Rapid protein stability prediction using deep learning representations
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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.
In: eLife, Vol. 12, e82593, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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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
N1 - Publisher Copyright: © 2023, eLife Sciences Publications Ltd. All rights reserved.
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