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

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Standard

Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. / Høie, Magnus Haraldson; Cagiada, Matteo; Frederiksen, Anders Haagen Beck; Stein, Amelie; Lindorff-Larsen, Kresten.

I: Cell Reports, Bind 38, Nr. 2, 110207, 2022.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Høie, MH, Cagiada, M, Frederiksen, AHB, Stein, A & Lindorff-Larsen, K 2022, 'Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation', Cell Reports, bind 38, nr. 2, 110207. https://doi.org/10.1016/j.celrep.2021.110207

APA

Høie, M. H., Cagiada, M., Frederiksen, A. H. B., Stein, A., & Lindorff-Larsen, K. (2022). Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Reports, 38(2), [110207]. https://doi.org/10.1016/j.celrep.2021.110207

Vancouver

Høie MH, Cagiada M, Frederiksen AHB, Stein A, Lindorff-Larsen K. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Reports. 2022;38(2). 110207. https://doi.org/10.1016/j.celrep.2021.110207

Author

Høie, Magnus Haraldson ; Cagiada, Matteo ; Frederiksen, Anders Haagen Beck ; Stein, Amelie ; Lindorff-Larsen, Kresten. / Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. I: Cell Reports. 2022 ; Bind 38, Nr. 2.

Bibtex

@article{9a2504b7e1c447fb91c2d05e4666af6f,

title = "Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation",

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.",

keywords = "machine learning, protein evolution, protein stability, variant effects",

author = "H{\o}ie, {Magnus Haraldson} and Matteo Cagiada and Frederiksen, {Anders Haagen Beck} and Amelie Stein and Kresten Lindorff-Larsen",

note = "Publisher Copyright: {\textcopyright} 2021 The Author(s)",

year = "2022",

doi = "10.1016/j.celrep.2021.110207",

language = "English",

volume = "38",

journal = "Cell Reports",

issn = "2211-1247",

publisher = "Cell Press",

number = "2",

}

RIS

TY - JOUR

T1 - Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

AU - Høie, Magnus Haraldson

AU - Cagiada, Matteo

AU - Frederiksen, Anders Haagen Beck

AU - Stein, Amelie

AU - Lindorff-Larsen, Kresten

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - machine learning

KW - protein evolution

KW - protein stability

KW - variant effects

U2 - 10.1016/j.celrep.2021.110207

DO - 10.1016/j.celrep.2021.110207

M3 - Journal article

C2 - 35021073

AN - SCOPUS:85122584956

VL - 38

JO - Cell Reports

JF - Cell Reports

SN - 2211-1247

IS - 2

M1 - 110207

ER -

ID: 291540399

Biologisk Institut