Ultrafast two-dimensional infrared (2D-IR) spectroscopy reveals picosecond protein and hydration dynamics of crowded hen egg white lysozyme (HEWL) labeled with a metal-carbonyl vibrational probe covalently attached to a solvent accessible His residue. HEWL is systematically crowded alternatively with polyethylene glycol (PEG) or excess lysozyme in order to distinguish the chemically inert polymer from the complex electrostatic profile of the protein crowder. The results are threefold: (1) A sharp dynamical jamming-like transition is observed in the picosecond protein and hydration dynamics that is attributed to an independent-to-collective hydration transition induced by macromolecular crowding that slows the hydration dynamics up to an order of magnitude relative to bulk water; (2) The interprotein distance at which the transition occurs suggests collective hydration of proteins over distances of 30-40 Å; and (3) Comparing the crowding effects of PEG400 to our previously reported experiments using glycerol exposes fundamental differences between small and macromolecular crowding agents.

The thermodynamic driving forces for protein folding, association and function are often determined by protein-water interactions. With a novel covalently bound labeling approach, we have used sensitive vibrational probes, site-selectively conjugated to two lysozyme variants–in conjunction with ultrafast two-dimensional infrared (2D-IR) spectroscopy–to investigate directly the protein-water interface. By probing alternatively a topologically flat, rigid domain and a flexible domain, we find direct experimental evidence for spatially heterogeneous hydration dynamics. The hydration environment around globular proteins can vary from exhibiting bulk-like hydration dynamics to dynamically constrained water, which results from stifled hydrogen bond switching dynamics near extended hydrophobic surfaces. Furthermore, we leverage preferential solvation exchange to demonstrate that the liberation of dynamically constrained water is a sufficient driving force for protein-surface association reactions. These results provide an intuitive picture of the dynamic aspects of hydrophobic hydration of proteins, illustrating an essential function of water in biological processes.

The bond dissociation energies of a set of 44 3d transition metal-containing diatomics are computed with phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) utilizing a correlated sampling technique. We investigate molecules with H, N, O, F, Cl, and S ligands, including those in the 3dMLBE20 database first compiled by Truhlar and co-workers with calculated and experimental values that have since been revised by various groups. In order to make a direct comparison of the accuracy of our ph-AFQMC calculations with previously published results from 10 DFT functionals, CCSD(T), and icMR-CCSD(T), we establish an objective selection protocol which utilizes the most recent experimental results except for a few cases with well-specified discrepancies. With 1 arXiv:1901.09464v1 [physics.chem-ph] 27 Jan 2019 the remaining set of 41 molecules, we find that ph-AFQMC gives robust agreement with experiment superior to that of all other methods, with a mean absolute error (MAE) of 1.4(4) kcal/mol and maximum error of 3(3) kcal/mol (parenthesis account for reported experimental uncertainties and the statistical errors of our ph-AFQMC calculations).In comparison, CCSD(T) and B97, the best performing DFT functional considered here, have MAEs of 2.8 and 3.7 kcal/mol, respectively, and maximum errors in excess of 17 kcal/mol for both methods. While a larger and more diverse data set would be required to demonstrate that ph-AFQMC is truly a benchmark method for transition metal systems, our results indicate that the method has tremendous potential, exhibiting unprecedented consistency and accuracy compared to other approximate quantum chemical approaches.

We present an implementation of phaseless Auxiliary-Field Quantum Monte Carlo (ph-AFQMC) utilizing graphical processing units (GPUs). The AFQMC method is recast in terms of matrix operations which are spread across thousands of processing cores and are executed in batches using custom Compute Unified Device Architecture kernels and the GPU-optimized cuBLAS matrix library. Algorithmic advances include a batched Sherman-Morrison-Woodbury algorithm to quickly update matrix determinants and inverses, density-fitting of the two-electron integrals, an energy algorithm involving a high-dimensional precomputed tensor, and the use of single-precision floating point arithmetic. These strategies accelerate ph-AFQMC calculations with both single- and multideterminant trial wave functions, though particularly dramatic wall-time reductions are achieved for the latter. For typical calculations we find speed-ups of roughly 2 orders of magnitude using just a single GPU card compared to a single modern CPU core. Furthermore, we achieve near-unity parallel efficiency using 8 GPU cards on a single node and can reach moderate system sizes via a local memory-slicing approach. We illustrate the robustness of our implementation on hydrogen chains of increasing length and through the calculation of all-electron ionization potentials of the first-row transition metal atoms. We compare long imaginary-time calculations utilizing a population control algorithm with our previously published correlated sampling approach and show that the latter improves not only the efficiency but also the accuracy of the computed ionization potentials. Taken together, the GPU implementation combined with correlated sampling provides a compelling computational method that will broaden the application of ph-AFQMC to the description of realistic correlated electronic systems.

The energy gap between the lowest-lying singlet and triplet states is an important quantity in chemical photocatalysis, with relevant applications ranging from triplet fusion in optical upconversion to the design of organic light-emitting devices. The ab initio prediction of singlet-triplet (ST) gaps is challenging due to the potentially biradical nature of the involved states, combined with the potentially large size of relevant molecules. In this work, we show that phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) can accurately predict ST gaps for chemical systems with singlet states of highly biradical nature, including a set of 13 small molecules and the ortho-, meta-, and para-isomers of benzyne. With respect to gas-phase experiments, ph-AFQMC using CASSCF trial wavefunctions achieves a mean averaged error of ∼1 1 arXiv:1905.13316v1 [physics.chem-ph] 30 May 2019 kcal/mol. Furthermore, we find that in the context of a spin-projection technique, ph-AFQMC using unrestricted single-determinant trial wavefunctions, which can be readily obtained for even very large systems, produces equivalently high accuracy. We proceed to show that this scalable methodology is capable of yielding accurate ST gaps for all linear polyacenes for which experimental measurements exist, i.e. naphthalene, anthracene, tetracene, and pentacene. Our results suggest a protocol for selecting either unrestricted Hartree-Fock or Kohn-Sham orbitals for the single-determinant trial wavefunction, based on the extent of spin-contamination. These findings pave the way for future investigations of specific photochemical processes involving large molecules with substantial biradical character.

Transition metal complexes are ubiquitous in biology and chemical catalysis, yet they remain difficult to accurately describe with ab initio methods due to the presence of a large degree of dynamic electron correlation, and, in some cases, strong static correlation which results from a manifold of low-lying states. Progress has been hindered by a scarcity of high quality gas-phase experimental data, while exact ab initio predictions are usually computationally unaffordable due to the large size of the relevant complexes.In this work, we present a data set of 34 tetrahedral, square planar, and octahedral 3d metal-containing complexes with gas-phase ligand-dissociation energies that have reported uncertainties of ≤ 2 kcal/mol. We perform all-electron phaseless auxiliaryfield quantum Monte Carlo (ph-AFQMC) calculations utilizing multi-determinant trial wavefunctions selected by a blackbox procedure. We compare the results with those from density functional theory (DFT) with the B3LYP, B97, M06, PBE0, ωB97X-V, and DSD-PBEP86/2013 functionals, and a localized orbital variant of coupled cluster theory with single, double, and perturbative triple excitations (DLPNO-CCSD(T)). We find mean averaged errors of 1.09 ± 0.28 kcal/mol for our best ph-AFQMC method, vs 2.89 kcal/mol for DLPNO-CCSD(T) and 1.57 -3.87 kcal/mol for DFT. We find maximum errors of 2.96 ± 1.71 kcal/mol for our best ph-AFQMC method, vs 9.15 kcal/mol for DLPNO-CCSD(T) and 5.98 -13.69 kcal/mol for DFT. The reasonable performance of a number of DFT functionals is in stark contrast to the much poorer accuracy previously demonstrated for diatomic species, suggesting a moderation in electron correlation due to ligand coordination. However, the unpredictably large errors for a small subset of cases with both DFT and DLPNO-CCSD(T) methods leave cause for concern, especially in light of the unreliability of common multi-reference indicators. In contrast, the robust and, in principle, systematically improvable results of ph-AFQMC for these realistic complexes establish the method as a useful tool for elucidating the electronic structure of transition metal-containing complexes and predicting their gas-phase properties.

Accurate computational methods of determining protein and nucleic acid pKa values are vital to understanding pH-dependent processes in biological systems. In this paper we use the recently developed method constant pH molecular dynamics (CPHMD) to explore the calculation of highly-perturbed pKa values in variants of staphylococcal nuclease (SNase). Simulations were performed using the replica exchange (REX) protocol for improved conformational sampling with eight temperature windows, and yielded converged proton populations in a total sampling time of 4 ns. Our REX-CPHMD simulations resulted in calculated pKa values with an average unsigned error (AUE) of 0.75 pK units for the acidic residues in Δ+PHS, a hyperstable variant of SNase. For highly pKa-perturbed SNase mutants with known crystal structures, our calculations yielded an AUE of 1.5 pK units and for those mutants based on modeled structures an AUE of 1.4 pK units was found. Although a systematic underestimate of pK shifts was observed in most of the cases for the highly perturbed pK mutants, correlations between conformational rearrangement and plasticity associated with the mutation and error in pKa prediction was not evident in the data. This study further extends the scope of electrostatic environments explored using the REX-CPHMD methodology and suggests it is a reliable tool for rapidly characterizing ionizable amino acids within proteins even when modeled structures are employed.

detected by mass spectrometry indicate that the phenyl group on the imidazole ring of SCH 66712 is one site of oxidation by CYP2D6 and could lead to methylene quinone formation. Three other metabolites were also observed. For understanding the metabolic pathway that leads to CYP2D6 inactivation, metabolism studies with CYP2C9 and CYP2C19 were performed because neither of these enzymes is significantly inhibited by SCH 66712. The metabolites formed by CYP2C9 and CYP2C19 are the same as those seen with CYP2D6, although in different abundance. Modeling studies with CYP2D6 revealed potential roles of various active site residues in the oxidation of SCH 66712 and inactivation of CYP2D6 and showed that the phenyl group of SCH 66712 is positioned at 2.2 Å from the heme iron.