ACS Publications最新文献

Polysiloxanes are versatile polymeric materials with widespread applications in industries ranging from electronics to biomedical devices because of their unique thermal and viscoelastic properties. Accurate molecular simulations of polysiloxanes are essential for understanding their broad applications from a microscopic perspective. However, the accuracy of these simulations is highly dependent on the quality of the force fields used. In this work, we present a comprehensive benchmark and development of force fields tailored for polydimethylsiloxane, which is one of the most widely used polysiloxane materials. Our focus is on their performance in predicting key thermophysical properties including density, heat capacities, isothermal compressibility, and transport properties such as viscosity and thermal conductivity. Experimental measurements are performed in parallel to rigorously validate simulation outcomes. Existing force fields for polydimethylsiloxane, including those derived for organic and inorganic systems, are systematically evaluated against experimental data to identify limitations in accuracy and transferability. Simulation results are compared extensively with experimental observations across a range of temperatures and pressures, revealing the strengths and shortcomings of these commonly utilized force fields for polydimethylsiloxane. Discrepancies between force field predictions and experimental measurements are analyzed in detail for thermodynamic and transport properties of polydimethylsiloxane. This benchmark study serves as a critical assessment of current force fields for polydimethylsiloxane and offers guidelines for their further development, enabling more reliable simulations of polysiloxane-based materials for various industrial applications.

We review MELD, an accelerator of Molecular Dynamics simulations of biomolecules. MELD (Modeling Employing Limited Data) integrates molecular dynamics (MD) with a variety of types of structural information through Bayesian inference, generating ensembles of protein and DNA structures having proper Boltzmann populations. MELD minimizes the computational sampling of irrelevant regions of phase space by applying energetic penalties to areas that conflict with the available data. MELD is effective in refining protein structures using NMR or cryo-EM data or predicting protein-ligand binding poses. As a plugin for OpenMM, MELD is interoperable with other enhanced sampling methods, offering a versatile tool for structural determination in computational chemistry and biophysics.

Machine learning (ML) models have become increasingly popular for predicting and designing structures and properties of peptides and proteins. These ML models typically use peptides and proteins containing only canonical amino acids as the training data. Consequently, these models struggle to make accurate predictions for peptides and proteins containing new amino acids that are absent in the training data set (e.g., noncanonical amino acids). One approach to improve the accuracy of the models is to collect more training data with the desired amino acids. However, this strategy is suboptimal as new data may not be easily attainable, and additional time is required to retrain the ML models. Alternatively, the extendibility of the ML models can be improved if the amino acid features used are representative and generalizable to the unseen amino acids. Herein, we develop amino acid features using molecular dynamics (MD) simulation results. Specifically, for a given amino acid, we perform MD simulation of its dipeptide to create features based on its backbone (ϕ, ψ) distributions and its electrostatic potentials. We demonstrate that these new features enable our ML models to more accurately predict the structural ensembles of cyclic peptides containing amino acids not present in the original training data set. For example, we build ML models to predict cyclic pentapeptide structures, with the training data set containing a library of 15 amino acids and the test data set containing the same 15-amino-acid library or an extended 50-amino-acid library. When using popular features such as Morgan fingerprints and MACCS keys to represent amino acids, the ML models achieve R² = 0.963 for structural predictions of test cyclic pentapeptides containing the same 15-amino-acid library. However, these models' performances decrease significantly to R² = 0.430 and R² = 0.508, respectively, when tasked to predict the structures of cyclic pentapeptides containing a library of 50 amino acids. On the other hand, the model using our backbone (ϕ, ψ) features outperforms those using Morgan fingerprints and MACCS keys, with R² = 0.700. Overall, instead of having to collect more training data, our new features enable predictions of peptide sequences containing amino acids not originally present in the training data set at the mere cost of performing new dipeptide simulations for the new amino acids.

Dengue virus (DENV) causes dengue fever, the leading mosquito-borne viral disease affecting millions globally. Licensed vaccines have their restrictions, and the development of vaccines is in progress to overcome the limitations. In this study, we expressed two types of virus-like particles (VLPs) and four DENV serotype antigens, 1EDIII-4EDIII (tetEDIII), in silkworm larvae and engineered them into tetravalent VLPs (tetVLPs) displaying tetEDIII. Canine parvovirus-like particles (CPV-LPs) were self-assembled in vivo from viral protein VP2 of CPV (CPV-VP2) as heterologous VLPs; dengue virus capsid-like particles (DENV C-LPs) from capsid protein of DENV serotype 2 (DENV-C2) as homologous VLPs. The tetEDIII was displayed on the surface of CPV-LPs and DENV C-LPs through in vitro SpyTag/SpyCatcher (SpT/SpC) covalent ligation. The EDIII display of CPV-LP is better than that of DENV C-LP. Both tetEDIII-displaying tetravalent CPV-LPs (tetCPV-LPs) and tetravalent DENV C-LPs (tetDENV C-LPs) elicited neutralizing antibodies in BALB/c mice assayed through the single-round infectious particles (SRIP) method. The immunogenicity of tetDENV C-LPs for anti-IgG EDIIIs was higher than that of tetCPV-LPs for serotypes 1 and 3. The neutralization activity of tetDENV C-LPs was higher than that of tetCPV-LPs for D1-SRIP, while tetCPV-LPs were higher than that of tetDENV C-LPs for D2- and D4-SRIP. These results suggest that homologous tetDENV C-LPs and heterologous tetCPV-LPs can be suitable vaccine candidates for further evaluation. This result is the first report to display a tetEDIII on the surface of the DENV C-LPs and the CPV-LPs by in vitro bioconjugation.

Quantum well intersubband transitions are critical for quantum cascade lasers and infrared photodetectors. Control of band offsets allows bound-to-bound intersubband transitions, with confinement of both initial and final states, and bound-to-continuum transitions, in which only the initial state is energetically confined within the potential well. Both types of transitions are also achieved in colloidal CdSe wells by changing the heterostructure shell. Bare wells have narrow intersubband transitions spanning the near-infrared spectrum following effective mass predictions. Atomically precise core/shells enable a readily adjusted potential well for electrons. For CdSe/ZnS, bound-to-bound transitions are narrow and redshift with shell thickness. By contrast, broad bound-to-continuum absorptions are found in CdSe/CdS. Due to small conduction band offsets, higher conduction band states of the well are more delocalized into the CdS shell. These measurements provide unique data to understand the electronic structure of colloidal quantum wells and chart a path to atomically precise optoelectronic materials for the mid-infrared.

Chemical reactions are regarded as transformations of chemical structures, and the question of which atoms in the reactants correspond to which atoms in the products has attracted chemists for a long time. Atom-to-atom mapping (AAM) is a procedure that establishes such correspondence(s) between the atoms of reactants and products in a chemical reaction. Currently, automatic AAM tools play a pivotal role in various chemoinformatics tasks. However, achieving accurate automatic AAM for complex or unknown reactions within a reasonable computation time remains a significant challenge due to the combinatorial nature of the problem and the difficulty in applying appropriate reaction rules. In this study, we propose a rule-free AAM algorithm, which enumerates all atom-to-atom correspondences that minimize the number of bond cleavages and formations during the reaction. To reduce the computational burden associated with the combinatorial optimization (i.e., minimizing bond changes), we introduce Ising computing, a computing paradigm that has gained significant attention for its efficiency in solving hard combinatorial optimization problems. We found that our Ising computing framework outperforms conventional combinatorial optimization algorithms in terms of computation times, making it feasible to solve the AAM problem without reaction rules in an acceptable time. Furthermore, our AAM algorithm successfully found the correct AAM solution for all problems in a benchmark data set. In contrast, conventional AAM algorithms based on chemical heuristics failed for several problems. Specifically, these algorithms either failed to find the optimal solution in terms of bond changes, or they identified only one optimal solution, which was incorrect when multiple optimal solutions exist. These results emphasize the importance of enumerating all optimal correspondences that minimize bond changes, which is effectively achieved by our Ising-computing framework.

The separation of m-cresol (m-cre), p-cresol (p-cre), and 2,6-xylenol (2,6-xyl) poses a significant challenge in industrial processes. This study focuses on selectively separating m-cre and p-cre from a ternary mixture using a cucurbit[7]uril (Q[7]) aqueous solution to achieve the near-perfect purification of 2,6-xyl. Experimental results show that m-cre and p-cre can be selectively encapsulated by the Q[7] host, while 2,6-xyl, due to its larger volume, cannot be encapsulated. The synergistic effect of steric hindrance and complex stability contributes to effective host-guest selective encapsulation separation. The Q[7] aqueous solution can be easily recovered and reused without a significant decrease in separation performance. In the simulated industrial separation experiment, the purity of the purified cresol reached 100%. This research underscores the importance of macrocyclic host molecules in enhancing industrial separations and reducing energy costs through precise guest molecule recognition.

Highly efficient electromagnetic interference (EMI) shielding materials are critical for portable hardware and flexible electronics, where mechanical durability often poses challenges. Here, the excellent wear resistance and flexibility of thermoplastic polyurethane are utilized to provide a "protective layer" for EMI equipment. A carbon nanotube/cellulose/thermoplastic polyurethane (CNT/paper/TPU) composite paper with a three-layer structure was prepared using a coating method. Strong hydrogen bonds between CNTs, cellulose, and TPU ensured robust integration. The composite, with a thickness of 0.54 mm and conductivity of 1040 S/m, achieved exceptional EMI shielding effectiveness of 69.0 dB. It demonstrated durability against water, solvents, bending, and friction while maintaining shielding performance. Furthermore, its excellent mechanical properties and fatigue resistance significantly enhance equipment lifespan. Therefore, it is expected that this work will open a simple strategy for developing materials with excellent durable EMI shielding properties.

A novel immune checkpoint, FGL1, is a potentially viable target for tumor immunotherapy. The development of FGL1-targeted PET probes could provide significant insights into the immune system's status and the evaluation of treatment efficacy. A ClusPro 2.0 server was used to analyze the interaction between FGL1 and LAG3, and the candidate peptides were identified by using the Rosetta peptide derivate protocol. Three candidate peptides targeting FGL1, named FGLP21, FGLP22, and FGLP23, with a simulated affinity of -9.56, -8.55, and -8.71 kcal/mol, respectively, were identified. The peptides were readily conjugated with p-NCS-benzyl-NODA-GA, and the resulting compounds were successfully labeled with ⁶⁸Ga in approximately 70% yields and radiochemical purity greater than 95%. In vitro competitive cell-binding assay demonstrated that all probes bound to FGL1 with IC₅₀ ranging from 100 nM to 160 nM. Among the probes, PET imaging revealed that ⁶⁸Ga-NODA-FGLP21 exhibited the best tumor imaging performance in mice bearing FGL1 positive Huh7 tumor. At 60 min p.i., the tumor uptake of ⁶⁸Ga-NODA-FGLP21 was significantly higher than those of ⁶⁸Ga-NODA-FGLP22 and ⁶⁸Ga-NODA-FGLP23, respectively (2.51 ± 0.11% ID/g vs 1.00 ± 0.16% ID/g and 1.49 ± 0.05% ID/g). Simultaneously, the tumor-to-muscle uptake ratios of the former were also higher than those of the latter, respectively (19.40 ± 2.30 vs 9.65 ± 0.62 and 12.45 ± 0.72). In the presence of unlabeled FGLP21, the uptake of ⁶⁸Ga-NODA-FGLP21 in Huh7 xenograft decreased to 0.81 ± 0.09% ID/g at 60 min p.i., which is similar to that observed in the FGL1 negative U87 MG tumor (0.46 ± 0.03% ID/g). The results were consistent with the immunohistochemical analysis and ex vivo autoradiography. No significant radioactivity was accumulated in normal organs, except for kidneys. In summary, a preclinical study confirmed that the tracer ⁶⁸Ga-NODA-FGLP21 has the potential to specifically detect FGL1 expression in tumors with good contrast to the background.