Your browser doesn't support javascript.
loading
PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures.
Jakubec, David; Skoda, Petr; Krivak, Radoslav; Novotny, Marian; Hoksza, David.
Afiliação
  • Jakubec D; Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Czech Republic.
  • Skoda P; Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Czech Republic.
  • Krivak R; Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Czech Republic.
  • Novotny M; Department of Cell Biology, Faculty of Science, Charles University, Czech Republic.
  • Hoksza D; Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Czech Republic.
Nucleic Acids Res ; 50(W1): W593-W597, 2022 07 05.
Article em En | MEDLINE | ID: mdl-35609995
ABSTRACT
Knowledge of protein-ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https//prankweb.cz/) for fast and accurate LBS prediction. Here, we present significant enhancements to PrankWeb. First, a new, more accurate evolutionary conservation estimation pipeline based on the UniRef50 sequence database and the HMMER3 package is introduced. Second, PrankWeb now allows users to enter UniProt ID to carry out LBS predictions in situations where no experimental structure is available by utilizing the AlphaFold model database. Additionally, a range of minor improvements has been implemented. These include the ability to deploy PrankWeb and P2Rank as Docker containers, support for the mmCIF file format, improved public REST API access, or the ability to batch download the LBS predictions for the whole PDB archive and parts of the AlphaFold database.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Proteínas Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Proteínas Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2022 Tipo de documento: Article