Systematic QM Region Construction in QM/MM Calculations Based on Uncertainty Quantification

01 November 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


While QM/MM studies of enzymatic reactions are widely used in computational chemistry, the results of such studies are subject to numerous sources of uncertainty, and the effect of different choices by the simulation scientist that are required when setting up QM/MM calculations is often unclear. In particular, the selection of the QM region is crucial for obtaining accurate and reliable results. Simply including amino acids by their distance to the active site is mostly not sufficient as necessary residues are missing or unimportant residues are included without evidence. Here, we take a first step towards quantifying uncertainties in QM/MM calculations by assessing the sensitivity of QM/MM reaction energies with respect to variations of the MM point charges. We show that such a point charge variation analysis (PCVA) can be employed to judge the accuracy of QM/MM reaction energies obtained with a selected QM region, and devise a protocol to systematically construct QM regions that minimize this uncertainty. We apply such a PCVA to the example of catechol \textit{O}-methyltransferase, and demonstrate that it provides a simple and reliable approach for the construction of the QM region. Our PCVA-based scheme is computationally efficient and requires only calculations for a system with a minimal QM region. Our work highlights the promise of applying methods of uncertainty quantification in computational chemistry.


uncertainty quantification

Supplementary materials

Supporting Information
Additional tables with details about the composition of distance-based, PCVA-based and 16-residue QM regions, an extended version of Table~\ref{tab:comparison}, and a comparison of the computational effort. Additional figures with information about the HOMO-LUMO gap, the SAM-CAT distance convergence, and the comparison of sensitivities and indicators for the different approximate PCVA schemes.

Supplementary weblinks


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