The Journal of Physical Chemistry B最新文献

The striking efficiency of exciton transfer in light-harvesting (LH) complexes has remained a topic of debate since the revision of the long-held role of electronic coherences. To address this issue, we have developed a neural network for the pigments in the LH2 complex of Rhodospirillum molischianum that allows nonadiabatic molecular dynamic (NAMD) simulations of exciton transfer in a coupled quantum mechanical/molecular mechanics (QM/MM) embedding. The calculated exciton occupations are averaged over hundreds of trajectories, each lasting several picoseconds. We have obtained transitions within the B800 and B850 rings that agree well with the experimental results, indicating an incoherent hopping process in the B800 ring and a more delocalized transfer in the B850 subsystem. The reorganization energies and excitonic couplings are comparable to each other, indicating that the "transient delocalization" transport model is the underlying cause of the highly efficient exciton transport in the B850 ring. This phenomenon can be attributed to a localized exciton that shows occasional large delocalization events. Our results indicate that the reason for the striking efficiency is the unusual electronic property of bacteriochlorophyll, manifested in minimal inner and outer sphere reorganization energies.

Allosteric signaling in proteins allows perturbations at one locale to modulate activity at an orthosteric distant site. This may explain how distal mutations disrupt protein activity and offer pathways for the development of allosteric therapeutics, a novel class of restorative compounds to reactivate native function. Despite the ubiquitous presence of allosteric control in nature and the promises that it holds for treating currently untreatable diseases, quantitative theory of the mechanism of allostery is lacking. Working to fill this critical gap, we have developed a novel method to identify groups of covarying residues which the sector hypothesis suggests are capable of transmitting allosteric signals in proteins. A major problem with sectors computed from covariance measures is the selection relies upon a full covariance matrix rather than on the covariance among the residues posited to be in the sector. We demonstrate a novel method which constructs sectors on the basis of cohesion within the residues in the sector to eliminate the incongruity between the sector idea and the way it is calculated. Furthermore, the refinement can be iteratively applied, enabling the extraction of more than one sector in a well-defined, systematic manner. In this study, we report on the development of MD multi-sector selector and its application to allosteric signaling in the tumor suppressor protein p53. We consider the implications of our findings on our long-term goal of allosterically reactivating mutant p53 as a means of curing cancer, and critically assess the broader applicability of MD multi-sector selector across diverse fields.

In our study, we combined classical molecular dynamics (MD) simulations with the simulated annealing (SA) method to explore the conformational landscape of water molecules. By using the K-means clustering method, we processed the MD simulation data to extract representative samples of water molecular structures used to train a deep potential (DP) model. Our DeePMD method showed accuracy in predicting water structural properties compared to DFT-MD results. Meanwhile, this approach achieves a balanced prediction of water density and self-diffusion coefficients compared with earlier DeePMD simulations. These results highlight the essential role of representative sampling techniques in training the DP model. Furthermore, we demonstrated the effectiveness of combining the DeePMD simulation with the centroid molecular dynamics (CMD) approach, which incorporates nuclear quantum effects (NQEs). This approach successfully reproduced the shoulder feature at 3250 cm⁻¹ in the Raman spectra for the O−H stretch. Incorporating the path integral method into the DeePMD simulations underscores the importance of considering NQEs in understanding water molecules’ structural and dynamic behaviors.

A detailed analysis of the structure and speciation of La³⁺ clusters in the 0.1 mol L^–1 La(NO₃)₃ salt methanol (MeOH) solution has been performed by means of molecular dynamics (MD) simulations. The time distribution and NO₃^–/MeOH ligand composition of these clusters have been computed using graph theory techniques. These analyses revealed the formation of branched-like polynuclear clusters in the solution, the predominant clusters being the 3, 7, and 8 La³⁺ clusters. In these clusters, the La³⁺ cations are bound by “monodentate” nitrate bridges. Moreover, the mechanism of aggregation of the La³⁺ clusters has been examined with the development of a 3-step model. Finally, the origin of the aggregation process has been identified by estimating the binding constant for the ion pair La³⁺-NO₃^– using the Bjerrum theory of dilute solutions, with pK° = 5.32 at 25 °C. The low value of the dielectric constant of methanol promotes the binding of the ion pair La³⁺-NO₃^– and the nitrato-bridging polymerization, resulting in the formation of clusters.

Allosteric signaling in proteins allows perturbations at one locale to modulate activity at an orthosteric distant site. This may explain how distal mutations disrupt protein activity and offer pathways for the development of allosteric therapeutics, a novel class of restorative compounds to reactivate native function. Despite the ubiquitous presence of allosteric control in nature and the promises that it holds for treating currently untreatable diseases, quantitative theory of the mechanism of allostery is lacking. Working to fill this critical gap, we have developed a novel method to identify groups of covarying residues which the sector hypothesis suggests are capable of transmitting allosteric signals in proteins. A major problem with sectors computed from covariance measures is the selection relies upon a full covariance matrix rather than on the covariance among the residues posited to be in the sector. We demonstrate a novel method which constructs sectors on the basis of cohesion within the residues in the sector to eliminate the incongruity between the sector idea and the way it is calculated. Furthermore, the refinement can be iteratively applied, enabling the extraction of more than one sector in a well-defined, systematic manner. In this study, we report on the development of MD multi-sector selector and its application to allosteric signaling in the tumor suppressor protein p53. We consider the implications of our findings on our long-term goal of allosterically reactivating mutant p53 as a means of curing cancer, and critically assess the broader applicability of MD multi-sector selector across diverse fields.

In our study, we combined classical molecular dynamics (MD) simulations with the simulated annealing (SA) method to explore the conformational landscape of water molecules. By using the K-means clustering method, we processed the MD simulation data to extract representative samples of water molecular structures used to train a deep potential (DP) model. Our DeePMD method showed accuracy in predicting water structural properties compared to DFT-MD results. Meanwhile, this approach achieves a balanced prediction of water density and self-diffusion coefficients compared with earlier DeePMD simulations. These results highlight the essential role of representative sampling techniques in training the DP model. Furthermore, we demonstrated the effectiveness of combining the DeePMD simulation with the centroid molecular dynamics (CMD) approach, which incorporates nuclear quantum effects (NQEs). This approach successfully reproduced the shoulder feature at 3250 cm^-1 in the Raman spectra for the O-H stretch. Incorporating the path integral method into the DeePMD simulations underscores the importance of considering NQEs in understanding water molecules' structural and dynamic behaviors.

High-concentration aqueous electrolytes (hydrate-melts) have attracted significant attention for lithium-ion batteries due to their nonflammability and low toxicity. In these electrolytes, the static and dynamic structures of the solvent play a crucial role in determining various properties, such as the ionic conductivity, of the system. To clarify the solvent structure and ion diffusion mechanism, we conducted molecular dynamics simulations using a machine learning potential for Li and Na hydrate-melts. By analyzing the dynamical interaction between ions and their coordinating molecules, we found the ligand exchange of H₂O molecules coordinated to cations occurs frequently. As a result, it is considered that the kinetic energy of H₂O is transferred to cations and drives the diffusion of cations in the hydrate-melts. This ion transport mechanism is different from the conventionally understood vehicle-type or hopping-type ion transport mechanism. The comparison of Na hydrate-melts and Li hydrate-melts shows the higher diffusion of Na relative to Li. It was suggested that there exists an optimal value for the strength of interaction between cations and H₂O molecules, which influences ion diffusion, and that the interaction for Na is close to this optimal value compared to that of the Li.

High-concentration aqueous electrolytes (hydrate-melts) have attracted significant attention for lithium-ion batteries due to their nonflammability and low toxicity. In these electrolytes, the static and dynamic structures of the solvent play a crucial role in determining various properties, such as the ionic conductivity, of the system. To clarify the solvent structure and ion diffusion mechanism, we conducted molecular dynamics simulations using a machine learning potential for Li and Na hydrate-melts. By analyzing the dynamical interaction between ions and their coordinating molecules, we found the ligand exchange of H₂O molecules coordinated to cations occurs frequently. As a result, it is considered that the kinetic energy of H₂O is transferred to cations and drives the diffusion of cations in the hydrate-melts. This ion transport mechanism is different from the conventionally understood vehicle-type or hopping-type ion transport mechanism. The comparison of Na hydrate-melts and Li hydrate-melts shows the higher diffusion of Na relative to Li. It was suggested that there exists an optimal value for the strength of interaction between cations and H₂O molecules, which influences ion diffusion, and that the interaction for Na is close to this optimal value compared to that of the Li.

A detailed analysis of the structure and speciation of La³⁺ clusters in the 0.1 mol L^-1 La(NO₃)₃ salt methanol (MeOH) solution has been performed by means of molecular dynamics (MD) simulations. The time distribution and NO₃^-/MeOH ligand composition of these clusters have been computed using graph theory techniques. These analyses revealed the formation of branched-like polynuclear clusters in the solution, the predominant clusters being the 3, 7, and 8 La³⁺ clusters. In these clusters, the La³⁺ cations are bound by "monodentate" nitrate bridges. Moreover, the mechanism of aggregation of the La³⁺ clusters has been examined with the development of a 3-step model. Finally, the origin of the aggregation process has been identified by estimating the binding constant for the ion pair La³⁺-NO₃^- using the Bjerrum theory of dilute solutions, with pK° = 5.32 at 25 °C. The low value of the dielectric constant of methanol promotes the binding of the ion pair La³⁺-NO₃^- and the nitrato-bridging polymerization, resulting in the formation of clusters.

The combined use of residual chemical shift anisotropy (RCSA) and residual dipolar coupling/residual dipole coupling (RDC) could provide highly complementary information about the structure and relative configuration of unknown organic molecules for their elucidation. However, tandem RCSA and RDC measurements in one sample remain a formidable challenge due to their varied testing requirements. Herein, a stimuli-responsive supramolecular liquid crystal self-assembled from an amphiphilic oligopeptide of C₁₉H₃₉-CONH-VVVVKKK-CONH₂ was constructed, which underwent a phasic transformation from anisotropy to isotropy when subjected to a thermal treatment. Both the anisotropic and isotropic phases exhibited good stability, facilitating tandem measurements of ¹³C-{¹H}-RCSA and (¹³C-¹H)-RDC in one sample with no need for special instruments or correction procedures. We expect that the joint use of RCSAs and RDCs will significantly improve data accuracy and utility for structural and configurational determination of small organic molecules and even biomacromolecules.