The Journal of Physical Chemistry B, Год журнала: 2024, Номер 128(42), С. 10506 - 10514
Опубликована: Окт. 10, 2024
We present a tutorial to carry out umbrella-sampling free-energy simulations with combined quantum mechanical and molecular (QM/MM) potential, which may also be used in computational or biophysical chemistry curriculum for first-year graduate undergraduate students. In this article, we choose the Type II S
Язык: Английский
Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown
Опубликована: Янв. 15, 2025
Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application been hindered by the time-consuming process of generating necessary training, validation, and test data MLP models through QM/MM simulations. Furthermore, entire needs to be repeated each specific enzyme system reaction. To overcome this bottleneck, it is required that trained MLPs exhibit transferability across different environments reacting species, thereby eliminating need retraining new variant. In study, we explore potential evaluating pretrained ΔMLP model mutations within MM environment using QM/MM-based ML architecture developed Pan, X. J. Chem. Theory Comput. 2021, 17(9), 5745–5758. The study includes scenarios such single point substitutions, homologous from even transition an aqueous environment, where last two systems have substantially used in training. results show effectively captures predicts effects on electrostatic interactions, producing reliable profiles enzyme-catalyzed reactions without retraining. also identified notable limitations transferability, particularly when transitioning water-rich environments. Overall, demonstrates robustness Pan et al.'s diverse systems, well further research development more sophisticated training methods.
Язык: Английский
Процитировано
2
Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown
Опубликована: Фев. 14, 2025
Enzymes play crucial roles in all biological systems by catalyzing a myriad of chemical reactions. These reactions range from simple one-step processes to intricate multistep cascades. Predicting mechanistically appropriate binding modes along reaction pathway for substrate, product, and intermediates transition states is daunting task. To address this challenge, special docking programs like EnzyDock have been developed. Yet, running such simulations complicated due the nature enzyme processes. This work presents CHARMM-GUI EnzyDocker, web-based cyberinfrastructure designed streamline preparation simulations. The development EnzyDocker has achieved through integration existing modules, as PDB Reader Manipulator, Ligand Designer, QM/MM Interfacer. In addition, new functionalities developed facilitate one-stop multistate multiscale enable interactive intuitive ligand modifications flexible protein residues selections. A setup related multiligand automatized user interfaces. offers support standard classical with CHARMM built-in semiempirical engines. Automated consensus restraints incorporating experimental knowledge into are facilitated via maximum common substructure algorithm. illustrate robustness we conducted three systems: dihydrofolate reductase, SARS-CoV-2 Mpro, diterpene synthase CotB2. created four tutorial videos about these systems, which can be found at https://www.charmm-gui.org/demo/enzydock. expected valuable accessible tool that simplifies accelerates process enzymes.
Язык: Английский
Процитировано
2
The Journal of Physical Chemistry A, Год журнала: 2024, Номер unknown
Опубликована: Дек. 4, 2024
Despite the success and widespread use of QM/MM methods in modeling (bio)chemically important processes, their accuracy is still not well understood. A key reason because these are ultimately approximations to direct QM calculations very large systems, which impractical perform most cases. We highlight recent progress toward development realistic model systems where it possible obtain full reference data directly systematically evaluate effectiveness different generation schemes. These highly flexible can be tailored probe sensitivity a reaction types simulation parameters such as pairing MM potentials, region size, composition. It envisaged that this strategy could used validate schemes spur more robust models future.
Язык: Английский
Процитировано
5
Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown
Опубликована: Апрель 2, 2025
A new module for quantum mechanical/molecular mechanical (QM/MM) calculations is implemented in a molecular dynamics (MD) program, SPDYN GENESIS, interfaced with an electronic structure QSimulate-QM. The periodic boundary condition (PBC) QM/MM simulation incorporated via QM calculation real space duplicated MM charges and particle mesh Ewald (PME) charges. highly optimized code QSimulate-QM, particularly the density functional tight-binding (DFTB) method, where interaction between regions computed utilizing multipole expansions. Together parallelized algorithms SPDYN, developed program performs MD simulations based on DFTB size of ∼100 atoms ∼100,000 better performance than 1 ns/day using one computer node. This feature paves way QM/MM-MD enhanced sampling simulations. Various methods namely, generalized replica exchange solute tempering (gREST), replica-exchange umbrella (REUS), path string are demonstrated at level to compute Ramachandran plot alanine dipeptide potential mean force (PMF) proton transfer reaction enzyme.
Язык: Английский
Процитировано
0
The Journal of Physical Chemistry B, Год журнала: 2024, Номер 128(42), С. 10506 - 10514
Опубликована: Окт. 10, 2024
We present a tutorial to carry out umbrella-sampling free-energy simulations with combined quantum mechanical and molecular (QM/MM) potential, which may also be used in computational or biophysical chemistry curriculum for first-year graduate undergraduate students. In this article, we choose the Type II S
Язык: Английский
Процитировано
2