Biophysical and Mechanistic Models for Disease-Causing Protein Variants. Issue 7 (July 2019)

• Record Type: Journal Article
• Authors: Stein, Amelie; Fowler, Douglas M.; Hartmann-Petersen, Rasmus; Lindorff-Larsen, Kresten
• Is Part Of: Trends in biochemical sciences. Volume 44:Issue 7(2019)
• Page Start: 575
• Page End: 588
• Publication Date: 2019-07
• Subjects: protein stability -- deep mutational scanning -- protein quality control -- variant classification -- computational biophysics -- genomics; Biochemistry -- Periodicals; 572
• DOI: 10.1016/j.tibs.2019.01.003
• Languages: English
• ISSNs: 0968-0004

Abstract

Abstract : The rapid decrease in DNA sequencing cost is revolutionizing medicine and science. In medicine, genome sequencing has revealed millions of missense variants that change protein sequences, yet we only understand the molecular and phenotypic consequences of a small fraction. Within protein science, high-throughput deep mutational scanning experiments enable us to probe thousands of variants in a single, multiplexed experiment. We review efforts that bring together these topics via experimental and computational approaches to determine the consequences of missense variants in proteins. We focus on the role of changes in protein stability as a driver for disease, and how experiments, biophysical models, and computation are providing a framework for understanding and predicting how changes in protein sequence affect cellular protein stability. Highlights: Human exome sequencing is revealing millions of missense variants that change protein sequences, but their phenotypic consequences are mostly unknown. Deep mutational scanning and other high-throughput experiments provide simultaneous insights into the effects of thousands of variants. Loss of protein stability is a common origin of inherited diseases, and computational predictions of protein stability are useful for assessing variant consequences. Cellular protein quality control provides a mechanistic link between altered protein stability and cellular protein levels and degradation. Computational biophysics, evolutionary sequence analyses, and machine learning methods each provide information about variant consequences and may potentially be combined. Mechanistic models for how mutations give rise to disease provide a starting point for therapeutic strategies.