Rapid Convergence of Energy and Free Energy Profiles with Quantum Mechanical Size in Quantum Mechanical–Molecular Mechanical Simulations of Proton Transfer in DNA

2018 ◽  
Vol 14 (3) ◽  
pp. 1695-1705 ◽  
Author(s):  
Susanta Das ◽  
Kwangho Nam ◽  
Dan Thomas Major
Keyword(s):  
Free Energy ◽  
Proton Transfer ◽  
Quantum Mechanical ◽  
Rapid Convergence ◽  
Energy Profiles ◽  
Free Energy Profiles

scholarly journals Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. II. Recalibrating Semi-Empirical Parameters with Force Matching

2019 ◽  
Author(s):  
Xiaoliang Pan ◽  
Pengfei Li ◽  
Junming Ho ◽  
Jingzhi Pu ◽  
Ye Mei ◽  
...  
Keyword(s):  
Free Energy ◽  
Ab Initio ◽  
Quantum Mechanical ◽  
Energy Profile ◽  
Free Energy Profile ◽  
Force Matching ◽  
Energy Profiles ◽  
Reference Potential ◽  
Semi Empirical ◽  
Free Energy Profiles

An efficient and accurate reference potential simulation protocol is proposed for producing ab initio quantum mechanical molecular mechanical (AI-QM/MM) quality free energy profiles for chemical<br>reactions in a solvent or macromolecular environment. This protocol involves three stages: (a) using force matching to recalibrate a semi-empirical quantum mechanical (SE-QM) Hamiltonian for the specific reaction under study; (b) employing the recalibrated SE-QM Hamiltonian (in combination with molecular mechanical force fields) as the reference potential to drive umbrella samplings along the reaction pathway; and (c) computing AI-QM/MM energy values for collected configurations from the sampling and performing weighted thermodynamic perturbation to acquire AI-QM/MM corrected reaction free energy profile. For three model reactions (identity SN2 reaction, Menshutkin reaction, and glycine proton transfer reaction) in aqueous solution and one enzyme reaction (Claisen arrangement in chorismate mutase), our simulations using recalibrated PM3 SE-QM Hamiltonians well reproduced AI-QM/MM free energy profiles (at the B3LYP/6-31G* level of theory) all within 1 kcal/mol with a 20 to 45 fold reduction in the computer time.

scholarly journals Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanical accuracy via a semi-empirical reference potential. II. Recalibrating semi-empirical parameters with force matching

2019 ◽  
Vol 21 (37) ◽  
pp. 20595-20605 ◽  
Author(s):  
Xiaoliang Pan ◽  
Pengfei Li ◽  
Junming Ho ◽  
Jingzhi Pu ◽  
Ye Mei ◽  
...  
Keyword(s):  
Free Energy ◽  
Ab Initio ◽  
Quantum Mechanical ◽  
Energy Profile ◽  
Force Matching ◽  
Energy Profiles ◽  
Reference Potential ◽  
Simulation Protocol ◽  
Semi Empirical ◽  
Free Energy Profiles

An efficient and accurate reference potential simulation protocol is proposed for producing ab initio quantum mechanical/molecular mechanical (AI-QM/MM) quality free energy profiles for chemical reactions in a solvent or macromolecular environment.

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

2021 ◽  
Author(s):  
Jinzhe Zeng ◽  
Timothy J Giese ◽  
Şölen Ekesan ◽  
Darrin M. York
Keyword(s):  
Free Energy ◽  
Chemical Reactions ◽  
High Energy ◽  
Quantum Mechanical ◽  
Training Procedure ◽  
Energy Profiles ◽  
Computational Performance ◽  
Range Correction ◽  
And Training ◽  
Free Energy Profiles

We develop a new Deep Potential - Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of 6 non-enzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free energy profiles generated from a target QM model. We perform comparisons using the MNDO/d and DFTB2 semiempirical models because they produce free energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce 4 different reactions and yielded good agreement with the free energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free energy surfaces and 1D free energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs, but was sped up almost 100-fold when using an NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free energy applications ranging from drug discovery to enzyme design.<br>

scholarly journals Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. II. Recalibrating Semi-Empirical Parameters with Force Matching

2019 ◽  
Author(s):  
Xiaoliang Pan ◽  
Pengfei Li ◽  
Junming Ho ◽  
Jingzhi Pu ◽  
Ye Mei ◽  
...  
Keyword(s):  
Free Energy ◽  
Ab Initio ◽  
Quantum Mechanical ◽  
Energy Profile ◽  
Free Energy Profile ◽  
Force Matching ◽  
Energy Profiles ◽  
Reference Potential ◽  
Semi Empirical ◽  
Free Energy Profiles

An efficient and accurate reference potential simulation protocol is proposed for producing ab initio quantum mechanical molecular mechanical (AI-QM/MM) quality free energy profiles for chemical<br>reactions in a solvent or macromolecular environment. This protocol involves three stages: (a) using force matching to recalibrate a semi-empirical quantum mechanical (SE-QM) Hamiltonian for the specific reaction under study; (b) employing the recalibrated SE-QM Hamiltonian (in combination with molecular mechanical force fields) as the reference potential to drive umbrella samplings along the reaction pathway; and (c) computing AI-QM/MM energy values for collected configurations from the sampling and performing weighted thermodynamic perturbation to acquire AI-QM/MM corrected reaction free energy profile. For three model reactions (identity SN2 reaction, Menshutkin reaction, and glycine proton transfer reaction) in aqueous solution and one enzyme reaction (Claisen arrangement in chorismate mutase), our simulations using recalibrated PM3 SE-QM Hamiltonians well reproduced AI-QM/MM free energy profiles (at the B3LYP/6-31G* level of theory) all within 1 kcal/mol with a 20 to 45 fold reduction in the computer time.

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

2021 ◽  
Author(s):  
Jinzhe Zeng ◽  
Timothy J Giese ◽  
Şölen Ekesan ◽  
Darrin M. York
Keyword(s):  
Free Energy ◽  
Chemical Reactions ◽  
High Energy ◽  
Quantum Mechanical ◽  
Training Procedure ◽  
Energy Profiles ◽  
Computational Performance ◽  
Range Correction ◽  
And Training ◽  
Free Energy Profiles

We develop a new Deep Potential - Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of 6 non-enzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free energy profiles generated from a target QM model. We perform comparisons using the MNDO/d and DFTB2 semiempirical models because they produce free energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce 4 different reactions and yielded good agreement with the free energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free energy surfaces and 1D free energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs, but was sped up almost 100-fold when using an NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free energy applications ranging from drug discovery to enzyme design.<br>

scholarly journals Particle Diffusivity and Free-Energy Profiles in Hydrogels from Time-Resolved Penetration Data

2021 ◽  
Author(s):  
Amanuel Wolde-Kidan ◽  
Anna Herrmann ◽  
Albert Prause ◽  
Michael Gradzielski ◽  
Rainer Haag ◽  
...  
Keyword(s):  
Free Energy ◽  
Time Resolved ◽  
Energy Profiles ◽  
Penetration Data ◽  
Free Energy Profiles

Free energy profile analysis to identify factors activating the aggregation-induced emission of a cyanostilbene derivative

2021 ◽  
Author(s):  
Norifumi Yamamoto
Keyword(s):  
Free Energy ◽  
Profile Analysis ◽  
Energy Profile ◽  
Contributing Factors ◽  
Free Energy Profile ◽  
Aggregation Induced Emission ◽  
Energy Profiles ◽  
Free Energy Profiles

The contributing factors that cause the aggregation-induced emission (AIE) are determined by identifying characteristic differences in the free energy profiles of the AIE processes of the AIE-active E-form of CN-MBE and the inactive Z-form.

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