Your browser doesn't support javascript.
loading
Current Computational Methods for Protein-Peptide Complex Structure Prediction.
Yang, Chao; Xu, Xianjin; Xiang, Changcheng.
Afiliação
  • Yang C; Department of Chemistry, New York University, New York, New York10003, United States.
  • Xu X; Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri65211, United States.
  • Xiang C; School of Computer Science and Technology, Aba Teachers University, Aba, Sichuan623002, China.
Curr Med Chem ; 2023 Oct 06.
Article em En | MEDLINE | ID: mdl-37888817
Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Curr Med Chem Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Curr Med Chem Ano de publicação: 2023 Tipo de documento: Article