In vivo functional phenotypes from a computational epistatic model of evolution

• 成果类型: Article
• 署名作者: Alvarez, Sophia; Nartey, Charisse M.; Mercado, Nicholas; de la Paz, Jose Alberto; Huseinbegovic, Tea; Morcos, Faruck
• 署名单位: University of Texas System; University of Texas Dallas; University of Texas System; University of Texas Dallas; University of Texas System; University of Texas Dallas
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9576
• DOI: 10.1073/pnas.2308895121
• 发表日期: 2024-02-06
• 关键词: fitness landscape; protein; sequence; INFORMATION; identification; residues; contacts; binding

摘要

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo fl-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family -like functionality while being more active than their wild -type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.