posted on 2023-11-07, 14:49authored byJun Shi Chang, Alexander A. Vinogradov, Yue Zhang, Yuki Goto, Hiroaki Suga
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 optimal mRNA libraries
and demonstrated their utility in affinity selections against IRAK4
kinase and the TLR10 cell surface receptor. The selections led to
the discovery of potent thiopeptide ligands against both target proteins
(KD up to 1.3 nM for the best compound against IRAK4 and
300 nM for TLR10). The IRAK4-targeting compounds also inhibited the
kinase at single-digit μM concentrations in vitro, exhibited efficient internalization into HEK293H cells, and suppressed
NF-kB-mediated signaling in cells. Altogether, the developed approach
streamlines the discovery of pseudonatural RiPPs with de novo designed biological activities and favorable pharmacological properties.