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MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Authors :
Zheng, Lingyan
Shi, Shuiyang
Sun, Xiuna
Lu, Mingkun
Liao, Yang
Zhu, Sisi
Zhang, Hongning
Pan, Ziqi
Fang, Pan
Zeng, Zhenyu
Li, Honglin
Li, Zhaorong
Xue, Weiwei
Zhu, Feng
Source :
Briefings in Bioinformatics; Mar2024, Vol. 25 Issue 2, p1-9, 9p
Publication Year :
2024

Abstract

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled ' MoDAFold ' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
25
Issue :
2
Database :
Complementary Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
176218799
Full Text :
https://doi.org/10.1093/bib/bbae006