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TM-search: An Efficient and Effective Tool for Protein Structure Database Search.
Liu, Zi; Zhang, Chengxin; Zhang, Qidi; Zhang, Yang; Yu, Dong-Jun.
Afiliação
  • Liu Z; School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China.
  • Zhang C; Computer Department, Jingdezhen Ceramic University, Jingdezhen 333403, China.
  • Zhang Q; Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw, Ann Arbor, Michigan 48109-2218, United States.
  • Zhang Y; Computer Department, Jingdezhen Ceramic University, Jingdezhen 333403, China.
  • Yu DJ; Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw, Ann Arbor, Michigan 48109-2218, United States.
J Chem Inf Model ; 64(3): 1043-1049, 2024 Feb 12.
Article em En | MEDLINE | ID: mdl-38270339
ABSTRACT
The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Proteínas Tipo de estudo: Prognostic_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Proteínas Tipo de estudo: Prognostic_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China