Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer Learning.

ABSTRACT: Ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes often exhibit promiscuous substrate preferences that cannot be reduced to simple rules. Large language models are promising tools for predicting such peptide fitness landscapes. However, state-of-the-art protein language models are trained on relatively few peptide sequences. A previous study comprehensively profiled the peptide substrate preferences of LazBF (a two-component serine dehydratase) and LazDEF (a three-component azole synthetase) from the lactazole biosynthetic pathway. We demonstrated that masked language modeling of LazBF substrate preferences produced language model embeddings that improved downstream classification models of both LazBF and LazDEF substrates. Similarly, masked lang

SUBMITTER: Clark JD 

PROVIDER: S-EPMC10925380 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature