The genetic architecture of protein stability.

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 ene

SUBMITTER: Faure AJ 

PROVIDER: S-EPMC11499273 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature