Browsing by Author "Pu, Jingzhi"

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    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
    (The Royal Society of Chemistry, 2019-08-15) Pan, Xiaoliang; Li, Pengfei; Ho, Junming; Pu, Jingzhi; Mei, Ye; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    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. 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 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.
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    Accelerating ab initio QM/MM Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semi-empirical QM/MM Hamiltonian
    (American Chemical Society, 2022-06-02) Pan, Xiaoliang; Van, Richard; Epifanovsky, Evgeny; Liu, Jian; Pu, Jingzhi; Nam, Kwangho; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.
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    Assessing the accuracy of the isotropic periodic sum method through Madelung energy computation
    (2014-04) Ojeda-May, Pedro; Pu, Jingzhi
    We tested the isotropic periodic sum (IPS) method for computing Madelung energies of ionic crystals. The performance of the method, both in its nonpolar (IPSn) and polar (IPSp) forms, was compared with that of the zero-charge and Wolf potentials [D. Wolf, P. Keblinski, S. R. Phillpot, and J. Eggebrecht, J. Chem. Phys.110, 8254 (1999)]. The results show that the IPSn and IPSp methods converge the Madelung energy to its reference value with an average deviation of ∼10−4 and ∼10−7 energy units, respectively, for a cutoff range of 18–24a (a/2 being the nearest-neighbor ion separation). However, minor oscillations were detected for the IPS methods when deviations of the computed Madelung energies were plotted on a logarithmic scale as a function of the cutoff distance. To remove such oscillations, we introduced a modified IPSn potential in which both the local-region and long-range electrostatic terms are damped, in analogy to the Wolf potential. With the damped-IPSn potential, a smoother convergence was achieved. In addition, we observed a better agreement between the damped-IPSn and IPSp methods, which suggests that damping the IPSn potential is in effect similar to adding a screening potential in IPSp.
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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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    CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
    (American Chemical Society, 2024) Hwang, Wonmuk; Austin, Steven L.; Blondel, Arnaud; Boittier, Eric D.; Boresch, Stefan; Buck, Matthias; Buckner, Joshua; Caflisch, Amedeo; Chang, Hao-Ting; Cheng, Xi; Choi, Yeol Kyo; Chu, Jhih-Wei; Crowley, Michael F.; Cui, Qiang; Damjanovic, Ana; Deng, Yuqing; Devereux, Mike; Ding, Xinqiang; Feig, Michael F.; Gao, Jiali; Glowacki, David R.; Gonzales, James E., II; Hamaneh, Mehdi Bagerhi; Harder, Edward D.; Hayes, Ryan L.; Huang, Jing; Huang, Yandong; Hudson, Phillip S.; Im, Wonpil; Islam, Shahidul M.; Jiang, Wei; Jones, Michael R.; Käser, Silvan; Kearns, Fiona L.; Kern, Nathan R.; Klauda, Jeffery B.; Lazaridis, Themis; Lee, Jinhyuk; Lemkul, Justin A.; Liu, Xiaorong; Luo, Yun; MacKerell, Alexander D., Jr.; Major, Dan T.; Meuwly, Markus; Nam, Kwangho; Nilsson, Lennart; Ovchinnikov, Victor; Paci, Emanuele; Park, Soohyung; Pastor, Richard W.; Pittman, Amanda R.; Post, Carol Beth; Prasad, Samarjeet; Pu, Jingzhi; Qi, Yifei; Rathinavelan, Thenmalarchelvi; Roe, Daniel R.; Roux, Benoit; Rowley, Christopher N.; Shen, Jana; Simmonett, Andrew C.; Sodt, Alexander J.; Töpfer, Kai; Upadhyay, Meenu; van der Vaart, Arjan; Vazquez-Salazar, Luis Itza; Venable, Richard M.; Warrensford, Luke C.; Woodcock, H. Lee; Wu, Yujin; Brooks, Charles L., III; Brooks, Bernard R.; Karplus, Martin; Chemistry and Chemical Biology, School of Science
    Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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    Combining Semiempirical QM Methods with Atom Dipole Interaction Model for Accurate and Efficient Polarizability Calculations
    (2022-12) Young, Ryan; Pu, Jingzhi; Long, Eric C.; Naumann, Christoph; Sardar, Rajesh
    Molecular polarizability plays a significant role in chemistry, biology, and medicine. Classical prediction of polarizability often relies on atomic-type specific polarizability optimized for training set molecules, which limits the calculations to systems of similar chemical environment. Although ab initio (AI) quantum mechanical (QM) methods are more transferable in predicting molecular polarizability, their high computational costs especially when used with large basis sets for obtaining quantitatively reliable results make them less practical. To obtain accurate QM polarizability in an efficient manner, we have developed a dual-level approach, where the polarizability (α) obtained from the efficient semiempirical QM (SE) method is corrected using a set of element-base atomic polarizabilities derived from the atomic dipole interaction model (ADIM) to reproduce the density functional theory (DFT) results. We have optimized the atomic polarizability correction parameters for CHON-containing systems using a small training set of molecules and tested the resulting SE-ADIM model on the neutral drug-like molecules in the QM7B database. SE-ADIM corrected AM1 showed substantial improvement with its relative percent error (RPE) compared to B3LYP reduced from 33.81% to 3.35%. To further test its robustness for larger molecules in broader chemical bonding situations, we applied this method to a collection of drug molecules from the e-Drug3D database. For the 1004 molecules tested, our SE-ADIM model, which only contains four empirical parameters, greatly reduces the RPE in AM1 polarizability relative to B3LYP from 26.8% to 2.9%. Error decomposition shows consistent improvements across molecules with diverse bond saturations, molecular sizes, and charge states. In addition, we have applied AlphaML, a promising machine learning (ML) technique for predicting molecular polarizability, to the e-Drug3D dataset to compare its performance with our SE-ADIM correction of AM1. We found SE-ADIM performs competitively with AlphaML bolstering our confidence in the value of our method. Errors distinct to AlphaML were also discovered. We found four molecules for which AlphaML predicts negative molecular polarizabilities, all of which were peroxides. In contrast, SE-ADIM has no such issue with these molecules or this chemical type. Finally, to improve performance of SE-ADIM when correcting AM1 molecular polarizability calculations for charged molecules, we introduce a charge dependent polarizability (CDP) enabled SE-ADIM. Training the CDP enabled SE-ADIM with a single additional parameter, B, we were able to reduce error in AM1 molecular polarizability calculations of charged molecules relative to B3LYP from 29.57% to 5.16%. By contrast, SE-ADIM without CDP corrected AM1 relative to B3LYP had an RPE of 8.56%. The most benefit of CDP was evident within negatively charged molecules where AM1 error relative to B3LYP fell from 32.20% to 3.77% while SE-ADIM without CDP enabled error for these same negative molecules was 10.06%.
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    A Computational Study of the Mechanism for F1-ATPase Inhibition by the Epsilon Subunit
    (2013) Thomson, Karen J.; Pu, Jingzhi; Ge, Haibo; Sardar, Rajesh; Long, Eric C. (Eric Charles)
    The multi-protein complex of F0F1 ATP synthase has been of great interest in the fields of microbiology and biochemistry, due to the ubiquitous use of ATP as a biological energy source. Efforts to better understand this complex have been made through structural determination of segments based on NMR and crystallographic data. Some experiments have provided useful data, while others have brought up more questions, especially when structures and functions are compared between bacteria and species with chloroplasts or mitochondria. The epsilon subunit is thought to play a signi cant role in the regulation of ATP synthesis and hydrolysis, yet the exact pathway is unknown due to the experimental difficulty in obtaining data along the transition pathway. Given starting and end point protein crystal structures, the transition pathway of the epsilon subunit was examined through computer simulation.The purpose of this investigation is to determine the likelihood of one such proposed mechanism for the involvement of the epsilon subunit in ATP regulation in bacterial species such as E. coli.
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    Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability
    (American Chemical Society, 2021) Kim, Bryant; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    A major shortcoming of semiempirical (SE) molecular orbital methods is their severe underestimation of molecular polarizability compared with experimental and ab initio (AI) benchmark data. In a combined quantum mechanical and molecular mechanical (QM/MM) treatment of solution-phase reactions, solute described by SE methods therefore tends to generate inadequate electronic polarization response to solvent electric fields, which often leads to large errors in free energy profiles. To address this problem, here we present a hybrid framework that improves the response property of SE/MM methods through high-level molecular-polarizability fitting. Specifically, we place on QM atoms a set of corrective polarizabilities (referred to as chaperone polarizabilities), whose magnitudes are determined from machine learning (ML) to reproduce the condensed-phase AI molecular polarizability along the minimum free energy path. These chaperone polarizabilities are then used in a machinery similar to a polarizable force field calculation to compensate for the missing polarization energy in the conventional SE/MM simulations. Because QM atoms in this treatment host SE wave functions as well as classical polarizabilities, both polarized by MM electric fields, we name this method doubly polarized QM/MM (dp-QM/MM). We demonstrate the new method on the free energy simulations of the Menshutkin reaction in water. Using AM1/MM as a base method, we show that ML chaperones greatly reduce the error in the solute molecular polarizability from 6.78 to 0.03 Å3 with respect to the density functional theory benchmark. The chaperone correction leads to ~10 kcal/mol of additional polarization energy in the product region, bringing the simulated free energy profiles to closer agreement with the experimental results. Furthermore, the solute-solvent radial distribution functions show that the chaperone polarizabilities modify the free energy profiles through enhanced solvation corrections when the system evolves from the charge-neutral reactant state to the charge-separated transition and product states. These results suggest that the dp-QM/MM method, enabled by ML chaperone polarizabilities, provides a very physical remedy for the underpolarization problem in SE/MM-based free energy simulations.
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    Elucidating the role of surface passivating ligand structural parameters in hole wave function delocalization in semiconductor cluster molecules
    (RSC, 2017-10) Teunis, Meghan B.; Nagaraju, Mulpuri; Dutta, Poulami; Pu, Jingzhi; Muhoberac, Barry B.; Sardar, Rajesh; Agarwal, Mangilal; Chemistry and Chemical Biology, School of Science
    This article describes the mechanisms underlying electronic interactions between surface passivating ligands and (CdSe)34 semiconductor cluster molecules (SCMs) that facilitate band-gap engineering through the delocalization of hole wave functions without altering their inorganic core. We show here both experimentally and through density functional theory calculations that the expansion of the hole wave function beyond the SCM boundary into the ligand monolayer depends not only on the pre-binding energetic alignment of interfacial orbitals between the SCM and surface passivating ligands but is also strongly influenced by definable ligand structural parameters such as the extent of their π-conjugation [π-delocalization energy; pyrene (Py), anthracene (Anth), naphthalene (Naph), and phenyl (Ph)], binding mode [dithiocarbamate (DTC, –NH–CS2−), carboxylate (–COO−), and amine (–NH2)], and binding head group [–SH, –SeH, and –TeH]. We observe an unprecedentedly large ∼650 meV red-shift in the lowest energy optical absorption band of (CdSe)34 SCMs upon passivating their surface with Py-DTC ligands and the trend is found to be Ph- < Naph- < Anth- < Py-DTC. This shift is reversible upon removal of Py-DTC by triethylphosphine gold(I) chloride treatment at room temperature. Furthermore, we performed temperature-dependent (80–300 K) photoluminescence lifetime measurements, which show longer lifetime at lower temperature, suggesting a strong influence of hole wave function delocalization rather than carrier trapping and/or phonon-mediated relaxation. Taken together, knowledge of how ligands electronically interact with the SCM surface is crucial to semiconductor nanomaterial research in general because it allows the tuning of electronic properties of nanomaterials for better charge separation and enhanced charge transfer, which in turn will increase optoelectronic device and photocatalytic efficiencies.
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    Facilitating Ab Initio QM/MM Free Energy Simulations by Gaussian Process Regression with Derivative Observations
    (Royal Society of Chemistry, 2022-10-27) Snyder, Ryan; Kim, Bryant; Pan, Xiaoliang; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results.