Biophysical and Mechanistic Models for Disease-Causing Protein Variants

Amelie Stein; Douglas M. Fowler; Rasmus Hartmann-Petersen; Kresten Lindorff-Larsen

doi:10.1016/j.tibs.2019.01.003

Biophysical and Mechanistic Models for Disease-Causing Protein Variants

Amelie Stein^*, Douglas M. Fowler, Rasmus Hartmann-Petersen, Kresten Lindorff-Larsen

^*Corresponding author for this work

Biomolecular Sciences

33 Citations (Scopus)

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.

Original language	English
Journal	Trends in Biochemical Sciences
Volume	44
Issue number	7
Pages (from-to)	575-588
ISSN	0968-0004
DOIs	https://doi.org/10.1016/j.tibs.2019.01.003
Publication status	Published - Jul 2019

Keywords

computational biophysics
deep mutational scanning
genomics
protein quality control
protein stability
variant classification

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10.1016/j.tibs.2019.01.003

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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.",

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AU - Fowler, Douglas M.

AU - Hartmann-Petersen, Rasmus

AU - Lindorff-Larsen, Kresten

PY - 2019/7

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AB - 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.

KW - computational biophysics

KW - deep mutational scanning

KW - genomics

KW - protein quality control

KW - protein stability

KW - variant classification

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JO - Trends in Biochemical Sciences

JF - Trends in Biochemical Sciences

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ER -

Biophysical and Mechanistic Models for Disease-Causing Protein Variants

Abstract

Keywords

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