Journal of Chemical Theory and Computation最新文献

Describing the puzzling phenomenon of long-range communication between distant protein sites, allostery is of paramount importance in biomolecular regulation and signal transduction. It is commonly assumed to arise from a conformational rearrangement of the protein, although the underlying dynamical process has remained largely elusive. This study introduces a dynamical model of allosteric communication based on "contact clusters"─localized groups of highly correlated contacts that facilitate interactions between secondary structures. The model shows that allostery involves a multistep process with cooperative contact changes within clusters and communication between distant clusters mediated by rigid secondary structures. Considering time-dependent experiments on a photoswitchable PDZ3 domain, extensive (in total ∼500 μs) molecular dynamics simulations are conducted that directly monitor the photoinduced allosteric transition. The structural reorganization is illustrated by the time evolution of the contact clusters and the ligand, which effects the nonlocal coupling between distant clusters. A time scale analysis reveals dynamics from nano- to microseconds, which are in excellent agreement with the experimentally measured time scales. While the simulation of larger systems may require enhanced sampling techniques, it is expected that the general picture of allostery mediated by communicating contact clusters will still be applicable.

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSD_UB) as rigid (RMSD_UB < 1.2 Å), moderately flexible (RMSD_UB ∈ [1.2, 2.2] Å), and flexible targets (RMSD_UB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

We develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces by using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed length bonds, with configurations updated through adaptive nonlocal Monte Carlo moves. This proposed method enables precise calculation of a polymer's response to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing the polymer conformation.

Heparin is a natural highly sulfated unbranched periodic polysaccharide that plays a critical role in regulating various cellular events through interactions with its protein targets such as growth factors and cytokines. Although all-atom simulations of heparin-containing systems provide valuable insights into their structural and dynamical properties, long chains of heparin participate in many biologically relevant processes at much bigger scales and longer times than the ones which all-atom MD is able to effectively deal with. Among these processes is the establishment of chemokine gradients, amyloidogenesis, or collagen network organization. To address this limitation, coarse-grained models simplify these systems by reducing the number of degrees of freedom, allowing for the efficient exploration of structural changes within protein/heparin complexes. We introduce and validate the accuracy of a new coarse-grained physics-based model designed for studying protein/heparin interactions, which has been incorporated into the UNRES software package. The effective energy functions from UNRES and SUGRES-1P have been employed for the protein and heparin components, respectively. A good agreement between the obtained coarse-grained simulation results and experimental data confirms the suitability of the combined coarse-grained UNRES and SUGRES-1P model for in silico analysis of complex biological phenomena involving heparin, spanning time scales and molecular system sizes not attainable by conventional atomistic molecular dynamics simulations.

Constraining molecules in simulations (such as with constant bond lengths and/or angles) reduces their degrees of freedom (DoF), which in turn affects temperature calculations in those simulations. When local temperatures are measured, e.g., from a set of atoms in a subvolume or from velocities in one Cartesian direction, the result can appear to unphysically violate equipartition of the kinetic energy if the local DoF are not correctly calculated. Here, we determine how to correctly calculate local temperatures from arbitrary Cartesian component kinetic energies, accounting for general geometric constraints, by self-consistently evaluating the DoF of atoms subjected to those constraints. The method is validated on a variety of test systems, including systems subject to a temperature gradient and those confined between walls. It is also shown to provide a sensitive test for the breakdown of kinetic energy equipartition caused by the approximate nature of numerical integration or insufficient equilibration times. As a practical demonstration, we show that kinetic energy equipartition between C and H atoms connected by rigid bonds can be violated even at the commonly used time step of 2 fs and that this equipartition violation appears to usefully indicate configurational overheating.

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time step integration. These NNPs serve to explore a puzzling solute-solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in agreement with experiment. These barriers are associated with an ensemble of transition states involving the direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example underscores the critical role that dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

In cells, adenosine triphosphate (ATP) and guanosine triphosphate (GTP) molecules typically form tricoordinated or bicoordinated ATP·Mg²⁺ or GTP·Mg²⁺ complexes with Mg²⁺ ions and bind to proteins, participating in and regulating many important cellular functions. The accuracy of their force field parameters plays a crucial role in studying the function-related conformations of ATP·Mg²⁺ or GTP·Mg²⁺ using molecular dynamics (MD) simulations. The parameters developed based on the methyl triphosphate model in existing AMBER force fields cannot accurately describe the conformational distribution of tricoordinated or bicoordinated ATP·Mg²⁺ or GTP·Mg²⁺ complexes in solution. In this study, we develop force field parameters for the triphosphate group based on the new ribosyl triphosphate model, considering the dihedral coupling effect, accurate van der Waals (vdW) interactions, and the influence of strongly polarized charges on conformational balance. The new force fields can accurately describe the conformational balance of tricoordinated and bicoordinated ATP·Mg²⁺ or GTP·Mg²⁺ conformations in solution and can be applied to simulate biological systems containing ATP·Mg²⁺ or GTP·Mg²⁺ complexes.

Nonadiabatic molecular dynamics (NAMD) has become an essential computational technique for studying the photophysical relaxation of molecular systems after light absorption. These phenomena require approximations that go beyond the Born-Oppenheimer approximation, and the accuracy of the results heavily depends on the electronic structure theory employed. Sophisticated electronic methods, however, make these techniques computationally expensive, even for medium size systems. Consequently, simulations are often performed on simplified models to interpret the experimental results. In this context, a variety of techniques have been developed to perform NAMD using approximate methods, particularly density functional tight binding (DFTB). Despite the use of these techniques on large systems, where ab initio methods are computationally prohibitive, a comprehensive validation has been lacking. In this work, we present a new implementation of trajectory surface hopping combined with DFTB, utilizing nonadiabatic coupling vectors. We selected the methaniminium cation and furan systems for validation, providing an exhaustive comparison with the higher-level electronic structure methods. As a case study, we simulated a system from the class of molecular motors, which has been extensively studied experimentally but remains challenging to simulate with ab initio methods due to its inherent complexity. Our approach effectively captures the key photophysical mechanism of dihedral rotation after the absorption of light. Additionally, we successfully reproduced the transition from the bright to dark states observed in the time-dependent fluorescence experiments, providing valuable insights into this critical part of the photophysical behavior in molecular motors.

In this work, we derive working equations for the linear response pair coupled cluster doubles (LR-pCCD) ansatz and its extension to singles (S), LR-pCCD+S. These methods allow us to compute electronic excitation energies and transition dipole moments based on a pCCD reference function. We benchmark the LR-pCCD+S model against the linear response coupled-cluster singles and doubles method for modeling electronic spectra (excitation energies and transition dipole moments) of the BH, H₂O, H₂CO, and furan molecules. We also analyze the effect of orbital optimization within pCCD on the resulting LR-pCCD+S transition dipole moments and oscillator strengths and perform a statistical error analysis. We show that the LR-pCCD+S method can correctly reproduce the transition dipole moments features, thus representing a reliable and cost-effective alternative to standard, more expensive electronic structure methods for modeling electronic spectra of simple molecules. Specifically, the proposed models require only mean-field-like computational cost, while excited-state properties may approach the CCSD level of accuracy. Moreover, we demonstrate the capability of our model to simulate electronic transitions with non-negligible contributions of double excitations and the electronic spectra of polyenes of various chain lengths, for which standard electronic structure methods perform purely.

Comprehending water dynamics is crucial in various fields, such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond time scales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamic properties of liquid water with ab initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, methods on the higher level of Jacob's ladder of DFT approximation perform significantly better. Intriguingly, we discovered that both D and η adhere to the established Stokes-Einstein (SE) relation for all of the ab initio water. The diversity observed across different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate the dynamic properties of liquid water. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path to designing better functionals for water but also underscore the significance of water's many-body characteristics.