Quantification of the effect of mutations using a global probability model of natural sequence variation

TL;DR

This paper introduces a statistical model based on natural sequence variation to predict the effects of mutations on protein function, leveraging evolutionary information to improve understanding of mutation impacts.

Contribution

It presents a novel probabilistic approach using an evolutionary Hamiltonian to quantify residue contributions and interactions affecting protein function, extending structural models to functional predictions.

Findings

Models predict mutation effects with reasonable accuracy.

Effective especially when selective pressure aligns with natural evolution.

Provides a new framework for understanding mutation impacts in proteins.

Abstract

Modern biomedicine is challenged to predict the effects of genetic variation. Systematic functional assays of point mutants of proteins have provided valuable empirical information, but vast regions of sequence space remain unexplored. Fortunately, the mutation-selection process of natural evolution has recorded rich information in the diversity of natural protein sequences. Here, building on probabilistic models for correlated amino-acid substitutions that have been successfully applied to determine the three-dimensional structures of proteins, we present a statistical approach for quantifying the contribution of residues and their interactions to protein function, using a statistical energy, the evolutionary Hamiltonian. We find that these probability models predict the experimental effects of mutations with reasonable accuracy for a number of proteins, especially where the selective pressure is similar to the evolutionary pressure on the protein, such as antibiotics.