Browsing by Subject "Free energy simulations"

Now showing 1 - 3 of 3
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    Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation
    (AIP, 2023) Snyder, Ryan; Kim, Bryant; Pan, Xiaoliang; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, NH3+CH3Cl→CH3NH3++Cl-, using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/Å and from 30.2 to 10.3 kcal/mol/Å for the N-C and C-Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from -12.4 to -41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks.
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    Machine Learning Facilitated Quantum Mechanic/Molecular Mechanic Free Energy Simulations
    (2023-08) Snyder, Ryan; Pu, Jingzhi; Naumann, Christoph; Webb, Ian; Deng, Yongming
    Bridging the accuracy of ab initio (AI) QM/MM with the efficiency of semi-empirical (SE) QM/MM methods has long been a goal in computational chemistry. This dissertation presents four ∆-Machine learning schemes aimed at achieving this objective. Firstly, the incorporation of negative force observations into the Gaussian process regression (GPR) model, resulting in GPR with derivative observations, demonstrates the remarkable capability to attain high-quality potential energy surfaces, accurate Cartesian force descriptions, and reliable free energy profiles using a training set of just 80 points. Secondly, the adaptation of the sparse streaming GPR algorithm showcases the potential of memory retention from previous phasespace, enabling energy-only models to converge using simple descriptors while faithfully reproducing high-quality potential energy surfaces and accurate free energy profiles. Thirdly, the utilization of GPR with atomic environmental vectors as input features proves effective in enhancing both potential energy surface and free energy description. Furthermore, incorporating derivative information on solute atoms further improves the accuracy of force predictions on molecular mechanical (MM) atoms, addressing discrepancies arising from QM/MM interaction energies between the target and base levels of theory. Finally, a comprehensive comparison of three distinct GPR schemes, namely GAP, GPR with an average kernel, and GPR with a system-specific sum kernel, is conducted to evaluate the impact of permutational invariance and atomistic learning on the model’s quality. Additionally, this dissertation introduces the adaptation of the GAP method to be compatible with the sparse variational Gaussian processes scheme and the streaming sparse GPR scheme, enhancing their efficiency and applicability. Through these four ∆-Machine learning schemes, this dissertation makes significant contributions to the field of computational chemistry, advancing the quest for accurate potential energy surfaces, reliable force descriptions, and informative free energy profiles in QM/MM simulations.
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    Mapping Free Energy Pathways for ATP Hydrolysis in the E. coli ABC Transporter HlyB by the String Method
    (MDPI, 2018-10-16) Zhou, Yan; Ojeda-May, Pedro; Nagaraju, Mulpuri; Kim, Bryant; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    HlyB functions as an adenosine triphosphate (ATP)-binding cassette (ABC) transporter that enables bacteria to secrete toxins at the expense of ATP hydrolysis. Our previous work, based on potential energy profiles from combined quantum mechanical and molecular mechanical (QM/MM) calculations, has suggested that the highly conserved H-loop His residue H662 in the nucleotide binding domain (NBD) of E. coli HlyB may catalyze the hydrolysis of ATP through proton relay. To further test this hypothesis when entropic contributions are taken into account, we obtained QM/MM minimum free energy paths (MFEPs) for the HlyB reaction, making use of the string method in collective variables. The free energy profiles along the MFEPs confirm the direct participation of H662 in catalysis. The MFEP simulations of HlyB also reveal an intimate coupling between the chemical steps and a local protein conformational change involving the signature-loop residue S607, which may serve a catalytic role similar to an Arg-finger motif in many ATPases and GTPases in stabilizing the phosphoryl-transfer transition state.