Interpreting the molecular mechanisms of disease variants in human transmembrane proteins

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Next-generation sequencing of human genomes reveals millions of missense variants, some of which may lead to loss of protein function and ultimately disease. Here, we investigate missense variants in membrane proteins—key drivers in cell signaling and recognition. We find enrichment of pathogenic variants in the transmembrane region across 19,000 functionally classified variants in human membrane proteins. To accurately predict variant consequences, one fundamentally needs to understand the underlying molecular processes. A key mechanism underlying pathogenicity in missense variants of soluble proteins has been shown to be loss of stability. Membrane proteins, however, are widely understudied. Here, we interpret variant effects on a larger scale by performing structure-based estimations of changes in thermodynamic stability using a membrane-specific energy function and analyses of sequence conservation during evolution of 15 transmembrane proteins. We find evidence for loss of stability being the cause of pathogenicity in more than half of the pathogenic variants, indicating that this is a driving factor also in membrane-protein-associated diseases. Our findings show how computational tools aid in gaining mechanistic insights into variant consequences for membrane proteins. To enable broader analyses of disease-related and population variants, we include variant mappings for the entire human proteome.
OriginalsprogEngelsk
TidsskriftBiophysical Journal
Vol/bind122
Udgave nummer11
Antal sider16
ISSN0006-3495
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
We thank Matteo Cagiada for providing an automatic pipeline for GEMME calculations and Kristoffer Enøe Johansson for resourcing us with his alignment and merging implementations. Additional thanks go to Julia Koehler Leman for helpful discussion regarding membrane protein implementations in Rosetta. This study was funded by the Protein Interactions and Stability in Medicine and Genomics (PRISM) center funded by the Novo Nordisk Foundation ( NNF18OC0033950 , to A.S. and K.L.-L.) and a grant from the Lundbeck Foundation ( R272-2017-4528 , to A.S.). We acknowledge access to resources from the Department of Biology’s core facility for biocomputing.

Publisher Copyright:
© 2023 Biophysical Society

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