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

, , ,
1 Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
2 Departments of Genome Sciences and Bioengineering, University of Washington, Seattle, Washington, United States
DOI
10.7287/peerj.preprints.27379v1
Published
Accepted
Subject Areas
Bioinformatics, Biophysics, Computational Biology, Genomics, Medical Genetics
Keywords
Protein quality control, Variant classification, Protein stability, Deep mutational scanning, Computational biophysics
Copyright
© 2018 Stein et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Stein A, Fowler DM, Hartmann-Petersen R, Lindorff-Larsen K. 2018. Towards predictive biophysical and mechanistic models for disease-causing protein variants. PeerJ Preprints 6:e27379v1

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.

Author Comment

This is a preprint submission to PeerJ Preprints.