Inferring protein fitness landscapes from laboratory evolution experiments.

ABSTRACT: Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspect

SUBMITTER: D'Costa S 

PROVIDER: S-EPMC10010530 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature