» Articles » PMID: 32920050

MPTherm-pred: Analysis and Prediction of Thermal Stability Changes Upon Mutations in Transmembrane Proteins

Overview
Journal J Mol Biol
Publisher Elsevier
Date 2020 Sep 13
PMID 32920050
Citations 8
Authors
Affiliations
Soon will be listed here.
Abstract

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔT) were experimentally determined, we found that the average ΔT due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔT values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔT due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔT for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔT values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.

Citing Articles

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.

Lin Y, Menon A, Hu Z, Brenner S Hum Genomics. 2024; 18(1):90.

PMID: 39198917 PMC: 11360829. DOI: 10.1186/s40246-024-00663-z.


PON-Tm: A Sequence-Based Method for Prediction of Missense Mutation Effects on Protein Thermal Stability Changes.

Kuang J, Zhao Z, Yang Y, Yan W Int J Mol Sci. 2024; 25(15).

PMID: 39125949 PMC: 11313349. DOI: 10.3390/ijms25158379.


Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.

Lin Y, Menon A, Hu Z, Brenner S bioRxiv. 2024; .

PMID: 38979289 PMC: 11230257. DOI: 10.1101/2024.06.25.600283.


Engineering G protein-coupled receptors for stabilization.

Velloso J, de Sa A, Pires D, Ascher D Protein Sci. 2024; 33(6):e5000.

PMID: 38747401 PMC: 11094779. DOI: 10.1002/pro.5000.


Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.

Dou Z, Sun Y, Jiang X, Wu X, Li Y, Gong B Acta Biochim Biophys Sin (Shanghai). 2023; 55(3):343-355.

PMID: 37143326 PMC: 10160227. DOI: 10.3724/abbs.2023033.