The Journal of Physical Chemistry B最新文献

One of the key glycolytic enzymes, pyruvate kinase (PKM2), is frequently found in mutated forms in cancer cells. While many have investigated the impact of the mutations on tumor size and progressions, their structural effects on the architecture of PKM2 have not been thoroughly studied. We examined 11 mutants using MD simulations and assessed their effects on structural dynamics, domain flexibility, and interaction networks. Among them, six mutants displayed significant perturbations compared to the WT, while five others retained WT-like behavior. RMSF and PCA demonstrated that mutations lead to destabilization of the B domain by disrupting its natural inward closure toward the A domain. Instead, they exhibited an outward or rotational movement, resulting in increased interdomain distances and a weakening of the native contacts between the A and B domains. Further analysis revealed that in the crucial region responsible for domain closure, there was a disruption of hydrogen bonds and salt bridges that are essential for stabilization. For the highly fluctuating mutants, R246S weakens the helical contacts to the hinge region, while K367 M and R399E compromise β-sheet pathways linked to the active and allosteric sites, P117L affects the anchor points in the hinge region, and R455Q and H464A destabilize the allosteric pockets by disrupting the connectivity between the helix, β-sheet, and the hinge. Collectively, these mutations impair communication with the domain closure region and suggest potential avenues for understanding cancer-related mutants.

Gas hydrates are of great relevance to both the oil industry and the environment. Understanding how these solid structures nucleate from aqueous solutions is essential for controlling their formation. Experimental studies have often suggested that hydrate nucleation originates at the interface between the aqueous phase and the guest-molecule reservoir. To assess this hypothesis, we perform molecular dynamics simulations of CO₂ hydrate nucleation. First, we place hydrate seeds at different positions relative to the interface and monitor their evolution, finding that seeds embedded in the bulk are more likely to grow than those located near or at the interface. Second, we analyze spontaneous nucleation simulations with and without an interface. Our previous work showed that nucleation rates are indistinguishable in both systems, strongly indicating that the interface does not play a role. Here, trajectory analysis reveals that hydrates nucleate in regions of locally high CO₂ concentration, which arise spontaneously in the bulk and are not associated with the interface. Our results indicate that hydrate nucleation does not preferentially occur at the interface, at least under the deep supercooling conditions explored in this work. Further work at higher temperatures, and considering alternative nucleation locations, is needed to reconcile experiments and simulations, and thereby reach a deep understanding of the mechanism of hydrate formation.

The Mn₄CaO₅ cluster, the catalytic center of water oxidation in photosystem II (PSII), is assembled from free Mn²⁺ and Ca²⁺ ions through a light-driven process known as photoactivation. Despite its importance, the molecular mechanism of photoactivation remains poorly understood. Here, we investigate the mechanism underlying the initial oxidation of Mn²⁺ at its binding site in apo-PSII using time-resolved infrared spectroscopy combined with density functional theory (DFT) calculations. Two distinct kinetic phases with time constants of 100-150 μs and 1.5-2.5 ms are observed at pH 6.5-7.5. The minimal H/D isotope effect indicates that electron transfer from Mn²⁺ to Y_Z^•, rather than proton release, is the rate-limiting step. DFT calculations of the Mn³⁺/Mn²⁺ redox potential, together with ΔG° estimates derived from electron transfer rates using semiempirical equations, reveal that Mn²⁺ oxidation proceeds via a slow, endergonic electron transfer from Mn²⁺ to Y_Z^•, followed by rapid proton transfer from a water ligand to D1-H332, which provides the driving force for the reaction. The faster kinetic component is attributed to a decreased ΔG° resulting from detachment of CP43-R357 in the "open" CP43 conformation, suggesting that thermal fluctuations of the CP43 lumenal domain facilitate initial Mn²⁺ oxidation during photoactivation.

Ion clustering has been proposed as a mechanism leading to the peculiar "anomalous underscreening" phenomenon seen for electrostatic interactions between charged surfaces immersed in concentrated electrolytes. These interactions have been measured using the Surface Force Apparatus, according to which there are strong repulsive interactions between like-charged surfaces, with a range that increases upon further addition of salt, above some threshold concentration. A common suggestion is that ionic aggregates, if they form in sufficient numbers, will reduce the concentration of free ions and thereby increase the nominal Debye length. In previous work, we investigated a cluster model using classical Density Functional Theory (cDFT) and a polymer-like description of the ion clusters. These clusters were monodisperse and of either a linear or branched architecture, and a fixed charge sequence along the chains. In this work, we generalize the cDFT to treat "living polymers" with variable chain lengths and charge arrangements along the chain. This approach allows clusters to become polarized by the presence of charged surfaces, manifested by like-charged bonding. We find that even with a small degree of like-charged bonding a full equilibrium treatment of our model predicts only weak repulsion between like-charged surfaces. When a global constraint is applied so that the charged surfaces are neutralized only by the dissociated ions, while the clusters contribute overall zero charge, even a very small fraction of clustering ions generate strong and long-ranged forces. Moreover, if the cluster fraction increase substantially upon the addition of further salt, then the strength of the surface forces will also increase, although the range remains roughly constant.

Dibenz[a,j]acridine (DBA) is a probable human carcinogen classified by the International Agency for Research on Cancer. Its metabolic activation in humans should not be directly extrapolated from that of classical polycyclic aromatic compounds, due to the presence of heteroatoms. This study combined molecular docking, molecular dynamics (MD) simulations, and quantum mechanical (QM) calculations to elucidate the binding modes, interactions, and regioselectivity metabolic mechanisms of DBA within human cytochrome P450 1A1 (CYP1A1). The results show that the nitrogen atom exhibits greater electrophilic reactivity than the ring carbons, but the geometric constraints of the active site force DBA to adopt a side-on mode toward heme, endowing the C2 and C3 sites with more favorable spatial conditions for metabolism. QM calculations further indicate C3 as the dominant metabolic site, with a lower rate-determining step energy barrier (19.37 kcal·mol^-1), and a more stable epoxide product (-13.69 kcal·mol^-1). The metabolic regioselectivity of DBA is governed by a synergy between its intrinsic properties and spatial factors. Moreover, the metabolic differences between DBA and the analogous 7H-dibenzo[c,g]carbazole originate from the distinct nitrogen characteristics. This study elucidates the metabolic activation of DBA in human CYP1A1 and improves the theoretical framework for heterocyclic aromatic compounds metabolism.

The search kinetics of specific DNA targets by transcription factors (TF) in eukaryotic cells is a complex process modulated by the structure of the chromatin, topologically associated domains, chromatin compartments, and the intracellular environment. In this work, we study the search for the target within the chromatin compartments under stochastic resetting of the TF to the transcription condensates. We show that intersegmental jumps and resetting can have dual effects by enhancing and reducing search efficiency depending on the resetting position of the TF. Intersegmental jumps can facilitate escape from chromatin compartments and increase search times by trapping the searcher. We further show that the size of the compartment critically influences the search dynamics and can help optimize search time. Resetting can improve search efficiency only when it occurs near the target site. In addition, resetting to a broad region rather than a point can further reduce search time. We also investigate the cost associated with resetting, which increases with resetting rate and decreases with intersegmental jump rate. Our analytical results, supported by numerical simulations, provide quantitative insights into the role of chromatin architecture and TF localization in regulating the search dynamics of TF.

Rapid and accurate screening of protein-ligand binding affinities using machine learning (ML) remains a challenging yet critical task in drug discovery. In this work, two closely related challenges are addressed: the inherent sensitivity of the binding affinity to the ligands' geometry within the protein-ligand complex and the absence of prior knowledge of this geometry for previously unseen compounds. Three representative ML methods of varying complexity─Kernel Ridge Regression (KRR), SchNet, and Polarizable Atom Interaction Neural Network (PaiNN)─were evaluated on prediction of the binding affinities of compounds against the main protease SARS-CoV-2 (M^pro). Employing a multi-instance learning framework, models were trained on multiple ligand conformers per compound and tested on sets of unseen compounds represented by multiple conformers unrelated to the binding geometry. Comprehensive error analysis reveals that the incorporation of multiple conformers of unseen compounds significantly improves the prediction accuracy, exceeding the gains achieved by refining individual models alone.

First-principles molecular dynamics simulations systematically elucidate the influence of atomic structure on ionic conductivity in BeF₂-NdF₃ (FBeNd) molten salt, a key constituent salt in electrochemical pyroprocessing for the molten salt reactor. The increase in ionic conductivity with Nd concentration is explained by multilevel structural analyses encompassing phonon modes, ionic pair structures, network architectures, and electronic characteristics. Phonon dispersion analysis demonstrates that high- and low-frequency vibrational modes are governed by Be and Nd ions, respectively. Detailed structural analyses confirm that enhanced Nd diffusivity correlates with improved Nd-Nd interactions manifested through shortened Nd-Nd distances, distorted Nd-F-Nd angles, emergent edge/face-sharing clusters, and intensified electronic polarization. Conversely, Be-F tetrahedra retain structural integrity with increasing Nd concentrations, and network fragmentation accelerates Be and F diffusion. The dual enhancement effect of ionic self-diffusion coefficients and charge carrier concentration synergistically elevates the bulk ionic conductivity of molten FBeNd. Overall, a composition-structure-property framework spanning macroscale conductivity to atomistic features is established, offering foundational insights for the predictive modeling of fission product accumulation effects and the rational design of separation protocols in pyroprocessing.

Despite the increase in computational power, traditional NMR structure determination remains semiquantitative or even qualitative, especially because of the complicated mathematics involved in modeling the nuclear dipole-dipole interactions in nuclear Overhauser effect (NOE) spectra. Even advanced exact NOE (eNOE) and residual dipolar coupling (RDC) methods neglect the effects of interproton angular motion, limiting the physical realism of generated ensembles. We present KEnRef, an open-source C++ library implementing the Kinetic Ensemble approach to refine multistate protein structures using restraints that rigorously account for interproton distance and angular fluctuation. We introduce a loss function, using fractional exponents, that balances sensitivity across the target distance range. KEnRef interfaces with GROMACS to introduce forces calculated at each molecular dynamics time step. On synthetic ubiquitin data sets, single-structure simulations with a fractional exponent of 0.25 achieved an interproton RMSD of ∼0.2 Å and convergence time down to 2 ns. Two-structure ensembles showed 100-fold restraint energy decreases at high force constants and reproduced both rigid and dynamic behaviors with distance and angular fluctuation highly correlated to reference data (R > 0.85), validating KEnRef's capacity to capture localized motions. KEnRef enables integrated refinement of distance and angular fluctuations, yielding ensembles that faithfully model both structural and dynamic properties. Its performance on synthetic benchmark tests and modular design lay the foundation for ultraquantitative NMR-based ensemble refinement.