Rapid protein stability prediction using deep learning representations.

Published version
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