Treating electrostatics with Wolf summation in combined quantum mechanical and molecular mechanical simulations

The Wolf summation approach [D. Wolf et al., J. Chem. Phys. 110, 8254 (1999)], in the damped shifted force (DSF) formalism [C. J. Fennell and J. D. Gezelter, J. Chem. Phys. 124, 234104 (2006)], is extended for treating electrostatics in combined quantum mechanical and molecular mechanical (QM/MM) molecular dynamics simulations. In this development, we split the QM/MM electrostatic potential energy function into the conventional Coulomb r −1 term and a term that contains the DSF contribution. The former is handled by the standard machinery of cutoff-based QM/MM simulations whereas the latter is incorporated into the QM/MM interaction Hamiltonian as a Fock matrix correction. We tested the resulting QM/MM-DSF method for two solution-phase reactions, i.e., the association of ammonium and chloride ions and a symmetric SN2 reaction in which a methyl group is exchanged between two chloride ions. The performance of the QM/MM-DSF method was assessed by comparing the potential of mean force (PMF) profiles with those from the QM/MM-Ewald and QM/MM-isotropic periodic sum (IPS) methods, both of which include long-range electrostatics explicitly. For ion association, the QM/MM-DSF method successfully eliminates the artificial free energy drift observed in the QM/MM-Cutoff simulations, in a remarkable agreement with the two long-range-containing methods. For the SN2 reaction, the free energy of activation obtained by the QM/MM-DSF method agrees well with both the QM/MM-Ewald and QM/MM-IPS results. The latter, however, requires a greater cutoff distance than QM/MM-DSF for a proper convergence of the PMF. Avoiding time-consuming lattice summation, the QM/MM-DSF method yields a 55% reduction in computational cost compared with the QM/MM-Ewald method. These results suggest that, in addition to QM/MM-IPS, the QM/MM-DSF method may serve as another efficient and accurate alternative to QM/MM-Ewald for treating electrostatics in condensed-phase simulations of chemical reactions

Assessing the accuracy of the isotropic periodic sum method through Madelung energy computation

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

HOW MALTOSE BINDING PROTEINS RECOGNIZE SUBSTRATES: INSIGHTS FROM COMPUTER SIMULATIONS

poster abstractAs a periplasmic component of the Maltose transporter system, the Maltose binding protein (MBP) is known to be able to capture the substrate with high affinity and load it to the entrance of the transmembrane portion of the transporter to initiate the ATP facilitated substrate transportation. Such substrate recognition and binding events trigger a large conformational change in MBP, as suggested from several X-ray crystal structures that capture MBP in an “open” or a “closed” state. To characterize the dynamical natures of these conformational states, we performed Molecular Dynamics (MD) simulations of MBP under three conditions: (1) a closed structure with ligand, (2) a closed structure without ligand, and (3) an open structure without ligand. Based on these equilibrium simulations and additional transition pathway simulations using Targeted Molecular Dynamics (TMD), possible reaction coordinates to describe the conformational change of MBP will be identified. Finally, free energy profiles of the conformational transition will be obtained by umbrella sampling simulations and the molecular recognition mechanism of MBP will be discussed

Mapping Free Energy Pathways for ATP Hydrolysis in the E. coli ABC Transporter HlyB by the String Method

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

Elucidating the role of surface passivating ligand structural parameters in hole wave function delocalization in semiconductor cluster molecules

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

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol–1 Å–1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost