The Journal of Physical Chemistry B最新文献

Flagellar-driven locomotion plays a critical role in the bacterial attachment and colonization of surfaces, contributing to the risks of contamination and infection. Extensive efforts to uncover the underlying principles governing bacterial motility near surfaces have relied on idealized assumptions about surrounding artificial surfaces. However, in the context of living systems, the role of cells from tissues and organs becomes increasingly critical, particularly in bacterial swimming and adhesion, yet it remains poorly understood. Here, we propose using biological surfaces composed of vascular endothelial cells to experimentally investigate bacterial motion and interaction behaviors. Our results reveal that bacterial trapping observed on inorganic surfaces is counteractively manifested with reduced radii of circular motion on cellular surfaces. Additionally, two distinct modes of bacterial adhesion were identified: tight and loose adhesion. Interestingly, the presence of living cells enhances bacterial surface enrichment, and imposed flow intensifies this accumulation via a bias-swimming effect. These results surprisingly indicate that physical effects remain the dominant factor regulating bacterial motility and accumulation at the single-cell-layer level in vitro, bridging the gap between simplified hydrodynamic mechanisms and complex biological surfaces with relevance to biofilm formation and bacterial contamination.

Exploring the vast chemical space of small molecules poses a significant challenge. We develop a new strategy to efficiently explore this space using coarse-grained toy-like molecules utilizing the Martini3 force field and graph representations. This yields initial proof-of-concept results for the approach enabling the identification of optimal molecules with specific properties targeting lipid bilayers. By leveraging genetic algorithms and coarse-grained molecular dynamics simulations, we demonstrate the potential of our method in designing simple, linear molecules. Our findings show a good convergence toward molecules with weak amphiphilic properties, resembling known (general anesthetic) molecules. While this study demonstrates the feasibility of our method, further refinement is needed to fully realize its potential and explore more complex molecular topologies. Nevertheless, these encouraging results suggest a new path for future research in small molecule discovery and design without relying on extensive data sets.

A considerable effort has been expended over the years to tune the properties of ionic liquids (ILs) by designing cations, anions, and pendant groups on the ions. A simple and effective approach to altering the properties of ILs is formulating IL-IL mixtures. However, the measurements and properties of such mixtures lag considerably behind those of pure ILs. From a molecular simulation point of view, binary IL mixtures have been investigated using charge distributions of pure ILs, which implicitly assumes that the ions of different polarizability do not influence the local electronic environment due to changing concentrations. To understand this effect, molecular dynamics (MD) simulations were conducted for a series of IL-IL mixtures containing the common cation 1-ethyl-3-methylimidazolium [C₂mim] varying the composition of various combinations of anions (tetrafluoroborate [BF₄] and dicyanamide [DCA], [BF₄] and bis(trifluoromethanesulfonyl)imide [NTF₂], [BF₄] and trifluoromethanesulfonate [TFO], and [TFO] and [NTF₂]). The effect of changing the electronic environment was evaluated by deriving partial charges using density functional theory (DFT) calculations in the condensed phase. It was observed that the overall charge on the cation and anion was a function of the cation-anion pairings for pure ILs. Moreover, the cation charge was found to vary linearly with anionic concentrations. Improved agreement of predicted density and ionic conductivity with experimental values was found for binary IL mixtures with this approach, in comparison to that when a fixed charge model is employed.

Intrinsically disordered proteins (IDPs) are macromolecules, which in contrast to well-folded proteins explore a large number of conformationally heterogeneous states. In this work, we investigate the conformational space of the disordered protein β-casein using Hamiltonian replica exchange atomistic molecular dynamics (MD) simulations in explicit water. The energy landscape contains a global minimum along with two shallow funnels. Employing static polymeric scaling laws separately for individual funnels, we find that they cannot be described by the same polymeric scaling exponent. Around the global minimum, the conformations are globular, whereas in the vicinity of local minima, we recover coil-like scaling. To elucidate the implications of structural diversity on equilibrium dynamics, we initiated standard MD simulations in the NVT ensemble with representative conformations from each funnel. Global and internal motions for different classes of trajectories show heterogeneous dynamics with globule to coil-like signatures. Thus, IDPs can behave as entirely different polymers in different regions of the conformational space.

Abnormal aggregation of tau protein is pathologically linked to Alzheimer's disease, while the aggregation of the prion-like RNA-binding protein (RBP) CPEB3 is functional and is associated with long-term memory. However, the interaction between these two memory-related proteins has not yet been explored. Our residue-specific NMR relaxation study revealed that the first prion domain of CPEB3 (PRD1) interacts with the ³⁰⁶VQIVYKPVDLSKV³¹⁸ segment of tau and prevents the aggregation of tau-K18. Notably, this interaction is synergistic as it not only inhibits tau-K18 aggregation but also enhances PRD1 fibril formation. We also studied the interaction of different PRD1 subdomains with tau-K18 to elucidate the precise region of PRD1 that inhibits tau-K18 aggregation. This revealed that the PRD1-Q region is responsible for preventing tau-K18 aggregation. Inspired by this, we synthesized a 15 amino acid Poly-Q peptide that inhibits tau-K18 aggregation, suggesting its potential as a small drug-like molecule for Alzheimer's disease therapeutics.

Alchemical free energy calculations are becoming an increasingly prevalent tool in drug discovery efforts. Over the past decade, significant progress has been made in automating various aspects of this technique. However, one aspect hampering wider application is the construction of perturbation networks to connect ligands of interest. More specifically, ligand pairs with large dissimilarities should be avoided since they can lower convergence and decrease accuracy. Here, we propose a technique for automatic generation of intermediate molecules to break up problematic edges─calculations connecting two different ligands or molecules─into smaller perturbations. To this end, a modular tool was developed that generates intermediates for a molecule pair by enumerating R-group combinations called IMERGE-FEP (Intermediate MolEculaR GEnerator for Free Energy Perturbation). Intermediate enumeration of multiple, representative congeneric series showed that intermediates increase similarity regarding shared substructures, geometry, and LOMAP scores. Taken together, this tool eases integration of intermediate steps into free energy calculation protocols.

Antimicrobial peptides (AMPs) have important developmental prospects as potential candidates for novel antibiotics. Although many studies have been devoted to the identification of AMPs and the qualitative prediction of their functional activities, few methods address the quantitative prediction of their activity values. In this paper, we propose a regression model called MSCMamba, which fuses multiscale convolutional neural network with Mamba module to accurately predict the activity values of AMPs. AMPs sequences are feature-extracted by multiple encoding methods and fed into a multiscale convolutional network and a Mamba module to capture local and long-range dependent features, respectively. The model fuses these two outputs and predicts the activity values of AMPs through a linear layer. Experimental results show that MSCMamba outperforms the current state-of-the-art methods in several performance metrics, especially with an increase in R² from 0.422 to 0.467, representing a 10.66% improvement. Additionally, we did a series of ablation experiments to verify the validity of each part of the MSCMamba model and the performance enhancement of feature diversification.This study provides a new method for activity prediction of AMPs, which is expected to accelerate the development of novel antibiotics.

Modeling polymer brushes is essential for understanding their complex behavior at surfaces and interfaces, enabling the design of materials with tunable properties. We present a computational protocol to model polymer brushes composed of grafted, brush-like chains of the charged polymer poly(N,N-dimethylaminoethyl methacrylate) (p(DMAEMA)) using an all-atom representation that captures detailed molecular interactions and structural properties. The approach is flexible and non-grid-based and allows for randomized strand configurations and the incorporation of periodic boundary conditions, enabling the construction of asymmetric polymer brush setups. An atactic p(DMAEMA) configuration is demonstrated as an example, though the protocol can be readily adapted to construct other brush-like polymer systems with varying tacticities or compositions, depending on the pH environment. Furthermore, this can be extended to stimuli-responsive materials, which generate conformation or charge upon changes in pH value or other external triggers. Molecular dynamics simulations are then employed to gain insights into the conformational behavior of the grafted p(DMAEMA) brushes and their surrounding aqueous environment, as well as their response to temperature, protonation, and variations in grafting densities, in terms of the solvent-accessible surface area, radius of gyration, and radial distribution functions. This versatile protocol provides a robust tool for simulating and analyzing the properties of diverse polyelectrolyte polymer brush systems and also as composite materials.

Water is vital to all facets of life and is anomalously behaving in its condensed states, making it continually a substance of interest to researchers. Therefore, attempting to capture its properties via correlations and equations of state is extremely valuable. Liquid water has not been studied as extensively in the low-temperature and high-pressure region as in other regions. Some key applications for correlations in this region are cryopreservation (specifically in certain methods of cryopreservation such as hyperbaric (high-pressure) and isochoric (constant-volume) cryopreservation), deep oceans, hydrospheres, clouds, and precipitation. Although there are not nearly as many models for water at low temperatures and high pressures as there are in other temperature and pressure ranges, there are some models that do currently exist. However, these either do not extend to temperatures and pressures as extreme as the data that exist, or they are complex with large numbers of parameters making them more difficult for application. Herein, we present a new correlation for liquid water valid for the temperature range of 200-300 K (-73-27 °C) and pressure range of 0.1-400 MPa that can analytically calculate volume, isothermal compressibility, isobaric expansivity, constant pressure heat capacity, and speed of sound, using only 17 adjustable parameters. The analytical expressions that we derived, and the fitting method that we used can also be applied to other fluids of interest in the future.

Simulating excited-state dynamics or computing spectra for molecules in condensed phases requires sampling the ground state to generate initial conditions. Initial conditions (or snapshots for spectra) are typically produced by QM/MM Boltzmann sampling following MM equilibration or optimization. Given the switch from a MM to a QM/MM potential energy surface, one should discard a set period of time (which we call the "healing time") from the beginning of the QM/MM trajectory. Ideally, the healing time is as short as possible (to avoid unnecessary computational effort), but long enough to equilibrate to the QM/MM ground state distribution. Healing times in previous studies range from tens of femtoseconds to tens of picoseconds, suggesting the need for guidelines to choose a healing time. We examine the effect of healing time on the nonadiabatic dynamics and spectrum of a first-generation Donor-Acceptor Stenhouse Adduct in chloroform. Insufficient healing times skew the branching ratio of ground state products and alter the relaxation time for one pathway. The influence of the healing time on the absorption spectrum is less pronounced, warning that the spectrum is not a sensitive indicator for the quality of a set of initial conditions for dynamics. We demonstrate that a reasonable estimate for the healing time can be obtained by monitoring the solute temperature during the healing trajectory. We suggest that this procedure should become standard practice for determining healing times to generate initial conditions for nonadiabatic QM/MM simulations in large molecules and condensed phases.