NepoIP/MM: Toward Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects DOI
Ge Song, Weitao Yang

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Май 21, 2025

Machine learning force fields offer the ability to simulate biomolecules with quantum mechanical accuracy while significantly reducing computational costs, attracting a growing amount of attention in biophysics. Meanwhile, by leveraging efficiency molecular mechanics modeling solvent molecules and long-range interactions, hybrid machine learning/molecular (ML/MM) model offers more realistic approach describing complex biomolecular systems solution. However, multiscale models electrostatic embedding require accounting for polarization ML region induced MM environment. To address this, we adapt state-of-the-art NequIP architecture into polarizable field, NepoIP, enabling effects based on external potential. We found that nanosecond MD simulations NepoIP/MM are stable periodic solvated dipeptide system, converged sampling shows excellent agreement reference QM/MM level. Moreover, show single NepoIP can be transferable across different fields, as well an extremely environment water proteins, laying foundation developing general field used ML/MM embedding.

Язык: Английский

Multiobjective Evolutionary Strategy for Improving Semiempirical Hamiltonians in the Study of Enzymatic Reactions at the QM/MM Level of Theory DOI
José Luis Velázquez‐Libera,

Rodrigo Recabarren,

Esteban Vöhringer‐Martinez

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency often struggle with in complex systems. This work presents a novel multiobjective evolutionary strategy optimizing semiempirical Hamiltonians, specifically designed to enhance performance QM/MM while remaining broadly applicable condensed-phase Our methodology combines automated parameter optimization, targeting ab initio or density functional theory (DFT)-reference potential energy surfaces, atomic charges, and gradients, comprehensive validation through minimum free path (MFEP) calculations. To demonstrate its effectiveness, we applied our approach improve GFN2-xTB Hamiltonian using two systems that involve hydride transfer reactions where activation barrier is severely underestimated: Crotonyl-CoA carboxylase/reductase (CCR) dihydrofolate reductase (DHFR). The optimized parameters showed significant improvements reproducing closely matching higher-level DFT Through an efficient two-stage optimization process, first developed CCR reaction data, then refined these DHFR by incorporating targeted set of additional training geometries. strategic minimized cost achieving accurate descriptions both systems, as validated Adaptive String Method (ASM). represents study larger longer time scales, applications mechanism studies, drug design, enzyme engineering.

Язык: Английский

Процитировано

0
NepoIP/MM: Toward Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects DOI
Ge Song, Weitao Yang

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Май 21, 2025

Machine learning force fields offer the ability to simulate biomolecules with quantum mechanical accuracy while significantly reducing computational costs, attracting a growing amount of attention in biophysics. Meanwhile, by leveraging efficiency molecular mechanics modeling solvent molecules and long-range interactions, hybrid machine learning/molecular (ML/MM) model offers more realistic approach describing complex biomolecular systems solution. However, multiscale models electrostatic embedding require accounting for polarization ML region induced MM environment. To address this, we adapt state-of-the-art NequIP architecture into polarizable field, NepoIP, enabling effects based on external potential. We found that nanosecond MD simulations NepoIP/MM are stable periodic solvated dipeptide system, converged sampling shows excellent agreement reference QM/MM level. Moreover, show single NepoIP can be transferable across different fields, as well an extremely environment water proteins, laying foundation developing general field used ML/MM embedding.

Язык: Английский

Процитировано

0