Rapid protein stability prediction using deep learning representations
- University of Copenhagen, Denmark
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.
Data availability
Scripts and data to repeat our analyses are available via: https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg/
Article and author information
Author details
Kresten Lindorff-Larsen
Funding
Novo Nordisk Fonden (NNF18OC0033950)
- Amelie Stein
- Kresten Lindorff-Larsen
Novo Nordisk Fonden (NNF20OC0062606)
- Wouter Boomsma
Novo Nordisk Fonden (NNF18OC0052719)
- Wouter Boomsma
Lundbeckfonden (R272-2017-4528)
- Amelie Stein
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Blaabjerg et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Rapid protein stability prediction using deep learning representations
eLife 12:e82593.
https://doi.org/10.7554/eLife.82593
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