CMDmpnn: Combining Comparative Molecular Dynamics and ProteinMPNN to Rapidly Expand Enzyme Substrate Spectrum.

Abstract

Expanding enzyme substrate spectra enhances industrial applications and drives sustainable biocatalysis. Despite advances, challenges in modification efficiency and high-throughput screening persist. Here, we developed a virtual screening method called CMDmpnn that combines comparative molecular dynamics (MD) simulations and ProteinMPNN to broaden enzyme substrate spectra without compromising other industrially important properties of enzymes, such as thermostability. Using glycosyltransferase as a model, we first established a dynamic model library of the wild-type enzyme through MD simulations and performed clustering. Subsequently, we utilized ProteinMPNN to generate a comprehensive set of new sequences for the entire library, enabling rapid identification of all possible enzyme variants. Short MD simulations were then conducted on variant-substrate complex models, with results compared to those of the wild-type enzyme. By analyzing catalytically relevant information such as substrate binding modes and key atomic distances, we identified multiple variants capable of catalyzing a broad spectrum of phenolic compounds, all within a timeframe of less than 2 weeks. The CMDmpnn method offers a powerful and efficient tool for rapidly expanding enzyme substrate spectra.