. © The Author(s) 2016. Published by Oxford University Press.

2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments.

PubMed

Allmer, Jens; Kuhlgert, Sebastian; Hippler, Michael

2008-07-07

The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed. In order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application. We present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.

VaProS: a database-integration approach for protein/genome information retrieval.

PubMed

Gojobori, Takashi; Ikeo, Kazuho; Katayama, Yukie; Kawabata, Takeshi; Kinjo, Akira R; Kinoshita, Kengo; Kwon, Yeondae; Migita, Ohsuke; Mizutani, Hisashi; Muraoka, Masafumi; Nagata, Koji; Omori, Satoshi; Sugawara, Hideaki; Yamada, Daichi; Yura, Kei

2016-12-01

Life science research now heavily relies on all sorts of databases for genome sequences, transcription, protein three-dimensional (3D) structures, protein-protein interactions, phenotypes and so forth. The knowledge accumulated by all the omics research is so vast that a computer-aided search of data is now a prerequisite for starting a new study. In addition, a combinatory search throughout these databases has a chance to extract new ideas and new hypotheses that can be examined by wet-lab experiments. By virtually integrating the related databases on the Internet, we have built a new web application that facilitates life science researchers for retrieving experts' knowledge stored in the databases and for building a new hypothesis of the research target. This web application, named VaProS, puts stress on the interconnection between the functional information of genome sequences and protein 3D structures, such as structural effect of the gene mutation. In this manuscript, we present the notion of VaProS, the databases and tools that can be accessed without any knowledge of database locations and data formats, and the power of search exemplified in quest of the molecular mechanisms of lysosomal storage disease. VaProS can be freely accessed at http://p4d-info.nig.ac.jp/vapros/ .

GRBase, a new gene regulation data base available by anonymous ftp.

PubMed Central

Collier, B; Danielsen, M

1994-01-01

The Gene Regulation Database (GRBase) is a compendium of information on the structure and function of proteins involved in the control of gene expression in eukaryotes. These proteins include transcription factors, proteins involved in signal transduction, and receptors. The database can be obtained by FTP in Filemaker Pro, text, and postscript formats. The database will be expanded in the coming year to include reviews on families of proteins involved in gene regulation and to allow online searching. PMID:7937071

Integrated Controlling System and Unified Database for High Throughput Protein Crystallography Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaponov, Yu.A.; Igarashi, N.; Hiraki, M.

2004-05-12

An integrated controlling system and a unified database for high throughput protein crystallography experiments have been developed. Main features of protein crystallography experiments (purification, crystallization, crystal harvesting, data collection, data processing) were integrated into the software under development. All information necessary to perform protein crystallography experiments is stored (except raw X-ray data that are stored in a central data server) in a MySQL relational database. The database contains four mutually linked hierarchical trees describing protein crystals, data collection of protein crystal and experimental data processing. A database editor was designed and developed. The editor supports basic database functions to view,more » create, modify and delete user records in the database. Two search engines were realized: direct search of necessary information in the database and object oriented search. The system is based on TCP/IP secure UNIX sockets with four predefined sending and receiving behaviors, which support communications between all connected servers and clients with remote control functions (creating and modifying data for experimental conditions, data acquisition, viewing experimental data, and performing data processing). Two secure login schemes were designed and developed: a direct method (using the developed Linux clients with secure connection) and an indirect method (using the secure SSL connection using secure X11 support from any operating system with X-terminal and SSH support). A part of the system has been implemented on a new MAD beam line, NW12, at the Photon Factory Advanced Ring for general user experiments.« less

AT_CHLORO, a comprehensive chloroplast proteome database with subplastidial localization and curated information on envelope proteins.

PubMed

Ferro, Myriam; Brugière, Sabine; Salvi, Daniel; Seigneurin-Berny, Daphné; Court, Magali; Moyet, Lucas; Ramus, Claire; Miras, Stéphane; Mellal, Mourad; Le Gall, Sophie; Kieffer-Jaquinod, Sylvie; Bruley, Christophe; Garin, Jérôme; Joyard, Jacques; Masselon, Christophe; Rolland, Norbert

2010-06-01

Recent advances in the proteomics field have allowed a series of high throughput experiments to be conducted on chloroplast samples, and the data are available in several public databases. However, the accurate localization of many chloroplast proteins often remains hypothetical. This is especially true for envelope proteins. We went a step further into the knowledge of the chloroplast proteome by focusing, in the same set of experiments, on the localization of proteins in the stroma, the thylakoids, and envelope membranes. LC-MS/MS-based analyses first allowed building the AT_CHLORO database (http://www.grenoble.prabi.fr/protehome/grenoble-plant-proteomics/), a comprehensive repertoire of the 1323 proteins, identified by 10,654 unique peptide sequences, present in highly purified chloroplasts and their subfractions prepared from Arabidopsis thaliana leaves. This database also provides extensive proteomics information (peptide sequences and molecular weight, chromatographic retention times, MS/MS spectra, and spectral count) for a unique chloroplast protein accurate mass and time tag database gathering identified peptides with their respective and precise analytical coordinates, molecular weight, and retention time. We assessed the partitioning of each protein in the three chloroplast compartments by using a semiquantitative proteomics approach (spectral count). These data together with an in-depth investigation of the literature were compiled to provide accurate subplastidial localization of previously known and newly identified proteins. A unique knowledge base containing extensive information on the proteins identified in envelope fractions was thus obtained, allowing new insights into this membrane system to be revealed. Altogether, the data we obtained provide unexpected information about plastidial or subplastidial localization of some proteins that were not suspected to be associated to this membrane system. The spectral counting-based strategy was further validated as the compartmentation of well known pathways (for instance, photosynthesis and amino acid, fatty acid, or glycerolipid biosynthesis) within chloroplasts could be dissected. It also allowed revisiting the compartmentation of the chloroplast metabolism and functions.

HypoxiaDB: a database of hypoxia-regulated proteins

PubMed Central

Khurana, Pankaj; Sugadev, Ragumani; Jain, Jaspreet; Singh, Shashi Bala

2013-01-01

There has been intense interest in the cellular response to hypoxia, and a large number of differentially expressed proteins have been identified through various high-throughput experiments. These valuable data are scattered, and there have been no systematic attempts to document the various proteins regulated by hypoxia. Compilation, curation and annotation of these data are important in deciphering their role in hypoxia and hypoxia-related disorders. Therefore, we have compiled HypoxiaDB, a database of hypoxia-regulated proteins. It is a comprehensive, manually-curated, non-redundant catalog of proteins whose expressions are shown experimentally to be altered at different levels and durations of hypoxia. The database currently contains 72 000 manually curated entries taken on 3500 proteins extracted from 73 peer-reviewed publications selected from PubMed. HypoxiaDB is distinctive from other generalized databases: (i) it compiles tissue-specific protein expression changes under different levels and duration of hypoxia. Also, it provides manually curated literature references to support the inclusion of the protein in the database and establish its association with hypoxia. (ii) For each protein, HypoxiaDB integrates data on gene ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, protein–protein interactions, protein family (Pfam), OMIM (Online Mendelian Inheritance in Man), PDB (Protein Data Bank) structures and homology to other sequenced genomes. (iii) It also provides pre-compiled information on hypoxia-proteins, which otherwise requires tedious computational analysis. This includes information like chromosomal location, identifiers like Entrez, HGNC, Unigene, Uniprot, Ensembl, Vega, GI numbers and Genbank accession numbers associated with the protein. These are further cross-linked to respective public databases augmenting HypoxiaDB to the external repositories. (iv) In addition, HypoxiaDB provides an online sequence-similarity search tool for users to compare their protein sequences with HypoxiaDB protein database. We hope that HypoxiaDB will enrich our knowledge about hypoxia-related biology and eventually will lead to the development of novel hypothesis and advancements in diagnostic and therapeutic activities. HypoxiaDB is freely accessible for academic and non-profit users via http://www.hypoxiadb.com. Database URL: http://www.hypoxiadb.com PMID:24178989

Analysis of high accuracy, quantitative proteomics data in the MaxQB database.

PubMed

Schaab, Christoph; Geiger, Tamar; Stoehr, Gabriele; Cox, Juergen; Mann, Matthias

2012-03-01

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.

SSEP: secondary structural elements of proteins

PubMed Central

Shanthi, V.; Selvarani, P.; Kiran Kumar, Ch.; Mohire, C. S.; Sekar, K.

2003-01-01

SSEP is a comprehensive resource for accessing information related to the secondary structural elements present in the 25 and 90% non-redundant protein chains. The database contains 1771 protein chains from 1670 protein structures and 6182 protein chains from 5425 protein structures in 25 and 90% non-redundant protein chains, respectively. The current version provides information about the α-helical segments and β-strand fragments of varying lengths. In addition, it also contains the information about 310-helix, β- and ν-turns and hairpin loops. The free graphics program RASMOL has been interfaced with the search engine to visualize the three-dimensional structures of the user queried secondary structural fragment. The database is updated regularly and is available through Bioinformatics web server at http://cluster.physics.iisc.ernet.in/ssep/ or http://144.16.71.148/ssep/. PMID:12824336

Meta sequence analysis of human blood peptides and their parent proteins.

PubMed

Bowden, Peter; Pendrak, Voitek; Zhu, Peihong; Marshall, John G

2010-04-18

Sequence analysis of the blood peptides and their qualities will be key to understanding the mechanisms that contribute to error in LC-ESI-MS/MS. Analysis of peptides and their proteins at the level of sequences is much more direct and informative than the comparison of disparate accession numbers. A portable database of all blood peptide and protein sequences with descriptor fields and gene ontology terms might be useful for designing immunological or MRM assays from human blood. The results of twelve studies of human blood peptides and/or proteins identified by LC-MS/MS and correlated against a disparate array of genetic libraries were parsed and matched to proteins from the human ENSEMBL, SwissProt and RefSeq databases by SQL. The reported peptide and protein sequences were organized into an SQL database with full protein sequences and up to five unique peptides in order of prevalence along with the peptide count for each protein. Structured query language or BLAST was used to acquire descriptive information in current databases. Sampling error at the level of peptides is the largest source of disparity between groups. Chi Square analysis of peptide to protein distributions confirmed the significant agreement between groups on identified proteins. Copyright 2010. Published by Elsevier B.V.

RPG: the Ribosomal Protein Gene database.

PubMed

Nakao, Akihiro; Yoshihama, Maki; Kenmochi, Naoya

2004-01-01

RPG (http://ribosome.miyazaki-med.ac.jp/) is a new database that provides detailed information about ribosomal protein (RP) genes. It contains data from humans and other organisms, including Drosophila melanogaster, Caenorhabditis elegans, Saccharo myces cerevisiae, Methanococcus jannaschii and Escherichia coli. Users can search the database by gene name and organism. Each record includes sequences (genomic, cDNA and amino acid sequences), intron/exon structures, genomic locations and information about orthologs. In addition, users can view and compare the gene structures of the above organisms and make multiple amino acid sequence alignments. RPG also provides information on small nucleolar RNAs (snoRNAs) that are encoded in the introns of RP genes.

RPG: the Ribosomal Protein Gene database

PubMed Central

Nakao, Akihiro; Yoshihama, Maki; Kenmochi, Naoya

2004-01-01

RPG (http://ribosome.miyazaki-med.ac.jp/) is a new database that provides detailed information about ribosomal protein (RP) genes. It contains data from humans and other organisms, including Drosophila melanogaster, Caenorhabditis elegans, Saccharo myces cerevisiae, Methanococcus jannaschii and Escherichia coli. Users can search the database by gene name and organism. Each record includes sequences (genomic, cDNA and amino acid sequences), intron/exon structures, genomic locations and information about orthologs. In addition, users can view and compare the gene structures of the above organisms and make multiple amino acid sequence alignments. RPG also provides information on small nucleolar RNAs (snoRNAs) that are encoded in the introns of RP genes. PMID:14681386

EvoDB: a database of evolutionary rate profiles, associated protein domains and phylogenetic trees for PFAM-A

PubMed Central

Ndhlovu, Andrew; Durand, Pierre M.; Hazelhurst, Scott

2015-01-01

The evolutionary rate at codon sites across protein-coding nucleotide sequences represents a valuable tier of information for aligning sequences, inferring homology and constructing phylogenetic profiles. However, a comprehensive resource for cataloguing the evolutionary rate at codon sites and their corresponding nucleotide and protein domain sequence alignments has not been developed. To address this gap in knowledge, EvoDB (an Evolutionary rates DataBase) was compiled. Nucleotide sequences and their corresponding protein domain data including the associated seed alignments from the PFAM-A (protein family) database were used to estimate evolutionary rate (ω = dN/dS) profiles at codon sites for each entry. EvoDB contains 98.83% of the gapped nucleotide sequence alignments and 97.1% of the evolutionary rate profiles for the corresponding information in PFAM-A. As the identification of codon sites under positive selection and their position in a sequence profile is usually the most sought after information for molecular evolutionary biologists, evolutionary rate profiles were determined under the M2a model using the CODEML algorithm in the PAML (Phylogenetic Analysis by Maximum Likelihood) suite of software. Validation of nucleotide sequences against amino acid data was implemented to ensure high data quality. EvoDB is a catalogue of the evolutionary rate profiles and provides the corresponding phylogenetic trees, PFAM-A alignments and annotated accession identifier data. In addition, the database can be explored and queried using known evolutionary rate profiles to identify domains under similar evolutionary constraints and pressures. EvoDB is a resource for evolutionary, phylogenetic studies and presents a tier of information untapped by current databases. Database URL: http://www.bioinf.wits.ac.za/software/fire/evodb PMID:26140928

Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.

PubMed

Ravikumar, Komandur Elayavilli; Wagholikar, Kavishwar B; Li, Dingcheng; Kocher, Jean-Pierre; Liu, Hongfang

2015-06-06

Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.

Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.

PubMed

Gromiha, M Michael; Anoosha, P; Huang, Liang-Tsung

2016-01-01

Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database.

PubMed

Huang, Weiliang; Brewer, Luke K; Jones, Jace W; Nguyen, Angela T; Marcu, Ana; Wishart, David S; Oglesby-Sherrouse, Amanda G; Kane, Maureen A; Wilks, Angela

2018-01-04

The Pseudomonas aeruginosaMetabolome Database (PAMDB, http://pseudomonas.umaryland.edu) is a searchable, richly annotated metabolite database specific to P. aeruginosa. P. aeruginosa is a soil organism and significant opportunistic pathogen that adapts to its environment through a versatile energy metabolism network. Furthermore, P. aeruginosa is a model organism for the study of biofilm formation, quorum sensing, and bioremediation processes, each of which are dependent on unique pathways and metabolites. The PAMDB is modelled on the Escherichia coli (ECMDB), yeast (YMDB) and human (HMDB) metabolome databases and contains >4370 metabolites and 938 pathways with links to over 1260 genes and proteins. The database information was compiled from electronic databases, journal articles and mass spectrometry (MS) metabolomic data obtained in our laboratories. For each metabolite entered, we provide detailed compound descriptions, names and synonyms, structural and physiochemical information, nuclear magnetic resonance (NMR) and MS spectra, enzymes and pathway information, as well as gene and protein sequences. The database allows extensive searching via chemical names, structure and molecular weight, together with gene, protein and pathway relationships. The PAMBD and its future iterations will provide a valuable resource to biologists, natural product chemists and clinicians in identifying active compounds, potential biomarkers and clinical diagnostics. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Viral Genome DataBase: storing and analyzing genes and proteins from complete viral genomes.

PubMed

Hiscock, D; Upton, C

2000-05-01

The Viral Genome DataBase (VGDB) contains detailed information of the genes and predicted protein sequences from 15 completely sequenced genomes of large (&100 kb) viruses (2847 genes). The data that is stored includes DNA sequence, protein sequence, GenBank and user-entered notes, molecular weight (MW), isoelectric point (pI), amino acid content, A + T%, nucleotide frequency, dinucleotide frequency and codon use. The VGDB is a mySQL database with a user-friendly JAVA GUI. Results of queries can be easily sorted by any of the individual parameters. The software and additional figures and information are available at http://athena.bioc.uvic.ca/genomes/index.html .

PARPs database: A LIMS systems for protein-protein interaction data mining or laboratory information management system

PubMed Central

Droit, Arnaud; Hunter, Joanna M; Rouleau, Michèle; Ethier, Chantal; Picard-Cloutier, Aude; Bourgais, David; Poirier, Guy G

2007-01-01

Background 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. Description 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. Conclusion 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. PMID:18093328

SGDB: a database of synthetic genes re-designed for optimizing protein over-expression.

PubMed

Wu, Gang; Zheng, Yuanpu; Qureshi, Imran; Zin, Htar Thant; Beck, Tyler; Bulka, Blazej; Freeland, Stephen J

2007-01-01

Here we present the Synthetic Gene Database (SGDB): a relational database that houses sequences and associated experimental information on synthetic (artificially engineered) genes from all peer-reviewed studies published to date. At present, the database comprises information from more than 200 published experiments. This resource not only provides reference material to guide experimentalists in designing new genes that improve protein expression, but also offers a dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that influence gene expression. The SGDB was built under MySQL database management system. We also offer an XML schema for standardized data description of synthetic genes. Users can access the database at http://www.evolvingcode.net/codon/sgdb/index.php, or batch downloads all information through XML files. Moreover, users may visually compare the coding sequences of a synthetic gene and its natural counterpart with an integrated web tool at http://www.evolvingcode.net/codon/sgdb/aligner.php, and discuss questions, findings and related information on an associated e-forum at http://www.evolvingcode.net/forum/viewforum.php?f=27.

FERN Ethnomedicinal Plant Database: Exploring Fern Ethnomedicinal Plants Knowledge for Computational Drug Discovery.

PubMed

Thakar, Sambhaji B; Ghorpade, Pradnya N; Kale, Manisha V; Sonawane, Kailas D

2015-01-01

Fern plants are known for their ethnomedicinal applications. Huge amount of fern medicinal plants information is scattered in the form of text. Hence, database development would be an appropriate endeavor to cope with the situation. So by looking at the importance of medicinally useful fern plants, we developed a web based database which contains information about several group of ferns, their medicinal uses, chemical constituents as well as protein/enzyme sequences isolated from different fern plants. Fern ethnomedicinal plant database is an all-embracing, content management web-based database system, used to retrieve collection of factual knowledge related to the ethnomedicinal fern species. Most of the protein/enzyme sequences have been extracted from NCBI Protein sequence database. The fern species, family name, identification, taxonomy ID from NCBI, geographical occurrence, trial for, plant parts used, ethnomedicinal importance, morphological characteristics, collected from various scientific literatures and journals available in the text form. NCBI's BLAST, InterPro, phylogeny, Clustal W web source has also been provided for the future comparative studies. So users can get information related to fern plants and their medicinal applications at one place. This Fern ethnomedicinal plant database includes information of 100 fern medicinal species. This web based database would be an advantageous to derive information specifically for computational drug discovery, botanists or botanical interested persons, pharmacologists, researchers, biochemists, plant biotechnologists, ayurvedic practitioners, doctors/pharmacists, traditional medicinal users, farmers, agricultural students and teachers from universities as well as colleges and finally fern plant lovers. This effort would be useful to provide essential knowledge for the users about the adventitious applications for drug discovery, applications, conservation of fern species around the world and finally to create social awareness.

A Bioinformatics Workflow for Variant Peptide Detection in Shotgun Proteomics*

PubMed Central

Li, Jing; Su, Zengliu; Ma, Ze-Qiang; Slebos, Robbert J. C.; Halvey, Patrick; Tabb, David L.; Liebler, Daniel C.; Pao, William; Zhang, Bing

2011-01-01

Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or cancer-related variations. Twenty-three out of 26 variants randomly selected from the 81 were confirmed by genomic sequencing. We further applied the workflow on data sets from three individual colorectal tumor specimens. A total of 204 distinct variant peptides were detected, and five carried known cancer-related mutations. Each individual showed a specific pattern of cancer-related mutations, suggesting potential use of this type of information for personalized medicine. Compatibility of the workflow has been tested with four popular database search engines including Sequest, Mascot, X!Tandem, and MyriMatch. In summary, we have developed a workflow that effectively uses existing genomic data to enable variant peptide detection in proteomics. PMID:21389108

MultitaskProtDB-II: an update of a database of multitasking/moonlighting proteins

PubMed Central

Franco-Serrano, Luís; Hernández, Sergio; Calvo, Alejandra; Severi, María A; Ferragut, Gabriela; Pérez-Pons, JosepAntoni; Piñol, Jaume; Pich, Òscar; Mozo-Villarias, Ángel; Amela, Isaac

2018-01-01

Abstract Multitasking, or moonlighting, is the capability of some proteins to execute two or more biological functions. MultitaskProtDB-II is a database of multifunctional proteins that has been updated. In the previous version, the information contained was: NCBI and UniProt accession numbers, canonical and additional biological functions, organism, monomeric/oligomeric states, PDB codes and bibliographic references. In the present update, the number of entries has been increased from 288 to 694 moonlighting proteins. MultitaskProtDB-II is continually being curated and updated. The new database also contains the following information: GO descriptors for the canonical and moonlighting functions, three-dimensional structure (for those proteins lacking PDB structure, a model was made using Itasser and Phyre), the involvement of the proteins in human diseases (78% of human moonlighting proteins) and whether the protein is a target of a current drug (48% of human moonlighting proteins). These numbers highlight the importance of these proteins for the analysis and explanation of human diseases and target-directed drug design. Moreover, 25% of the proteins of the database are involved in virulence of pathogenic microorganisms, largely in the mechanism of adhesion to the host. This highlights their importance for the mechanism of microorganism infection and vaccine design. MultitaskProtDB-II is available at http://wallace.uab.es/multitaskII. PMID:29136215

GMDD: a database of GMO detection methods.

PubMed

Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans J P; Guo, Rong; Liang, Wanqi; Zhang, Dabing

2008-06-04

Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier.

The Multinational Arabidopsis Steering Subcommittee for Proteomics Assembles the Largest Proteome Database Resource for Plant Systems Biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weckwerth, Wolfram; Baginsky, Sacha; Van Wijk, Klass

2009-12-01

In the past 10 years, we have witnessed remarkable advances in the field of plant molecular biology. The rapid development of proteomic technologies and the speed with which these techniques have been applied to the field have altered our perception of how we can analyze proteins in complex systems. At nearly the same time, the availability of the complete genome for the model plant Arabidopsis thaliana was released; this effort provides an unsurpassed resource for the identification of proteins when researchers use MS to analyze plant samples. Recognizing the growth in this area, the Multinational Arabidopsis Steering Committee (MASC) establishedmore » a subcommittee for A. thaliana proteomics in 2006 with the objective of consolidating databases, technique standards, and experimentally validated candidate genes and functions. Since the establishment of the Multinational Arabidopsis Steering Subcommittee for Proteomics (MASCP), many new approaches and resources have become available. Recently, the subcommittee established a webpage to consolidate this information (www.masc-proteomics.org). It includes links to plant proteomic databases, general information about proteomic techniques, meeting information, a summary of proteomic standards, and other relevant resources. Altogether, this website provides a useful resource for the Arabidopsis proteomics community. In the future, the website will host discussions and investigate the cross-linking of databases. The subcommittee members have extensive experience in arabidopsis proteomics and collectively have produced some of the most extensive proteomics data sets for this model plant (Table S1 in the Supporting Information has a list of resources). The largest collection of proteomics data from a single study in A. thaliana was assembled into an accessible database (AtProteome; http://fgcz-atproteome.unizh.ch/index.php) and was recently published by the Baginsky lab.1 The database provides links to major Arabidopsis online resources, and raw data have been deposited in PRIDE and PRIDE BioMart. Included in this database is an Arabidopsis proteome map that provides evidence for the expression of {approx}50% of all predicted gene models, including several alternative gene models that are not represented in The Arabidopsis Information Resource (TAIR) protein database. A set of organ-specific biomarkers is provided, as well as organ-specific proteotypic peptides for 4105 proteins that can be used to facilitate targeted quantitative proteomic surveys. In the future, the AtProteome database will be linked to additional existing resources developed by MASCP members, such as PPDB, ProMEX, and SUBA. The most comprehensive study on the Arabidopsis chloroplast proteome, which includes information on chloroplast sorting signals, posttranslational modifications (PTMs), and protein abundances (analyzed by high-accuracy MS [Orbitrap]), was recently published by the van Wijk lab.2 These and previous data are available via the plant proteome database (PPDB; http://ppdb.tc.cornell.edu) for A. thaliana and maize. PPDB provides genome-wide experimental and functional characterization of the A. thaliana and maize proteomes, including PTMs and subcellular localization information, with an emphasis on leaf and plastid proteins. Maize and Arabidopsis proteome entries are directly linked via internal BLAST alignments within PPDB. Direct links for each protein to TAIR, SUBA, ProMEX, and other resources are also provided.« less

An integrated metagenome and -proteome analysis of the microbial community residing in a biogas production plant.

PubMed

Ortseifen, Vera; Stolze, Yvonne; Maus, Irena; Sczyrba, Alexander; Bremges, Andreas; Albaum, Stefan P; Jaenicke, Sebastian; Fracowiak, Jochen; Pühler, Alfred; Schlüter, Andreas

2016-08-10

To study the metaproteome of a biogas-producing microbial community, fermentation samples were taken from an agricultural biogas plant for microbial cell and protein extraction and corresponding metagenome analyses. Based on metagenome sequence data, taxonomic community profiling was performed to elucidate the composition of bacterial and archaeal sub-communities. The community's cytosolic metaproteome was represented in a 2D-PAGE approach. Metaproteome databases for protein identification were compiled based on the assembled metagenome sequence dataset for the biogas plant analyzed and non-corresponding biogas metagenomes. Protein identification results revealed that the corresponding biogas protein database facilitated the highest identification rate followed by other biogas-specific databases, whereas common public databases yielded insufficient identification rates. Proteins of the biogas microbiome identified as highly abundant were assigned to the pathways involved in methanogenesis, transport and carbon metabolism. Moreover, the integrated metagenome/-proteome approach enabled the examination of genetic-context information for genes encoding identified proteins by studying neighboring genes on the corresponding contig. Exemplarily, this approach led to the identification of a Methanoculleus sp. contig encoding 16 methanogenesis-related gene products, three of which were also detected as abundant proteins within the community's metaproteome. Thus, metagenome contigs provide additional information on the genetic environment of identified abundant proteins. Copyright © 2016 Elsevier B.V. All rights reserved.

Integrating the intrinsic conformational preferences of non-coded α-amino acids modified at the peptide bond into the NCAD database

PubMed Central

Revilla-López, Guillem; Rodríguez-Ropero, Francisco; Curcó, David; Torras, Juan; Calaza, M. Isabel; Zanuy, David; Jiménez, Ana I.; Cativiela, Carlos; Nussinov, Ruth; Alemán, Carlos

2011-01-01

Recently, we reported a database (NCAD, Non-Coded Amino acids Database; http://recerca.upc.edu/imem/index.htm) that was built to compile information about the intrinsic conformational preferences of non-proteinogenic residues determined by quantum mechanical calculations, as well as bibliographic information about their synthesis, physical and spectroscopic characterization, the experimentally-established conformational propensities, and applications (J. Phys. Chem. B 2010, 114, 7413). The database initially contained the information available for α-tetrasubstituted α-amino acids. In this work, we extend NCAD to three families of compounds, which can be used to engineer peptides and proteins incorporating modifications at the –NHCO– peptide bond. Such families are: N-substituted α-amino acids, thio-α-amino acids, and diamines and diacids used to build retropeptides. The conformational preferences of these compounds have been analyzed and described based on the information captured in the database. In addition, we provide an example of the utility of the database and of the compounds it compiles in protein and peptide engineering. Specifically, the symmetry of a sequence engineered to stabilize the 310-helix with respect to the α-helix has been broken without perturbing significantly the secondary structure through targeted replacements using the information contained in the database. PMID:21491493

Curated protein information in the Saccharomyces genome database.

PubMed

Hellerstedt, Sage T; Nash, Robert S; Weng, Shuai; Paskov, Kelley M; Wong, Edith D; Karra, Kalpana; Engel, Stacia R; Cherry, J Michael

2017-01-01

Due to recent advancements in the production of experimental proteomic data, the Saccharomyces genome database (SGD; www.yeastgenome.org ) has been expanding our protein curation activities to make new data types available to our users. Because of broad interest in post-translational modifications (PTM) and their importance to protein function and regulation, we have recently started incorporating expertly curated PTM information on individual protein pages. Here we also present the inclusion of new abundance and protein half-life data obtained from high-throughput proteome studies. These new data types have been included with the aim to facilitate cellular biology research. : www.yeastgenome.org. © The Author(s) 2017. Published by Oxford University Press.

VerSeDa: vertebrate secretome database

PubMed Central

Cortazar, Ana R.; Oguiza, José A.

2017-01-01

Based on the current tools, de novo secretome (full set of proteins secreted by an organism) prediction is a time consuming bioinformatic task that requires a multifactorial analysis in order to obtain reliable in silico predictions. Hence, to accelerate this process and offer researchers a reliable repository where secretome information can be obtained for vertebrates and model organisms, we have developed VerSeDa (Vertebrate Secretome Database). This freely available database stores information about proteins that are predicted to be secreted through the classical and non-classical mechanisms, for the wide range of vertebrate species deposited at the NCBI, UCSC and ENSEMBL sites. To our knowledge, VerSeDa is the only state-of-the-art database designed to store secretome data from multiple vertebrate genomes, thus, saving an important amount of time spent in the prediction of protein features that can be retrieved from this repository directly. Database URL: VerSeDa is freely available at http://genomics.cicbiogune.es/VerSeDa/index.php PMID:28365718

DITOP: drug-induced toxicity related protein database.

PubMed

Zhang, Jing-Xian; Huang, Wei-Juan; Zeng, Jing-Hua; Huang, Wen-Hui; Wang, Yi; Zhao, Rui; Han, Bu-Cong; Liu, Qing-Feng; Chen, Yu-Zong; Ji, Zhi-Liang

2007-07-01

Drug-induced toxicity related proteins (DITRPs) are proteins that mediate adverse drug reactions (ADRs) or toxicities through their binding to drugs or reactive metabolites. Collection of these proteins facilitates better understanding of the molecular mechanisms of drug-induced toxicity and the rational drug discovery. Drug-induced toxicity related protein database (DITOP) is such a database that is intending to provide comprehensive information of DITRPs. Currently, DITOP contains 1501 records, covering 618 distinct literature-reported DITRPs, 529 drugs/ligands and 418 distinct toxicity terms. These proteins were confirmed experimentally to interact with drugs or their reactive metabolites, thus directly or indirectly cause adverse effects or toxicities. Five major types of drug-induced toxicities or ADRs are included in DITOP, which are the idiosyncratic adverse drug reactions, the dose-dependent toxicities, the drug-drug interactions, the immune-mediated adverse drug effects (IMADEs) and the toxicities caused by genetic susceptibility. Molecular mechanisms underlying the toxicity and cross-links to related resources are also provided while available. Moreover, a series of user-friendly interfaces were designed for flexible retrieval of DITRPs-related information. The DITOP can be accessed freely at http://bioinf.xmu.edu.cn/databases/ADR/index.html. Supplementary data are available at Bioinformatics online.

Follicle Online: an integrated database of follicle assembly, development and ovulation.

PubMed

Hua, Juan; Xu, Bo; Yang, Yifan; Ban, Rongjun; Iqbal, Furhan; Cooke, Howard J; Zhang, Yuanwei; Shi, Qinghua

2015-01-01

Folliculogenesis is an important part of ovarian function as it provides the oocytes for female reproductive life. Characterizing genes/proteins involved in folliculogenesis is fundamental for understanding the mechanisms associated with this biological function and to cure the diseases associated with folliculogenesis. A large number of genes/proteins associated with folliculogenesis have been identified from different species. However, no dedicated public resource is currently available for folliculogenesis-related genes/proteins that are validated by experiments. Here, we are reporting a database 'Follicle Online' that provides the experimentally validated gene/protein map of the folliculogenesis in a number of species. Follicle Online is a web-based database system for storing and retrieving folliculogenesis-related experimental data. It provides detailed information for 580 genes/proteins (from 23 model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Mesocricetus auratus, Bos Taurus, Drosophila and Xenopus laevis) that have been reported to be involved in folliculogenesis, POF (premature ovarian failure) and PCOS (polycystic ovary syndrome). The literature was manually curated from more than 43,000 published articles (till 1 March 2014). The Follicle Online database is implemented in PHP + MySQL + JavaScript and this user-friendly web application provides access to the stored data. In summary, we have developed a centralized database that provides users with comprehensive information about genes/proteins involved in folliculogenesis. This database can be accessed freely and all the stored data can be viewed without any registration. Database URL: http://mcg.ustc.edu.cn/sdap1/follicle/index.php © The Author(s) 2015. Published by Oxford University Press.

Follicle Online: an integrated database of follicle assembly, development and ovulation

PubMed Central

Hua, Juan; Xu, Bo; Yang, Yifan; Ban, Rongjun; Iqbal, Furhan; Zhang, Yuanwei; Shi, Qinghua

2015-01-01

Folliculogenesis is an important part of ovarian function as it provides the oocytes for female reproductive life. Characterizing genes/proteins involved in folliculogenesis is fundamental for understanding the mechanisms associated with this biological function and to cure the diseases associated with folliculogenesis. A large number of genes/proteins associated with folliculogenesis have been identified from different species. However, no dedicated public resource is currently available for folliculogenesis-related genes/proteins that are validated by experiments. Here, we are reporting a database ‘Follicle Online’ that provides the experimentally validated gene/protein map of the folliculogenesis in a number of species. Follicle Online is a web-based database system for storing and retrieving folliculogenesis-related experimental data. It provides detailed information for 580 genes/proteins (from 23 model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Mesocricetus auratus, Bos Taurus, Drosophila and Xenopus laevis) that have been reported to be involved in folliculogenesis, POF (premature ovarian failure) and PCOS (polycystic ovary syndrome). The literature was manually curated from more than 43 000 published articles (till 1 March 2014). The Follicle Online database is implemented in PHP + MySQL + JavaScript and this user-friendly web application provides access to the stored data. In summary, we have developed a centralized database that provides users with comprehensive information about genes/proteins involved in folliculogenesis. This database can be accessed freely and all the stored data can be viewed without any registration. Database URL: http://mcg.ustc.edu.cn/sdap1/follicle/index.php PMID:25931457

The Protein Information Management System (PiMS): a generic tool for any structural biology research laboratory

PubMed Central

Morris, Chris; Pajon, Anne; Griffiths, Susanne L.; Daniel, Ed; Savitsky, Marc; Lin, Bill; Diprose, Jonathan M.; Wilter da Silva, Alan; Pilicheva, Katya; Troshin, Peter; van Niekerk, Johannes; Isaacs, Neil; Naismith, James; Nave, Colin; Blake, Richard; Wilson, Keith S.; Stuart, David I.; Henrick, Kim; Esnouf, Robert M.

2011-01-01

The techniques used in protein production and structural biology have been developing rapidly, but techniques for recording the laboratory information produced have not kept pace. One approach is the development of laboratory information-management systems (LIMS), which typically use a relational database schema to model and store results from a laboratory workflow. The underlying philosophy and implementation of the Protein Information Management System (PiMS), a LIMS development specifically targeted at the flexible and unpredictable workflows of protein-production research laboratories of all scales, is described. PiMS is a web-based Java application that uses either Postgres or Oracle as the underlying relational database-management system. PiMS is available under a free licence to all academic laboratories either for local installation or for use as a managed service. PMID:21460443

The Protein Information Management System (PiMS): a generic tool for any structural biology research laboratory.

PubMed

Morris, Chris; Pajon, Anne; Griffiths, Susanne L; Daniel, Ed; Savitsky, Marc; Lin, Bill; Diprose, Jonathan M; da Silva, Alan Wilter; Pilicheva, Katya; Troshin, Peter; van Niekerk, Johannes; Isaacs, Neil; Naismith, James; Nave, Colin; Blake, Richard; Wilson, Keith S; Stuart, David I; Henrick, Kim; Esnouf, Robert M

2011-04-01

The techniques used in protein production and structural biology have been developing rapidly, but techniques for recording the laboratory information produced have not kept pace. One approach is the development of laboratory information-management systems (LIMS), which typically use a relational database schema to model and store results from a laboratory workflow. The underlying philosophy and implementation of the Protein Information Management System (PiMS), a LIMS development specifically targeted at the flexible and unpredictable workflows of protein-production research laboratories of all scales, is described. PiMS is a web-based Java application that uses either Postgres or Oracle as the underlying relational database-management system. PiMS is available under a free licence to all academic laboratories either for local installation or for use as a managed service.

TryTransDB: A web-based resource for transport proteins in Trypanosomatidae.

PubMed

Sonar, Krushna; Kabra, Ritika; Singh, Shailza

2018-03-12

TryTransDB is a web-based resource that stores transport protein data which can be retrieved using a standalone BLAST tool. We have attempted to create an integrated database that can be a one-stop shop for the researchers working with transport proteins of Trypanosomatidae family. TryTransDB (Trypanosomatidae Transport Protein Database) is a web based comprehensive resource that can fire a BLAST search against most of the transport protein sequences (protein and nucleotide) from Trypanosomatidae family organisms. This web resource further allows to compute a phylogenetic tree by performing multiple sequence alignment (MSA) using CLUSTALW suite embedded in it. Also, cross-linking to other databases helps in gathering more information for a certain transport protein in a single website.

Cazymes Analysis Toolkit (CAT): Webservice for searching and analyzing carbohydrateactive enzymes in a newly sequenced organism using CAZy database

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karpinets, Tatiana V; Park, Byung; Syed, Mustafa H

2010-01-01

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire non-redundant sequences of the CAZy database. Themore » second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains (DUF) and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit (CAT), and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.« less

CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database.

PubMed

Park, Byung H; Karpinets, Tatiana V; Syed, Mustafa H; Leuze, Michael R; Uberbacher, Edward C

2010-12-01

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.

HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

PubMed

López, Yosvany; Nakai, Kenta; Patil, Ashwini

2015-01-01

HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

Detection of alternative splice variants at the proteome level in Aspergillus flavus.

PubMed

Chang, Kung-Yen; Georgianna, D Ryan; Heber, Steffen; Payne, Gary A; Muddiman, David C

2010-03-05

Identification of proteins from proteolytic peptides or intact proteins plays an essential role in proteomics. Researchers use search engines to match the acquired peptide sequences to the target proteins. However, search engines depend on protein databases to provide candidates for consideration. Alternative splicing (AS), the mechanism where the exon of pre-mRNAs can be spliced and rearranged to generate distinct mRNA and therefore protein variants, enable higher eukaryotic organisms, with only a limited number of genes, to have the requisite complexity and diversity at the proteome level. Multiple alternative isoforms from one gene often share common segments of sequences. However, many protein databases only include a limited number of isoforms to keep minimal redundancy. As a result, the database search might not identify a target protein even with high quality tandem MS data and accurate intact precursor ion mass. We computationally predicted an exhaustive list of putative isoforms of Aspergillus flavus proteins from 20 371 expressed sequence tags to investigate whether an alternative splicing protein database can assign a greater proportion of mass spectrometry data. The newly constructed AS database provided 9807 new alternatively spliced variants in addition to 12 832 previously annotated proteins. The searches of the existing tandem MS spectra data set using the AS database identified 29 new proteins encoded by 26 genes. Nine fungal genes appeared to have multiple protein isoforms. In addition to the discovery of splice variants, AS database also showed potential to improve genome annotation. In summary, the introduction of an alternative splicing database helps identify more proteins and unveils more information about a proteome.

ToxReporter: viewing the genome through the eyes of a toxicologist.

PubMed

Gosink, Mark

2016-01-01

One of the many roles of a toxicologist is to determine if an observed adverse event (AE) is related to a previously unrecognized function of a given gene/protein. Towards that end, he or she will search a variety of public and propriety databases for information linking that protein to the observed AE. However, these databases tend to present all available information about a protein, which can be overwhelming, limiting the ability to find information about the specific toxicity being investigated. ToxReporter compiles information from a broad selection of resources and limits display of the information to user-selected areas of interest. ToxReporter is a PERL-based web-application which utilizes a MySQL database to streamline this process by categorizing public and proprietary domain-derived information into predefined safety categories according to a customizable lexicon. Users can view gene information that is 'red-flagged' according to the safety issue under investigation. ToxReporter also uses a scoring system based on relative counts of the red-flags to rank all genes for the amount of information pertaining to each safety issue and to display their scored ranking as an easily interpretable 'Tox-At-A-Glance' chart. Although ToxReporter was originally developed to display safety information, its flexible design could easily be adapted to display disease information as well.Database URL: ToxReporter is freely available at https://github.com/mgosink/ToxReporter. © The Author(s) 2016. Published by Oxford University Press.

Metagenomic Taxonomy-Guided Database-Searching Strategy for Improving Metaproteomic Analysis.

PubMed

Xiao, Jinqiu; Tanca, Alessandro; Jia, Ben; Yang, Runqing; Wang, Bo; Zhang, Yu; Li, Jing

2018-04-06

Metaproteomics provides a direct measure of the functional information by investigating all proteins expressed by a microbiota. However, due to the complexity and heterogeneity of microbial communities, it is very hard to construct a sequence database suitable for a metaproteomic study. Using a public database, researchers might not be able to identify proteins from poorly characterized microbial species, while a sequencing-based metagenomic database may not provide adequate coverage for all potentially expressed protein sequences. To address this challenge, we propose a metagenomic taxonomy-guided database-search strategy (MT), in which a merged database is employed, consisting of both taxonomy-guided reference protein sequences from public databases and proteins from metagenome assembly. By applying our MT strategy to a mock microbial mixture, about two times as many peptides were detected as with the metagenomic database only. According to the evaluation of the reliability of taxonomic attribution, the rate of misassignments was comparable to that obtained using an a priori matched database. We also evaluated the MT strategy with a human gut microbial sample, and we found 1.7 times as many peptides as using a standard metagenomic database. In conclusion, our MT strategy allows the construction of databases able to provide high sensitivity and precision in peptide identification in metaproteomic studies, enabling the detection of proteins from poorly characterized species within the microbiota.

The Molecule Pages database

PubMed Central

Saunders, Brian; Lyon, Stephen; Day, Matthew; Riley, Brenda; Chenette, Emily; Subramaniam, Shankar

2008-01-01

The UCSD-Nature Signaling Gateway Molecule Pages (http://www.signaling-gateway.org/molecule) provides essential information on more than 3800 mammalian proteins involved in cellular signaling. The Molecule Pages contain expert-authored and peer-reviewed information based on the published literature, complemented by regularly updated information derived from public data source references and sequence analysis. The expert-authored data includes both a full-text review about the molecule, with citations, and highly structured data for bioinformatics interrogation, including information on protein interactions and states, transitions between states and protein function. The expert-authored pages are anonymously peer reviewed by the Nature Publishing Group. The Molecule Pages data is present in an object-relational database format and is freely accessible to the authors, the reviewers and the public from a web browser that serves as a presentation layer. The Molecule Pages are supported by several applications that along with the database and the interfaces form a multi-tier architecture. The Molecule Pages and the Signaling Gateway are routinely accessed by a very large research community. PMID:17965093

The Molecule Pages database.

PubMed

Saunders, Brian; Lyon, Stephen; Day, Matthew; Riley, Brenda; Chenette, Emily; Subramaniam, Shankar; Vadivelu, Ilango

2008-01-01

The UCSD-Nature Signaling Gateway Molecule Pages (http://www.signaling-gateway.org/molecule) provides essential information on more than 3800 mammalian proteins involved in cellular signaling. The Molecule Pages contain expert-authored and peer-reviewed information based on the published literature, complemented by regularly updated information derived from public data source references and sequence analysis. The expert-authored data includes both a full-text review about the molecule, with citations, and highly structured data for bioinformatics interrogation, including information on protein interactions and states, transitions between states and protein function. The expert-authored pages are anonymously peer reviewed by the Nature Publishing Group. The Molecule Pages data is present in an object-relational database format and is freely accessible to the authors, the reviewers and the public from a web browser that serves as a presentation layer. The Molecule Pages are supported by several applications that along with the database and the interfaces form a multi-tier architecture. The Molecule Pages and the Signaling Gateway are routinely accessed by a very large research community.

Rice proteome database: a step toward functional analysis of the rice genome.

PubMed

Komatsu, Setsuko

2005-09-01

The technique of proteome analysis using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) has the power to monitor global changes that occur in the protein complement of tissues and subcellular compartments. In this study, the proteins of rice were cataloged, a rice proteome database was constructed, and a functional characterization of some of the identified proteins was undertaken. Proteins extracted from various tissues and subcellular compartments in rice were separated by 2D-PAGE and an image analyzer was used to construct a display of the proteins. The Rice Proteome Database contains 23 reference maps based on 2D-PAGE of proteins from various rice tissues and subcellular compartments. These reference maps comprise 13129 identified proteins, and the amino acid sequences of 5092 proteins are entered in the database. Major proteins involved in growth or stress responses were identified using the proteome approach. Some of these proteins, including a beta-tubulin, calreticulin, and ribulose-1,5-bisphosphate carboxylase/oxygenase activase in rice, have unexpected functions. The information obtained from the Rice Proteome Database will aid in cloning the genes for and predicting the function of unknown proteins.

EvoDB: a database of evolutionary rate profiles, associated protein domains and phylogenetic trees for PFAM-A.

PubMed

Ndhlovu, Andrew; Durand, Pierre M; Hazelhurst, Scott

2015-01-01

The evolutionary rate at codon sites across protein-coding nucleotide sequences represents a valuable tier of information for aligning sequences, inferring homology and constructing phylogenetic profiles. However, a comprehensive resource for cataloguing the evolutionary rate at codon sites and their corresponding nucleotide and protein domain sequence alignments has not been developed. To address this gap in knowledge, EvoDB (an Evolutionary rates DataBase) was compiled. Nucleotide sequences and their corresponding protein domain data including the associated seed alignments from the PFAM-A (protein family) database were used to estimate evolutionary rate (ω = dN/dS) profiles at codon sites for each entry. EvoDB contains 98.83% of the gapped nucleotide sequence alignments and 97.1% of the evolutionary rate profiles for the corresponding information in PFAM-A. As the identification of codon sites under positive selection and their position in a sequence profile is usually the most sought after information for molecular evolutionary biologists, evolutionary rate profiles were determined under the M2a model using the CODEML algorithm in the PAML (Phylogenetic Analysis by Maximum Likelihood) suite of software. Validation of nucleotide sequences against amino acid data was implemented to ensure high data quality. EvoDB is a catalogue of the evolutionary rate profiles and provides the corresponding phylogenetic trees, PFAM-A alignments and annotated accession identifier data. In addition, the database can be explored and queried using known evolutionary rate profiles to identify domains under similar evolutionary constraints and pressures. EvoDB is a resource for evolutionary, phylogenetic studies and presents a tier of information untapped by current databases. © The Author(s) 2015. Published by Oxford University Press.

Consolidation of proteomics data in the Cancer Proteomics database.

PubMed

Arntzen, Magnus Ø; Boddie, Paul; Frick, Rahel; Koehler, Christian J; Thiede, Bernd

2015-11-01

Cancer is a class of diseases characterized by abnormal cell growth and one of the major reasons for human deaths. Proteins are involved in the molecular mechanisms leading to cancer, furthermore they are affected by anti-cancer drugs, and protein biomarkers can be used to diagnose certain cancer types. Therefore, it is important to explore the proteomics background of cancer. In this report, we developed the Cancer Proteomics database to re-interrogate published proteome studies investigating cancer. The database is divided in three sections related to cancer processes, cancer types, and anti-cancer drugs. Currently, the Cancer Proteomics database contains 9778 entries of 4118 proteins extracted from 143 scientific articles covering all three sections: cell death (cancer process), prostate cancer (cancer type) and platinum-based anti-cancer drugs including carboplatin, cisplatin, and oxaliplatin (anti-cancer drugs). The detailed information extracted from the literature includes basic information about the articles (e.g., PubMed ID, authors, journal name, publication year), information about the samples (type, study/reference, prognosis factor), and the proteomics workflow (Subcellular fractionation, protein, and peptide separation, mass spectrometry, quantification). Useful annotations such as hyperlinks to UniProt and PubMed were included. In addition, many filtering options were established as well as export functions. The database is freely available at http://cancerproteomics.uio.no. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SITEX 2.0: Projections of protein functional sites on eukaryotic genes. Extension with orthologous genes.

PubMed

Medvedeva, Irina V; Demenkov, Pavel S; Ivanisenko, Vladimir A

2017-04-01

Functional sites define the diversity of protein functions and are the central object of research of the structural and functional organization of proteins. The mechanisms underlying protein functional sites emergence and their variability during evolution are distinguished by duplication, shuffling, insertion and deletion of the exons in genes. The study of the correlation between a site structure and exon structure serves as the basis for the in-depth understanding of sites organization. In this regard, the development of programming resources that allow the realization of the mutual projection of exon structure of genes and primary and tertiary structures of encoded proteins is still the actual problem. Previously, we developed the SitEx system that provides information about protein and gene sequences with mapped exon borders and protein functional sites amino acid positions. The database included information on proteins with known 3D structure. However, data with respect to orthologs was not available. Therefore, we added the projection of sites positions to the exon structures of orthologs in SitEx 2.0. We implemented a search through database using site conservation variability and site discontinuity through exon structure. Inclusion of the information on orthologs allowed to expand the possibilities of SitEx usage for solving problems regarding the analysis of the structural and functional organization of proteins. Database URL: http://www-bionet.sscc.ru/sitex/ .

GALT protein database, a bioinformatics resource for the management and analysis of structural features of a galactosemia-related protein and its mutants.

PubMed

d'Acierno, Antonio; Facchiano, Angelo; Marabotti, Anna

2009-06-01

We describe the GALT-Prot database and its related web-based application that have been developed to collect information about the structural and functional effects of mutations on the human enzyme galactose-1-phosphate uridyltransferase (GALT) involved in the genetic disease named galactosemia type I. Besides a list of missense mutations at gene and protein sequence levels, GALT-Prot reports the analysis results of mutant GALT structures. In addition to the structural information about the wild-type enzyme, the database also includes structures of over 100 single point mutants simulated by means of a computational procedure, and the analysis to each mutant was made with several bioinformatics programs in order to investigate the effect of the mutations. The web-based interface allows querying of the database, and several links are also provided in order to guarantee a high integration with other resources already present on the web. Moreover, the architecture of the database and the web application is flexible and can be easily adapted to store data related to other proteins with point mutations. GALT-Prot is freely available at http://bioinformatica.isa.cnr.it/GALT/.

ExtraTrain: a database of Extragenic regions and Transcriptional information in prokaryotic organisms

PubMed Central

Pareja, Eduardo; Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Bonal, Javier; Tobes, Raquel

2006-01-01

Background Transcriptional regulation processes are the principal mechanisms of adaptation in prokaryotes. In these processes, the regulatory proteins and the regulatory DNA signals located in extragenic regions are the key elements involved. As all extragenic spaces are putative regulatory regions, ExtraTrain covers all extragenic regions of available genomes and regulatory proteins from bacteria and archaea included in the UniProt database. Description ExtraTrain provides integrated and easily manageable information for 679816 extragenic regions and for the genes delimiting each of them. In addition ExtraTrain supplies a tool to explore extragenic regions, named Palinsight, oriented to detect and search palindromic patterns. This interactive visual tool is totally integrated in the database, allowing the search for regulatory signals in user defined sets of extragenic regions. The 26046 regulatory proteins included in ExtraTrain belong to the families AraC/XylS, ArsR, AsnC, Cold shock domain, CRP-FNR, DeoR, GntR, IclR, LacI, LuxR, LysR, MarR, MerR, NtrC/Fis, OmpR and TetR. The database follows the InterPro criteria to define these families. The information about regulators includes manually curated sets of references specifically associated to regulator entries. In order to achieve a sustainable and maintainable knowledge database ExtraTrain is a platform open to the contribution of knowledge by the scientific community providing a system for the incorporation of textual knowledge. Conclusion ExtraTrain is a new database for exploring Extragenic regions and Transcriptional information in bacteria and archaea. ExtraTrain database is available at . PMID:16539733

PhosphoBase: a database of phosphorylation sites.

PubMed Central

Blom, N; Kreegipuu, A; Brunak, S

1998-01-01

PhosphoBase is a database of experimentally verified phosphorylation sites. Version 1.0 contains 156 entries and 398 experimentally determined phosphorylation sites. Entries are compiled and revised from the literature and from major protein sequence databases such as SwissProt and PIR. The entries provide information about the phosphoprotein and the exact position of its phosphorylation sites. Furthermore, part of the entries contain information about kinetic data obtained from enzyme assays on specific peptides. To illustrate the use of data extracted from PhosphoBase we present a sequence logo displaying the overall conservation of positions around serines phosphorylated by protein kinase A (PKA). PhosphoBase is available on the WWW at http://www.cbs.dtu.dk/databases/PhosphoBase/ PMID:9399879

Proteomic analysis of tardigrades: towards a better understanding of molecular mechanisms by anhydrobiotic organisms.

PubMed

Schokraie, Elham; Hotz-Wagenblatt, Agnes; Warnken, Uwe; Mali, Brahim; Frohme, Marcus; Förster, Frank; Dandekar, Thomas; Hengherr, Steffen; Schill, Ralph O; Schnölzer, Martina

2010-03-03

Tardigrades are small, multicellular invertebrates which are able to survive times of unfavourable environmental conditions using their well-known capability to undergo cryptobiosis at any stage of their life cycle. Milnesium tardigradum has become a powerful model system for the analysis of cryptobiosis. While some genetic information is already available for Milnesium tardigradum the proteome is still to be discovered. Here we present to the best of our knowledge the first comprehensive study of Milnesium tardigradum on the protein level. To establish a proteome reference map we developed optimized protocols for protein extraction from tardigrades in the active state and for separation of proteins by high resolution two-dimensional gel electrophoresis. Since only limited sequence information of M. tardigradum on the genome and gene expression level is available to date in public databases we initiated in parallel a tardigrade EST sequencing project to allow for protein identification by electrospray ionization tandem mass spectrometry. 271 out of 606 analyzed protein spots could be identified by searching against the publicly available NCBInr database as well as our newly established tardigrade protein database corresponding to 144 unique proteins. Another 150 spots could be identified in the tardigrade clustered EST database corresponding to 36 unique contigs and ESTs. Proteins with annotated function were further categorized in more detail by their molecular function, biological process and cellular component. For the proteins of unknown function more information could be obtained by performing a protein domain annotation analysis. Our results include proteins like protein member of different heat shock protein families and LEA group 3, which might play important roles in surviving extreme conditions. The proteome reference map of Milnesium tardigradum provides the basis for further studies in order to identify and characterize the biochemical mechanisms of tolerance to extreme desiccation. The optimized proteomics workflow will enable application of sensitive quantification techniques to detect differences in protein expression, which are characteristic of the active and anhydrobiotic states of tardigrades.

Proteomic Analysis of Tardigrades: Towards a Better Understanding of Molecular Mechanisms by Anhydrobiotic Organisms

PubMed Central

Schokraie, Elham; Hotz-Wagenblatt, Agnes; Warnken, Uwe; Mali, Brahim; Frohme, Marcus; Förster, Frank; Dandekar, Thomas; Hengherr, Steffen; Schill, Ralph O.; Schnölzer, Martina

2010-01-01

Background Tardigrades are small, multicellular invertebrates which are able to survive times of unfavourable environmental conditions using their well-known capability to undergo cryptobiosis at any stage of their life cycle. Milnesium tardigradum has become a powerful model system for the analysis of cryptobiosis. While some genetic information is already available for Milnesium tardigradum the proteome is still to be discovered. Principal Findings Here we present to the best of our knowledge the first comprehensive study of Milnesium tardigradum on the protein level. To establish a proteome reference map we developed optimized protocols for protein extraction from tardigrades in the active state and for separation of proteins by high resolution two-dimensional gel electrophoresis. Since only limited sequence information of M. tardigradum on the genome and gene expression level is available to date in public databases we initiated in parallel a tardigrade EST sequencing project to allow for protein identification by electrospray ionization tandem mass spectrometry. 271 out of 606 analyzed protein spots could be identified by searching against the publicly available NCBInr database as well as our newly established tardigrade protein database corresponding to 144 unique proteins. Another 150 spots could be identified in the tardigrade clustered EST database corresponding to 36 unique contigs and ESTs. Proteins with annotated function were further categorized in more detail by their molecular function, biological process and cellular component. For the proteins of unknown function more information could be obtained by performing a protein domain annotation analysis. Our results include proteins like protein member of different heat shock protein families and LEA group 3, which might play important roles in surviving extreme conditions. Conclusions The proteome reference map of Milnesium tardigradum provides the basis for further studies in order to identify and characterize the biochemical mechanisms of tolerance to extreme desiccation. The optimized proteomics workflow will enable application of sensitive quantification techniques to detect differences in protein expression, which are characteristic of the active and anhydrobiotic states of tardigrades. PMID:20224743

GMDD: a database of GMO detection methods

PubMed Central

Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans JP; Guo, Rong; Liang, Wanqi; Zhang, Dabing

2008-01-01

Background Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. Results GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. Conclusion GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier. PMID:18522755

EcoCyc: a comprehensive database resource for Escherichia coli

PubMed Central

Keseler, Ingrid M.; Collado-Vides, Julio; Gama-Castro, Socorro; Ingraham, John; Paley, Suzanne; Paulsen, Ian T.; Peralta-Gil, Martín; Karp, Peter D.

2005-01-01

The EcoCyc database (http://EcoCyc.org/) is a comprehensive source of information on the biology of the prototypical model organism Escherichia coli K12. The mission for EcoCyc is to contain both computable descriptions of, and detailed comments describing, all genes, proteins, pathways and molecular interactions in E.coli. Through ongoing manual curation, extensive information such as summary comments, regulatory information, literature citations and evidence types has been extracted from 8862 publications and added to Version 8.5 of the EcoCyc database. The EcoCyc database can be accessed through a World Wide Web interface, while the downloadable Pathway Tools software and data files enable computational exploration of the data and provide enhanced querying capabilities that web interfaces cannot support. For example, EcoCyc contains carefully curated information that can be used as training sets for bioinformatics prediction of entities such as promoters, operons, genetic networks, transcription factor binding sites, metabolic pathways, functionally related genes, protein complexes and protein–ligand interactions. PMID:15608210

Protein-protein interaction predictions using text mining methods.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

2015-03-01

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein-DNA interactions.

PubMed

Hume, Maxwell A; Barrera, Luis A; Gisselbrecht, Stephen S; Bulyk, Martha L

2015-01-01

The Universal PBM Resource for Oligonucleotide Binding Evaluation (UniPROBE) serves as a convenient source of information on published data generated using universal protein-binding microarray (PBM) technology, which provides in vitro data about the relative DNA-binding preferences of transcription factors for all possible sequence variants of a length k ('k-mers'). The database displays important information about the proteins and displays their DNA-binding specificity data in terms of k-mers, position weight matrices and graphical sequence logos. This update to the database documents the growth of UniPROBE since the last update 4 years ago, and introduces a variety of new features and tools, including a new streamlined pipeline that facilitates data deposition by universal PBM data generators in the research community, a tool that generates putative nonbinding (i.e. negative control) DNA sequences for one or more proteins and novel motifs obtained by analyzing the PBM data using the BEEML-PBM algorithm for motif inference. The UniPROBE database is available at http://uniprobe.org. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

THPdb: Database of FDA-approved peptide and protein therapeutics.

PubMed

Usmani, Salman Sadullah; Bedi, Gursimran; Samuel, Jesse S; Singh, Sandeep; Kalra, Sourav; Kumar, Pawan; Ahuja, Anjuman Arora; Sharma, Meenu; Gautam, Ankur; Raghava, Gajendra P S

2017-01-01

THPdb (http://crdd.osdd.net/raghava/thpdb/) is a manually curated repository of Food and Drug Administration (FDA) approved therapeutic peptides and proteins. The information in THPdb has been compiled from 985 research publications, 70 patents and other resources like DrugBank. The current version of the database holds a total of 852 entries, providing comprehensive information on 239 US-FDA approved therapeutic peptides and proteins and their 380 drug variants. The information on each peptide and protein includes their sequences, chemical properties, composition, disease area, mode of activity, physical appearance, category or pharmacological class, pharmacodynamics, route of administration, toxicity, target of activity, etc. In addition, we have annotated the structure of most of the protein and peptides. A number of user-friendly tools have been integrated to facilitate easy browsing and data analysis. To assist scientific community, a web interface and mobile App have also been developed.

Using SQL Databases for Sequence Similarity Searching and Analysis.

PubMed

Pearson, William R; Mackey, Aaron J

2017-09-13

Relational databases can integrate diverse types of information and manage large sets of similarity search results, greatly simplifying genome-scale analyses. By focusing on taxonomic subsets of sequences, relational databases can reduce the size and redundancy of sequence libraries and improve the statistical significance of homologs. In addition, by loading similarity search results into a relational database, it becomes possible to explore and summarize the relationships between all of the proteins in an organism and those in other biological kingdoms. This unit describes how to use relational databases to improve the efficiency of sequence similarity searching and demonstrates various large-scale genomic analyses of homology-related data. It also describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. The unit also introduces search_demo, a database that stores sequence similarity search results. The search_demo database is then used to explore the evolutionary relationships between E. coli proteins and proteins in other organisms in a large-scale comparative genomic analysis. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

CORUM: the comprehensive resource of mammalian protein complexes

PubMed Central

Ruepp, Andreas; Brauner, Barbara; Dunger-Kaltenbach, Irmtraud; Frishman, Goar; Montrone, Corinna; Stransky, Michael; Waegele, Brigitte; Schmidt, Thorsten; Doudieu, Octave Noubibou; Stümpflen, Volker; Mewes, H. Werner

2008-01-01

Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. The CORUM (http://mips.gsf.de/genre/proj/corum/index.html) database is a collection of experimentally verified mammalian protein complexes. Information is manually derived by critical reading of the scientific literature from expert annotators. Information about protein complexes includes protein complex names, subunits, literature references as well as the function of the complexes. For functional annotation, we use the FunCat catalogue that enables to organize the protein complex space into biologically meaningful subsets. The database contains more than 1750 protein complexes that are built from 2400 different genes, thus representing 12% of the protein-coding genes in human. A web-based system is available to query, view and download the data. CORUM provides a comprehensive dataset of protein complexes for discoveries in systems biology, analyses of protein networks and protein complex-associated diseases. Comparable to the MIPS reference dataset of protein complexes from yeast, CORUM intends to serve as a reference for mammalian protein complexes. PMID:17965090

RStrucFam: a web server to associate structure and cognate RNA for RNA-binding proteins from sequence information.

PubMed

Ghosh, Pritha; Mathew, Oommen K; Sowdhamini, Ramanathan

2016-10-07

RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information. The web server employs Hidden Markov Model scan (hmmscan) to enable association to a back-end database of structural and sequence families. The database (HMMRBP) comprises of 437 HMMs of RBP families of known structure that have been generated using structure-based sequence alignments and 746 sequence-centric RBP family HMMs. The input protein sequence is associated with structural or sequence domain families, if structure or sequence signatures exist. In case of association of the protein with a family of known structures, output features like, multiple structure-based sequence alignment (MSSA) of the query with all others members of that family is provided. Further, cognate RNA partner(s) for that protein, Gene Ontology (GO) annotations, if any and a homology model of the protein can be obtained. The users can also browse through the database for details pertaining to each family, protein or RNA and their related information based on keyword search or RNA motif search. RStrucFam is a web server that exploits structurally conserved features of RBPs, derived from known family members and imprinted in mathematical profiles, to predict putative RBPs from sequence information. Proteins that fail to associate with such structure-centric families are further queried against the sequence-centric RBP family HMMs in the HMMRBP database. Further, all other essential information pertaining to an RBP, like overall function annotations, are provided. The web server can be accessed at the following link: http://caps.ncbs.res.in/rstrucfam .

Identifying functionally informative evolutionary sequence profiles.

PubMed

Gil, Nelson; Fiser, Andras

2018-04-15

Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually. We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git. andras.fiser@einstein.yu.edu. Supplementary data are available at Bioinformatics online.

LocSigDB: a database of protein localization signals

PubMed Central

Negi, Simarjeet; Pandey, Sanjit; Srinivasan, Satish M.; Mohammed, Akram; Guda, Chittibabu

2015-01-01

LocSigDB (http://genome.unmc.edu/LocSigDB/) is a manually curated database of experimental protein localization signals for eight distinct subcellular locations; primarily in a eukaryotic cell with brief coverage of bacterial proteins. Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Mislocalization of proteins to unintended locations is a causative factor for many human diseases; therefore, collection of known sorting signals will help support many important areas of biomedical research. By performing an extensive literature study, we compiled a collection of 533 experimentally determined localization signals, along with the proteins that harbor such signals. Each signal in the LocSigDB is annotated with its localization, source, PubMed references and is linked to the proteins in UniProt database along with the organism information that contain the same amino acid pattern as the given signal. From LocSigDB webserver, users can download the whole database or browse/search for data using an intuitive query interface. To date, LocSigDB is the most comprehensive compendium of protein localization signals for eight distinct subcellular locations. Database URL: http://genome.unmc.edu/LocSigDB/ PMID:25725059

Rice proteome analysis: a step toward functional analysis of the rice genome.

PubMed

Komatsu, Setsuko; Tanaka, Naoki

2005-03-01

The technique of proteome analysis using 2-DE has the power to monitor global changes that occur in the protein complement of tissues and subcellular compartments. In this review, we describe construction of the rice proteome database, the cataloging of rice proteins, and the functional characterization of some of the proteins identified. Initially, proteins extracted from various tissues and organelles were separated by 2-DE and an image analyzer was used to construct a display or reference map of the proteins. The rice proteome database currently contains 23 reference maps based on 2-DE of proteins from different rice tissues and subcellular compartments. These reference maps comprise 13 129 rice proteins, and the amino acid sequences of 5092 of these proteins are entered in the database. Major proteins involved in growth or stress responses have been identified by using a proteomics approach and some of these proteins have unique functions. Furthermore, initial work has also begun on analyzing the phosphoproteome and protein-protein interactions in rice. The information obtained from the rice proteome database will aid in the molecular cloning of rice genes and in predicting the function of unknown proteins.

VerSeDa: vertebrate secretome database.

PubMed

Cortazar, Ana R; Oguiza, José A; Aransay, Ana M; Lavín, José L

2017-01-01

Based on the current tools, de novo secretome (full set of proteins secreted by an organism) prediction is a time consuming bioinformatic task that requires a multifactorial analysis in order to obtain reliable in silico predictions. Hence, to accelerate this process and offer researchers a reliable repository where secretome information can be obtained for vertebrates and model organisms, we have developed VerSeDa (Vertebrate Secretome Database). This freely available database stores information about proteins that are predicted to be secreted through the classical and non-classical mechanisms, for the wide range of vertebrate species deposited at the NCBI, UCSC and ENSEMBL sites. To our knowledge, VerSeDa is the only state-of-the-art database designed to store secretome data from multiple vertebrate genomes, thus, saving an important amount of time spent in the prediction of protein features that can be retrieved from this repository directly. VerSeDa is freely available at http://genomics.cicbiogune.es/VerSeDa/index.php. © The Author(s) 2017. Published by Oxford University Press.

Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification.

PubMed

Schuemie, Martijn J; Mons, Barend; Weeber, Marc; Kors, Jan A

2007-06-01

Gene and protein name identification in text requires a dictionary approach to relate synonyms to the same gene or protein, and to link names to external databases. However, existing dictionaries are incomplete. We investigate two complementary methods for automatic generation of a comprehensive dictionary: combination of information from existing gene and protein databases and rule-based generation of spelling variations. Both methods have been reported in literature before, but have hitherto not been combined and evaluated systematically. We combined gene and protein names from several existing databases of four different organisms. The combined dictionaries showed a substantial increase in recall on three different test sets, as compared to any single database. Application of 23 spelling variation rules to the combined dictionaries further increased recall. However, many rules appeared to have no effect and some appear to have a detrimental effect on precision.

GALT protein database: querying structural and functional features of GALT enzyme.

PubMed

d'Acierno, Antonio; Facchiano, Angelo; Marabotti, Anna

2014-09-01

Knowledge of the impact of variations on protein structure can enhance the comprehension of the mechanisms of genetic diseases related to that protein. Here, we present a new version of GALT Protein Database, a Web-accessible data repository for the storage and interrogation of structural effects of variations of the enzyme galactose-1-phosphate uridylyltransferase (GALT), the impairment of which leads to classic Galactosemia, a rare genetic disease. This new version of this database now contains the models of 201 missense variants of GALT enzyme, including heterozygous variants, and it allows users not only to retrieve information about the missense variations affecting this protein, but also to investigate their impact on substrate binding, intersubunit interactions, stability, and other structural features. In addition, it allows the interactive visualization of the models of variants collected into the database. We have developed additional tools to improve the use of the database by nonspecialized users. This Web-accessible database (http://bioinformatica.isa.cnr.it/GALT/GALT2.0) represents a model of tools potentially suitable for application to other proteins that are involved in human pathologies and that are subjected to genetic variations. © 2014 WILEY PERIODICALS, INC.

ImmunemiR - A Database of Prioritized Immune miRNA Disease Associations and its Interactome.

PubMed

Prabahar, Archana; Natarajan, Jeyakumar

2017-01-01

MicroRNAs are the key regulators of gene expression and their abnormal expression in the immune system may be associated with several human diseases such as inflammation, cancer and autoimmune diseases. Elucidation of miRNA disease association through the interactome will deepen the understanding of its disease mechanisms. A specialized database for immune miRNAs is highly desirable to demonstrate the immune miRNA disease associations in the interactome. miRNAs specific to immune related diseases were retrieved from curated databases such as HMDD, miR2disease and PubMed literature based on MeSH classification of immune system diseases. The additional data such as miRNA target genes, genes coding protein-protein interaction information were compiled from related resources. Further, miRNAs were prioritized to specific immune diseases using random walk ranking algorithm. In total 245 immune miRNAs associated with 92 OMIM disease categories were identified from external databases. The resultant data were compiled as ImmunemiR, a database of prioritized immune miRNA disease associations. This database provides both text based annotation information and network visualization of its interactome. To our knowledge, ImmunemiR is the first available database to provide a comprehensive repository of human immune disease associated miRNAs with network visualization options of its target genes, protein-protein interactions (PPI) and its disease associations. It is freely available at http://www.biominingbu.org/immunemir/. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Extension of the COG and arCOG databases by amino acid and nucleotide sequences

PubMed Central

Meereis, Florian; Kaufmann, Michael

2008-01-01

Background The current versions of the COG and arCOG databases, both excellent frameworks for studies in comparative and functional genomics, do not contain the nucleotide sequences corresponding to their protein or protein domain entries. Results Using sequence information obtained from GenBank flat files covering the completely sequenced genomes of the COG and arCOG databases, we constructed NUCOCOG (nucleotide sequences containing COG databases) as an extended version including all nucleotide sequences and in addition the amino acid sequences originally utilized to construct the current COG and arCOG databases. We make available three comprehensive single XML files containing the complete databases including all sequence information. In addition, we provide a web interface as a utility suitable to browse the NUCOCOG database for sequence retrieval. The database is accessible at . Conclusion NUCOCOG offers the possibility to analyze any sequence related property in the context of the COG and arCOG framework simply by using script languages such as PERL applied to a large but single XML document. PMID:19014535

DBSecSys: a database of Burkholderia mallei secretion systems.

PubMed

Memišević, Vesna; Kumar, Kamal; Cheng, Li; Zavaljevski, Nela; DeShazer, David; Wallqvist, Anders; Reifman, Jaques

2014-07-16

Bacterial pathogenicity represents a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they provide the means for bacterial proteins to penetrate host-cell membranes and insert themselves directly into the host cells' cytosol. Burkholderia mallei is a Gram-negative bacterium that uses multiple secretion systems during its host infection life cycle. To date, the identities of secretion system proteins for B. mallei are not well known, and their pathogenic mechanisms of action and host factors are largely uncharacterized. We present the Database of Burkholderia malleiSecretion Systems (DBSecSys), a compilation of manually curated and computationally predicted bacterial secretion system proteins and their host factors. Currently, DBSecSys contains comprehensive experimentally and computationally derived information about B. mallei strain ATCC 23344. The database includes 143 B. mallei proteins associated with five secretion systems, their 1,635 human and murine interacting targets, and the corresponding 2,400 host-B. mallei interactions. The database also includes information about 10 pathogenic mechanisms of action for B. mallei secretion system proteins inferred from the available literature. Additionally, DBSecSys provides details about 42 virulence attenuation experiments for 27 B. mallei secretion system proteins. Users interact with DBSecSys through a Web interface that allows for data browsing, querying, visualizing, and downloading. DBSecSys provides a comprehensive, systematically organized resource of experimental and computational data associated with B. mallei secretion systems. It provides the unique ability to study secretion systems not only through characterization of their corresponding pathogen proteins, but also through characterization of their host-interacting partners.The database is available at https://applications.bhsai.org/dbsecsys.

The Histone Database: an integrated resource for histones and histone fold-containing proteins

PubMed Central

Mariño-Ramírez, Leonardo; Levine, Kevin M.; Morales, Mario; Zhang, Suiyuan; Moreland, R. Travis; Baxevanis, Andreas D.; Landsman, David

2011-01-01

Eukaryotic chromatin is composed of DNA and protein components—core histones—that act to compactly pack the DNA into nucleosomes, the fundamental building blocks of chromatin. These nucleosomes are connected to adjacent nucleosomes by linker histones. Nucleosomes are highly dynamic and, through various core histone post-translational modifications and incorporation of diverse histone variants, can serve as epigenetic marks to control processes such as gene expression and recombination. The Histone Sequence Database is a curated collection of sequences and structures of histones and non-histone proteins containing histone folds, assembled from major public databases. Here, we report a substantial increase in the number of sequences and taxonomic coverage for histone and histone fold-containing proteins available in the database. Additionally, the database now contains an expanded dataset that includes archaeal histone sequences. The database also provides comprehensive multiple sequence alignments for each of the four core histones (H2A, H2B, H3 and H4), the linker histones (H1/H5) and the archaeal histones. The database also includes current information on solved histone fold-containing structures. The Histone Sequence Database is an inclusive resource for the analysis of chromatin structure and function focused on histones and histone fold-containing proteins. Database URL: The Histone Sequence Database is freely available and can be accessed at http://research.nhgri.nih.gov/histones/. PMID:22025671

DNAtraffic--a new database for systems biology of DNA dynamics during the cell life.

PubMed

Kuchta, Krzysztof; Barszcz, Daniela; Grzesiuk, Elzbieta; Pomorski, Pawel; Krwawicz, Joanna

2012-01-01

DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications.

DNAtraffic—a new database for systems biology of DNA dynamics during the cell life

PubMed Central

Kuchta, Krzysztof; Barszcz, Daniela; Grzesiuk, Elzbieta; Pomorski, Pawel; Krwawicz, Joanna

2012-01-01

DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications. PMID:22110027

MIPS: curated databases and comprehensive secondary data resources in 2010.

PubMed

Mewes, H Werner; Ruepp, Andreas; Theis, Fabian; Rattei, Thomas; Walter, Mathias; Frishman, Dmitrij; Suhre, Karsten; Spannagl, Manuel; Mayer, Klaus F X; Stümpflen, Volker; Antonov, Alexey

2011-01-01

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38,000,000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

MIPS: curated databases and comprehensive secondary data resources in 2010

PubMed Central

Mewes, H. Werner; Ruepp, Andreas; Theis, Fabian; Rattei, Thomas; Walter, Mathias; Frishman, Dmitrij; Suhre, Karsten; Spannagl, Manuel; Mayer, Klaus F.X.; Stümpflen, Volker; Antonov, Alexey

2011-01-01

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de). PMID:21109531

SNAPPI-DB: a database and API of Structures, iNterfaces and Alignments for Protein–Protein Interactions

PubMed Central

Jefferson, Emily R.; Walsh, Thomas P.; Roberts, Timothy J.; Barton, Geoffrey J.

2007-01-01

SNAPPI-DB, a high performance database of Structures, iNterfaces and Alignments of Protein–Protein Interactions, and its associated Java Application Programming Interface (API) is described. SNAPPI-DB contains structural data, down to the level of atom co-ordinates, for each structure in the Protein Data Bank (PDB) together with associated data including SCOP, CATH, Pfam, SWISSPROT, InterPro, GO terms, Protein Quaternary Structures (PQS) and secondary structure information. Domain–domain interactions are stored for multiple domain definitions and are classified by their Superfamily/Family pair and interaction interface. Each set of classified domain–domain interactions has an associated multiple structure alignment for each partner. The API facilitates data access via PDB entries, domains and domain–domain interactions. Rapid development, fast database access and the ability to perform advanced queries without the requirement for complex SQL statements are provided via an object oriented database and the Java Data Objects (JDO) API. SNAPPI-DB contains many features which are not available in other databases of structural protein–protein interactions. It has been applied in three studies on the properties of protein–protein interactions and is currently being employed to train a protein–protein interaction predictor and a functional residue predictor. The database, API and manual are available for download at: . PMID:17202171

Interactome of the hepatitis C virus: Literature mining with ANDSystem.

PubMed

Saik, Olga V; Ivanisenko, Timofey V; Demenkov, Pavel S; Ivanisenko, Vladimir A

2016-06-15

A study of the molecular genetics mechanisms of host-pathogen interactions is of paramount importance in developing drugs against viral diseases. Currently, the literature contains a huge amount of information that describes interactions between HCV and human proteins. In addition, there are many factual databases that contain experimentally verified data on HCV-host interactions. The sources of such data are the original data along with the data manually extracted from the literature. However, the manual analysis of scientific publications is time consuming and, because of this, databases created with such an approach often do not have complete information. One of the most promising methods to provide actualisation and completeness of information is text mining. Here, with the use of a previously developed method by the authors using ANDSystem, an automated extraction of information on the interactions between HCV and human proteins was conducted. As a data source for the text mining approach, PubMed abstracts and full text articles were used. Additionally, external factual databases were analyzed. On the basis of this analysis, a special version of ANDSystem, extended with the HCV interactome, was created. The HCV interactome contains information about the interactions between 969 human and 11 HCV proteins. Among the 969 proteins, 153 'new' proteins were found not previously referred to in any external databases of protein-protein interactions for HCV-host interactions. Thus, the extended ANDSystem possesses a more comprehensive detailing of HCV-host interactions versus other existing databases. It was interesting that HCV proteins more preferably interact with human proteins that were already involved in a large number of protein-protein interactions as well as those associated with many diseases. Among human proteins of the HCV interactome, there were a large number of proteins regulated by microRNAs. It turned out that the results obtained for protein-protein interactions and microRNA-regulation did not depend on how well the proteins were studied, while protein-disease interactions appeared to be dependent on the level of study. In particular, the mean number of diseases linked to well-studied proteins (proteins were considered well-studied if they were mentioned in 50 or more PubMed publications) from the HCV interactome was 20.8, significantly exceeding the mean number of associations with diseases (10.1) for the total set of well-studied human proteins present in ANDSystem. For proteins not highly poorly-studied investigated, proteins from the HCV interactome (each protein was referred to in less than 50 publications) distribution of the number of diseases associated with them had no statistically significant differences from the distribution of the number of diseases associated with poorly-studied proteins based on the total set of human proteins stored in ANDSystem. With this, the average number of associations with diseases for the HCV interactome and the total set of human proteins were 0.3 and 0.2, respectively. Thus, ANDSystem, extended with the HCV interactome, can be helpful in a wide range of issues related to analyzing HCV-host interactions in the search for anti-HCV drug targets. The demo version of the extended ANDSystem covered here containing only interactions between human proteins, genes, metabolites, diseases, miRNAs and molecular-genetic pathways, as well as interactions between human proteins/genes and HCV proteins, is freely available at the following web address: http://www-bionet.sscc.ru/psd/andhcv/. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

CanisOme--The protein signatures of Canis lupus familiaris diseases.

PubMed

Fernandes, Mónica; Rosa, Nuno; Esteves, Eduardo; Correia, Maria José; Arrais, Joel; Ribeiro, Paulo; Vala, Helena; Barros, Marlene

2016-03-16

Although the applications of Proteomics in Human Biomedicine have been explored for some time now, in animal and veterinary research, the potential of this resource has just started to be explored, especially when companion animal health is considered. In the last years, knowledge on the Canis lupus familiaris proteome has been accumulating in the literature and a resource compiling all this information and critically reviewing it was lacking. This article presents such a resource for the first time. CanisOme is a database of all proteins identified in Canis lupus familiaris tissues, either in health or in disease, annotated with information on the proteins present on the sample and on the donors. This database reunites information on 549 proteins, associated with 63 dog diseases and 33 dog breeds. Examples of how this information may be used to produce new hypothesis on disease mechanisms is presented both through the functional analysis of the proteins quantified in canine cutaneous mast cell tumors and through the study of the interactome of C. lupus familiaris and Leishmania infantum. Therefore, the usefulness of CanisOme for researchers looking for protein biomarkers in dogs and interested in a comprehensive analysis of disease mechanisms is demonstrated. This paper presents CanisOme, a database of proteomic studies with relevant protein annotation, allowing the enlightenment of disease mechanisms and the discovery of novel disease biomarkers for C. lupus familiaris. This knowledge is important not only for the improvement of animal health but also for the use of dogs as models for human health studies. Copyright © 2016 Elsevier B.V. All rights reserved.

Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model.

PubMed

Chen, Mingchen; Lin, Xingcheng; Zheng, Weihua; Onuchic, José N; Wolynes, Peter G

2016-08-25

The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequences that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six α-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

PubMed

Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem

2016-01-01

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

Human Variome Project Quality Assessment Criteria for Variation Databases.

PubMed

Vihinen, Mauno; Hancock, John M; Maglott, Donna R; Landrum, Melissa J; Schaafsma, Gerard C P; Taschner, Peter

2016-06-01

Numerous databases containing information about DNA, RNA, and protein variations are available. Gene-specific variant databases (locus-specific variation databases, LSDBs) are typically curated and maintained for single genes or groups of genes for a certain disease(s). These databases are widely considered as the most reliable information source for a particular gene/protein/disease, but it should also be made clear they may have widely varying contents, infrastructure, and quality. Quality is very important to evaluate because these databases may affect health decision-making, research, and clinical practice. The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment. The basic principle was to develop a simple system that nevertheless provides a good overview of the quality of a database. The HVP quality evaluation criteria that resulted are divided into four main components: data quality, technical quality, accessibility, and timeliness. This report elaborates on the developed quality criteria and how implementation of the quality scheme can be achieved. Examples are provided for the current status of the quality items in two different databases, BTKbase, an LSDB, and ClinVar, a central archive of submissions about variants and their clinical significance. © 2016 WILEY PERIODICALS, INC.

Protein sequence annotation in the genome era: the annotation concept of SWISS-PROT+TREMBL.

PubMed

Apweiler, R; Gateau, A; Contrino, S; Martin, M J; Junker, V; O'Donovan, C; Lang, F; Mitaritonna, N; Kappus, S; Bairoch, A

1997-01-01

SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation, a minimal level of redundancy and high level of integration with other databases. Ongoing genome sequencing projects have dramatically increased the number of protein sequences to be incorporated into SWISS-PROT. Since we do not want to dilute the quality standards of SWISS-PROT by incorporating sequences without proper sequence analysis and annotation, we cannot speed up the incorporation of new incoming data indefinitely. However, as we also want to make the sequences available as fast as possible, we introduced TREMBL (TRanslation of EMBL nucleotide sequence database), a supplement to SWISS-PROT. TREMBL consists of computer-annotated entries in SWISS-PROT format derived from the translation of all coding sequences (CDS) in the EMBL nucleotide sequence database, except for CDS already included in SWISS-PROT. While TREMBL is already of immense value, its computer-generated annotation does not match the quality of SWISS-PROTs. The main difference is in the protein functional information attached to sequences. With this in mind, we are dedicating substantial effort to develop and apply computer methods to enhance the functional information attached to TREMBL entries.

O-GLYCBASE Version 3.0: a revised database of O-glycosylated proteins.

PubMed Central

Hansen, J E; Lund, O; Nilsson, J; Rapacki, K; Brunak, S

1998-01-01

O-GLYCBASE is a revised database of information on glycoproteins and their O-linked glycosylation sites. Entries are compiled and revised from the literature, and from the sequence databases. Entries include information about species, sequence, glycosylation sites and glycan type and is fully cross-referenced. Compared to version 2.0 the number of entries has increased by 20%. Sequence logos displaying the acceptor specificity patterns for the GalNAc, mannose and GlcNAc transferases are shown. The O-GLYCBASE database is available through the WWW at http://www.cbs.dtu. dk/databases/OGLYCBASE/ PMID:9399880

RICD: a rice indica cDNA database resource for rice functional genomics.

PubMed

Lu, Tingting; Huang, Xuehui; Zhu, Chuanrang; Huang, Tao; Zhao, Qiang; Xie, Kabing; Xiong, Lizhong; Zhang, Qifa; Han, Bin

2008-11-26

The Oryza sativa L. indica subspecies is the most widely cultivated rice. During the last few years, we have collected over 20,000 putative full-length cDNAs and over 40,000 ESTs isolated from various cDNA libraries of two indica varieties Guangluai 4 and Minghui 63. A database of the rice indica cDNAs was therefore built to provide a comprehensive web data source for searching and retrieving the indica cDNA clones. Rice Indica cDNA Database (RICD) is an online MySQL-PHP driven database with a user-friendly web interface. It allows investigators to query the cDNA clones by keyword, genome position, nucleotide or protein sequence, and putative function. It also provides a series of information, including sequences, protein domain annotations, similarity search results, SNPs and InDels information, and hyperlinks to gene annotation in both The Rice Annotation Project Database (RAP-DB) and The TIGR Rice Genome Annotation Resource, expression atlas in RiceGE and variation report in Gramene of each cDNA. The online rice indica cDNA database provides cDNA resource with comprehensive information to researchers for functional analysis of indica subspecies and for comparative genomics. The RICD database is available through our website http://www.ncgr.ac.cn/ricd.

Data on publications, structural analyses, and queries used to build and utilize the AlloRep database.

PubMed

Sousa, Filipa L; Parente, Daniel J; Hessman, Jacob A; Chazelle, Allen; Teichmann, Sarah A; Swint-Kruse, Liskin

2016-09-01

The AlloRep database (www.AlloRep.org) (Sousa et al., 2016) [1] compiles extensive sequence, mutagenesis, and structural information for the LacI/GalR family of transcription regulators. Sequence alignments are presented for >3000 proteins in 45 paralog subfamilies and as a subsampled alignment of the whole family. Phenotypic and biochemical data on almost 6000 mutants have been compiled from an exhaustive search of the literature; citations for these data are included herein. These data include information about oligomerization state, stability, DNA binding and allosteric regulation. Protein structural data for 65 proteins are presented as easily-accessible, residue-contact networks. Finally, this article includes example queries to enable the use of the AlloRep database. See the related article, "AlloRep: a repository of sequence, structural and mutagenesis data for the LacI/GalR transcription regulators" (Sousa et al., 2016) [1].

Haemophilus influenzae Genome Database (HIGDB): a single point web resource for Haemophilus influenzae.

PubMed

Swetha, Rayapadi G; Kala Sekar, Dinesh Kumar; Ramaiah, Sudha; Anbarasu, Anand; Sekar, Kanagaraj

2014-12-01

Haemophilus influenzae (H. Influenzae) is the causative agent of pneumonia, bacteraemia and meningitis. The organism is responsible for large number of deaths in both developed and developing countries. Even-though the first bacterial genome to be sequenced was that of H. Influenzae, there is no exclusive database dedicated for H. Influenzae. This prompted us to develop the Haemophilus influenzae Genome Database (HIGDB). All data of HIGDB are stored and managed in MySQL database. The HIGDB is hosted on Solaris server and developed using PERL modules. Ajax and JavaScript are used for the interface development. The HIGDB contains detailed information on 42,741 proteins, 18,077 genes including 10 whole genome sequences and also 284 three dimensional structures of proteins of H. influenzae. In addition, the database provides "Motif search" and "GBrowse". The HIGDB is freely accessible through the URL: http://bioserver1.physics.iisc.ernet.in/HIGDB/. The HIGDB will be a single point access for bacteriological, clinical, genomic and proteomic information of H. influenzae. The database can also be used to identify DNA motifs within H. influenzae genomes and to compare gene or protein sequences of a particular strain with other strains of H. influenzae. Copyright © 2014 Elsevier Ltd. All rights reserved.

MAPU: Max-Planck Unified database of organellar, cellular, tissue and body fluid proteomes

PubMed Central

Zhang, Yanling; Zhang, Yong; Adachi, Jun; Olsen, Jesper V.; Shi, Rong; de Souza, Gustavo; Pasini, Erica; Foster, Leonard J.; Macek, Boris; Zougman, Alexandre; Kumar, Chanchal; Wiśniewski, Jacek R.; Jun, Wang; Mann, Matthias

2007-01-01

Mass spectrometry (MS)-based proteomics has become a powerful technology to map the protein composition of organelles, cell types and tissues. In our department, a large-scale effort to map these proteomes is complemented by the Max-Planck Unified (MAPU) proteome database. MAPU contains several body fluid proteomes; including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities and is freely available at using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools. PMID:17090601

ORFer--retrieval of protein sequences and open reading frames from GenBank and storage into relational databases or text files.

PubMed

Büssow, Konrad; Hoffmann, Steve; Sievert, Volker

2002-12-19

Functional genomics involves the parallel experimentation with large sets of proteins. This requires management of large sets of open reading frames as a prerequisite of the cloning and recombinant expression of these proteins. A Java program was developed for retrieval of protein and nucleic acid sequences and annotations from NCBI GenBank, using the XML sequence format. Annotations retrieved by ORFer include sequence name, organism and also the completeness of the sequence. The program has a graphical user interface, although it can be used in a non-interactive mode. For protein sequences, the program also extracts the open reading frame sequence, if available, and checks its correct translation. ORFer accepts user input in the form of single or lists of GenBank GI identifiers or accession numbers. It can be used to extract complete sets of open reading frames and protein sequences from any kind of GenBank sequence entry, including complete genomes or chromosomes. Sequences are either stored with their features in a relational database or can be exported as text files in Fasta or tabulator delimited format. The ORFer program is freely available at http://www.proteinstrukturfabrik.de/orfer. The ORFer program allows for fast retrieval of DNA sequences, protein sequences and their open reading frames and sequence annotations from GenBank. Furthermore, storage of sequences and features in a relational database is supported. Such a database can supplement a laboratory information system (LIMS) with appropriate sequence information.

Verification of Ribosomal Proteins of Aspergillus fumigatus for Use as Biomarkers in MALDI-TOF MS Identification.

PubMed

Nakamura, Sayaka; Sato, Hiroaki; Tanaka, Reiko; Yaguchi, Takashi

2016-01-01

We have previously proposed a rapid identification method for bacterial strains based on the profiles of their ribosomal subunit proteins (RSPs), observed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). This method can perform phylogenetic characterization based on the mass of housekeeping RSP biomarkers, ideally calculated from amino acid sequence information registered in public protein databases. With the aim of extending its field of application to medical mycology, this study investigates the actual state of information of RSPs of eukaryotic fungi registered in public protein databases through the characterization of ribosomal protein fractions extracted from genome-sequenced Aspergillus fumigatus strains Af293 and A1163 as a model. In this process, we have found that the public protein databases harbor problems. The RSP names are in confusion, so we have provisionally unified them using the yeast naming system. The most serious problem is that many incorrect sequences are registered in the public protein databases. Surprisingly, more than half of the sequences are incorrect, due chiefly to mis-annotation of exon/intron structures. These errors could be corrected by a combination of in silico inspection by sequence homology analysis and MALDI-TOF MS measurements. We were also able to confirm conserved post-translational modifications in eleven RSPs. After these verifications, the masses of 31 expressed RSPs under 20,000 Da could be accurately confirmed. These RSPs have a potential to be useful biomarkers for identifying clinical isolates of A. fumigatus .

iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence

PubMed Central

Turner, Brian; Razick, Sabry; Turinsky, Andrei L.; Vlasblom, James; Crowdy, Edgard K.; Cho, Emerson; Morrison, Kyle; Wodak, Shoshana J.

2010-01-01

We present iRefWeb, a web interface to protein interaction data consolidated from 10 public databases: BIND, BioGRID, CORUM, DIP, IntAct, HPRD, MINT, MPact, MPPI and OPHID. iRefWeb enables users to examine aggregated interactions for a protein of interest, and presents various statistical summaries of the data across databases, such as the number of organism-specific interactions, proteins and cited publications. Through links to source databases and supporting evidence, researchers may gauge the reliability of an interaction using simple criteria, such as the detection methods, the scale of the study (high- or low-throughput) or the number of cited publications. Furthermore, iRefWeb compares the information extracted from the same publication by different databases, and offers means to follow-up possible inconsistencies. We provide an overview of the consolidated protein–protein interaction landscape and show how it can be automatically cropped to aid the generation of meaningful organism-specific interactomes. iRefWeb can be accessed at: http://wodaklab.org/iRefWeb. Database URL: http://wodaklab.org/iRefWeb/ PMID:20940177

Bioinformatics Approaches to Classifying Allergens and Predicting Cross-Reactivity

PubMed Central

Schein, Catherine H.; Ivanciuc, Ovidiu; Braun, Werner

2007-01-01

The major advances in understanding why patients respond to several seemingly different stimuli have been through the isolation, sequencing and structural analysis of proteins that induce an IgE response. The most significant finding is that allergenic proteins from very different sources can have nearly identical sequences and structures, and that this similarity can account for clinically observed cross-reactivity. The increasing amount of information on the sequence, structure and IgE epitopes of allergens is now available in several databases and powerful bioinformatics search tools allow user access to relevant information. Here, we provide an overview of these databases and describe state-of-the art bioinformatics tools to identify the common proteins that may be at the root of multiple allergy syndromes. Progress has also been made in quantitatively defining characteristics that discriminate allergens from non-allergens. Search and software tools for this purpose have been developed and implemented in the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/). SDAP contains information for over 800 allergens and extensive bibliographic references in a relational database with links to other publicly available databases. SDAP is freely available on the Web to clinicians and patients, and can be used to find structural and functional relations among known allergens and to identify potentially cross-reacting antigens. Here we illustrate how these bioinformatics tools can be used to group allergens, and to detect areas that may account for common patterns of IgE binding and cross-reactivity. Such results can be used to guide treatment regimens for allergy sufferers. PMID:17276876

mpMoRFsDB: a database of molecular recognition features in membrane proteins.

PubMed

Gypas, Foivos; Tsaousis, Georgios N; Hamodrakas, Stavros J

2013-10-01

Molecular recognition features (MoRFs) are small, intrinsically disordered regions in proteins that undergo a disorder-to-order transition on binding to their partners. MoRFs are involved in protein-protein interactions and may function as the initial step in molecular recognition. The aim of this work was to collect, organize and store all membrane proteins that contain MoRFs. Membrane proteins constitute ∼30% of fully sequenced proteomes and are responsible for a wide variety of cellular functions. MoRFs were classified according to their secondary structure, after interacting with their partners. We identified MoRFs in transmembrane and peripheral membrane proteins. The position of transmembrane protein MoRFs was determined in relation to a protein's topology. All information was stored in a publicly available mySQL database with a user-friendly web interface. A Jmol applet is integrated for visualization of the structures. mpMoRFsDB provides valuable information related to disorder-based protein-protein interactions in membrane proteins. http://bioinformatics.biol.uoa.gr/mpMoRFsDB

Thermodynamic database for proteins: features and applications.

PubMed

Gromiha, M Michael; Sarai, Akinori

2010-01-01

We have developed a thermodynamic database for proteins and mutants, ProTherm, which is a collection of a large number of thermodynamic data on protein stability along with the sequence and structure information, experimental methods and conditions, and literature information. This is a valuable resource for understanding/predicting the stability of proteins, and it can be accessible at http://www.gibk26.bse.kyutech.ac.jp/jouhou/Protherm/protherm.html . ProTherm has several features including various search, display, and sorting options and visualization tools. We have analyzed the data in ProTherm to examine the relationship among thermodynamics, structure, and function of proteins. We describe the progress on the development of methods for understanding/predicting protein stability, such as (i) relationship between the stability of protein mutants and amino acid properties, (ii) average assignment method, (iii) empirical energy functions, (iv) torsion, distance, and contact potentials, and (v) machine learning techniques. The list of online resources for predicting protein stability has also been provided.

Columba: an integrated database of proteins, structures, and annotations.

PubMed

Trissl, Silke; Rother, Kristian; Müller, Heiko; Steinke, Thomas; Koch, Ina; Preissner, Robert; Frömmel, Cornelius; Leser, Ulf

2005-03-31

Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures. COLUMBA currently integrates twelve different databases, including PDB, KEGG, Swiss-Prot, CATH, SCOP, the Gene Ontology, and ENZYME. The database can be searched using either keyword search or data source-specific web forms. Users can thus quickly select and download PDB entries that, for instance, participate in a particular pathway, are classified as containing a certain CATH architecture, are annotated as having a certain molecular function in the Gene Ontology, and whose structures have a resolution under a defined threshold. The results of queries are provided in both machine-readable extensible markup language and human-readable format. The structures themselves can be viewed interactively on the web. The COLUMBA database facilitates the creation of protein structure data sets for many structure-based studies. It allows to combine queries on a number of structure-related databases not covered by other projects at present. Thus, information on both many and few protein structures can be used efficiently. The web interface for COLUMBA is available at http://www.columba-db.de.

Plant Genome Resources at the National Center for Biotechnology Information

PubMed Central

Wheeler, David L.; Smith-White, Brian; Chetvernin, Vyacheslav; Resenchuk, Sergei; Dombrowski, Susan M.; Pechous, Steven W.; Tatusova, Tatiana; Ostell, James

2005-01-01

The National Center for Biotechnology Information (NCBI) integrates data from more than 20 biological databases through a flexible search and retrieval system called Entrez. A core Entrez database, Entrez Nucleotide, includes GenBank and is tightly linked to the NCBI Taxonomy database, the Entrez Protein database, and the scientific literature in PubMed. A suite of more specialized databases for genomes, genes, gene families, gene expression, gene variation, and protein domains dovetails with the core databases to make Entrez a powerful system for genomic research. Linked to the full range of Entrez databases is the NCBI Map Viewer, which displays aligned genetic, physical, and sequence maps for eukaryotic genomes including those of many plants. A specialized plant query page allow maps from all plant genomes covered by the Map Viewer to be searched in tandem to produce a display of aligned maps from several species. PlantBLAST searches against the sequences shown in the Map Viewer allow BLAST alignments to be viewed within a genomic context. In addition, precomputed sequence similarities, such as those for proteins offered by BLAST Link, enable fluid navigation from unannotated to annotated sequences, quickening the pace of discovery. NCBI Web pages for plants, such as Plant Genome Central, complete the system by providing centralized access to NCBI's genomic resources as well as links to organism-specific Web pages beyond NCBI. PMID:16010002

Resource for structure related information on transmembrane proteins

NASA Astrophysics Data System (ADS)

Tusnády, Gábor E.; Simon, István

Transmembrane proteins are involved in a wide variety of vital biological processes including transport of water-soluble molecules, flow of information and energy production. Despite significant efforts to determine the structures of these proteins, only a few thousand solved structures are known so far. Here, we review the various resources for structure-related information on these types of proteins ranging from the 3D structure to the topology and from the up-to-date databases to the various Internet sites and servers dealing with structure prediction and structure analysis. Abbreviations: 3D, three dimensional; PDB, Protein Data Bank; TMP, transmembrane protein.

LymPHOS 2.0: an update of a phosphosite database of primary human T cells

PubMed Central

Nguyen, Tien Dung; Vidal-Cortes, Oriol; Gallardo, Oscar; Abian, Joaquin; Carrascal, Montserrat

2015-01-01

LymPHOS is a web-oriented database containing peptide and protein sequences and spectrometric information on the phosphoproteome of primary human T-Lymphocytes. Current release 2.0 contains 15 566 phosphorylation sites from 8273 unique phosphopeptides and 4937 proteins, which correspond to a 45-fold increase over the original database description. It now includes quantitative data on phosphorylation changes after time-dependent treatment with activators of the TCR-mediated signal transduction pathway. Sequence data quality has also been improved with the use of multiple search engines for database searching. LymPHOS can be publicly accessed at http://www.lymphos.org. Database URL: http://www.lymphos.org. PMID:26708986

CADB: Conformation Angles DataBase of proteins

PubMed Central

Sheik, S. S.; Ananthalakshmi, P.; Bhargavi, G. Ramya; Sekar, K.

2003-01-01

Conformation Angles DataBase (CADB) provides an online resource to access data on conformation angles (both main-chain and side-chain) of protein structures in two data sets corresponding to 25% and 90% sequence identity between any two proteins, available in the Protein Data Bank. In addition, the database contains the necessary crystallographic parameters. The package has several flexible options and display facilities to visualize the main-chain and side-chain conformation angles for a particular amino acid residue. The package can also be used to study the interrelationship between the main-chain and side-chain conformation angles. A web based JAVA graphics interface has been deployed to display the user interested information on the client machine. The database is being updated at regular intervals and can be accessed over the World Wide Web interface at the following URL: http://144.16.71.148/cadb/. PMID:12520049

The COG database: new developments in phylogenetic classification of proteins from complete genomes

PubMed Central

Tatusov, Roman L.; Natale, Darren A.; Garkavtsev, Igor V.; Tatusova, Tatiana A.; Shankavaram, Uma T.; Rao, Bachoti S.; Kiryutin, Boris; Galperin, Michael Y.; Fedorova, Natalie D.; Koonin, Eugene V.

2001-01-01

The database of Clusters of Orthologous Groups of proteins (COGs), which represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes, currently consists of 2791 COGs including 45 350 proteins from 30 genomes of bacteria, archaea and the yeast Saccharomyces cerevisiae (http://www.ncbi.nlm.nih.gov/COG). In addition, a supplement to the COGs is available, in which proteins encoded in the genomes of two multicellular eukaryotes, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and shared with bacteria and/or archaea were included. The new features added to the COG database include information pages with structural and functional details on each COG and literature references, improvements of the COGNITOR program that is used to fit new proteins into the COGs, and classification of genomes and COGs constructed by using principal component analysis. PMID:11125040

The Biological Macromolecule Crystallization Database and NASA Protein Crystal Growth Archive

PubMed Central

Gilliland, Gary L.; Tung, Michael; Ladner, Jane

1996-01-01

The NIST/NASA/CARB Biological Macromolecule Crystallization Database (BMCD), NIST Standard Reference Database 21, contains crystal data and crystallization conditions for biological macromolecules. The database entries include data abstracted from published crystallographic reports. Each entry consists of information describing the biological macromolecule crystallized and crystal data and the crystallization conditions for each crystal form. The BMCD serves as the NASA Protein Crystal Growth Archive in that it contains protocols and results of crystallization experiments undertaken in microgravity (space). These database entries report the results, whether successful or not, from NASA-sponsored protein crystal growth experiments in microgravity and from microgravity crystallization studies sponsored by other international organizations. The BMCD was designed as a tool to assist x-ray crystallographers in the development of protocols to crystallize biological macromolecules, those that have previously been crystallized, and those that have not been crystallized. PMID:11542472

Intelligent Access to Sequence and Structure Databases (IASSD) - an interface for accessing information from major web databases.

PubMed

Ganguli, Sayak; Gupta, Manoj Kumar; Basu, Protip; Banik, Rahul; Singh, Pankaj Kumar; Vishal, Vineet; Bera, Abhisek Ranjan; Chakraborty, Hirak Jyoti; Das, Sasti Gopal

2014-01-01

With the advent of age of big data and advances in high throughput technology accessing data has become one of the most important step in the entire knowledge discovery process. Most users are not able to decipher the query result that is obtained when non specific keywords or a combination of keywords are used. Intelligent access to sequence and structure databases (IASSD) is a desktop application for windows operating system. It is written in Java and utilizes the web service description language (wsdl) files and Jar files of E-utilities of various databases such as National Centre for Biotechnology Information (NCBI) and Protein Data Bank (PDB). Apart from that IASSD allows the user to view protein structure using a JMOL application which supports conditional editing. The Jar file is freely available through e-mail from the corresponding author.

A series of PDB related databases for everyday needs.

PubMed

Joosten, Robbie P; te Beek, Tim A H; Krieger, Elmar; Hekkelman, Maarten L; Hooft, Rob W W; Schneider, Reinhard; Sander, Chris; Vriend, Gert

2011-01-01

The Protein Data Bank (PDB) is the world-wide repository of macromolecular structure information. We present a series of databases that run parallel to the PDB. Each database holds one entry, if possible, for each PDB entry. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content, augmented with essentials from the other systems. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray. WHY_NOT summarizes why certain files could not be produced. All these systems are updated weekly. The data sets can be used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design.

PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification.

PubMed

Thomas, Paul D; Kejariwal, Anish; Campbell, Michael J; Mi, Huaiyu; Diemer, Karen; Guo, Nan; Ladunga, Istvan; Ulitsky-Lazareva, Betty; Muruganujan, Anushya; Rabkin, Steven; Vandergriff, Jody A; Doremieux, Olivier

2003-01-01

The PANTHER database was designed for high-throughput analysis of protein sequences. One of the key features is a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators have associated the ontology terms with groups of protein sequences rather than individual sequences. Statistical models (Hidden Markov Models, or HMMs) are built from each of these groups. The advantage of this approach is that new sequences can be automatically classified as they become available. To ensure accurate functional classification, HMMs are constructed not only for families, but also for functionally distinct subfamilies. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, are available for each family. The current version of the PANTHER database includes training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs have been used to classify gene products across the entire genomes of human, and Drosophila melanogaster. The ontology terms and protein families and subfamilies, as well as Drosophila gene c;assifications, can be browsed and searched for free. Due to outstanding contractual obligations, access to human gene classifications and to protein family trees and multiple sequence alignments will temporarily require a nominal registration fee. PANTHER is publicly available on the web at http://panther.celera.com.

Database resources of the National Center for Biotechnology Information.

PubMed

Sayers, Eric W; Barrett, Tanya; Benson, Dennis A; Bolton, Evan; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; Dicuccio, Michael; Federhen, Scott; Feolo, Michael; Fingerman, Ian M; Geer, Lewis Y; Helmberg, Wolfgang; Kapustin, Yuri; Krasnov, Sergey; Landsman, David; Lipman, David J; Lu, Zhiyong; Madden, Thomas L; Madej, Tom; Maglott, Donna R; Marchler-Bauer, Aron; Miller, Vadim; Karsch-Mizrachi, Ilene; Ostell, James; Panchenko, Anna; Phan, Lon; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Stephen T; Shumway, Martin; Sirotkin, Karl; Slotta, Douglas; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A; Wagner, Lukas; Wang, Yanli; Wilbur, W John; Yaschenko, Eugene; Ye, Jian

2012-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Website. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Genome and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Probe, Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Database resources of the National Center for Biotechnology Information

PubMed Central

Acland, Abigail; Agarwala, Richa; Barrett, Tanya; Beck, Jeff; Benson, Dennis A.; Bollin, Colleen; Bolton, Evan; Bryant, Stephen H.; Canese, Kathi; Church, Deanna M.; Clark, Karen; DiCuccio, Michael; Dondoshansky, Ilya; Federhen, Scott; Feolo, Michael; Geer, Lewis Y.; Gorelenkov, Viatcheslav; Hoeppner, Marilu; Johnson, Mark; Kelly, Christopher; Khotomlianski, Viatcheslav; Kimchi, Avi; Kimelman, Michael; Kitts, Paul; Krasnov, Sergey; Kuznetsov, Anatoliy; Landsman, David; Lipman, David J.; Lu, Zhiyong; Madden, Thomas L.; Madej, Tom; Maglott, Donna R.; Marchler-Bauer, Aron; Karsch-Mizrachi, Ilene; Murphy, Terence; Ostell, James; O'Sullivan, Christopher; Panchenko, Anna; Phan, Lon; Pruitt, Don Preussm Kim D.; Rubinstein, Wendy; Sayers, Eric W.; Schneider, Valerie; Schuler, Gregory D.; Sequeira, Edwin; Sherry, Stephen T.; Shumway, Martin; Sirotkin, Karl; Siyan, Karanjit; Slotta, Douglas; Soboleva, Alexandra; Soussov, Vladimir; Starchenko, Grigory; Tatusova, Tatiana A.; Trawick, Bart W.; Vakatov, Denis; Wang, Yanli; Ward, Minghong; John Wilbur, W.; Yaschenko, Eugene; Zbicz, Kerry

2014-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, PubReader, Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link, Primer-BLAST, COBALT, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, the Genetic Testing Registry, Genome and related tools, the Map Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, ClinVar, MedGen, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Probe, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool, Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All these resources can be accessed through the NCBI home page. PMID:24259429

Global De Novo Protein-Protein Interactome Elucidates Interactions of Drought-Responsive Proteins in Horse Gram (Macrotyloma uniflorum).

PubMed

Bhardwaj, Jyoti; Gangwar, Indu; Panzade, Ganesh; Shankar, Ravi; Yadav, Sudesh Kumar

2016-06-03

Inspired by the availability of de novo transcriptome of horse gram (Macrotyloma uniflorum) and recent developments in systems biology studies, the first ever global protein-protein interactome (PPI) map was constructed for this highly drought-tolerant legume. Large-scale studies of PPIs and the constructed database would provide rationale behind the interplay at cascading translational levels for drought stress-adaptive mechanisms in horse gram. Using a bidirectional approach (interolog and domain-based), a high-confidence interactome map and database for horse gram was constructed. Available transcriptomic information for shoot and root tissues of a sensitive (M-191; genotype 1) and a drought-tolerant (M-249; genotype 2) genotype of horse gram was utilized to draw comparative PPI subnetworks under drought stress. High-confidence 6804 interactions were predicted among 1812 proteins covering about one-fourth of the horse gram proteome. The highest number of interactions (33.86%) in horse gram interactome matched with Arabidopsis PPI data. The top five hub nodes mostly included ubiquitin and heat-shock-related proteins. Higher numbers of PPIs were found to be responsive in shoot tissue (416) and root tissue (2228) of genotype 2 compared with shoot tissue (136) and root tissue (579) of genotype 1. Characterization of PPIs using gene ontology analysis revealed that kinase and transferase activities involved in signal transduction, cellular processes, nucleocytoplasmic transport, protein ubiquitination, and localization of molecules were most responsive to drought stress. Hence, these could be framed in stress adaptive mechanisms of horse gram. Being the first legume global PPI map, it would provide new insights into gene and protein regulatory networks for drought stress tolerance mechanisms in horse gram. Information compiled in the form of database (MauPIR) will provide the much needed high-confidence systems biology information for horse gram genes, proteins, and involved processes. This information would ease the effort and increase the efficacy for similar studies on other legumes. Public access is available at http://14.139.59.221/MauPIR/ .

LIFEdb: a database for functional genomics experiments integrating information from external sources, and serving as a sample tracking system

PubMed Central

Bannasch, Detlev; Mehrle, Alexander; Glatting, Karl-Heinz; Pepperkok, Rainer; Poustka, Annemarie; Wiemann, Stefan

2004-01-01

We have implemented LIFEdb (http://www.dkfz.de/LIFEdb) to link information regarding novel human full-length cDNAs generated and sequenced by the German cDNA Consortium with functional information on the encoded proteins produced in functional genomics and proteomics approaches. The database also serves as a sample-tracking system to manage the process from cDNA to experimental read-out and data interpretation. A web interface enables the scientific community to explore and visualize features of the annotated cDNAs and ORFs combined with experimental results, and thus helps to unravel new features of proteins with as yet unknown functions. PMID:14681468

Verification of Ribosomal Proteins of Aspergillus fumigatus for Use as Biomarkers in MALDI-TOF MS Identification

PubMed Central

Nakamura, Sayaka; Sato, Hiroaki; Tanaka, Reiko; Yaguchi, Takashi

2016-01-01

We have previously proposed a rapid identification method for bacterial strains based on the profiles of their ribosomal subunit proteins (RSPs), observed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). This method can perform phylogenetic characterization based on the mass of housekeeping RSP biomarkers, ideally calculated from amino acid sequence information registered in public protein databases. With the aim of extending its field of application to medical mycology, this study investigates the actual state of information of RSPs of eukaryotic fungi registered in public protein databases through the characterization of ribosomal protein fractions extracted from genome-sequenced Aspergillus fumigatus strains Af293 and A1163 as a model. In this process, we have found that the public protein databases harbor problems. The RSP names are in confusion, so we have provisionally unified them using the yeast naming system. The most serious problem is that many incorrect sequences are registered in the public protein databases. Surprisingly, more than half of the sequences are incorrect, due chiefly to mis-annotation of exon/intron structures. These errors could be corrected by a combination of in silico inspection by sequence homology analysis and MALDI-TOF MS measurements. We were also able to confirm conserved post-translational modifications in eleven RSPs. After these verifications, the masses of 31 expressed RSPs under 20,000 Da could be accurately confirmed. These RSPs have a potential to be useful biomarkers for identifying clinical isolates of A. fumigatus. PMID:27843740

Web server to identify similarity of amino acid motifs to compounds (SAAMCO).

PubMed

Casey, Fergal P; Davey, Norman E; Baran, Ivan; Varekova, Radka Svobodova; Shields, Denis C

2008-07-01

Protein-protein interactions are fundamental in mediating biological processes including metabolism, cell growth, and signaling. To be able to selectively inhibit or induce protein activity or complex formation is a key feature in controlling disease. For those situations in which protein-protein interactions derive substantial affinity from short linear peptide sequences, or motifs, we can develop search algorithms for peptidomimetic compounds that resemble the short peptide's structure but are not compromised by poor pharmacological properties. SAAMCO is a Web service ( http://bioware.ucd.ie/ approximately saamco) that facilitates the screening of motifs with known structures against bioactive compound databases. It is built on an algorithm that defines compound similarity based on the presence of appropriate amino acid side chain fragments and a favorable Root Mean Squared Deviation (RMSD) between compound and motif structure. The methodology is efficient as the available compound databases are preprocessed and fast regular expression searches filter potential matches before time-intensive 3D superposition is performed. The required input information is minimal, and the compound databases have been selected to maximize the availability of information on biological activity. "Hits" are accompanied with a visualization window and links to source database entries. Motif matching can be defined on partial or full similarity which will increase or reduce respectively the number of potential mimetic compounds. The Web server provides the functionality for rapid screening of known or putative interaction motifs against prepared compound libraries using a novel search algorithm. The tabulated results can be analyzed by linking to appropriate databases and by visualization.

Meta-All: a system for managing metabolic pathway information.

PubMed

Weise, Stephan; Grosse, Ivo; Klukas, Christian; Koschützki, Dirk; Scholz, Uwe; Schreiber, Falk; Junker, Björn H

2006-10-23

Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed. We have designed META-ALL, an information system that allows the management of metabolic pathways, including reaction kinetics, detailed locations, environmental factors and taxonomic information. Data can be stored together with quality tags and in different parallel versions. META-ALL uses Oracle DBMS and Oracle Application Express. We provide the META-ALL information system for download and use. In this paper, we describe the database structure and give information about the tools for submitting and accessing the data. As a first application of META-ALL, we show how the information contained in a detailed kinetic model can be stored and accessed. META-ALL is a system for managing information about metabolic pathways. It facilitates the handling of pathway-related data and is designed to help biochemists and molecular biologists in their daily research. It is available on the Web at http://bic-gh.de/meta-all and can be downloaded free of charge and installed locally.

Meta-All: a system for managing metabolic pathway information

PubMed Central

Weise, Stephan; Grosse, Ivo; Klukas, Christian; Koschützki, Dirk; Scholz, Uwe; Schreiber, Falk; Junker, Björn H

2006-01-01

Background Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed. Results We have designed META-ALL, an information system that allows the management of metabolic pathways, including reaction kinetics, detailed locations, environmental factors and taxonomic information. Data can be stored together with quality tags and in different parallel versions. META-ALL uses Oracle DBMS and Oracle Application Express. We provide the META-ALL information system for download and use. In this paper, we describe the database structure and give information about the tools for submitting and accessing the data. As a first application of META-ALL, we show how the information contained in a detailed kinetic model can be stored and accessed. Conclusion META-ALL is a system for managing information about metabolic pathways. It facilitates the handling of pathway-related data and is designed to help biochemists and molecular biologists in their daily research. It is available on the Web at and can be downloaded free of charge and installed locally. PMID:17059592

SEQATOMS: a web tool for identifying missing regions in PDB in sequence context.

PubMed

Brandt, Bernd W; Heringa, Jaap; Leunissen, Jack A M

2008-07-01

With over 46 000 proteins, the Protein Data Bank (PDB) is the most important database with structural information of biological macromolecules. PDB files contain sequence and coordinate information. Residues present in the sequence can be absent from the coordinate section, which means their position in space is unknown. Similarity searches are routinely carried out against sequences taken from PDB SEQRES. However, there no distinction is made between residues that have a known or unknown position in the 3D protein structure. We present a FASTA sequence database that is produced by combining the sequence and coordinate information. All residues absent from the PDB coordinate section are masked with lower-case letters, thereby providing a view of these residues in the context of the entire protein sequence, which facilitates inspecting 'missing' regions. We also provide a masked version of the CATH domain database. A user-friendly BLAST interface is available for similarity searching. In contrast to standard (stand-alone) BLAST output, which only contains upper-case letters, our output retains the lower-case letters of the masked regions. Thus, our server can be used to perform BLAST searching case-sensitively. Here, we have applied it to the study of missing regions in their sequence context. SEQATOMS is available at http://www.bioinformatics.nl/tools/seqatoms/.

PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine

PubMed Central

Lee, Ji-Hyun; Park, Kyoung Mii; Han, Dong-Jin; Bang, Nam Young; Kim, Do-Hee; Na, Hyeongjin; Lim, Semi; Kim, Tae Bum; Kim, Dae Gyu; Kim, Hyun-Jung; Chung, Yeonseok; Sung, Sang Hyun; Surh, Young-Joon; Kim, Sunghoon; Han, Byung Woo

2015-01-01

Despite the growing attention given to Traditional Medicine (TM) worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM) database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound), disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software) and BioMart (a data federation framework) for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org. PMID:26555441

ASD: a comprehensive database of allosteric proteins and modulators

PubMed Central

Huang, Zhimin; Zhu, Liang; Cao, Yan; Wu, Geng; Liu, Xinyi; Chen, Yingyi; Wang, Qi; Shi, Ting; Zhao, Yaxue; Wang, Yuefei; Li, Weihua; Li, Yixue; Chen, Haifeng; Chen, Guoqiang; Zhang, Jian

2011-01-01

Allostery is the most direct, rapid and efficient way of regulating protein function, ranging from the control of metabolic mechanisms to signal-transduction pathways. However, an enormous amount of unsystematic allostery information has deterred scientists who could benefit from this field. Here, we present the AlloSteric Database (ASD), the first online database that provides a central resource for the display, search and analysis of structure, function and related annotation for allosteric molecules. Currently, ASD contains 336 allosteric proteins from 101 species and 8095 modulators in three categories (activators, inhibitors and regulators). Proteins are annotated with a detailed description of allostery, biological process and related diseases, and modulators with binding affinity, physicochemical properties and therapeutic area. Integrating the information of allosteric proteins in ASD should allow for the identification of specific allosteric sites of a given subtype among proteins of the same family that can potentially serve as ideal targets for experimental validation. In addition, modulators curated in ASD can be used to investigate potent allosteric targets for the query compound, and also help chemists to implement structure modifications for novel allosteric drug design. Therefore, ASD could be a platform and a starting point for biologists and medicinal chemists for furthering allosteric research. ASD is freely available at http://mdl.shsmu.edu.cn/ASD/. PMID:21051350

MAPU: Max-Planck Unified database of organellar, cellular, tissue and body fluid proteomes.

PubMed

Zhang, Yanling; Zhang, Yong; Adachi, Jun; Olsen, Jesper V; Shi, Rong; de Souza, Gustavo; Pasini, Erica; Foster, Leonard J; Macek, Boris; Zougman, Alexandre; Kumar, Chanchal; Wisniewski, Jacek R; Jun, Wang; Mann, Matthias

2007-01-01

Mass spectrometry (MS)-based proteomics has become a powerful technology to map the protein composition of organelles, cell types and tissues. In our department, a large-scale effort to map these proteomes is complemented by the Max-Planck Unified (MAPU) proteome database. MAPU contains several body fluid proteomes; including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities and is freely available at http://www.mapuproteome.com using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

SinEx DB: a database for single exon coding sequences in mammalian genomes.

PubMed

Jorquera, Roddy; Ortiz, Rodrigo; Ossandon, F; Cárdenas, Juan Pablo; Sepúlveda, Rene; González, Carolina; Holmes, David S

2016-01-01

Eukaryotic genes are typically interrupted by intragenic, noncoding sequences termed introns. However, some genes lack introns in their coding sequence (CDS) and are generally known as 'single exon genes' (SEGs). In this work, a SEG is defined as a nuclear, protein-coding gene that lacks introns in its CDS. Whereas, many public databases of Eukaryotic multi-exon genes are available, there are only two specialized databases for SEGs. The present work addresses the need for a more extensive and diverse database by creating SinEx DB, a publicly available, searchable database of predicted SEGs from 10 completely sequenced mammalian genomes including human. SinEx DB houses the DNA and protein sequence information of these SEGs and includes their functional predictions (KOG) and the relative distribution of these functions within species. The information is stored in a relational database built with My SQL Server 5.1.33 and the complete dataset of SEG sequences and their functional predictions are available for downloading. SinEx DB can be interrogated by: (i) a browsable phylogenetic schema, (ii) carrying out BLAST searches to the in-house SinEx DB of SEGs and (iii) via an advanced search mode in which the database can be searched by key words and any combination of searches by species and predicted functions. SinEx DB provides a rich source of information for advancing our understanding of the evolution and function of SEGs.Database URL: www.sinex.cl. © The Author(s) 2016. Published by Oxford University Press.

SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1.

PubMed

Senachak, Jittisak; Cheevadhanarak, Supapon; Hongsthong, Apiradee

2015-07-29

Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria. A web-based repository was developed including an embedded database, SpirPro, and tools for network visualization. Proteome data were analyzed integrated with protein-protein interactions and/or metabolic pathways from KEGG. The repository provides various information, ranging from raw data (2D-gel images) to associated results, such as data from interaction and/or pathway analyses. This integration allows in silico analyses of protein-protein interactions affected at the metabolic level and, particularly, analyses of interactions between and within the affected metabolic pathways under temperature stresses for comparative proteomic analysis. The developed tool, which is coded in HTML with CSS/JavaScript and depicted in Scalable Vector Graphics (SVG), is designed for interactive analysis and exploration of the constructed network. SpirPro is publicly available on the web at http://spirpro.sbi.kmutt.ac.th . SpirPro is an analysis platform containing an integrated proteome and PPI database that provides the most comprehensive data on this cyanobacterium at the systematic level. As an integrated database, SpirPro can be applied in various analyses, such as temperature stress response networking analysis in cyanobacterial models and interacting domain-domain analysis between proteins of interest.

The structure and dipole moment of globular proteins in solution and crystalline states: use of NMR and X-ray databases for the numerical calculation of dipole moment.

PubMed

Takashima, S

2001-04-05

The large dipole moment of globular proteins has been well known because of the detailed studies using dielectric relaxation and electro-optical methods. The search for the origin of these dipolemoments, however, must be based on the detailed knowledge on protein structure with atomic resolutions. At present, we have two sources of information on the structure of protein molecules: (1) x-ray databases obtained in crystalline state; (2) NMR databases obtained in solution state. While x-ray databases consist of only one model, NMR databases, because of the fluctuation of the protein folding in solution, consist of a number of models, thus enabling the computation of dipole moment repeated for all these models. The aim of this work, using these databases, is the detailed investigation on the interdependence between the structure and dipole moment of protein molecules. The dipole moment of protein molecules has roughly two components: one dipole moment is due to surface charges and the other, core dipole moment, is due to polar groups such as N--H and C==O bonds. The computation of surface charge dipole moment consists of two steps: (A) calculation of the pK shifts of charged groups for electrostatic interactions and (B) calculation of the dipole moment using the pK corrected for electrostatic shifts. The dipole moments of several proteins were computed using both NMR and x-ray databases. The dipole moments of these two sets of calculations are, with a few exceptions, in good agreement with one another and also with measured dipole moments.

DWARF – a data warehouse system for analyzing protein families

PubMed Central

Fischer, Markus; Thai, Quan K; Grieb, Melanie; Pleiss, Jürgen

2006-01-01

Background The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. Description The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of α/β-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. Conclusion DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering. PMID:17094801

Meta4: a web application for sharing and annotating metagenomic gene predictions using web services.

PubMed

Richardson, Emily J; Escalettes, Franck; Fotheringham, Ian; Wallace, Robert J; Watson, Mick

2013-01-01

Whole-genome shotgun metagenomics experiments produce DNA sequence data from entire ecosystems, and provide a huge amount of novel information. Gene discovery projects require up-to-date information about sequence homology and domain structure for millions of predicted proteins to be presented in a simple, easy-to-use system. There is a lack of simple, open, flexible tools that allow the rapid sharing of metagenomics datasets with collaborators in a format they can easily interrogate. We present Meta4, a flexible and extensible web application that can be used to share and annotate metagenomic gene predictions. Proteins and predicted domains are stored in a simple relational database, with a dynamic front-end which displays the results in an internet browser. Web services are used to provide up-to-date information about the proteins from homology searches against public databases. Information about Meta4 can be found on the project website, code is available on Github, a cloud image is available, and an example implementation can be seen at.

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

PubMed

Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

2018-05-08

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/ .

The history of the CATH structural classification of protein domains.

PubMed

Sillitoe, Ian; Dawson, Natalie; Thornton, Janet; Orengo, Christine

2015-12-01

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Exploring Protein Function Using the Saccharomyces Genome Database.

PubMed

Wong, Edith D

2017-01-01

Elucidating the function of individual proteins will help to create a comprehensive picture of cell biology, as well as shed light on human disease mechanisms, possible treatments, and cures. Due to its compact genome, and extensive history of experimentation and annotation, the budding yeast Saccharomyces cerevisiae is an ideal model organism in which to determine protein function. This information can then be leveraged to infer functions of human homologs. Despite the large amount of research and biological data about S. cerevisiae, many proteins' functions remain unknown. Here, we explore ways to use the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org ) to predict the function of proteins and gain insight into their roles in various cellular processes.

3DNALandscapes: a database for exploring the conformational features of DNA.

PubMed

Zheng, Guohui; Colasanti, Andrew V; Lu, Xiang-Jun; Olson, Wilma K

2010-01-01

3DNALandscapes, located at: http://3DNAscapes.rutgers.edu, is a new database for exploring the conformational features of DNA. In contrast to most structural databases, which archive the Cartesian coordinates and/or derived parameters and images for individual structures, 3DNALandscapes enables searches of conformational information across multiple structures. The database contains a wide variety of structural parameters and molecular images, computed with the 3DNA software package and known to be useful for characterizing and understanding the sequence-dependent spatial arrangements of the DNA sugar-phosphate backbone, sugar-base side groups, base pairs, base-pair steps, groove structure, etc. The data comprise all DNA-containing structures--both free and bound to proteins, drugs and other ligands--currently available in the Protein Data Bank. The web interface allows the user to link, report, plot and analyze this information from numerous perspectives and thereby gain insight into DNA conformation, deformability and interactions in different sequence and structural contexts. The data accumulated from known, well-resolved DNA structures can serve as useful benchmarks for the analysis and simulation of new structures. The collective data can also help to understand how DNA deforms in response to proteins and other molecules and undergoes conformational rearrangements.

Cry-Bt identifier: a biological database for PCR detection of Cry genes present in transgenic plants.

PubMed

Singh, Vinay Kumar; Ambwani, Sonu; Marla, Soma; Kumar, Anil

2009-10-23

We describe the development of a user friendly tool that would assist in the retrieval of information relating to Cry genes in transgenic crops. The tool also helps in detection of transformed Cry genes from Bacillus thuringiensis present in transgenic plants by providing suitable designed primers for PCR identification of these genes. The tool designed based on relational database model enables easy retrieval of information from the database with simple user queries. The tool also enables users to access related information about Cry genes present in various databases by interacting with different sources (nucleotide sequences, protein sequence, sequence comparison tools, published literature, conserved domains, evolutionary and structural data). http://insilicogenomics.in/Cry-btIdentifier/welcome.html.

Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell.

PubMed

De Las Rivas, Javier; Fontanillo, Celia

2012-11-01

Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics.

E-MSD: an integrated data resource for bioinformatics.

PubMed

Velankar, S; McNeil, P; Mittard-Runte, V; Suarez, A; Barrell, D; Apweiler, R; Henrick, K

2005-01-01

The Macromolecular Structure Database (MSD) group (http://www.ebi.ac.uk/msd/) continues to enhance the quality and consistency of macromolecular structure data in the worldwide Protein Data Bank (wwPDB) and to work towards the integration of various bioinformatics data resources. One of the major obstacles to the improved integration of structural databases such as MSD and sequence databases like UniProt is the absence of up to date and well-maintained mapping between corresponding entries. We have worked closely with the UniProt group at the EBI to clean up the taxonomy and sequence cross-reference information in the MSD and UniProt databases. This information is vital for the reliable integration of the sequence family databases such as Pfam and Interpro with the structure-oriented databases of SCOP and CATH. This information has been made available to the eFamily group (http://www.efamily.org.uk/) and now forms the basis of the regular interchange of information between the member databases (MSD, UniProt, Pfam, Interpro, SCOP and CATH). This exchange of annotation information has enriched the structural information in the MSD database with annotation from wider sequence-oriented resources. This work was carried out under the 'Structure Integration with Function, Taxonomy and Sequences (SIFTS)' initiative (http://www.ebi.ac.uk/msd-srv/docs/sifts) in the MSD group.

SNPdbe: constructing an nsSNP functional impacts database.

PubMed

Schaefer, Christian; Meier, Alice; Rost, Burkhard; Bromberg, Yana

2012-02-15

Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe-SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from >2600 organisms; 'human' being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs. http://www.rostlab.org/services/snpdbe.

GenBank.

PubMed

Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Sayers, Eric W

2009-01-01

GenBank is a comprehensive database that contains publicly available nucleotide sequences for more than 300,000 organisms named at the genus level or lower, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and accession numbers are assigned by GenBank(R) staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through the National Center for Biotechnology Information (NCBI) Entrez retrieval system, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov.

Establishment of an international database for genetic variants in esophageal cancer.

PubMed

Vihinen, Mauno

2016-10-01

The establishment of a database has been suggested in order to collect, organize, and distribute genetic information about esophageal cancer. The World Organization for Specialized Studies on Diseases of the Esophagus and the Human Variome Project will be in charge of a central database of information about esophageal cancer-related variations from publications, databases, and laboratories; in addition to genetic details, clinical parameters will also be included. The aim will be to get all the central players in research, clinical, and commercial laboratories to contribute. The database will follow established recommendations and guidelines. The database will require a team of dedicated curators with different backgrounds. Numerous layers of systematics will be applied to facilitate computational analyses. The data items will be extensively integrated with other information sources. The database will be distributed as open access to ensure exchange of the data with other databases. Variations will be reported in relation to reference sequences on three levels--DNA, RNA, and protein-whenever applicable. In the first phase, the database will concentrate on genetic variations including both somatic and germline variations for susceptibility genes. Additional types of information can be integrated at a later stage. © 2016 New York Academy of Sciences.

ChemProt-2.0: visual navigation in a disease chemical biology database

PubMed Central

Kim Kjærulff, Sonny; Wich, Louis; Kringelum, Jens; Jacobsen, Ulrik P.; Kouskoumvekaki, Irene; Audouze, Karine; Lund, Ole; Brunak, Søren; Oprea, Tudor I.; Taboureau, Olivier

2013-01-01

ChemProt-2.0 (http://www.cbs.dtu.dk/services/ChemProt-2.0) is a public available compilation of multiple chemical–protein annotation resources integrated with diseases and clinical outcomes information. The database has been updated to >1.15 million compounds with 5.32 millions bioactivity measurements for 15 290 proteins. Each protein is linked to quality-scored human protein–protein interactions data based on more than half a million interactions, for studying diseases and biological outcomes (diseases, pathways and GO terms) through protein complexes. In ChemProt-2.0, therapeutic effects as well as adverse drug reactions have been integrated allowing for suggesting proteins associated to clinical outcomes. New chemical structure fingerprints were computed based on the similarity ensemble approach. Protein sequence similarity search was also integrated to evaluate the promiscuity of proteins, which can help in the prediction of off-target effects. Finally, the database was integrated into a visual interface that enables navigation of the pharmacological space for small molecules. Filtering options were included in order to facilitate and to guide dynamic search of specific queries. PMID:23185041

Database resources of the National Center for Biotechnology Information

PubMed Central

Sayers, Eric W.; Barrett, Tanya; Benson, Dennis A.; Bolton, Evan; Bryant, Stephen H.; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M.; DiCuccio, Michael; Federhen, Scott; Feolo, Michael; Fingerman, Ian M.; Geer, Lewis Y.; Helmberg, Wolfgang; Kapustin, Yuri; Krasnov, Sergey; Landsman, David; Lipman, David J.; Lu, Zhiyong; Madden, Thomas L.; Madej, Tom; Maglott, Donna R.; Marchler-Bauer, Aron; Miller, Vadim; Karsch-Mizrachi, Ilene; Ostell, James; Panchenko, Anna; Phan, Lon; Pruitt, Kim D.; Schuler, Gregory D.; Sequeira, Edwin; Sherry, Stephen T.; Shumway, Martin; Sirotkin, Karl; Slotta, Douglas; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A.; Wagner, Lukas; Wang, Yanli; Wilbur, W. John; Yaschenko, Eugene; Ye, Jian

2012-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Website. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Genome and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Probe, Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. PMID:22140104

Database resources of the National Center for Biotechnology Information

PubMed Central

2013-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, the Genetic Testing Registry, Genome and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Probe, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool, Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page. PMID:23193264

Seeds in Chernobyl: the database on proteome response on radioactive environment

PubMed Central

Klubicová, Katarína; Vesel, Martin; Rashydov, Namik M.; Hajduch, Martin

2012-01-01

Two serious nuclear accidents during the last quarter century (Chernobyl, 1986 and Fukushima, 2011) contaminated large agricultural areas with radioactivity. The database “Seeds in Chernobyl” (http://www.chernobylproteomics.sav.sk) contains the information about the abundances of hundreds of proteins from on-going investigation of mature and developing seed harvested from plants grown in radioactive Chernobyl area. This database provides a useful source of information concerning the response of the seed proteome to permanently increased level of ionizing radiation in a user-friendly format. PMID:23087698

A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data.

PubMed

Mo, Fan; Hong, Xu; Gao, Feng; Du, Lin; Wang, Jun; Omenn, Gilbert S; Lin, Biaoyang

2008-12-16

Alternative splicing is an important gene regulation mechanism. It is estimated that about 74% of multi-exon human genes have alternative splicing. High throughput tandem (MS/MS) mass spectrometry provides valuable information for rapidly identifying potentially novel alternatively-spliced protein products from experimental datasets. However, the ability to identify alternative splicing events through tandem mass spectrometry depends on the database against which the spectra are searched. We wrote scripts in perl, Bioperl, mysql and Ensembl API and built a theoretical exon-exon junction protein database to account for all possible combinations of exons for a gene while keeping the frame of translation (i.e., keeping only in-phase exon-exon combinations) from the Ensembl Core Database. Using our liver cancer MS/MS dataset, we identified a total of 488 non-redundant peptides that represent putative exon skipping events. Our exon-exon junction database provides the scientific community with an efficient means to identify novel alternatively spliced (exon skipping) protein isoforms using mass spectrometry data. This database will be useful in annotating genome structures using rapidly accumulating proteomics data.

Publication of nuclear magnetic resonance experimental data with semantic web technology and the application thereof to biomedical research of proteins.

PubMed

Yokochi, Masashi; Kobayashi, Naohiro; Ulrich, Eldon L; Kinjo, Akira R; Iwata, Takeshi; Ioannidis, Yannis E; Livny, Miron; Markley, John L; Nakamura, Haruki; Kojima, Chojiro; Fujiwara, Toshimichi

2016-05-05

The nuclear magnetic resonance (NMR) spectroscopic data for biological macromolecules archived at the BioMagResBank (BMRB) provide a rich resource of biophysical information at atomic resolution. The NMR data archived in NMR-STAR ASCII format have been implemented in a relational database. However, it is still fairly difficult for users to retrieve data from the NMR-STAR files or the relational database in association with data from other biological databases. To enhance the interoperability of the BMRB database, we present a full conversion of BMRB entries to two standard structured data formats, XML and RDF, as common open representations of the NMR-STAR data. Moreover, a SPARQL endpoint has been deployed. The described case study demonstrates that a simple query of the SPARQL endpoints of the BMRB, UniProt, and Online Mendelian Inheritance in Man (OMIM), can be used in NMR and structure-based analysis of proteins combined with information of single nucleotide polymorphisms (SNPs) and their phenotypes. We have developed BMRB/XML and BMRB/RDF and demonstrate their use in performing a federated SPARQL query linking the BMRB to other databases through standard semantic web technologies. This will facilitate data exchange across diverse information resources.

Regulators of Androgen Action Resource: a one-stop shop for the comprehensive study of androgen receptor action.

PubMed

DePriest, Adam D; Fiandalo, Michael V; Schlanger, Simon; Heemers, Frederike; Mohler, James L; Liu, Song; Heemers, Hannelore V

2016-01-01

Androgen receptor (AR) is a ligand-activated transcription factor that is the main target for treatment of non-organ-confined prostate cancer (CaP). Failure of life-prolonging AR-targeting androgen deprivation therapy is due to flexibility in steroidogenic pathways that control intracrine androgen levels and variability in the AR transcriptional output. Androgen biosynthesis enzymes, androgen transporters and AR-associated coregulators are attractive novel CaP treatment targets. These proteins, however, are characterized by multiple transcript variants and isoforms, are subject to genomic alterations, and are differentially expressed among CaPs. Determining their therapeutic potential requires evaluation of extensive, diverse datasets that are dispersed over multiple databases, websites and literature reports. Mining and integrating these datasets are cumbersome, time-consuming tasks and provide only snapshots of relevant information. To overcome this impediment to effective, efficient study of AR and potential drug targets, we developed the Regulators of Androgen Action Resource (RAAR), a non-redundant, curated and user-friendly searchable web interface. RAAR centralizes information on gene function, clinical relevance, and resources for 55 genes that encode proteins involved in biosynthesis, metabolism and transport of androgens and for 274 AR-associated coregulator genes. Data in RAAR are organized in two levels: (i) Information pertaining to production of androgens is contained in a 'pre-receptor level' database, and coregulator gene information is provided in a 'post-receptor level' database, and (ii) an 'other resources' database contains links to additional databases that are complementary to and useful to pursue further the information provided in RAAR. For each of its 329 entries, RAAR provides access to more than 20 well-curated publicly available databases, and thus, access to thousands of data points. Hyperlinks provide direct access to gene-specific entries in the respective database(s). RAAR is a novel, freely available resource that provides fast, reliable and easy access to integrated information that is needed to develop alternative CaP therapies. Database URL: http://www.lerner.ccf.org/cancerbio/heemers/RAAR/search/. © The Author(s) 2016. Published by Oxford University Press.

SASD: the Synthetic Alternative Splicing Database for identifying novel isoform from proteomics

PubMed Central

2013-01-01

Background Alternative splicing is an important and widespread mechanism for generating protein diversity and regulating protein expression. High-throughput identification and analysis of alternative splicing in the protein level has more advantages than in the mRNA level. The combination of alternative splicing database and tandem mass spectrometry provides a powerful technique for identification, analysis and characterization of potential novel alternative splicing protein isoforms from proteomics. Therefore, based on the peptidomic database of human protein isoforms for proteomics experiments, our objective is to design a new alternative splicing database to 1) provide more coverage of genes, transcripts and alternative splicing, 2) exclusively focus on the alternative splicing, and 3) perform context-specific alternative splicing analysis. Results We used a three-step pipeline to create a synthetic alternative splicing database (SASD) to identify novel alternative splicing isoforms and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing. First, we extracted information on gene structures of all genes in the Ensembl Genes 71 database and incorporated the Integrated Pathway Analysis Database. Then, we compiled artificial splicing transcripts. Lastly, we translated the artificial transcripts into alternative splicing peptides. The SASD is a comprehensive database containing 56,630 genes (Ensembl gene IDs), 95,260 transcripts (Ensembl transcript IDs), and 11,919,779 Alternative Splicing peptides, and also covering about 1,956 pathways, 6,704 diseases, 5,615 drugs, and 52 organs. The database has a web-based user interface that allows users to search, display and download a single gene/transcript/protein, custom gene set, pathway, disease, drug, organ related alternative splicing. Moreover, the quality of the database was validated with comparison to other known databases and two case studies: 1) in liver cancer and 2) in breast cancer. Conclusions The SASD provides the scientific community with an efficient means to identify, analyze, and characterize novel Exon Skipping and Intron Retention protein isoforms from mass spectrometry and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing. PMID:24267658

Gene Unprediction with Spurio: A tool to identify spurious protein sequences.

PubMed

Höps, Wolfram; Jeffryes, Matt; Bateman, Alex

2018-01-01

We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation.  Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases.  We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes.  Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.

Mycobacteriophage genome database.

PubMed

Joseph, Jerrine; Rajendran, Vasanthi; Hassan, Sameer; Kumar, Vanaja

2011-01-01

Mycobacteriophage genome database (MGDB) is an exclusive repository of the 64 completely sequenced mycobacteriophages with annotated information. It is a comprehensive compilation of the various gene parameters captured from several databases pooled together to empower mycobacteriophage researchers. The MGDB (Version No.1.0) comprises of 6086 genes from 64 mycobacteriophages classified into 72 families based on ACLAME database. Manual curation was aided by information available from public databases which was enriched further by analysis. Its web interface allows browsing as well as querying the classification. The main objective is to collect and organize the complexity inherent to mycobacteriophage protein classification in a rational way. The other objective is to browse the existing and new genomes and describe their functional annotation. The database is available for free at http://mpgdb.ibioinformatics.org/mpgdb.php.

GPCR & company: databases and servers for GPCRs and interacting partners.

PubMed

Kowalsman, Noga; Niv, Masha Y

2014-01-01

G-protein-coupled receptors (GPCRs) are a large superfamily of membrane receptors that are involved in a wide range of signaling pathways. To fulfill their tasks, GPCRs interact with a variety of partners, including small molecules, lipids and proteins. They are accompanied by different proteins during all phases of their life cycle. Therefore, GPCR interactions with their partners are of great interest in basic cell-signaling research and in drug discovery.Due to the rapid development of computers and internet communication, knowledge and data can be easily shared within the worldwide research community via freely available databases and servers. These provide an abundance of biological, chemical and pharmacological information.This chapter describes the available web resources for investigating GPCR interactions. We review about 40 freely available databases and servers, and provide a few sentences about the essence and the data they supply. For simplification, the databases and servers were grouped under the following topics: general GPCR-ligand interactions; particular families of GPCRs and their ligands; GPCR oligomerization; GPCR interactions with intracellular partners; and structural information on GPCRs. In conclusion, a multitude of useful tools are currently available. Summary tables are provided to ease navigation between the numerous and partially overlapping resources. Suggestions for future enhancements of the online tools include the addition of links from general to specialized databases and enabling usage of user-supplied template for GPCR structural modeling.

SORTEZ: a relational translator for NCBI's ASN.1 database.

PubMed

Hart, K W; Searls, D B; Overton, G C

1994-07-01

The National Center for Biotechnology Information (NCBI) has created a database collection that includes several protein and nucleic acid sequence databases, a biosequence-specific subset of MEDLINE, as well as value-added information such as links between similar sequences. Information in the NCBI database is modeled in Abstract Syntax Notation 1 (ASN.1) an Open Systems Interconnection protocol designed for the purpose of exchanging structured data between software applications rather than as a data model for database systems. While the NCBI database is distributed with an easy-to-use information retrieval system, ENTREZ, the ASN.1 data model currently lacks an ad hoc query language for general-purpose data access. For that reason, we have developed a software package, SORTEZ, that transforms the ASN.1 database (or other databases with nested data structures) to a relational data model and subsequently to a relational database management system (Sybase) where information can be accessed through the relational query language, SQL. Because the need to transform data from one data model and schema to another arises naturally in several important contexts, including efficient execution of specific applications, access to multiple databases and adaptation to database evolution this work also serves as a practical study of the issues involved in the various stages of database transformation. We show that transformation from the ASN.1 data model to a relational data model can be largely automated, but that schema transformation and data conversion require considerable domain expertise and would greatly benefit from additional support tools.

Gene: a gene-centered information resource at NCBI.

PubMed

Brown, Garth R; Hem, Vichet; Katz, Kenneth S; Ovetsky, Michael; Wallin, Craig; Ermolaeva, Olga; Tolstoy, Igor; Tatusova, Tatiana; Pruitt, Kim D; Maglott, Donna R; Murphy, Terence D

2015-01-01

The National Center for Biotechnology Information's (NCBI) Gene database (www.ncbi.nlm.nih.gov/gene) integrates gene-specific information from multiple data sources. NCBI Reference Sequence (RefSeq) genomes for viruses, prokaryotes and eukaryotes are the primary foundation for Gene records in that they form the critical association between sequence and a tracked gene upon which additional functional and descriptive content is anchored. Additional content is integrated based on the genomic location and RefSeq transcript and protein sequence data. The content of a Gene record represents the integration of curation and automated processing from RefSeq, collaborating model organism databases, consortia such as Gene Ontology, and other databases within NCBI. Records in Gene are assigned unique, tracked integers as identifiers. The content (citations, nomenclature, genomic location, gene products and their attributes, phenotypes, sequences, interactions, variation details, maps, expression, homologs, protein domains and external databases) is available via interactive browsing through NCBI's Entrez system, via NCBI's Entrez programming utilities (E-Utilities and Entrez Direct) and for bulk transfer by FTP. Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

E-MSD: an integrated data resource for bioinformatics

PubMed Central

Velankar, S.; McNeil, P.; Mittard-Runte, V.; Suarez, A.; Barrell, D.; Apweiler, R.; Henrick, K.

2005-01-01

The Macromolecular Structure Database (MSD) group (http://www.ebi.ac.uk/msd/) continues to enhance the quality and consistency of macromolecular structure data in the worldwide Protein Data Bank (wwPDB) and to work towards the integration of various bioinformatics data resources. One of the major obstacles to the improved integration of structural databases such as MSD and sequence databases like UniProt is the absence of up to date and well-maintained mapping between corresponding entries. We have worked closely with the UniProt group at the EBI to clean up the taxonomy and sequence cross-reference information in the MSD and UniProt databases. This information is vital for the reliable integration of the sequence family databases such as Pfam and Interpro with the structure-oriented databases of SCOP and CATH. This information has been made available to the eFamily group (http://www.efamily.org.uk/) and now forms the basis of the regular interchange of information between the member databases (MSD, UniProt, Pfam, Interpro, SCOP and CATH). This exchange of annotation information has enriched the structural information in the MSD database with annotation from wider sequence-oriented resources. This work was carried out under the ‘Structure Integration with Function, Taxonomy and Sequences (SIFTS)’ initiative (http://www.ebi.ac.uk/msd-srv/docs/sifts) in the MSD group. PMID:15608192

Genome-Wide Identification of Arabidopsis Coiled-Coil Proteins and Establishment of the ARABI-COIL Database1

PubMed Central

Rose, Annkatrin; Manikantan, Sankaraganesh; Schraegle, Shannon J.; Maloy, Michael A.; Stahlberg, Eric A.; Meier, Iris

2004-01-01

Increasing evidence demonstrates the importance of long coiled-coil proteins for the spatial organization of cellular processes. Although several protein classes with long coiled-coil domains have been studied in animals and yeast, our knowledge about plant long coiled-coil proteins is very limited. The repeat nature of the coiled-coil sequence motif often prevents the simple identification of homologs of animal coiled-coil proteins by generic sequence similarity searches. As a consequence, counterparts of many animal proteins with long coiled-coil domains, like lamins, golgins, or microtubule organization center components, have not been identified yet in plants. Here, all Arabidopsis proteins predicted to contain long stretches of coiled-coil domains were identified by applying the algorithm MultiCoil to a genome-wide screen. A searchable protein database, ARABI-COIL (http://www.coiled-coil.org/arabidopsis), was established that integrates information on number, size, and position of predicted coiled-coil domains with subcellular localization signals, transmembrane domains, and available functional annotations. ARABI-COIL serves as a tool to sort and browse Arabidopsis long coiled-coil proteins to facilitate the identification and selection of candidate proteins of potential interest for specific research areas. Using the database, candidate proteins were identified for Arabidopsis membrane-bound, nuclear, and organellar long coiled-coil proteins. PMID:15020757

EKPD: a hierarchical database of eukaryotic protein kinases and protein phosphatases.

PubMed

Wang, Yongbo; Liu, Zexian; Cheng, Han; Gao, Tianshun; Pan, Zhicheng; Yang, Qing; Guo, Anyuan; Xue, Yu

2014-01-01

We present here EKPD (http://ekpd.biocuckoo.org), a hierarchical database of eukaryotic protein kinases (PKs) and protein phosphatases (PPs), the key molecules responsible for the reversible phosphorylation of proteins that are involved in almost all aspects of biological processes. As extensive experimental and computational efforts have been carried out to identify PKs and PPs, an integrative resource with detailed classification and annotation information would be of great value for both experimentalists and computational biologists. In this work, we first collected 1855 PKs and 347 PPs from the scientific literature and various public databases. Based on previously established rationales, we classified all of the known PKs and PPs into a hierarchical structure with three levels, i.e. group, family and individual PK/PP. There are 10 groups with 149 families for the PKs and 10 groups with 33 families for the PPs. We constructed 139 and 27 Hidden Markov Model profiles for PK and PP families, respectively. Then we systematically characterized ∼50,000 PKs and >10,000 PPs in eukaryotes. In addition, >500 PKs and >400 PPs were computationally identified by ortholog search. Finally, the online service of the EKPD database was implemented in PHP + MySQL + JavaScript.

PrionScan: an online database of predicted prion domains in complete proteomes.

PubMed

Espinosa Angarica, Vladimir; Angulo, Alfonso; Giner, Arturo; Losilla, Guillermo; Ventura, Salvador; Sancho, Javier

2014-02-05

Prions are a particular type of amyloids related to a large variety of important processes in cells, but also responsible for serious diseases in mammals and humans. The number of experimentally characterized prions is still low and corresponds to a handful of examples in microorganisms and mammals. Prion aggregation is mediated by specific protein domains with a remarkable compositional bias towards glutamine/asparagine and against charged residues and prolines. These compositional features have been used to predict new prion proteins in the genomes of different organisms. Despite these efforts, there are only a few available data sources containing prion predictions at a genomic scale. Here we present PrionScan, a new database of predicted prion-like domains in complete proteomes. We have previously developed a predictive methodology to identify and score prionogenic stretches in protein sequences. In the present work, we exploit this approach to scan all the protein sequences in public databases and compile a repository containing relevant information of proteins bearing prion-like domains. The database is updated regularly alongside UniprotKB and in its present version contains approximately 28000 predictions in proteins from different functional categories in more than 3200 organisms from all the taxonomic subdivisions. PrionScan can be used in two different ways: database query and analysis of protein sequences submitted by the users. In the first mode, simple queries allow to retrieve a detailed description of the properties of a defined protein. Queries can also be combined to generate more complex and specific searching patterns. In the second mode, users can submit and analyze their own sequences. It is expected that this database would provide relevant insights on prion functions and regulation from a genome-wide perspective, allowing researches performing cross-species prion biology studies. Our database might also be useful for guiding experimentalists in the identification of new candidates for further experimental characterization.

Databases for Microbiologists

DOE PAGES

Zhulin, Igor B.

2015-05-26

Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. Finally, the purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists.

Databases for Microbiologists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhulin, Igor B.

Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. Finally, the purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists.

Databases for Microbiologists

PubMed Central

2015-01-01

Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. The purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists. PMID:26013493

Organizing, exploring, and analyzing antibody sequence data: the case for relational-database managers.

PubMed

Owens, John

2009-01-01

Technological advances in the acquisition of DNA and protein sequence information and the resulting onrush of data can quickly overwhelm the scientist unprepared for the volume of information that must be evaluated and carefully dissected to discover its significance. Few laboratories have the luxury of dedicated personnel to organize, analyze, or consistently record a mix of arriving sequence data. A methodology based on a modern relational-database manager is presented that is both a natural storage vessel for antibody sequence information and a conduit for organizing and exploring sequence data and accompanying annotation text. The expertise necessary to implement such a plan is equal to that required by electronic word processors or spreadsheet applications. Antibody sequence projects maintained as independent databases are selectively unified by the relational-database manager into larger database families that contribute to local analyses, reports, interactive HTML pages, or exported to facilities dedicated to sophisticated sequence analysis techniques. Database files are transposable among current versions of Microsoft, Macintosh, and UNIX operating systems.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions.

PubMed

Gerlt, John A

2017-08-22

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of "genomic enzymology" web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence-function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence–Function Space and Genome Context to Discover Novel Functions

PubMed Central

2017-01-01

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of “genomic enzymology” web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence–function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems. PMID:28826221

Database resources of the National Center for Biotechnology Information.

PubMed

Sayers, Eric W; Barrett, Tanya; Benson, Dennis A; Bolton, Evan; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; DiCuccio, Michael; Federhen, Scott; Feolo, Michael; Fingerman, Ian M; Geer, Lewis Y; Helmberg, Wolfgang; Kapustin, Yuri; Landsman, David; Lipman, David J; Lu, Zhiyong; Madden, Thomas L; Madej, Tom; Maglott, Donna R; Marchler-Bauer, Aron; Miller, Vadim; Mizrachi, Ilene; Ostell, James; Panchenko, Anna; Phan, Lon; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Stephen T; Shumway, Martin; Sirotkin, Karl; Slotta, Douglas; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A; Wagner, Lukas; Wang, Yanli; Wilbur, W John; Yaschenko, Eugene; Ye, Jian

2011-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Electronic PCR, OrfFinder, Splign, ProSplign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), IBIS, Biosystems, Peptidome, OMSSA, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

HNdb: an integrated database of gene and protein information on head and neck squamous cell carcinoma

PubMed Central

Henrique, Tiago; José Freitas da Silveira, Nelson; Henrique Cunha Volpato, Arthur; Mioto, Mayra Mataruco; Carolina Buzzo Stefanini, Ana; Bachir Fares, Adil; Gustavo da Silva Castro Andrade, João; Masson, Carolina; Verónica Mendoza López, Rossana; Daumas Nunes, Fabio; Paulo Kowalski, Luis; Severino, Patricia; Tajara, Eloiza Helena

2016-01-01

The total amount of scientific literature has grown rapidly in recent years. Specifically, there are several million citations in the field of cancer. This makes it difficult, if not impossible, to manually retrieve relevant information on the mechanisms that govern tumor behavior or the neoplastic process. Furthermore, cancer is a complex disease or, more accurately, a set of diseases. The heterogeneity that permeates many tumors is particularly evident in head and neck (HN) cancer, one of the most common types of cancer worldwide. In this study, we present HNdb, a free database that aims to provide a unified and comprehensive resource of information on genes and proteins involved in HN squamous cell carcinoma, covering data on genomics, transcriptomics, proteomics, literature citations and also cross-references of external databases. Different literature searches of MEDLINE abstracts were performed using specific Medical Subject Headings (MeSH terms) for oral, oropharyngeal, hypopharyngeal and laryngeal squamous cell carcinomas. A curated gene-to-publication assignment yielded a total of 1370 genes related to HN cancer. The diversity of results allowed identifying novel and mostly unexplored gene associations, revealing, for example, that processes linked to response to steroid hormone stimulus are significantly enriched in genes related to HN carcinomas. Thus, our database expands the possibilities for gene networks investigation, providing potential hypothesis to be tested. Database URL: http://www.gencapo.famerp.br/hndb PMID:27013077

Gene and protein nomenclature in public databases

PubMed Central

Fundel, Katrin; Zimmer, Ralf

2006-01-01

Background Frequently, several alternative names are in use for biological objects such as genes and proteins. Applications like manual literature search, automated text-mining, named entity identification, gene/protein annotation, and linking of knowledge from different information sources require the knowledge of all used names referring to a given gene or protein. Various organism-specific or general public databases aim at organizing knowledge about genes and proteins. These databases can be used for deriving gene and protein name dictionaries. So far, little is known about the differences between databases in terms of size, ambiguities and overlap. Results We compiled five gene and protein name dictionaries for each of the five model organisms (yeast, fly, mouse, rat, and human) from different organism-specific and general public databases. We analyzed the degree of ambiguity of gene and protein names within and between dictionaries, to a lexicon of common English words and domain-related non-gene terms, and we compared different data sources in terms of size of extracted dictionaries and overlap of synonyms between those. The study shows that the number of genes/proteins and synonyms covered in individual databases varies significantly for a given organism, and that the degree of ambiguity of synonyms varies significantly between different organisms. Furthermore, it shows that, despite considerable efforts of co-curation, the overlap of synonyms in different data sources is rather moderate and that the degree of ambiguity of gene names with common English words and domain-related non-gene terms varies depending on the considered organism. Conclusion In conclusion, these results indicate that the combination of data contained in different databases allows the generation of gene and protein name dictionaries that contain significantly more used names than dictionaries obtained from individual data sources. Furthermore, curation of combined dictionaries considerably increases size and decreases ambiguity. The entries of the curated synonym dictionary are available for manual querying, editing, and PubMed- or Google-search via the ProThesaurus-wiki. For automated querying via custom software, we offer a web service and an exemplary client application. PMID:16899134

Secure web book to store structural genomics research data.

PubMed

Manjasetty, Babu A; Höppner, Klaus; Mueller, Uwe; Heinemann, Udo

2003-01-01

Recently established collaborative structural genomics programs aim at significantly accelerating the crystal structure analysis of proteins. These large-scale projects require efficient data management systems to ensure seamless collaboration between different groups of scientists working towards the same goal. Within the Berlin-based Protein Structure Factory, the synchrotron X-ray data collection and the subsequent crystal structure analysis tasks are located at BESSY, a third-generation synchrotron source. To organize file-based communication and data transfer at the BESSY site of the Protein Structure Factory, we have developed the web-based BCLIMS, the BESSY Crystallography Laboratory Information Management System. BCLIMS is a relational data management system which is powered by MySQL as the database engine and Apache HTTP as the web server. The database interface routines are written in Python programing language. The software is freely available to academic users. Here we describe the storage, retrieval and manipulation of laboratory information, mainly pertaining to the synchrotron X-ray diffraction experiments and the subsequent protein structure analysis, using BCLIMS.

Automated quantitative assessment of proteins' biological function in protein knowledge bases.

PubMed

Mayr, Gabriele; Lepperdinger, Günter; Lackner, Peter

2008-01-01

Primary protein sequence data are archived in databases together with information regarding corresponding biological functions. In this respect, UniProt/Swiss-Prot is currently the most comprehensive collection and it is routinely cross-examined when trying to unravel the biological role of hypothetical proteins. Bioscientists frequently extract single entries and further evaluate those on a subjective basis. In lieu of a standardized procedure for scoring the existing knowledge regarding individual proteins, we here report about a computer-assisted method, which we applied to score the present knowledge about any given Swiss-Prot entry. Applying this quantitative score allows the comparison of proteins with respect to their sequence yet highlights the comprehension of functional data. pfs analysis may be also applied for quality control of individual entries or for database management in order to rank entry listings.

AllerML: markup language for allergens.

PubMed

Ivanciuc, Ovidiu; Gendel, Steven M; Power, Trevor D; Schein, Catherine H; Braun, Werner

2011-06-01

Many concerns have been raised about the potential allergenicity of novel, recombinant proteins into food crops. Guidelines, proposed by WHO/FAO and EFSA, include the use of bioinformatics screening to assess the risk of potential allergenicity or cross-reactivities of all proteins introduced, for example, to improve nutritional value or promote crop resistance. However, there are no universally accepted standards that can be used to encode data on the biology of allergens to facilitate using data from multiple databases in this screening. Therefore, we developed AllerML a markup language for allergens to assist in the automated exchange of information between databases and in the integration of the bioinformatics tools that are used to investigate allergenicity and cross-reactivity. As proof of concept, AllerML was implemented using the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/) database. General implementation of AllerML will promote automatic flow of validated data that will aid in allergy research and regulatory analysis. Copyright © 2011 Elsevier Inc. All rights reserved.

AllerML: Markup Language for Allergens

PubMed Central

Ivanciuc, Ovidiu; Gendel, Steven M.; Power, Trevor D.; Schein, Catherine H.; Braun, Werner

2011-01-01

Many concerns have been raised about the potential allergenicity of novel, recombinant proteins into food crops. Guidelines, proposed by WHO/FAO and EFSA, include the use of bioinformatics screening to assess the risk of potential allergenicity or cross-reactivities of all proteins introduced, for example, to improve nutritional value or promote crop resistance. However, there are no universally accepted standards that can be used to encode data on the biology of allergens to facilitate using data from multiple databases in this screening. Therefore, we developed AllerML a markup language for allergens to assist in the automated exchange of information between databases and in the integration of the bioinformatics tools that are used to investigate allergenicity and cross-reactivity. As proof of concept, AllerML was implemented using the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/) database. General implementation of AllerML will promote automatic flow of validated data that will aid in allergy research and regulatory analysis. PMID:21420460

RaftProt: mammalian lipid raft proteome database.

PubMed

Shah, Anup; Chen, David; Boda, Akash R; Foster, Leonard J; Davis, Melissa J; Hill, Michelle M

2015-01-01

RaftProt (http://lipid-raft-database.di.uq.edu.au/) is a database of mammalian lipid raft-associated proteins as reported in high-throughput mass spectrometry studies. Lipid rafts are specialized membrane microdomains enriched in cholesterol and sphingolipids thought to act as dynamic signalling and sorting platforms. Given their fundamental roles in cellular regulation, there is a plethora of information on the size, composition and regulation of these membrane microdomains, including a large number of proteomics studies. To facilitate the mining and analysis of published lipid raft proteomics studies, we have developed a searchable database RaftProt. In addition to browsing the studies, performing basic queries by protein and gene names, searching experiments by cell, tissue and organisms; we have implemented several advanced features to facilitate data mining. To address the issue of potential bias due to biochemical preparation procedures used, we have captured the lipid raft preparation methods and implemented advanced search option for methodology and sample treatment conditions, such as cholesterol depletion. Furthermore, we have identified a list of high confidence proteins, and enabled searching only from this list of likely bona fide lipid raft proteins. Given the apparent biological importance of lipid raft and their associated proteins, this database would constitute a key resource for the scientific community. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

A proteomic analysis of leaf sheaths from rice.

PubMed

Shen, Shihua; Matsubae, Masami; Takao, Toshifumi; Tanaka, Naoki; Komatsu, Setsuko

2002-10-01

The proteins extracted from the leaf sheaths of rice seedlings were separated by 2-D PAGE, and analyzed by Edman sequencing and mass spectrometry, followed by database searching. Image analysis revealed 352 protein spots on 2-D PAGE after staining with Coomassie Brilliant Blue. The amino acid sequences of 44 of 84 proteins were determined; for 31 of these proteins, a clear function could be assigned, whereas for 12 proteins, no function could be assigned. Forty proteins did not yield amino acid sequence information, because they were N-terminally blocked, or the obtained sequences were too short and/or did not give unambiguous results. Fifty-nine proteins were analyzed by mass spectrometry; all of these proteins were identified by matching to the protein database. The amino acid sequences of 19 of 27 proteins analyzed by mass spectrometry were similar to the results of Edman sequencing. These results suggest that 2-D PAGE combined with Edman sequencing and mass spectrometry analysis can be effectively used to identify plant proteins.

WWW Entrez: A Hypertext Retrieval Tool for Molecular Biology.

ERIC Educational Resources Information Center

Epstein, Jonathan A.; Kans, Jonathan A.; Schuler, Gregory D.

This article describes the World Wide Web (WWW) Entrez server which is based upon the National Center for Biotechnology Information's (NCBI) Entrez retrieval database and software. Entrez is a molecular sequence retrieval system that contains an integrated view of portions of Medline and all publicly available nucleotide and protein databases,…

Using GenBank.

PubMed

Wheeler, David

2007-01-01

GenBank(R) is a comprehensive database of publicly available DNA sequences for more than 205,000 named organisms and for more than 60,000 within the embryophyta, obtained through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Daily data exchange with the European Molecular Biology Laboratory (EMBL) in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through the National Center for Biotechnology Information (NCBI) retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases with taxonomy, genome, mapping, protein structure, and domain information and the biomedical journal literature through PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available through FTP. GenBank usage scenarios ranging from local analyses of the data available through FTP to online analyses supported by the NCBI Web-based tools are discussed. To access GenBank and its related retrieval and analysis services, go to the NCBI Homepage at http://www.ncbi.nlm.nih.gov.

ZifBASE: a database of zinc finger proteins and associated resources.

PubMed

Jayakanthan, Mannu; Muthukumaran, Jayaraman; Chandrasekar, Sanniyasi; Chawla, Konika; Punetha, Ankita; Sundar, Durai

2009-09-09

Information on the occurrence of zinc finger protein motifs in genomes is crucial to the developing field of molecular genome engineering. The knowledge of their target DNA-binding sequences is vital to develop chimeric proteins for targeted genome engineering and site-specific gene correction. There is a need to develop a computational resource of zinc finger proteins (ZFP) to identify the potential binding sites and its location, which reduce the time of in vivo task, and overcome the difficulties in selecting the specific type of zinc finger protein and the target site in the DNA sequence. ZifBASE provides an extensive collection of various natural and engineered ZFP. It uses standard names and a genetic and structural classification scheme to present data retrieved from UniProtKB, GenBank, Protein Data Bank, ModBase, Protein Model Portal and the literature. It also incorporates specialized features of ZFP including finger sequences and positions, number of fingers, physiochemical properties, classes, framework, PubMed citations with links to experimental structures (PDB, if available) and modeled structures of natural zinc finger proteins. ZifBASE provides information on zinc finger proteins (both natural and engineered ones), the number of finger units in each of the zinc finger proteins (with multiple fingers), the synergy between the adjacent fingers and their positions. Additionally, it gives the individual finger sequence and their target DNA site to which it binds for better and clear understanding on the interactions of adjacent fingers. The current version of ZifBASE contains 139 entries of which 89 are engineered ZFPs, containing 3-7F totaling to 296 fingers. There are 50 natural zinc finger protein entries ranging from 2-13F, totaling to 307 fingers. It has sequences and structures from literature, Protein Data Bank, ModBase and Protein Model Portal. The interface is cross linked to other public databases like UniprotKB, PDB, ModBase and Protein Model Portal and PubMed for making it more informative. A database is established to maintain the information of the sequence features, including the class, framework, number of fingers, residues, position, recognition site and physio-chemical properties (molecular weight, isoelectric point) of both natural and engineered zinc finger proteins and dissociation constant of few. ZifBASE can provide more effective and efficient way of accessing the zinc finger protein sequences and their target binding sites with the links to their three-dimensional structures. All the data and functions are available at the advanced web-based search interface http://web.iitd.ac.in/~sundar/zifbase.

FARME DB: a functional antibiotic resistance element database

PubMed Central

Wallace, James C.; Port, Jesse A.; Smith, Marissa N.; Faustman, Elaine M.

2017-01-01

Antibiotic resistance (AR) is a major global public health threat but few resources exist that catalog AR genes outside of a clinical context. Current AR sequence databases are assembled almost exclusively from genomic sequences derived from clinical bacterial isolates and thus do not include many microbial sequences derived from environmental samples that confer resistance in functional metagenomic studies. These environmental metagenomic sequences often show little or no similarity to AR sequences from clinical isolates using standard classification criteria. In addition, existing AR databases provide no information about flanking sequences containing regulatory or mobile genetic elements. To help address this issue, we created an annotated database of DNA and protein sequences derived exclusively from environmental metagenomic sequences showing AR in laboratory experiments. Our Functional Antibiotic Resistant Metagenomic Element (FARME) database is a compilation of publically available DNA sequences and predicted protein sequences conferring AR as well as regulatory elements, mobile genetic elements and predicted proteins flanking antibiotic resistant genes. FARME is the first database to focus on functional metagenomic AR gene elements and provides a resource to better understand AR in the 99% of bacteria which cannot be cultured and the relationship between environmental AR sequences and antibiotic resistant genes derived from cultured isolates. Database URL: http://staff.washington.edu/jwallace/farme PMID:28077567

BtoxDB: a comprehensive database of protein structural data on toxin-antitoxin systems.

PubMed

Barbosa, Luiz Carlos Bertucci; Garrido, Saulo Santesso; Marchetto, Reinaldo

2015-03-01

Toxin-antitoxin (TA) systems are diverse and abundant genetic modules in prokaryotic cells that are typically formed by two genes encoding a stable toxin and a labile antitoxin. Because TA systems are able to repress growth or kill cells and are considered to be important actors in cell persistence (multidrug resistance without genetic change), these modules are considered potential targets for alternative drug design. In this scenario, structural information for the proteins in these systems is highly valuable. In this report, we describe the development of a web-based system, named BtoxDB, that stores all protein structural data on TA systems. The BtoxDB database was implemented as a MySQL relational database using PHP scripting language. Web interfaces were developed using HTML, CSS and JavaScript. The data were collected from the PDB, UniProt and Entrez databases. These data were appropriately filtered using specialized literature and our previous knowledge about toxin-antitoxin systems. The database provides three modules ("Search", "Browse" and "Statistics") that enable searches, acquisition of contents and access to statistical data. Direct links to matching external databases are also available. The compilation of all protein structural data on TA systems in one platform is highly useful for researchers interested in this content. BtoxDB is publicly available at http://www.gurupi.uft.edu.br/btoxdb. Copyright © 2015 Elsevier Ltd. All rights reserved.

MIPS: analysis and annotation of genome information in 2007

PubMed Central

Mewes, H. W.; Dietmann, S.; Frishman, D.; Gregory, R.; Mannhaupt, G.; Mayer, K. F. X.; Münsterkötter, M.; Ruepp, A.; Spannagl, M.; Stümpflen, V.; Rattei, T.

2008-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de). PMID:18158298

MIPS: analysis and annotation of genome information in 2007.

PubMed

Mewes, H W; Dietmann, S; Frishman, D; Gregory, R; Mannhaupt, G; Mayer, K F X; Münsterkötter, M; Ruepp, A; Spannagl, M; Stümpflen, V; Rattei, T

2008-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).

'RetinoGenetics': a comprehensive mutation database for genes related to inherited retinal degeneration.

PubMed

Ran, Xia; Cai, Wei-Jun; Huang, Xiu-Feng; Liu, Qi; Lu, Fan; Qu, Jia; Wu, Jinyu; Jin, Zi-Bing

2014-01-01

Inherited retinal degeneration (IRD), a leading cause of human blindness worldwide, is exceptionally heterogeneous with clinical heterogeneity and genetic variety. During the past decades, tremendous efforts have been made to explore the complex heterogeneity, and massive mutations have been identified in different genes underlying IRD with the significant advancement of sequencing technology. In this study, we developed a comprehensive database, 'RetinoGenetics', which contains informative knowledge about all known IRD-related genes and mutations for IRD. 'RetinoGenetics' currently contains 4270 mutations in 186 genes, with detailed information associated with 164 phenotypes from 934 publications and various types of functional annotations. Then extensive annotations were performed to each gene using various resources, including Gene Ontology, KEGG pathways, protein-protein interaction, mutational annotations and gene-disease network. Furthermore, by using the search functions, convenient browsing ways and intuitive graphical displays, 'RetinoGenetics' could serve as a valuable resource for unveiling the genetic basis of IRD. Taken together, 'RetinoGenetics' is an integrative, informative and updatable resource for IRD-related genetic predispositions. Database URL: http://www.retinogenetics.org/. © The Author(s) 2014. Published by Oxford University Press.

Query3d: a new method for high-throughput analysis of functional residues in protein structures.

PubMed

Ausiello, Gabriele; Via, Allegra; Helmer-Citterich, Manuela

2005-12-01

The identification of local similarities between two protein structures can provide clues of a common function. Many different methods exist for searching for similar subsets of residues in proteins of known structure. However, the lack of functional and structural information on single residues, together with the low level of integration of this information in comparison methods, is a limitation that prevents these methods from being fully exploited in high-throughput analyses. Here we describe Query3d, a program that is both a structural DBMS (Database Management System) and a local comparison method. The method conserves a copy of all the residues of the Protein Data Bank annotated with a variety of functional and structural information. New annotations can be easily added from a variety of methods and known databases. The algorithm makes it possible to create complex queries based on the residues' function and then to compare only subsets of the selected residues. Functional information is also essential to speed up the comparison and the analysis of the results. With Query3d, users can easily obtain statistics on how many and which residues share certain properties in all proteins of known structure. At the same time, the method also finds their structural neighbours in the whole PDB. Programs and data can be accessed through the PdbFun web interface.

Query3d: a new method for high-throughput analysis of functional residues in protein structures

PubMed Central

Ausiello, Gabriele; Via, Allegra; Helmer-Citterich, Manuela

2005-01-01

Background The identification of local similarities between two protein structures can provide clues of a common function. Many different methods exist for searching for similar subsets of residues in proteins of known structure. However, the lack of functional and structural information on single residues, together with the low level of integration of this information in comparison methods, is a limitation that prevents these methods from being fully exploited in high-throughput analyses. Results Here we describe Query3d, a program that is both a structural DBMS (Database Management System) and a local comparison method. The method conserves a copy of all the residues of the Protein Data Bank annotated with a variety of functional and structural information. New annotations can be easily added from a variety of methods and known databases. The algorithm makes it possible to create complex queries based on the residues' function and then to compare only subsets of the selected residues. Functional information is also essential to speed up the comparison and the analysis of the results. Conclusion With Query3d, users can easily obtain statistics on how many and which residues share certain properties in all proteins of known structure. At the same time, the method also finds their structural neighbours in the whole PDB. Programs and data can be accessed through the PdbFun web interface. PMID:16351754

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

PubMed

Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

2018-06-14

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less

A three-way approach for protein function classification

PubMed Central

2017-01-01

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy. PMID:28234929

A three-way approach for protein function classification.

PubMed

Ur Rehman, Hafeez; Azam, Nouman; Yao, JingTao; Benso, Alfredo

2017-01-01

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.

Cell death proteomics database: consolidating proteomics data on cell death.

PubMed

Arntzen, Magnus Ø; Bull, Vibeke H; Thiede, Bernd

2013-05-03

Programmed cell death is a ubiquitous process of utmost importance for the development and maintenance of multicellular organisms. More than 10 different types of programmed cell death forms have been discovered. Several proteomics analyses have been performed to gain insight in proteins involved in the different forms of programmed cell death. To consolidate these studies, we have developed the cell death proteomics (CDP) database, which comprehends data from apoptosis, autophagy, cytotoxic granule-mediated cell death, excitotoxicity, mitotic catastrophe, paraptosis, pyroptosis, and Wallerian degeneration. The CDP database is available as a web-based database to compare protein identifications and quantitative information across different experimental setups. The proteomics data of 73 publications were integrated and unified with protein annotations from UniProt-KB and gene ontology (GO). Currently, more than 6,500 records of more than 3,700 proteins are included in the CDP. Comparing apoptosis and autophagy using overrepresentation analysis of GO terms, the majority of enriched processes were found in both, but also some clear differences were perceived. Furthermore, the analysis revealed differences and similarities of the proteome between autophagosomal and overall autophagy. The CDP database represents a useful tool to consolidate data from proteome analyses of programmed cell death and is available at http://celldeathproteomics.uio.no.

AN OVERVIEW OF COMPUTATIONAL LIFE SCIENCE DATABASES & EXCHANGE FORMATS OF RELEVANCE TO CHEMICAL BIOLOGY RESEARCH

PubMed Central

Hall, Aaron Smalter; Shan, Yunfeng; Lushington, Gerald; Visvanathan, Mahesh

2016-01-01

Databases and exchange formats describing biological entities such as chemicals and proteins, along with their relationships, are a critical component of research in life sciences disciplines, including chemical biology wherein small information about small molecule properties converges with cellular and molecular biology. Databases for storing biological entities are growing not only in size, but also in type, with many similarities between them and often subtle differences. The data formats available to describe and exchange these entities are numerous as well. In general, each format is optimized for a particular purpose or database, and hence some understanding of these formats is required when choosing one for research purposes. This paper reviews a selection of different databases and data formats with the goal of summarizing their purposes, features, and limitations. Databases are reviewed under the categories of 1) protein interactions, 2) metabolic pathways, 3) chemical interactions, and 4) drug discovery. Representation formats will be discussed according to those describing chemical structures, and those describing genomic/proteomic entities. PMID:22934944

An overview of computational life science databases & exchange formats of relevance to chemical biology research.

PubMed

Smalter Hall, Aaron; Shan, Yunfeng; Lushington, Gerald; Visvanathan, Mahesh

2013-03-01

Databases and exchange formats describing biological entities such as chemicals and proteins, along with their relationships, are a critical component of research in life sciences disciplines, including chemical biology wherein small information about small molecule properties converges with cellular and molecular biology. Databases for storing biological entities are growing not only in size, but also in type, with many similarities between them and often subtle differences. The data formats available to describe and exchange these entities are numerous as well. In general, each format is optimized for a particular purpose or database, and hence some understanding of these formats is required when choosing one for research purposes. This paper reviews a selection of different databases and data formats with the goal of summarizing their purposes, features, and limitations. Databases are reviewed under the categories of 1) protein interactions, 2) metabolic pathways, 3) chemical interactions, and 4) drug discovery. Representation formats will be discussed according to those describing chemical structures, and those describing genomic/proteomic entities.

NCAD, a database integrating the intrinsic conformational preferences of non-coded amino acids

PubMed Central

Revilla-López, Guillem; Torras, Juan; Curcó, David; Casanovas, Jordi; Calaza, M. Isabel; Zanuy, David; Jiménez, Ana I.; Cativiela, Carlos; Nussinov, Ruth; Grodzinski, Piotr; Alemán, Carlos

2010-01-01

Peptides and proteins find an ever-increasing number of applications in the biomedical and materials engineering fields. The use of non-proteinogenic amino acids endowed with diverse physicochemical and structural features opens the possibility to design proteins and peptides with novel properties and functions. Moreover, non-proteinogenic residues are particularly useful to control the three-dimensional arrangement of peptidic chains, which is a crucial issue for most applications. However, information regarding such amino acids –also called non-coded, non-canonical or non-standard– is usually scattered among publications specialized in quite diverse fields as well as in patents. Making all these data useful to the scientific community requires new tools and a framework for their assembly and coherent organization. We have successfully compiled, organized and built a database (NCAD, Non-Coded Amino acids Database) containing information about the intrinsic conformational preferences of non-proteinogenic residues determined by quantum mechanical calculations, as well as bibliographic information about their synthesis, physical and spectroscopic characterization, conformational propensities established experimentally, and applications. The architecture of the database is presented in this work together with the first family of non-coded residues included, namely, α-tetrasubstituted α-amino acids. Furthermore, the NCAD usefulness is demonstrated through a test-case application example. PMID:20455555

Databases in the Area of Pharmacogenetics

PubMed Central

Sim, Sarah C.; Altman, Russ B.; Ingelman-Sundberg, Magnus

2012-01-01

In the area of pharmacogenetics and personalized health care it is obvious that databases, providing important information of the occurrence and consequences of variant genes encoding drug metabolizing enzymes, drug transporters, drug targets, and other proteins of importance for drug response or toxicity, are of critical value for scientists, physicians, and industry. The primary outcome of the pharmacogenomic field is the identification of biomarkers that can predict drug toxicity and drug response, thereby individualizing and improving drug treatment of patients. The drug in question and the polymorphic gene exerting the impact are the main issues to be searched for in the databases. Here, we review the databases that provide useful information in this respect, of benefit for the development of the pharmacogenomic field. PMID:21309040

Biases in the Experimental Annotations of Protein Function and Their Effect on Our Understanding of Protein Function Space

PubMed Central

Schnoes, Alexandra M.; Ream, David C.; Thorman, Alexander W.; Babbitt, Patricia C.; Friedberg, Iddo

2013-01-01

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the “few articles - many proteins” phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments. PMID:23737737

Localizome: a server for identifying transmembrane topologies and TM helices of eukaryotic proteins utilizing domain information

PubMed Central

Lee, Sunghoon; Lee, Byungwook; Jang, Insoo; Kim, Sangsoo; Bhak, Jong

2006-01-01

The Localizome server predicts the transmembrane (TM) helix number and TM topology of a user-supplied eukaryotic protein and presents the result as an intuitive graphic representation. It utilizes hmmpfam to detect the presence of Pfam domains and a prediction algorithm, Phobius, to predict the TM helices. The results are combined and checked against the TM topology rules stored in a protein domain database called LocaloDom. LocaloDom is a curated database that contains TM topologies and TM helix numbers of known protein domains. It was constructed from Pfam domains combined with Swiss-Prot annotations and Phobius predictions. The Localizome server corrects the combined results of the user sequence to conform to the rules stored in LocaloDom. Compared with other programs, this server showed the highest accuracy for TM topology prediction: for soluble proteins, the accuracy and coverage were 99 and 75%, respectively, while for TM protein domain regions, they were 96 and 68%, respectively. With a graphical representation of TM topology and TM helix positions with the domain units, the Localizome server is a highly accurate and comprehensive information source for subcellular localization for soluble proteins as well as membrane proteins. The Localizome server can be found at . PMID:16845118

GenoMycDB: a database for comparative analysis of mycobacterial genes and genomes.

PubMed

Catanho, Marcos; Mascarenhas, Daniel; Degrave, Wim; Miranda, Antonio Basílio de

2006-03-31

Several databases and computational tools have been created with the aim of organizing, integrating and analyzing the wealth of information generated by large-scale sequencing projects of mycobacterial genomes and those of other organisms. However, with very few exceptions, these databases and tools do not allow for massive and/or dynamic comparison of these data. GenoMycDB (http://www.dbbm.fiocruz.br/GenoMycDB) is a relational database built for large-scale comparative analyses of completely sequenced mycobacterial genomes, based on their predicted protein content. Its central structure is composed of the results obtained after pair-wise sequence alignments among all the predicted proteins coded by the genomes of six mycobacteria: Mycobacterium tuberculosis (strains H37Rv and CDC1551), M. bovis AF2122/97, M. avium subsp. paratuberculosis K10, M. leprae TN, and M. smegmatis MC2 155. The database stores the computed similarity parameters of every aligned pair, providing for each protein sequence the predicted subcellular localization, the assigned cluster of orthologous groups, the features of the corresponding gene, and links to several important databases. Tables containing pairs or groups of potential homologs between selected species/strains can be produced dynamically by user-defined criteria, based on one or multiple sequence similarity parameters. In addition, searches can be restricted according to the predicted subcellular localization of the protein, the DNA strand of the corresponding gene and/or the description of the protein. Massive data search and/or retrieval are available, and different ways of exporting the result are offered. GenoMycDB provides an on-line resource for the functional classification of mycobacterial proteins as well as for the analysis of genome structure, organization, and evolution.

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources.

PubMed

Karchin, Rachel; Diekhans, Mark; Kelly, Libusha; Thomas, Daryl J; Pieper, Ursula; Eswar, Narayanan; Haussler, David; Sali, Andrej

2005-06-15

The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org http://salilab.org/LS-SNP/supp-info.pdf.

Shewregdb: Database and visualization environment for experimental and predicted regulatory information in Shewanella oneidensis mr-1

PubMed Central

Syed, Mustafa H; Karpinets, Tatiana V; Leuze, Michael R; Kora, Guruprasad H; Romine, Margaret R; Uberbacher, Edward C

2009-01-01

Shewanella oneidensis MR-1 is an important model organism for environmental research as it has an exceptional metabolic and respiratory versatility regulated by a complex regulatory network. We have developed a database to collect experimental and computational data relating to regulation of gene and protein expression, and, a visualization environment that enables integration of these data types. The regulatory information in the database includes predictions of DNA regulator binding sites, sigma factor binding sites, transcription units, operons, promoters, and RNA regulators including non-coding RNAs, riboswitches, and different types of terminators. Availability http://shewanella-knowledgebase.org:8080/Shewanella/gbrowserLanding.jsp PMID:20198195

PASS2: an automated database of protein alignments organised as structural superfamilies.

PubMed

Bhaduri, Anirban; Pugalenthi, Ganesan; Sowdhamini, Ramanathan

2004-04-02

The functional selection and three-dimensional structural constraints of proteins in nature often relates to the retention of significant sequence similarity between proteins of similar fold and function despite poor sequence identity. Organization of structure-based sequence alignments for distantly related proteins, provides a map of the conserved and critical regions of the protein universe that is useful for the analysis of folding principles, for the evolutionary unification of protein families and for maximizing the information return from experimental structure determination. The Protein Alignment organised as Structural Superfamily (PASS2) database represents continuously updated, structural alignments for evolutionary related, sequentially distant proteins. An automated and updated version of PASS2 is, in direct correspondence with SCOP 1.63, consisting of sequences having identity below 40% among themselves. Protein domains have been grouped into 628 multi-member superfamilies and 566 single member superfamilies. Structure-based sequence alignments for the superfamilies have been obtained using COMPARER, while initial equivalencies have been derived from a preliminary superposition using LSQMAN or STAMP 4.0. The final sequence alignments have been annotated for structural features using JOY4.0. The database is supplemented with sequence relatives belonging to different genomes, conserved spatially interacting and structural motifs, probabilistic hidden markov models of superfamilies based on the alignments and useful links to other databases. Probabilistic models and sensitive position specific profiles obtained from reliable superfamily alignments aid annotation of remote homologues and are useful tools in structural and functional genomics. PASS2 presents the phylogeny of its members both based on sequence and structural dissimilarities. Clustering of members allows us to understand diversification of the family members. The search engine has been improved for simpler browsing of the database. The database resolves alignments among the structural domains consisting of evolutionarily diverged set of sequences. Availability of reliable sequence alignments of distantly related proteins despite poor sequence identity and single-member superfamilies permit better sampling of structures in libraries for fold recognition of new sequences and for the understanding of protein structure-function relationships of individual superfamilies. PASS2 is accessible at http://www.ncbs.res.in/~faculty/mini/campass/pass2.html

CPLA 1.0: an integrated database of protein lysine acetylation.

PubMed

Liu, Zexian; Cao, Jun; Gao, Xinjiao; Zhou, Yanhong; Wen, Longping; Yang, Xiangjiao; Yao, Xuebiao; Ren, Jian; Xue, Yu

2011-01-01

As a reversible post-translational modification (PTM) discovered decades ago, protein lysine acetylation was known for its regulation of transcription through the modification of histones. Recent studies discovered that lysine acetylation targets broad substrates and especially plays an essential role in cellular metabolic regulation. Although acetylation is comparable with other major PTMs such as phosphorylation, an integrated resource still remains to be developed. In this work, we presented the compendium of protein lysine acetylation (CPLA) database for lysine acetylated substrates with their sites. From the scientific literature, we manually collected 7151 experimentally identified acetylation sites in 3311 targets. We statistically studied the regulatory roles of lysine acetylation by analyzing the Gene Ontology (GO) and InterPro annotations. Combined with protein-protein interaction information, we systematically discovered a potential human lysine acetylation network (HLAN) among histone acetyltransferases (HATs), substrates and histone deacetylases (HDACs). In particular, there are 1862 triplet relationships of HAT-substrate-HDAC retrieved from the HLAN, at least 13 of which were previously experimentally verified. The online services of CPLA database was implemented in PHP + MySQL + JavaScript, while the local packages were developed in JAVA 1.5 (J2SE 5.0). The CPLA database is freely available for all users at: http://cpla.biocuckoo.org.

REFOLDdb: a new and sustainable gateway to experimental protocols for protein refolding.

PubMed

Mizutani, Hisashi; Sugawara, Hideaki; Buckle, Ashley M; Sangawa, Takeshi; Miyazono, Ken-Ichi; Ohtsuka, Jun; Nagata, Koji; Shojima, Tomoki; Nosaki, Shohei; Xu, Yuqun; Wang, Delong; Hu, Xiao; Tanokura, Masaru; Yura, Kei

2017-04-24

More than 7000 papers related to "protein refolding" have been published to date, with approximately 300 reports each year during the last decade. Whilst some of these papers provide experimental protocols for protein refolding, a survey in the structural life science communities showed a necessity for a comprehensive database for refolding techniques. We therefore have developed a new resource - "REFOLDdb" that collects refolding techniques into a single, searchable repository to help researchers develop refolding protocols for proteins of interest. We based our resource on the existing REFOLD database, which has not been updated since 2009. We redesigned the data format to be more concise, allowing consistent representations among data entries compared with the original REFOLD database. The remodeled data architecture enhances the search efficiency and improves the sustainability of the database. After an exhaustive literature search we added experimental refolding protocols from reports published 2009 to early 2017. In addition to this new data, we fully converted and integrated existing REFOLD data into our new resource. REFOLDdb contains 1877 entries as of March 17 th , 2017, and is freely available at http://p4d-info.nig.ac.jp/refolddb/ . REFOLDdb is a unique database for the life sciences research community, providing annotated information for designing new refolding protocols and customizing existing methodologies. We envisage that this resource will find wide utility across broad disciplines that rely on the production of pure, active, recombinant proteins. Furthermore, the database also provides a useful overview of the recent trends and statistics in refolding technology development.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rayl, K.D.; Gaasterland, T.

This paper presents an overview of the purpose, content, and design of a subset of the currently available biological databases, with an emphasis on protein databases. Databases included in this summary are 3D-ALI, Berlin RNA databank, Blocks, DSSP, EMBL Nucleotide Database, EMP, ENZYME, FSSP, GDB, GenBank, HSSP, LiMB, PDB, PIR, PKCDD, ProSite, and SWISS-PROT. The goal is to provide a starting point for researchers who wish to take advantage of the myriad available databases. Rather than providing a complete explanation of each database, we present its content and form by explaining the details of typical entries. Pointers to more completemore » ``user guides`` are included, along with general information on where to search for a new database.« less

Updated regulation curation model at the Saccharomyces Genome Database

PubMed Central

Engel, Stacia R; Skrzypek, Marek S; Hellerstedt, Sage T; Wong, Edith D; Nash, Robert S; Weng, Shuai; Binkley, Gail; Sheppard, Travis K; Karra, Kalpana; Cherry, J Michael

2018-01-01

Abstract The Saccharomyces Genome Database (SGD) provides comprehensive, integrated biological information for the budding yeast Saccharomyces cerevisiae, along with search and analysis tools to explore these data, enabling the discovery of functional relationships between sequence and gene products in fungi and higher organisms. We have recently expanded our data model for regulation curation to address regulation at the protein level in addition to transcription, and are presenting the expanded data on the ‘Regulation’ pages at SGD. These pages include a summary describing the context under which the regulator acts, manually curated and high-throughput annotations showing the regulatory relationships for that gene and a graphical visualization of its regulatory network and connected networks. For genes whose products regulate other genes or proteins, the Regulation page includes Gene Ontology enrichment analysis of the biological processes in which those targets participate. For DNA-binding transcription factors, we also provide other information relevant to their regulatory function, such as DNA binding site motifs and protein domains. As with other data types at SGD, all regulatory relationships and accompanying data are available through YeastMine, SGD’s data warehouse based on InterMine. Database URL: http://www.yeastgenome.org PMID:29688362

Gaining knowledge from previously unexplained spectra-application of the PTM-Explorer software to detect PTM in HUPO BPP MS/MS data.

PubMed

Chamrad, Daniel C; Körting, Gerhard; Schäfer, Heike; Stephan, Christian; Thiele, Herbert; Apweiler, Rolf; Meyer, Helmut E; Marcus, Katrin; Blüggel, Martin

2006-09-01

A novel software tool named PTM-Explorer has been applied to LC-MS/MS datasets acquired within the Human Proteome Organisation (HUPO) Brain Proteome Project (BPP). PTM-Explorer enables automatic identification of peptide MS/MS spectra that were not explained in typical sequence database searches. The main focus was detection of PTMs, but PTM-Explorer detects also unspecific peptide cleavage, mass measurement errors, experimental modifications, amino acid substitutions, transpeptidation products and unknown mass shifts. To avoid a combinatorial problem the search is restricted to a set of selected protein sequences, which stem from previous protein identifications using a common sequence database search. Prior to application to the HUPO BPP data, PTM-Explorer was evaluated on excellently manually characterized and evaluated LC-MS/MS data sets from Alpha-A-Crystallin gel spots obtained from mouse eye lens. Besides various PTMs including phosphorylation, a wealth of experimental modifications and unspecific cleavage products were successfully detected, completing the primary structure information of the measured proteins. Our results indicate that a large amount of MS/MS spectra that currently remain unidentified in standard database searches contain valuable information that can only be elucidated using suitable software tools.

Update on Genomic Databases and Resources at the National Center for Biotechnology Information.

PubMed

Tatusova, Tatiana

2016-01-01

The National Center for Biotechnology Information (NCBI), as a primary public repository of genomic sequence data, collects and maintains enormous amounts of heterogeneous data. Data for genomes, genes, gene expressions, gene variation, gene families, proteins, and protein domains are integrated with the analytical, search, and retrieval resources through the NCBI website, text-based search and retrieval system, provides a fast and easy way to navigate across diverse biological databases.Comparative genome analysis tools lead to further understanding of evolution processes quickening the pace of discovery. Recent technological innovations have ignited an explosion in genome sequencing that has fundamentally changed our understanding of the biology of living organisms. This huge increase in DNA sequence data presents new challenges for the information management system and the visualization tools. New strategies have been designed to bring an order to this genome sequence shockwave and improve the usability of associated data.

Integrated web visualizations for protein-protein interaction databases.

PubMed

Jeanquartier, Fleur; Jean-Quartier, Claire; Holzinger, Andreas

2015-06-16

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. We selected M=10 out of N=53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

Oral cancer databases: A comprehensive review.

PubMed

Sarode, Gargi S; Sarode, Sachin C; Maniyar, Nikunj; Anand, Rahul; Patil, Shankargouda

2017-11-29

Cancer database is a systematic collection and analysis of information on various human cancers at genomic and molecular level that can be utilized to understand various steps in carcinogenesis and for therapeutic advancement in cancer field. Oral cancer is one of the leading causes of morbidity and mortality all over the world. The current research efforts in this field are aimed at cancer etiology and therapy. Advanced genomic technologies including microarrays, proteomics, transcrpitomics, and gene sequencing development have culminated in generation of extensive data and subjection of several genes and microRNAs that are distinctively expressed and this information is stored in the form of various databases. Extensive data from various resources have brought the need for collaboration and data sharing to make effective use of this new knowledge. The current review provides comprehensive information of various publicly accessible databases that contain information pertinent to oral squamous cell carcinoma (OSCC) and databases designed exclusively for OSCC. The databases discussed in this paper are Protein-Coding Gene Databases and microRNA Databases. This paper also describes gene overlap in various databases, which will help researchers to reduce redundancy and focus on only those genes, which are common to more than one databases. We hope such introduction will promote awareness and facilitate the usage of these resources in the cancer research community, and researchers can explore the molecular mechanisms involved in the development of cancer, which can help in subsequent crafting of therapeutic strategies. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Considerations to improve functional annotations in biological databases.

PubMed

Benítez-Páez, Alfonso

2009-12-01

Despite the great effort to design efficient systems allowing the electronic indexation of information concerning genes, proteins, structures, and interactions published daily in scientific journals, some problems are still observed in specific tasks such as functional annotation. The annotation of function is a critical issue for bioinformatic routines, such as for instance, in functional genomics and the further prediction of unknown protein function, which are highly dependent of the quality of existing annotations. Some information management systems evolve to efficiently incorporate information from large-scale projects, but often, annotation of single records from the literature is difficult and slow. In this short report, functional characterizations of a representative sample of the entire set of uncharacterized proteins from Escherichia coli K12 was compiled from Swiss-Prot, PubMed, and EcoCyc and demonstrate a functional annotation deficit in biological databases. Some issues are postulated as causes of the lack of annotation, and different solutions are evaluated and proposed to avoid them. The hope is that as a consequence of these observations, there will be new impetus to improve the speed and quality of functional annotation and ultimately provide updated, reliable information to the scientific community.

PDB-wide collection of binding data: current status of the PDBbind database.

PubMed

Liu, Zhihai; Li, Yan; Han, Li; Li, Jie; Liu, Jie; Zhao, Zhixiong; Nie, Wei; Liu, Yuchen; Wang, Renxiao

2015-02-01

Molecular recognition between biological macromolecules and organic small molecules plays an important role in various life processes. Both structural information and binding data of biomolecular complexes are indispensable for depicting the underlying mechanism in such an event. The PDBbind database was created to collect experimentally measured binding data for the biomolecular complexes throughout the Protein Data Bank (PDB). It thus provides the linkage between structural information and energetic properties of biomolecular complexes, which is especially desirable for computational studies or statistical analyses. Since its first public release in 2004, the PDBbind database has been updated on an annual basis. The latest release (version 2013) provides experimental binding affinity data for 10,776 biomolecular complexes in PDB, including 8302 protein-ligand complexes and 2474 other types of complexes. In this article, we will describe the current methods used for compiling PDBbind and the updated status of this database. We will also review some typical applications of PDBbind published in the scientific literature. All contents of this database are freely accessible at the PDBbind-CN Web server at http://www.pdbbind-cn.org/. wangrx@mail.sioc.ac.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Data set for phylogenetic tree and RAMPAGE Ramachandran plot analysis of SODs in Gossypium raimondii and G. arboreum.

PubMed

Wang, Wei; Xia, Minxuan; Chen, Jie; Deng, Fenni; Yuan, Rui; Zhang, Xiaopei; Shen, Fafu

2016-12-01

The data presented in this paper is supporting the research article "Genome-Wide Analysis of Superoxide Dismutase Gene Family in Gossypium raimondii and G. arboreum" [1]. In this data article, we present phylogenetic tree showing dichotomy with two different clusters of SODs inferred by the Bayesian method of MrBayes (version 3.2.4), "Bayesian phylogenetic inference under mixed models" [2], Ramachandran plots of G. raimondii and G. arboreum SODs, the protein sequence used to generate 3D sructure of proteins and the template accession via SWISS-MODEL server, "SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information." [3] and motif sequences of SODs identified by InterProScan (version 4.8) with the Pfam database, "Pfam: the protein families database" [4].

Software Tool for Researching Annotations of Proteins (STRAP): Open-Source Protein Annotation Software with Data Visualization

PubMed Central

Bhatia, Vivek N.; Perlman, David H.; Costello, Catherine E.; McComb, Mark E.

2009-01-01

In order that biological meaning may be derived and testable hypotheses may be built from proteomics experiments, assignments of proteins identified by mass spectrometry or other techniques must be supplemented with additional notation, such as information on known protein functions, protein-protein interactions, or biological pathway associations. Collecting, organizing, and interpreting this data often requires the input of experts in the biological field of study, in addition to the time-consuming search for and compilation of information from online protein databases. Furthermore, visualizing this bulk of information can be challenging due to the limited availability of easy-to-use and freely available tools for this process. In response to these constraints, we have undertaken the design of software to automate annotation and visualization of proteomics data in order to accelerate the pace of research. Here we present the Software Tool for Researching Annotations of Proteins (STRAP) – a user-friendly, open-source C# application. STRAP automatically obtains gene ontology (GO) terms associated with proteins in a proteomics results ID list using the freely accessible UniProtKB and EBI GOA databases. Summarized in an easy-to-navigate tabular format, STRAP includes meta-information on the protein in addition to complimentary GO terminology. Additionally, this information can be edited by the user so that in-house expertise on particular proteins may be integrated into the larger dataset. STRAP provides a sortable tabular view for all terms, as well as graphical representations of GO-term association data in pie (biological process, cellular component and molecular function) and bar charts (cross comparison of sample sets) to aid in the interpretation of large datasets and differential analyses experiments. Furthermore, proteins of interest may be exported as a unique FASTA-formatted file to allow for customizable re-searching of mass spectrometry data, and gene names corresponding to the proteins in the lists may be encoded in the Gaggle microformat for further characterization, including pathway analysis. STRAP, a tutorial, and the C# source code are freely available from http://cpctools.sourceforge.net. PMID:19839595

UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View.

PubMed

Boutet, Emmanuel; Lieberherr, Damien; Tognolli, Michael; Schneider, Michel; Bansal, Parit; Bridge, Alan J; Poux, Sylvain; Bougueleret, Lydie; Xenarios, Ioannis

2016-01-01

The Universal Protein Resource (UniProt, http://www.uniprot.org ) consortium is an initiative of the SIB Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI) and the Protein Information Resource (PIR) to provide the scientific community with a central resource for protein sequences and functional information. The UniProt consortium maintains the UniProt KnowledgeBase (UniProtKB), updated every 4 weeks, and several supplementary databases including the UniProt Reference Clusters (UniRef) and the UniProt Archive (UniParc).The Swiss-Prot section of the UniProt KnowledgeBase (UniProtKB/Swiss-Prot) contains publicly available expertly manually annotated protein sequences obtained from a broad spectrum of organisms. Plant protein entries are produced in the frame of the Plant Proteome Annotation Program (PPAP), with an emphasis on characterized proteins of Arabidopsis thaliana and Oryza sativa. High level annotations provided by UniProtKB/Swiss-Prot are widely used to predict annotation of newly available proteins through automatic pipelines.The purpose of this chapter is to present a guided tour of a UniProtKB/Swiss-Prot entry. We will also present some of the tools and databases that are linked to each entry.

ANCAC: amino acid, nucleotide, and codon analysis of COGs--a tool for sequence bias analysis in microbial orthologs.

PubMed

Meiler, Arno; Klinger, Claudia; Kaufmann, Michael

2012-09-08

The COG database is the most popular collection of orthologous proteins from many different completely sequenced microbial genomes. Per definition, a cluster of orthologous groups (COG) within this database exclusively contains proteins that most likely achieve the same cellular function. Recently, the COG database was extended by assigning to every protein both the corresponding amino acid and its encoding nucleotide sequence resulting in the NUCOCOG database. This extended version of the COG database is a valuable resource connecting sequence features with the functionality of the respective proteins. Here we present ANCAC, a web tool and MySQL database for the analysis of amino acid, nucleotide, and codon frequencies in COGs on the basis of freely definable phylogenetic patterns. We demonstrate the usefulness of ANCAC by analyzing amino acid frequencies, codon usage, and GC-content in a species- or function-specific context. With respect to amino acids we, at least in part, confirm the cognate bias hypothesis by using ANCAC's NUCOCOG dataset as the largest one available for that purpose thus far. Using the NUCOCOG datasets, ANCAC connects taxonomic, amino acid, and nucleotide sequence information with the functional classification via COGs and provides a GUI for flexible mining for sequence-bias. Thereby, to our knowledge, it is the only tool for the analysis of sequence composition in the light of physiological roles and phylogenetic context without requirement of substantial programming-skills.

ANCAC: amino acid, nucleotide, and codon analysis of COGs – a tool for sequence bias analysis in microbial orthologs

PubMed Central

2012-01-01

Background The COG database is the most popular collection of orthologous proteins from many different completely sequenced microbial genomes. Per definition, a cluster of orthologous groups (COG) within this database exclusively contains proteins that most likely achieve the same cellular function. Recently, the COG database was extended by assigning to every protein both the corresponding amino acid and its encoding nucleotide sequence resulting in the NUCOCOG database. This extended version of the COG database is a valuable resource connecting sequence features with the functionality of the respective proteins. Results Here we present ANCAC, a web tool and MySQL database for the analysis of amino acid, nucleotide, and codon frequencies in COGs on the basis of freely definable phylogenetic patterns. We demonstrate the usefulness of ANCAC by analyzing amino acid frequencies, codon usage, and GC-content in a species- or function-specific context. With respect to amino acids we, at least in part, confirm the cognate bias hypothesis by using ANCAC’s NUCOCOG dataset as the largest one available for that purpose thus far. Conclusions Using the NUCOCOG datasets, ANCAC connects taxonomic, amino acid, and nucleotide sequence information with the functional classification via COGs and provides a GUI for flexible mining for sequence-bias. Thereby, to our knowledge, it is the only tool for the analysis of sequence composition in the light of physiological roles and phylogenetic context without requirement of substantial programming-skills. PMID:22958836

In Silico Screening of the Human Gut Metaproteome Identifies Th17-Promoting Peptides Encrypted in Proteins of Commensal Bacteria

PubMed Central

Hidalgo-Cantabrana, Claudio; Moro-García, Marco A.; Blanco-Míguez, Aitor; Fdez-Riverola, Florentino; Lourenço, Anália; Alonso-Arias, Rebeca; Sánchez, Borja

2017-01-01

Scientific studies focused on the role of the human microbiome over human health have generated billions of gigabits of genetic information during the last decade. Nowadays integration of all this information in public databases and development of pipelines allowing us to biotechnologically exploit this information are urgently needed. Prediction of the potential bioactivity of the products encoded by the human gut microbiome, or metaproteome, is the first step for identifying proteins responsible for the molecular interaction between microorganisms and the immune system. We have recently published the Mechanism of Action of the Human Microbiome (MAHMI) database (http://www.mahmi.org), conceived as a resource compiling peptide sequences with a potential immunomodulatory activity. Fifteen out of the 300 hundred million peptides contained in the MAHMI database were synthesized. These peptides were identified as being encrypted in proteins produced by gut microbiota members, they do not contain cleavage points for the major intestinal endoproteases and displayed high probability to have immunomodulatory bioactivity. The bacterial peptides FR-16 and LR-17 encrypted in proteins from Bifidobacterium longum DJ010A and Bifidobacterium fragilis YCH46 respectively, showed the higher immune modulation capability over human peripheral blood mononuclear cells. Both peptides modulated the immune response toward increases in the Th17 and decreases in the Th1 cell response, together with an induction of IL-22 production. These results strongly suggest the combined use of bioinformatics and in vitro tools as a first stage in the screening of bioactive peptides encrypted in the human gut metaproteome. PMID:28943872

ProCarDB: a database of bacterial carotenoids.

PubMed

Nupur, L N U; Vats, Asheema; Dhanda, Sandeep Kumar; Raghava, Gajendra P S; Pinnaka, Anil Kumar; Kumar, Ashwani

2016-05-26

Carotenoids have important functions in bacteria, ranging from harvesting light energy to neutralizing oxidants and acting as virulence factors. However, information pertaining to the carotenoids is scattered throughout the literature. Furthermore, information about the genes/proteins involved in the biosynthesis of carotenoids has tremendously increased in the post-genomic era. A web server providing the information about microbial carotenoids in a structured manner is required and will be a valuable resource for the scientific community working with microbial carotenoids. Here, we have created a manually curated, open access, comprehensive compilation of bacterial carotenoids named as ProCarDB- Prokaryotic Carotenoid Database. ProCarDB includes 304 unique carotenoids arising from 50 biosynthetic pathways distributed among 611 prokaryotes. ProCarDB provides important information on carotenoids, such as 2D and 3D structures, molecular weight, molecular formula, SMILES, InChI, InChIKey, IUPAC name, KEGG Id, PubChem Id, and ChEBI Id. The database also provides NMR data, UV-vis absorption data, IR data, MS data and HPLC data that play key roles in the identification of carotenoids. An important feature of this database is the extension of biosynthetic pathways from the literature and through the presence of the genes/enzymes in different organisms. The information contained in the database was mined from published literature and databases such as KEGG, PubChem, ChEBI, LipidBank, LPSN, and Uniprot. The database integrates user-friendly browsing and searching with carotenoid analysis tools to help the user. We believe that this database will serve as a major information centre for researchers working on bacterial carotenoids.

Pfarao: a web application for protein family analysis customized for cytoskeletal and motor proteins (CyMoBase).

PubMed

Odronitz, Florian; Kollmar, Martin

2006-11-29

Annotation of protein sequences of eukaryotic organisms is crucial for the understanding of their function in the cell. Manual annotation is still by far the most accurate way to correctly predict genes. The classification of protein sequences, their phylogenetic relation and the assignment of function involves information from various sources. This often leads to a collection of heterogeneous data, which is hard to track. Cytoskeletal and motor proteins consist of large and diverse superfamilies comprising up to several dozen members per organism. Up to date there is no integrated tool available to assist in the manual large-scale comparative genomic analysis of protein families. Pfarao (Protein Family Application for Retrieval, Analysis and Organisation) is a database driven online working environment for the analysis of manually annotated protein sequences and their relationship. Currently, the system can store and interrelate a wide range of information about protein sequences, species, phylogenetic relations and sequencing projects as well as links to literature and domain predictions. Sequences can be imported from multiple sequence alignments that are generated during the annotation process. A web interface allows to conveniently browse the database and to compile tabular and graphical summaries of its content. We implemented a protein sequence-centric web application to store, organize, interrelate, and present heterogeneous data that is generated in manual genome annotation and comparative genomics. The application has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be adapted for any protein.

Genic insights from integrated human proteomics in GeneCards.

PubMed

Fishilevich, Simon; Zimmerman, Shahar; Kohn, Asher; Iny Stein, Tsippi; Olender, Tsviya; Kolker, Eugene; Safran, Marilyn; Lancet, Doron

2016-01-01

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite's next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein-RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome. Database URL:http://www.genecards.org/. © The Author(s) 2016. Published by Oxford University Press.

GenBank.

PubMed

Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Wheeler, David L

2008-01-01

GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 260 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov.

GenBank

PubMed Central

Benson, Dennis A.; Karsch-Mizrachi, Ilene; Lipman, David J.; Ostell, James; Wheeler, David L.

2008-01-01

GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 260 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov PMID:18073190

In silico analysis of cacao (Theobroma cacao L.) genes that involved in pathogen and disease responses

NASA Astrophysics Data System (ADS)

Agung, Muhammad Budi; Budiarsa, I. Made; Suwastika, I. Nengah

2017-02-01

Cocoa bean is one of the main commodities from Indonesia for the world, which still have problem regarding yield degradation due to pathogens and disease attack. Developing robust cacao plant that genetically resistant to pathogen and disease attack is an ideal solution in over taking on this problem. The aim of this study was to identify Theobroma cacao genes on database of cacao genome that homolog to response genes of pathogen and disease attack in other plant, through in silico analysis. Basic information survey and gene identification were performed in GenBank and The Arabidopsis Information Resource database. The In silico analysis contains protein BLAST, homology test of each gene's protein candidates, and identification of homologue gene in Cacao Genome Database using data source "Theobroma cacao cv. Matina 1-6 v1.1" genome. Identification found that Thecc1EG011959t1 (EDS1), Thecc1EG006803t1 (EDS5), Thecc1EG013842t1 (ICS1), and Thecc1EG015614t1 (BG_PPAP) gene of Cacao Genome Database were Theobroma cacao genes that homolog to plant's resistance genes which highly possible to have similar functions of each gene's homologue gene.

GenBank

PubMed Central

Benson, Dennis A.; Karsch-Mizrachi, Ilene; Lipman, David J.; Ostell, James; Wheeler, David L.

2007-01-01

GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 240 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage (). PMID:17202161

SANSparallel: interactive homology search against Uniprot

PubMed Central

Somervuo, Panu; Holm, Liisa

2015-01-01

Proteins evolve by mutations and natural selection. The network of sequence similarities is a rich source for mining homologous relationships that inform on protein structure and function. There are many servers available to browse the network of homology relationships but one has to wait up to a minute for results. The SANSparallel webserver provides protein sequence database searches with immediate response and professional alignment visualization by third-party software. The output is a list, pairwise alignment or stacked alignment of sequence-similar proteins from Uniprot, UniRef90/50, Swissprot or Protein Data Bank. The stacked alignments are viewed in Jalview or as sequence logos. The database search uses the suffix array neighborhood search (SANS) method, which has been re-implemented as a client-server, improved and parallelized. The method is extremely fast and as sensitive as BLAST above 50% sequence identity. Benchmarks show that the method is highly competitive compared to previously published fast database search programs: UBLAST, DIAMOND, LAST, LAMBDA, RAPSEARCH2 and BLAT. The web server can be accessed interactively or programmatically at http://ekhidna2.biocenter.helsinki.fi/cgi-bin/sans/sans.cgi. It can be used to make protein functional annotation pipelines more efficient, and it is useful in interactive exploration of the detailed evidence supporting the annotation of particular proteins of interest. PMID:25855811

Hmrbase: a database of hormones and their receptors

PubMed Central

Rashid, Mamoon; Singla, Deepak; Sharma, Arun; Kumar, Manish; Raghava, Gajendra PS

2009-01-01

Background Hormones are signaling molecules that play vital roles in various life processes, like growth and differentiation, physiology, and reproduction. These molecules are mostly secreted by endocrine glands, and transported to target organs through the bloodstream. Deficient, or excessive, levels of hormones are associated with several diseases such as cancer, osteoporosis, diabetes etc. Thus, it is important to collect and compile information about hormones and their receptors. Description This manuscript describes a database called Hmrbase which has been developed for managing information about hormones and their receptors. It is a highly curated database for which information has been collected from the literature and the public databases. The current version of Hmrbase contains comprehensive information about ~2000 hormones, e.g., about their function, source organism, receptors, mature sequences, structures etc. Hmrbase also contains information about ~3000 hormone receptors, in terms of amino acid sequences, subcellular localizations, ligands, and post-translational modifications etc. One of the major features of this database is that it provides data about ~4100 hormone-receptor pairs. A number of online tools have been integrated into the database, to provide the facilities like keyword search, structure-based search, mapping of a given peptide(s) on the hormone/receptor sequence, sequence similarity search. This database also provides a number of external links to other resources/databases in order to help in the retrieving of further related information. Conclusion Owing to the high impact of endocrine research in the biomedical sciences, the Hmrbase could become a leading data portal for researchers. The salient features of Hmrbase are hormone-receptor pair-related information, mapping of peptide stretches on the protein sequences of hormones and receptors, Pfam domain annotations, categorical browsing options, online data submission, DrugPedia linkage etc. Hmrbase is available online for public from . PMID:19589147

Tachyon search speeds up retrieval of similar sequences by several orders of magnitude

PubMed Central

Tan, Joshua; Kuchibhatla, Durga; Sirota, Fernanda L.; Sherman, Westley A.; Gattermayer, Tobias; Kwoh, Chia Yee; Eisenhaber, Frank; Schneider, Georg; Maurer-Stroh, Sebastian

2012-01-01

Summary: The usage of current sequence search tools becomes increasingly slower as databases of protein sequences continue to grow exponentially. Tachyon, a new algorithm that identifies closely related protein sequences ~200 times faster than standard BLAST, circumvents this limitation with a reduced database and oligopeptide matching heuristic. Availability and implementation: The tool is publicly accessible as a webserver at http://tachyon.bii.a-star.edu.sg and can also be accessed programmatically through SOAP. Contact: sebastianms@bii.a-star.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. PMID:22531216

Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL).

PubMed

Hooper, Cornelia M; Castleden, Ian R; Aryamanesh, Nader; Jacoby, Richard P; Millar, A Harvey

2016-01-01

Barley, wheat, rice and maize provide the bulk of human nutrition and have extensive industrial use as agricultural products. The genomes of these crops each contains >40,000 genes encoding proteins; however, the major genome databases for these species lack annotation information of protein subcellular location for >80% of these gene products. We address this gap, by constructing the compendium of crop protein subcellular locations called crop Proteins with Annotated Locations (cropPAL). Subcellular location is most commonly determined by fluorescent protein tagging of live cells or mass spectrometry detection in subcellular purifications, but can also be predicted from amino acid sequence or protein expression patterns. The cropPAL database collates 556 published studies, from >300 research institutes in >30 countries that have been previously published, as well as compiling eight pre-computed subcellular predictions for all Hordeum vulgare, Triticum aestivum, Oryza sativa and Zea mays protein sequences. The data collection including metadata for proteins and published studies can be accessed through a search portal http://crop-PAL.org. The subcellular localization information housed in cropPAL helps to depict plant cells as compartmentalized protein networks that can be investigated for improving crop yield and quality, and developing new biotechnological solutions to agricultural challenges. © The Author 2015. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Omics databases on kidney disease: where they can be found and how to benefit from them.

PubMed

Papadopoulos, Theofilos; Krochmal, Magdalena; Cisek, Katryna; Fernandes, Marco; Husi, Holger; Stevens, Robert; Bascands, Jean-Loup; Schanstra, Joost P; Klein, Julie

2016-06-01

In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.

Towards an understanding of wheat chloroplasts: a methodical investigation of thylakoid proteome.

PubMed

Kamal, Abu Hena Mostafa; Cho, Kun; Komatsu, Setsuko; Uozumi, Nobuyuki; Choi, Jong-Soon; Woo, Sun Hee

2012-05-01

We utilized Percoll density gradient centrifugation to isolate and fractionate chloroplasts of Korean winter wheat cultivar cv. Kumgang (Triticum aestivum L.). The resulting protein fractions were separated by one dimensional polyacrylamide gel electrophoresis (1D-PAGE) coupled with LTQ-FTICR mass spectrometry. This enabled us to detect and identify 767 unique proteins. Our findings represent the most comprehensive exploration of a proteome to date. Based on annotation information from the UniProtKB/Swiss-Prot database and our analyses via WoLF PSORT and PSORT, these proteins are localized in the chloroplast (607 proteins), chloroplast stroma (145), thylakoid membrane (342), lumens (163), and integral membranes (166). In all, 67% were confirmed as chloroplast thylakoid proteins. Although nearly complete protein coverage (89% proteins) has been accomplished for the key chloroplast pathways in wheat, such as for photosynthesis, many other proteins are involved in regulating carbon metabolism. The identified proteins were assigned to 103 functional categories according to a classification system developed by the iProClass database and provided through Protein Information Resources. Those functions include electron transport, energy, cellular organization and biogenesis, transport, stress responses, and other metabolic processes. Whereas most of these proteins are associated with known complexes and metabolic pathways, about 13% of the proteins have unknown functions. The chloroplast proteome contains many proteins that are localized to the thylakoids but as yet have no known function. We propose that some of these familiar proteins participate in the photosynthetic pathway. Thus, our new and comprehensive protein profile may provide clues for better understanding that photosynthetic process in wheat.

The MAR databases: development and implementation of databases specific for marine metagenomics

PubMed Central

Klemetsen, Terje; Raknes, Inge A; Fu, Juan; Agafonov, Alexander; Balasundaram, Sudhagar V; Tartari, Giacomo; Robertsen, Espen

2018-01-01

Abstract We introduce the marine databases; MarRef, MarDB and MarCat (https://mmp.sfb.uit.no/databases/), which are publicly available resources that promote marine research and innovation. These data resources, which have been implemented in the Marine Metagenomics Portal (MMP) (https://mmp.sfb.uit.no/), are collections of richly annotated and manually curated contextual (metadata) and sequence databases representing three tiers of accuracy. While MarRef is a database for completely sequenced marine prokaryotic genomes, which represent a marine prokaryote reference genome database, MarDB includes all incomplete sequenced prokaryotic genomes regardless level of completeness. The last database, MarCat, represents a gene (protein) catalog of uncultivable (and cultivable) marine genes and proteins derived from marine metagenomics samples. The first versions of MarRef and MarDB contain 612 and 3726 records, respectively. Each record is built up of 106 metadata fields including attributes for sampling, sequencing, assembly and annotation in addition to the organism and taxonomic information. Currently, MarCat contains 1227 records with 55 metadata fields. Ontologies and controlled vocabularies are used in the contextual databases to enhance consistency. The user-friendly web interface lets the visitors browse, filter and search in the contextual databases and perform BLAST searches against the corresponding sequence databases. All contextual and sequence databases are freely accessible and downloadable from https://s1.sfb.uit.no/public/mar/. PMID:29106641

MDB: the Metalloprotein Database and Browser at The Scripps Research Institute

PubMed Central

Castagnetto, Jesus M.; Hennessy, Sean W.; Roberts, Victoria A.; Getzoff, Elizabeth D.; Tainer, John A.; Pique, Michael E.

2002-01-01

The Metalloprotein Database and Browser (MDB; http://metallo.scripps.edu) at The Scripps Research Institute is a web-accessible resource for metalloprotein research. It offers the scientific community quantitative information on geometrical parameters of metal-binding sites in protein structures available from the Protein Data Bank (PDB). The MDB also offers analytical tools for the examination of trends or patterns in the indexed metal-binding sites. A user can perform interactive searches, metal-site structure visualization (via a Java applet), and analysis of the quantitative data by accessing the MDB through a web browser without requiring an external application or platform-dependent plugin. The MDB also has a non-interactive interface with which other web sites and network-aware applications can seamlessly incorporate data or statistical analysis results from metal-binding sites. The information contained in the MDB is periodically updated with automated algorithms that find and index metal sites from new protein structures released by the PDB. PMID:11752342

Complementary DNA sequencing and identification of mRNAs from the venomous gland of Agkistrodon piscivorus leucostoma.

PubMed

Jia, Ying; Cantu, Bruno A; Sánchez, Elda E; Pérez, John C

2008-06-15

To advance our knowledge on the snake venom composition and transcripts expressed in venom gland at the molecular level, we constructed a cDNA library from the venom gland of Agkistrodon piscivorus leucostoma for the generation of expressed sequence tags (ESTs) database. From the randomly sequenced 2112 independent clones, we have obtained ESTs for 1309 (62%) cDNAs, which showed significant deduced amino acid sequence similarity (scores >80) to previously characterized proteins in National Center for Biotechnology Information (NCBI) database. Ribosomal proteins make up 47 clones (2%) and the remaining 756 (36%) cDNAs represent either unknown identity or show BLASTX sequence identity scores of <80 with known GenBank accessions. The most highly expressed gene encoding phospholipase A(2) (PLA(2)) accounting for 35% of A. p. leucostoma venom gland cDNAs was identified and further confirmed by crude venom applied to sodium dodecyl sulfate/polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis and protein sequencing. A total of 180 representative genes were obtained from the sequence assemblies and deposited to EST database. Clones showing sequence identity to disintegrins, thrombin-like enzymes, hemorrhagic toxins, fibrinogen clotting inhibitors and plasminogen activators were also identified in our EST database. These data can be used to develop a research program that will help us identify genes encoding proteins that are of medical importance or proteins involved in the mechanisms of the toxin venom.

MutHTP: Mutations in Human Transmembrane Proteins.

PubMed

A, Kulandaisamy; S, Binny Priya; R, Sakthivel; Tarnovskaya, Svetlana; Bizin, Ilya; Hönigschmid, Peter; Frishman, Dmitrij; Gromiha, M Michael

2018-02-01

We have developed a novel database, MutHTP, which contains information on 183395 disease-associated and 17827 neutral mutations in human transmembrane proteins. For each mutation site MutHTP provides a description of its location with respect to the membrane protein topology, structural environment (if available) and functional features. Comprehensive visualization, search, display and download options are available. The database is publicly available at http://www.iitm.ac.in/bioinfo/MutHTP/. The website is implemented using HTML, PHP and javascript and supports recent versions of all major browsers, such as Firefox, Chrome and Opera. gromiha@iitm.ac.in. Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

An annotated database of Arabidopsis mutants of acyl lipid metabolism

DOE PAGES

McGlew, Kathleen; Shaw, Vincent; Zhang, Meng; ...

2014-12-10

Mutants have played a fundamental role in gene discovery and in understanding the function of genes involved in plant acyl lipid metabolism. The first mutant in Arabidopsis lipid metabolism ( fad4) was described in 1985. Since that time, characterization of mutants in more than 280 genes associated with acyl lipid metabolism has been reported. This review provides a brief background and history on identification of mutants in acyl lipid metabolism, an analysis of the distribution of mutants in different areas of acyl lipid metabolism and presents an annotated database (ARALIPmutantDB) of these mutants. The database provides information on the phenotypesmore » of mutants, pathways and enzymes/proteins associated with the mutants, and allows rapid access via hyperlinks to summaries of information about each mutant and to literature that provides information on the lipid composition of the mutants. Mutants for at least 30 % of the genes in the database have multiple names, which have been compiled here to reduce ambiguities in searches for information. Furthermore, the database should also provide a tool for exploring the relationships between mutants in acyl lipid-related genes and their lipid phenotypes and point to opportunities for further research.« less

From metaphor to practices: The introduction of "information engineers" into the first DNA sequence database.

PubMed

García-Sancho, Miguel

2011-01-01

This paper explores the introduction of professional systems engineers and information management practices into the first centralized DNA sequence database, developed at the European Molecular Biology Laboratory (EMBL) during the 1980s. In so doing, it complements the literature on the emergence of an information discourse after World War II and its subsequent influence in biological research. By the careers of the database creators and the computer algorithms they designed, analyzing, from the mid-1960s onwards information in biology gradually shifted from a pervasive metaphor to be embodied in practices and professionals such as those incorporated at the EMBL. I then investigate the reception of these database professionals by the EMBL biological staff, which evolved from initial disregard to necessary collaboration as the relationship between DNA, genes, and proteins turned out to be more complex than expected. The trajectories of the database professionals at the EMBL suggest that the initial subject matter of the historiography of genomics should be the long-standing practices that emerged after World War II and to a large extent originated outside biomedicine and academia. Only after addressing these practices, historians may turn to their further disciplinary assemblage in fields such as bioinformatics or biotechnology.

[A basic research to share Fourier transform near-infrared spectrum information resource].

PubMed

Zhang, Lu-Da; Li, Jun-Hui; Zhao, Long-Lian; Zhao, Li-Li; Qin, Fang-Li; Yan, Yan-Lu

2004-08-01

A method to share the information resource in the database of Fourier transform near-infrared(FTNIR) spectrum information of agricultural products and utilize the spectrum information sufficiently is explored in this paper. Mapping spectrum information from one instrument to another is studied to express the spectrum information accurately between the instruments. Then mapping spectrum information is used to establish a mathematical model of quantitative analysis without including standard samples. The analysis result is that the relative coefficient r is 0.941 and the relative error is 3.28% between the model estimate values and the Kjeldahl's value for the protein content of twenty-two wheat samples, while the relative coefficient r is 0.963 and the relative error is 2.4% for the other model, which is established by using standard samples. It is shown that the spectrum information can be shared by using the mapping spectrum information. So it can be concluded that the spectrum information in one FTNIR spectrum information database can be transformed to another instrument's mapping spectrum information, which makes full use of the information resource in the database of FTNIR spectrum information to realize the resource sharing between different instruments.

CPLA 1.0: an integrated database of protein lysine acetylation

PubMed Central

Liu, Zexian; Cao, Jun; Gao, Xinjiao; Zhou, Yanhong; Wen, Longping; Yang, Xiangjiao; Yao, Xuebiao; Ren, Jian; Xue, Yu

2011-01-01

As a reversible post-translational modification (PTM) discovered decades ago, protein lysine acetylation was known for its regulation of transcription through the modification of histones. Recent studies discovered that lysine acetylation targets broad substrates and especially plays an essential role in cellular metabolic regulation. Although acetylation is comparable with other major PTMs such as phosphorylation, an integrated resource still remains to be developed. In this work, we presented the compendium of protein lysine acetylation (CPLA) database for lysine acetylated substrates with their sites. From the scientific literature, we manually collected 7151 experimentally identified acetylation sites in 3311 targets. We statistically studied the regulatory roles of lysine acetylation by analyzing the Gene Ontology (GO) and InterPro annotations. Combined with protein–protein interaction information, we systematically discovered a potential human lysine acetylation network (HLAN) among histone acetyltransferases (HATs), substrates and histone deacetylases (HDACs). In particular, there are 1862 triplet relationships of HAT-substrate-HDAC retrieved from the HLAN, at least 13 of which were previously experimentally verified. The online services of CPLA database was implemented in PHP + MySQL + JavaScript, while the local packages were developed in JAVA 1.5 (J2SE 5.0). The CPLA database is freely available for all users at: http://cpla.biocuckoo.org. PMID:21059677

Real-Time Ligand Binding Pocket Database Search Using Local Surface Descriptors

PubMed Central

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-01-01

Due to the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of a particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two dimensional pseudo-Zernike moments or the 3D Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark study employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed. PMID:20455259

Real-time ligand binding pocket database search using local surface descriptors.

PubMed

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-07-01

Because of the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two-dimensional pseudo-Zernike moments or the three-dimensional Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark studies employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed.

Detection of Metastatic Potential in Breast Cancer by RhoC-GTPase and WISP3 Proteins

DTIC Science & Technology

2005-05-01

clinical utility of RhoC- GTPase and WISP3 proteins in breast cancer patients. These two genes were identified as key genetic determinants of...information, linked to a clinical database, and to better understand the functional significance of the WISP3 gene in Inflammatory Breast Cancer (IBC), to...pathological and clinical information. The idea behind this decision was to be able to link the results of the TMA scoring with the patient pathological

Liverome: a curated database of liver cancer-related gene signatures with self-contained context information.

PubMed

Lee, Langho; Wang, Kai; Li, Gang; Xie, Zhi; Wang, Yuli; Xu, Jiangchun; Sun, Shaoxian; Pocalyko, David; Bhak, Jong; Kim, Chulhong; Lee, Kee-Ho; Jang, Ye Jin; Yeom, Young Il; Yoo, Hyang-Sook; Hwang, Seungwoo

2011-11-30

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. A number of molecular profiling studies have investigated the changes in gene and protein expression that are associated with various clinicopathological characteristics of HCC and generated a wealth of scattered information, usually in the form of gene signature tables. A database of the published HCC gene signatures would be useful to liver cancer researchers seeking to retrieve existing differential expression information on a candidate gene and to make comparisons between signatures for prioritization of common genes. A challenge in constructing such database is that a direct import of the signatures as appeared in articles would lead to a loss or ambiguity of their context information that is essential for a correct biological interpretation of a gene's expression change. This challenge arises because designation of compared sample groups is most often abbreviated, ad hoc, or even missing from published signature tables. Without manual curation, the context information becomes lost, leading to uninformative database contents. Although several databases of gene signatures are available, none of them contains informative form of signatures nor shows comprehensive coverage on liver cancer. Thus we constructed Liverome, a curated database of liver cancer-related gene signatures with self-contained context information. Liverome's data coverage is more than three times larger than any other signature database, consisting of 143 signatures taken from 98 HCC studies, mostly microarray and proteome, and involving 6,927 genes. The signatures were post-processed into an informative and uniform representation and annotated with an itemized summary so that all context information is unambiguously self-contained within the database. The signatures were further informatively named and meaningfully organized according to ten functional categories for guided browsing. Its web interface enables a straightforward retrieval of known differential expression information on a query gene and a comparison of signatures to prioritize common genes. The utility of Liverome-collected data is shown by case studies in which useful biological insights on HCC are produced. Liverome database provides a comprehensive collection of well-curated HCC gene signatures and straightforward interfaces for gene search and signature comparison as well. Liverome is available at http://liverome.kobic.re.kr.

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining.

PubMed

Oliveira, S R M; Almeida, G V; Souza, K R R; Rodrigues, D N; Kuser-Falcão, P R; Yamagishi, M E B; Santos, E H; Vieira, F D; Jardine, J G; Neshich, G

2007-10-05

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.

A chromosome-centric human proteome project (C-HPP) to characterize the sets of proteins encoded in chromosome 17.

PubMed

Liu, Suli; Im, Hogune; Bairoch, Amos; Cristofanilli, Massimo; Chen, Rui; Deutsch, Eric W; Dalton, Stephen; Fenyo, David; Fanayan, Susan; Gates, Chris; Gaudet, Pascale; Hincapie, Marina; Hanash, Samir; Kim, Hoguen; Jeong, Seul-Ki; Lundberg, Emma; Mias, George; Menon, Rajasree; Mu, Zhaomei; Nice, Edouard; Paik, Young-Ki; Uhlen, Mathias; Wells, Lance; Wu, Shiaw-Lin; Yan, Fangfei; Zhang, Fan; Zhang, Yue; Snyder, Michael; Omenn, Gilbert S; Beavis, Ronald C; Hancock, William S

2013-01-04

We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 "missing" proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of "missing" proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.

HIV Molecular Immunology 2014

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yusim, Karina; Korber, Bette Tina Marie; Barouch, Dan

HIV Molecular Immunology is a companion volume to HIV Sequence Compendium. This publication, the 2014 edition, is the PDF version of the web-based HIV Immunology Database (http://www.hiv.lanl.gov/content/immunology/). The web interface for this relational database has many search options, as well as interactive tools to help immunologists design reagents and interpret their results. In the HIV Immunology Database, HIV-specific B-cell and T-cell responses are summarized and annotated. Immunological responses are divided into three parts, CTL, T helper, and antibody. Within these parts, defined epitopes are organized by protein and binding sites within each protein, moving from left to right through themore » coding regions spanning the HIV genome. We include human responses to natural HIV infections, as well as vaccine studies in a range of animal models and human trials. Responses that are not specifically defined, such as responses to whole proteins or monoclonal antibody responses to discontinuous epitopes, are summarized at the end of each protein section. Studies describing general HIV responses to the virus, but not to any specific protein, are included at the end of each part. The annotation includes information such as crossreactivity, escape mutations, antibody sequence, TCR usage, functional domains that overlap with an epitope, immune response associations with rates of progression and therapy, and how specific epitopes were experimentally defined. Basic information such as HLA specificities for T-cell epitopes, isotypes of monoclonal antibodies, and epitope sequences are included whenever possible. All studies that we can find that incorporate the use of a specific monoclonal antibody are included in the entry for that antibody. A single T-cell epitope can have multiple entries, generally one entry per study. Finally, maps of all defined linear epitopes relative to the HXB2 reference proteins are provided.« less

The Biomolecular Interaction Network Database and related tools 2005 update

PubMed Central

Alfarano, C.; Andrade, C. E.; Anthony, K.; Bahroos, N.; Bajec, M.; Bantoft, K.; Betel, D.; Bobechko, B.; Boutilier, K.; Burgess, E.; Buzadzija, K.; Cavero, R.; D'Abreo, C.; Donaldson, I.; Dorairajoo, D.; Dumontier, M. J.; Dumontier, M. R.; Earles, V.; Farrall, R.; Feldman, H.; Garderman, E.; Gong, Y.; Gonzaga, R.; Grytsan, V.; Gryz, E.; Gu, V.; Haldorsen, E.; Halupa, A.; Haw, R.; Hrvojic, A.; Hurrell, L.; Isserlin, R.; Jack, F.; Juma, F.; Khan, A.; Kon, T.; Konopinsky, S.; Le, V.; Lee, E.; Ling, S.; Magidin, M.; Moniakis, J.; Montojo, J.; Moore, S.; Muskat, B.; Ng, I.; Paraiso, J. P.; Parker, B.; Pintilie, G.; Pirone, R.; Salama, J. J.; Sgro, S.; Shan, T.; Shu, Y.; Siew, J.; Skinner, D.; Snyder, K.; Stasiuk, R.; Strumpf, D.; Tuekam, B.; Tao, S.; Wang, Z.; White, M.; Willis, R.; Wolting, C.; Wong, S.; Wrong, A.; Xin, C.; Yao, R.; Yates, B.; Zhang, S.; Zheng, K.; Pawson, T.; Ouellette, B. F. F.; Hogue, C. W. V.

2005-01-01

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. PMID:15608229

Challenges in the association of human single nucleotide polymorphism mentions with unique database identifiers

PubMed Central

2011-01-01

Background Most information on genomic variations and their associations with phenotypes are covered exclusively in scientific publications rather than in structured databases. These texts commonly describe variations using natural language; database identifiers are seldom mentioned. This complicates the retrieval of variations, associated articles, as well as information extraction, e. g. the search for biological implications. To overcome these challenges, procedures to map textual mentions of variations to database identifiers need to be developed. Results This article describes a workflow for normalization of variation mentions, i.e. the association of them to unique database identifiers. Common pitfalls in the interpretation of single nucleotide polymorphism (SNP) mentions are highlighted and discussed. The developed normalization procedure achieves a precision of 98.1 % and a recall of 67.5% for unambiguous association of variation mentions with dbSNP identifiers on a text corpus based on 296 MEDLINE abstracts containing 527 mentions of SNPs. The annotated corpus is freely available at http://www.scai.fraunhofer.de/snp-normalization-corpus.html. Conclusions Comparable approaches usually focus on variations mentioned on the protein sequence and neglect problems for other SNP mentions. The results presented here indicate that normalizing SNPs described on DNA level is more difficult than the normalization of SNPs described on protein level. The challenges associated with normalization are exemplified with ambiguities and errors, which occur in this corpus. PMID:21992066

Pfarao: a web application for protein family analysis customized for cytoskeletal and motor proteins (CyMoBase)

PubMed Central

Odronitz, Florian; Kollmar, Martin

2006-01-01

Background Annotation of protein sequences of eukaryotic organisms is crucial for the understanding of their function in the cell. Manual annotation is still by far the most accurate way to correctly predict genes. The classification of protein sequences, their phylogenetic relation and the assignment of function involves information from various sources. This often leads to a collection of heterogeneous data, which is hard to track. Cytoskeletal and motor proteins consist of large and diverse superfamilies comprising up to several dozen members per organism. Up to date there is no integrated tool available to assist in the manual large-scale comparative genomic analysis of protein families. Description Pfarao (Protein Family Application for Retrieval, Analysis and Organisation) is a database driven online working environment for the analysis of manually annotated protein sequences and their relationship. Currently, the system can store and interrelate a wide range of information about protein sequences, species, phylogenetic relations and sequencing projects as well as links to literature and domain predictions. Sequences can be imported from multiple sequence alignments that are generated during the annotation process. A web interface allows to conveniently browse the database and to compile tabular and graphical summaries of its content. Conclusion We implemented a protein sequence-centric web application to store, organize, interrelate, and present heterogeneous data that is generated in manual genome annotation and comparative genomics. The application has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be adapted for any protein. PMID:17134497

PLI: a web-based tool for the comparison of protein-ligand interactions observed on PDB structures.

PubMed

Gallina, Anna Maria; Bisignano, Paola; Bergamino, Maurizio; Bordo, Domenico

2013-02-01

A large fraction of the entries contained in the Protein Data Bank describe proteins in complex with low molecular weight molecules such as physiological compounds or synthetic drugs. In many cases, the same molecule is found in distinct protein-ligand complexes. There is an increasing interest in Medicinal Chemistry in comparing protein binding sites to get insight on interactions that modulate the binding specificity, as this structural information can be correlated with other experimental data of biochemical or physiological nature and may help in rational drug design. The web service protein-ligand interaction presented here provides a tool to analyse and compare the binding pockets of homologous proteins in complex with a selected ligand. The information is deduced from protein-ligand complexes present in the Protein Data Bank and stored in the underlying database. Freely accessible at http://bioinformatics.istge.it/pli/.

The Importance of Biological Databases in Biological Discovery.

PubMed

Baxevanis, Andreas D; Bateman, Alex

2015-06-19

Biological databases play a central role in bioinformatics. They offer scientists the opportunity to access a wide variety of biologically relevant data, including the genomic sequences of an increasingly broad range of organisms. This unit provides a brief overview of major sequence databases and portals, such as GenBank, the UCSC Genome Browser, and Ensembl. Model organism databases, including WormBase, The Arabidopsis Information Resource (TAIR), and those made available through the Mouse Genome Informatics (MGI) resource, are also covered. Non-sequence-centric databases, such as Online Mendelian Inheritance in Man (OMIM), the Protein Data Bank (PDB), MetaCyc, and the Kyoto Encyclopedia of Genes and Genomes (KEGG), are also discussed. Copyright © 2015 John Wiley & Sons, Inc.

MACSIMS : multiple alignment of complete sequences information management system

PubMed Central

Thompson, Julie D; Muller, Arnaud; Waterhouse, Andrew; Procter, Jim; Barton, Geoffrey J; Plewniak, Frédéric; Poch, Olivier

2006-01-01

Background In the post-genomic era, systems-level studies are being performed that seek to explain complex biological systems by integrating diverse resources from fields such as genomics, proteomics or transcriptomics. New information management systems are now needed for the collection, validation and analysis of the vast amount of heterogeneous data available. Multiple alignments of complete sequences provide an ideal environment for the integration of this information in the context of the protein family. Results MACSIMS is a multiple alignment-based information management program that combines the advantages of both knowledge-based and ab initio sequence analysis methods. Structural and functional information is retrieved automatically from the public databases. In the multiple alignment, homologous regions are identified and the retrieved data is evaluated and propagated from known to unknown sequences with these reliable regions. In a large-scale evaluation, the specificity of the propagated sequence features is estimated to be >99%, i.e. very few false positive predictions are made. MACSIMS is then used to characterise mutations in a test set of 100 proteins that are known to be involved in human genetic diseases. The number of sequence features associated with these proteins was increased by 60%, compared to the features available in the public databases. An XML format output file allows automatic parsing of the MACSIM results, while a graphical display using the JalView program allows manual analysis. Conclusion MACSIMS is a new information management system that incorporates detailed analyses of protein families at the structural, functional and evolutionary levels. MACSIMS thus provides a unique environment that facilitates knowledge extraction and the presentation of the most pertinent information to the biologist. A web server and the source code are available at . PMID:16792820

Comprehensive proteomic analysis of Penicillium verrucosum.

PubMed

Nöbauer, Katharina; Hummel, Karin; Mayrhofer, Corina; Ahrens, Maike; Setyabudi, Francis M C; Schmidt-Heydt, Markus; Eisenacher, Martin; Razzazi-Fazeli, Ebrahim

2017-05-01

Mass spectrometric identification of proteins in species lacking validated sequence information is a major problem in veterinary science. In the present study, we used ochratoxin A producing Penicillium verrucosum to identify and quantitatively analyze proteins of an organism with yet no protein information available. The work presented here aimed to provide a comprehensive protein identification of P. verrucosum using shotgun proteomics. We were able to identify 3631 proteins in an "ab initio" translated database from DNA sequences of P. verrucosum. Additionally, a sequential window acquisition of all theoretical fragment-ion spectra analysis was done to find differentially regulated proteins at two different time points of the growth curve. We compared the proteins at the beginning (day 3) and at the end of the log phase (day 12). © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

CoSMoS: Conserved Sequence Motif Search in the proteome

PubMed Central

Liu, Xiao I; Korde, Neeraj; Jakob, Ursula; Leichert, Lars I

2006-01-01

Background With the ever-increasing number of gene sequences in the public databases, generating and analyzing multiple sequence alignments becomes increasingly time consuming. Nevertheless it is a task performed on a regular basis by researchers in many labs. Results We have now created a database called CoSMoS to find the occurrences and at the same time evaluate the significance of sequence motifs and amino acids encoded in the whole genome of the model organism Escherichia coli K12. We provide a precomputed set of multiple sequence alignments for each individual E. coli protein with all of its homologues in the RefSeq database. The alignments themselves, information about the occurrence of sequence motifs together with information on the conservation of each of the more than 1.3 million amino acids encoded in the E. coli genome can be accessed via the web interface of CoSMoS. Conclusion CoSMoS is a valuable tool to identify highly conserved sequence motifs, to find regions suitable for mutational studies in functional analyses and to predict important structural features in E. coli proteins. PMID:16433915

Importance of databases of nucleic acids for bioinformatic analysis focused to genomics

NASA Astrophysics Data System (ADS)

Jimenez-Gutierrez, L. R.; Barrios-Hernández, C. J.; Pedraza-Ferreira, G. R.; Vera-Cala, L.; Martinez-Perez, F.

2016-08-01

Recently, bioinformatics has become a new field of science, indispensable in the analysis of millions of nucleic acids sequences, which are currently deposited in international databases (public or private); these databases contain information of genes, RNA, ORF, proteins, intergenic regions, including entire genomes from some species. The analysis of this information requires computer programs; which were renewed in the use of new mathematical methods, and the introduction of the use of artificial intelligence. In addition to the constant creation of supercomputing units trained to withstand the heavy workload of sequence analysis. However, it is still necessary the innovation on platforms that allow genomic analyses, faster and more effectively, with a technological understanding of all biological processes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Denef, Vincent; Shah, Manesh B; Verberkmoes, Nathan C

The recent surge in microbial genomic sequencing, combined with the development of high-throughput liquid chromatography-mass-spectrometry-based (LC/LC-MS/MS) proteomics, has raised the question of the extent to which genomic information of one strain or environmental sample can be used to profile proteomes of related strains or samples. Even with decreasing sequencing costs, it remains impractical to obtain genomic sequence for every strain or sample analyzed. Here, we evaluate how shotgun proteomics is affected by amino acid divergence between the sample and the genomic database using a probability-based model and a random mutation simulation model constrained by experimental data. To assess the effectsmore » of nonrandom distribution of mutations, we also evaluated identification levels using in silico peptide data from sequenced isolates with average amino acid identities (AAI) varying between 76 and 98%. We compared the predictions to experimental protein identification levels for a sample that was evaluated using a database that included genomic information for the dominant organism and for a closely related variant (95% AAI). The range of models set the boundaries at which half of the proteins in a proteomic experiment can be identified to be 77-92% AAI between orthologs in the sample and database. Consistent with this prediction, experimental data indicated loss of half the identifiable proteins at 90% AAI. Additional analysis indicated a 6.4% reduction of the initial protein coverage per 1% amino acid divergence and total identification loss at 86% AAI. Consequently, shotgun proteomics is capable of cross-strain identifications but avoids most crossspecies false positives.« less

Indel PDB: a database of structural insertions and deletions derived from sequence alignments of closely related proteins.

PubMed

Hsing, Michael; Cherkasov, Artem

2008-06-25

Insertions and deletions (indels) represent a common type of sequence variations, which are less studied and pose many important biological questions. Recent research has shown that the presence of sizable indels in protein sequences may be indicative of protein essentiality and their role in protein interaction networks. Examples of utilization of indels for structure-based drug design have also been recently demonstrated. Nonetheless many structural and functional characteristics of indels remain less researched or unknown. We have created a web-based resource, Indel PDB, representing a structural database of insertions/deletions identified from the sequence alignments of highly similar proteins found in the Protein Data Bank (PDB). Indel PDB utilized large amounts of available structural information to characterize 1-, 2- and 3-dimensional features of indel sites. Indel PDB contains 117,266 non-redundant indel sites extracted from 11,294 indel-containing proteins. Unlike loop databases, Indel PDB features more indel sequences with secondary structures including alpha-helices and beta-sheets in addition to loops. The insertion fragments have been characterized by their sequences, lengths, locations, secondary structure composition, solvent accessibility, protein domain association and three dimensional structures. By utilizing the data available in Indel PDB, we have studied and presented here several sequence and structural features of indels. We anticipate that Indel PDB will not only enable future functional studies of indels, but will also assist protein modeling efforts and identification of indel-directed drug binding sites.

TOPSAN: a dynamic web database for structural genomics.

PubMed

Ellrott, Kyle; Zmasek, Christian M; Weekes, Dana; Sri Krishna, S; Bakolitsa, Constantina; Godzik, Adam; Wooley, John

2011-01-01

The Open Protein Structure Annotation Network (TOPSAN) is a web-based collaboration platform for exploring and annotating structures determined by structural genomics efforts. Characterization of those structures presents a challenge since the majority of the proteins themselves have not yet been characterized. Responding to this challenge, the TOPSAN platform facilitates collaborative annotation and investigation via a user-friendly web-based interface pre-populated with automatically generated information. Semantic web technologies expand and enrich TOPSAN's content through links to larger sets of related databases, and thus, enable data integration from disparate sources and data mining via conventional query languages. TOPSAN can be found at http://www.topsan.org.

SANSparallel: interactive homology search against Uniprot.

PubMed

Somervuo, Panu; Holm, Liisa

2015-07-01

Proteins evolve by mutations and natural selection. The network of sequence similarities is a rich source for mining homologous relationships that inform on protein structure and function. There are many servers available to browse the network of homology relationships but one has to wait up to a minute for results. The SANSparallel webserver provides protein sequence database searches with immediate response and professional alignment visualization by third-party software. The output is a list, pairwise alignment or stacked alignment of sequence-similar proteins from Uniprot, UniRef90/50, Swissprot or Protein Data Bank. The stacked alignments are viewed in Jalview or as sequence logos. The database search uses the suffix array neighborhood search (SANS) method, which has been re-implemented as a client-server, improved and parallelized. The method is extremely fast and as sensitive as BLAST above 50% sequence identity. Benchmarks show that the method is highly competitive compared to previously published fast database search programs: UBLAST, DIAMOND, LAST, LAMBDA, RAPSEARCH2 and BLAT. The web server can be accessed interactively or programmatically at http://ekhidna2.biocenter.helsinki.fi/cgi-bin/sans/sans.cgi. It can be used to make protein functional annotation pipelines more efficient, and it is useful in interactive exploration of the detailed evidence supporting the annotation of particular proteins of interest. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

The value of protein structure classification information-Surveying the scientific literature

DOE PAGES

Fox, Naomi K.; Brenner, Steven E.; Chandonia, John -Marc

2015-08-27

The Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases have been valuable resources for protein structure classification for over 20 years. Development of SCOP (version 1) concluded in June 2009 with SCOP 1.75. The SCOPe (SCOP-extended) database offers continued development of the classic SCOP hierarchy, adding over 33,000 structures. We have attempted to assess the impact of these two decade old resources and guide future development. To this end, we surveyed recent articles to learn how structure classification data are used. Of 571 articles published in 2012-2013 that cite SCOP, 439 actually use data from themore » resource. We found that the type of use was fairly evenly distributed among four top categories: A) study protein structure or evolution (27% of articles), B) train and/or benchmark algorithms (28% of articles), C) augment non-SCOP datasets with SCOP classification (21% of articles), and D) examine the classification of one protein/a small set of proteins (22% of articles). Most articles described computational research, although 11% described purely experimental research, and a further 9% included both. We examined how CATH and SCOP were used in 158 articles that cited both databases: while some studies used only one dataset, the majority used data from both resources. Protein structure classification remains highly relevant for a diverse range of problems and settings.« less

The value of protein structure classification information-Surveying the scientific literature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, Naomi K.; Brenner, Steven E.; Chandonia, John -Marc

The Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases have been valuable resources for protein structure classification for over 20 years. Development of SCOP (version 1) concluded in June 2009 with SCOP 1.75. The SCOPe (SCOP-extended) database offers continued development of the classic SCOP hierarchy, adding over 33,000 structures. We have attempted to assess the impact of these two decade old resources and guide future development. To this end, we surveyed recent articles to learn how structure classification data are used. Of 571 articles published in 2012-2013 that cite SCOP, 439 actually use data from themore » resource. We found that the type of use was fairly evenly distributed among four top categories: A) study protein structure or evolution (27% of articles), B) train and/or benchmark algorithms (28% of articles), C) augment non-SCOP datasets with SCOP classification (21% of articles), and D) examine the classification of one protein/a small set of proteins (22% of articles). Most articles described computational research, although 11% described purely experimental research, and a further 9% included both. We examined how CATH and SCOP were used in 158 articles that cited both databases: while some studies used only one dataset, the majority used data from both resources. Protein structure classification remains highly relevant for a diverse range of problems and settings.« less

The MAR databases: development and implementation of databases specific for marine metagenomics.

PubMed

Klemetsen, Terje; Raknes, Inge A; Fu, Juan; Agafonov, Alexander; Balasundaram, Sudhagar V; Tartari, Giacomo; Robertsen, Espen; Willassen, Nils P

2018-01-04

We introduce the marine databases; MarRef, MarDB and MarCat (https://mmp.sfb.uit.no/databases/), which are publicly available resources that promote marine research and innovation. These data resources, which have been implemented in the Marine Metagenomics Portal (MMP) (https://mmp.sfb.uit.no/), are collections of richly annotated and manually curated contextual (metadata) and sequence databases representing three tiers of accuracy. While MarRef is a database for completely sequenced marine prokaryotic genomes, which represent a marine prokaryote reference genome database, MarDB includes all incomplete sequenced prokaryotic genomes regardless level of completeness. The last database, MarCat, represents a gene (protein) catalog of uncultivable (and cultivable) marine genes and proteins derived from marine metagenomics samples. The first versions of MarRef and MarDB contain 612 and 3726 records, respectively. Each record is built up of 106 metadata fields including attributes for sampling, sequencing, assembly and annotation in addition to the organism and taxonomic information. Currently, MarCat contains 1227 records with 55 metadata fields. Ontologies and controlled vocabularies are used in the contextual databases to enhance consistency. The user-friendly web interface lets the visitors browse, filter and search in the contextual databases and perform BLAST searches against the corresponding sequence databases. All contextual and sequence databases are freely accessible and downloadable from https://s1.sfb.uit.no/public/mar/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Characterization of the Proteome of Theobroma cacao Beans by Nano-UHPLC-ESI MS/MS.

PubMed

Scollo, Emanuele; Neville, David; Oruna-Concha, M Jose; Trotin, Martine; Cramer, Rainer

2018-02-01

Cocoa seed storage proteins play an important role in flavour development as aroma precursors are formed from their degradation during fermentation. Major proteins in the beans of Theobroma cacao are the storage proteins belonging to the vicilin and albumin classes. Although both these classes of proteins have been extensively characterized, there is still limited information on the expression and abundance of other proteins present in cocoa beans. This work is the first attempt to characterize the whole cocoa bean proteome by nano-UHPLC-ESI MS/MS analysis using tryptic digests of cocoa bean protein extracts. The results of this analysis show that >1000 proteins could be identified using a species-specific Theobroma cacao database. The majority of the identified proteins were involved with metabolism and energy. Additionally, a significant number of the identified proteins were linked to protein synthesis and processing. Several proteins were also involved with plant response to stress conditions and defence. Albumin and vicilin storage proteins showed the highest intensity values among all detected proteins, although only seven entries were identified as storage proteins. A comparison of MS/MS data searches carried out against larger non-specific databases confirmed that using a species-specific database can increase the number of identified proteins, and at the same time reduce the number of false positives. The results of this work will be useful in developing tools that can allow the comparison of the proteomic profile of cocoa beans from different genotypes and geographic origins. Data are available via ProteomeXchange with identifier PXD005586. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

IMGT/3Dstructure-DB and IMGT/StructuralQuery, a database and a tool for immunoglobulin, T cell receptor and MHC structural data

PubMed Central

Kaas, Quentin; Ruiz, Manuel; Lefranc, Marie-Paule

2004-01-01

IMGT/3Dstructure-DB and IMGT/Structural-Query are a novel 3D structure database and a new tool for immunological proteins. They are part of IMGT, the international ImMunoGenetics information system®, a high-quality integrated knowledge resource specializing in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC) and related proteins of the immune system (RPI) of human and other vertebrate species, which consists of databases, Web resources and interactive on-line tools. IMGT/3Dstructure-DB data are described according to the IMGT Scientific chart rules based on the IMGT-ONTOLOGY concepts. IMGT/3Dstructure-DB provides IMGT gene and allele identification of IG, TR and MHC proteins with known 3D structures, domain delimitations, amino acid positions according to the IMGT unique numbering and renumbered coordinate flat files. Moreover IMGT/3Dstructure-DB provides 2D graphical representations (or Collier de Perles) and results of contact analysis. The IMGT/StructuralQuery tool allows search of this database based on specific structural characteristics. IMGT/3Dstructure-DB and IMGT/StructuralQuery are freely available at http://imgt.cines.fr. PMID:14681396

HotRegion: a database of predicted hot spot clusters.

PubMed

Cukuroglu, Engin; Gursoy, Attila; Keskin, Ozlem

2012-01-01

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.

PpTFDB: A pigeonpea transcription factor database for exploring functional genomics in legumes

PubMed Central

Singh, Akshay; Sharma, Ajay Kumar; Singh, Nagendra Kumar

2017-01-01

Pigeonpea (Cajanus cajan L.), a diploid legume crop, is a member of the tribe Phaseoleae. This tribe is descended from the millettioid (tropical) clade of the subfamily Papilionoideae, which includes many important legume crop species such as soybean (Glycine max), mung bean (Vigna radiata), cowpea (Vigna ungiculata), and common bean (Phaseolus vulgaris). It plays major role in food and nutritional security, being rich source of proteins, minerals and vitamins. We have developed a comprehensive Pigeonpea Transcription Factors Database (PpTFDB) that encompasses information about 1829 putative transcription factors (TFs) and their 55 TF families. PpTFDB provides a comprehensive information about each of the identified TFs that includes chromosomal location, protein physicochemical properties, sequence data, protein functional annotation, simple sequence repeats (SSRs) with primers derived from their motifs, orthology with related legume crops, and gene ontology (GO) assignment to respective TFs. (PpTFDB: http://14.139.229.199/PpTFDB/Home.aspx) is a freely available and user friendly web resource that facilitates users to retrieve the information of individual members of a TF family through a set of query interfaces including TF ID or protein functional annotation. In addition, users can also get the information by browsing interfaces, which include browsing by TF Categories and by, GO Categories. This PpTFDB will serve as a promising central resource for researchers as well as breeders who are working towards crop improvement of legume crops. PMID:28651001

Using the TIGR gene index databases for biological discovery.

PubMed

Lee, Yuandan; Quackenbush, John

2003-11-01

The TIGR Gene Index web pages provide access to analyses of ESTs and gene sequences for nearly 60 species, as well as a number of resources derived from these. Each species-specific database is presented using a common format with a homepage. A variety of methods exist that allow users to search each species-specific database. Methods implemented currently include nucleotide or protein sequence queries using WU-BLAST, text-based searches using various sequence identifiers, searches by gene, tissue and library name, and searches using functional classes through Gene Ontology assignments. This protocol provides guidance for using the Gene Index Databases to extract information.

Systematic Differences in Signal Emitting and Receiving Revealed by PageRank Analysis of a Human Protein Interactome

PubMed Central

Li, Xiu-Qing

2012-01-01

Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell. PMID:23028653

ProBiS-database: precalculated binding site similarities and local pairwise alignments of PDB structures.

PubMed

Konc, Janez; Cesnik, Tomo; Konc, Joanna Trykowska; Penca, Matej; Janežič, Dušanka

2012-02-27

ProBiS-Database is a searchable repository of precalculated local structural alignments in proteins detected by the ProBiS algorithm in the Protein Data Bank. Identification of functionally important binding regions of the protein is facilitated by structural similarity scores mapped to the query protein structure. PDB structures that have been aligned with a query protein may be rapidly retrieved from the ProBiS-Database, which is thus able to generate hypotheses concerning the roles of uncharacterized proteins. Presented with uncharacterized protein structure, ProBiS-Database can discern relationships between such a query protein and other better known proteins in the PDB. Fast access and a user-friendly graphical interface promote easy exploration of this database of over 420 million local structural alignments. The ProBiS-Database is updated weekly and is freely available online at http://probis.cmm.ki.si/database.

YPD™, PombePD™ and WormPD™: model organism volumes of the BioKnowledge™ Library, an integrated resource for protein information

PubMed Central

Costanzo, Maria C.; Crawford, Matthew E.; Hirschman, Jodi E.; Kranz, Janice E.; Olsen, Philip; Robertson, Laura S.; Skrzypek, Marek S.; Braun, Burkhard R.; Hopkins, Kelley Lennon; Kondu, Pinar; Lengieza, Carey; Lew-Smith, Jodi E.; Tillberg, Michael; Garrels, James I.

2001-01-01

The BioKnowledge Library is a relational database and web site (http://www.proteome.com) composed of protein-specific information collected from the scientific literature. Each Protein Report on the web site summarizes and displays published information about a single protein, including its biochemical function, role in the cell and in the whole organism, localization, mutant phenotype and genetic interactions, regulation, domains and motifs, interactions with other proteins and other relevant data. This report describes four species-specific volumes of the BioKnowledge Library, concerned with the model organisms Saccharo­myces cerevisiae (YPD), Schizosaccharomyces pombe (PombePD) and Caenorhabditis elegans (WormPD), and with the fungal pathogen Candida albicans (CalPD™). Protein Reports of each species are unified in format, easily searchable and extensively cross-referenced between species. The relevance of these comprehensively curated resources to analysis of proteins in other species is discussed, and is illustrated by a survey of model organism proteins that have similarity to human proteins involved in disease. PMID:11125054

BEAUTY-X: enhanced BLAST searches for DNA queries.

PubMed

Worley, K C; Culpepper, P; Wiese, B A; Smith, R F

1998-01-01

BEAUTY (BLAST Enhanced Alignment Utility) is an enhanced version of the BLAST database search tool that facilitates identification of the functions of matched sequences. Three recent improvements to the BEAUTY program described here make the enhanced output (1) available for DNA queries, (2) available for searches of any protein database, and (3) more up-to-date, with periodic updates of the domain information. BEAUTY searches of the NCBI and EMBL non-redundant protein sequence databases are available from the BCM Search Launcher Web pages (http://gc.bcm.tmc. edu:8088/search-launcher/launcher.html). BEAUTY Post-Processing of submitted search results is available using the BCM Search Launcher Batch Client (version 2.6) (ftp://gc.bcm.tmc. edu/pub/software/search-launcher/). Example figures are available at http://dot.bcm.tmc. edu:9331/papers/beautypp.html (kworley,culpep)@bcm.tmc.edu

NUREBASE: database of nuclear hormone receptors.

PubMed

Duarte, Jorge; Perrière, Guy; Laudet, Vincent; Robinson-Rechavi, Marc

2002-01-01

Nuclear hormone receptors are an abundant class of ligand activated transcriptional regulators, found in varying numbers in all animals. Based on our experience of managing the official nomenclature of nuclear receptors, we have developed NUREBASE, a database containing protein and DNA sequences, reviewed protein alignments and phylogenies, taxonomy and annotations for all nuclear receptors. The reviewed NUREBASE is completed by NUREBASE_DAILY, automatically updated every 24 h. Both databases are organized under a client/server architecture, with a client written in Java which runs on any platform. This client, named FamFetch, integrates a graphical interface allowing selection of families, and manipulation of phylogenies and alignments. NUREBASE sequence data is also accessible through a World Wide Web server, allowing complex queries. All information on accessing and installing NUREBASE may be found at http://www.ens-lyon.fr/LBMC/laudet/nurebase.html.

FPD: A comprehensive phosphorylation database in fungi.

PubMed

Bai, Youhuang; Chen, Bin; Li, Mingzhu; Zhou, Yincong; Ren, Silin; Xu, Qin; Chen, Ming; Wang, Shihua

2017-10-01

Protein phosphorylation, one of the most classic post-translational modification, plays a critical role in diverse cellular processes including cell cycle, growth, and signal transduction pathways. However, the available information about phosphorylation in fungi is limited. Here, we provided a Fungi Phosphorylation Database (FPD) that comprises high-confidence in vivo phosphosites identified by MS-based proteomics in various fungal species. This comprehensive phosphorylation database contains 62 272 non-redundant phosphorylation sites in 11 222 proteins across eight organisms, including Aspergillus flavus, Aspergillus nidulans, Fusarium graminearum, Magnaporthe oryzae, Neurospora crassa, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Cryptococcus neoformans. A fungi-specific phosphothreonine motif and several conserved phosphorylation motifs were discovered by comparatively analysing the pattern of phosphorylation sites in plants, animals, and fungi. Copyright © 2017 British Mycological Society. Published by Elsevier Ltd. All rights reserved.

The VirusBanker database uses a Java program to allow flexible searching through Bunyaviridae sequences.

PubMed

Fourment, Mathieu; Gibbs, Mark J

2008-02-05

Viruses of the Bunyaviridae have segmented negative-stranded RNA genomes and several of them cause significant disease. Many partial sequences have been obtained from the segments so that GenBank searches give complex results. Sequence databases usually use HTML pages to mediate remote sorting, but this approach can be limiting and may discourage a user from exploring a database. The VirusBanker database contains Bunyaviridae sequences and alignments and is presented as two spreadsheets generated by a Java program that interacts with a MySQL database on a server. Sequences are displayed in rows and may be sorted using information that is displayed in columns and includes data relating to the segment, gene, protein, species, strain, sequence length, terminal sequence and date and country of isolation. Bunyaviridae sequences and alignments may be downloaded from the second spreadsheet with titles defined by the user from the columns, or viewed when passed directly to the sequence editor, Jalview. VirusBanker allows large datasets of aligned nucleotide and protein sequences from the Bunyaviridae to be compiled and winnowed rapidly using criteria that are formulated heuristically.

GenBank.

PubMed

Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Sayers, Eric W

2010-01-01

GenBank is a comprehensive database that contains publicly available nucleotide sequences for more than 300,000 organisms named at the genus level or lower, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bi-monthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI homepage: www.ncbi.nlm.nih.gov.

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks.

PubMed

Zhang, Menghuan; Li, Hong; He, Ying; Sun, Han; Xia, Li; Wang, Lishun; Sun, Bo; Ma, Liangxiao; Zhang, Guoqing; Li, Jing; Li, Yixue; Xie, Lu

2015-07-02

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.

LenVarDB: database of length-variant protein domains.

PubMed

Mutt, Eshita; Mathew, Oommen K; Sowdhamini, Ramanathan

2014-01-01

Protein domains are functionally and structurally independent modules, which add to the functional variety of proteins. This array of functional diversity has been enabled by evolutionary changes, such as amino acid substitutions or insertions or deletions, occurring in these protein domains. Length variations (indels) can introduce changes at structural, functional and interaction levels. LenVarDB (freely available at http://caps.ncbs.res.in/lenvardb/) traces these length variations, starting from structure-based sequence alignments in our Protein Alignments organized as Structural Superfamilies (PASS2) database, across 731 structural classification of proteins (SCOP)-based protein domain superfamilies connected to 2 730 625 sequence homologues. Alignment of sequence homologues corresponding to a structural domain is available, starting from a structure-based sequence alignment of the superfamily. Orientation of the length-variant (indel) regions in protein domains can be visualized by mapping them on the structure and on the alignment. Knowledge about location of length variations within protein domains and their visual representation will be useful in predicting changes within structurally or functionally relevant sites, which may ultimately regulate protein function. Non-technical summary: Evolutionary changes bring about natural changes to proteins that may be found in many organisms. Such changes could be reflected as amino acid substitutions or insertions-deletions (indels) in protein sequences. LenVarDB is a database that provides an early overview of observed length variations that were set among 731 protein families and after examining >2 million sequences. Indels are followed up to observe if they are close to the active site such that they can affect the activity of proteins. Inclusion of such information can aid the design of bioengineering experiments.

Data to knowledge: how to get meaning from your result.

PubMed

Berman, Helen M; Gabanyi, Margaret J; Groom, Colin R; Johnson, John E; Murshudov, Garib N; Nicholls, Robert A; Reddy, Vijay; Schwede, Torsten; Zimmerman, Matthew D; Westbrook, John; Minor, Wladek

2015-01-01

Structural and functional studies require the development of sophisticated 'Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB 'super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

GenBank.

PubMed

Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Wheeler, David L

2007-01-01

GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 240 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage (www.ncbi.nlm.nih.gov).

GenBank.

PubMed

Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Wheeler, David L

2005-01-01

GenBank is a comprehensive database that contains publicly available DNA sequences for more than 165,000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in the UK and the DNA Data Bank of Japan helps to ensure worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, go to the NCBI Homepage at http://www.ncbi.nlm.nih.gov.

GenBank.

PubMed

Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Wheeler, David L

2006-01-01

GenBank (R) is a comprehensive database that contains publicly available DNA sequences for more than 205 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the Web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, go to the NCBI Homepage at www.ncbi.nlm.nih.gov.

ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects.

PubMed

Zhang, Yaoyang; Xu, Tao; Shan, Bing; Hart, Jonathan; Aslanian, Aaron; Han, Xuemei; Zong, Nobel; Li, Haomin; Choi, Howard; Wang, Dong; Acharya, Lipi; Du, Lisa; Vogt, Peter K; Ping, Peipei; Yates, John R

2015-11-03

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable. This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015. Published by Elsevier B.V.

AFAL: a web service for profiling amino acids surrounding ligands in proteins

NASA Astrophysics Data System (ADS)

Arenas-Salinas, Mauricio; Ortega-Salazar, Samuel; Gonzales-Nilo, Fernando; Pohl, Ehmke; Holmes, David S.; Quatrini, Raquel

2014-11-01

With advancements in crystallographic technology and the increasing wealth of information populating structural databases, there is an increasing need for prediction tools based on spatial information that will support the characterization of proteins and protein-ligand interactions. Herein, a new web service is presented termed amino acid frequency around ligand (AFAL) for determining amino acids type and frequencies surrounding ligands within proteins deposited in the Protein Data Bank and for assessing the atoms and atom-ligand distances involved in each interaction (availability: http://structuralbio.utalca.cl/AFAL/index.html). AFAL allows the user to define a wide variety of filtering criteria (protein family, source organism, resolution, sequence redundancy and distance) in order to uncover trends and evolutionary differences in amino acid preferences that define interactions with particular ligands. Results obtained from AFAL provide valuable statistical information about amino acids that may be responsible for establishing particular ligand-protein interactions. The analysis will enable investigators to compare ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. Such patterns can be used subsequently to predict ligand binding in proteins that currently have no structural information and to refine the interpretation of existing protein models. The application of AFAL is illustrated by the analysis of proteins interacting with adenosine-5'-triphosphate.

AFAL: a web service for profiling amino acids surrounding ligands in proteins.

PubMed

Arenas-Salinas, Mauricio; Ortega-Salazar, Samuel; Gonzales-Nilo, Fernando; Pohl, Ehmke; Holmes, David S; Quatrini, Raquel

2014-11-01

With advancements in crystallographic technology and the increasing wealth of information populating structural databases, there is an increasing need for prediction tools based on spatial information that will support the characterization of proteins and protein-ligand interactions. Herein, a new web service is presented termed amino acid frequency around ligand (AFAL) for determining amino acids type and frequencies surrounding ligands within proteins deposited in the Protein Data Bank and for assessing the atoms and atom-ligand distances involved in each interaction (availability: http://structuralbio.utalca.cl/AFAL/index.html ). AFAL allows the user to define a wide variety of filtering criteria (protein family, source organism, resolution, sequence redundancy and distance) in order to uncover trends and evolutionary differences in amino acid preferences that define interactions with particular ligands. Results obtained from AFAL provide valuable statistical information about amino acids that may be responsible for establishing particular ligand-protein interactions. The analysis will enable investigators to compare ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. Such patterns can be used subsequently to predict ligand binding in proteins that currently have no structural information and to refine the interpretation of existing protein models. The application of AFAL is illustrated by the analysis of proteins interacting with adenosine-5'-triphosphate.

The Papillomavirus Episteme: a central resource for papillomavirus sequence data and analysis.

PubMed

Van Doorslaer, Koenraad; Tan, Qina; Xirasagar, Sandhya; Bandaru, Sandya; Gopalan, Vivek; Mohamoud, Yasmin; Huyen, Yentram; McBride, Alison A

2013-01-01

The goal of the Papillomavirus Episteme (PaVE) is to provide an integrated resource for the analysis of papillomavirus (PV) genome sequences and related information. The PaVE is a freely accessible, web-based tool (http://pave.niaid.nih.gov) created around a relational database, which enables storage, analysis and exchange of sequence information. From a design perspective, the PaVE adopts an Open Source software approach and stresses the integration and reuse of existing tools. Reference PV genome sequences have been extracted from publicly available databases and reannotated using a custom-created tool. To date, the PaVE contains 241 annotated PV genomes, 2245 genes and regions, 2004 protein sequences and 47 protein structures, which users can explore, analyze or download. The PaVE provides scientists with the data and tools needed to accelerate scientific progress for the study and treatment of diseases caused by PVs.

BIND: the Biomolecular Interaction Network Database

PubMed Central

Bader, Gary D.; Betel, Doron; Hogue, Christopher W. V.

2003-01-01

The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD. PMID:12519993

Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data.

PubMed

Kumar, Dhirendra; Yadav, Amit Kumar; Dash, Debasis

2017-01-01

Database searching is the preferred method for protein identification from digital spectra of mass to charge ratios (m/z) detected for protein samples through mass spectrometers. The search database is one of the major influencing factors in discovering proteins present in the sample and thus in deriving biological conclusions. In most cases the choice of search database is arbitrary. Here we describe common search databases used in proteomic studies and their impact on final list of identified proteins. We also elaborate upon factors like composition and size of the search database that can influence the protein identification process. In conclusion, we suggest that choice of the database depends on the type of inferences to be derived from proteomics data. However, making additional efforts to build a compact and concise database for a targeted question should generally be rewarding in achieving confident protein identifications.

Text-mining and information-retrieval services for molecular biology

PubMed Central

Krallinger, Martin; Valencia, Alfonso

2005-01-01

Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators. PMID:15998455

The Functional Human C-Terminome

PubMed Central

Hedden, Michael; Lyon, Kenneth F.; Brooks, Steven B.; David, Roxanne P.; Limtong, Justin; Newsome, Jacklyn M.; Novakovic, Nemanja; Rajasekaran, Sanguthevar; Thapar, Vishal; Williams, Sean R.; Schiller, Martin R.

2016-01-01

All translated proteins end with a carboxylic acid commonly called the C-terminus. Many short functional sequences (minimotifs) are located on or immediately proximal to the C-terminus. However, information about the function of protein C-termini has not been consolidated into a single source. Here, we built a new “C-terminome” database and web system focused on human proteins. Approximately 3,600 C-termini in the human proteome have a minimotif with an established molecular function. To help evaluate the function of the remaining C-termini in the human proteome, we inferred minimotifs identified by experimentation in rodent cells, predicted minimotifs based upon consensus sequence matches, and predicted novel highly repetitive sequences in C-termini. Predictions can be ranked by enrichment scores or Gene Evolutionary Rate Profiling (GERP) scores, a measurement of evolutionary constraint. By searching for new anchored sequences on the last 10 amino acids of proteins in the human proteome with lengths between 3–10 residues and up to 5 degenerate positions in the consensus sequences, we have identified new consensus sequences that predict instances in the majority of human genes. All of this information is consolidated into a database that can be accessed through a C-terminome web system with search and browse functions for minimotifs and human proteins. A known consensus sequence-based predicted function is assigned to nearly half the proteins in the human proteome. Weblink: http://cterminome.bio-toolkit.com. PMID:27050421

Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces.

PubMed

Venselaar, Hanka; Te Beek, Tim A H; Kuipers, Remko K P; Hekkelman, Maarten L; Vriend, Gert

2010-11-08

Many newly detected point mutations are located in protein-coding regions of the human genome. Knowledge of their effects on the protein's 3D structure provides insight into the protein's mechanism, can aid the design of further experiments, and eventually can lead to the development of new medicines and diagnostic tools. In this article we describe HOPE, a fully automatic program that analyzes the structural and functional effects of point mutations. HOPE collects information from a wide range of information sources including calculations on the 3D coordinates of the protein by using WHAT IF Web services, sequence annotations from the UniProt database, and predictions by DAS services. Homology models are built with YASARA. Data is stored in a database and used in a decision scheme to identify the effects of a mutation on the protein's 3D structure and function. HOPE builds a report with text, figures, and animations that is easy to use and understandable for (bio)medical researchers. We tested HOPE by comparing its output to the results of manually performed projects. In all straightforward cases HOPE performed similar to a trained bioinformatician. The use of 3D structures helps optimize the results in terms of reliability and details. HOPE's results are easy to understand and are presented in a way that is attractive for researchers without an extensive bioinformatics background.

Database resources of the National Center for Biotechnology Information

PubMed Central

Wheeler, David L.; Barrett, Tanya; Benson, Dennis A.; Bryant, Stephen H.; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M.; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Geer, Lewis Y.; Helmberg, Wolfgang; Kapustin, Yuri; Kenton, David L.; Khovayko, Oleg; Lipman, David J.; Madden, Thomas L.; Maglott, Donna R.; Ostell, James; Pruitt, Kim D.; Schuler, Gregory D.; Schriml, Lynn M.; Sequeira, Edwin; Sherry, Stephen T.; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Suzek, Tugba O.; Tatusov, Roman; Tatusova, Tatiana A.; Wagner, Lukas; Yaschenko, Eugene

2006-01-01

In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups, Retroviral Genotyping Tools, HIV-1, Human Protein Interaction Database, SAGEmap, Gene Expression Omnibus, Entrez Probe, GENSAT, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of the resources can be accessed through the NCBI home page at: . PMID:16381840

Global, quantitative and dynamic mapping of protein subcellular localization.

PubMed

Itzhak, Daniel N; Tyanova, Stefka; Cox, Jürgen; Borner, Georg Hh

2016-06-09

Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology.

HIV Molecular Immunology 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yusim, Karina; Korber, Bette Tina; Brander, Christian

The scope and purpose of the HIV molecular immunology database: HIV Molecular Immunology is a companion volume to HIV Sequence Compendium. This publication, the 2015 edition, is the PDF version of the web-based HIV Immunology Database (http://www.hiv.lanl.gov/ content/immunology/). The web interface for this relational database has many search options, as well as interactive tools to help immunologists design reagents and interpret their results. In the HIV Immunology Database, HIV-specific B-cell and T-cell responses are summarized and annotated. Immunological responses are divided into three parts, CTL, T helper, and antibody. Within these parts, defined epitopes are organized by protein and bindingmore » sites within each protein, moving from left to right through the coding regions spanning the HIV genome. We include human responses to natural HIV infections, as well as vaccine studies in a range of animal models and human trials. Responses that are not specifically defined, such as responses to whole proteins or monoclonal antibody responses to discontinuous epitopes, are summarized at the end of each protein section. Studies describing general HIV responses to the virus, but not to any specific protein, are included at the end of each part. The annotation includes information such as cross-reactivity, escape mutations, antibody sequence, TCR usage, functional domains that overlap with an epitope, immune response associations with rates of progression and therapy, and how specific epitopes were experimentally defined. Basic information such as HLA specificities for T-cell epitopes, isotypes of monoclonal antibodies, and epitope sequences are included whenever possible. All studies that we can find that incorporate the use of a specific monoclonal antibody are included in the entry for that antibody. A single T-cell epitope can have multiple entries, generally one entry per study. Finally, maps of all defined linear epitopes relative to the HXB2 reference proteins are provided. Alignments of CTL, helper T-cell, and antibody epitopes are available through the search interface on our web site at http:// www.hiv.lanl.gov/content/immunology.« less

TFBSshape: a motif database for DNA shape features of transcription factor binding sites.

PubMed

Yang, Lin; Zhou, Tianyin; Dror, Iris; Mathelier, Anthony; Wasserman, Wyeth W; Gordân, Raluca; Rohs, Remo

2014-01-01

Transcription factor binding sites (TFBSs) are most commonly characterized by the nucleotide preferences at each position of the DNA target. Whereas these sequence motifs are quite accurate descriptions of DNA binding specificities of transcription factors (TFs), proteins recognize DNA as a three-dimensional object. DNA structural features refine the description of TF binding specificities and provide mechanistic insights into protein-DNA recognition. Existing motif databases contain extensive nucleotide sequences identified in binding experiments based on their selection by a TF. To utilize DNA shape information when analysing the DNA binding specificities of TFs, we developed a new tool, the TFBSshape database (available at http://rohslab.cmb.usc.edu/TFBSshape/), for calculating DNA structural features from nucleotide sequences provided by motif databases. The TFBSshape database can be used to generate heat maps and quantitative data for DNA structural features (i.e., minor groove width, roll, propeller twist and helix twist) for 739 TF datasets from 23 different species derived from the motif databases JASPAR and UniPROBE. As demonstrated for the basic helix-loop-helix and homeodomain TF families, our TFBSshape database can be used to compare, qualitatively and quantitatively, the DNA binding specificities of closely related TFs and, thus, uncover differential DNA binding specificities that are not apparent from nucleotide sequence alone.

HIPdb: a database of experimentally validated HIV inhibiting peptides.

PubMed

Qureshi, Abid; Thakur, Nishant; Kumar, Manoj

2013-01-01

Besides antiretroviral drugs, peptides have also demonstrated potential to inhibit the Human immunodeficiency virus (HIV). For example, T20 has been discovered to effectively block the HIV entry and was approved by the FDA as a novel anti-HIV peptide (AHP). We have collated all experimental information on AHPs at a single platform. HIPdb is a manually curated database of experimentally verified HIV inhibiting peptides targeting various steps or proteins involved in the life cycle of HIV e.g. fusion, integration, reverse transcription etc. This database provides experimental information of 981 peptides. These are of varying length obtained from natural as well as synthetic sources and tested on different cell lines. Important fields included are peptide sequence, length, source, target, cell line, inhibition/IC(50), assay and reference. The database provides user friendly browse, search, sort and filter options. It also contains useful services like BLAST and 'Map' for alignment with user provided sequences. In addition, predicted structure and physicochemical properties of the peptides are also included. HIPdb database is freely available at http://crdd.osdd.net/servers/hipdb. Comprehensive information of this database will be helpful in selecting/designing effective anti-HIV peptides. Thus it may prove a useful resource to researchers for peptide based therapeutics development.

ISAAC - InterSpecies Analysing Application using Containers.

PubMed

Baier, Herbert; Schultz, Jörg

2014-01-15

Information about genes, transcripts and proteins is spread over a wide variety of databases. Different tools have been developed using these databases to identify biological signals in gene lists from large scale analysis. Mostly, they search for enrichments of specific features. But, these tools do not allow an explorative walk through different views and to change the gene lists according to newly upcoming stories. To fill this niche, we have developed ISAAC, the InterSpecies Analysing Application using Containers. The central idea of this web based tool is to enable the analysis of sets of genes, transcripts and proteins under different biological viewpoints and to interactively modify these sets at any point of the analysis. Detailed history and snapshot information allows tracing each action. Furthermore, one can easily switch back to previous states and perform new analyses. Currently, sets can be viewed in the context of genomes, protein functions, protein interactions, pathways, regulation, diseases and drugs. Additionally, users can switch between species with an automatic, orthology based translation of existing gene sets. As todays research usually is performed in larger teams and consortia, ISAAC provides group based functionalities. Here, sets as well as results of analyses can be exchanged between members of groups. ISAAC fills the gap between primary databases and tools for the analysis of large gene lists. With its highly modular, JavaEE based design, the implementation of new modules is straight forward. Furthermore, ISAAC comes with an extensive web-based administration interface including tools for the integration of third party data. Thus, a local installation is easily feasible. In summary, ISAAC is tailor made for highly explorative interactive analyses of gene, transcript and protein sets in a collaborative environment.

Motivated Proteins: A web application for studying small three-dimensional protein motifs

PubMed Central

Leader, David P; Milner-White, E James

2009-01-01

Background Small loop-shaped motifs are common constituents of the three-dimensional structure of proteins. Typically they comprise between three and seven amino acid residues, and are defined by a combination of dihedral angles and hydrogen bonding partners. The most abundant of these are αβ-motifs, asx-motifs, asx-turns, β-bulges, β-bulge loops, β-turns, nests, niches, Schellmann loops, ST-motifs, ST-staples and ST-turns. We have constructed a database of such motifs from a range of high-quality protein structures and built a web application as a visual interface to this. Description The web application, Motivated Proteins, provides access to these 12 motifs (with 48 sub-categories) in a database of over 400 representative proteins. Queries can be made for specific categories or sub-categories of motif, motifs in the vicinity of ligands, motifs which include part of an enzyme active site, overlapping motifs, or motifs which include a particular amino acid sequence. Individual proteins can be specified, or, where appropriate, motifs for all proteins listed. The results of queries are presented in textual form as an (X)HTML table, and may be saved as parsable plain text or XML. Motifs can be viewed and manipulated either individually or in the context of the protein in the Jmol applet structural viewer. Cartoons of the motifs imposed on a linear representation of protein secondary structure are also provided. Summary information for the motifs is available, as are histograms of amino acid distribution, and graphs of dihedral angles at individual positions in the motifs. Conclusion Motivated Proteins is a publicly and freely accessible web application that enables protein scientists to study small three-dimensional motifs without requiring knowledge of either Structured Query Language or the underlying database schema. PMID:19210785

Sequence-Based Prediction of RNA-Binding Residues in Proteins.

PubMed

Walia, Rasna R; El-Manzalawy, Yasser; Honavar, Vasant G; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome.

PubMed

Wang, Julia; Al-Ouran, Rami; Hu, Yanhui; Kim, Seon-Young; Wan, Ying-Wooi; Wangler, Michael F; Yamamoto, Shinya; Chao, Hsiao-Tuan; Comjean, Aram; Mohr, Stephanie E; Perrimon, Norbert; Liu, Zhandong; Bellen, Hugo J

2017-06-01

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

PrionHome: a database of prions and other sequences relevant to prion phenomena.

PubMed

Harbi, Djamel; Parthiban, Marimuthu; Gendoo, Deena M A; Ehsani, Sepehr; Kumar, Manish; Schmitt-Ulms, Gerold; Sowdhamini, Ramanathan; Harrison, Paul M

2012-01-01

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion.

PrionHome: A Database of Prions and Other Sequences Relevant to Prion Phenomena

PubMed Central

Harbi, Djamel; Parthiban, Marimuthu; Gendoo, Deena M. A.; Ehsani, Sepehr; Kumar, Manish; Schmitt-Ulms, Gerold; Sowdhamini, Ramanathan; Harrison, Paul M.

2012-01-01

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion. PMID:22363733

URS DataBase: universe of RNA structures and their motifs.

PubMed

Baulin, Eugene; Yacovlev, Victor; Khachko, Denis; Spirin, Sergei; Roytberg, Mikhail

2016-01-01

The Universe of RNA Structures DataBase (URSDB) stores information obtained from all RNA-containing PDB entries (2935 entries in October 2015). The content of the database is updated regularly. The database consists of 51 tables containing indexed data on various elements of the RNA structures. The database provides a web interface allowing user to select a subset of structures with desired features and to obtain various statistical data for a selected subset of structures or for all structures. In particular, one can easily obtain statistics on geometric parameters of base pairs, on structural motifs (stems, loops, etc.) or on different types of pseudoknots. The user can also view and get information on an individual structure or its selected parts, e.g. RNA-protein hydrogen bonds. URSDB employs a new original definition of loops in RNA structures. That definition fits both pseudoknot-free and pseudoknotted secondary structures and coincides with the classical definition in case of pseudoknot-free structures. To our knowledge, URSDB is the first database supporting searches based on topological classification of pseudoknots and on extended loop classification.Database URL: http://server3.lpm.org.ru/urs/. © The Author(s) 2016. Published by Oxford University Press.

URS DataBase: universe of RNA structures and their motifs

PubMed Central

Baulin, Eugene; Yacovlev, Victor; Khachko, Denis; Spirin, Sergei; Roytberg, Mikhail

2016-01-01

The Universe of RNA Structures DataBase (URSDB) stores information obtained from all RNA-containing PDB entries (2935 entries in October 2015). The content of the database is updated regularly. The database consists of 51 tables containing indexed data on various elements of the RNA structures. The database provides a web interface allowing user to select a subset of structures with desired features and to obtain various statistical data for a selected subset of structures or for all structures. In particular, one can easily obtain statistics on geometric parameters of base pairs, on structural motifs (stems, loops, etc.) or on different types of pseudoknots. The user can also view and get information on an individual structure or its selected parts, e.g. RNA–protein hydrogen bonds. URSDB employs a new original definition of loops in RNA structures. That definition fits both pseudoknot-free and pseudoknotted secondary structures and coincides with the classical definition in case of pseudoknot-free structures. To our knowledge, URSDB is the first database supporting searches based on topological classification of pseudoknots and on extended loop classification. Database URL: http://server3.lpm.org.ru/urs/ PMID:27242032

Database resources of the National Center for Biotechnology Information

PubMed Central

Wheeler, David L.; Barrett, Tanya; Benson, Dennis A.; Bryant, Stephen H.; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M.; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Feolo, Michael; Geer, Lewis Y.; Helmberg, Wolfgang; Kapustin, Yuri; Khovayko, Oleg; Landsman, David; Lipman, David J.; Madden, Thomas L.; Maglott, Donna R.; Miller, Vadim; Ostell, James; Pruitt, Kim D.; Schuler, Gregory D.; Shumway, Martin; Sequeira, Edwin; Sherry, Steven T.; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Tatusov, Roman L.; Tatusova, Tatiana A.; Wagner, Lukas; Yaschenko, Eugene

2008-01-01

In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data available through NCBI's web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link, Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace, Assembly, and Short Read Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Entrez Probe, GENSAT, Database of Genotype and Phenotype, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool and the PubChem suite of small molecule databases. Augmenting the web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. PMID:18045790

LISTA, LISTA-HOP and LISTA-HON: a comprehensive compilation of protein encoding sequences and its associated homology databases from the yeast Saccharomyces.

PubMed Central

Dölz, R; Mossé, M O; Slonimski, P P; Bairoch, A; Linder, P

1996-01-01

We continued our effort to make a comprehensive database (LISTA) for the yeast Saccharomyces cerevisiae. As in previous editions the genetic names are consistently associated to each sequence with a known and confirmed ORF. If necessary, synonyms are given in the case of allelic duplicated sequences. Although the first publication of a sequence gives-according to our rules-the genetic name of a gene, in some instances more commonly used names are given to avoid nomenclature problems and the use of ancient designations which are no longer used. In these cases the old designation is given as synonym. Thus sequences can be found either by the name or by synonyms given in LISTA. Each entry contains the genetic name, the mnemonic from the EMBL data bank, the codon bias, reference of the publication of the sequence, Chromosomal location as far as known, SWISSPROT and EMBL accession numbers. New entries will also contain the name from the systematic sequencing efforts. Since the release of LISTA4.1 we update the database continuously. To obtain more information on the included sequences, each entry has been screened against non-redundant nucleotide and protein data bank collections resulting in LISTA-HON and LISTA-HOP. This release includes reports from full Smith and Watermann peptide-level searches against a non-redundant protein sequence database. The LISTA data base can be linked to the associated data sets or to nucleotide and protein banks by the Sequence Retrieval System (SRS). The database is available by FTP and on World Wide Web. PMID:8594599

Applicability of computational systems biology in toxicology.

PubMed

Kongsbak, Kristine; Hadrup, Niels; Audouze, Karine; Vinggaard, Anne Marie

2014-07-01

Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research. © 2014 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).

Aminoacyl-tRNA synthetases database Y2K

PubMed Central

Szymanski, Maciej; Barciszewski, Jan

2000-01-01

The aminoacyl-tRNA synthetases (AARS) are a diverse group of enzymes that ensure the fidelity of transfer of genetic information from DNA into protein. They catalyse the attachment of amino acids to transfer RNAs and thereby establish the rules of the genetic code by virtue of matching the nucleotide triplet of the anticodon with its cognate amino acid. Currently, 818 AARS primary structures have been reported from archaebacteria, eubacteria, mitochondria, chloroplasts and eukaryotic cells. The database is a compilation of the amino acid sequences of all AARSs, known to date, which are available as separate entries or alignments of related proteins via the WWW at http://rose.man.poznan.pl/aars/index.html PMID:10592262

Aminoacyl-tRNA synthetases database Y2K.

PubMed

Szymanski, M; Barciszewski, J

2000-01-01

The aminoacyl-tRNA synthetases (AARS) are a diverse group of enzymes that ensure the fidelity of transfer of genetic information from DNA into protein. They catalyse the attachment of amino acids to transfer RNAs and thereby establish the rules of the genetic code by virtue of matching the nucleotide triplet of the anticodon with its cognate amino acid. Currently, 818 AARS primary structures have been reported from archaebacteria, eubacteria, mitochondria, chloro-plasts and eukaryotic cells. The database is a compilation of the amino acid sequences of all AARSs, known to date, which are available as separate entries or alignments of related proteins via the WWW at http://rose.man.poznan.pl/aars/index.html

Genome databases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Courteau, J.

1991-10-11

Since the Genome Project began several years ago, a plethora of databases have been developed or are in the works. They range from the massive Genome Data Base at Johns Hopkins University, the central repository of all gene mapping information, to small databases focusing on single chromosomes or organisms. Some are publicly available, others are essentially private electronic lab notebooks. Still others limit access to a consortium of researchers working on, say, a single human chromosome. An increasing number incorporate sophisticated search and analytical software, while others operate as little more than data lists. In consultation with numerous experts inmore » the field, a list has been compiled of some key genome-related databases. The list was not limited to map and sequence databases but also included the tools investigators use to interpret and elucidate genetic data, such as protein sequence and protein structure databases. Because a major goal of the Genome Project is to map and sequence the genomes of several experimental animals, including E. coli, yeast, fruit fly, nematode, and mouse, the available databases for those organisms are listed as well. The author also includes several databases that are still under development - including some ambitious efforts that go beyond data compilation to create what are being called electronic research communities, enabling many users, rather than just one or a few curators, to add or edit the data and tag it as raw or confirmed.« less

The Halophile protein database.

PubMed

Sharma, Naveen; Farooqi, Mohammad Samir; Chaturvedi, Krishna Kumar; Lal, Shashi Bhushan; Grover, Monendra; Rai, Anil; Pandey, Pankaj

2014-01-01

Halophilic archaea/bacteria adapt to different salt concentration, namely extreme, moderate and low. These type of adaptations may occur as a result of modification of protein structure and other changes in different cell organelles. Thus proteins may play an important role in the adaptation of halophilic archaea/bacteria to saline conditions. The Halophile protein database (HProtDB) is a systematic attempt to document the biochemical and biophysical properties of proteins from halophilic archaea/bacteria which may be involved in adaptation of these organisms to saline conditions. In this database, various physicochemical properties such as molecular weight, theoretical pI, amino acid composition, atomic composition, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (Gravy) have been listed. These physicochemical properties play an important role in identifying the protein structure, bonding pattern and function of the specific proteins. This database is comprehensive, manually curated, non-redundant catalogue of proteins. The database currently contains 59 897 proteins properties extracted from 21 different strains of halophilic archaea/bacteria. The database can be accessed through link. Database URL: http://webapp.cabgrid.res.in/protein/ © The Author(s) 2014. Published by Oxford University Press.

TrypsNetDB: An integrated framework for the functional characterization of trypanosomatid proteins

PubMed Central

Gazestani, Vahid H.; Yip, Chun Wai; Nikpour, Najmeh; Berghuis, Natasha

2017-01-01

Trypanosomatid parasites cause serious infections in humans and production losses in livestock. Due to the high divergence from other eukaryotes, such as humans and model organisms, the functional roles of many trypanosomatid proteins cannot be predicted by homology-based methods, rendering a significant portion of their proteins as uncharacterized. Recent technological advances have led to the availability of multiple systematic and genome-wide datasets on trypanosomatid parasites that are informative regarding the biological role(s) of their proteins. Here, we report TrypsNetDB (http://trypsNetDB.org), a web-based resource for the functional annotation of 16 different species/strains of trypanosomatid parasites. The database not only visualizes the network context of the queried protein(s) in an intuitive way but also examines the response of the represented network in more than 50 different biological contexts and its enrichment for various biological terms and pathways, protein sequence signatures, and potential RNA regulatory elements. The interactome core of the database, as of Jan 23, 2017, contains 101,187 interactions among 13,395 trypanosomatid proteins inferred from 97 genome-wide and focused studies on the interactome of these organisms. PMID:28158179

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

PubMed Central

Franke, Lude; Bakel, Harm van; Fokkens, Like; de Jong, Edwin D.; Egmont-Petersen, Michael; Wijmenga, Cisca

2006-01-01

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown. PMID:16685651

Structure and function of seed storage proteins in faba bean (Vicia faba L.).

PubMed

Liu, Yujiao; Wu, Xuexia; Hou, Wanwei; Li, Ping; Sha, Weichao; Tian, Yingying

2017-05-01

The protein subunit is the most important basic unit of protein, and its study can unravel the structure and function of seed storage proteins in faba bean. In this study, we identified six specific protein subunits in Faba bean (cv. Qinghai 13) combining liquid chromatography (LC), liquid chromatography-electronic spray ionization mass (LC-ESI-MS/MS) and bio-information technology. The results suggested a diversity of seed storage proteins in faba bean, and a total of 16 proteins (four GroEL molecular chaperones and 12 plant-specific proteins) were identified from 97-, 96-, 64-, 47-, 42-, and 38-kD-specific protein subunits in faba bean based on the peptide sequence. We also analyzed the composition and abundance of the amino acids, the physicochemical characteristics, secondary structure, three-dimensional structure, transmembrane domain, and possible subcellular localization of these identified proteins in faba bean seed, and finally predicted function and structure. The three-dimensional structures were generated based on homologous modeling, and the protein function was analyzed based on the annotation from the non-redundant protein database (NR database, NCBI) and function analysis of optimal modeling. The objective of this study was to identify the seed storage proteins in faba bean and confirm the structure and function of these proteins. Our results can be useful for the study of protein nutrition and achieve breeding goals for optimal protein quality in faba bean.

ACLAME: a CLAssification of Mobile genetic Elements, update 2010.

PubMed

Leplae, Raphaël; Lima-Mendez, Gipsi; Toussaint, Ariane

2010-01-01

The ACLAME database is dedicated to the collection, analysis and classification of sequenced mobile genetic elements (MGEs, in particular phages and plasmids). In addition to providing information on the MGEs content, classifications are available at various levels of organization. At the gene/protein level, families group similar sequences that are expected to share the same function. Families of four or more proteins are manually assigned with a functional annotation using the GeneOntology and the locally developed ontology MeGO dedicated to MGEs. At the genome level, evolutionary cohesive modules group sets of protein families shared among MGEs. At the population level, networks display the reticulate evolutionary relationships among MGEs. To increase the coverage of the phage sequence space, ACLAME version 0.4 incorporates 760 high-quality predicted prophages selected from the Prophinder database. Most of the data can be downloaded from the freely accessible ACLAME web site (http://aclame.ulb.ac.be). The BLAST interface for querying the database has been extended and numerous tools for in-depth analysis of the results have been added.

Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME

PubMed Central

Berthold, Michael R.; Hedrick, Michael P.; Gilson, Michael K.

2015-01-01

Today’s large, public databases of protein–small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to custom scripting for informatics and data analysis. Here, we illustrate how the large protein-ligand database BindingDB may be incorporated into KNIME workflows as a step toward the integration of pharmacological data with broader biomolecular analyses. Thus, we describe a collection of KNIME workflows that access BindingDB data via RESTful webservices and, for more intensive queries, via a local distillation of the full BindingDB dataset. We focus in particular on the KNIME implementation of knowledge-based tools to generate informed hypotheses regarding protein targets of bioactive compounds, based on notions of chemical similarity. A number of variants of this basic approach are tested for seven existing drugs with relatively ill-defined therapeutic targets, leading to replication of some previously confirmed results and discovery of new, high-quality hits. Implications for future development are discussed. Database URL: www.bindingdb.org PMID:26384374

LISTA, LISTA-HOP and LISTA-HON: a comprehensive compilation of protein encoding sequences and its associated homology databases from the yeast Saccharomyces.

PubMed Central

Dölz, R; Mossé, M O; Slonimski, P P; Bairoch, A; Linder, P

1994-01-01

We continued our effort to make a comprehensive database (LISTA) for the yeast Saccharomyces cerevisiae. In this database each sequence has been attributed a single genetic name. In the case of duplicated sequences a simple method has been applied to distinguish between sequences of one and the same gene from non-allelic sequences of duplicated genes. If necessary, synonyms are given in the case of allelic duplicated sequences. Thus sequences can be found either by the name or by synonyms given in LISTA. Each entry contains the genetic name, the mnemonic from the EMBL data bank, the codon bias, reference of the publication of the sequence, Chromosomal location as far as known, Swissprot and EMBL accession numbers. To obtain more information on the included sequences, each entry has been screened against non-redundant nucleotide and protein data bank collections resulting in LISTA-HON and LISTA-HOP. The LISTA data base can be linked to the associated data sets or to nucleotide and protein banks by the Sequence Retrieval System (SRS). PMID:7937046

GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle.

PubMed

Lardenois, Aurélie; Gattiker, Alexandre; Collin, Olivier; Chalmel, Frédéric; Primig, Michael

2010-01-01

GermOnline 4.0 is a cross-species database portal focusing on high-throughput expression data relevant for germline development, the meiotic cell cycle and mitosis in healthy versus malignant cells. It is thus a source of information for life scientists as well as clinicians who are interested in gene expression and regulatory networks. The GermOnline gateway provides unlimited access to information produced with high-density oligonucleotide microarrays (3'-UTR GeneChips), genome-wide protein-DNA binding assays and protein-protein interaction studies in the context of Ensembl genome annotation. Samples used to produce high-throughput expression data and to carry out genome-wide in vivo DNA binding assays are annotated via the MIAME-compliant Multiomics Information Management and Annotation System (MIMAS 3.0). Furthermore, the Saccharomyces Genomics Viewer (SGV) was developed and integrated into the gateway. SGV is a visualization tool that outputs genome annotation and DNA-strand specific expression data produced with high-density oligonucleotide tiling microarrays (Sc_tlg GeneChips) which cover the complete budding yeast genome on both DNA strands. It facilitates the interpretation of expression levels and transcript structures determined for various cell types cultured under different growth and differentiation conditions. Database URL: www.germonline.org/

A Reference Proteomic Database of Lactobacillus plantarum CMCC-P0002

PubMed Central

Tian, Wanhong; Yu, Gang; Liu, Xiankai; Wang, Jie; Feng, Erling; Zhang, Xuemin; Chen, Bei; Zeng, Ming; Wang, Hengliang

2011-01-01

Lactobacillus plantarum is a widespread probiotic bacteria found in many fermented food products. In this study, the whole-cell proteins and secretory proteins of L. plantarum were separated by two-dimensional electrophoresis method. A total of 434 proteins were identified by tandem mass spectrometry, including a plasmid-encoded hypothetical protein pLP9000_05. The information of first 20 highest abundance proteins was listed for the further genetic manipulation of L. plantarum, such as construction of high-level expressions system. Furthermore, the first interaction map of L. plantarum was established by Blue-Native/SDS-PAGE technique. A heterodimeric complex composed of maltose phosphorylase Map3 and Map2, and two homodimeric complexes composed of Map3 and Map2 respectively, were identified at the same time, indicating the important roles of these proteins. These findings provided valuable information for the further proteomic researches of L. plantarum. PMID:21998671

PDBFlex: exploring flexibility in protein structures

PubMed Central

Hrabe, Thomas; Li, Zhanwen; Sedova, Mayya; Rotkiewicz, Piotr; Jaroszewski, Lukasz; Godzik, Adam

2016-01-01

The PDBFlex database, available freely and with no login requirements at http://pdbflex.org, provides information on flexibility of protein structures as revealed by the analysis of variations between depositions of different structural models of the same protein in the Protein Data Bank (PDB). PDBFlex collects information on all instances of such depositions, identifying them by a 95% sequence identity threshold, performs analysis of their structural differences and clusters them according to their structural similarities for easy analysis. The PDBFlex contains tools and viewers enabling in-depth examination of structural variability including: 2D-scaling visualization of RMSD distances between structures of the same protein, graphs of average local RMSD in the aligned structures of protein chains, graphical presentation of differences in secondary structure and observed structural disorder (unresolved residues), difference distance maps between all sets of coordinates and 3D views of individual structures and simulated transitions between different conformations, the latter displayed using JSMol visualization software. PMID:26615193

A reference proteomic database of Lactobacillus plantarum CMCC-P0002.

PubMed

Zhu, Li; Hu, Wei; Liu, Datao; Tian, Wanhong; Yu, Gang; Liu, Xiankai; Wang, Jie; Feng, Erling; Zhang, Xuemin; Chen, Bei; Zeng, Ming; Wang, Hengliang

2011-01-01

Lactobacillus plantarum is a widespread probiotic bacteria found in many fermented food products. In this study, the whole-cell proteins and secretory proteins of L. plantarum were separated by two-dimensional electrophoresis method. A total of 434 proteins were identified by tandem mass spectrometry, including a plasmid-encoded hypothetical protein pLP9000_05. The information of first 20 highest abundance proteins was listed for the further genetic manipulation of L. plantarum, such as construction of high-level expressions system. Furthermore, the first interaction map of L. plantarum was established by Blue-Native/SDS-PAGE technique. A heterodimeric complex composed of maltose phosphorylase Map3 and Map2, and two homodimeric complexes composed of Map3 and Map2 respectively, were identified at the same time, indicating the important roles of these proteins. These findings provided valuable information for the further proteomic researches of L. plantarum.

Distribution and cluster analysis of predicted intrinsically disordered protein Pfam domains

PubMed Central

Williams, Robert W; Xue, Bin; Uversky, Vladimir N; Dunker, A Keith

2013-01-01

The Pfam database groups regions of proteins by how well hidden Markov models (HMMs) can be trained to recognize similarities among them. Conservation pressure is probably in play here. The Pfam seed training set includes sequence and structure information, being drawn largely from the PDB. A long standing hypothesis among intrinsically disordered protein (IDP) investigators has held that conservation pressures are also at play in the evolution of different kinds of intrinsic disorder, but we find that predicted intrinsic disorder (PID) is not always conserved across Pfam domains. Here we analyze distributions and clusters of PID regions in 193024 members of the version 23.0 Pfam seed database. To include the maximum information available for proteins that remain unfolded in solution, we employ the 10 linearly independent Kidera factors1–3 for the amino acids, combined with PONDR4 predictions of disorder tendency, to transform the sequences of these Pfam members into an 11 column matrix where the number of rows is the length of each Pfam region. Cluster analyses of the set of all regions, including those that are folded, show 6 groupings of domains. Cluster analyses of domains with mean VSL2b scores greater than 0.5 (half predicted disorder or more) show at least 3 separated groups. It is hypothesized that grouping sets into shorter sequences with more uniform length will reveal more information about intrinsic disorder and lead to more finely structured and perhaps more accurate predictions. HMMs could be trained to include this information. PMID:28516017

RNA Bricks—a database of RNA 3D motifs and their interactions

PubMed Central

Chojnowski, Grzegorz; Waleń, Tomasz; Bujnicki, Janusz M.

2014-01-01

The RNA Bricks database (http://iimcb.genesilico.pl/rnabricks), stores information about recurrent RNA 3D motifs and their interactions, found in experimentally determined RNA structures and in RNA–protein complexes. In contrast to other similar tools (RNA 3D Motif Atlas, RNA Frabase, Rloom) RNA motifs, i.e. ‘RNA bricks’ are presented in the molecular environment, in which they were determined, including RNA, protein, metal ions, water molecules and ligands. All nucleotide residues in RNA bricks are annotated with structural quality scores that describe real-space correlation coefficients with the electron density data (if available), backbone geometry and possible steric conflicts, which can be used to identify poorly modeled residues. The database is also equipped with an algorithm for 3D motif search and comparison. The algorithm compares spatial positions of backbone atoms of the user-provided query structure and of stored RNA motifs, without relying on sequence or secondary structure information. This enables the identification of local structural similarities among evolutionarily related and unrelated RNA molecules. Besides, the search utility enables searching ‘RNA bricks’ according to sequence similarity, and makes it possible to identify motifs with modified ribonucleotide residues at specific positions. PMID:24220091

PeptideDepot: flexible relational database for visual analysis of quantitative proteomic data and integration of existing protein information.

PubMed

Yu, Kebing; Salomon, Arthur R

2009-12-01

Recently, dramatic progress has been achieved in expanding the sensitivity, resolution, mass accuracy, and scan rate of mass spectrometers able to fragment and identify peptides through MS/MS. Unfortunately, this enhanced ability to acquire proteomic data has not been accompanied by a concomitant increase in the availability of flexible tools allowing users to rapidly assimilate, explore, and analyze this data and adapt to various experimental workflows with minimal user intervention. Here we fill this critical gap by providing a flexible relational database called PeptideDepot for organization of expansive proteomic data sets, collation of proteomic data with available protein information resources, and visual comparison of multiple quantitative proteomic experiments. Our software design, built upon the synergistic combination of a MySQL database for safe warehousing of proteomic data with a FileMaker-driven graphical user interface for flexible adaptation to diverse workflows, enables proteomic end-users to directly tailor the presentation of proteomic data to the unique analysis requirements of the individual proteomics lab. PeptideDepot may be deployed as an independent software tool or integrated directly with our high throughput autonomous proteomic pipeline used in the automated acquisition and post-acquisition analysis of proteomic data.

RiceFOX: a database of Arabidopsis mutant lines overexpressing rice full-length cDNA that contains a wide range of trait information to facilitate analysis of gene function.

PubMed

Sakurai, Tetsuya; Kondou, Youichi; Akiyama, Kenji; Kurotani, Atsushi; Higuchi, Mieko; Ichikawa, Takanari; Kuroda, Hirofumi; Kusano, Miyako; Mori, Masaki; Saitou, Tsutomu; Sakakibara, Hitoshi; Sugano, Shoji; Suzuki, Makoto; Takahashi, Hideki; Takahashi, Shinya; Takatsuji, Hiroshi; Yokotani, Naoki; Yoshizumi, Takeshi; Saito, Kazuki; Shinozaki, Kazuo; Oda, Kenji; Hirochika, Hirohiko; Matsui, Minami

2011-02-01

Identification of gene function is important not only for basic research but also for applied science, especially with regard to improvements in crop production. For rapid and efficient elucidation of useful traits, we developed a system named FOX hunting (Full-length cDNA Over-eXpressor gene hunting) using full-length cDNAs (fl-cDNAs). A heterologous expression approach provides a solution for the high-throughput characterization of gene functions in agricultural plant species. Since fl-cDNAs contain all the information of functional mRNAs and proteins, we introduced rice fl-cDNAs into Arabidopsis plants for systematic gain-of-function mutation. We generated >30,000 independent Arabidopsis transgenic lines expressing rice fl-cDNAs (rice FOX Arabidopsis mutant lines). These rice FOX Arabidopsis lines were screened systematically for various criteria such as morphology, photosynthesis, UV resistance, element composition, plant hormone profile, metabolite profile/fingerprinting, bacterial resistance, and heat and salt tolerance. The information obtained from these screenings was compiled into a database named 'RiceFOX'. This database contains around 18,000 records of rice FOX Arabidopsis lines and allows users to search against all the observed results, ranging from morphological to invisible traits. The number of searchable items is approximately 100; moreover, the rice FOX Arabidopsis lines can be searched by rice and Arabidopsis gene/protein identifiers, sequence similarity to the introduced rice fl-cDNA and traits. The RiceFOX database is available at http://ricefox.psc.riken.jp/.

RiceFOX: A Database of Arabidopsis Mutant Lines Overexpressing Rice Full-Length cDNA that Contains a Wide Range of Trait Information to Facilitate Analysis of Gene Function

PubMed Central

Sakurai, Tetsuya; Kondou, Youichi; Akiyama, Kenji; Kurotani, Atsushi; Higuchi, Mieko; Ichikawa, Takanari; Kuroda, Hirofumi; Kusano, Miyako; Mori, Masaki; Saitou, Tsutomu; Sakakibara, Hitoshi; Sugano, Shoji; Suzuki, Makoto; Takahashi, Hideki; Takahashi, Shinya; Takatsuji, Hiroshi; Yokotani, Naoki; Yoshizumi, Takeshi; Saito, Kazuki; Shinozaki, Kazuo; Oda, Kenji; Hirochika, Hirohiko; Matsui, Minami

2011-01-01

Identification of gene function is important not only for basic research but also for applied science, especially with regard to improvements in crop production. For rapid and efficient elucidation of useful traits, we developed a system named FOX hunting (Full-length cDNA Over-eXpressor gene hunting) using full-length cDNAs (fl-cDNAs). A heterologous expression approach provides a solution for the high-throughput characterization of gene functions in agricultural plant species. Since fl-cDNAs contain all the information of functional mRNAs and proteins, we introduced rice fl-cDNAs into Arabidopsis plants for systematic gain-of-function mutation. We generated >30,000 independent Arabidopsis transgenic lines expressing rice fl-cDNAs (rice FOX Arabidopsis mutant lines). These rice FOX Arabidopsis lines were screened systematically for various criteria such as morphology, photosynthesis, UV resistance, element composition, plant hormone profile, metabolite profile/fingerprinting, bacterial resistance, and heat and salt tolerance. The information obtained from these screenings was compiled into a database named ‘RiceFOX’. This database contains around 18,000 records of rice FOX Arabidopsis lines and allows users to search against all the observed results, ranging from morphological to invisible traits. The number of searchable items is approximately 100; moreover, the rice FOX Arabidopsis lines can be searched by rice and Arabidopsis gene/protein identifiers, sequence similarity to the introduced rice fl-cDNA and traits. The RiceFOX database is available at http://ricefox.psc.riken.jp/. PMID:21186176

Data to knowledge: how to get meaning from your result

PubMed Central

Berman, Helen M.; Gabanyi, Margaret J.; Groom, Colin R.; Johnson, John E.; Murshudov, Garib N.; Nicholls, Robert A.; Reddy, Vijay; Schwede, Torsten; Zimmerman, Matthew D.; Westbrook, John; Minor, Wladek

2015-01-01

Structural and functional studies require the development of sophisticated ‘Big Data’ technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB ‘super’ laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results. PMID:25610627

Defining and predicting structurally conserved regions in protein superfamilies

PubMed Central

Huang, Ivan K.; Grishin, Nick V.

2013-01-01

Motivation: The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. Results: Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. Availability: The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR. Contact: 91huangi@gmail.com or grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics Online PMID:23193223

pseudoMap: an innovative and comprehensive resource for identification of siRNA-mediated mechanisms in human transcribed pseudogenes.

PubMed

Chan, Wen-Ling; Yang, Wen-Kuang; Huang, Hsien-Da; Chang, Jan-Gowth

2013-01-01

RNA interference (RNAi) is a gene silencing process within living cells, which is controlled by the RNA-induced silencing complex with a sequence-specific manner. In flies and mice, the pseudogene transcripts can be processed into short interfering RNAs (siRNAs) that regulate protein-coding genes through the RNAi pathway. Following these findings, we construct an innovative and comprehensive database to elucidate siRNA-mediated mechanism in human transcribed pseudogenes (TPGs). To investigate TPG producing siRNAs that regulate protein-coding genes, we mapped the TPGs to small RNAs (sRNAs) that were supported by publicly deep sequencing data from various sRNA libraries and constructed the TPG-derived siRNA-target interactions. In addition, we also presented that TPGs can act as a target for miRNAs that actually regulate the parental gene. To enable the systematic compilation and updating of these results and additional information, we have developed a database, pseudoMap, capturing various types of information, including sequence data, TPG and cognate annotation, deep sequencing data, RNA-folding structure, gene expression profiles, miRNA annotation and target prediction. As our knowledge, pseudoMap is the first database to demonstrate two mechanisms of human TPGs: encoding siRNAs and decoying miRNAs that target the parental gene. pseudoMap is freely accessible at http://pseudomap.mbc.nctu.edu.tw/. Database URL: http://pseudomap.mbc.nctu.edu.tw/

NemaPath: online exploration of KEGG-based metabolic pathways for nematodes

PubMed Central

Wylie, Todd; Martin, John; Abubucker, Sahar; Yin, Yong; Messina, David; Wang, Zhengyuan; McCarter, James P; Mitreva, Makedonka

2008-01-01

Background Nematode.net is a web-accessible resource for investigating gene sequences from parasitic and free-living nematode genomes. Beyond the well-characterized model nematode C. elegans, over 500,000 expressed sequence tags (ESTs) and nearly 600,000 genome survey sequences (GSSs) have been generated from 36 nematode species as part of the Parasitic Nematode Genomics Program undertaken by the Genome Center at Washington University School of Medicine. However, these sequencing data are not present in most publicly available protein databases, which only include sequences in Swiss-Prot. Swiss-Prot, in turn, relies on GenBank/Embl/DDJP for predicted proteins from complete genomes or full-length proteins. Description Here we present the NemaPath pathway server, a web-based pathway-level visualization tool for navigating putative metabolic pathways for over 30 nematode species, including 27 parasites. The NemaPath approach consists of two parts: 1) a backend tool to align and evaluate nematode genomic sequences (curated EST contigs) against the annotated Kyoto Encyclopedia of Genes and Genomes (KEGG) protein database; 2) a web viewing application that displays annotated KEGG pathway maps based on desired confidence levels of primary sequence similarity as defined by a user. NemaPath also provides cross-referenced access to nematode genome information provided by other tools available on Nematode.net, including: detailed NemaGene EST cluster information; putative translations; GBrowse EST cluster views; links from nematode data to external databases for corresponding synonymous C. elegans counterparts, subject matches in KEGG's gene database, and also KEGG Ontology (KO) identification. Conclusion The NemaPath server hosts metabolic pathway mappings for 30 nematode species and is available on the World Wide Web at . The nematode source sequences used for the metabolic pathway mappings are available via FTP , as provided by the Genome Center at Washington University School of Medicine. PMID:18983679

Comprehensive analysis of orthologous protein domains using the HOPS database.

PubMed

Storm, Christian E V; Sonnhammer, Erik L L

2003-10-01

One of the most reliable methods for protein function annotation is to transfer experimentally known functions from orthologous proteins in other organisms. Most methods for identifying orthologs operate on a subset of organisms with a completely sequenced genome, and treat proteins as single-domain units. However, it is well known that proteins are often made up of several independent domains, and there is a wealth of protein sequences from genomes that are not completely sequenced. A comprehensive set of protein domain families is found in the Pfam database. We wanted to apply orthology detection to Pfam families, but first some issues needed to be addressed. First, orthology detection becomes impractical and unreliable when too many species are included. Second, shorter domains contain less information. It is therefore important to assess the quality of the orthology assignment and avoid very short domains altogether. We present a database of orthologous protein domains in Pfam called HOPS: Hierarchical grouping of Orthologous and Paralogous Sequences. Orthology is inferred in a hierarchic system of phylogenetic subgroups using ortholog bootstrapping. To avoid the frequent errors stemming from horizontally transferred genes in bacteria, the analysis is presently limited to eukaryotic genes. The results are accessible in the graphical browser NIFAS, a Java tool originally developed for analyzing phylogenetic relations within Pfam families. The method was tested on a set of curated orthologs with experimentally verified function. In comparison to tree reconciliation with a complete species tree, our approach finds significantly more orthologs in the test set. Examples for investigating gene fusions and domain recombination using HOPS are given.

MPIC: a mitochondrial protein import components database for plant and non-plant species.

PubMed

Murcha, Monika W; Narsai, Reena; Devenish, James; Kubiszewski-Jakubiak, Szymon; Whelan, James

2015-01-01

In the 2 billion years since the endosymbiotic event that gave rise to mitochondria, variations in mitochondrial protein import have evolved across different species. With the genomes of an increasing number of plant species sequenced, it is possible to gain novel insights into mitochondrial protein import pathways. We have generated the Mitochondrial Protein Import Components (MPIC) Database (DB; http://www.plantenergy.uwa.edu.au/applications/mpic) providing searchable information on the protein import apparatus of plant and non-plant mitochondria. An in silico analysis was carried out, comparing the mitochondrial protein import apparatus from 24 species representing various lineages from Saccharomyces cerevisiae (yeast) and algae to Homo sapiens (human) and higher plants, including Arabidopsis thaliana (Arabidopsis), Oryza sativa (rice) and other more recently sequenced plant species. Each of these species was extensively searched and manually assembled for analysis in the MPIC DB. The database presents an interactive diagram in a user-friendly manner, allowing users to select their import component of interest. The MPIC DB presents an extensive resource facilitating detailed investigation of the mitochondrial protein import machinery and allowing patterns of conservation and divergence to be recognized that would otherwise have been missed. To demonstrate the usefulness of the MPIC DB, we present a comparative analysis of the mitochondrial protein import machinery in plants and non-plant species, revealing plant-specific features that have evolved. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Biomineralization of Schlumbergerella floresiana, a significant carbonate-producing benthic foraminifer.

PubMed

Sabbatini, A; Bédouet, L; Marie, A; Bartolini, A; Landemarre, L; Weber, M X; Gusti Ngurah Kade Mahardika, I; Berland, S; Zito, F; Vénec-Peyré, M-T

2014-07-01

Most foraminifera that produce a shell are efficient biomineralizers. We analyzed the calcitic shell of the large tropical benthic foraminifer Schlumbergerella floresiana. We found a suite of macromolecules containing many charged and polar amino acids and glycine that are also abundant in biomineralization proteins of other phyla. As neither genomic nor transcriptomic data are available for foraminiferal biomineralization yet, de novo-generated sequences, obtained from organic matrices submitted to ms blast database search, led to the characterization of 156 peptides. Very few homologous proteins were matched in the proteomic database, implying that the peptides are derived from unknown proteins present in the foraminiferal organic matrices. The amino acid distribution of these peptides was queried against the uniprot database and the mollusk uniprot database for comparison. The mollusks compose a well-studied phylum that yield a large variety of biomineralization proteins. These results showed that proteins extracted from S. floresiana shells contained sequences enriched with glycine, alanine, and proline, making a set of residues that provided a signature unique to foraminifera. Three of the de novo peptides exhibited sequence similarities to peptides found in proteins such as pre-collagen-P and a group of P-type ATPases including a calcium-transporting ATPase. Surprisingly, the peptide that was most similar to the collagen-like protein was a glycine-rich peptide reported from the test and spine proteome of sea urchin. The molecules, identified by matrix-assisted laser desorption ionization-time of flight mass spectrometry analyses, included acid-soluble N-glycoproteins with its sugar moieties represented by high-mannose-type glycans and carbohydrates. Describing the nature of the proteins, and associated molecules in the skeletal structure of living foraminifera, can elucidate the biomineralization mechanisms of these major carbonate producers in marine ecosystems. As fossil foraminifera provide important paleoenvironmental and paleoclimatic information, a better understanding of biomineralization in these organisms will have far-reaching impacts. © 2014 John Wiley & Sons Ltd.

The VirusBanker database uses a Java program to allow flexible searching through Bunyaviridae sequences

PubMed Central

Fourment, Mathieu; Gibbs, Mark J

2008-01-01

Background Viruses of the Bunyaviridae have segmented negative-stranded RNA genomes and several of them cause significant disease. Many partial sequences have been obtained from the segments so that GenBank searches give complex results. Sequence databases usually use HTML pages to mediate remote sorting, but this approach can be limiting and may discourage a user from exploring a database. Results The VirusBanker database contains Bunyaviridae sequences and alignments and is presented as two spreadsheets generated by a Java program that interacts with a MySQL database on a server. Sequences are displayed in rows and may be sorted using information that is displayed in columns and includes data relating to the segment, gene, protein, species, strain, sequence length, terminal sequence and date and country of isolation. Bunyaviridae sequences and alignments may be downloaded from the second spreadsheet with titles defined by the user from the columns, or viewed when passed directly to the sequence editor, Jalview. Conclusion VirusBanker allows large datasets of aligned nucleotide and protein sequences from the Bunyaviridae to be compiled and winnowed rapidly using criteria that are formulated heuristically. PMID:18251994

PDB explorer -- a web based algorithm for protein annotation viewer and 3D visualization.

PubMed

Nayarisseri, Anuraj; Shardiwal, Rakesh Kumar; Yadav, Mukesh; Kanungo, Neha; Singh, Pooja; Shah, Pratik; Ahmed, Sheaza

2014-12-01

The PDB file format, is a text format characterizing the three dimensional structures of macro molecules available in the Protein Data Bank (PDB). Determined protein structure are found in coalition with other molecules or ions such as nucleic acids, water, ions, Drug molecules and so on, which therefore can be described in the PDB format and have been deposited in PDB database. PDB is a machine generated file, it's not human readable format, to read this file we need any computational tool to understand it. The objective of our present study is to develop a free online software for retrieval, visualization and reading of annotation of a protein 3D structure which is available in PDB database. Main aim is to create PDB file in human readable format, i.e., the information in PDB file is converted in readable sentences. It displays all possible information from a PDB file including 3D structure of that file. Programming languages and scripting languages like Perl, CSS, Javascript, Ajax, and HTML have been used for the development of PDB Explorer. The PDB Explorer directly parses the PDB file, calling methods for parsed element secondary structure element, atoms, coordinates etc. PDB Explorer is freely available at http://www.pdbexplorer.eminentbio.com/home with no requirement of log-in.

Actinobase: Database on molecular diversity, phylogeny and biocatalytic potential of salt tolerant alkaliphilic actinomycetes.

PubMed

Sharma, Amit K; Gohel, Sangeeta; Singh, Satya P

2012-01-01

Actinobase is a relational database of molecular diversity, phylogeny and biocatalytic potential of haloalkaliphilic actinomycetes. The main objective of this data base is to provide easy access to range of information, data storage, comparison and analysis apart from reduced data redundancy, data entry, storage, retrieval costs and improve data security. Information related to habitat, cell morphology, Gram reaction, biochemical characterization and molecular features would allow researchers in understanding identification and stress adaptation of the existing and new candidates belonging to salt tolerant alkaliphilic actinomycetes. The PHP front end helps to add nucleotides and protein sequence of reported entries which directly help researchers to obtain the required details. Analysis of the genus wise status of the salt tolerant alkaliphilic actinomycetes indicated 6 different genera among the 40 classified entries of the salt tolerant alkaliphilic actinomycetes. The results represented wide spread occurrence of salt tolerant alkaliphilic actinomycetes belonging to diverse taxonomic positions. Entries and information related to actinomycetes in the database are publicly accessible at http://www.actinobase.in. On clustalW/X multiple sequence alignment of the alkaline protease gene sequences, different clusters emerged among the groups. The narrow search and limit options of the constructed database provided comparable information. The user friendly access to PHP front end facilitates would facilitate addition of sequences of reported entries. The database is available for free at http://www.actinobase.in.

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system.

PubMed

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

2015-11-19

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database in which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. This database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.

TIM Barrel Protein Structure Classification Using Alignment Approach and Best Hit Strategy

NASA Astrophysics Data System (ADS)

Chu, Jia-Han; Lin, Chun Yuan; Chang, Cheng-Wen; Lee, Chihan; Yang, Yuh-Shyong; Tang, Chuan Yi

2007-11-01

The classification of protein structures is essential for their function determination in bioinformatics. It has been estimated that around 10% of all known enzymes have TIM barrel domains from the Structural Classification of Proteins (SCOP) database. With its high sequence variation and diverse functionalities, TIM barrel protein becomes to be an attractive target for protein engineering and for the evolution study. Hence, in this paper, an alignment approach with the best hit strategy is proposed to classify the TIM barrel protein structure in terms of superfamily and family levels in the SCOP. This work is also used to do the classification for class level in the Enzyme nomenclature (ENZYME) database. Two testing data sets, TIM40D and TIM95D, both are used to evaluate this approach. The resulting classification has an overall prediction accuracy rate of 90.3% for the superfamily level in the SCOP, 89.5% for the family level in the SCOP and 70.1% for the class level in the ENZYME. These results demonstrate that the alignment approach with the best hit strategy is a simple and viable method for the TIM barrel protein structure classification, even only has the amino acid sequences information.

Atomic analysis of protein-protein interfaces with known inhibitors: the 2P2I database.

PubMed

Bourgeas, Raphaël; Basse, Marie-Jeanne; Morelli, Xavier; Roche, Philippe

2010-03-09

In the last decade, the inhibition of protein-protein interactions (PPIs) has emerged from both academic and private research as a new way to modulate the activity of proteins. Inhibitors of these original interactions are certainly the next generation of highly innovative drugs that will reach the market in the next decade. However, in silico design of such compounds still remains challenging. Here we describe this particular PPI chemical space through the presentation of 2P2I(DB), a hand-curated database dedicated to the structure of PPIs with known inhibitors. We have analyzed protein/protein and protein/inhibitor interfaces in terms of geometrical parameters, atom and residue properties, buried accessible surface area and other biophysical parameters. The interfaces found in 2P2I(DB) were then compared to those of representative datasets of heterodimeric complexes. We propose a new classification of PPIs with known inhibitors into two classes depending on the number of segments present at the interface and corresponding to either a single secondary structure element or to a more globular interacting domain. 2P2I(DB) complexes share global shape properties with standard transient heterodimer complexes, but their accessible surface areas are significantly smaller. No major conformational changes are seen between the different states of the proteins. The interfaces are more hydrophobic than general PPI's interfaces, with less charged residues and more non-polar atoms. Finally, fifty percent of the complexes in the 2P2I(DB) dataset possess more hydrogen bonds than typical protein-protein complexes. Potential areas of study for the future are proposed, which include a new classification system consisting of specific families and the identification of PPI targets with high druggability potential based on key descriptors of the interaction. 2P2I database stores structural information about PPIs with known inhibitors and provides a useful tool for biologists to assess the potential druggability of their interfaces. The database can be accessed at http://2p2idb.cnrs-mrs.fr.

Achieving high confidence protein annotations in a sea of unknowns

NASA Astrophysics Data System (ADS)

Timmins-Schiffman, E.; May, D. H.; Noble, W. S.; Nunn, B. L.; Mikan, M.; Harvey, H. R.

2016-02-01

Increased sensitivity of mass spectrometry (MS) technology allows deep and broad insight into community functional analyses. Metaproteomics holds the promise to reveal functional responses of natural microbial communities, whereas metagenomics alone can only hint at potential functions. The complex datasets resulting from ocean MS have the potential to inform diverse realms of the biological, chemical, and physical ocean sciences, yet the extent of bacterial functional diversity and redundancy has not been fully explored. To take advantage of these impressive datasets, we need a clear bioinformatics pipeline for metaproteomics peptide identification and annotation with a database that can provide confident identifications. Researchers must consider whether it is sufficient to leverage the vast quantities of available ocean sequence data or if they must invest in site-specific metagenomic sequencing. We have sequenced, to our knowledge, the first western arctic metagenomes from the Bering Strait and the Chukchi Sea. We have addressed the long standing question: Is a metagenome required to accurately complete metaproteomics and assess the biological distribution of metabolic functions controlling nutrient acquisition in the ocean? Two different protein databases were constructed from 1) a site-specific metagenome and 2) subarctic/arctic groups available in NCBI's non-redundant database. Multiple proteomic search strategies were employed, against each individual database and against both databases combined, to determine the algorithm and approach that yielded the balance of high sensitivity and confident identification. Results yielded over 8200 confidently identified proteins. Our comparison of these results allows us to quantify the utility of investing resources in a metagenome versus using the constantly expanding and immediately available public databases for metaproteomic studies.

Evaluating Functional Annotations of Enzymes Using the Gene Ontology.

PubMed

Holliday, Gemma L; Davidson, Rebecca; Akiva, Eyal; Babbitt, Patricia C

2017-01-01

The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

Can we replace curation with information extraction software?

PubMed

Karp, Peter D

2016-01-01

Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL. © The Author(s) 2016. Published by Oxford University Press.

The chordate proteome history database.

PubMed

Levasseur, Anthony; Paganini, Julien; Dainat, Jacques; Thompson, Julie D; Poch, Olivier; Pontarotti, Pierre; Gouret, Philippe

2012-01-01

The chordate proteome history database (http://ioda.univ-provence.fr) comprises some 20,000 evolutionary analyses of proteins from chordate species. Our main objective was to characterize and study the evolutionary histories of the chordate proteome, and in particular to detect genomic events and automatic functional searches. Firstly, phylogenetic analyses based on high quality multiple sequence alignments and a robust phylogenetic pipeline were performed for the whole protein and for each individual domain. Novel approaches were developed to identify orthologs/paralogs, and predict gene duplication/gain/loss events and the occurrence of new protein architectures (domain gains, losses and shuffling). These important genetic events were localized on the phylogenetic trees and on the genomic sequence. Secondly, the phylogenetic trees were enhanced by the creation of phylogroups, whereby groups of orthologous sequences created using OrthoMCL were corrected based on the phylogenetic trees; gene family size and gene gain/loss in a given lineage could be deduced from the phylogroups. For each ortholog group obtained from the phylogenetic or the phylogroup analysis, functional information and expression data can be retrieved. Database searches can be performed easily using biological objects: protein identifier, keyword or domain, but can also be based on events, eg, domain exchange events can be retrieved. To our knowledge, this is the first database that links group clustering, phylogeny and automatic functional searches along with the detection of important events occurring during genome evolution, such as the appearance of a new domain architecture.

Effect of the sequence data deluge on the performance of methods for detecting protein functional residues.

PubMed

Garrido-Martín, Diego; Pazos, Florencio

2018-02-27

The exponential accumulation of new sequences in public databases is expected to improve the performance of all the approaches for predicting protein structural and functional features. Nevertheless, this was never assessed or quantified for some widely used methodologies, such as those aimed at detecting functional sites and functional subfamilies in protein multiple sequence alignments. Using raw protein sequences as only input, these approaches can detect fully conserved positions, as well as those with a family-dependent conservation pattern. Both types of residues are routinely used as predictors of functional sites and, consequently, understanding how the sequence content of the databases affects them is relevant and timely. In this work we evaluate how the growth and change with time in the content of sequence databases affect five sequence-based approaches for detecting functional sites and subfamilies. We do that by recreating historical versions of the multiple sequence alignments that would have been obtained in the past based on the database contents at different time points, covering a period of 20 years. Applying the methods to these historical alignments allows quantifying the temporal variation in their performance. Our results show that the number of families to which these methods can be applied sharply increases with time, while their ability to detect potentially functional residues remains almost constant. These results are informative for the methods' developers and final users, and may have implications in the design of new sequencing initiatives.

P19-S Managing Proteomics Data from Data Generation and Data Warehousing to Central Data Repository and Journal Reviewing Processes

PubMed Central

Thiele, H.; Glandorf, J.; Koerting, G.; Reidegeld, K.; Blüggel, M.; Meyer, H.; Stephan, C.

2007-01-01

In today’s proteomics research, various techniques and instrumentation bioinformatics tools are necessary to manage the large amount of heterogeneous data with an automatic quality control to produce reliable and comparable results. Therefore a data-processing pipeline is mandatory for data validation and comparison in a data-warehousing system. The proteome bioinformatics platform ProteinScape has been proven to cover these needs. The reprocessing of HUPO BPP participants’ MS data was done within ProteinScape. The reprocessed information was transferred into the global data repository PRIDE. ProteinScape as a data-warehousing system covers two main aspects: archiving relevant data of the proteomics workflow and information extraction functionality (protein identification, quantification and generation of biological knowledge). As a strategy for automatic data validation, different protein search engines are integrated. Result analysis is performed using a decoy database search strategy, which allows the measurement of the false-positive identification rate. Peptide identifications across different workflows, different MS techniques, and different search engines are merged to obtain a quality-controlled protein list. The proteomics identifications database (PRIDE), as a public data repository, is an archiving system where data are finally stored and no longer changed by further processing steps. Data submission to PRIDE is open to proteomics laboratories generating protein and peptide identifications. An export tool has been developed for transferring all relevant HUPO BPP data from ProteinScape into PRIDE using the PRIDE.xml format. The EU-funded ProDac project will coordinate the development of software tools covering international standards for the representation of proteomics data. The implementation of data submission pipelines and systematic data collection in public standards–compliant repositories will cover all aspects, from the generation of MS data in each laboratory to the conversion of all the annotating information and identifications to a standardized format. Such datasets can be used in the course of publishing in scientific journals.

Mining protein database using machine learning techniques.

PubMed

Camargo, Renata da Silva; Niranjan, Mahesan

2008-08-25

With a large amount of information relating to proteins accumulating in databases widely available online, it is of interest to apply machine learning techniques that, by extracting underlying statistical regularities in the data, make predictions about the functional and evolutionary characteristics of unseen proteins. Such predictions can help in achieving a reduction in the space over which experiment designers need to search in order to improve our understanding of the biochemical properties. Previously it has been suggested that an integration of features computable by comparing a pair of proteins can be achieved by an artificial neural network, hence predicting the degree to which they may be evolutionary related and homologous.
We compiled two datasets of pairs of proteins, each pair being characterised by seven distinct features. We performed an exhaustive search through all possible combinations of features, for the problem of separating remote homologous from analogous pairs, we note that significant performance gain was obtained by the inclusion of sequence and structure information. We find that the use of a linear classifier was enough to discriminate a protein pair at the family level. However, at the superfamily level, to detect remote homologous pairs was a relatively harder problem. We find that the use of nonlinear classifiers achieve significantly higher accuracies.
In this paper, we compare three different pattern classification methods on two problems formulated as detecting evolutionary and functional relationships between pairs of proteins, and from extensive cross validation and feature selection based studies quantify the average limits and uncertainties with which such predictions may be made. Feature selection points to a \\"knowledge gap\\" in currently available functional annotations. We demonstrate how the scheme may be employed in a framework to associate an individual protein with an existing family of evolutionarily related proteins.

Introducing meta-services for biomedical information extraction

PubMed Central

Leitner, Florian; Krallinger, Martin; Rodriguez-Penagos, Carlos; Hakenberg, Jörg; Plake, Conrad; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Hung, Hsi-Chuan; Lau, William W; Johnson, Calvin A; Sætre, Rune; Yoshida, Kazuhiro; Chen, Yan Hua; Kim, Sun; Shin, Soo-Yong; Zhang, Byoung-Tak; Baumgartner, William A; Hunter, Lawrence; Haddow, Barry; Matthews, Michael; Wang, Xinglong; Ruch, Patrick; Ehrler, Frédéric; Özgür, Arzucan; Erkan, Güneş; Radev, Dragomir R; Krauthammer, Michael; Luong, ThaiBinh; Hoffmann, Robert; Sander, Chris; Valencia, Alfonso

2008-01-01

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations. PMID:18834497

Database resources of the National Center for Biotechnology Information.

PubMed

Wheeler, David L; Barrett, Tanya; Benson, Dennis A; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Geer, Lewis Y; Kapustin, Yuri; Khovayko, Oleg; Landsman, David; Lipman, David J; Madden, Thomas L; Maglott, Donna R; Ostell, James; Miller, Vadim; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Steven T; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Tatusov, Roman L; Tatusova, Tatiana A; Wagner, Lukas; Yaschenko, Eugene

2007-01-01

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link(BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace and Assembly Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Viral Genotyping Tools, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Database resources of the National Center for Biotechnology Information.

PubMed

Sayers, Eric W; Barrett, Tanya; Benson, Dennis A; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Feolo, Michael; Geer, Lewis Y; Helmberg, Wolfgang; Kapustin, Yuri; Landsman, David; Lipman, David J; Madden, Thomas L; Maglott, Donna R; Miller, Vadim; Mizrachi, Ilene; Ostell, James; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Stephen T; Shumway, Martin; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A; Wagner, Lukas; Yaschenko, Eugene; Ye, Jian

2009-01-01

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the web applications is custom implementation of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

GenBank.

PubMed

Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Sayers, Eric W

2011-01-01

GenBank® is a comprehensive database that contains publicly available nucleotide sequences for more than 380,000 organisms named at the genus level or lower, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system that integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov.

HHsvm: fast and accurate classification of profile–profile matches identified by HHsearch

PubMed Central

Dlakić, Mensur

2009-01-01

Motivation: Recently developed profile–profile methods rival structural comparisons in their ability to detect homology between distantly related proteins. Despite this tremendous progress, many genuine relationships between protein families cannot be recognized as comparisons of their profiles result in scores that are statistically insignificant. Results: Using known evolutionary relationships among protein superfamilies in SCOP database, support vector machines were trained on four sets of discriminatory features derived from the output of HHsearch. Upon validation, it was shown that the automatic classification of all profile–profile matches was superior to fixed threshold-based annotation in terms of sensitivity and specificity. The effectiveness of this approach was demonstrated by annotating several domains of unknown function from the Pfam database. Availability: Programs and scripts implementing the methods described in this manuscript are freely available from http://hhsvm.dlakiclab.org/. Contact: mdlakic@montana.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19773335

Proteomic profile of dormant Trichophyton Rubrum conidia

PubMed Central

Leng, Wenchuan; Liu, Tao; Li, Rui; Yang, Jian; Wei, Candong; Zhang, Wenliang; Jin, Qi

2008-01-01

Background Trichophyton rubrum is the most common dermatophyte causing fungal skin infections in humans. Asexual sporulation is an important means of propagation for T. rubrum, and conidia produced by this way are thought to be the primary cause of human infections. Despite their importance in pathogenesis, the conidia of T. rubrum remain understudied. We intend to intensively investigate the proteome of dormant T. rubrum conidia to characterize its molecular and cellular features and to enhance the development of novel therapeutic strategies. Results The proteome of T. rubrum conidia was analyzed by combining shotgun proteomics with sample prefractionation and multiple enzyme digestion. In total, 1026 proteins were identified. All identified proteins were compared to those in the NCBI non-redundant protein database, the eukaryotic orthologous groups database, and the gene ontology database to obtain functional annotation information. Functional classification revealed that the identified proteins covered nearly all major biological processes. Some proteins were spore specific and related to the survival and dispersal of T. rubrum conidia, and many proteins were important to conidial germination and response to environmental conditions. Conclusion Our results suggest that the proteome of T. rubrum conidia is considerably complex, and that the maintenance of conidial dormancy is an intricate and elaborate process. This data set provides the first global framework for the dormant T. rubrum conidia proteome and is a stepping stone on the way to further study of the molecular mechanisms of T. rubrum conidial germination and the maintenance of conidial dormancy. PMID:18578874

Navigating through the Jungle of Allergens: Features and Applications of Allergen Databases.

PubMed

Radauer, Christian

2017-01-01

The increasing number of available data on allergenic proteins demanded the establishment of structured, freely accessible allergen databases. In this review article, features and applications of 6 of the most widely used allergen databases are discussed. The WHO/IUIS Allergen Nomenclature Database is the official resource of allergen designations. Allergome is the most comprehensive collection of data on allergens and allergen sources. AllergenOnline is aimed at providing a peer-reviewed database of allergen sequences for prediction of allergenicity of proteins, such as those planned to be inserted into genetically modified crops. The Structural Database of Allergenic Proteins (SDAP) provides a database of allergen sequences, structures, and epitopes linked to bioinformatics tools for sequence analysis and comparison. The Immune Epitope Database (IEDB) is the largest repository of T-cell, B-cell, and major histocompatibility complex protein epitopes including epitopes of allergens. AllFam classifies allergens into families of evolutionarily related proteins using definitions from the Pfam protein family database. These databases contain mostly overlapping data, but also show differences in terms of their targeted users, the criteria for including allergens, data shown for each allergen, and the availability of bioinformatics tools. © 2017 S. Karger AG, Basel.

The Human Ageing Genomic Resources: online databases and tools for biogerontologists

PubMed Central

de Magalhães, João Pedro; Budovsky, Arie; Lehmann, Gilad; Costa, Joana; Li, Yang; Fraifeld, Vadim; Church, George M.

2009-01-01

Summary Ageing is a complex, challenging phenomenon that will require multiple, interdisciplinary approaches to unravel its puzzles. To assist basic research on ageing, we developed the Human Ageing Genomic Resources (HAGR). This work provides an overview of the databases and tools in HAGR and describes how the gerontology research community can employ them. Several recent changes and improvements to HAGR are also presented. The two centrepieces in HAGR are GenAge and AnAge. GenAge is a gene database featuring genes associated with ageing and longevity in model organisms, a curated database of genes potentially associated with human ageing, and a list of genes tested for their association with human longevity. A myriad of biological data and information is included for hundreds of genes, making GenAge a reference for research that reflects our current understanding of the genetic basis of ageing. GenAge can also serve as a platform for the systems biology of ageing, and tools for the visualization of protein-protein interactions are also included. AnAge is a database of ageing in animals, featuring over 4,000 species, primarily assembled as a resource for comparative and evolutionary studies of ageing. Longevity records, developmental and reproductive traits, taxonomic information, basic metabolic characteristics, and key observations related to ageing are included in AnAge. Software is also available to aid researchers in the form of Perl modules to automate numerous tasks and as an SPSS script to analyse demographic mortality data. The Human Ageing Genomic Resources are available online at http://genomics.senescence.info. PMID:18986374

APPRIS 2017: principal isoforms for multiple gene sets

PubMed Central

Rodriguez-Rivas, Juan; Di Domenico, Tomás; Vázquez, Jesús; Valencia, Alfonso

2018-01-01

Abstract The APPRIS database (http://appris-tools.org) uses protein structural and functional features and information from cross-species conservation to annotate splice isoforms in protein-coding genes. APPRIS selects a single protein isoform, the ‘principal’ isoform, as the reference for each gene based on these annotations. A single main splice isoform reflects the biological reality for most protein coding genes and APPRIS principal isoforms are the best predictors of these main proteins isoforms. Here, we present the updates to the database, new developments that include the addition of three new species (chimpanzee, Drosophila melangaster and Caenorhabditis elegans), the expansion of APPRIS to cover the RefSeq gene set and the UniProtKB proteome for six species and refinements in the core methods that make up the annotation pipeline. In addition APPRIS now provides a measure of reliability for individual principal isoforms and updates with each release of the GENCODE/Ensembl and RefSeq reference sets. The individual GENCODE/Ensembl, RefSeq and UniProtKB reference gene sets for six organisms have been merged to produce common sets of splice variants. PMID:29069475

LoopX: A Graphical User Interface-Based Database for Comprehensive Analysis and Comparative Evaluation of Loops from Protein Structures.

PubMed

Kadumuri, Rajashekar Varma; Vadrevu, Ramakrishna

2017-10-01

Due to their crucial role in function, folding, and stability, protein loops are being targeted for grafting/designing to create novel or alter existing functionality and improve stability and foldability. With a view to facilitate a thorough analysis and effectual search options for extracting and comparing loops for sequence and structural compatibility, we developed, LoopX a comprehensively compiled library of sequence and conformational features of ∼700,000 loops from protein structures. The database equipped with a graphical user interface is empowered with diverse query tools and search algorithms, with various rendering options to visualize the sequence- and structural-level information along with hydrogen bonding patterns, backbone φ, ψ dihedral angles of both the target and candidate loops. Two new features (i) conservation of the polar/nonpolar environment and (ii) conservation of sequence and conformation of specific residues within the loops have also been incorporated in the search and retrieval of compatible loops for a chosen target loop. Thus, the LoopX server not only serves as a database and visualization tool for sequence and structural analysis of protein loops but also aids in extracting and comparing candidate loops for a given target loop based on user-defined search options.

Non-redundant patent sequence databases with value-added annotations at two levels

PubMed Central

Li, Weizhong; McWilliam, Hamish; de la Torre, Ana Richart; Grodowski, Adam; Benediktovich, Irina; Goujon, Mickael; Nauche, Stephane; Lopez, Rodrigo

2010-01-01

The European Bioinformatics Institute (EMBL-EBI) provides public access to patent data, including abstracts, chemical compounds and sequences. Sequences can appear multiple times due to the filing of the same invention with multiple patent offices, or the use of the same sequence by different inventors in different contexts. Information relating to the source invention may be incomplete, and biological information available in patent documents elsewhere may not be reflected in the annotation of the sequence. Search and analysis of these data have become increasingly challenging for both the scientific and intellectual-property communities. Here, we report a collection of non-redundant patent sequence databases, which cover the EMBL-Bank nucleotides patent class and the patent protein databases and contain value-added annotations from patent documents. The databases were created at two levels by the use of sequence MD5 checksums. Sequences within a level-1 cluster are 100% identical over their whole length. Level-2 clusters were defined by sub-grouping level-1 clusters based on patent family information. Value-added annotations, such as publication number corrections, earliest publication dates and feature collations, significantly enhance the quality of the data, allowing for better tracking and cross-referencing. The databases are available format: http://www.ebi.ac.uk/patentdata/nr/. PMID:19884134

Non-redundant patent sequence databases with value-added annotations at two levels.

PubMed

Li, Weizhong; McWilliam, Hamish; de la Torre, Ana Richart; Grodowski, Adam; Benediktovich, Irina; Goujon, Mickael; Nauche, Stephane; Lopez, Rodrigo

2010-01-01

The European Bioinformatics Institute (EMBL-EBI) provides public access to patent data, including abstracts, chemical compounds and sequences. Sequences can appear multiple times due to the filing of the same invention with multiple patent offices, or the use of the same sequence by different inventors in different contexts. Information relating to the source invention may be incomplete, and biological information available in patent documents elsewhere may not be reflected in the annotation of the sequence. Search and analysis of these data have become increasingly challenging for both the scientific and intellectual-property communities. Here, we report a collection of non-redundant patent sequence databases, which cover the EMBL-Bank nucleotides patent class and the patent protein databases and contain value-added annotations from patent documents. The databases were created at two levels by the use of sequence MD5 checksums. Sequences within a level-1 cluster are 100% identical over their whole length. Level-2 clusters were defined by sub-grouping level-1 clusters based on patent family information. Value-added annotations, such as publication number corrections, earliest publication dates and feature collations, significantly enhance the quality of the data, allowing for better tracking and cross-referencing. The databases are available format: http://www.ebi.ac.uk/patentdata/nr/.

CCDB: a curated database of genes involved in cervix cancer.

PubMed

Agarwal, Subhash M; Raghav, Dhwani; Singh, Harinder; Raghava, G P S

2011-01-01

The Cervical Cancer gene DataBase (CCDB, http://crdd.osdd.net/raghava/ccdb) is a manually curated catalog of experimentally validated genes that are thought, or are known to be involved in the different stages of cervical carcinogenesis. In spite of the large women population that is presently affected from this malignancy still at present, no database exists that catalogs information on genes associated with cervical cancer. Therefore, we have compiled 537 genes in CCDB that are linked with cervical cancer causation processes such as methylation, gene amplification, mutation, polymorphism and change in expression level, as evident from published literature. Each record contains details related to gene like architecture (exon-intron structure), location, function, sequences (mRNA/CDS/protein), ontology, interacting partners, homology to other eukaryotic genomes, structure and links to other public databases, thus augmenting CCDB with external data. Also, manually curated literature references have been provided to support the inclusion of the gene in the database and establish its association with cervix cancer. In addition, CCDB provides information on microRNA altered in cervical cancer as well as search facility for querying, several browse options and an online tool for sequence similarity search, thereby providing researchers with easy access to the latest information on genes involved in cervix cancer.

Mass spectrometry analysis and in silico prediction of allergenicity of peptides in tryptic hydrolysates of the proteins from Ruditapes philippinarum.

PubMed

Yu, Yue; Liu, Hongwei; Tu, Maolin; Qiao, Meiling; Wang, Zhenyu; Du, Ming

2017-12-01

Ruditapes philippinarum is nutrient-rich and widely-distributed, but little attention has been paid to the identification and characterization of the bioactive peptides in the bivalve. In the present study, we evaluated the peptides of the R. philippinarum that were enzymolysised by trypsin using a combination of ultra-performance liquid chromatography separation and electrospray ionization quadrupole time-of-flight tandem mass spectrometry, followed by data processing and sequence-similarity database searching. The potential allergenicity of the peptides was assessed in silico. The enzymolysis was performed under the conditions: E:S 3:100 (w/w), pH 9.0, 45 °C for 4 h. After separation and detection, the Swiss-Prot database and a Ruditapes philippinarum sequence database were used: 966 unique peptides were identified by non-error tolerant database searching; 173 peptides matching 55 precursor proteins comprised highly conserved cytoskeleton proteins. The remaining 793 peptides were identified from the R. philippinarum sequence database. The results showed that 510 peptides were labeled as allergens and 31 peptides were potential allergens; 425 peptides were predicted to be nonallergenic. The abundant peptide information contributes to further investigations of the structure and potential function of R. philippinarum. Additional in vitro studies are required to demonstrate and ensure the correct production of the hydrolysates for use in the food industry with respect to R. philippinarum. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

The Protein Identifier Cross-Referencing (PICR) service: reconciling protein identifiers across multiple source databases.

PubMed

Côté, Richard G; Jones, Philip; Martens, Lennart; Kerrien, Samuel; Reisinger, Florian; Lin, Quan; Leinonen, Rasko; Apweiler, Rolf; Hermjakob, Henning

2007-10-18

Each major protein database uses its own conventions when assigning protein identifiers. Resolving the various, potentially unstable, identifiers that refer to identical proteins is a major challenge. This is a common problem when attempting to unify datasets that have been annotated with proteins from multiple data sources or querying data providers with one flavour of protein identifiers when the source database uses another. Partial solutions for protein identifier mapping exist but they are limited to specific species or techniques and to a very small number of databases. As a result, we have not found a solution that is generic enough and broad enough in mapping scope to suit our needs. We have created the Protein Identifier Cross-Reference (PICR) service, a web application that provides interactive and programmatic (SOAP and REST) access to a mapping algorithm that uses the UniProt Archive (UniParc) as a data warehouse to offer protein cross-references based on 100% sequence identity to proteins from over 70 distinct source databases loaded into UniParc. Mappings can be limited by source database, taxonomic ID and activity status in the source database. Users can copy/paste or upload files containing protein identifiers or sequences in FASTA format to obtain mappings using the interactive interface. Search results can be viewed in simple or detailed HTML tables or downloaded as comma-separated values (CSV) or Microsoft Excel (XLS) files suitable for use in a local database or a spreadsheet. Alternatively, a SOAP interface is available to integrate PICR functionality in other applications, as is a lightweight REST interface. We offer a publicly available service that can interactively map protein identifiers and protein sequences to the majority of commonly used protein databases. Programmatic access is available through a standards-compliant SOAP interface or a lightweight REST interface. The PICR interface, documentation and code examples are available at http://www.ebi.ac.uk/Tools/picr.

The Protein Identifier Cross-Referencing (PICR) service: reconciling protein identifiers across multiple source databases

PubMed Central

Côté, Richard G; Jones, Philip; Martens, Lennart; Kerrien, Samuel; Reisinger, Florian; Lin, Quan; Leinonen, Rasko; Apweiler, Rolf; Hermjakob, Henning

2007-01-01

Background Each major protein database uses its own conventions when assigning protein identifiers. Resolving the various, potentially unstable, identifiers that refer to identical proteins is a major challenge. This is a common problem when attempting to unify datasets that have been annotated with proteins from multiple data sources or querying data providers with one flavour of protein identifiers when the source database uses another. Partial solutions for protein identifier mapping exist but they are limited to specific species or techniques and to a very small number of databases. As a result, we have not found a solution that is generic enough and broad enough in mapping scope to suit our needs. Results We have created the Protein Identifier Cross-Reference (PICR) service, a web application that provides interactive and programmatic (SOAP and REST) access to a mapping algorithm that uses the UniProt Archive (UniParc) as a data warehouse to offer protein cross-references based on 100% sequence identity to proteins from over 70 distinct source databases loaded into UniParc. Mappings can be limited by source database, taxonomic ID and activity status in the source database. Users can copy/paste or upload files containing protein identifiers or sequences in FASTA format to obtain mappings using the interactive interface. Search results can be viewed in simple or detailed HTML tables or downloaded as comma-separated values (CSV) or Microsoft Excel (XLS) files suitable for use in a local database or a spreadsheet. Alternatively, a SOAP interface is available to integrate PICR functionality in other applications, as is a lightweight REST interface. Conclusion We offer a publicly available service that can interactively map protein identifiers and protein sequences to the majority of commonly used protein databases. Programmatic access is available through a standards-compliant SOAP interface or a lightweight REST interface. The PICR interface, documentation and code examples are available at . PMID:17945017

MIPS plant genome information resources.

PubMed

Spannagl, Manuel; Haberer, Georg; Ernst, Rebecca; Schoof, Heiko; Mayer, Klaus F X

2007-01-01

The Munich Institute for Protein Sequences (MIPS) has been involved in maintaining plant genome databases since the Arabidopsis thaliana genome project. Genome databases and analysis resources have focused on individual genomes and aim to provide flexible and maintainable data sets for model plant genomes as a backbone against which experimental data, for example from high-throughput functional genomics, can be organized and evaluated. In addition, model genomes also form a scaffold for comparative genomics, and much can be learned from genome-wide evolutionary studies.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

PubMed

Saidi, Rabie; Boudellioua, Imane; Martin, Maria J; Solovyev, Victor

2017-01-01

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

Global, quantitative and dynamic mapping of protein subcellular localization

PubMed Central

Itzhak, Daniel N; Tyanova, Stefka; Cox, Jürgen; Borner, Georg HH

2016-01-01

Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology. DOI: http://dx.doi.org/10.7554/eLife.16950.001 PMID:27278775

Biomine: predicting links between biological entities using network models of heterogeneous databases.

PubMed

Eronen, Lauri; Toivonen, Hannu

2012-06-06

Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching for and visualizing connections between given biological entities.

Analysis of commercial and public bioactivity databases.

PubMed

Tiikkainen, Pekka; Franke, Lutz

2012-02-27

Activity data for small molecules are invaluable in chemoinformatics. Various bioactivity databases exist containing detailed information of target proteins and quantitative binding data for small molecules extracted from journals and patents. In the current work, we have merged several public and commercial bioactivity databases into one bioactivity metabase. The molecular presentation, target information, and activity data of the vendor databases were standardized. The main motivation of the work was to create a single relational database which allows fast and simple data retrieval by in-house scientists. Second, we wanted to know the amount of overlap between databases by commercial and public vendors to see whether the former contain data complementing the latter. Third, we quantified the degree of inconsistency between data sources by comparing data points derived from the same scientific article cited by more than one vendor. We found that each data source contains unique data which is due to different scientific articles cited by the vendors. When comparing data derived from the same article we found that inconsistencies between the vendors are common. In conclusion, using databases of different vendors is still useful since the data overlap is not complete. It should be noted that this can be partially explained by the inconsistencies and errors in the source data.

Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB.

PubMed

Yao, Qiuming; Xu, Dong

2017-01-01

Protein phosphorylation is one of the most pervasive protein post-translational modification events in plant cells. It is involved in many plant biological processes, such as plant growth, organ development, and plant immunology, by regulating or switching signaling and metabolic pathways. High-throughput experimental methods like mass spectrometry can easily characterize hundreds to thousands of phosphorylation events in a single experiment. With the increasing volume of the data sets, Plant Protein Phosphorylation DataBase (P3DB, http://p3db.org ) provides a comprehensive, systematic, and interactive online platform to deposit, query, analyze, and visualize these phosphorylation events in many plant species. It stores the protein phosphorylation sites in the context of identified mass spectra, phosphopeptides, and phosphoproteins contributed from various plant proteome studies. In addition, P3DB associates these plant phosphorylation sites to protein physicochemical information in the protein charts and tertiary structures, while various protein annotations from hierarchical kinase phosphatase families, protein domains, and gene ontology are also added into the database. P3DB not only provides rich information, but also interconnects and provides visualization of the data in networks, in systems biology context. Currently, P3DB includes the KiC (Kinase Client) assay network, the protein-protein interaction network, the kinase-substrate network, the phosphatase-substrate network, and the protein domain co-occurrence network. All of these are available to query for and visualize existing phosphorylation events. Although P3DB only hosts experimentally identified phosphorylation data, it provides a plant phosphorylation prediction model for any unknown queries on the fly. P3DB is an entry point to the plant phosphorylation community to deposit and visualize any customized data sets within this systems biology framework. Nowadays, P3DB has become one of the major bioinformatics platforms of protein phosphorylation in plant biology.

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

PubMed Central

Xia, Kai; Dong, Dong; Han, Jing-Dong J

2006-01-01

Background Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. Results By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. Conclusion IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer. PMID:17112386

Fast protein tertiary structure retrieval based on global surface shape similarity.

PubMed

Sael, Lee; Li, Bin; La, David; Fang, Yi; Ramani, Karthik; Rustamov, Raif; Kihara, Daisuke

2008-09-01

Characterization and identification of similar tertiary structure of proteins provides rich information for investigating function and evolution. The importance of structure similarity searches is increasing as structure databases continue to expand, partly due to the structural genomics projects. A crucial drawback of conventional protein structure comparison methods, which compare structures by their main-chain orientation or the spatial arrangement of secondary structure, is that a database search is too slow to be done in real-time. Here we introduce a global surface shape representation by three-dimensional (3D) Zernike descriptors, which represent a protein structure compactly as a series expansion of 3D functions. With this simplified representation, the search speed against a few thousand structures takes less than a minute. To investigate the agreement between surface representation defined by 3D Zernike descriptor and conventional main-chain based representation, a benchmark was performed against a protein classification generated by the combinatorial extension algorithm. Despite the different representation, 3D Zernike descriptor retrieved proteins of the same conformation defined by combinatorial extension in 89.6% of the cases within the top five closest structures. The real-time protein structure search by 3D Zernike descriptor will open up new possibility of large-scale global and local protein surface shape comparison. 2008 Wiley-Liss, Inc.

The YeastGenome app: the Saccharomyces Genome Database at your fingertips.

PubMed

Wong, Edith D; Karra, Kalpana; Hitz, Benjamin C; Hong, Eurie L; Cherry, J Michael

2013-01-01

The Saccharomyces Genome Database (SGD) is a scientific database that provides researchers with high-quality curated data about the genes and gene products of Saccharomyces cerevisiae. To provide instant and easy access to this information on mobile devices, we have developed YeastGenome, a native application for the Apple iPhone and iPad. YeastGenome can be used to quickly find basic information about S. cerevisiae genes and chromosomal features regardless of internet connectivity. With or without network access, you can view basic information and Gene Ontology annotations about a gene of interest by searching gene names and gene descriptions or by browsing the database within the app to find the gene of interest. With internet access, the app provides more detailed information about the gene, including mutant phenotypes, references and protein and genetic interactions, as well as provides hyperlinks to retrieve detailed information by showing SGD pages and views of the genome browser. SGD provides online help describing basic ways to navigate the mobile version of SGD, highlights key features and answers frequently asked questions related to the app. The app is available from iTunes (http://itunes.com/apps/yeastgenome). The YeastGenome app is provided freely as a service to our community, as part of SGD's mission to provide free and open access to all its data and annotations.

The PMDB Protein Model Database

PubMed Central

Castrignanò, Tiziana; De Meo, Paolo D'Onorio; Cozzetto, Domenico; Talamo, Ivano Giuseppe; Tramontano, Anna

2006-01-01

The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins. The database is designed to provide access to models published in the scientific literature, together with validating experimental data. It is a relational database and it currently contains >74 000 models for ∼240 proteins. The system is accessible at and allows predictors to submit models along with related supporting evidence and users to download them through a simple and intuitive interface. Users can navigate in the database and retrieve models referring to the same target protein or to different regions of the same protein. Each model is assigned a unique identifier that allows interested users to directly access the data. PMID:16381873

Proteomic characterization of hempseed (Cannabis sativa L.).

PubMed

Aiello, Gilda; Fasoli, Elisa; Boschin, Giovanna; Lammi, Carmen; Zanoni, Chiara; Citterio, Attilio; Arnoldi, Anna

2016-09-16

This paper presents an investigation on hempseed proteome. The experimental approach, based on combinatorial peptide ligand libraries (CPLLs), SDS-PAGE separation, nLC-ESI-MS/MS identification, and database search, permitted identifying in total 181 expressed proteins. This very large number of identifications was achieved by searching in two databases: Cannabis sativa L. (56 gene products identified) and Arabidopsis thaliana (125 gene products identified). By performing a protein-protein association network analysis using the STRING software, it was possible to build the first interactomic map of all detected proteins, characterized by 137 nodes and 410 interactions. Finally, a Gene Ontology analysis of the identified species permitted to classify their molecular functions: the great majority is involved in the seed metabolic processes (41%), responses to stimulus (8%), and biological process (7%). Hempseed is an underexploited non-legume protein-rich seed. Although its protein is well known for its digestibility, essential amino acid composition, and useful techno-functional properties, a comprehensive proteome characterization is still lacking. The objective of this work was to fill this knowledge gap and provide information useful for a better exploitation of this seed in different food products. Copyright © 2016 Elsevier B.V. All rights reserved.

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system

DOE Office of Scientific and Technical Information (OSTI.GOV)

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database inmore » which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. Lastly, this database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.« less

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system

DOE PAGES

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

2015-11-19

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database inmore » which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. Lastly, this database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.« less

Single step generation of protein arrays from DNA by cell-free expression and in situ immobilisation (PISA method).

PubMed

He, M; Taussig, M J

2001-08-01

We describe a format for production of protein arrays termed 'protein in situ array' (PISA). A PISA is rapidly generated in one step directly from PCR-generated DNA fragments by cell-free protein expression and in situ immobilisation at a surface. The template for expression is DNA encoding individual proteins or domains, which is produced by PCR using primers designed from information in DNA databases. Coupled transcription and translation is carried out on a surface to which the tagged protein adheres as soon as it is synthesised. Because proteins generated by cell-free synthesis are usually soluble and functional, this method can overcome problems of insolubility or degradation associated with bacterial expression of recombinant proteins. Moreover, the use of PCR-generated DNA enables rapid production of proteins or domains based on genome information alone and will be particularly useful where cloned material is not available. Here we show that human single-chain antibody fragments (three domain, V(H)/K form) and an enzyme (luciferase) can be functionally arrayed by the PISA method.

Database-independent Protein Sequencing (DiPS) Enables Full-length de Novo Protein and Antibody Sequence Determination.

PubMed

Savidor, Alon; Barzilay, Rotem; Elinger, Dalia; Yarden, Yosef; Lindzen, Moshit; Gabashvili, Alexandra; Adiv Tal, Ophir; Levin, Yishai

2017-06-01

Traditional "bottom-up" proteomic approaches use proteolytic digestion, LC-MS/MS, and database searching to elucidate peptide identities and their parent proteins. Protein sequences absent from the database cannot be identified, and even if present in the database, complete sequence coverage is rarely achieved even for the most abundant proteins in the sample. Thus, sequencing of unknown proteins such as antibodies or constituents of metaproteomes remains a challenging problem. To date, there is no available method for full-length protein sequencing, independent of a reference database, in high throughput. Here, we present Database-independent Protein Sequencing, a method for unambiguous, rapid, database-independent, full-length protein sequencing. The method is a novel combination of non-enzymatic, semi-random cleavage of the protein, LC-MS/MS analysis, peptide de novo sequencing, extraction of peptide tags, and their assembly into a consensus sequence using an algorithm named "Peptide Tag Assembler." As proof-of-concept, the method was applied to samples of three known proteins representing three size classes and to a previously un-sequenced, clinically relevant monoclonal antibody. Excluding leucine/isoleucine and glutamic acid/deamidated glutamine ambiguities, end-to-end full-length de novo sequencing was achieved with 99-100% accuracy for all benchmarking proteins and the antibody light chain. Accuracy of the sequenced antibody heavy chain, including the entire variable region, was also 100%, but there was a 23-residue gap in the constant region sequence. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Knowledge-based prediction of protein backbone conformation using a structural alphabet.

PubMed

Vetrivel, Iyanar; Mahajan, Swapnil; Tyagi, Manoj; Hoffmann, Lionel; Sanejouand, Yves-Henri; Srinivasan, Narayanaswamy; de Brevern, Alexandre G; Cadet, Frédéric; Offmann, Bernard

2017-01-01

Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

Rice DB: an Oryza Information Portal linking annotation, subcellular location, function, expression, regulation, and evolutionary information for rice and Arabidopsis

PubMed Central

Narsai, Reena; Devenish, James; Castleden, Ian; Narsai, Kabir; Xu, Lin; Shou, Huixia; Whelan, James

2013-01-01

Omics research in Oryza sativa (rice) relies on the use of multiple databases to obtain different types of information to define gene function. We present Rice DB, an Oryza information portal that is a functional genomics database, linking gene loci to comprehensive annotations, expression data and the subcellular location of encoded proteins. Rice DB has been designed to integrate the direct comparison of rice with Arabidopsis (Arabidopsis thaliana), based on orthology or ‘expressology’, thus using and combining available information from two pre-eminent plant models. To establish Rice DB, gene identifiers (more than 40 types) and annotations from a variety of sources were compiled, functional information based on large-scale and individual studies was manually collated, hundreds of microarrays were analysed to generate expression annotations, and the occurrences of potential functional regulatory motifs in promoter regions were calculated. A range of computational subcellular localization predictions were also run for all putative proteins encoded in the rice genome, and experimentally confirmed protein localizations have been collated, curated and linked to functional studies in rice. A single search box allows anything from gene identifiers (for rice and/or Arabidopsis), motif sequences, subcellular location, to keyword searches to be entered, with the capability of Boolean searches (such as AND/OR). To demonstrate the utility of Rice DB, several examples are presented including a rice mitochondrial proteome, which draws on a variety of sources for subcellular location data within Rice DB. Comparisons of subcellular location, functional annotations, as well as transcript expression in parallel with Arabidopsis reveals examples of conservation between rice and Arabidopsis, using Rice DB (http://ricedb.plantenergy.uwa.edu.au). PMID:24147765

Rice DB: an Oryza Information Portal linking annotation, subcellular location, function, expression, regulation, and evolutionary information for rice and Arabidopsis.

PubMed

Narsai, Reena; Devenish, James; Castleden, Ian; Narsai, Kabir; Xu, Lin; Shou, Huixia; Whelan, James

2013-12-01

Omics research in Oryza sativa (rice) relies on the use of multiple databases to obtain different types of information to define gene function. We present Rice DB, an Oryza information portal that is a functional genomics database, linking gene loci to comprehensive annotations, expression data and the subcellular location of encoded proteins. Rice DB has been designed to integrate the direct comparison of rice with Arabidopsis (Arabidopsis thaliana), based on orthology or 'expressology', thus using and combining available information from two pre-eminent plant models. To establish Rice DB, gene identifiers (more than 40 types) and annotations from a variety of sources were compiled, functional information based on large-scale and individual studies was manually collated, hundreds of microarrays were analysed to generate expression annotations, and the occurrences of potential functional regulatory motifs in promoter regions were calculated. A range of computational subcellular localization predictions were also run for all putative proteins encoded in the rice genome, and experimentally confirmed protein localizations have been collated, curated and linked to functional studies in rice. A single search box allows anything from gene identifiers (for rice and/or Arabidopsis), motif sequences, subcellular location, to keyword searches to be entered, with the capability of Boolean searches (such as AND/OR). To demonstrate the utility of Rice DB, several examples are presented including a rice mitochondrial proteome, which draws on a variety of sources for subcellular location data within Rice DB. Comparisons of subcellular location, functional annotations, as well as transcript expression in parallel with Arabidopsis reveals examples of conservation between rice and Arabidopsis, using Rice DB (http://ricedb.plantenergy.uwa.edu.au). © 2013 The Authors The Plant Journal © 2013 John Wiley & Sons Ltd.

DEPPDB - DNA electrostatic potential properties database. Electrostatic properties of genome DNA elements.

PubMed

Osypov, Alexander A; Krutinin, Gleb G; Krutinina, Eugenia A; Kamzolova, Svetlana G

2012-04-01

Electrostatic properties of genome DNA are important to its interactions with different proteins, in particular, related to transcription. DEPPDB - DNA Electrostatic Potential (and other Physical) Properties Database - provides information on the electrostatic and other physical properties of genome DNA combined with its sequence and annotation of biological and structural properties of genomes and their elements. Genomes are organized on taxonomical basis, supporting comparative and evolutionary studies. Currently, DEPPDB contains all completely sequenced bacterial, viral, mitochondrial, and plastids genomes according to the NCBI RefSeq, and some model eukaryotic genomes. Data for promoters, regulation sites, binding proteins, etc., are incorporated from established DBs and literature. The database is complemented by analytical tools. User sequences calculations are available. Case studies discovered electrostatics complementing DNA bending in E.coli plasmid BNT2 promoter functioning, possibly affecting host-environment metabolic switch. Transcription factors binding sites gravitate to high potential regions, confirming the electrostatics universal importance in protein-DNA interactions beyond the classical promoter-RNA polymerase recognition and regulation. Other genome elements, such as terminators, also show electrostatic peculiarities. Most intriguing are gene starts, exhibiting taxonomic correlations. The necessity of the genome electrostatic properties studies is discussed.

Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial.

PubMed

Crosara, Karla Tonelli Bicalho; Moffa, Eduardo Buozi; Xiao, Yizhi; Siqueira, Walter Luiz

2018-01-16

Protein-protein interaction is a common physiological mechanism for protection and actions of proteins in an organism. The identification and characterization of protein-protein interactions in different organisms is necessary to better understand their physiology and to determine their efficacy. In a previous in vitro study using mass spectrometry, we identified 43 proteins that interact with histatin 1. Six previously documented interactors were confirmed and 37 novel partners were identified. In this tutorial, we aimed to demonstrate the usefulness of the STRING database for studying protein-protein interactions. We used an in-silico approach along with the STRING database (http://string-db.org/) and successfully performed a fast simulation of a novel constructed histatin 1 protein-protein network, including both the previously known and the predicted interactors, along with our newly identified interactors. Our study highlights the advantages and importance of applying bioinformatics tools to merge in-silico tactics with experimental in vitro findings for rapid advancement of our knowledge about protein-protein interactions. Our findings also indicate that bioinformatics tools such as the STRING protein network database can help predict potential interactions between proteins and thus serve as a guide for future steps in our exploration of the Human Interactome. Our study highlights the usefulness of the STRING protein database for studying protein-protein interactions. The STRING database can collect and integrate data about known and predicted protein-protein associations from many organisms, including both direct (physical) and indirect (functional) interactions, in an easy-to-use interface. Copyright © 2017 Elsevier B.V. All rights reserved.

Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.

PubMed

O'Leary, Nuala A; Wright, Mathew W; Brister, J Rodney; Ciufo, Stacy; Haddad, Diana; McVeigh, Rich; Rajput, Bhanu; Robbertse, Barbara; Smith-White, Brian; Ako-Adjei, Danso; Astashyn, Alexander; Badretdin, Azat; Bao, Yiming; Blinkova, Olga; Brover, Vyacheslav; Chetvernin, Vyacheslav; Choi, Jinna; Cox, Eric; Ermolaeva, Olga; Farrell, Catherine M; Goldfarb, Tamara; Gupta, Tripti; Haft, Daniel; Hatcher, Eneida; Hlavina, Wratko; Joardar, Vinita S; Kodali, Vamsi K; Li, Wenjun; Maglott, Donna; Masterson, Patrick; McGarvey, Kelly M; Murphy, Michael R; O'Neill, Kathleen; Pujar, Shashikant; Rangwala, Sanjida H; Rausch, Daniel; Riddick, Lillian D; Schoch, Conrad; Shkeda, Andrei; Storz, Susan S; Sun, Hanzhen; Thibaud-Nissen, Francoise; Tolstoy, Igor; Tully, Raymond E; Vatsan, Anjana R; Wallin, Craig; Webb, David; Wu, Wendy; Landrum, Melissa J; Kimchi, Avi; Tatusova, Tatiana; DiCuccio, Michael; Kitts, Paul; Murphy, Terence D; Pruitt, Kim D

2016-01-04

The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management. Published by Oxford University Press on behalf of Nucleic Acids Research 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

The Human Transcript Database: A Catalogue of Full Length cDNA Inserts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bouckk John; Michael McLeod; Kim Worley

1999-09-10

The BCM Search Launcher provided improved access to web-based sequence analysis services during the granting period and beyond. The Search Launcher web site grouped analysis procedures by function and provided default parameters that provided reasonable search results for most applications. For instance, most queries were automatically masked for repeat sequences prior to sequence database searches to avoid spurious matches. In addition to the web-based access and arrangements that were made using the functions easier, the BCM Search Launcher provided unique value-added applications like the BEAUTY sequence database search tool that combined information about protein domains and sequence database search resultsmore » to give an enhanced, more complete picture of the reliability and relative value of the information reported. This enhanced search tool made evaluating search results more straight-forward and consistent. Some of the favorite features of the web site are the sequence utilities and the batch client functionality that allows processing of multiple samples from the command line interface. One measure of the success of the BCM Search Launcher is the number of sites that have adopted the models first developed on the site. The graphic display on the BLAST search from the NCBI web site is one such outgrowth, as is the display of protein domain search results within BLAST search results, and the design of the Biology Workbench application. The logs of usage and comments from users confirm the great utility of this resource.« less

Annotation, submission and screening of repetitive elements in Repbase: RepbaseSubmitter and Censor.

PubMed

Kohany, Oleksiy; Gentles, Andrew J; Hankus, Lukasz; Jurka, Jerzy

2006-10-25

Repbase is a reference database of eukaryotic repetitive DNA, which includes prototypic sequences of repeats and basic information described in annotations. Updating and maintenance of the database requires specialized tools, which we have created and made available for use with Repbase, and which may be useful as a template for other curated databases. We describe the software tools RepbaseSubmitter and Censor, which are designed to facilitate updating and screening the content of Repbase. RepbaseSubmitter is a java-based interface for formatting and annotating Repbase entries. It eliminates many common formatting errors, and automates actions such as calculation of sequence lengths and composition, thus facilitating curation of Repbase sequences. In addition, it has several features for predicting protein coding regions in sequences; searching and including Pubmed references in Repbase entries; and searching the NCBI taxonomy database for correct inclusion of species information and taxonomic position. Censor is a tool to rapidly identify repetitive elements by comparison to known repeats. It uses WU-BLAST for speed and sensitivity, and can conduct DNA-DNA, DNA-protein, or translated DNA-translated DNA searches of genomic sequence. Defragmented output includes a map of repeats present in the query sequence, with the options to report masked query sequence(s), repeat sequences found in the query, and alignments. Censor and RepbaseSubmitter are available as both web-based services and downloadable versions. They can be found at http://www.girinst.org/repbase/submission.html (RepbaseSubmitter) and http://www.girinst.org/censor/index.php (Censor).

Visualization portal for genetic variation (VizGVar): a tool for interactive visualization of SNPs and somatic mutations in exons, genes and protein domains.

PubMed

Solano-Román, Antonio; Alfaro-Arias, Verónica; Cruz-Castillo, Carlos; Orozco-Solano, Allan

2018-03-15

VizGVar was designed to meet the growing need of the research community for improved genomic and proteomic data viewers that benefit from better information visualization. We implemented a new information architecture and applied user centered design principles to provide a new improved way of visualizing genetic information and protein data related to human disease. VizGVar connects the entire database of Ensembl protein motifs, domains, genes and exons with annotated SNPs and somatic variations from PharmGKB and COSMIC. VizGVar precisely represents genetic variations and their respective location by colored curves to designate different types of variations. The structured hierarchy of biological data is reflected in aggregated patterns through different levels, integrating several layers of information at once. VizGVar provides a new interactive, web-based JavaScript visualization of somatic mutations and protein variation, enabling fast and easy discovery of clinically relevant variation patterns. VizGVar is accessible at http://vizport.io/vizgvar; http://vizport.io/vizgvar/doc/. asolano@broadinstitute.org or allan.orozcosolano@ucr.ac.cr.

PeptideDepot: Flexible Relational Database for Visual Analysis of Quantitative Proteomic Data and Integration of Existing Protein Information

PubMed Central

Yu, Kebing; Salomon, Arthur R.

2010-01-01

Recently, dramatic progress has been achieved in expanding the sensitivity, resolution, mass accuracy, and scan rate of mass spectrometers able to fragment and identify peptides through tandem mass spectrometry (MS/MS). Unfortunately, this enhanced ability to acquire proteomic data has not been accompanied by a concomitant increase in the availability of flexible tools allowing users to rapidly assimilate, explore, and analyze this data and adapt to a variety of experimental workflows with minimal user intervention. Here we fill this critical gap by providing a flexible relational database called PeptideDepot for organization of expansive proteomic data sets, collation of proteomic data with available protein information resources, and visual comparison of multiple quantitative proteomic experiments. Our software design, built upon the synergistic combination of a MySQL database for safe warehousing of proteomic data with a FileMaker-driven graphical user interface for flexible adaptation to diverse workflows, enables proteomic end-users to directly tailor the presentation of proteomic data to the unique analysis requirements of the individual proteomics lab. PeptideDepot may be deployed as an independent software tool or integrated directly with our High Throughput Autonomous Proteomic Pipeline (HTAPP) used in the automated acquisition and post-acquisition analysis of proteomic data. PMID:19834895

Carbohydrate Structure Database: tools for statistical analysis of bacterial, plant and fungal glycomes

PubMed Central

Egorova, K.S.; Kondakova, A.N.; Toukach, Ph.V.

2015-01-01

Carbohydrates are biological blocks participating in diverse and crucial processes both at cellular and organism levels. They protect individual cells, establish intracellular interactions, take part in the immune reaction and participate in many other processes. Glycosylation is considered as one of the most important modifications of proteins and other biologically active molecules. Still, the data on the enzymatic machinery involved in the carbohydrate synthesis and processing are scattered, and the advance on its study is hindered by the vast bulk of accumulated genetic information not supported by any experimental evidences for functions of proteins that are encoded by these genes. In this article, we present novel instruments for statistical analysis of glycomes in taxa. These tools may be helpful for investigating carbohydrate-related enzymatic activities in various groups of organisms and for comparison of their carbohydrate content. The instruments are developed on the Carbohydrate Structure Database (CSDB) platform and are available freely on the CSDB web-site at http://csdb.glycoscience.ru. Database URL: http://csdb.glycoscience.ru PMID:26337239

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space.

PubMed

Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ohue, Masahito; Akiyama, Yutaka

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

Sequence-Based Prediction of RNA-Binding Residues in Proteins

PubMed Central

Walia, Rasna R.; EL-Manzalawy, Yasser; Honavar, Vasant G.; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein–RNA complexes is important for understanding the molecular determinants of protein–RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein–RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein–RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner. PMID:27787829

Extending CATH: increasing coverage of the protein structure universe and linking structure with function

PubMed Central

Cuff, Alison L.; Sillitoe, Ian; Lewis, Tony; Clegg, Andrew B.; Rentzsch, Robert; Furnham, Nicholas; Pellegrini-Calace, Marialuisa; Jones, David; Thornton, Janet; Orengo, Christine A.

2011-01-01

CATH version 3.3 (class, architecture, topology, homology) contains 128 688 domains, 2386 homologous superfamilies and 1233 fold groups, and reflects a major focus on classifying structural genomics (SG) structures and transmembrane proteins, both of which are likely to add structural novelty to the database and therefore increase the coverage of protein fold space within CATH. For CATH version 3.4 we have significantly improved the presentation of sequence information and associated functional information for CATH superfamilies. The CATH superfamily pages now reflect both the functional and structural diversity within the superfamily and include structural alignments of close and distant relatives within the superfamily, annotated with functional information and details of conserved residues. A significantly more efficient search function for CATH has been established by implementing the search server Solr (http://lucene.apache.org/solr/). The CATH v3.4 webpages have been built using the Catalyst web framework. PMID:21097779

SIBIS: a Bayesian model for inconsistent protein sequence estimation.

PubMed

Khenoussi, Walyd; Vanhoutrève, Renaud; Poch, Olivier; Thompson, Julie D

2014-09-01

The prediction of protein coding genes is a major challenge that depends on the quality of genome sequencing, the accuracy of the model used to elucidate the exonic structure of the genes and the complexity of the gene splicing process leading to different protein variants. As a consequence, today's protein databases contain a huge amount of inconsistency, due to both natural variants and sequence prediction errors. We have developed a new method, called SIBIS, to detect such inconsistencies based on the evolutionary information in multiple sequence alignments. A Bayesian framework, combined with Dirichlet mixture models, is used to estimate the probability of observing specific amino acids and to detect inconsistent or erroneous sequence segments. We evaluated the performance of SIBIS on a reference set of protein sequences with experimentally validated errors and showed that the sensitivity is significantly higher than previous methods, with only a small loss of specificity. We also assessed a large set of human sequences from the UniProt database and found evidence of inconsistency in 48% of the previously uncharacterized sequences. We conclude that the integration of quality control methods like SIBIS in automatic analysis pipelines will be critical for the robust inference of structural, functional and phylogenetic information from these sequences. Source code, implemented in C on a linux system, and the datasets of protein sequences are freely available for download at http://www.lbgi.fr/∼julie/SIBIS. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Complex network theory for the identification and assessment of candidate protein targets.

PubMed

McGarry, Ken; McDonald, Sharon

2018-06-01

In this work we use complex network theory to provide a statistical model of the connectivity patterns of human proteins and their interaction partners. Our intention is to identify important proteins that may be predisposed to be potential candidates as drug targets for therapeutic interventions. Target proteins usually have more interaction partners than non-target proteins, but there are no hard-and-fast rules for defining the actual number of interactions. We devise a statistical measure for identifying hub proteins, we score our target proteins with gene ontology annotations. The important druggable protein targets are likely to have similar biological functions that can be assessed for their potential therapeutic value. Our system provides a statistical analysis of the local and distant neighborhood protein interactions of the potential targets using complex network measures. This approach builds a more accurate model of drug-to-target activity and therefore the likely impact on treating diseases. We integrate high quality protein interaction data from the HINT database and disease associated proteins from the DrugTarget database. Other sources include biological knowledge from Gene Ontology and drug information from DrugBank. The problem is a very challenging one since the data is highly imbalanced between target proteins and the more numerous nontargets. We use undersampling on the training data and build Random Forest classifier models which are used to identify previously unclassified target proteins. We validate and corroborate these findings from the available literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Computational tools for exploring sequence databases as a resource for antimicrobial peptides.

PubMed

Porto, W F; Pires, A S; Franco, O L

Data mining has been recognized by many researchers as a hot topic in different areas. In the post-genomic era, the growing number of sequences deposited in databases has been the reason why these databases have become a resource for novel biological information. In recent years, the identification of antimicrobial peptides (AMPs) in databases has gained attention. The identification of unannotated AMPs has shed some light on the distribution and evolution of AMPs and, in some cases, indicated suitable candidates for developing novel antimicrobial agents. The data mining process has been performed mainly by local alignments and/or regular expressions. Nevertheless, for the identification of distant homologous sequences, other techniques such as antimicrobial activity prediction and molecular modelling are required. In this context, this review addresses the tools and techniques, and also their limitations, for mining AMPs from databases. These methods could be helpful not only for the development of novel AMPs, but also for other kinds of proteins, at a higher level of structural genomics. Moreover, solving the problem of unannotated proteins could bring immeasurable benefits to society, especially in the case of AMPs, which could be helpful for developing novel antimicrobial agents and combating resistant bacteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Proteomic analysis of Rhodotorula mucilaginosa: dealing with the issues of a non-conventional yeast.

PubMed

Addis, Maria Filippa; Tanca, Alessandro; Landolfo, Sara; Abbondio, Marcello; Cutzu, Raffaela; Biosa, Grazia; Pagnozzi, Daniela; Uzzau, Sergio; Mannazzu, Ilaria

2016-08-01

Red yeasts ascribed to the species Rhodotorula mucilaginosa are gaining increasing attention, due to their numerous biotechnological applications, spanning carotenoid production, liquid bioremediation, heavy metal biotransformation and antifungal and plant growth-promoting actions, but also for their role as opportunistic pathogens. Nevertheless, their characterization at the 'omic' level is still scarce. Here, we applied different proteomic workflows to R. mucilaginosa with the aim of assessing their potential in generating information on proteins and functions of biotechnological interest, with a particular focus on the carotenogenic pathway. After optimization of protein extraction, we tested several gel-based (including 2D-DIGE) and gel-free sample preparation techniques, followed by tandem mass spectrometry analysis. Contextually, we evaluated different bioinformatic strategies for protein identification and interpretation of the biological significance of the dataset. When 2D-DIGE analysis was applied, not all spots returned a unambiguous identification and no carotenogenic enzymes were identified, even upon the application of different database search strategies. Then, the application of shotgun proteomic workflows with varying levels of sensitivity provided a picture of the information depth that can be reached with different analytical resources, and resulted in a plethora of information on R. mucilaginosa metabolism. However, also in these cases no proteins related to the carotenogenic pathway were identified, thus indicating that further improvements in sequence databases and functional annotations are strictly needed for increasing the outcome of proteomic analysis of this and other non-conventional yeasts. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE.

PubMed

Névéol, Aurélie; Wilbur, W John; Lu, Zhiyong

2012-01-01

High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation. Database URLs: http://www.ncbi.nlm.nih.gov/PubMed, http://www.ncbi.nlm.nih.gov/geo/, http://www.rcsb.org/pdb/

Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE

PubMed Central

Névéol, Aurélie; Wilbur, W. John; Lu, Zhiyong

2012-01-01

High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation. Database URLs: http://www.ncbi.nlm.nih.gov/PubMed, http://www.ncbi.nlm.nih.gov/geo/, http://www.rcsb.org/pdb/ PMID:22685160

Proteome-wide Subcellular Topologies of E. coli Polypeptides Database (STEPdb)*

PubMed Central

Orfanoudaki, Georgia; Economou, Anastassios

2014-01-01

Cell compartmentalization serves both the isolation and the specialization of cell functions. After synthesis in the cytoplasm, over a third of all proteins are targeted to other subcellular compartments. Knowing how proteins are distributed within the cell and how they interact is a prerequisite for understanding it as a whole. Surface and secreted proteins are important pathogenicity determinants. Here we present the STEP database (STEPdb) that contains a comprehensive characterization of subcellular localization and topology of the complete proteome of Escherichia coli. Two widely used E. coli proteomes (K-12 and BL21) are presented organized into thirteen subcellular classes. STEPdb exploits the wealth of genetic, proteomic, biochemical, and functional information on protein localization, secretion, and targeting in E. coli, one of the best understood model organisms. Subcellular annotations were derived from a combination of bioinformatics prediction, proteomic, biochemical, functional, topological data and extensive literature re-examination that were refined through manual curation. Strong experimental support for the location of 1553 out of 4303 proteins was based on 426 articles and some experimental indications for another 526. Annotations were provided for another 320 proteins based on firm bioinformatic predictions. STEPdb is the first database that contains an extensive set of peripheral IM proteins (PIM proteins) and includes their graphical visualization into complexes, cellular functions, and interactions. It also summarizes all currently known protein export machineries of E. coli K-12 and pairs them, where available, with the secretory proteins that use them. It catalogs the Sec- and TAT-utilizing secretomes and summarizes their topological features such as signal peptides and transmembrane regions, transmembrane topologies and orientations. It also catalogs physicochemical and structural features that influence topology such as abundance, solubility, disorder, heat resistance, and structural domain families. Finally, STEPdb incorporates prediction tools for topology (TMHMM, SignalP, and Phobius) and disorder (IUPred) and implements the BLAST2STEP that performs protein homology searches against the STEPdb. PMID:25210196

The Protein Disease Database of human body fluids: II. Computer methods and data issues.

PubMed

Lemkin, P F; Orr, G A; Goldstein, M P; Creed, G J; Myrick, J E; Merril, C R

1995-01-01

The Protein Disease Database (PDD) is a relational database of proteins and diseases. With this database it is possible to screen for quantitative protein abnormalities associated with disease states. These quantitative relationships use data drawn from the peer-reviewed biomedical literature. Assays may also include those observed in high-resolution electrophoretic gels that offer the potential to quantitate many proteins in a single test as well as data gathered by enzymatic or immunologic assays. We are using the Internet World Wide Web (WWW) and the Web browser paradigm as an access method for wide distribution and querying of the Protein Disease Database. The WWW hypertext transfer protocol and its Common Gateway Interface make it possible to build powerful graphical user interfaces that can support easy-to-use data retrieval using query specification forms or images. The details of these interactions are totally transparent to the users of these forms. Using a client-server SQL relational database, user query access, initial data entry and database maintenance are all performed over the Internet with a Web browser. We discuss the underlying design issues, mapping mechanisms and assumptions that we used in constructing the system, data entry, access to the database server, security, and synthesis of derived two-dimensional gel image maps and hypertext documents resulting from SQL database searches.

Domain fusion analysis by applying relational algebra to protein sequence and domain databases

PubMed Central

Truong, Kevin; Ikura, Mitsuhiko

2003-01-01

Background Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. Results This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at . Conclusion As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time. PMID:12734020

TRX-LOGOS - a graphical tool to demonstrate DNA information content dependent upon backbone dynamics in addition to base sequence.

PubMed

Fortin, Connor H; Schulze, Katharina V; Babbitt, Gregory A

2015-01-01

It is now widely-accepted that DNA sequences defining DNA-protein interactions functionally depend upon local biophysical features of DNA backbone that are important in defining sites of binding interaction in the genome (e.g. DNA shape, charge and intrinsic dynamics). However, these physical features of DNA polymer are not directly apparent when analyzing and viewing Shannon information content calculated at single nucleobases in a traditional sequence logo plot. Thus, sequence logos plots are severely limited in that they convey no explicit information regarding the structural dynamics of DNA backbone, a feature often critical to binding specificity. We present TRX-LOGOS, an R software package and Perl wrapper code that interfaces the JASPAR database for computational regulatory genomics. TRX-LOGOS extends the traditional sequence logo plot to include Shannon information content calculated with regard to the dinucleotide-based BI-BII conformation shifts in phosphate linkages on the DNA backbone, thereby adding a visual measure of intrinsic DNA flexibility that can be critical for many DNA-protein interactions. TRX-LOGOS is available as an R graphics module offered at both SourceForge and as a download supplement at this journal. To demonstrate the general utility of TRX logo plots, we first calculated the information content for 416 Saccharomyces cerevisiae transcription factor binding sites functionally confirmed in the Yeastract database and matched to previously published yeast genomic alignments. We discovered that flanking regions contain significantly elevated information content at phosphate linkages than can be observed at nucleobases. We also examined broader transcription factor classifications defined by the JASPAR database, and discovered that many general signatures of transcription factor binding are locally more information rich at the level of DNA backbone dynamics than nucleobase sequence. We used TRX-logos in combination with MEGA 6.0 software for molecular evolutionary genetics analysis to visually compare the human Forkhead box/FOX protein evolution to its binding site evolution. We also compared the DNA binding signatures of human TP53 tumor suppressor determined by two different laboratory methods (SELEX and ChIP-seq). Further analysis of the entire yeast genome, center aligned at the start codon, also revealed a distinct sequence-independent 3 bp periodic pattern in information content, present only in coding region, and perhaps indicative of the non-random organization of the genetic code. TRX-LOGOS is useful in any situation in which important information content in DNA can be better visualized at the positions of phosphate linkages (i.e. dinucleotides) where the dynamic properties of the DNA backbone functions to facilitate DNA-protein interaction.

Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors.

PubMed

König, Caroline; Cárdenas, Martha I; Giraldo, Jesús; Alquézar, René; Vellido, Alfredo

2015-09-29

The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences. In this study, we describe a systematic approach, using Support Vector Machine classifiers, to the analysis of G protein-coupled receptor misclassifications. As a proof of concept, this approach is used to assist the discovery of labeling quality problems in a curated, publicly accessible database of this type of proteins. We also investigate the extent to which physico-chemical transformations of the protein sequences reflect G protein-coupled receptor subtype labeling. The candidate mislabeled cases detected with this approach are externally validated with phylogenetic trees and against further trusted sources such as the National Center for Biotechnology Information, Universal Protein Resource, European Bioinformatics Institute and Ensembl Genome Browser information repositories. In quantitative classification problems, class labels are often by default assumed to be correct. Label noise, though, is bound to be a pervasive problem in bioinformatics, where labels may be obtained indirectly through complex, many-step similarity modelling processes. In the case of G protein-coupled receptors, methods capable of singling out and characterizing those sequences with consistent misclassification behaviour are required to minimize this problem. A systematic, Support Vector Machine-based method has been proposed in this study for such purpose. The proposed method enables a filtering approach to the label noise problem and might become a support tool for database curators in proteomics.

Human Disease Insight: An integrated knowledge-based platform for disease-gene-drug information.

PubMed

Tasleem, Munazzah; Ishrat, Romana; Islam, Asimul; Ahmad, Faizan; Hassan, Md Imtaiyaz

2016-01-01

The scope of the Human Disease Insight (HDI) database is not limited to researchers or physicians as it also provides basic information to non-professionals and creates disease awareness, thereby reducing the chances of patient suffering due to ignorance. HDI is a knowledge-based resource providing information on human diseases to both scientists and the general public. Here, our mission is to provide a comprehensive human disease database containing most of the available useful information, with extensive cross-referencing. HDI is a knowledge management system that acts as a central hub to access information about human diseases and associated drugs and genes. In addition, HDI contains well-classified bioinformatics tools with helpful descriptions. These integrated bioinformatics tools enable researchers to annotate disease-specific genes and perform protein analysis, search for biomarkers and identify potential vaccine candidates. Eventually, these tools will facilitate the analysis of disease-associated data. The HDI provides two types of search capabilities and includes provisions for downloading, uploading and searching disease/gene/drug-related information. The logistical design of the HDI allows for regular updating. The database is designed to work best with Mozilla Firefox and Google Chrome and is freely accessible at http://humandiseaseinsight.com. Copyright © 2015 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Prediction of hot spots in protein interfaces using a random forest model with hybrid features.

PubMed

Wang, Lin; Liu, Zhi-Ping; Zhang, Xiang-Sun; Chen, Luonan

2012-03-01

Prediction of hot spots in protein interfaces provides crucial information for the research on protein-protein interaction and drug design. Existing machine learning methods generally judge whether a given residue is likely to be a hot spot by extracting features only from the target residue. However, hot spots usually form a small cluster of residues which are tightly packed together at the center of protein interface. With this in mind, we present a novel method to extract hybrid features which incorporate a wide range of information of the target residue and its spatially neighboring residues, i.e. the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). We provide a novel random forest (RF) model to effectively integrate these hybrid features for predicting hot spots in protein interfaces. Our method can achieve accuracy (ACC) of 82.4% and Matthew's correlation coefficient (MCC) of 0.482 in Alanine Scanning Energetics Database, and ACC of 77.6% and MCC of 0.429 in Binding Interface Database. In a comparison study, performance of our RF model exceeds other existing methods, such as Robetta, FOLDEF, KFC, KFC2, MINERVA and HotPoint. Of our hybrid features, three physicochemical features of target residues (mass, polarizability and isoelectric point), the relative side-chain accessible surface area and the average depth index of mirror-contact residues are found to be the main discriminative features in hot spots prediction. We also confirm that hot spots tend to form large contact surface areas between two interacting proteins. Source data and code are available at: http://www.aporc.org/doc/wiki/HotSpot.

TOPDOM: database of conservatively located domains and motifs in proteins.

PubMed

Varga, Julia; Dobson, László; Tusnády, Gábor E

2016-09-01

The TOPDOM database-originally created as a collection of domains and motifs located consistently on the same side of the membranes in α-helical transmembrane proteins-has been updated and extended by taking into consideration consistently localized domains and motifs in globular proteins, too. By taking advantage of the recently developed CCTOP algorithm to determine the type of a protein and predict topology in case of transmembrane proteins, and by applying a thorough search for domains and motifs as well as utilizing the most up-to-date version of all source databases, we managed to reach a 6-fold increase in the size of the whole database and a 2-fold increase in the number of transmembrane proteins. TOPDOM database is available at http://topdom.enzim.hu The webpage utilizes the common Apache, PHP5 and MySQL software to provide the user interface for accessing and searching the database. The database itself is generated on a high performance computer. tusnady.gabor@ttk.mta.hu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions.

PubMed

Yue, Junyang; Zhang, Danfeng; Ban, Rongjun; Ma, Xiaojing; Chen, Danyang; Li, Guangwei; Liu, Jia; Wisniewski, Michael; Droby, Samir; Liu, Yongsheng

2017-01-01

Penicillium expansum , the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium -crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein-protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium -Crop Protein-Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen-plant systems and available domain-domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community. : http://bdg.hfut.edu.cn/pcppi/index.html. © The Author(s) 2017. Published by Oxford University Press.

NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database

PubMed Central

Mangal, Manu; Sagar, Parul; Singh, Harinder; Raghava, Gajendra P. S.; Agarwal, Subhash M.

2013-01-01

Plant-derived molecules have been highly valued by biomedical researchers and pharmaceutical companies for developing drugs, as they are thought to be optimized during evolution. Therefore, we have collected and compiled a central resource Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database (NPACT, http://crdd.osdd.net/raghava/npact/) that gathers the information related to experimentally validated plant-derived natural compounds exhibiting anti-cancerous activity (in vitro and in vivo), to complement the other databases. It currently contains 1574 compound entries, and each record provides information on their structure, manually curated published data on in vitro and in vivo experiments along with reference for users referral, inhibitory values (IC50/ED50/EC50/GI50), properties (physical, elemental and topological), cancer types, cell lines, protein targets, commercial suppliers and drug likeness of compounds. NPACT can easily be browsed or queried using various options, and an online similarity tool has also been made available. Further, to facilitate retrieval of existing data, each record is hyperlinked to similar databases like SuperNatural, Herbal Ingredients’ Targets, Comparative Toxicogenomics Database, PubChem and NCI-60 GI50 data. PMID:23203877

The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.

PubMed

Hermjakob, Henning; Montecchi-Palazzi, Luisa; Bader, Gary; Wojcik, Jérôme; Salwinski, Lukasz; Ceol, Arnaud; Moore, Susan; Orchard, Sandra; Sarkans, Ugis; von Mering, Christian; Roechert, Bernd; Poux, Sylvain; Jung, Eva; Mersch, Henning; Kersey, Paul; Lappe, Michael; Li, Yixue; Zeng, Rong; Rana, Debashis; Nikolski, Macha; Husi, Holger; Brun, Christine; Shanker, K; Grant, Seth G N; Sander, Chris; Bork, Peer; Zhu, Weimin; Pandey, Akhilesh; Brazma, Alvis; Jacq, Bernard; Vidal, Marc; Sherman, David; Legrain, Pierre; Cesareni, Gianni; Xenarios, Ioannis; Eisenberg, David; Steipe, Boris; Hogue, Chris; Apweiler, Rolf

2004-02-01

A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).

SCOWLP classification: Structural comparison and analysis of protein binding regions

PubMed Central

Teyra, Joan; Paszkowski-Rogacz, Maciej; Anders, Gerd; Pisabarro, M Teresa

2008-01-01

Background Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design. Description Protein binding regions (PBRs) might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed. We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions. The hierarchical classification of PBRs is implemented into the SCOWLP database and extends the SCOP classification with three additional family sub-levels: Binding Region, Interface and Contacting Domains. SCOWLP contains 9,334 binding regions distributed within 2,561 families. In 65% of the cases we observe families containing more than one binding region. Besides, 22% of the regions are forming complex with more than one different protein family. Conclusion The current SCOWLP classification and its web application represent a framework for the study of protein interfaces and comparative analysis of protein family binding regions. This comparison can be performed at atomic level and allows the user to study interactome conservation and variability. The new SCOWLP classification may be of great utility for reconstruction of protein complexes, understanding protein networks and ligand design. SCOWLP will be updated with every SCOP release. The web application is available at . PMID:18182098

EDCs DataBank: 3D-Structure database of endocrine disrupting chemicals.

PubMed

Montes-Grajales, Diana; Olivero-Verbel, Jesus

2015-01-02

Endocrine disrupting chemicals (EDCs) are a group of compounds that affect the endocrine system, frequently found in everyday products and epidemiologically associated with several diseases. The purpose of this work was to develop EDCs DataBank, the only database of EDCs with three-dimensional structures. This database was built on MySQL using the EU list of potential endocrine disruptors and TEDX list. It contains the three-dimensional structures available on PubChem, as well as a wide variety of information from different databases and text mining tools, useful for almost any kind of research regarding EDCs. The web platform was developed employing HTML, CSS and PHP languages, with dynamic contents in a graphic environment, facilitating information analysis. Currently EDCs DataBank has 615 molecules, including pesticides, natural and industrial products, cosmetics, drugs and food additives, among other low molecular weight xenobiotics. Therefore, this database can be used to study the toxicological effects of these molecules, or to develop pharmaceuticals targeting hormone receptors, through docking studies, high-throughput virtual screening and ligand-protein interaction analysis. EDCs DataBank is totally user-friendly and the 3D-structures of the molecules can be downloaded in several formats. This database is freely available at http://edcs.unicartagena.edu.co. Copyright © 2014. Published by Elsevier Ireland Ltd.

A computational platform to maintain and migrate manual functional annotations for BioCyc databases.

PubMed

Walsh, Jesse R; Sen, Taner Z; Dickerson, Julie A

2014-10-12

BioCyc databases are an important resource for information on biological pathways and genomic data. Such databases represent the accumulation of biological data, some of which has been manually curated from literature. An essential feature of these databases is the continuing data integration as new knowledge is discovered. As functional annotations are improved, scalable methods are needed for curators to manage annotations without detailed knowledge of the specific design of the BioCyc database. We have developed CycTools, a software tool which allows curators to maintain functional annotations in a model organism database. This tool builds on existing software to improve and simplify annotation data imports of user provided data into BioCyc databases. Additionally, CycTools automatically resolves synonyms and alternate identifiers contained within the database into the appropriate internal identifiers. Automating steps in the manual data entry process can improve curation efforts for major biological databases. The functionality of CycTools is demonstrated by transferring GO term annotations from MaizeCyc to matching proteins in CornCyc, both maize metabolic pathway databases available at MaizeGDB, and by creating strain specific databases for metabolic engineering.

BIG: a large-scale data integration tool for renal physiology.

PubMed

Zhao, Yue; Yang, Chin-Rang; Raghuram, Viswanathan; Parulekar, Jaya; Knepper, Mark A

2016-10-01

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

The Protein Structure Initiative Structural Biology Knowledgebase Technology Portal: a structural biology web resource.

PubMed

Gifford, Lida K; Carter, Lester G; Gabanyi, Margaret J; Berman, Helen M; Adams, Paul D

2012-06-01

The Technology Portal of the Protein Structure Initiative Structural Biology Knowledgebase (PSI SBKB; http://technology.sbkb.org/portal/ ) is a web resource providing information about methods and tools that can be used to relieve bottlenecks in many areas of protein production and structural biology research. Several useful features are available on the web site, including multiple ways to search the database of over 250 technological advances, a link to videos of methods on YouTube, and access to a technology forum where scientists can connect, ask questions, get news, and develop collaborations. The Technology Portal is a component of the PSI SBKB ( http://sbkb.org ), which presents integrated genomic, structural, and functional information for all protein sequence targets selected by the Protein Structure Initiative. Created in collaboration with the Nature Publishing Group, the SBKB offers an array of resources for structural biologists, such as a research library, editorials about new research advances, a featured biological system each month, and a functional sleuth for searching protein structures of unknown function. An overview of the various features and examples of user searches highlight the information, tools, and avenues for scientific interaction available through the Technology Portal.

Transmembrane proteins in the Protein Data Bank: identification and classification.

PubMed

Tusnády, Gábor E; Dosztányi, Zsuzsanna; Simon, István

2004-11-22

Integral membrane proteins play important roles in living cells. Although these proteins are estimated to constitute 25% of proteins at a genomic scale, the Protein Data Bank (PDB) contains only a few hundred membrane proteins due to the difficulties with experimental techniques. The presence of transmembrane proteins in the structure data bank, however, is quite invisible, as the annotation of these entries is rather poor. Even if a protein is identified as a transmembrane one, the possible location of the lipid bilayer is not indicated in the PDB because these proteins are crystallized without their natural lipid bilayer, and currently no method is publicly available to detect the possible membrane plane using the atomic coordinates of membrane proteins. Here, we present a new geometrical approach to distinguish between transmembrane and globular proteins using structural information only and to locate the most likely position of the lipid bilayer. An automated algorithm (TMDET) is given to determine the membrane planes relative to the position of atomic coordinates, together with a discrimination function which is able to separate transmembrane and globular proteins even in cases of low resolution or incomplete structures such as fragments or parts of large multi chain complexes. This method can be used for the proper annotation of protein structures containing transmembrane segments and paves the way to an up-to-date database containing the structure of all known transmembrane proteins and fragments (PDB_TM) which can be automatically updated. The algorithm is equally important for the purpose of constructing databases purely of globular proteins.

Functional Genomics Analysis of Singapore Grouper Iridovirus: Complete Sequence Determination and Proteomic Analysis

PubMed Central

Song, Wen Jun; Qin, Qi Wei; Qiu, Jin; Huang, Can Hua; Wang, Fan; Hew, Choy Leong

2004-01-01

Here we report the complete genome sequence of Singapore grouper iridovirus (SGIV). Sequencing of the random shotgun and restriction endonuclease genomic libraries showed that the entire SGIV genome consists of 140,131 nucleotide bp. One hundred sixty-two open reading frames (ORFs) from the sense and antisense DNA strands, coding for lengths varying from 41 to 1,268 amino acids, were identified. Computer-assisted analyses of the deduced amino acid sequences revealed that 77 of the ORFs exhibited homologies to known virus genes, 23 of which matched functional iridovirus proteins. Forty-two putative conserved domains or signatures were detected in the National Center for Biotechnology Information CD-Search database and PROSITE database. An assortment of enzyme activities involved in DNA replication, transcription, nucleotide metabolism, cell signaling, etc., were identified. Viruses were cultured on a cell line derived from the embryonated egg of the grouper Epinephelus tauvina, isolated, and purified by sucrose gradient ultracentrifugation. The protein extract from the purified virions was analyzed by polyacrylamide gel electrophoresis followed by in-gel digestion of protein bands. Matrix-assisted laser desorption ionization-time of flight mass spectrometry and database searching led to identification of 26 proteins. Twenty of these represented novel or previously unidentified genes, which were further confirmed by reverse transcription-PCR (RT-PCR) and DNA sequencing of their respective RT-PCR products. PMID:15507645

Tandem mass spectrometry for the detection of plant pathogenic fungi and the effects of database composition on protein inferences.

PubMed

Padliya, Neerav D; Garrett, Wesley M; Campbell, Kimberly B; Tabb, David L; Cooper, Bret

2007-11-01

LC-MS/MS has demonstrated potential for detecting plant pathogens. Unlike PCR or ELISA, LC-MS/MS does not require pathogen-specific reagents for the detection of pathogen-specific proteins and peptides. However, the MS/MS approach we and others have explored does require a protein sequence reference database and database-search software to interpret tandem mass spectra. To evaluate the limitations of database composition on pathogen identification, we analyzed proteins from cultured Ustilago maydis, Phytophthora sojae, Fusarium graminearum, and Rhizoctonia solani by LC-MS/MS. When the search database did not contain sequences for a target pathogen, or contained sequences to related pathogens, target pathogen spectra were reliably matched to protein sequences from nontarget organisms, giving an illusion that proteins from nontarget organisms were identified. Our analysis demonstrates that when database-search software is used as part of the identification process, a paradox exists whereby additional sequences needed to detect a wide variety of possible organisms may lead to more cross-species protein matches and misidentification of pathogens.

Kinetic parameters of cholinesterase interactions with organophosphates: retrieval and comparison tools available through ESTHER database: ESTerases, alpha/beta Hydrolase Enzymes and Relatives.

PubMed

Chatonnet, A; Hotelier, T; Cousin, X

1999-05-14

Cholinesterases are targets for organophosphorus compounds which are used as insecticides, chemical warfare agents and drugs for the treatment of disease such as glaucoma, or parasitic infections. The widespread use of these chemicals explains the growing of this area of research and the ever increasing number of sequences, structures, or biochemical data available. Future advances will depend upon effective management of existing information as well as upon creation of new knowledge. The ESTHER database goal is to facilitate retrieval and comparison of data about structure and function of proteins presenting the alpha/beta hydrolase fold. Protein engineering and in vitro production of enzymes allow direct comparison of biochemical parameters. Kinetic parameters of enzymatic reactions are now included in the database. These parameters can be searched and compared with a table construction tool. ESTHER can be reached through internet (http://www.ensam.inra.fr/cholinesterase). The full database or the specialised X-window Client-server system can be downloaded from our ftp server (ftp://ftp.toulouse.inra.fr./pub/esther). Forms can be used to send updates or corrections directly from the web.

PROFESS: a PROtein Function, Evolution, Structure and Sequence database

PubMed Central

Triplet, Thomas; Shortridge, Matthew D.; Griep, Mark A.; Stark, Jaime L.; Powers, Robert; Revesz, Peter

2010-01-01

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are ∼1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein–protein interaction networks. Database URL: http://cse.unl.edu/∼profess/ PMID:20624718

In-depth proteomic analysis of banana (Musa spp.) fruit with combinatorial peptide ligand libraries.

PubMed

Esteve, Clara; D'Amato, Alfonsina; Marina, María Luisa; García, María Concepción; Righetti, Pier Giorgio

2013-01-01

Musa ssp. is among the world's leading fruit crops. Although a strong interest on banana biochemistry exists in the scientific community, focused on metabolite composition, proteins have been scarcely investigated even if they play an important role in food allergy and stability, are a source of biologically active peptides, and can provide information about nutritional aspects of this fruit. In this work we have employed the combinatorial peptide ligand libraries after different types of protein extractions, for searching the very low-abundance proteins in banana. The use of advanced MS techniques and Musa ssp. mRNAs database in combination with the Uniprot_viridiplantae database allowed us to identify 1131 proteins. Among this huge amount of proteins we found several already known allergens such as Mus a 1, pectinesterase, superoxide dismutase, and potentially new allergens. Additionally several enzymes involved in degradation of starch granules and strictly correlated to ripening stage were identified. This is the first in-depth exploration of the banana fruit proteome and one of the largest descriptions of the proteome of any vegetable system. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SwePep, a database designed for endogenous peptides and mass spectrometry.

PubMed

Fälth, Maria; Sköld, Karl; Norrman, Mathias; Svensson, Marcus; Fenyö, David; Andren, Per E

2006-06-01

A new database, SwePep, specifically designed for endogenous peptides, has been constructed to significantly speed up the identification process from complex tissue samples utilizing mass spectrometry. In the identification process the experimental peptide masses are compared with the peptide masses stored in the database both with and without possible post-translational modifications. This intermediate identification step is fast and singles out peptides that are potential endogenous peptides and can later be confirmed with tandem mass spectrometry data. Successful applications of this methodology are presented. The SwePep database is a relational database developed using MySql and Java. The database contains 4180 annotated endogenous peptides from different tissues originating from 394 different species as well as 50 novel peptides from brain tissue identified in our laboratory. Information about the peptides, including mass, isoelectric point, sequence, and precursor protein, is also stored in the database. This new approach holds great potential for removing the bottleneck that occurs during the identification process in the field of peptidomics. The SwePep database is available to the public.

A PDB-wide, evolution-based assessment of protein-protein interfaces.

PubMed

Baskaran, Kumaran; Duarte, Jose M; Biyani, Nikhil; Bliven, Spencer; Capitani, Guido

2014-10-18

Thanks to the growth in sequence and structure databases, more than 50 million sequences are now available in UniProt and 100,000 structures in the PDB. Rich information about protein-protein interfaces can be obtained by a comprehensive study of protein contacts in the PDB, their sequence conservation and geometric features. An automated computational pipeline was developed to run our Evolutionary Protein-Protein Interface Classifier (EPPIC) software on the entire PDB and store the results in a relational database, currently containing > 800,000 interfaces. This allows the analysis of interface data on a PDB-wide scale. Two large benchmark datasets of biological interfaces and crystal contacts, each containing about 3000 entries, were automatically generated based on criteria thought to be strong indicators of interface type. The BioMany set of biological interfaces includes NMR dimers solved as crystal structures and interfaces that are preserved across diverse crystal forms, as catalogued by the Protein Common Interface Database (ProtCID) from Xu and Dunbrack. The second dataset, XtalMany, is derived from interfaces that would lead to infinite assemblies and are therefore crystal contacts. BioMany and XtalMany were used to benchmark the EPPIC approach. The performance of EPPIC was also compared to classifications from the Protein Interfaces, Surfaces, and Assemblies (PISA) program on a PDB-wide scale, finding that the two approaches give the same call in about 88% of PDB interfaces. By comparing our safest predictions to the PDB author annotations, we provide a lower-bound estimate of the error rate of biological unit annotations in the PDB. Additionally, we developed a PyMOL plugin for direct download and easy visualization of EPPIC interfaces for any PDB entry. Both the datasets and the PyMOL plugin are available at http://www.eppic-web.org/ewui/\\#downloads. Our computational pipeline allows us to analyze protein-protein contacts and their sequence conservation across the entire PDB. Two new benchmark datasets are provided, which are over an order of magnitude larger than existing manually curated ones. These tools enable the comprehensive study of several aspects of protein-protein contacts in the PDB and represent a basis for future, even larger scale studies of protein-protein interactions.

ASGARD: an open-access database of annotated transcriptomes for emerging model arthropod species.

PubMed

Zeng, Victor; Extavour, Cassandra G

2012-01-01

The increased throughput and decreased cost of next-generation sequencing (NGS) have shifted the bottleneck genomic research from sequencing to annotation, analysis and accessibility. This is particularly challenging for research communities working on organisms that lack the basic infrastructure of a sequenced genome, or an efficient way to utilize whatever sequence data may be available. Here we present a new database, the Assembled Searchable Giant Arthropod Read Database (ASGARD). This database is a repository and search engine for transcriptomic data from arthropods that are of high interest to multiple research communities but currently lack sequenced genomes. We demonstrate the functionality and utility of ASGARD using de novo assembled transcriptomes from the milkweed bug Oncopeltus fasciatus, the cricket Gryllus bimaculatus and the amphipod crustacean Parhyale hawaiensis. We have annotated these transcriptomes to assign putative orthology, coding region determination, protein domain identification and Gene Ontology (GO) term annotation to all possible assembly products. ASGARD allows users to search all assemblies by orthology annotation, GO term annotation or Basic Local Alignment Search Tool. User-friendly features of ASGARD include search term auto-completion suggestions based on database content, the ability to download assembly product sequences in FASTA format, direct links to NCBI data for predicted orthologs and graphical representation of the location of protein domains and matches to similar sequences from the NCBI non-redundant database. ASGARD will be a useful repository for transcriptome data from future NGS studies on these and other emerging model arthropods, regardless of sequencing platform, assembly or annotation status. This database thus provides easy, one-stop access to multi-species annotated transcriptome information. We anticipate that this database will be useful for members of multiple research communities, including developmental biology, physiology, evolutionary biology, ecology, comparative genomics and phylogenomics. Database URL: asgard.rc.fas.harvard.edu.

P2P proteomics -- data sharing for enhanced protein identification

PubMed Central

2012-01-01

Background In order to tackle the important and challenging problem in proteomics of identifying known and new protein sequences using high-throughput methods, we propose a data-sharing platform that uses fully distributed P2P technologies to share specifications of peer-interaction protocols and service components. By using such a platform, information to be searched is no longer centralised in a few repositories but gathered from experiments in peer proteomics laboratories, which can subsequently be searched by fellow researchers. Methods The system distributively runs a data-sharing protocol specified in the Lightweight Communication Calculus underlying the system through which researchers interact via message passing. For this, researchers interact with the system through particular components that link to database querying systems based on BLAST and/or OMSSA and GUI-based visualisation environments. We have tested the proposed platform with data drawn from preexisting MS/MS data reservoirs from the 2006 ABRF (Association of Biomolecular Resource Facilities) test sample, which was extensively tested during the ABRF Proteomics Standards Research Group 2006 worldwide survey. In particular we have taken the data available from a subset of proteomics laboratories of Spain's National Institute for Proteomics, ProteoRed, a network for the coordination, integration and development of the Spanish proteomics facilities. Results and Discussion We performed queries against nine databases including seven ProteoRed proteomics laboratories, the NCBI Swiss-Prot database and the local database of the CSIC/UAB Proteomics Laboratory. A detailed analysis of the results indicated the presence of a protein that was supported by other NCBI matches and highly scored matches in several proteomics labs. The analysis clearly indicated that the protein was a relatively high concentrated contaminant that could be present in the ABRF sample. This fact is evident from the information that could be derived from the proposed P2P proteomics system, however it is not straightforward to arrive to the same conclusion by conventional means as it is difficult to discard organic contamination of samples. The actual presence of this contaminant was only stated after the ABRF study of all the identifications reported by the laboratories. PMID:22293032

Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites.

PubMed

Chou, Kuo-Chen; Shen, Hong-Bin

2007-05-01

One of the critical challenges in predicting protein subcellular localization is how to deal with the case of multiple location sites. Unfortunately, so far, no efforts have been made in this regard except for the one focused on the proteins in budding yeast only. For most existing predictors, the multiple-site proteins are either excluded from consideration or assumed even not existing. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. For instance, according to the Swiss-Prot database (version 50.7, released 19-Sept-2006), among the 33,925 eukaryotic protein entries that have experimentally observed subcellular location annotations, 2715 have multiple location sites, meaning about 8% bearing the multiplex feature. Proteins with multiple locations or dynamic feature of this kind are particularly interesting because they may have some very special biological functions intriguing to investigators in both basic research and drug discovery. Meanwhile, according to the same Swiss-Prot database, the number of total eukaryotic protein entries (except those annotated with "fragment" or those with less than 50 amino acids) is 90,909, meaning a gap of (90,909-33,925) = 56,984 entries for which no knowledge is available about their subcellular locations. Although one can use the computational approach to predict the desired information for the blank, so far, all the existing methods for predicting eukaryotic protein subcellular localization are limited in the case of single location site only. To overcome such a barrier, a new ensemble classifier, named Euk-mPLoc, was developed that can be used to deal with the case of multiple location sites as well. Euk-mPLoc is freely accessible to the public as a Web server at http://202.120.37.186/bioinf/euk-multi. Meanwhile, to support the people working in the relevant areas, Euk-mPLoc has been used to identify all eukaryotic protein entries in the Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results thus obtained have been deposited at the same Web site via a downloadable file prepared with Microsoft Excel and named "Tab_Euk-mPLoc.xls". Furthermore, to include new entries of eukaryotic proteins and reflect the continuous development of Euk-mPLoc in both the coverage scope and prediction accuracy, we will timely update the downloadable file as well as the predictor, and keep users informed by publishing a short note in the Journal and making an announcement in the Web Page.

TDR Targets: a chemogenomics resource for neglected diseases.

PubMed

Magariños, María P; Carmona, Santiago J; Crowther, Gregory J; Ralph, Stuart A; Roos, David S; Shanmugam, Dhanasekaran; Van Voorhis, Wesley C; Agüero, Fernán

2012-01-01

The TDR Targets Database (http://tdrtargets.org) has been designed and developed as an online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data (e.g. expression, phylogeny, essentiality) for genes collected from various sources, including literature curation. This information can be browsed and queried using an extensive web interface with functionalities for combining, saving, exporting and sharing the query results. Target genes can be ranked and prioritized using numerical weights assigned to the criteria used for querying. In this report we describe recent updates to the TDR Targets database, including the addition of new genomes (specifically helminths), and integration of chemical structure, property and bioactivity information for biological ligands, drugs and inhibitors and cheminformatic tools for querying and visualizing these chemical data. These changes greatly facilitate exploration of linkages (both known and predicted) between genes and small molecules, yielding insight into whether particular proteins may be druggable, effectively allowing the navigation of chemical space in a genomics context.

TDR Targets: a chemogenomics resource for neglected diseases

PubMed Central

Magariños, María P.; Carmona, Santiago J.; Crowther, Gregory J.; Ralph, Stuart A.; Roos, David S.; Shanmugam, Dhanasekaran; Van Voorhis, Wesley C.; Agüero, Fernán

2012-01-01

The TDR Targets Database (http://tdrtargets.org) has been designed and developed as an online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data (e.g. expression, phylogeny, essentiality) for genes collected from various sources, including literature curation. This information can be browsed and queried using an extensive web interface with functionalities for combining, saving, exporting and sharing the query results. Target genes can be ranked and prioritized using numerical weights assigned to the criteria used for querying. In this report we describe recent updates to the TDR Targets database, including the addition of new genomes (specifically helminths), and integration of chemical structure, property and bioactivity information for biological ligands, drugs and inhibitors and cheminformatic tools for querying and visualizing these chemical data. These changes greatly facilitate exploration of linkages (both known and predicted) between genes and small molecules, yielding insight into whether particular proteins may be druggable, effectively allowing the navigation of chemical space in a genomics context. PMID:22116064

Protein structure and evolution: are they constrained globally by a principle derived from information theory?

PubMed

Hatton, Leslie; Warr, Gregory

2015-01-01

That the physicochemical properties of amino acids constrain the structure, function and evolution of proteins is not in doubt. However, principles derived from information theory may also set bounds on the structure (and thus also the evolution) of proteins. Here we analyze the global properties of the full set of proteins in release 13-11 of the SwissProt database, showing by experimental test of predictions from information theory that their collective structure exhibits properties that are consistent with their being guided by a conservation principle. This principle (Conservation of Information) defines the global properties of systems composed of discrete components each of which is in turn assembled from discrete smaller pieces. In the system of proteins, each protein is a component, and each protein is assembled from amino acids. Central to this principle is the inter-relationship of the unique amino acid count and total length of a protein and its implications for both average protein length and occurrence of proteins with specific unique amino acid counts. The unique amino acid count is simply the number of distinct amino acids (including those that are post-translationally modified) that occur in a protein, and is independent of the number of times that the particular amino acid occurs in the sequence. Conservation of Information does not operate at the local level (it is independent of the physicochemical properties of the amino acids) where the influences of natural selection are manifest in the variety of protein structure and function that is well understood. Rather, this analysis implies that Conservation of Information would define the global bounds within which the whole system of proteins is constrained; thus it appears to be acting to constrain evolution at a level different from natural selection, a conclusion that appears counter-intuitive but is supported by the studies described herein.

Proteome Speciation by Mass Spectrometry: Characterization of Composite Protein Mixtures in Milk Replacers.

PubMed

Gaspari, Marco; Chiesa, Luca; Nicastri, Annalisa; Gabriele, Caterina; Harper, Valeria; Britti, Domenico; Cuda, Giovanni; Procopio, Antonio

2016-12-06

The ability of tandem mass spectrometry to determine the primary structure of proteolytic peptides can be exploited to trace back the organisms from which the corresponding proteins were extracted. This information can be important when food products, such as protein powders, can be supplemented with lower-quality starting materials. In order to dissect the origin of proteinaceous material composing a given unknown mixture, a two-step database search strategy for bottom-up nanoscale liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) data was implemented. A single nanoLC-MS/MS analysis was sufficient not only to determine the qualitative composition of the mixtures under examination, but also to assess the relative percent composition of the various proteomes, if dedicated calibration curves were previously generated. The approach of two-step database search for qualitative analysis and proteome total ion current (pTIC) calculation for quantitative analysis was applied to several binary and ternary mixtures which mimic the composition of milk replacers typically used in calf feeding.

The Biomolecular Crystallization Database Version 4: expanded content and new features.

PubMed

Tung, Michael; Gallagher, D Travis

2009-01-01

The Biological Macromolecular Crystallization Database (BMCD) has been a publicly available resource since 1988, providing a curated archive of information on crystal growth for proteins and other biological macromolecules. The BMCD content has recently been expanded to include 14 372 crystal entries. The resource continues to be freely available at http://xpdb.nist.gov:8060/BMCD4. In addition, the software has been adapted to support the Java-based Lucene query language, enabling detailed searching over specific parameters, and explicit search of parameter ranges is offered for five numeric variables. Extensive tools have been developed for import and handling of data from the RCSB Protein Data Bank. The updated BMCD is called version 4.02 or BMCD4. BMCD4 entries have been expanded to include macromolecule sequence, enabling more elaborate analysis of relations among protein properties, crystal-growth conditions and the geometric and diffraction properties of the crystals. The BMCD version 4.02 contains greatly expanded content and enhanced search capabilities to facilitate scientific analysis and design of crystal-growth strategies.

Using relational databases for improved sequence similarity searching and large-scale genomic analyses.

PubMed

Mackey, Aaron J; Pearson, William R

2004-10-01

Relational databases are designed to integrate diverse types of information and manage large sets of search results, greatly simplifying genome-scale analyses. Relational databases are essential for management and analysis of large-scale sequence analyses, and can also be used to improve the statistical significance of similarity searches by focusing on subsets of sequence libraries most likely to contain homologs. This unit describes using relational databases to improve the efficiency of sequence similarity searching and to demonstrate various large-scale genomic analyses of homology-related data. This unit describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. These include basic use of the database to generate a novel sequence library subset, how to extend and use seqdb_demo for the storage of sequence similarity search results and making use of various kinds of stored search results to address aspects of comparative genomic analysis.

Update of the androgen receptor gene mutations database.

PubMed

Gottlieb, B; Beitel, L K; Lumbroso, R; Pinsky, L; Trifiro, M

1999-01-01

The current version of the androgen receptor (AR) gene mutations database is described. The total number of reported mutations has risen from 309 to 374 during the past year. We have expanded the database by adding information on AR-interacting proteins; and we have improved the database by identifying those mutation entries that have been updated. Mutations of unknown significance have now been reported in both the 5' and 3' untranslated regions of the AR gene, and in individuals who are somatic mosaics constitutionally. In addition, single nucleotide polymorphisms, including silent mutations, have been discovered in normal individuals and in individuals with male infertility. A mutation hotspot associated with prostatic cancer has been identified in exon 5. The database is available on the internet (http://www.mcgill.ca/androgendb/), from EMBL-European Bioinformatics Institute (ftp.ebi.ac.uk/pub/databases/androgen), or as a Macintosh FilemakerPro or Word file (MC33@musica.mcgill.ca). Copyright 1999 Wiley-Liss, Inc.

Domain fusion analysis by applying relational algebra to protein sequence and domain databases.

PubMed

Truong, Kevin; Ikura, Mitsuhiko

2003-05-06

Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at http://calcium.uhnres.utoronto.ca/pi. As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time.

msBiodat analysis tool, big data analysis for high-throughput experiments.

PubMed

Muñoz-Torres, Pau M; Rokć, Filip; Belužic, Robert; Grbeša, Ivana; Vugrek, Oliver

2016-01-01

Mass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of proteins. Filtering those data efficiently is the first step for extracting biologically relevant information. The filtering may increase interest by merging previous data with the data obtained from public databases, resulting in an accurate list of proteins which meet the predetermined conditions. In this article we present msBiodat Analysis Tool, a web-based application thought to approach proteomics to the big data analysis. With this tool, researchers can easily select the most relevant information from their MS experiments using an easy-to-use web interface. An interesting feature of msBiodat analysis tool is the possibility of selecting proteins by its annotation on Gene Ontology using its Gene Id, ensembl or UniProt codes. The msBiodat analysis tool is a web-based application that allows researchers with any programming experience to deal with efficient database querying advantages. Its versatility and user-friendly interface makes easy to perform fast and accurate data screening by using complex queries. Once the analysis is finished, the result is delivered by e-mail. msBiodat analysis tool is freely available at http://msbiodata.irb.hr.

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

PubMed Central

Mu, Lin

2018-01-01

This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination. PMID:29309403

HDAPD: a web tool for searching the disease-associated protein structures

PubMed Central

2010-01-01

Background The protein structures of the disease-associated proteins are important for proceeding with the structure-based drug design to against a particular disease. Up until now, proteins structures are usually searched through a PDB id or some sequence information. However, in the HDAPD database presented here the protein structure of a disease-associated protein can be directly searched through the associated disease name keyed in. Description The search in HDAPD can be easily initiated by keying some key words of a disease, protein name, protein type, or PDB id. The protein sequence can be presented in FASTA format and directly copied for a BLAST search. HDAPD is also interfaced with Jmol so that users can observe and operate a protein structure with Jmol. The gene ontological data such as cellular components, molecular functions, and biological processes are provided once a hyperlink to Gene Ontology (GO) is clicked. Further, HDAPD provides a link to the KEGG map such that where the protein is placed and its relationship with other proteins in a metabolic pathway can be found from the map. The latest literatures namely titles, journals, authors, and abstracts searched from PubMed for the protein are also presented as a length controllable list. Conclusions Since the HDAPD data content can be routinely updated through a PHP-MySQL web page built, the new database presented is useful for searching the structures for some disease-associated proteins that may play important roles in the disease developing process for performing the structure-based drug design to against the diseases. PMID:20158919

Identification and Analysis of Jasmonate Pathway Genes in Coffea canephora (Robusta Coffee) by In Silico Approach.

PubMed

Bharathi, Kosaraju; Sreenath, H L

2017-07-01

Coffea canephora is the commonly cultivated coffee species in the world along with Coffea arabica . Different pests and pathogens affect the production and quality of the coffee. Jasmonic acid (JA) is a plant hormone which plays an important role in plants growth, development, and defense mechanisms, particularly against insect pests. The key enzymes involved in the production of JA are lipoxygenase, allene oxide synthase, allene oxide cyclase, and 12-oxo-phytodienoic reductase. There is no report on the genes involved in JA pathway in coffee plants. We made an attempt to identify and analyze the genes coding for these enzymes in C. canephora . First, protein sequences of jasmonate pathway genes from model plant Arabidopsis thaliana were identified in the National Center for Biotechnology Information (NCBI) database. These protein sequences were used to search the web-based database Coffee Genome Hub to identify homologous protein sequences in C. canephora genome using Basic Local Alignment Search Tool (BLAST). Homologous protein sequences for key genes were identified in the C. canephora genome database. Protein sequences of the top matches were in turn used to search in NCBI database using BLAST tool to confirm the identity of the selected proteins and to identify closely related genes in species. The protein sequences from C. canephora database and the top matches in NCBI were aligned, and phylogenetic trees were constructed using MEGA6 software and identified the genetic distance of the respective genes. The study identified the four key genes of JA pathway in C. canephora , confirming the conserved nature of the pathway in coffee. The study expected to be useful to further explore the defense mechanisms of coffee plants. JA is a plant hormone that plays an important role in plant defense against insect pests. Genes coding for the 4 key enzymes involved in the production of JA viz., LOX, AOS, AOC, and OPR are identified in C. canephora (robusta coffee) by bioinformatic approaches confirming the conserved nature of the pathway in coffee. The findings are useful to understand the defense mechanisms of C. canephora and coffee breeding in the long run. JA is a plant hormone that plays an important role in plant defense against insect pests. Genes coding for the 4 key enzymes involved in the production of JA viz., LOX, AOS, AOC and OPR were identified and analyzed in C. canephora (robusta coffee) by in silico approach. The study has confirmed the conserved nature of JA pathway in coffee; the findings are useful to further explore the defense mechanisms of coffee plants. Abbreviations used: C. canephora : Coffea canephora ; C. arabica : Coffea arabica ; JA: Jasmonic acid; CGH: Coffee Genome Hub; NCBI: National Centre for Biotechnology Information; BLAST: Basic Local Alignment Search Tool; A. thaliana : Arabidopsis thaliana ; LOX: Lipoxygenase, AOS: Allene oxide synthase; AOC: Allene oxide cyclase; OPR: 12 oxo phytodienoic reductase.

The Classification of Protein Domains.

PubMed

Dawson, Natalie; Sillitoe, Ian; Marsden, Russell L; Orengo, Christine A

2017-01-01

The significant expansion in protein sequence and structure data that we are now witnessing brings with it a pressing need to bring order to the protein world. Such order enables us to gain insights into the evolution of proteins, their function and the extent to which the functional repertoire can vary across the three kingdoms of life. This has lead to the creation of a wide range of protein family classifications that aim to group proteins based upon their evolutionary relationships.In this chapter we discuss the approaches and methods that are frequently used in the classification of proteins, with a specific emphasis on the classification of protein domains. The construction of both domain sequence and domain structure databases is considered and we show how the use of domain family annotations to assign structural and functional information is enhancing our understanding of genomes.

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods, and Applications

PubMed Central

Zhao, Mingzhu; Wei, Dong-Qing

2013-01-01

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM. PMID:23818932

JavaProtein Dossier: a novel web-based data visualization tool for comprehensive analysis of protein structure

PubMed Central

Neshich, Goran; Rocchia, Walter; Mancini, Adauto L.; Yamagishi, Michel E. B.; Kuser, Paula R.; Fileto, Renato; Baudet, Christian; Pinto, Ivan P.; Montagner, Arnaldo J.; Palandrani, Juliana F.; Krauchenco, Joao N.; Torres, Renato C.; Souza, Savio; Togawa, Roberto C.; Higa, Roberto H.

2004-01-01

JavaProtein Dossier (JPD) is a new concept, database and visualization tool providing one of the largest collections of the physicochemical parameters describing proteins' structure, stability, function and interaction with other macromolecules. By collecting as many descriptors/parameters as possible within a single database, we can achieve a better use of the available data and information. Furthermore, data grouping allows us to generate different parameters with the potential to provide new insights into the sequence–structure–function relationship. In JPD, residue selection can be performed according to multiple criteria. JPD can simultaneously display and analyze all the physicochemical parameters of any pair of structures, using precalculated structural alignments, allowing direct parameter comparison at corresponding amino acid positions among homologous structures. In order to focus on the physicochemical (and consequently pharmacological) profile of proteins, visualization tools (showing the structure and structural parameters) also had to be optimized. Our response to this challenge was the use of Java technology with its exceptional level of interactivity. JPD is freely accessible (within the Gold Sting Suite) at http://sms.cbi.cnptia.embrapa.br, http://mirrors.rcsb.org/SMS, http://trantor.bioc.columbia.edu/SMS and http://www.es.embnet.org/SMS/ (Option: JavaProtein Dossier). PMID:15215458

Genome-wide identification, classification, and functional analysis of the basic helix-loop-helix transcription factors in the cattle, Bos Taurus.

PubMed

Li, Fengmei; Liu, Wuyi

2017-06-01

The basic helix-loop-helix (bHLH) transcription factors (TFs) form a huge superfamily and play crucial roles in many essential developmental, genetic, and physiological-biochemical processes of eukaryotes. In total, 109 putative bHLH TFs were identified and categorized successfully in the genomic databases of cattle, Bos Taurus, after removing redundant sequences and merging genetic isoforms. Through phylogenetic analyses, 105 proteins among these bHLH TFs were classified into 44 families with 46, 25, 14, 3, 13, and 4 members in the high-order groups A, B, C, D, E, and F, respectively. The remaining 4 bHLH proteins were sorted out as 'orphans.' Next, these 109 putative bHLH proteins identified were further characterized as significantly enriched in 524 significant Gene Ontology (GO) annotations (corrected P value ≤ 0.05) and 21 significantly enriched pathways (corrected P value ≤ 0.05) that had been mapped by the web server KOBAS 2.0. Furthermore, 95 bHLH proteins were further screened and analyzed together with two uncharacterized proteins in the STRING online database to reconstruct the protein-protein interaction network of cattle bHLH TFs. Ultimately, 89 bHLH proteins were fully mapped in a network with 67 biological process, 13 molecular functions, 5 KEGG pathways, 12 PFAM protein domains, and 25 INTERPRO classified protein domains and features. These results provide much useful information and a good reference for further functional investigations and updated researches on cattle bHLH TFs.

A protein relational database and protein family knowledge bases to facilitate structure-based design analyses.

PubMed

Mobilio, Dominick; Walker, Gary; Brooijmans, Natasja; Nilakantan, Ramaswamy; Denny, R Aldrin; Dejoannis, Jason; Feyfant, Eric; Kowticwar, Rupesh K; Mankala, Jyoti; Palli, Satish; Punyamantula, Sairam; Tatipally, Maneesh; John, Reji K; Humblet, Christine

2010-08-01

The Protein Data Bank is the most comprehensive source of experimental macromolecular structures. It can, however, be difficult at times to locate relevant structures with the Protein Data Bank search interface. This is particularly true when searching for complexes containing specific interactions between protein and ligand atoms. Moreover, searching within a family of proteins can be tedious. For example, one cannot search for some conserved residue as residue numbers vary across structures. We describe herein three databases, Protein Relational Database, Kinase Knowledge Base, and Matrix Metalloproteinase Knowledge Base, containing protein structures from the Protein Data Bank. In Protein Relational Database, atom-atom distances between protein and ligand have been precalculated allowing for millisecond retrieval based on atom identity and distance constraints. Ring centroids, centroid-centroid and centroid-atom distances and angles have also been included permitting queries for pi-stacking interactions and other structural motifs involving rings. Other geometric features can be searched through the inclusion of residue pair and triplet distances. In Kinase Knowledge Base and Matrix Metalloproteinase Knowledge Base, the catalytic domains have been aligned into common residue numbering schemes. Thus, by searching across Protein Relational Database and Kinase Knowledge Base, one can easily retrieve structures wherein, for example, a ligand of interest is making contact with the gatekeeper residue.

Why Choose This One? Factors in Scientists' Selection of Bioinformatics Tools

ERIC Educational Resources Information Center

Bartlett, Joan C.; Ishimura, Yusuke; Kloda, Lorie A.

2011-01-01

Purpose: The objective was to identify and understand the factors involved in scientists' selection of preferred bioinformatics tools, such as databases of gene or protein sequence information (e.g., GenBank) or programs that manipulate and analyse biological data (e.g., BLAST). Methods: Eight scientists maintained research diaries for a two-week…

Protein Function Prediction: Problems and Pitfalls.

PubMed

Pearson, William R

2015-09-03

The characterization of new genomes based on their protein sets has been revolutionized by new sequencing technologies, but biologists seeking to exploit new sequence information are often frustrated by the challenges associated with accurately assigning biological functions to newly identified proteins. Here, we highlight some of the challenges in functional inference from sequence similarity. Investigators can improve the accuracy of function prediction by (1) being conservative about the evolutionary distance to a protein of known function; (2) considering the ambiguous meaning of "functional similarity," and (3) being aware of the limitations of annotations in functional databases. Protein function prediction does not offer "one-size-fits-all" solutions. Prediction strategies work better when the idiosyncrasies of function and functional annotation are better understood. Copyright © 2015 John Wiley & Sons, Inc.

Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information.

PubMed

Kim, Sun; Kim, Won; Wei, Chih-Hsuan; Lu, Zhiyong; Wilbur, W John

2012-01-01

The Comparative Toxicogenomics Database (CTD) contains manually curated literature that describes chemical-gene interactions, chemical-disease relationships and gene-disease relationships. Finding articles containing this information is the first and an important step to assist manual curation efficiency. However, the complex nature of named entities and their relationships make it challenging to choose relevant articles. In this article, we introduce a machine learning framework for prioritizing CTD-relevant articles based on our prior system for the protein-protein interaction article classification task in BioCreative III. To address new challenges in the CTD task, we explore a new entity identification method for genes, chemicals and diseases. In addition, latent topics are analyzed and used as a feature type to overcome the small size of the training set. Applied to the BioCreative 2012 Triage dataset, our method achieved 0.8030 mean average precision (MAP) in the official runs, resulting in the top MAP system among participants. Integrated with PubTator, a Web interface for annotating biomedical literature, the proposed system also received a positive review from the CTD curation team.

Evolution and function of CAG/polyglutamine repeats in protein–protein interaction networks

PubMed Central

Schaefer, Martin H.; Wanker, Erich E.; Andrade-Navarro, Miguel A.

2012-01-01

Expanded runs of consecutive trinucleotide CAG repeats encoding polyglutamine (polyQ) stretches are observed in the genes of a large number of patients with different genetic diseases such as Huntington's and several Ataxias. Protein aggregation, which is a key feature of most of these diseases, is thought to be triggered by these expanded polyQ sequences in disease-related proteins. However, polyQ tracts are a normal feature of many human proteins, suggesting that they have an important cellular function. To clarify the potential function of polyQ repeats in biological systems, we systematically analyzed available information stored in sequence and protein interaction databases. By integrating genomic, phylogenetic, protein interaction network and functional information, we obtained evidence that polyQ tracts in proteins stabilize protein interactions. This happens most likely through structural changes whereby the polyQ sequence extends a neighboring coiled-coil region to facilitate its interaction with a coiled-coil region in another protein. Alteration of this important biological function due to polyQ expansion results in gain of abnormal interactions, leading to pathological effects like protein aggregation. Our analyses suggest that research on polyQ proteins should shift focus from expanded polyQ proteins into the characterization of the influence of the wild-type polyQ on protein interactions. PMID:22287626

CCProf: exploring conformational change profile of proteins

PubMed Central

Chang, Che-Wei; Chou, Chai-Wei; Chang, Darby Tien-Hao

2016-01-01

In many biological processes, proteins have important interactions with various molecules such as proteins, ions or ligands. Many proteins undergo conformational changes upon these interactions, where regions with large conformational changes are critical to the interactions. This work presents the CCProf platform, which provides conformational changes of entire proteins, named conformational change profile (CCP) in the context. CCProf aims to be a platform where users can study potential causes of novel conformational changes. It provides 10 biological features, including conformational change, potential binding target site, secondary structure, conservation, disorder propensity, hydropathy propensity, sequence domain, structural domain, phosphorylation site and catalytic site. All these information are integrated into a well-aligned view, so that researchers can capture important relevance between different biological features visually. The CCProf contains 986 187 protein structure pairs for 3123 proteins. In addition, CCProf provides a 3D view in which users can see the protein structures before and after conformational changes as well as binding targets that induce conformational changes. All information (e.g. CCP, binding targets and protein structures) shown in CCProf, including intermediate data are available for download to expedite further analyses. Database URL: http://zoro.ee.ncku.edu.tw/ccprof/ PMID:27016699

THGS: a web-based database of Transmembrane Helices in Genome Sequences

PubMed Central

Fernando, S. A.; Selvarani, P.; Das, Soma; Kumar, Ch. Kiran; Mondal, Sukanta; Ramakumar, S.; Sekar, K.

2004-01-01

Transmembrane Helices in Genome Sequences (THGS) is an interactive web-based database, developed to search the transmembrane helices in the user-interested gene sequences available in the Genome Database (GDB). The proposed database has provision to search sequence motifs in transmembrane and globular proteins. In addition, the motif can be searched in the other sequence databases (Swiss-Prot and PIR) or in the macromolecular structure database, Protein Data Bank (PDB). Further, the 3D structure of the corresponding queried motif, if it is available in the solved protein structures deposited in the Protein Data Bank, can also be visualized using the widely used graphics package RASMOL. All the sequence databases used in the present work are updated frequently and hence the results produced are up to date. The database THGS is freely available via the world wide web and can be accessed at http://pranag.physics.iisc.ernet.in/thgs/ or http://144.16.71.10/thgs/. PMID:14681375

A Computational Approach From Gene to Structure Analysis of the Human ABCA4 Transporter Involved in Genetic Retinal Diseases.

PubMed

Trezza, Alfonso; Bernini, Andrea; Langella, Andrea; Ascher, David B; Pires, Douglas E V; Sodi, Andrea; Passerini, Ilaria; Pelo, Elisabetta; Rizzo, Stanislao; Niccolai, Neri; Spiga, Ottavia

2017-10-01

The aim of this article is to report the investigation of the structural features of ABCA4, a protein associated with a genetic retinal disease. A new database collecting knowledge of ABCA4 structure may facilitate predictions about the possible functional consequences of gene mutations observed in clinical practice. In order to correlate structural and functional effects of the observed mutations, the structure of mouse P-glycoprotein was used as a template for homology modeling. The obtained structural information and genetic data are the basis of our relational database (ABCA4Database). Sequence variability among all ABCA4-deposited entries was calculated and reported as Shannon entropy score at the residue level. The three-dimensional model of ABCA4 structure was used to locate the spatial distribution of the observed variable regions. Our predictions from structural in silico tools were able to accurately link the functional effects of mutations to phenotype. The development of the ABCA4Database gathers all the available genetic and structural information, yielding a global view of the molecular basis of some retinal diseases. ABCA4 modeled structure provides a molecular basis on which to analyze protein sequence mutations related to genetic retinal disease in order to predict the risk of retinal disease across all possible ABCA4 mutations. Additionally, our ABCA4 predicted structure is a good starting point for the creation of a new data analysis model, appropriate for precision medicine, in order to develop a deeper knowledge network of the disease and to improve the management of patients.

Phylogenetic analysis of β-xylanase SRXL1 of Sporisorium reilianum and its relationship with families (GH10 and GH11) of Ascomycetes and Basidiomycetes

PubMed Central

Álvarez-Cervantes, Jorge; Díaz-Godínez, Gerardo; Mercado-Flores, Yuridia; Gupta, Vijai Kumar; Anducho-Reyes, Miguel Angel

2016-01-01

In this paper, the amino acid sequence of the β-xylanase SRXL1 of Sporisorium reilianum, which is a pathogenic fungus of maize was used as a model protein to find its phylogenetic relationship with other xylanases of Ascomycetes and Basidiomycetes and the information obtained allowed to establish a hypothesis of monophyly and of biological role. 84 amino acid sequences of β-xylanase obtained from the GenBank database was used. Groupings analysis of higher-level in the Pfam database allowed to determine that the proteins under study were classified into the GH10 and GH11 families, based on the regions of highly conserved amino acids, 233–318 and 180–193 respectively, where glutamate residues are responsible for the catalysis. PMID:27040368

From sequence to enzyme mechanism using multi-label machine learning.

PubMed

De Ferrari, Luna; Mitchell, John B O

2014-05-19

In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm. We find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.

Deep mRNA Sequencing of the Tritonia diomedea Brain Transcriptome Provides Access to Gene Homologues for Neuronal Excitability, Synaptic Transmission and Peptidergic Signalling

PubMed Central

Senatore, Adriano; Edirisinghe, Neranjan; Katz, Paul S.

2015-01-01

Background The sea slug Tritonia diomedea (Mollusca, Gastropoda, Nudibranchia), has a simple and highly accessible nervous system, making it useful for studying neuronal and synaptic mechanisms underlying behavior. Although many important contributions have been made using Tritonia, until now, a lack of genetic information has impeded exploration at the molecular level. Results We performed Illumina sequencing of central nervous system mRNAs from Tritonia, generating 133.1 million 100 base pair, paired-end reads. De novo reconstruction of the RNA-Seq data yielded a total of 185,546 contigs, which partitioned into 123,154 non-redundant gene clusters (unigenes). BLAST comparison with RefSeq and Swiss-Prot protein databases, as well as mRNA data from other invertebrates (gastropod molluscs: Aplysia californica, Lymnaea stagnalis and Biomphalaria glabrata; cnidarian: Nematostella vectensis) revealed that up to 76,292 unigenes in the Tritonia transcriptome have putative homologues in other databases, 18,246 of which are below a more stringent E-value cut-off of 1x10-6. In silico prediction of secreted proteins from the Tritonia transcriptome shotgun assembly (TSA) produced a database of 579 unique sequences of secreted proteins, which also exhibited markedly higher expression levels compared to other genes in the TSA. Conclusions Our efforts greatly expand the availability of gene sequences available for Tritonia diomedea. We were able to extract full length protein sequences for most queried genes, including those involved in electrical excitability, synaptic vesicle release and neurotransmission, thus confirming that the transcriptome will serve as a useful tool for probing the molecular correlates of behavior in this species. We also generated a neurosecretome database that will serve as a useful tool for probing peptidergic signalling systems in the Tritonia brain. PMID:25719197

SorghumFDB: sorghum functional genomics database with multidimensional network analysis.

PubMed

Tian, Tian; You, Qi; Zhang, Liwei; Yi, Xin; Yan, Hengyu; Xu, Wenying; Su, Zhen

2016-01-01

Sorghum (Sorghum bicolor [L.] Moench) has excellent agronomic traits and biological properties, such as heat and drought-tolerance. It is a C4 grass and potential bioenergy-producing plant, which makes it an important crop worldwide. With the sorghum genome sequence released, it is essential to establish a sorghum functional genomics data mining platform. We collected genomic data and some functional annotations to construct a sorghum functional genomics database (SorghumFDB). SorghumFDB integrated knowledge of sorghum gene family classifications (transcription regulators/factors, carbohydrate-active enzymes, protein kinases, ubiquitins, cytochrome P450, monolignol biosynthesis related enzymes, R-genes and organelle-genes), detailed gene annotations, miRNA and target gene information, orthologous pairs in the model plants Arabidopsis, rice and maize, gene loci conversions and a genome browser. We further constructed a dynamic network of multidimensional biological relationships, comprised of the co-expression data, protein-protein interactions and miRNA-target pairs. We took effective measures to combine the network, gene set enrichment and motif analyses to determine the key regulators that participate in related metabolic pathways, such as the lignin pathway, which is a major biological process in bioenergy-producing plants.Database URL: http://structuralbiology.cau.edu.cn/sorghum/index.html. © The Author(s) 2016. Published by Oxford University Press.