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Real time structural search of the Protein Data Bank.
Guzenko, Dmytro; Burley, Stephen K; Duarte, Jose M.
Afiliação
  • Guzenko D; RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, United States of America.
  • Burley SK; RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, United States of America.
  • Duarte JM; RCSB Protein Data Bank, Institute for Quantitative Biomedicine Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.
PLoS Comput Biol ; 16(7): e1007970, 2020 07.
Article em En | MEDLINE | ID: mdl-32639954
ABSTRACT
Detection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, together with recent growth of oligomeric structures in the Protein Data Bank (PDB), demands more efficient approaches to oligomeric assembly alignment/retrieval. Traditional methods use atom level information, which can be complicated by the presence of topological permutations within a polypeptide chain and/or subunit rearrangements. These challenges can be overcome by comparing electron density volumes directly. But, brute force alignment of 3D data is a compute intensive search problem. We developed a 3D Zernike moment normalization procedure to orient electron density volumes and assess similarity with unprecedented speed. Similarity searching with this approach enables real-time retrieval of proteins/protein assemblies resembling a target, from PDB or user input, together with resulting alignments (http//shape.rcsb.org).
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteínas / Biologia Computacional / Bases de Dados de Proteínas Idioma: En Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteínas / Biologia Computacional / Bases de Dados de Proteínas Idioma: En Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos