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Rapid protein stability prediction using deep learning representations.

Published version
Peer-reviewed

Repository DOI


Type

Article

Change log

Authors

Kassem, Maher M 
Cagiada, Matteo 

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 ∼ 230 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.

Description

Keywords

biophysics, computational biology, genomic variants, machine learning, molecular biophysics, none, protein stability, structural biology, systems biology, Humans, Deep Learning, Proteins, Mutagenesis, Amino Acids, Protein Stability, Computational Biology

Journal Title

Elife

Conference Name

Journal ISSN

2050-084X
2050-084X

Volume Title

12

Publisher

eLife Sciences Publications, Ltd