SIGMA Leverages Protein Structural Information To Predict the Pathogenicity of Missense Variants

Authors

Hengqiang Zhao
Huakang Du
Sen Zhao
Zefu Chen
Yaqi Li
Kexin Xu
Bowen Liu
Xi Cheng
Wen Wen
Guozhuang Li
Guilin Chen
Zhengye Zhao
Guixing Qiu
Deciphering Disorders Involving Scoliosis & Comorbidities (DISCO) Study
Pengfei Liu
Terry Jianguo Zhang
Zhihong Wu
Nan Wu

Publication Date

1-22-2024

Journal

Cell Reports Methods

DOI

10.1016/j.crmeth.2023.100687

PMID

38211594

PMCID

PMC10831939

PubMedCentral® Posted Date

1-10-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Mutation, Missense, Virulence, Computational Biology, Proteins, Mutation, variant effect predictor, protein structure, machine learning, missense variant

Abstract

Leveraging protein structural information to evaluate pathogenicity has been hindered by the scarcity of experimentally determined 3D protein. With the aid of AlphaFold2 predictions, we developed the structure-informed genetic missense mutation assessor (SIGMA) to predict missense variant pathogenicity. In comparison with existing predictors across labeled variant datasets and experimental datasets, SIGMA demonstrates superior performance in predicting missense variant pathogenicity (AUC = 0.933). We found that the relative solvent accessibility of the mutated residue contributed greatly to the predictive ability of SIGMA. We further explored combining SIGMA with other top-tier predictors to create SIGMA+, proving highly effective for variant pathogenicity prediction (AUC = 0.966). To facilitate the application of SIGMA, we pre-computed SIGMA scores for over 48 million possible missense variants across 3,454 disease-associated genes and developed an interactive online platform (https://www.sigma-pred.org/). Overall, by leveraging protein structure information, SIGMA offers an accurate structure-based approach to evaluating the pathogenicity of missense variants.