Exponential averaging versus umbrella sampling for computing the QM/MM free energy barrier of the initial step of the desuccinylation reaction catalyzed by sirtuin 5. Issue 13 (16th March 2022)

• Record Type: Journal Article
• Authors: Dietschreit, Johannes C. B.; von der Esch, Beatriz; Ochsenfeld, Christian
• Is Part Of: Physical chemistry chemical physics. Volume 24:Issue 13(2022)
• Page Start: 7723
• Page End: 7731
• Publication Date: 2022-03-16
• Subjects: Chemistry, Physical and theoretical -- Periodicals; 541.3
• DOI: 10.1039/d1cp05007a
• Languages: English
• ISSNs: 1463-9076

Abstract

Abstract : Free energy surface confirms conserved SN 2 type mechanism. Comparison of the activation barrier with 150 previously calculated minimum energy paths and machine learning results illustrates the importance of sampling when studying complex reactions. Abstract : The computational characterization of enzymatic reactions poses a great challenge which arises from the high dimensional and often rough potential energy surfaces commonly explored by static QM/MM methods such as adiabatic mapping (AM). The present study highlights the difficulties in estimating free energy barriers via exponential averaging over AM pathways. Based on our previous study [von der Esch et al., J. Chem. Theory Comput., 2019, 15, 6660–6667], where we analyzed the first reaction step of the desuccinylation reaction catalyzed by human sirtuin 5 (SIRT5) by means of QM/MM adiabatic mapping and machine learning, we use, here, umbrella sampling to compute the free energy profile of the initial reaction step. The computational investigations show that the initial step of the desuccinylation reaction proceeds via an SN 2-type reaction mechanism in SIRT5, suggesting that the first step of the deacylation reactions catalyzed by sirtuins is highly conserved. In addition, the direct comparison of the extrapolated free energy barrier from minimal energy paths and the computed free energy path from umbrella sampling further underlines the importance of extensive sampling.