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GPU-I-TASSER: a GPU accelerated I-TASSER protein structure prediction tool.
MacCarthy, Elijah A; Zhang, Chengxin; Zhang, Yang; Kc, Dukka B.
Afiliação
  • MacCarthy EA; Department of Mathematics, Lane College, Jackson, TN 38301, USA.
  • Zhang C; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
  • Zhang Y; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
  • Kc DB; Computer Science Department, Michigan Technological University, Houghton, MI 49931, USA.
Bioinformatics ; 38(6): 1754-1755, 2022 03 04.
Article em En | MEDLINE | ID: mdl-34978562
ABSTRACT
MOTIVATION Accurate and efficient predictions of protein structures play an important role in understanding their functions. Iterative Threading Assembly Refinement (I-TASSER) is one of the most successful and widely used protein structure prediction methods in the recent community-wide CASP experiments. Yet, the computational efficiency of I-TASSER is one of the limiting factors that prevent its application for large-scale structure modeling.

RESULTS:

We present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelerated I-TASSER protein structure prediction tool for fast and accurate protein structure prediction. Our implementation is based on OpenACC parallelization of the replica-exchange Monte Carlo simulations to enhance the speed of I-TASSER by extending its capabilities to the GPU architecture. On a benchmark dataset of 71 protein structures, GPU-I-TASSER achieves on average a 10× speedup with comparable structure prediction accuracy compared to the CPU version of the I-TASSER. AVAILABILITY AND IMPLEMENTATION The complete source code for GPU-I-TASSER can be downloaded and used without restriction from https//zhanggroup.org/GPU-I-TASSER/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteínas Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteínas Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article