An efficient method to predict protein thermostability in alanine mutation

Ya Gao; Bo Wang; Shiyu Hu; Tong Zhu; John Z. H. Zhang

doi:10.1039/D2CP04236C

An efficient method to predict protein thermostability in alanine mutation†

Ya Gao,

^a Bo Wang,^b Shiyu Hu,^c Tong Zhu

*^bc and John Z. H. Zhang*^bcd

Author affiliations

* Corresponding authors

^a School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China

^b Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
E-mail: tzhu@lps.ecnu.edu.cn, zhzhang@phy.ecnu.edu.cn

^c NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China

^d Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Abstract

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔG_exp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

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DOI: https://doi.org/10.1039/D2CP04236C
Article type: Paper
Submitted: 11 Sep 2022
Accepted: 15 Nov 2022
First published: 16 Nov 2022

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Phys. Chem. Chem. Phys., 2022,24, 29629-29639

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An efficient method to predict protein thermostability in alanine mutation

Y. Gao, B. Wang, S. Hu, T. Zhu and J. Z. H. Zhang, Phys. Chem. Chem. Phys., 2022, 24, 29629 DOI: 10.1039/D2CP04236C

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Physical Chemistry Chemical Physics

An efficient method to predict protein thermostability in alanine mutation†

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An efficient method to predict protein thermostability in alanine mutation

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