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Missense3D-TM: Predicting the Effect of Missense Variants in Helical Transmembrane Protein Regions Using 3D Protein Structures

Gordon Hanna, Tarun Khanna, Suhail A. Islam, Alessia David, Michael J.E. Sternberg

2023Journal of Molecular Biology12 citationsDOIOpen Access PDF

Abstract

Variant effect predictors assess if a substitution is pathogenic or benign. Most predictors, including those that are structure-based, are designed for globular proteins in aqueous environments and do not consider that the variant residue is located within the membrane. We report Missense3D-TM that provides a structure-based assessment of the impact of a missense variant located within a membrane. On a dataset of 2,078 pathogenic and 1,060 benign variants, spanning 711 proteins from 706 structures, Missense3D-TM achieved an accuracy of 66%, Mathews correlation coefficient of 0.37, sensitivity of 58% and specificity of 81%. Missense3D-TM performed similarly to mCSM-membrane: accuracy 66% vs 61% (p = 0.02) on an unbalanced test set and 70% vs 67% (p = 0.20) on a balanced test set. The Missense3D-TM website provides an analysis of the structural effects of the variant along with its predicted position within the membrane. The web server is available at http://missense3d.bc.ic.ac.uk/.

Topics & Concepts

Missense mutationMembrane proteinMatthews correlation coefficientTransmembrane proteinPhenotypeChemistryBiologyComputational biologyGeneticsMembraneComputer scienceArtificial intelligenceGeneSupport vector machineReceptorRNA and protein synthesis mechanismsGenomics and Rare DiseasesGenomics and Phylogenetic Studies