The Journal of Physical Chemistry B最新文献

Understanding the molecular basis of interprotein electron transfer (ET) is essential for elucidating the mechanisms of bioenergetic processes. In this study, we characterize the ET kinetics between cytochrome c (Cyt c) and cytochrome c oxidase (CcO) by determining the ET rate constant (k_ET) within their complex using temperature-dependent flow-flash spectroscopy. The measured k_ET was on the order of 10⁴ s^–1, corresponding to an ET distance of ∼13 Å, as estimated via Marcus theory, significantly shorter than the ∼23 Å distance inferred for the ES complex based on docking simulations using Michaelis constants (K_M). These results provide strong evidence that ET does not occur within the canonical ES complex but rather within a distinct ET-active complex characterized by a shorter ET distance. Docking simulations further support the existence of this ET-active complex by identifying configurations with restricted ET distances. Importantly, the observed k_ET is approximately 80 times faster than the catalytic constant (k_cat), indicating that ET is not the rate-limiting step in the overall Cyt c–CcO reaction. Given that k_cat reflects a millisecond-scale conformational transition from the ES complex to the ET-active complex, it is likely governed by the structural fluctuation of the proteins. These findings support a conformational gating mechanism, wherein thermal fluctuations of protein structure critically regulate ET efficiency. This study advances our understanding of protein–protein ET from Cyt c to CcO by highlighting the role of dynamic structural transitions in modulating the reaction kinetics.

Small molecule organic matter significantly influences methane adsorption and desorption in coal. To understand how these molecules affect methane capacity, this study investigated CO₂ injection’s microscopic effects on methane recovery. Models incorporated three abundant small molecules at varying compositions and concentrations. Density functional theory (DFT) and molecular dynamics simulations analyzed their impact on methane adsorption. Results show 2,3–2-methylnaphthalene binds methane most strongly, while alkanes exhibit the weakest adsorption. Variations in these molecules’ composition and concentration obstruct or block coal pores, reducing methane adsorption capacity. Methane adsorption also decreases with rising temperature and higher concentrations of small organic molecules. Correspondingly, the isosteric heat of methane adsorption declines with temperature and is sensitive to small molecule concentration. Injecting gas (like CO₂) into coal rich in small organic molecules effectively enhances methane recovery. However, efficiency declines with increasing alkane concentrations, suggesting that extending injection duration could improve outcomes. These findings provide crucial insights into the microscopic mechanisms of coal methane adsorption, aiding optimization of coalbed methane extraction and mine gas hazard mitigation.

Engineered glycomaterials represent an exciting new field of biomaterials, owing to their vast structural diversity, yielding a myriad of potential properties and applications. Glycomaterials can be composed of naturally occurring polysaccharides (cellulose, hyaluronic acid, chondroitin sulfate, etc.), but these are also amenable to chemical derivatization, resulting in engineered glycomaterials with altered chemical and material properties. However, rules for predicting the properties of glycomaterials, based on their chemical structure, are not well established, hindering their rational design. Computational methods, such as molecular dynamics (MD) simulation, can accurately characterize the spatial and temporal properties, of glycomaterials; however, the application of MD simulations to predict material properties, such as diffusion, solubility, viscosity, and hydrogel formation, has received less attention. This work demonstrates that diffusion properties of well-known glycomaterial constituents, measured by DOSY NMR spectroscopy and calculated from explicit solvent MD simulations with the GLYCAM06 force field, generally agree well. However, the theoretical results are found to be heavily dependent on the water model, with the TIP5P and OPC models outperforming the widely used TIP3P model. Lastly, an empirical method for estimating the diffusion properties of carbohydrates, based on assessing the number of tightly bound waters, is proposed. Together, these results illustrate the potential of computational approaches to guide the rational design of engineered glycomaterials.

Point mutations in circulating tumor DNA (ctDNA) represent critical biomarkers for minimally invasive cancer management. However, their detection remains challenging because single-base mismatches impose only a modest energetic penalty within long ctDNA fragments and ensemble signal averaging in conventional transducer configurations obscures such subtle differences. Here, we present a kinetically tuned single-walled carbon nanotube nanosensor that selectively resolves the single-nucleotide KRAS G12D mutation against wild-type backgrounds through near-infrared (nIR) fluorescence modulation. The DNA corona was rationally engineered by computational anchoring-domain selection to maximize nanotube affinity and transduction gain, followed by capture-domain length tuning to suppress wild-type hybridization while preserving mutant complementarity. Stronger anchoring yielded amplified and reproducible spectral shifts, and systematic probe truncation revealed that the site of shortening governs the balance between mutant affinity and wild-type exclusion. By fitting single-analyte and cotitration assays to a competitive binding model, we extracted quantitative kinetic parameters, and these kinetic binding models rationalize the observed selectivity trends in terms of mismatch-dependent hybridization thermodynamics, thereby defining generalizable design rules for point mutation-selective DNA/single-walled carbon nanotube (SWCNT) sensors. Guided by this framework, the optimized construct achieved a limit of detection (LOD) of 151.6 nM in serum spiked with wild-type DNA, demonstrating robustness in complex biofluids. This kinetic-model-driven nanosensor strategy introduces a principled route for precise point mutation detection directly in liquid biopsy samples, providing a disruptive alternative to sequencing-based assays for portable, real-time cancer monitoring.

The binding of rare earth elements to extractants is a key step in their separation and purification by solvent extraction. Classical MD simulations are used to investigate the equilibration configuration and binding dynamics of one, two, and three DEHP^– to Er³⁺ in both bulk water and at the water–dodecane interface. The equilibrium radial distribution function g(r) shows that Er–O and Er–P radial separations within the first and second hydration shells are essentially identical for all bulk and interface simulations, though other aspects of the spatial configuration of heteroleptic Er³⁺(H₂O)_x(DEHP^–)_n complexes are different. The binding dynamics was fast for all interfacial and bulk binding events. Hydration water molecules rotate about the Er³⁺ ion and away from the binding DEHP^– upon its approach and water molecules are ejected in well-defined directions from the incoming DEHP^–. Potential of mean force calculations yield the height of the reaction barrier, which provides insight into the results of calculations of the binding dynamics. Interfacial binding of DEHP^– to rare earth ions is expected to dominate the solvent extraction process, and we find that the probability of interfacial binding of a third DEHP^– to Er³⁺ is an order of magnitude lower than the probabilities of binding the first two DEHP^– or of binding one, two, or three DEHP^– to Er³⁺ in bulk water.

Nanoparticles (NP) produced in or added to biological milieu will spontaneously form a shell composed of biomolecules, most commonly proteins, around the NP core. The explicit interaction of the protein shell with the NP core remains poorly resolved, particularly for NP based on metals essential to life. Red emissive copper nanoclusters (RCuNC) serve as a synthetic model for the Cu⁰ NP-protein interface, and have been developed as biocompatible sensors, though the mechanism underlying their red emission is still unclear. Herein, we identify that the red emission from RCuNC does not originate from the Cu⁰ NP but instead from previously unidentified Cu^I-metallothionine (MT)-like clusters. Emission decay measurements, Cu^I-quantification assays, native polyacrylamide gel electrophoresis imaging experiments, and direct protein metalation with Cu^I identify at least two distinct populations of Cu that form during the reduction of Cu^II in the presence of proteins. Our findings reveal that approximately 47% of the total Cu in the as-prepared bovine serum albumin-stabilized RCuNC is present as Cu^I. Our results underscore the need for the scrutiny of the assignment of emitting species in copper-treated protein samples prepared under reducing conditions, while revealing the opportunity for the development of protein-based sensors with red-emitting embedded Cu^I-MT-like clusters.

Glucose mutarotation plays a fundamental role in carbohydrate chemistry by governing the interconversion between α- and β-anomers in solution, thereby influencing the physical, chemical, and biological properties of glucose. Two mechanisms have been proposed for the mutarotation of glucose in aqueous solution. However, it remains unclear which pathway predominates under typical conditions, as both have been suggested, and definitive experimental or theoretical evidence distinguishing them is still lacking. To clarify the mutarotation mechanism of glucose, deep learning potential molecular dynamics (DLPMD) simulations were performed to investigate the underlying mechanism and free energy profiles of glucose mutarotation. Compared with previous ab initio molecular dynamics results, the DLPMD simulations provide a more accurate and statistically converged description of the reaction landscape, revealing that mutarotation preferentially proceeds via the ring-opening pathway. This route exhibits a lower activation barrier and avoids the formation of a high-energy C1 carbocation. Within the ring-opening mechanism, the formation of the β-anomer is kinetically favored. These results demonstrate that DLPMD simulations reliably capture both reaction pathways and conformational preferences in aqueous solution, offering a computationally efficient alternative to conventional density functional theory (DFT) methods.

Modeling vibrational circular dichroism (VCD) spectra in solution remains a complex task due to the intricate interactions involved. Recent advances in computational chemistry, particularly the implementation of polarizable force fields, have greatly improved the description of environmental effects in classical molecular dynamics simulations. In this study, we use the AMOEBA polarizable model to calculate VCD spectra through the expression of the electric and magnetic dipole moments developed recently [Bowles, J. . ChemPhysChem 2024, 25, e202300982.] in combination with the use of induced dipole models. This methodology enables the reliable treatment of flexible molecules and has been applied to the 1-Phenyl-1,2-Cyclohexanediol (PC) molecule solvated in dimethyl sulfoxide (DMSO). The computed infrared and VCD spectra provide detailed insights into molecular conformations and solute–solvent interactions. An in-depth investigation was conducted into the possible effects of system size, concentration, and spectral convergence. Comparison with experimental vibrational frequencies in deuterated solvent further supports the interpretation of spectral features, although residual differences in the spectral shifts and broadenings prove to be challenging for theory, especially in the case of VCD.

Understanding heterogeneous ice nucleation on solid surfaces is essential for controlling water freezing in natural and technological contexts. Using molecular dynamics simulations, we investigated how the nanoscale roughness of surfaces regulates heterogeneous ice nucleation. Our results indicate that ice nucleation preferentially occurs on locally flat surface regions that are capable of generating smooth, ordered interfacial-water domains, which act as active sites for ice formation. The maximum size of these smooth interfacial-water domains serves as a practical descriptor for the nucleation capability of a surface. In contrast, atomic-scale protrusions, cavities, and other defects in surfaces disrupt the smoothness of interfacial water and thereby hinder nucleation. These results reveal that the ice-nucleation ability of rough solid surfaces shows a strong association with their ability to organize interfacial water into sufficiently large and ordered domains, rather than with average surface roughness, and provide new insights for the rational design of anti-icing and ice-promoting materials.