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PIRSitePredict for protein functional site prediction using position-specific rules.
Chen, Chuming; Wang, Qinghua; Huang, Hongzhan; Vinayaka, Cholanayakanahalli R; Garavelli, John S; Arighi, Cecilia N; Natale, Darren A; Wu, Cathy H.
Afiliación
  • Chen C; Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
  • Wang Q; Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
  • Huang H; Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
  • Vinayaka CR; Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
  • Garavelli JS; Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
  • Arighi CN; Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
  • Natale DA; Protein Information Resource, Georgetown University Medical Center, Washington, DC, USA.
  • Wu CH; Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
Database (Oxford) ; 20192019 01 01.
Article en En | MEDLINE | ID: mdl-30805646
Methods focused on predicting 'global' annotations for proteins (such as molecular function, biological process and presence of domains or membership in a family) have reached a relatively mature stage. Methods to provide fine-grained 'local' annotation of functional sites (at the level of individual amino acid) are now coming to the forefront, especially in light of the rapid accumulation of genetic variant data. We have developed a computational method and workflow that predicts functional sites within proteins using position-specific conditional template annotation rules (namely PIR Site Rules or PIRSRs for short). Such rules are curated through review of known protein structural and other experimental data by structural biologists and are used to generate high-quality annotations for the UniProt Knowledgebase (UniProtKB) unreviewed section. To share the PIRSR functional site prediction method with the broader scientific community, we have streamlined our workflow and developed a stand-alone Java software package named PIRSitePredict. We demonstrate the use of PIRSitePredict for functional annotation of de novo assembled genome/transcriptome by annotating uncharacterized proteins from Trinity RNA-seq assembly of embryonic transcriptomes of the following three cartilaginous fishes: Leucoraja erinacea (Little Skate), Scyliorhinus canicula (Small-spotted Catshark) and Callorhinchus milii (Elephant Shark). On average about 1200 lines of annotations were predicted for each species.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Bases de Datos de Proteínas / Anotación de Secuencia Molecular Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Database (Oxford) Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Bases de Datos de Proteínas / Anotación de Secuencia Molecular Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Database (Oxford) Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido