A Unified Multiscale Framework for De Novo Enzyme Design: Integrating Quantum Mechanics, Machine Learning, and Bayesian Uncertainty Quantification for Ab Initio Catalyst Synthesis

This paper presents a conceptual theoretical framework for de novo enzyme design, aiming to generate efficient biocatalysts for chemical reactions from first principles. The integration of quantum mechanical calculations, molecular dynamics simulations, and generative artificial intelligence is proposed within a Bayesian uncertainty quantification framework. A multiscale modeling approach combines quantum mechanics for transition state characterization, molecular dynamics for conformational sampling, and machine learning for sequence optimization. Fundamental mathematical equations governing catalytic efficiency are derived, including extensions of Marcus theory for enzyme environments, with a thermodynamically consistent model for enzyme preorganization that accounts for desolvation penalties and entropic contributions. Sensitivity analysis identifies critical parameters affecting catalytic activity, and Bayesian inference ensures robust uncertainty quantification. The framework is implemented in a reproducible Python pipeline. Application to olefin metathesis, a challenging abiological reaction, is demonstrated through retrospective simulation, yielding predicted turnover numbers consistent with experimental benchmarks in artificial metalloenzymes (e.g., TONs up to 1,000) [jeschek2016directed]. This work builds on advances such as anchoring of ruthenium cofactors in proteins [jeschek2016directed], providing a foundation for enzyme design platforms while addressing limitations in current methodologies.