Journal of Chemical Theory and Computation最新文献

The mechanistic origin of CO₂ capture in aqueous ammonia has long remained debated, with competing proposals invoking concerted versus stepwise pathways, ambiguous catalytic roles of ammonia, and uncertain product distributions. Here, we introduce an active learning, data-driven framework (ADRML) that integrates reactive molecular dynamics (RMD) with dimensionality-reduced sampling. RMD simulations employing the trained machine-learned interatomic potentials (MLIPs) on cluster models with periodic boundary conditions reveal that the [CO₂]/[NH₃] concentration ratio (R_[C]/[A]) is a critical determinant of product distributions, kinetics, and underlying mechanisms. At high R_[C]/[A], carbonic species are favored via water-mediated hydration, whereas low R_[C]/[A] markedly promotes carbamate formation through a concerted ammonia–ammonia pair mechanism that becomes accessible only under ammonia-rich (low R_[C]/[A]) conditions. This disparity underscores a distinct concentration-dependent mechanistic shift in carbamate formation─from a stepwise to a concerted pathway. In addition, the hydronium ion (H₃O⁺) generated in the carbonate-formation channel ultimately suppresses further reactivity by promoting the reverse process of carbonate hydrolysis and by depleting NH₃ through protonation. Overall, at low R_[C]/[A] ≈ 0.2, carbamate production surpasses carbonate formation in both rates and yields, occurring before significant H₃O⁺ accumulation from the carbonate channel, thereby maximizing CO₂ uptake. Both carbonate and carbamate formation reactions compete across the entire range of R_[C]/[A]. However, the dramatic enhancement of carbamate formation at low R_[C]/[A] is likely the primary source of long-standing ambiguities in these systems.

Despite considerable advances in computational chemistry, bridging the gap between the accuracy of all-atom molecular dynamics (AA-MD) and the high-throughput capabilities of docking remains an unsolved problem in protein–ligand binding free energy predictions. In this work, we propose to address this challenge through coarse-grained funnel metadynamics (CG-FMD) with the Martini 3 force field. This approach combines the reduced computational cost of a CG representation with state-of-the-art enhanced sampling techniques and the interpretability of a physics-based force field. Specifically, the binding of colchicine to two different protein targets was modeled at both AA and CG resolutions, and the corresponding ΔG_bind predictions were compared with experimental references. Additionally, the robustness of CG-FMD-based ΔG_bind predictions was evaluated with respect to various aspects of the simulation setup by collecting more than 7 ms of CG-FMD simulations. The optimal simulation protocol has been further validated against a limited set of compounds chemically different from colchicine. The results demonstrate that CG-FMD yields ΔG_bind estimates comparable to experimental values while requiring only a fraction of the computational cost of AA-MD simulations. Moreover, the extensive sampling achievable with CG-FMD reduces statistical uncertainty in the final predictions, effectively compensating for the simplified system representation. Future work should build upon these methodological insights to broaden the scope of ligands and targets explored.

Both RNA- and protein-based strategies have been developed to mitigate off-target cleavage by CRISPR–Cas9, yielding noncanonical guide RNAs (gRNAs) and Cas9 variants with enhanced gene-editing precision. However, the molecular mechanisms by which such PAM-distal alterations─remote from the nuclease centers─modulate Cas9 activity and specificity remain incompletely understood. Here, we performed near-millisecond all-atom molecular dynamics simulations to elucidate how diverse PAM-distal perturbations─including gRNA truncation, base mismatching, and evolved mutations─reshape the conformational dynamics and allosteric regulation of Cas9. Despite their distinct origins, all perturbations ultimately modulate Cas9 function by altering HNH dynamics that impede the transition from the checkpoint to the catalytically active state, yet they do so through distinct allosteric routes. The 16-nt gRNA induces a pronounced REC3 reorientation toward the L2 linker and HNH domain, while PAM-distal mismatches with the 18-nt gRNA promote engagement of the unwound target DNA strand with L2─both effectively restraining HNH rotation. In contrast, evolved mutations remodel the global motional modes so that REC2 swivels inward, constraining the HNH flexibility. These perturbations delineate multiple structural paths converging on a shared allosteric outcome─HNH immobilization and catalytic suppression─thereby unifying RNA-, DNA-, and protein-level effects within a single dynamic framework linking distal structural perturbations to activity control. This work provides mechanistic insight into the regulation of Cas9 fidelity and offers principles for the design of next-generation genome editors.

Accelerating conformational sampling through changes in molecular mass is an attractive option in biomolecular modeling. Here, we examine the utility and compare the efficiency of hydrogen mass repartitioning (HMR) and light water (LW) models in the context of replica exchange (RE) simulations of an alanine dipeptide. To maintain integrator stability, we introduced scaling of integration steps with RE temperatures and determined their maximum values, assuring the stability of RE simulations. HMR2 and HMR3 models featuring doubled and tripled hydrogen masses and, to a lesser extent, the LW model reproduce the energetic and conformational properties of alanine dipeptide in water compared to the HMR1 reference. This conclusion is based on comparing kinetic and potential energies, free energy landscapes of the peptide, as well as its structural properties, including hydrogen bonding, water counts in the peptide first solvation shell, and RMSD distributions. Thereby, our results demonstrate that both HMR and LW models can be integrated into RE simulations. We then compared HMR and LW models with respect to the computational efforts required to equilibrate alanine dipeptide. HMR2 and HMR3 are up to 4-fold more computationally efficient than the HMR1 reference, whereas LW lags behind being less than a factor of 2 more efficient. As a result, LW efficiency is 2-fold lower than that of HMR3. This outcome means that increasing the integration step provides faster sampling than boosting water diffusion. Even if the computation of long-range interactions is adjusted with the length of the integration step and the step in LW simulations is further increased, the model remains less efficient than HMR3. We considered a hybrid variant of LW, hLW, featuring heavier water and mass repartitioning applied to all hydrogens, affording longer integration steps than LW does. hLW improves computational efficiency and provides more accurate reproduction of energetic and conformational properties of alanine dipeptide than LW. We concluded that HMR3 and hLW models demonstrate good performance in replica exchange simulation, but the former is preferable due to broader applicability and simplicity. hLW remains an excellent alternative to HMR3, but its scope is limited to “water-rich” systems. More generally, our findings suggest that among the two approaches, HMR or decreasing water mass, the former is more effective. Since LW simulations are not currently supported out-of-the-box by the NAMD molecular dynamics program, we implemented a patch enabling LW functionality.

We introduce a size-consistent and orbital-invariant formalism for constructing correlation functionals based on the adiabatic connection for density functional theory (DFT). By constructing correlation energy matrices for the weak and strong correlation limits in the space of occupied orbitals, our method, which we call orbital-based size-consistent matrix interpolation (OSMI), avoids previous difficulties in the construction of size-consistent adiabatic connection functionals. We design a simple, nonempirical adiabatic connection and a one-parameter strong-interaction limit functional, and we show that the resulting method reproduces the correlation energy of the uniform electron gas over a wide range of densities. When applied to subsets of the GMTKN55 thermochemistry database, OSMI is more accurate on average than MP2 and nonempirical density functionals. Most notably, OSMI provides excellent predictions of the barrier heights we tested, with average errors of less than 2 kcal mol^–1. Finally, we find that OSMI improves the trade-off between fractional spin and fractional charge errors for bond dissociation curves compared to DFT and MP2. The fact that OSMI provides a good description of molecular systems and the uniform electron gas, while also maintaining low self-interaction error and size-consistency, suggests that it could provide a framework for studying heterogeneous chemical systems.

Semiempirical quantum chemistry (SQC) methods offer fast quantum chemical insights by constructing and solving a parametric effective minimal basis Fock matrix. Establishing suitable parametrizations has long been a challenging and time-consuming task involving tedious grid searches or costly finite-difference gradients of carefully crafted loss functions based on select experimental data. The growing availability of differentiable programming environments that leverage algorithmic differentiation to obtain complicated derivatives together with access to a wealth of reliable reference data from ab initio calculations offers a new and more efficient approach. In this work, we extend a previous, basic implementation of SQC methods in PyTorch [Zhou, G. J. Chem. Theory Comput. 2020, 16, 4951–4962] by including global algorithmic considerations in the code design. This allows for improved general applicability and establishes a robust back-end for rapid SQC parametrizations. In particular, we address the general differentiability of the eigensolver and the iterative SCF procedure. The new implementation offers dramatic improvements in both computing cost and memory footprint, while simultaneously increasing numeric stability in gradient evaluation. We highlight the importance of these advances and their improvements over existing formulations and illustrate their role in the context of SQC parametrization.

Potassium channels exhibit high selectivity and conductance, yet the atomic details of ion permeation, particularly the involvement of water molecules, remain debated. Two main conduction mechanisms have been proposed: the hard knock-on, in which ions traverse the selectivity filter in direct contact, and the soft knock-on, which involves copermeation of water molecules. Using microsecond molecular dynamics simulations with the OPC water model, the AMBER19SB protein force field, and the 12–6–4 Sengupta et al. ion model, and an analysis strategy based on Markov State Models, we observed that both hard and soft knock-on mechanisms are accessible and, notably, can reversibly transition in the MthK and KcsA channels across all simulated membrane potentials. These reversible transitions contrast with previous observations using the TIP3P water model, where water entry either disrupted conduction or was expelled, favoring exclusive hard knock-on events. Our results suggest that the choice of the water model, force field, and ion parameters significantly influences the observed conduction mechanism. Importantly, the coexistence of hard and soft knock-on in these simulations provides a reconciliation between structural data supporting hard knock-on and streaming potential measurements demonstrating water copermeation. These findings reintroduce soft knock-on as a viable conduction mechanism and highlight the critical role of simulation parameters in reproducing potassium channel permeation behavior.

We present BindFlow, a Python-based software for automated absolute binding free energy (ABFE) calculations at the free energy perturbation (FEP) or at the molecular mechanics Poisson–Boltzmann/generalized Born surface area [MM(PB/GB)SA] level of theory. BindFlow is free, open-source, user-friendly, and easily customizable, runs on workstations or distributed computing platforms, and provides extensive documentation and tutorials. BindFlow uses GROMACS as a molecular dynamics engine and provides built-in support for the small-molecule force fields GAFF, OpenFF, and Espaloma, as well as support for user-provided custom force fields. We test BindFlow by computing affinities for 139 receptor–ligand pairs, involving eight different targets, including six soluble proteins, one membrane protein, and one nonprotein host–guest system. We find that the agreement of BindFlow predictions with experiments is overall similar to gold standards in the field. Interestingly, we find that MM(PB/GB)SA achieves correlations that, for some systems and force fields, approach those obtained with FEP while requiring only a fraction of the computational cost. This study establishes BindFlow as a validated and accessible tool for ABFE calculations.

Accurately modeling compounds with partially filled d and f shells remains a hard challenge for density-functional theory, due to large self-interaction errors stemming from local or semilocal exchange-correlation functionals. Hubbard U corrections can mitigate such errors, but are often detrimental to the description of hybridized states, leading to spurious force contributions and wrong lattice structures. Here, we show that careful disentanglement of localized and delocalized states leads to accurate predictions of electronic states and structural distortions in ternary monouranates (AUO₄, where A represents Mn, Co, or Ni), for which standard U corrections generally fail. Crucial to achieving such accuracy is a minimization of the mismatch between the spatial extension of the projector functions and the true coordination geometry. This requires Wannier-like alternatives to atomic-orbital projector functions, or corrections of Hubbard manifolds exclusively comprised of the most localized A-3d, U-5f and O-2p orbitals. These findings open up the computational prediction of fundamental properties of actinide solids of critical technological importance.

Membrane fission is a fundamental process underlying cellular trafficking, endocytosis, cytokinesis, and viral budding. The canonical fission pathway proceeds through two key steps: hemifission and rupture. Despite structural insights, the energetics of these intermediates remain experimentally elusive. Here, we establish a simulation framework to map the free-energy landscape of fission in cylindrical lipid bilayers using coarse-grained molecular dynamics simulations. By employing a collective variable (reaction coordinate), the potential of mean force is reconstructed to capture both the intact-to-hemifission and hemifission-to-rupture transitions. Our results reveal a complex influence of the tube radius on the fission energy barriers: while the hemifission barrier increases with tube radius due to enhanced membrane rigidity, the rupture barrier decreases as the curvature stress destabilizes the intermediate state. Lipid composition further modulates the pathway, with DOPE stabilizing the hemifission state more effectively than DOPC owing to its higher negative spontaneous curvature. Elevated membrane tension markedly lowers the hemifission barrier by lowering the inner tube radius and the lipid density. To demonstrate the broader applicability of our approach, we show that the influenza A M2 protein lowers the hemifission energy barrier, which is consistent with previous experimental observations. Together, these findings provide a mechanistic framework linking lipid mechanics, protein interactions, and external forces to the energetics of membrane fission.