{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:05:44Z","timestamp":1790111144693,"version":"4.0.1"},"posted":{"date-parts":[[2026,9,17]]},"group-title":"Biophysics","reference-count":66,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Enzyme engineering aims to tailor natural enzymes for industrial and therapeutic applications, yet physically grounded rational design has been limited by a trade-off between accuracy and cost, leaving the field heavily dependent on expert intuition. Here we present a scalable physics-based framework that combines field-aware machine learning with molecular mechanics to capture enzyme electrostatics at quantum-mechanical accuracy while enabling efficient, atomistic exploration of reaction free-energy landscapes. Coupled with microkinetic modelling, the framework translates molecular free-energy landscapes into catalytic rates and selectivity across competing, multistep reaction pathways. Applied to a newly engineered oxidative amidase (OxiAm), the framework predicts catalytic rate constants with near-experimental accuracy, quantitatively resolves the selectivity between hydrolysis and aminolysis, and generalizes across substrates, mutations and enzyme homologues. Transition-state ensemble analysis further reveals the reaction mechanism and guides the design of enzyme variants for pharmaceutical synthesis. By bringing chemical accuracy and high-throughput sampling to enzyme catalysis, this approach shifts rational design from static, empirical practice toward dynamic, free-energy-driven design, and should accelerate the engineering of biocatalysts.<\/jats:p>","DOI":"10.64898\/2026.09.15.751901","type":"posted-content","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:25:11Z","timestamp":1789705511000},"source":"Crossref","is-referenced-by-count":0,"title":["High-throughput physics-based enzyme engineering"],"prefix":"10.64898","author":[{"given":"Xujian","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States"},{"name":"Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, United States"},{"name":"Department of Computational and Systems Biology, School of Medicine, 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