Prediction of enzyme catalysis by computing reaction energy barriers via steered QM/MM Molecular Dynamics Simulations and Machine Learning

Daniel Platero-Rochart, Tatyana Krivobokova, Michael Gastegger, Gilbert Reibnegger, Pedro Alejandro Sánchez Murcia

Publications: Contribution to journalArticlePeer Reviewed

Abstract

The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase ofIdeonella sakaiensis. As a training data set, we use steered quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulation snapshots and their corresponding pulling work values. We have explored three algorithms together with three chemical representations. As an outcome, our trained models are able to predict pulling works along the steered QM/MM MD simulations with a mean absolute error below 3 kcal mol–1 and a score value above 0.90. More challenging is the prediction of the energy maximum with a single geometry. Whereas the use of the initial snapshot of the QM/MM MD trajectory as input geometry yields a very poor prediction of the reaction energy barrier, the use of an intermediate snapshot of the former trajectory brings the score value above 0.40 with a low mean absolute error (ca. 3 kcal mol–1). Altogether, we have faced in this work some initial challenges of the final goal of getting an efficient workflow for the semiautomatic prediction of enzyme-catalyzed energy barriers and catalytic efficiencies.
Translated title of the contributionVorhersage der Enzymkatalyse durch Berechnung von Reaktionsenergiebarrieren mittels gesteuerter QM/MM-Molekulardynamiksimulationen und maschinellem Lernen
Original languageEnglish
Pages (from-to)4623-4632
Number of pages10
JournalJournal of Chemical Information and Modeling
Volume63
Issue number15
Early online date21 Jul 2023
Publication statusPublished - 14 Aug 2023

Austrian Fields of Science 2012

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