» Articles » PMID: 39918182

A Robust and Versatile QM/MM Interface for Molecular Dynamics in GROMOS

Overview
Journal J Comput Chem
Publisher Wiley
Date 2025 Feb 7
PMID 39918182
Authors
Affiliations
Soon will be listed here.
Abstract

The integration of quantum mechanics and molecular mechanics (QM/MM) within molecular dynamics simulations is crucial to accurately model complex biochemical systems. Here, we present an enhanced implementation of the QM/MM interface in the GROMOS simulation package, introducing significant improvements in functionality and user control. We present new features, including the link atom scheme, which allows the modeling of QM regions as a part of bigger molecules. Benchmark tests on various systems, including QM water in water, amino acids in water, and tripeptides validate the reliability of the new functionalities. Performance evaluations demonstrate that the updated implementation is efficient, with the primary computational burden attributed to the QM program rather than the QM/MM interface or the MD program itself. The improved QM/MM interface enables more advanced investigations into biomolecular reactivity, enzyme catalysis, and other phenomena requiring detailed quantum mechanical treatment within classical simulations. This work represents a significant advancement in the capabilities of GROMOS, providing enhanced tools to explore complex molecular systems.

Citing Articles

Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.

Pultar F, Thurlemann M, Gordiy I, Doloszeski E, Riniker S J Am Chem Soc. 2025; 147(8):6835-6856.

PMID: 39961342 PMC: 11869291. DOI: 10.1021/jacs.4c17015.

References
1.
van Gunsteren W, Daura X, Fuchs P, Hansen N, Horta B, Hunenberger P . On the Effect of the Various Assumptions and Approximations used in Molecular Simulations on the Properties of Bio-Molecular Systems: Overview and Perspective on Issues. Chemphyschem. 2020; 22(3):264-282. DOI: 10.1002/cphc.202000968. View

2.
Unke O, Chmiela S, Sauceda H, Gastegger M, Poltavsky I, Schutt K . Machine Learning Force Fields. Chem Rev. 2021; 121(16):10142-10186. PMC: 8391964. DOI: 10.1021/acs.chemrev.0c01111. View

3.
Chai J, Head-Gordon M . Long-range corrected hybrid density functionals with damped atom-atom dispersion corrections. Phys Chem Chem Phys. 2008; 10(44):6615-20. DOI: 10.1039/b810189b. View

4.
Clemente C, Capece L, Marti M . Best Practices on QM/MM Simulations of Biological Systems. J Chem Inf Model. 2023; 63(9):2609-2627. DOI: 10.1021/acs.jcim.2c01522. View

5.
Riccardi D, Schaefer P, Yang Y, Yu H, Ghosh N, Prat-Resina X . Development of effective quantum mechanical/molecular mechanical (QM/MM) methods for complex biological processes. J Phys Chem B. 2006; 110(13):6458-69. DOI: 10.1021/jp056361o. View