Deep Learning-Driven Library Design for the De Novo Discovery of Bioactive Thiopeptides.

ABSTRACT: Broad substrate tolerance of ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes has allowed numerous strategies for RiPP engineering. However, despite relaxed specificities, exact substrate preferences of RiPP enzymes are often difficult to pinpoint. Thus, when designing combinatorial libraries of RiPP precursors, balancing the compound diversity with the substrate fitness can be challenging. Here, we employed a deep learning model to streamline the design of mRNA display libraries. Using an in vitro reconstituted thiopeptide biosynthesis platform, we performed mRNA display-based profiling of substrate fitness for the biosynthetic pathway involving five enzymes to train an accurate deep learning model. We then utilized the model to design o

SUBMITTER: Chang JS 

PROVIDER: S-EPMC10683472 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature