Rapid protein stability prediction using deep learning representations

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Standard

Rapid protein stability prediction using deep learning representations. / Blaabjerg, Lasse M.; Kassem, Maher M.; Good, Lydia L.; Jonsson, Nicolas; Cagiada, Matteo; Johansson, Kristoffer E.; Boomsma, Wouter; Stein, Amelie; Lindorff-Larsen, Kresten.

I: eLife, Bind 12, e82593, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Blaabjerg, LM, Kassem, MM, Good, LL, Jonsson, N, Cagiada, M, Johansson, KE, Boomsma, W, Stein, A & Lindorff-Larsen, K 2023, 'Rapid protein stability prediction using deep learning representations', eLife, bind 12, e82593. https://doi.org/10.7554/eLife.82593

APA

Blaabjerg, L. M., Kassem, M. M., Good, L. L., Jonsson, N., Cagiada, M., Johansson, K. E., Boomsma, W., Stein, A., & Lindorff-Larsen, K. (2023). Rapid protein stability prediction using deep learning representations. eLife, 12, [e82593]. https://doi.org/10.7554/eLife.82593

Vancouver

Blaabjerg LM, Kassem MM, Good LL, Jonsson N, Cagiada M, Johansson KE o.a. Rapid protein stability prediction using deep learning representations. eLife. 2023;12. e82593. https://doi.org/10.7554/eLife.82593

Author

Blaabjerg, Lasse M. ; Kassem, Maher M. ; Good, Lydia L. ; Jonsson, Nicolas ; Cagiada, Matteo ; Johansson, Kristoffer E. ; Boomsma, Wouter ; Stein, Amelie ; Lindorff-Larsen, Kresten. / Rapid protein stability prediction using deep learning representations. I: eLife. 2023 ; Bind 12.

Bibtex

@article{8934c474ac8e4ea59509a6fb0a76d3ba,

title = "Rapid protein stability prediction using deep learning representations",

abstract = "Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available—including via a Web interface—and enables large-scale analyses of stability in experimental and predicted protein structures.",

author = "Blaabjerg, {Lasse M.} and Kassem, {Maher M.} and Good, {Lydia L.} and Nicolas Jonsson and Matteo Cagiada and Johansson, {Kristoffer E.} and Wouter Boomsma and Amelie Stein and Kresten Lindorff-Larsen",

year = "2023",

doi = "10.7554/eLife.82593",

language = "English",

volume = "12",

journal = "eLife",

issn = "2050-084X",

publisher = "eLife Sciences Publications Ltd.",

}

RIS

TY - JOUR

T1 - Rapid protein stability prediction using deep learning representations

AU - Blaabjerg, Lasse M.

AU - Kassem, Maher M.

AU - Good, Lydia L.

AU - Jonsson, Nicolas

AU - Cagiada, Matteo

AU - Johansson, Kristoffer E.

AU - Boomsma, Wouter

AU - Stein, Amelie

AU - Lindorff-Larsen, Kresten

PY - 2023

Y1 - 2023

N2 - Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available—including via a Web interface—and enables large-scale analyses of stability in experimental and predicted protein structures.

AB - Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available—including via a Web interface—and enables large-scale analyses of stability in experimental and predicted protein structures.

U2 - 10.7554/eLife.82593

DO - 10.7554/eLife.82593

M3 - Journal article

C2 - 37184062

AN - SCOPUS:85161448362

VL - 12

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e82593

ER -

ID: 356971944

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