Protein structure-based evaluation of missense variants: Resources, challenges and future directions

Alessia David; Michael J E Sternberg

doi:10.1016/j.sbi.2023.102600

Protein structure-based evaluation of missense variants: Resources, challenges and future directions

Curr Opin Struct Biol. 2023 Jun:80:102600. doi: 10.1016/j.sbi.2023.102600. Epub 2023 Apr 29.

Authors

Alessia David¹, Michael J E Sternberg²

Affiliations

¹ Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK. Electronic address: alessia.david09@imperial.ac.uk.
² Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.

PMID: 37126977
DOI: 10.1016/j.sbi.2023.102600

Abstract

We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.

Keywords: Algorithms; Missense variants; Prediction; Protein structure.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computational Biology / methods
Mutation, Missense*
Proteins* / chemistry

Substances

Proteins

Abstract

Publication types

MeSH terms

Substances

Grants and funding