The genetic architecture of protein stability

• 成果类型: Article
• 署名作者: Faure, Andre J.; Marti-Aranda, Aina; Hidalgo-Carcedo, Cristina; Beltran, Antoni; Schmiedel, Joern M.; Lehner, Ben
• 署名单位: Barcelona Institute of Science & Technology; Pompeu Fabra University; Centre de Regulacio Genomica (CRG); Wellcome Trust Sanger Institute; Pompeu Fabra University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-3680
• DOI: 10.1038/s41586-024-07966-0
• 发表日期: 2024-10-10
• 关键词: epistasis

摘要

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. By experimentally sampling from sequence spaces larger than 1010 and using thermodynamic models, the genetic structure of at least some proteins can be well described, indicating that protein genetics is simpler than anticipated.