Towards predictive biophysical and mechanistic models for disease-causing protein variants

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 mutations in a single, multiplexed experiment. We review efforts that bring together these topics via experimental and computational approaches to determine the consequences of missense mutations 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 together providing a framework for understanding and predicting how mutations affect cellular protein stability.