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Although quantum mechanics\/molecular mechanics (QM\/MM) simulations can in principle compute the activation free energy associated with enzymatic reactions, their high computational cost limits systematic studies across many variants. Here, we benchmark a mechanical-embedding machine learning potential\/molecular mechanics (ML\/MM) protocol for predicting mutation effects on chorismate mutase catalysis, a model system extensively studied both experimentally and computationally. In this framework, the QM-region potential energy surface is represented by an actively learned machine learning potential, while QM\/MM electrostatic interactions are treated classically using partial charges predicted from instantaneous geometries for the QM region. Combined with umbrella sampling, the ML\/MM approach enables efficient estimation of activation free energies and direct comparison with experimental kinetics. The method shows reasonable correlations with experiment across both nonpolar and polar active-site mutations and is quantitatively accurate for nonpolar mutations despite their narrow energetic range (&amp;lt;1 kcal mol\u20131). However, it substantially underestimates the activation free energy for polar mutations. The results highlight both the promise and limitations of mechanical-embedding ML\/MM approaches for predicting mutation effects on enzyme catalysis.<\/jats:p>","DOI":"10.1021\/acs.jctc.6c00761","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T20:02:13Z","timestamp":1785960133000},"page":"8595-8606","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating Mechanical-Embedding\nML\/MM for Predicting\nMutation Effects in Chorismate Mutase Catalysis"],"prefix":"10.1021","volume":"22","author":[{"given":"Zichen","family":"Sun","sequence":"first","affiliation":[{"name":"University of Chicago , , , ,","place":["Chicago, Illinois, United States, 60637"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3217-0592","authenticated-orcid":true,"given":"Yifan","family":"Li","sequence":"additional","affiliation":[{"name":"Princeton University , , , ,","place":["Princeton, New Jersey, United States, 08544"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9913-3118","authenticated-orcid":true,"given":"Wenqiang","family":"Cui","sequence":"additional","affiliation":[{"name":"University of Florida , , , ,","place":["Gainesville, Florida, United States, 32610"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3982-9305","authenticated-orcid":true,"given":"Wen Jun","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Florida , , , ,","place":["Gainesville, Florida, United States, 32610"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"2026083118580108800_cit1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2025.168998","article-title":"Identification\nand understanding of allostery hotspots in proteins: Integration of\ndeep mutational scanning and multi-faceted computational analyses","volume":"437","author":"Cui","year":"2025","journal-title":"J. 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