Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

Magnus Haraldson Høie, Matteo Cagiada, Anders Haagen Beck Frederiksen, Amelie Stein*, Kresten Lindorff-Larsen*

*Corresponding author af dette arbejde

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Abstract

Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.

OriginalsprogEngelsk
Artikelnummer110207
TidsskriftCell Reports
Vol/bind38
Udgave nummer2
Antal sider15
ISSN2211-1247
DOI
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
Our research is supported by the Protein Interactions and Stability in Medicine and Genomics (PRISM) center funded by the Novo Nordisk Foundation ( NNF18OC0033950 , to A.S. and K.L.-L.) and a grant from the Lundbeck Foundation ( R272-2017-4528 , to A.S.)

Publisher Copyright:
© 2021 The Author(s)

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