Communications Chemistry最新文献

Silk fibres derive their exceptional properties from hierarchical protein organisation, yet the molecular pathways that guide this structural transformation remain poorly resolved. During regenerated silk fibroin gelation under biomimetic gradual acidification, we identify a stepwise assembly pathway comprising nanoscale clustering, domain growth within clusters, and mesoscale network formation. Time-resolved small-angle neutron scattering performed simultaneously with turbidity and fluorescence emission (NUrF) identifies unique intermediates and a regulated onset of β-contacts and β-sheets assembly, indicating that fibril formation requires prior compaction and network connectivity. By contrast, methanol-induced gelation bypasses these intermediates, driving rapid aggregation. These findings define the sequence and timing of events that construct silk's hierarchical architecture without accidental aggregation, showing how pathway selection governs material outcomes. This multiscale resolution achieved by NUrF provides a broadly applicable strategy for probing hierarchical assembly in silk and other protein materials.

CYP3A4 metabolizes a significant proportion of all approved drugs in the human body. However, the mechanism by which substrates access the catalytic center in concert with conformational changes remains unresolved. Here, we captured spontaneous substrate-binding events from outside the enzyme to the catalytic center of CYP3A4 using long-timescale, unbiased MD simulations. During each entry process, the ligand initially resided on the surface of CYP3A4 with the F-F' loop oriented downward for over 5 μs. The F-F' loop then moved upward to permit substrate entry and restricts its exit. After entry, the substrate underwent a conformational rearrangement, while the F-F' loop began to move downward again. One of the final bound poses closely resembled the experimentally determined conformation. The dissociation constant derived from a Markov state model agreed well with experimental data. Overall, our work provides an atomistic, dynamic view of the substrate entry mechanism in CYP3A4.

Conotoxins are small, disulfide-rich peptides that display exceptional affinity and selectivity for ion channels and receptors, making them valuable templates for therapeutic development. However, their optimization remains challenging due to the limited diversity of naturally occurring variants and the labor-intensive nature of conventional engineering strategies. Here, we present CreoPep, a deep learning-based generative framework specifically developed to design and optimize conotoxins targeting defined receptors. CreoPep integrates masked language modeling with a progressive masking scheme and employs an augmentation pipeline that combines physics-based energy screening with temperature-controlled multinomial sampling. This enables the generation of structurally and functionally diverse peptide variants while retaining essential pharmacological features. Structural analysis shows that CreoPep-generated variants adopt both conserved and previously unobserved binding modes, including disulfide-deficient forms. Together, these findings establish CreoPep as a powerful computational-experimental framework for the rational design of conotoxin-based peptides and provide a foundation for extending similar approaches to other peptide families.

Enhancing electron transfer rates is a critical challenge in improving electrochemical efficiency and advancing energy storage technologies. Ionic liquids (ILs), which can significantly enhance charge transport and interfacial electron transfer, have emerged as versatile components in electrochemical systems, particularly as binders in carbon paste electrodes (CPEs). We combined molecular dynamics (MD) simulations with experiments to further elucidate how aprotic quaternary ammonium (QA) ionic liquids can function as binders CPEs. Simulation revealed QA-ILs films selectively wet graphite basal planes while leaving edge sites exposed, unlike paraffinic binders that uniformly coat all surfaces. This interfacial selectivity supports the hypothesis of edge-plane exposure in enhancing electron-transfer kinetics. Additionally, peak-widths of correlation function indicate that the aliphatic QA cation experiences low friction on graphite, enabling barrierless "gliding" motion, in contrast to the rigid, hopping behavior of aromatic imidazolium ILs. Together with charge delocalization in the flexible [NTf₂]^- anion, this promotes rapid ion dynamics at the graphite interface. Cyclic voltammetry confirms fast electron transfer and a pronounced non-Faradaic response for QA-IL-based CPEs relative to pyridinium- and imidazolium-derived systems. These results identify QA-ILs as promising binders for next-generation supercapacitors and high-performance electrochemical devices.

Mn-site doping in spinel LiMn₂O₄ (LMO) mitigates Mn³⁺-induced Jahn-Teller distortion. However, this strategy faces inherent trade-offs. Specifically, low-valent doping weakens oxygen bonding, while high-valent doping increases Mn³⁺ content. To overcome these limitations, this work proposes dual-lanthanide (La³⁺/Ce³⁺) co-doping. Through sol-gel synthesis, LiLa_0.1Ce_0.1Mn_1.8O₄ (LLCMO) achieves synergistic performance enhancements. Particularly, La reduces Mn³⁺ content to 43.13%, suppressing lattice distortion and widening Li+ diffusion pathways via its large ionic radius. Concurrently, Ce (in a mixed Ce³⁺/Ce⁴⁺ state) enhances charge delocalization, lowering electron transfer barriers and boosting conductivity. Critically, La-Ce cooperation mitigates Mn dissolution while stabilizing the spinel framework. Consequently, LLCMO exhibits a 3.2-fold higher Li+ diffusion coefficient than pristine LMO. Furthermore, it delivers 111.2 mAh g^-1 at 0.5 C with 90.9% retention after 100 cycles, and remarkably retains 76.0 mAh g^-1 after 1000 cycles even at 10 C. Thus, this dual-doping strategy establishes a generalizable design principle for enhancing stability/kinetics in diverse cathodes via a synergistic division-of-labor mechanism.

Efficient photocatalytic hydrogen production from water splitting remains a critical challenge for sustainable energy solutions. Here, we report dual Ni(OH)₂ (NO)/Ni_xP (NP) cocatalysts photo-deposited and ZnS decorated ZnIn₂S₄ photocatalyst (NOP/ZIS-Z). It exhibits efficient photocatalytic hydrogen production (PHP) with the rate of 5.46 mmol·g^-1·h^-1 and 420 nm quantum yield of 55.2% in triethanolamine (TEOA) system upon Xe lamp visible light combined with excellent stability. Impressively, its PHP rate reaches 0.54 mmol·g^-1·h^-1 in pure water under natural sunlight, showing tremendous practical potentials. The synergistic mechanism among ZnS, NO, and NP was revealed: (i) ZnS could transfer electron from ZIS and facilitates charge carrier separation, (ii) NP acts as reduction cocatalysts for proton reduction, (iii) NO functions as oxidation cocatalysts to trap holes for sacrificial reagent/water oxidation. Our work highlights coinstantaneous enhancing photocatalytic reduction and oxidation half-reaction by loading dual cocatalysts onto the heterojunctions.

Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen-2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables GPT-4o to reach perfect accuracy and smaller LLMs to match or exceed single-agent GPT-4o's performance in these benchmarks.

Ordered membrane nanodomains colloquially known as "lipid rafts" have many proposed cellular functions. However, pharmacological tools to modulate protein affinity for rafts and to manipulate raft formation are currently lacking. We screened 24,000 small molecules for compounds that impact the raft affinity of a known raft-preferring model protein, peripheral myelin protein 22 (PMP22), in giant plasma membrane vesicles (GPMVs). Hits were tested against another model raft protein, MAL, and also tested for their impact on raft stability. We identified three chemically distinct tools for manipulating lipid rafts. Two compounds were found to destabilize ordered domains (VU0607402 and VU0519975) while a third (primaquine diphosphate) increased PMP22 partitioning and stabilized ordered domains. While discovered in a PMP22-focused screen, all three were seen to modulate raft formation in a protein-independent manner by altering lipid-lipid interactions and membrane fluidity. Acute treatment of live cells with the raft destabilizing compound, VU0607402 was seen to modulate TRPM8 channel function, highlighting the utility of this compound in live-cell experiments for dissecting the role that membrane order and fluidity play in cell signaling. These compounds provide pharmacological tools for probing lipid raft properties and function in biophysical experiments and in living cells.

Deep learning models have become fundamental tools in drug design. In particular, large language models trained on biochemical sequences learn feature vectors that guide drug discovery through virtual screening. However, such models do not capture the molecular interactions important for binding affinity and specificity. Therefore, there is a need to merge representations from distinct biological modalities to effectively represent molecular complexes. We present an overview of the methods to combine molecular representations and propose that future work should develop biochemical foundation models that jointly encode diverse molecular modalities. Specifically, learning to merge the representations from internal layers of domain specific biological language models could improve generalizability in the context of interaction prediction. We demonstrate that 'composing' biochemical language models performs similar or better than standard methods representing molecular interactions despite having significantly fewer features. We also discuss recent methods for interpreting and democratizing large language models that could aid the development of interaction aware foundation models for biology. Finally, we present a vision for future research that allows for predicting the evolution of molecular interactions across biophysical contexts.

Photothermal therapy (PTT) has emerged as a promising strategy for treating solid tumors and topical infections by converting the incident light energy into localized heat using photothermal agents. Among these, gold nanoparticles (GNPs) are particularly attractive due to their strong surface plasmon resonance, tunable surface chemistry, biocompatibility and scalability. However, their limited biodegradability and inefficient clearance remain significant translational challenges. In this study, we have developed gold-coated calcium peroxide nanoparticles (CPAu-NPs) that offer dual advantages, enhanced photothermal conversion and intrinsic reactive oxygen species generation. The self-release of oxygen and hydrogen peroxide from CPAu-NPs addresses tumor hypoxia, a key barrier to effective therapy. To further augment therapeutic efficacy, we incorporated Sorafenib, a multi-kinase inhibitor known to induce ferroptosis and inhibit tumor progression in melanoma, a cancer type marked by dysregulated iron metabolism and vulnerability to ferroptosis. This combinatorial approach disrupts critical survival pathways while promoting lipid peroxidation, potentially overcoming resistance to standard treatments. Additionally, we explored the antifungal potential of this system, recognizing the increased susceptibility of immunocompromised cancer patients to fungal infections. Our results suggest that CPAu-NPs, in combination with Sorafenib, provide a multifunctional theranostic platform capable of targeting melanoma cells, modulating the tumor microenvironment, and addressing opportunistic fungal infections.