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Despite the progress in modeling complex molecular systems of ever-increasing complexity, a quantitatively accurate computational treatment of ion permeation through narrow membrane channels remains challenging. An important factor to reach this goal is induced electronic polarization, which is likely to impact the permeation rate of small ions through narrow molecular pores. In this work, we extended the recently developed polarizable force field based on the classical Drude oscillators to assess the role of induced polarization effects on the energetics of sodium and potassium ion transport across the gramicidin A (gA) ion channel. The inclusion of induced polarization lowers barriers present in 1D potential of mean force (PMF) for cation permeation by ∼50% compared to those obtained with the additive force field. Conductance properties calculated with 1D PMFs from Drude simulations are in better agreement with experimental results. Polarization of single-file water molecules and protein atoms forming the narrow pore has a direct impact on the free-energy barriers and cation-specific solid-state NMR chemical shifts. Sensitivity analysis indicates that small changes to water–channel interactions can alter the free energy barrier for ion permeation. These results, illustrating polarization effects present in the complex electrostatic environment of the gA channel, have broad implications for revising proposed mechanisms of ion permeation and selectivity in a variety of ion channels.

Guanine quadruplex nucleic acids (G4s) are involved in key biological processes such as replication or transcription. Beyond their biological relevance, G4s find applications as biotechnological tools since they readily bind hemin and enhance its peroxidase activity, creating a G4-DNAzyme. The biocatalytic properties of G4-DNAzymes have been thoroughly studied and used for biosensing purposes. Despite hundreds of applications and massive experimental efforts, the atomistic details of the reaction mechanism remain unclear. To help select between the different hypotheses currently under investigation, we use extended explicit-solvent molecular dynamics (MD) simulations to scrutinize the G4/hemin interaction. We find that besides the dominant conformation in which hemin is stacked atop the external G-quartets, hemin can also transiently bind to the loops and be brought to the external G-quartets through diverse delivery mechanisms. The simulations do not support the catalytic mechanism relying on a wobbling guanine. Similarly, catalytic role of the iron-bound water molecule is not in line with our results, however, given the simulation limitations, this observation should be considered with some caution. The simulations rather suggest tentative mechanisms in which the external G-quartet itself could be responsible for the unique H2O2-promoted biocatalytic properties of the G4/hemin complexes. Once stacked atop a terminal G-quartet, hemin rotates about its vertical axis while readily sampling shifted geometries where the iron transiently contacts oxygen atoms of the adjacent G-quartet. This dynamics is not apparent from the ensemble-averaged structure. We also visualize transient interactions between the stacked hemin and the G4 loops. Finally, we investigated interactions between hemin and on-pathway folding intermediates of the parallel-stranded G4 fold. The simulations suggest that hemin drives the folding of parallel-stranded G4s from slip-stranded intermediates, acting as a G4 chaperone. Limitations of the MD technique are briefly discussed.For Table of Contents Only

We describe an efficient method for the simultaneous solution of all free energies within a relative binding free-energy (RBFE) network with cycle closure and experimental/reference constraint conditions using Bennett Acceptance Ratio (BAR) and Multistate BAR (MBAR) analysis. Rather than solving the BAR or MBAR equations for each transformation independently, the simultaneous solution of all transformations are obtained by performing a constrained minimization of a global objective function. The nonlinear optimization of the objective function is subjected to affine linear constraints that couple the free energies between the network edges. The constraints are used to enforce the closure of thermodynamic cycles within the RBFE network, and to enforce an additional set of linear constraint conditions demonstrated here to be subsets of (1 or 2) experimental values. We describe details of the practical implementation of the network BAR/MBAR procedure, including use of generalized coordinates in the minimization of the free-energy objective function, propagation of bootstrap errors from those coordinates, and performance and memory optimization. In some cases it is found that use of restraints in the optimization is more practical than use of generalized coordinates for enforcing constraint conditions. The fast BARnet and MBARnet methods are used to analyze the RBFEs of six prototypical protein-ligand systems, and it is shown that enforcement of cycle closure conditions reduces the error in the predictions only modestly, and further reduction in errors can be achieved when one or two experimental RBFEs are included in the optimization procedure. These methods have been implemented into FE-ToolKit, a new free-energy analysis toolkit. The BARnet/MBARnet framework presented here opens the door to new, more efficient and robust free-energy analysis with enhanced predictive capability for drug discovery applications.

A model, including the chemical details of core nanoparticles as well as explicit surface charges and hydrophobic patches, of triblock Janus particles is employed to simulate nucleation and solid-solid phase transitions in two-dimensional layers. An explicit solvent and a substrate are included in the model, and hydrodynamic and many-body interactions were taken into account within many-body dissipative particle dynamics simulation. In order not to impose a mechanism a priori, we performed free (unbiased) simulations, leaving the system the freedom to choose its own pathways. In agreement with the experiment and previous biased simulations, a two-step mechanism for the nucleation of a kagome lattice from solution was detected. However, a distinct feature of the present unbiased versus biased simulations is that multiple nuclei emerge from the solution; upon their growth, the aligned and misaligned facets at the grain boundaries are introduced into the system. The liquid-like particles trapped between the neighboring nuclei connect them together. A mismatch in the symmetry planes of neighboring nuclei hinders the growth of less stable (smaller) nuclei. Unification of such nuclei at the grain boundaries of misaligned facets obeys a two-step mechanism: melting of the smaller nuclei, followed by subsequent nucleation of liquid-like particles at the interface of bigger neighboring nuclei. Besides, multiple postcritical nuclei are formed in the simulation box; the growth of some of which stops due to introduction of a strain in the system. Such an incomplete nucleation/growth mechanism is in complete agreement with the recent experiments. The solid-solid (hexagonal-to-kagome) phase transition, at weak superheatings, obeys a two-step mechanism: a slower step (formation of a liquid droplet), followed by a faster step (nucleation of kagome from the liquid droplet).

Protein-embedded chromophores are responsible for light harvesting, excitation energy transfer, and charge separation in photosynthesis. A critical part of the photosynthetic apparatus are reaction centers (RCs), which comprise groups of (bacterio)chlorophyll and (bacterio)pheophytin molecules that transform the excitation energy derived from light absorption into charge separation. The lowest excitation energies of individual pigments (site energies) are key for understanding photosynthetic systems, and form a prime target for quantum chemistry. A major theoretical challenge is to accurately describe the electrochromic (Stark) shifts in site energies produced by the inhomogeneous electric field of the protein matrix. Here, we present large-scale quantum mechanics/molecular mechanics calculations of electrochromic shifts for the RC chromophores of photosystem II (PSII) using various quantum chemical methods evaluated against the domain-based local pair natural orbital (DLPNO) implementation of the similarity-transformed equation of motion coupled cluster theory with single and double excitations (STEOM-CCSD). We show that certain range-separated density functionals (ωΒ97, ωΒ97X-V, ωΒ2PLYP, and LC-BLYP) correctly reproduce RC site energy shifts with time-dependent density functional theory (TD-DFT). The popular CAM-B3LYP functional underestimates the shifts and is not recommended. Global hybrid functionals are too insensitive to the environment and should be avoided, while nonhybrid functionals are strictly nonapplicable. Among the applicable approximate coupled cluster methods, the canonical versions of CC2 and ADC(2) were found to deviate significantly from the reference results both for the description of the lowest excited state and for the electrochromic shifts. By contrast, their spin-component-scaled (SCS) and particularly the scale-opposite-spin (SOS) variants compare well with the reference DLPNO-STEOM-CCSD and the best range-separated DFT methods. The emergence of RC excitation asymmetry is discussed in terms of intrinsic and protein electrostatic potentials. In addition, we evaluate a minimal structural scaffold of PSII, the D1-D2-CytB559 RC complex often employed in experimental studies, and show that it would have the same site energy distribution of RC chromophores as the full PSII supercomplex, but only under the unlikely conditions that the core protein organization and cofactor arrangement remain identical to those of the intact enzyme.

Funding Information: This work was supported by the Academy of Finland (project numbers 308647, 314298, and 316601) and through their Flagship program: Finnish Center for Artificial Intelligence FCAI. We thank CSC, the Finnish IT Center for Science, and Aalto Science IT for computational resources. This work is supported by COST (European Cooperation in Science and Technology) Action 18234. L.F. thanks Guoxu Zhang, Marc Dvorak, Jingrui Li, and Annika Stuke for the help with FHI-Aims. He also acknowledges financial support from the Chinese Scholarship Council (grant no. [2017]3109). 1 Publisher Copyright: © 2020 American Chemical Society. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only 1000 single-point calculations and approximately 80 structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. To test the transferability of our method, we also repeated the conformer search of serine, tryptophan, and aspartic acid. The results agree well with previous conformer search studies.

Kinesin-1 is a motor protein moving along a microtubule with its two identical motor heads dimerized by two neck linkers and a coiled-coil stalk. When both motor heads bind the microtubule, an internal strain is built up between the two heads, which is indispensable to ensure proper coordination of the two motor heads during kinesin-1's mechanochemical cycle. The internal strain forms a tensile force along the neck linker that tends to unwind the neck coiled coil (NCC). Experiments showed that the kinesin-1's NCC has a high antiunwinding ability compared with conventional coiled coils, which was mainly attributed to the enhanced hydrophobic pressure arising from the unconventional sequence of kinesin-1's NCC. However, hydrophobic pressure cannot provide the shearing force which is needed to balance the tensile force on the interface between two helices. To find out the true origin of the mechanical stability of kinesin-1's NCC, we perform a novel and detailed mechanical analysis for the system based on molecular dynamics simulation at an atomic level. We find that the needed shearing force is provided by a buckle structure formed by two tyrosines which form effective steric hindrance in the presence of tensile forces. The tensile force is balanced by the tensile direction component of the contact force between the two tyrosines which forms the shearing force. The hydrophobic pressure balances the other component of the contact force perpendicular to the tensile direction. The antiunwinding strength of NCC is defined by the maximum shearing force, which is finally determined by the hydrophobic pressure. Kinesin-1 uses residues with plane side chains, tryptophans and tyrosines, to form the hydrophobic center and to shorten the interhelix distance so that a high antiunwinding strength is obtained. The special design of NCC ensures exquisite cooperation of steric hindrance and hydrophobic pressure that results in the surprising mechanical stability of NCC.

Experimentally measured infrared spectra are often compared to their computed equivalents. However, the accordance is typically characterized by visual inspection, which is prone to subjective judgment. The primary challenge for a similarity-based analysis is that the artifacts introduced by each approach are very different and, therefore, may require preprocessing steps to determine and correct impeding irregularities. To allow for automated objective assessment, we propose a practical and comprehensive workflow involving scaling factors, a novel baseline correction scheme, and peak smoothing. The resulting spectra can then easily be compared quantitatively using similarity measures, for which we found the Pearson correlation coefficient to be the most suitable. The proposed procedure is then applied to compare the agreement of the experimental infrared spectra from the NIST Chemistry Web book with the calculated spectra using standard harmonic frequency analysis and spectra extracted from ab initio molecular dynamics simulations at different levels of theory. We conclude that the direct, quantitative comparison of calculated and measured IR spectra might become a novel, sophisticated approach to benchmark quantum-chemical methods. In the present benchmark, simulated spectra based on ab initio molecular dynamics show in general better agreement with the experiment than static calculations.

Recently, a novel type of a multiscale simulation, called Relative Resolution (RelRes), was introduced. In a single system, molecules switch their resolution in terms of their relative separation, with near neighbors interacting via fine-grained potentials yet far neighbors interacting via coarse-grained potentials; notably, these two potentials are analytically parameterized by a multipole approximation. This multiscale approach is consequently able to correctly retrieve across state space, the structural and thermal, as well as static and dynamic, behavior of various nonpolar mixtures. Our current work focuses on the practical implementation of RelRes in LAMMPS, specifically for the commonly used Lennard-Jones potential. By examining various correlations and properties of several alkane liquids, including complex solutions of alternate cooligomers and block copolymers, we confirm the validity of this automated LAMMPS algorithm. Most importantly, we demonstrate that this RelRes implementation gains almost an order of magnitude in computational efficiency, as compared with conventional simulations. We thus recommend this novel LAMMPS algorithm for anyone studying systems governed by Lennard-Jones interactions., 38 pages, 10 figures, 4 tables

A double exponential (DE) functional form for Lennard-Jones (LJ) interactions, proposed in our previous study, has many advantages over LJ potentials including a natural softcore characteristic for the convenience of the pathway-based free-energy calculations, fast convergence, and flexibility in use. In this work, we put the first step on the application of the DE functional form by identifying a DE potential, coined DE-TIP3P, for molecular simulations using the TIP3P water model. The developed DE-TIP3 potential was better than LJ potential in reproducing the experimental water properties. Afterward, we developed the nonbonded models of 15 divalent metal ions, which frequently appear and play vital roles in biological systems, to be consistent with the DE-TIP3P potential and TIP3P water model. Our nonbonded models were as good as the complicated nonbonded dummy cationic models by Jiang et al. and the nonbonded 12-6-4 LJ models by Li and Merz in reproducing the experimental properties of those ions. Moreover, our nonbonded models achieved a better performance than the compromise (CM) LJ models and 12-6-4 LJ models, developed by Li and Merz, in reproducing the properties of MgCl2 in aqueous solution.

We use the quantum-classical path integral (QCPI) methodology to investigate the relaxation dynamics of an excess proton that has been inserted in a water dimer embedded in the gramicidin A channel at room temperature. We obtain one-dimensional potential slices for the quantum degree of freedom through a proper transformation to internal coordinates. Our results indicate that the proton transfer is driven by the oscillation of the oxygen pair, and that the transfer occurs primarily at single-well or nearby low-barrier configurations. Yet, we find that tunneling and zero-point energy lead to a significant acceleration of the proton transfer dynamics.

Adsorption processes are often governed by weak interactions for which the estimation of entropy contributions by means of the harmonic approximation is prone to be inaccurate. Thermodynamic integration (TI) from the harmonic to the fully interacting system (λ-path integration) can be used to compute anharmonic corrections. Here, we combine TI with (curvilinear) internal coordinates in periodic systems to make the formalism available in computational studies. Our implementation of ab initio molecular dynamics in VASP is independent of the reaction path and can be thus applied to study adsorption processes relative to the gas phase and does hence provide a useful tool for computational catalysis. We discuss the application of the approach on three model systems for which exact semianalytical solutions exist and illustrate and quantify the importance of anharmonic vibrations, hindered rotations, and hindered translations (dissociation). Eventually, we apply the method to study the adsorption of small adsorbates in a zeolite (H-SSZ-13).

We have proposed a new constant-pH (CpH) hybrid Monte Carlo (MC) method with a configuration-selection (CS) scheme, called the CS-CpH method, to obtain pH-dependent physical properties within a framework of atomistic molecular simulation. The CS-CpH method consists of carrying out a short equilibrium molecular dynamics (MD) and a searching MD coupled with thermostats and barostats to generate physically plausible configurations with changed protonation states (PSs) that are subsequently accepted or rejected according to the Metropolis MC procedure. As an example, we have applied it to glutamic acid in aqueous solution and have demonstrated that it can work to generate reasonably the pH-dependent microscopic configuration ensemble compatible with the experimental pKa value and also to show interestingly the molecular diffusivity correlated with pH-dependent solvation shell. In conclusion, we believe that the present CS-CpH method becomes a quite useful tool to study the microscopic origin of various pH-dependent phenomena, interpreting them in the atomistic chemical processes.

We present very accurate theoretical results of Penning ionization rate coefficients of the excited metastable helium atoms (4He(23S) and 3He(23S)) colliding with the hydrogen isotopologues (H2, HD, D2) in the ground and first excited rotational and vibrational states at subkelvin regime. The calculations are performed using the current best ab initio interaction energy surface, which takes into account the nonrigidity effects of the molecule. The results confirm a recently observed substantial quantum kinetic isotope effect (Nat. Chem. 2014, 6, 332–335) and reveal that the change of the rotational or vibrational state of the molecule can strongly enhance or suppress the reaction. Moreover, we demonstrate the mechanism of the appearance and disappearance of resonances in Penning ionization. The additional model computations, with the morphed interaction energy surface and mass, give better insight into the behavior of the resonances and thereby the reaction dynamics under study. Our theoretical findings are compared with all available measurements, and comprehensive data for prospective experiments are provided.

Though quasi-Newton methods have been widely adopted in computational chemistry software for molecular geometry optimization, it is well known that these methods might not perform well for initial guess geometries far away from the local minima, where the quadratic approximation might be inaccurate. We propose a reinforcement learning approach to develop a model that produces a correction term for the quasi-Newton step calculated with the BFGS algorithm to improve the overall optimization performance. Our model is able to complete the optimization in about 30% fewer steps than pure BFGS for molecules starting from perturbed geometries. The new method has similar convergence to BFGS when complemented with a line search procedure, but it is much faster since it avoids the multiple gradient evaluations associated with line searches.

Photoredox catalysts (PCs) have contributed to the advancement of organic chemistry by accelerating conventional reactions and enabling new pathways through the use of reactive electrons in excited states. With a number of successful applications, chemists continue to seek new promising organic PCs to achieve their objectives. Instead of labor-intensive manual experimentation, quantum chemical simulations could explore the enormous chemical space more efficiently. The reliability and accuracy of quantum chemical simulations have become important factors for material screening. We designed a theoretical protocol capable of predicting redox properties in excited states with high accuracy for a selected model system of dihydroquinoxalino[2,3-b]quinoxaline derivatives. Herein, three factors were considered as critical to achieving reliable predictions with accurate physics: the solvent medium effect on excited-state geometries, an adequate amount of Hartree-Fock exchange (HFX), and the consideration of double-excitation character in excited states. We determined that it is necessary to incorporate solvent medium during geometry optimizations to obtain planar excited-state structures that are consistent with the experimentally observed modest Stokes shift. While density functionals belonging to the generalized gradient approximation family perform well for the prediction of photoelectrochemical properties, an incorrect description of exciton boundedness (spontaneous dissociation of excitons or extremely weak boundedness) on small organic molecules was predicted. The inclusion of an adequate amount of Hartree-Fock exchange was suggested as one approach to obtain bound excitons, which is physically reasonable. The last consideration is the double-excitation character in S1 states. As revealed by the second-order algebraic diagrammatic construction theory, non-negligible double excitations exist in S1 states in our model systems. Time-dependent density functional theory (TDDFT) is blind to doubly excited states, and this motivated us to use spin-flip DFT (SF-DFT). We established a theoretical protocol that could provide highly accurate estimations of photophysical properties and ground-/excited-state redox properties, focusing on the three factors mentioned above. Geometry optimization with DFT and TDDFT employing the B3LYP functional (20% HFX) in solution and energy refinement by SF-DFT reproduced the experimental redox properties in the excited and ground states remarkably well with mean signed deviations (MSDs) of 0.01 and -0.15 V, respectively. This theoretical protocol is expected to contribute to the understanding of exciton behavior in organic PCs and to the efficient design of new promising PC candidates.

The many-body expansion (MBE) of energies of molecular clusters or solids offers a way to detect and analyze errors of theoretical methods that could go unnoticed if only the total energy of the system was considered. In this regard, the interaction between the methane molecule and its enclosing dodecahedral water cage, CH$_4$(H$_2$O)$_{20}$, is a stringent test for approximate methods, including density-functional theory (DFT) approximations. Hybrid and semilocal DFT approximations behave erratically for this system, with three- and four-body nonadditive terms having neither the correct sign nor magnitude. Here we analyze to what extent these qualitative errors in different MBE contributions are conveyed to post-Kohn-Sham random-phase approximation (RPA). The results reveal a correlation between the quality of the DFT input states and the RPA results. Moreover, the renormalized singles energy (RSE) corrections play a crucial role in all orders of MBE. For dimers, RSE corrects the RPA underbinding for every tested Kohn-Sham model: generalized-gradient approximation (GGA), meta-GGA, (meta-)GGA hybrids, as well as the optimized effective potential at the correlated level. Remarkably, the inclusion of singles in RPA can also correct the wrong signs of three- and four-body nonadditive energies as well as mitigate the excessive higher-order contributions to the MBE. The RPA errors are dominated by the contributions of compact clusters. As a workable method for large systems, we propose to replace those compact contributions with CCSD(T) energies and to sum up the remaining many-body contributions up to infinity with supermolecular or periodic RPA. As a demonstration of this approach, we show that for RPA(PBE0)+RSE it suffices to apply CCSD(T) to dimers and 30 compact, hydrogen-bonded trimers to get the methane-water cage interaction energy to within 1.6% of the reference value., Comment: Data available at https://doi.org/10.5281/zenodo.4429677

We have benchmarked the surface hopping method to capture nuclear quantum effects in the spin-Boson model in the deep tunneling regime. The thermal populations and the rate constants calculated using the surface hopping method are compared with those calculated using Boltzmann theory and Fermi's golden rule, respectively. Additionally, we have proposed a simple kinetic model that partially includes nuclear quantum effects within Marcus theory, and the results of the surface hopping method are analyzed under the framework of this simple kinetic model. A broad range of parameters are investigated to identify the regimes for the successes and failures of the surface hopping method. This work shows that with the accurate treatment of decoherence and velocity reversal, surface hopping can generally capture the nuclear quantum effects in the deep tunneling and weak diabatic coupling regime.

Single-stranded DNA (ssDNA) plays an important role in biological processes and is used in DNA nanotechnology and other novel applications. Many important research questions can be addressed with molecular simulations of ssDNA molecules; however, no dedicated force field for ssDNA has been developed, and there is limited experimental information about ssDNA structures. This study assesses the accuracy and applicability of existing Amber force fields for all-atom simulations of ssDNA, such as ff99, bsc0, bsc1, and OL15, in implicit and explicit solvents via comparison to available experimental data, such as Forster resonance energy transfer and small angle X-ray scattering. We observed that some force fields agree better with experiments than others mainly due to the difference in parameterization of the propensity for hydrogen bonding and base stacking. Overall, the Amber ff99 force field in the IGB5 or IGB8 implicit solvent and the bsc1 force field in the explicit TIP3P solvent had the best agreement with experiment.

Entropy is a key thermodynamic property governing most biomolecular processes, including binding. Nonetheless, quantification of the configurational entropy of a single molecule in solution remains a grand challenge. Here, we present an original approach for the calculation of absolute molecular entropies based on the analysis of converged molecular dynamics (MD) simulations. Our method, named quasi-harmonic multibasin (QHMB), relies on a multibasin decomposition of the simulated trajectory by root-mean-square deviation clustering and subsequent quasi-harmonic analysis (QHA) of extracted sub-trajectories. Last, the entropy of the landscape is evaluated using the Gibbs formula. Because of the nature of QHA, this method is directly applicable to explicit-solvent simulations to access configurational entropies in solution. When compared with calorimetric data from NIST, QHMB is shown to predict absolute entropies in the gas phase for 23 small molecules with a root-mean-squared error of 0.36 kcal/mol from the experiments. In addition, the introduction of a QHMB correction in MM/GBSA calculations to account for the ligand configurational entropy loss on binding is shown to improve the correlation between calculated and experimental binding affinities with R2 increasing from 0.67 to 0.78. Because this entropy correction penalizes large and flexible ligands more strongly, it might be useful to reduce the false-positive rate in virtual screening. The availability of an automatic procedure to compute QHMB entropies makes it a new available tool in the field of drug discovery.

The cluster-in-molecule (CIM) local correlation approach with an accurate distant pair correlation energy correction is presented. For large systems, the inclusion of distant pair correlation energies is essential for the accurate prediction of absolute correlation energies and relative energies. Here, we propose a simple and efficient scheme for evaluating the distant pair correlation energy correction for the CIM approaches. The corrections can be readily extracted from electron correlation calculations of clusters with almost no additional effort. Benchmark calculations show that the improved CIM approach can recover more than 99.94% of the correlation energy calculated by the parent method. By combining the CIM approach with the domain-based local pair natural orbital (DLPNO) local correlation approach, we have provided accurate binding energies at the CIM-DLPNO-CCSD(T) level for a test set consisting of eight weakly bound complexes ranging in size from 200 to 1027 atoms. With these results as the reference data, the accuracy and applicability of other electron correlation methods and a few density functional methods for large systems have been assessed.

While CCSD(T) with spin-restricted Hartree-Fock (RHF) orbitals has long been lauded for its ability to accurately describe closed-shell interactions, the performance of CCSD(T) on open-shell species is much more erratic, especially when using a spin-unrestricted HF (UHF) reference. Previous studies have shown improved treatment of open-shell systems when a non-HF set of molecular orbitals, like Brueckner or Kohn-Sham density functional theory (DFT) orbitals, is used as a reference. Inspired by the success of regularized orbital-optimized second-order Møller-Plesset perturbation theory (κ-OOMP2) orbitals as reference orbitals for MP3, we investigate the use of κ-OOMP2 orbitals and various DFT orbitals as reference orbitals for CCSD(T) calculations of the corrected ground-state harmonic vibrational frequencies of a set of 36 closed-shell (29 neutrals, 6 cations, 1 anion) and 59 open-shell diatomic species (38 neutrals, 15 cations, 6 anions). The aug-cc-pwCVTZ basis set is used for all calculations. The use of κ-OOMP2 orbitals in this context alleviates difficult cases observed for both UHF orbitals and OOMP2 orbitals. Removing two multireference systems and 12 systems with ambiguous experimental data leaves a pruned data set. Overall performance on the pruned data set highlights CCSD(T) with a B97 orbital reference (CCSD(T):B97), CCSD(T) with a κ-OOMP2 orbital reference (CCSD(T):κ-OOMP2), and CCSD(T) with a B97M-rV orbital reference (CCSD(T):B97M-rV) with RMSDs of 8.48 cm-1, and 8.50 cm-1, and 8.75 cm-1 respectively, outperforming CCSD(T):UHF by nearly a factor of 5. Moreover, the performance on the closed- and open-shell subsets shows these methods are able to treat open-shell and closed-shell systems with comparable accuracy and robustness. CCSD(T) with RHF orbitals is seen to improve upon UHF for the closed-shell species, while spatial symmetry breaking in a number of restricted open-shell HF (ROHF) references leads CCSD(T) with ROHF reference orbitals to exhibit the poorest statistical performance of all methods surveyed for open-shell species. The use of κ-OOMP2 orbitals has also proven useful in diagnosing multireference character that can hinder the reliability of CCSD(T).

Simulations of electronically nonadiabatic processes may employ either the adiabatic or diabatic representation. Direct dynamics calculations are usually carried out in the adiabatic basis because the energy, force, and state coupling can be evaluated directly by many electronic structure methods. However, although its straightforwardness is appealing, direct dynamics is expensive when combined with quantitatively accurate electronic structure theories. This generates interest in analytically fitted surfaces to cut the expense, but the cuspidal ridges of the potentials and the singularities and vector nature of the couplings at high-dimensional, nonsymmetry-determined intersections in the adiabatic representation make accurate fitting almost impossible. This motivates using diabatic representations, where the surfaces are smooth and the couplings are also smooth and-importantly-scalar. In a recent previous work, we have developed a method called diabatization by deep neural network (DDNN) that takes advantage of the smoothness and nonuniqueness of diabatic bases to obtain them by machine learning. The diabatic potential energy matrices (DPEMs) learned by the DDNN method yield not only diabatic potential energy surfaces (PESs) and couplings in an analytic form useful for dynamics calculations, but also adiabatic surfaces and couplings in the adiabatic representation can be calculated inexpensively from the transformation. In the present work, we show how to extend the DDNN method to produce good approximations to global permutationally invariant adiabatic PESs simultaneously with DPEMs. The extended method is called permutationally restrained DDNN.

A proper treatment of electron correlation effects is indispensable for accurate simulation of compounds. Various post-Hartree-Fock methods have been adopted to calculate correlation energies of chemical systems, but time complexity usually prevents their usage in a large scale. Here, we propose a density functional approximation, based on machine learning using neural networks, which can be readily employed to produce results comparable to second-order Møller-Plesset perturbation (MP2) ones for organic compounds with reduced computational cost. Various systems have been tested and the transferability across basis sets, structures, and nuclear configurations has been evaluated. Only a small number of molecules at the equilibrium structure has been needed for the training, and generally less than 5% relative error has been achieved for structures outside the training domain and systems containing about 140 atoms. In addition, this approach has been applied to make predictions on correlation energies of nuclear configurations extracted from density functional theory-based molecular dynamics trajectories with only one or two structures as training data.

The popular MARTINI coarse-grained model is used as a test case to analyze the adherence of top-down coarse-grained molecular dynamics models (i.e., models primarily parametrized to match experimental results) to the known features of statistical mechanics for the underlying all-atom representations. Specifically, the temperature dependence of various pair distribution functions, and hence their underlying potentials of mean force via the reversible work theorem, are compared between MARTINI 2.0, Dry MARTINI, and all-atom simulations mapped onto equivalent coarse-grained sites for certain lipid bilayers. It is found that the MARTINI models do not completely capture the lipid structure seen in atomistic simulations as projected onto the coarse-grained mappings and that issues of accuracy and temperature transferability arise due to an incorrect enthalpy-entropy decomposition of these potentials of mean force. The potential of mean force for the association of two amphipathic helices in a lipid bilayer is also calculated, and especially at shorter ranges, the MARTINI and all-atom projection results differ substantially. The former is much less repulsive and hence will lead to a higher probability of MARTINI helix association in the MARTINI bilayer than occurs in the actual all-atom case. Additionally, the bilayer height fluctuation spectra are calculated for the MARTINI model, and compared to the all-atom results, it is found that the magnitude of thermally averaged amplitudes at intermediate length scales are quite different, pointing to a number of possible consequences for realistic modeling of membrane processes. Taken as a whole, the results presented here show disagreement in the enthalpic and entropic driving forces driving lateral structure in lipid bilayers as well as quantitative differences in association of embedded amphipathic helices, which can help direct future efforts to parametrize CG models with better agreement to the all-atom systems they aspire to represent.

A simple and robust range-separated (RS) double-hybrid (DH) time-dependent density functional approach is presented for the accurate calculation of excitation energies of molecules within the Tamm-Dancoff approximation. The scheme can be considered as an excited-state extension of the ansatz proposed by Toulouse and co-workers [J. Chem. Phys. 2018, 148, 164105], which is based on the two-parameter decomposition of the Coulomb potential, for which both the exchange and correlation contributions are range-separated. A flexible and efficient implementation of the new scheme is also presented, which facilitates its extension to any combination of exchange and correlation functionals. The performance of the new approximation is tested for singlet excitations on several benchmark compilations and thoroughly compared to that of representative DH, RS hybrid, and RS DH functionals. The one-electron basis set dependence and computation times are also assessed. Our results show that the new approach improves on standard DHs in most cases, and it can provide a more robust and accurate alternative. In addition, on average, it noticeably surpasses the existing RS hybrid and RS DH functionals.

Gaussian process regression has recently been explored as an alternative to standard surrogate models in molecular equilibrium geometry optimization. In particular, the gradient-enhanced Kriging approach in association with internal coordinates, restricted-variance optimization, and an efficient and fast estimate of hyperparameters have demonstrated performance on par or better than standard methods. In this report we extend the approach to constrained optimizations and transition states, and benchmark it for a set of reactions. We compare the performance of the new developed method with the standard techniques in the location of transition states and in constrained optimizations, both isolated and in the context of reaction path computation. The results show that the method outperforms the current standard in efficiency as well as in robustness.

We investigated gramicidin A (gA) subunit dimerization in lipid bilayers using microsecond-long replica-exchange umbrella sampling simulations, millisecond-long unbiased molecular dynamics simulations, and machine learning. Our simulations led to a dimer structure that is indistinguishable from the experimentally determined gA channel structures, with the two gA subunits joined by six hydrogen bonds (6HB). The simulations also uncovered two additional dimer structures, with different gA-gA stacking orientations that were stabilized by four or two hydrogen bonds (4HB or 2HB). When examining the temporal evolution of the dimerization, we found that two bilayer-inserted gA subunits can form the 6HB dimer directly, with no discernible intermediate states, as well as through paths that involve the 2HB and 4HB dimers.

Orbital-optimized multiple self-consistent-field (SCF) solutions are increasingly being interpreted as mean-field approximations of diabatic or excited electronic states. However, surprisingly little is known about the topology of the electronic energy landscape from which these multiple solutions emerge. In this contribution, we extend energy landscape methods, developed for investigating molecular potential energy surfaces, to investigate and understand the structure of the electronic SCF energy surface. Using analytic gradients and Hessians, we systematically identify every real SCF minimum for the prototypical H4 molecule with the 3-21G basis set, and the index-1 saddles that connect these minima. The resulting SCF energy landscape has a double-funnel structure, with no high-energy local minima. The effect of molecular symmetry on the pathways is analyzed, and we demonstrate how the SCF energy landscape changes with the basis set, SCF potential, molecular structure, and spin state. These results provide guiding principles for the future development of algorithms to systematically identify multiple SCF solutions from an orbital optimization perspective.

A novel state-averaged version of ab initio nonorthogonal valence bond method is described, for the sake of accurate theoretical studies of excited states in the valence bond framework. With respect to standard calculations in the molecular orbital framework, the state-averaged breathing-orbital valence bond (BOVB) method has the advantage to be free from the penalizing constraint for the ground and excited state(s) to share the same unique set of orbitals. The ability of the BOVB method to faithfully describe excited states and to compute accurate transition energies from the ground state is tested on the five lowest-lying singlet electronic states of ozone and sulfur dioxide, among which 11B2 and 21A1 are the challenging ones. As the 11A2, 11B1, and 11B2 states are of different symmetries than the ground state, they can be calculated at the state-specific BOVB level. On the other hand, the 21A1 states and the 11A1 ground states, which are of like symmetry, are calculated with the state-averaged BOVB technique. In all cases, the calculated vertical energies are close to the experimental values when available, and at par with the most sophisticated calculations in the molecular framework, despite the extreme compactness of the BOVB wave functions, made of no more than 5-9 valence bond structures in all cases. The features that allow the combination of compactness and accuracy in challenging cases are analyzed. For the "ionic" 11B2 states, which are the site of important charge fluctuations, it is because of the built-in dynamic correlation inherent to the BOVB method. For the 21A1 ones, this is the fact that these states have the degree of freedom of having different orbitals than the ground states, even though they are of like symmetry and calculated simultaneously using the newly implemented state-average BOVB algorithm. Finally, the description of the excited states in terms of Lewis structures is insightful, rationalizing the fast ring closure for the 21A1 state of ozone and predicting some diradical character in the so-called "ionic" 11B2 states.

Disordered proteins and nucleic acids can condense into droplets that resemble the membraneless organelles observed in living cells. MD simulations offer a unique tool to characterize the molecular interactions governing the formation of these biomolecular condensates, their physicochemical properties, and the factors controlling their composition and size. However, biopolymer condensation depends sensitively on the balance between different energetic and entropic contributions. Here, we develop a general strategy to fine-tune the potential energy function for molecular dynamics simulations of biopolymer phase separation. We rebalance protein–protein interactions against solvation and entropic contributions to match the excess free energy of transferring proteins between dilute solution and condensate. We illustrate this formalism by simulating liquid droplet formation of the FUS low-complexity domain (LCD) with a rebalanced MARTINI model. By scaling the strength of the nonbonded interactions in the coarse-grained MARTINI potential energy function, we map out a phase diagram in the plane of protein concentration and interaction strength. Above a critical scaling factor of αc ≈ 0.6, FUS-LCD condensation is observed, where α = 1 and 0 correspond to full and repulsive interactions in the MARTINI model. For a scaling factor α = 0.65, we recover experimental densities of the dilute and dense phases, and thus the excess protein transfer free energy into the droplet and the saturation concentration where FUS-LCD condenses. In the region of phase separation, we simulate FUS-LCD droplets of four different sizes in stable equilibrium with the dilute phase and slabs of condensed FUS-LCD for tens of microseconds, and over one millisecond in aggregate. We determine surface tensions in the range of 0.01–0.4 mN/m from the fluctuations of the droplet shape and from the capillary-wave-like broadening of the interface between the two phases. From the dynamics of the protein end-to-end distance, we estimate shear viscosities from 0.001 to 0.02 Pa s for the FUS-LCD droplets with scaling factors α in the range of 0.625–0.75, where we observe liquid droplets. Significant hydration of the interior of the droplets keeps the proteins mobile and the droplets fluid.

The greatest restriction to the theoretical study of the dynamics of photoinduced processes is computationally expensive electronic structure calculations. Machine learning algorithms have the potential to reduce the number of these computations significantly. Here, PySurf is introduced as an innovative code framework, which is specifically designed for rapid prototyping and development tasks for data science applications in computational chemistry. It comes with powerful Plugin and Workflow engines, which allows intuitive customization for individual tasks. Data is automatically stored through the database framework, which enables additional interpolation of properties in previously evaluated regions of the conformational space. To illustrate the potential of the framework, a code for nonadiabatic surface hopping simulations based on the Landau-Zener algorithm is presented here. Deriving gradients from the interpolated potential energy surfaces allows for full-dimensional nonadiabatic surface hopping simulations using only adiabatic energies (energy only). Simulations of a pyrazine model and ab initio-based calculations of the SO2 molecule show that energy-only calculations with PySurf are able to correctly predict the nonadiabatic dynamics of these prototype systems. The results reveal the degree of sophistication, which can be achieved by the database accelerated energy-only surface hopping simulations being competitive to commonly used semiclassical approaches.

The premise for Kohn-Sham density functional theory is that the exact electron density can be generated by a set of orbitals in a single Slater determinant. While this is ensured in a complete basis set, it has been shown that it cannot hold in small basis sets. The present work probes how accurately a reference electron density of the full-CI type can be reproduced by a set of orbitals in a single Slater determinant, as a function of the basis set used for the fitting electron density. The key finding is that the fitting error may be significant for basis sets of double- or triple-ζ quality. It is also shown that it is important that the fitting basis set includes the same basis functions as used for generating the reference electron density. The main limitation in a given basis set is the lack of higher order polarization functions. The error for practical purposes becomes insignificant for basis sets of quadruple-ζ or better quality, and this should be the choice when assessing the accuracy of exchange-correlation functionals by comparing electron densities to accurate reference results generated by wave function methods. The methodology in the present work can be used to transform an electron density from a multideterminant wave function into a set of orbitals in a single Slater determinant, and this may be useful for developing and testing new exchange-correlation functionals.

A recent development in quantum chemistry has established the quantum mutual information between orbitals as a major descriptor of electronic structure. This has already facilitated remarkable improvements of numerical methods and may lead to a more comprehensive foundation for chemical bonding theory. Building on this promising development, our work provides a refined discussion of quantum information theoretical concepts by introducing the physical correlation and its separation into classical and quantum parts as distinctive quantifiers of electronic structure. In particular, we succeed in quantifying the entanglement. Intriguingly, our results for different molecules reveal that the total correlation between orbitals is mainly classical, raising questions about the general significance of entanglement in chemical bonding. Our work also shows that implementing the fundamental particle number superselection rule, so far not accounted for in quantum chemistry, removes a major part of correlation and entanglement previously seen. In that respect, realizing quantum information processing tasks with molecular systems might be more challenging than anticipated., Comment: Close to published version

Thermodynamic integration (TI) is a commonly used method to determine free-energy differences. One of its disadvantages is that many intermediate λ-states need to be sampled in order to be able to integrate accurately over ⟨∂H/∂λ⟩. Here, we use the recently introduced extended TI to study alternative parameterizations of H(λ) and its influence on the smoothness of the ⟨∂H/∂λ⟩ curves as well as the efficiency of the simulations. We find that the extended TI approach can be used to select curves of low curvature. An optimal parameterization is suggested for the calculation of hydration free energies. For calculations of relative binding free energies, we show that optimized parameterizations of the Hamiltonian in the unbound state also effectively lower the curvature in the bound state of the ligand.

Two different potentials, the modified TraPPE-UA model of Fischer, J. [ J. Phys. Chem. B 2008, 112, 2388–2398] and the modified general AMBER force-field (GAFF) model of Barbosa, N. S. V. [ J. Mol....

A multiscale approach to the dynamics of resonant energy transfer (RET) is presented, combining DFT and TD-DFT results on the energy donor (D) and acceptor (A) moieties with an extensive equilibrium and non-equilibrium molecular dynamics (MD) analysis of a bound D–A pair in solution to build a coarse-grained kinetic model. We demonstrate that a thorough MD study is needed to properly address RET: the enormous configuration space visited by the system cannot be reliably sampled accounting only for a few representative configurations. Moreover, the conformational motion of the RET pair, occurring in a similar time scale as the RET process itself, leads to a sizable increase of the overall process efficiency.

Molecular dynamic (MD) simulations are an important tool for studying protein aggregation processes, which play a central role in a number of diseases including Alzheimer's disease. However, MD simulations produce large amounts of data, requiring advanced methods to extract mechanistic insight into the process under study. Transition networks (TNs) provide an elegant method to identify (meta)stable states and the transitions between them from MD simulations. Here, we apply two different methods to generate TNs for protein aggregation: Markov state models (MSMs), which are based on kinetic clustering the state space, and TNs using conformational clustering. The similarities and differences of both methods are elucidated for the aggregation of the fragment Aβ16-22 of the Alzheimer's amyloid-β peptide. In general, both methods perform excellently in identifying the main aggregation pathways. The strength of MSMs is that they provide a rather coarse and thus simply to interpret picture of the aggregation process. Conformation-sorting TNs, on the other hand, outperform MSMs in uncovering mechanistic details. We thus recommend to apply both methods to MD data of protein aggregation in order to obtain a complete picture of this process. As part of this work, a Python script called ATRANET for automated TN generation based on a correlation analysis of the descriptors used for conformational sorting is made publicly available.

A major field of current research in chemistry and biology is the development of the tools that enable in situ analysis of complex systems. However, the long-time dynamics simulation for an extremely large system in solution is almost impossible by an all-atom force field combined with an explicit solvent model. The results show that the larger the periodic box is, the closer the properties of the system are to the experimental values. Therefore, how can we carry out simulations for systems that are fast, accurate, and large enough? A method of dividing the periodic box into subdivisions with their surroundings (DBSS) is presented here, and it clearly increases the computation speed without losing accuracy and enables the simulation of extremely large systems by strongly decreasing the dimension of the charge matrix. The DBSS method divides a single periodic box or unit in an extremely large system into several subdivisions with a suitable choice according to atomic coordinates. This method ensures that these subdivisions are always changing and allows the atoms to communicate with each other. Intermolecular communication is important for molecular properties and functions but is not possible with other fragment methods. The partial charges are calculated in each subdivision with an overlapping surrounding used to take hydrogen bond interaction between the subdivisions into account. This is an iterative process because the charge population will be recalculated at intervals during the dynamics simulations. Taking a water system as an example, each subdivision is extended by 4 Å as the surrounding. The computation time scales almost linearly with the size of the system, and the slope is small. MD simulations for several properties have been performed by the ABEEM-DBSS method. The results indicate that the ABEEM-DBSS method can accurately simulate the properties of water system, and the accuracy can reach or approach that of the experimental data or of other water potentials. Interestingly, the properties become closer to the experimental data as the sizes of the periodic box increase, further validating the need for the simulation of a large system and demonstrating the value of the DBSS method.

We present a high-performance, GPU (graphics processing unit)-accelerated algorithm for building the Fock matrix. The algorithm is designed for efficient calculations on large molecular systems and uses a novel dynamic load balancing scheme that maximizes the GPU throughput and avoids thread divergence that could occur due to integral screening. Additionally, the code adopts a novel ERI digestion algorithm that exploits all forms of permutational symmetry, combines efficiently the evaluation of both Coulomb and exchange terms together, and eliminates explicit thread synchronization requirements. Performance results obtained using a number of large molecules reveal remarkable speedups up to 24.4× with respect to the QUICK GPU code and up to 237× with respect to the GAMESS CPU parallel code.

Connections between the Görling-Levy (GL) perturbation theory and the parameters of double-hybrid (DH) density functional are established via adiabatic connection formalism. Moreover, we present a more general DH density functional theory, where the higher-order perturbation terms beyond the second-order GL2 one, such as GL3 and GL4, also contribute. It is shown that a class of DH functionals including previously proposed ones can be formed using the present construction. Based on the proposed formalism, we assess the performance of higher-order DH and long-range corrected DH formed on the Perdew-Burke-Ernzerhof (PBE) semilocal functional and second-order GL2 correlation energy. The underlying construction of DH functionals based on the generalized many-body perturbation approaches is physically appealing in terms of the development of the non-local forms using more accurate and sophisticated semilocal functionals.

Interpreting response properties such as the polarizability, optical rotation (OR), or hyperpolarizabilities is a complex task for which a uniform strategy would be desirable. We propose a response analysis procedure called the RespA approach with two interrelated schemes to describe molecular optical response properties in terms of natural response orbitals (NROs) and chemical fragment response for convenient elucidation of structure-(optical)property relationships. These quantities can be easily extracted from the frequency-dependent perturbed one-electron transition/current density matrix obtained from any quantum mechanical response function calculation. NROs provide the most compact representation of the virtual excitations occurring in the (hyper)scattering process. It is decomposed in hole and electron NRO pairs providing a simple exciton picture. For a chemist, it is natural to interpret a property by decomposing it into functional groups or fragment contributions. In this spirit, the response is partitioned into on-site and between-site fragment responses, allowing a property mapping into real space. The new RespA procedure was implemented and tested at the simplified time-dependent density functional theory (sTD-DFT) level enabling calculations for large systems. The RespA strategy is a method-independent route for the understanding of a wide variety of response properties. We showcase how the chemically intuitive RespA approach extracts easily structure-property relationships for the particularly difficult case of OR. As examples, we demonstrate how to enhance the OR of [5]helicene and norbornenone, provide an analysis of the change of the OR observed for camphor and fenchone, and finally investigate the case of a (P,P)-bis-helicenic 2,2'-bipyridine chiroptical switch.

We examine the effect of equilibration methodology and sampling on ab initio molecular dynamics (AIMD) simulations of systems of common solvents and salts found in lithium-oxygen batteries. We compare two equilibration methods: (1) using an AIMD temperature ramp and (2) using a classical MD simulation followed by a short AIMD simulation both at the target simulation temperature of 300 K. We also compare two different classical all-atom force fields: PCFF+ and OPLS. By comparing the simulated association/dissociation behavior of lithium salts in different solvents with the experimental behavior, we find that equilibration with the classical force field that produces more physically accurate behavior in the classical MD simulations, namely, OPLS, also results in more physically accurate behavior in the AIMD runs compared to equilibration with PCFF+ or with the AIMD temperature ramp. Equilibration with OPLS outperforms even the pure AIMD equilibration because the classical MD equilibration is much longer than the AIMD equilibration (nanosecond vs picosecond timescales). These longer classical simulations allow the systems to find a more physically accurate initial configuration, and in the short simulation times available for the AIMD production runs, the initial configuration has a large impact on the system behavior. We also demonstrate the importance of averaging coordination number over multiple starting configurations and Li+ ions, as the majority of Li+ ions do not undergo a single association or dissociation event even in an ∼40 ps long simulation and thus do not sample a statistically significant portion of the phase space. These results show the importance of both equilibration method and sufficient independent sampling for extracting experimentally relevant quantities from AIMD simulations.

Several methods of constructing the active orbital space for multiconfigurational wave functions are compared on typical moderately strongly or strongly correlated ground-state molecules. The relative merits of these methods and problems inherent in multiconfigurational calculations are discussed. Strong correlation in the ground electronic state is found typically in larger conjugated and antiaromatic systems, transition states which involve bond breaking or formation, and transition metal complexes. Our examples include polyenes, polyacenes, the reactant, product and transition state of the Bergman cyclization, and two transition metal complexes: Hieber’s anion [(CO)(3)FeNO](−) and ferrocene. For the systems investigated, the simplest and oldest selection method, based on the fractional occupancy of unrestricted Hartree–Fock natural orbitals (the UNO criterion), yields the same active space as much more expensive approximate full CI methods. A disadvantage of this method used to be the difficulty of finding broken spin symmetry UHF solutions. However, our analytical method, accurate to fourth order in the orbital rotation angles (Tóth and Pulay J. Chem. Phys. 2016, 145, 164102.), has solved this problem. Two further advantages of the UNO criterion are that, unlike most other methods, it measures not only the energetic proximity to the Fermi level but also the magnitude of the exchange interaction with strongly occupied orbitals and therefore allows the estimation of the correlation strength for orbital selection in Restricted Active Space methods.

To study the elastic properties of rodlike DNA nanostructures, we perform long simulations of these structures using the oxDNA coarse-grained model. By analyzing the fluctuations in these trajectories, we obtain estimates of the bend and twist persistence lengths and the underlying bend and twist elastic moduli and couplings between them. Only on length scales beyond those associated with the spacings between the interhelix crossovers do the bending fluctuations behave like those of a wormlike chain. The obtained bending persistence lengths are much larger than that for double-stranded DNA and increase nonlinearly with the number of helices, whereas the twist moduli increase approximately linearly. To within the numerical error in our data, the twist-bend coupling constants are of order zero. That the bending persistence lengths that we obtain are generally somewhat higher than in experiment probably reflects both that the simulated origamis have no assembly defects and that the oxDNA extensional modulus for double-stranded DNA is too large.

Inelastic neutron scattering (INS) is a widely used technique to study atomic and molecular vibrations. With the increasing complexity of materials and thus the INS spectra, being able to simulate the spectra from various atomistic models becomes an essential step and also a major bottleneck for INS data analysis. The conventional approach using density functional theory and lattice dynamics often falls short when the materials of interest are complex (e.g., defective, disordered, heterogeneous, amorphous, large-scale), for which molecular dynamics driven by an interatomic force field is a more common approach. In this paper, we demonstrate a method to directly convert molecular dynamics trajectories into simulated INS spectra, including not only fundamental but also higher order excitations. The results are compared with data collected on various representative samples from different neutron spectrometers. This development will open great opportunities by providing the key tool to perform in-depth analysis of INS data and to validate and optimize computer models.

Imatinib is a drug for the treatment of Chronic Myeloid Leukemia (CML) and other cancers that works by blocking the catalytic site of pathological constitutively active Abl kinase. While the binding pose is known from X-ray crystallography, the different steps leading to the formation of the complex are not well understood. The results from extensive molecular dynamics simulations show that imatinib can primarily exit the known crystallographic binding pose through the cleft of the binding site or by sliding under the αC helix. Once displaced from the crystallographic binding pose, imatinib becomes trapped in intermediate states. These intermediates are characterized by a high diversity of ligand orientations and conformations, and relaxation timescales within this region may exceed 3–4 ms. Analysis indicates that the metastable intermediate states should be spectroscopically indistinguishable from the crystallographic binding pose, in agreement with tryptophan stopped-flow fluorescence experiments.

We present a new method for calculating internal forces in DNA structures using coarse-grained models and demonstrate its utility with the oxDNA model. The instantaneous forces on individual nucleotides are explored and related to model potentials, and using our framework, internal forces are calculated for two simple DNA systems and for a recently-published nanoscopic force clamp. Our results highlight some pitfalls associated with conventional methods for estimating internal forces, which are based on elastic polymer models, and emphasise the importance of carefully considering secondary structure and ionic conditions when modelling the elastic behaviour of single-stranded DNA. Beyond its relevance to the DNA nanotechnological community, we expect our approach to be broadly applicable to calculations of internal force in a variety of structures -- from DNA to protein -- and across other coarse-grained simulation models., 39 pages, 9 figures

Currently, two different methods dominate the field of biomolecular free-energy calculations for the prediction of binding affinities. Pathway methods are frequently used for large ligands that bind on the surface of a host, such as protein–protein complexes. Alchemical methods, on the other hand, are preferably applied for small ligands that bind to deeply buried binding sites. The latter methods are also widely known to be heavily artifacted by the representation of electrostatic energies in periodic simulation boxes, in particular, when net-charge changes are involved. Different methods have been described to deal with these artifacts, including postsimulation correction schemes and instantaneous correction schemes (e.g., co-alchemical perturbation of ions). Here, we use very simple test systems to show that instantaneous correction schemes with no change in the system net charge lower the artifacts but do not eliminate them. Furthermore, we show that free energies from pathway methods suffer from the same artifacts.

Because of their anisotropic electron distribution and electron deficiency, halonium ions are unusually strong halogen-bond donors that form strong and directional three-center, four-electron halogen bonds. These halogen bonds have received considerable attention owing to their applicability in supramolecular and synthetic chemistry and have been intensely studied using spectroscopic and crystallographic techniques over the past decade. Their computational treatment faces different challenges to those of conventional weak and neutral halogen bonds. Literature studies have used a variety of wave functions and DFT functionals for prediction of their geometries and NMR chemical shifts, however, without any systematic evaluation of the accuracy of these methods being available. In order to provide guidance for future studies, we present the assessment of the accuracy of 12 common DFT functionals along with the Hartree–Fock (HF) and the second-order Møller–Plesset perturbation theory (MP2) methods, selected from an initial set of 36 prescreened functionals, for the prediction of 1H, 13C, and 15N NMR chemical shifts of [N–X–N]+ halogen-bond complexes, where X = F, Cl, Br, and I. Using a benchmark set of 14 complexes, providing 170 high-quality experimental chemical shifts, we show that the choice of the DFT functional is more important than that of the basis set. The M06 functional in combination with the aug-cc-pVTZ basis set is demonstrated to provide the overall most accurate NMR chemical shifts, whereas LC-ωPBE, ωB97X-D, LC-TPSS, CAM-B3LYP, and B3LYP to show acceptable performance. Our results are expected to provide a guideline to facilitate future developments and applications of the [N–X–N]+ halogen bond.