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 af dette arbejde

Biomolecular Sciences

33 Citationer (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.

Originalsprog	Engelsk
Tidsskrift	Trends in Biochemical Sciences
Vol/bind	44
Udgave nummer	7
Sider (fra-til)	575-588
ISSN	0968-0004
DOI	https://doi.org/10.1016/j.tibs.2019.01.003
Status	Udgivet - jul. 2019

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10.1016/j.tibs.2019.01.003

Citationsformater

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title = "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 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.",

keywords = "computational biophysics, deep mutational scanning, genomics, protein quality control, protein stability, variant classification",

author = "Amelie Stein and Fowler, {Douglas M.} and Rasmus Hartmann-Petersen and Kresten Lindorff-Larsen",

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AU - Hartmann-Petersen, Rasmus

AU - Lindorff-Larsen, Kresten

PY - 2019/7

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N2 - 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.

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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DO - 10.1016/j.tibs.2019.01.003

M3 - Review

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SP - 575

EP - 588

JO - Trends in Biochemical Sciences

JF - Trends in Biochemical Sciences

IS - 7

ER -

Biophysical and Mechanistic Models for Disease-Causing Protein Variants

Abstract

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