The genetic architecture of protein stability

Andre J. Faure (), Aina Martí-Aranda, Cristina Hidalgo-Carcedo, Antoni Beltran, Jörn M. Schmiedel and Ben Lehner ()
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Andre J. Faure: The Barcelona Institute of Science and Technology
Aina Martí-Aranda: The Barcelona Institute of Science and Technology
Cristina Hidalgo-Carcedo: The Barcelona Institute of Science and Technology
Antoni Beltran: The Barcelona Institute of Science and Technology
Jörn M. Schmiedel: The Barcelona Institute of Science and Technology
Ben Lehner: The Barcelona Institute of Science and Technology

Nature, 2024, vol. 634, issue 8035, 995-1003

Abstract: Abstract There are more ways to synthesize a 100-amino acid (aa) protein (20100) than there are atoms in the universe. Only a very small fraction of such a vast sequence space can ever be experimentally or computationally surveyed. Deep neural networks are increasingly being used to navigate high-dimensional sequence spaces1. However, these models are extremely complicated. Here, by experimentally sampling from sequence spaces larger than 1010, we show that the genetic architecture of at least some proteins is remarkably simple, allowing accurate genetic prediction in high-dimensional sequence spaces with fully interpretable energy models. These models capture the nonlinear relationships between free energies and phenotypes but otherwise consist of additive free energy changes with a small contribution from pairwise energetic couplings. These energetic couplings are sparse and associated with structural contacts and backbone proximity. Our results indicate that protein genetics is actually both rather simple and intelligible.

Date: 2024
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DOI: 10.1038/s41586-024-07966-0

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