This study uses the ultrahigh-throughput mRNA display technique to map the sequence compatibility of ProcM across a focused library and provides a high-throughput view of ProcM reactivity and highlights differences with previous high-throughput studies on substrate selectivity of RiPP modification enzymes.
Abstract
The biosynthetic machineries of ribosomally synthesized and post-translationally modified peptides (RiPPs) are often substrate tolerant. A remarkable example is the class II lanthipeptide synthetase ProcM, which naturally functions as a generalist enzyme that has not evolved to use a specific substrate during its evolutionary history. Although ProcM has been studied extensively, the sequence features associated with productive modification remain underexplored. In this study, we use the ultrahigh-throughput mRNA display technique to map the sequence compatibility of ProcM across a focused library. This approach expands the landscape of ProcM reactivity beyond native substrates and individually characterized variants. Machine learning (ML) is used as a tool to demonstrate that the selected dataset contains learnable signatures and classification architectures revealed a balanced accuracy of 0.73. This performance contrasts sharply with the near-perfect accuracy of specialized enzyme models as the sequence-fitness landscape of the generalist enzymes are characterized by class imbalance and limited by intrinsic dataset features. Our results provide a high-throughput view of ProcM reactivity and highlight differences with previous high-throughput studies on substrate selectivity of RiPP modification enzymes. Future studies will need to assess whether these differences are common when comparing generalist with specialist enzymes. Table of Contents Graphic
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