Affiliation:
1. Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry, CSIC , 28006 Madrid, Spain
2. Bioinformatic Unit, Centro de Biología Molecular “Severo Ochoa,” CSIC-UAM Cantoblanco , Madrid 28049, Spain
Abstract
Abstract
Motivation
Structure-based stability prediction upon mutation is crucial for protein engineering and design, and for understanding genetic diseases or drug resistance events. For this task, we adopted a simple residue-based orientational potential that considers only three backbone atoms, previously applied in protein modeling. Its application to stability prediction only requires parametrizing 12 amino acid-dependent weights using cross-validation strategies on a curated dataset in which we tried to reduce the mutations that belong to protein–protein or protein–ligand interfaces, extreme conditions and the alanine over-representation.
Results
Our method, called KORPM, accurately predicts mutational effects on an independent benchmark dataset, whether the wild-type or mutated structure is used as starting point. Compared with state-of-the-art methods on this balanced dataset, our approach obtained the lowest root mean square error (RMSE) and the highest correlation between predicted and experimental ΔΔG measures, as well as better receiver operating characteristics and precision-recall curves. Our method is almost anti-symmetric by construction, and it performs thus similarly for the direct and reverse mutations with the corresponding wild-type and mutated structures. Despite the strong limitations of the available experimental mutation data in terms of size, variability, and heterogeneity, we show competitive results with a simple sum of energy terms, which is more efficient and less prone to overfitting.
Availability and implementation
https://github.com/chaconlab/korpm.
Supplementary information
Supplementary data are available at Bioinformatics online.
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
6 articles.
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