The Journal of Physical Chemistry B最新文献

Passive and targeted delivery of peptides to cells and organelles is a fundamental biophysical process controlled by membranes surrounding biological compartments. Embedded proteins, phospholipid composition, and solution conditions contribute to targeted transport. An anticancer peptide, NAF-1^44-67, permeates to cancer cells but not to normal cells. The mechanism of this selectivity is of significant interest. However, the complexity of biomembranes makes pinpointing passive targeting mechanisms difficult. To dissect contributions to selective transport by membrane components, we constructed simplified phospholipid vesicles as plasma membrane (PM) models of cancer and normal cells and investigated NAF-1^44-67 permeation computationally and experimentally. We use atomically detailed simulations with enhanced sampling techniques to study kinetics and thermodynamics of the interaction. Experimentally, we study the interaction of the peptide with large and giant unilamellar vesicles. The large vesicles were investigated with fluorescence spectroscopy and the giant vesicles with confocal microscopy. Peptide permeation across a model of cancer PM is more efficient than permeation across a PM model of normal cells. The investigations agree on the mechanism of selectivity, which consists of three steps: (i) early electrostatic attraction of the peptide to the negatively charged membrane, (ii) the penetration of the peptide hydrophobic N-terminal segment into the lipid bilayer, and (iii) exploiting short-range electrostatic forces to create a defect in the membrane and complete the permeation process. The first step is kinetically less efficient in a normal membrane with fewer negatively charged phospholipids. The model of a normal membrane is less receptive to defect creation in the third step.

In the Ras/Raf/MAPK signaling pathway, Ras and Raf proteins interact synergistically to form a tetrameric complex. NMR experiments have demonstrated that Ras dimerizes in solution and binds stably to Raf, forming Ras·Raf complexes. In this study, we constructed the ternary and quaternary complexes of KRas and CRaf based on crystal structures, denoted as (KRas)₂·CRaf and (KRas)₂·(CRaf)₂, respectively. Molecular dynamics (MD) simulations were performed to investigate the stability of these complexes, while hydrogen bonds as well as salt bridges formed at the protein-protein interaction interfaces were analyzed based on simulation trajectories. The results revealed that the KRas·CRaf complex is more stable in explicit solvent compared with the KRas dimer. Formation of the stable quaternary complex (KRas)₂·(CRaf)₂ might be attributed to the association of two binary KRas·CRaf complexes. Additionally, MD simulations of the KRasG12D·CRaf complex revealed a stable and extended binding site at the KRas-CRaf interaction interface. This binding site was identified as a potential therapeutic target to block abnormal signal transmission in the pathway.

We develop coarse-grained models for carbon dioxide (CO₂) and nitrogen (N₂) that capture the vapor-liquid equilibria of both their single components and their binary mixtures over a wide range of temperatures and pressures. To achieve this, we used an equation of state (EoS), namely Statistical Associating Fluid Theory (SAFT), which utilizes a molecular-based algebraic description of the free energy of chain fluids. This significantly accelerates the exploration of the parameter space, enabling the development of coarse-grained models that provide an optimal description of the macroscopic experimental data. SAFT creates models of fluids by chaining together spheres, which represent coarse-grained parts of a molecule. The result is a series of fitted parameters, such as bead size, bond length, and interaction strengths, that seem amenable to molecular simulation. However, only a limited set of models can be directly implemented in a particle-based simulation; this is predominantly due to how SAFT handles overlap between bonded monomers with parameters that do not translate to physical features, such as bond length. To translate such parameters to bond lengths in a coarse-grained force-field, we performed Wang-Landau transition-matrix Monte Carlo (WL-TMMC) simulations in the grand canonical ensemble on homonuclear fused two-segment Mie models and evaluated the phase behavior at different bond lengths. In the spirit of the law of corresponding states, we found that a force field, which matches SAFT predictions, can be derived by rescaling length and energy scales based on ratios of critical point properties of simulations and experiments. The phase behavior of CO₂ and N₂ mixtures was also investigated. Overall, we found excellent agreement over a wide range of temperatures and pressures in pure components and mixtures, similar to TraPPE CO₂ and N₂ models. Our proposed approach is the first step to establishing a more robust bridge between SAFT and molecular simulation modeling.

Green fluorescent protein (GFP) is famous for noninvasively observing the internal biological processes of cells and organisms, revolutionizing the field of cell biology. GFP was first discovered in jellyfish Aequorea victoria (AV). The GFP bioluminescence (BL) in AV can be divided into three stages: the first singlet excited state coelenteramide (S₁-CTD) is formed in aequorin; GFP acquires energy from S₁-CTD via an energy transfer (ET) process; and GFP emits green light. The first and final stages have been well studied, whereas the detailed mechanism of the second stage remains unclear, with only sporadic experimental evidence. The purpose of this study is to clarify how GFP acquires energy before emitting green light in AV. Through protein-protein docking, molecular dynamics simulations, and combined quantum mechanics and molecular mechanics calculations, we demonstrate that the ET process occurs via the Förster resonance energy transfer (FRET) mechanism. The calculated FRET rate is faster than the radiative and nonradiative decay ones of S₁-CTD, which means the ET process can occur efficiently. Additionally, the calculated fluorescence quantum yield explains the experimentally observed BL enhancement after the ET. This is the first theoretical report on the ET mechanism in BL. This study not only clearly interprets how GFP acquires energy for emitting light but also helps to understand the ET mechanism in other bioluminescent systems and sheds new light on bioluminescence resonance energy transfer.

This study examines the foams generated by three surfactants: sodium dodecyl sulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), and sodium lauryl polyoxyethylene ether sulfate (AES). By analyzing the hydrogen bond at the gas-liquid interface, the research provides novel insights into the mechanisms by which surfactants stabilize foams. Surfactants adsorb at the gas-liquid interface, establishing hydrogen bonds with water molecules while simultaneously retarding the structural relaxation of water-water hydrogen bonds within the hydration layer. This phenomenon can impede drainage during the surface tension drainage phase. Surfactants that readily form hydrogen bonds with water are more likely to adsorb at the gas-liquid interface, thereby enhancing the foam stability. The presence of robust hydrogen bonds and the frequent reconstruction of these bonds contribute to the establishment of a stable hydrogen bond network, which can reinforce the gas-liquid film and potentially augment its elasticity, enabling it to better withstand external perturbations. Although the Marangoni effect typically promotes bubble coalescence, a stable hydrogen bond network may mitigate this process.

Block copolymers of amphiphilic nature represent a distinctive class of macromolecules that have been extensively studied due to their intriguing surface-active properties. Their ability to reduce interfacial tension and create disperse phases, such as emulsions, has made them crucial in industries that rely on the interfacial effects of these molecules. Experimental and computational studies have reported the effects of changing various properties associated with the polymeric chains including stiffness, molecular weight, and other structural attributes. In this work, extensive molecular simulations were performed to understand how the sequence of an AB multiblock copolymer impacts the interfacial tension between two immiscible liquids. To efficiently explore a range of surface concentration values and four different block copolymer sequences, a coarse-grained model was employed. Simulation results indicate that at a fixed composition, block sequence has a strong effect on the rate of interfacial tension reduction as polymer surface concentration increases. Of all studied sequences, the alternating sequence was able to greatly reduce the interfacial tension at low surface concentrations, whereas pentablock and triblock sequences were able to reduce it even more than the alternating sequence, but it required a higher polymer surface concentration to achieve this. To correlate polymer conformations with interfacial effects, several structural descriptors were computed to quantify the conformations adopted by the macromolecules at the interface.

Metal dithiolene compounds are attracting considerable attention in the field of molecular electronics, particularly as constituents of materials with high charge-carrier mobilities. Recent experiments on cable bacteria that perform centimeter-scale charge transport suggest that Ni-bis(dithiolene) cofactors are important components of the bacterial conductive network. Further, current-voltage experiments of cable-bacteria-conductive sheaths have measured high conductivity values as compared to other electron-transfer bacteria. An important question is how the Ni-bis(dithiolene) structures participating as electron donors/acceptors contribute to the high conductivity. Currently, the protein and cofactor structures of these bacterial networks are largely unknown. Given this limitation, in this work, we explore the more general question of how Ni-bis(dithiolene) molecules would perform as electron donor and acceptor centers in protein-mediated charge transfer. Our aim is to deduce order-of-magnitude higher bounds for charge-transfer rates in such systems as a function of donor-acceptor distance, protein-bridge (amino acid) sequence, cofactor size, and redox state. These bounds are useful for predicting charge-transfer mechanisms and estimating rates in the absence of detailed structural information on protein wires that may use Ni-bis(dithiolene) redox cofactors. Our analysis is also relevant to the design of artificial Ni-bis(dithiolene) protein wires.

Mitochondrial neurogastrointestinal encephalopathy (MNGIE) is a rare metabolic disorder caused by missense mutations in the TYMP gene, leading to the loss of human thymidine phosphorylase (HTP) activity and subsequent mitochondrial dysfunction. Despite its well-characterized biochemical basis, the molecular mechanisms by which MNGIE-associated mutations alter HTP's structural stability, dynamics, and substrate (thymidine) binding remain unclear. In this study, we employ a multiscale computational approach, integrating AlphaFold2-based structural modeling, all-atom and coarse-grained molecular dynamics (MD) simulations, protein-ligand (HTP-thymidine) docking, HTP-thymidine complex simulations, binding free-energy landscape analysis, and systematic mutational profiling to investigate the impact of key MNGIE-associated mutations (R44Q, G145R, G153S, K222S, and E289A) on HTP function. Analyses of our long-duration multiscale simulations (comprising 9 μs coarse-grained, 1.2 μs all-atom apo HTP, and 1.2 μs HTP-thymidine complex MD simulations) and physicochemical properties reveal that while wild-type HTP maintains structural integrity and strong thymidine-binding affinity, MNGIE-associated mutations induce substantial destabilization, increased flexibility, and reduced enzymatic efficiency. Free-energy landscape analysis highlights a shift toward less stable conformational states in mutant HTPs, further supporting their functional impairment. Additionally, the G145R mutation introduces steric hindrance at the active site, preventing thymidine binding and causing off-site interactions. These findings not only provide fundamental insights into the physicochemical and dynamic alterations underlying HTP dysfunction in MNGIE but also establish a computational framework for guiding future experimental studies and the rational design of therapeutic interventions aimed at restoring HTP function.

The development of nonfused ring electron acceptors (NFREAs) has garnered significant attention due to their simplified molecular design and cost-effectiveness. Recent advancements have pushed the power conversion efficiency (PCE) of NFREAs beyond 19%. Despite these advantages, most NFREAs adopt A-D-A structures, where the electron-donating core is typically a benzene ring substituted with fluorine or alkoxy groups. This design restricts the tunability of energy levels, and the selection of substituents for benzene rings as central units is relatively constrained, which hampers further optimization of material properties. In this work, we designed three A-D-A'-D-A structured fully NFREAs featuring distinct nitrogen heterocyclic cores: linear-shaped TT, star-shaped TYT, and quad-rotor-shaped TTVP. The nitrogen-containing aromatic units, typically strong electron-withdrawing groups, enable precise tuning of energy levels. Moreover, these electron-withdrawing cores enhance molecular rigidity, facilitating efficient π-π stacking and improving electron mobility. Although these NFREAs share identical π-bridges and terminal groups, their unique nitrogen heterocyclic cores exert divergent effects on photovoltaic performance. Theoretical calculations reveal that TT and TTVP exhibit higher electron affinity, greater absorption intensity, lower exciton binding energy, and higher electron mobility compared to the high-performance reference NFREA, TBT-26. Notably, TTVP, featuring an electron-withdrawing core and four terminal groups, exhibits exceptional electronic properties. It achieves the highest electron affinity, the narrowest bandgap of 1.76 eV, and a predicted electron mobility of 4.43 × 10^-4 cm² V^-1 s^-1, surpassing TBT-26. These findings underscore the potential of nitrogen heterocyclic cores in diversifying NFREA design and advancing the development of next-generation high-performance NFREAs.

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.