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Despite their biological significance, the study of hydropersulfides (RSSH) is often limited due to their inherent instability. Here, we introduce arylsulfonothioates as thiol activated RSSH donors and provide insight into cellular reactive sulfur species homeostasis. These precursors persulfidate physiologically relevant thiols (RSH) to form the corresponding RSSH. Real-time monitoring of hydrogen sulfide (H2S) generation via membrane inlet mass spectrometry (MIMS) was employed to follow RSSH production, revealing that electron-donating aryl substituents marginally slow RSSH release rates, whereas electron-withdrawing substituents slightly accelerate release. Furthermore, arylsulfonothioates with strong electron-withdrawing substituents offer superior protection against doxorubicin (DOX)-induced cardiotoxicity. Experiments using H9c2 cardiomyocytes affirmed the cell-permeability of arylsulfonothioates and their ability to increase intracellular RSSH levels and protein persulfidation levels. Notably, we observe the excretion of RSSH into the extracellular medium. Further investigations revealed the involvement of the cystine/glutamate antiporter SLC7A11, as cotreatment with its inhibitor, sulfasalazine, significantly reduce extracellular RSSH release. H9c2 cells exhibit tolerance to arylsulfonothioate 1g with an electronwithdrawing 4-cyano group at 1 mM; however, inhibition of the cystine antiporter results in a minor decrease in cell viability. Under oxidative stress conditions induced by DOX or hydrogen peroxide (H2O2), cotreatment with 1g diminishes the excretion of RSSH and confers cytoprotection against DOX or H2O2-mediated toxicity. Our findings show adaptive cellular responses to RSSH levels, demonstrating excretion under elevated conditions to maintain redox homeostasis and intracellular retention as a protective response during oxidative stress. 

Abstract Designing protein-binding proteins is critical for drug discovery. However, artificial-intelligence-based design of such proteins is challenging due to the complexity of protein–ligand interactions, the flexibility of ligand molecules and amino acid side chains, and sequence–structure dependencies. We introduce PocketGen, a deep generative model that produces residue sequence and atomic structure of the protein regions in which ligand interactions occur. PocketGen promotes consistency between protein sequence and structure by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The graph transformer captures interactions at multiple scales, including atom, residue and ligand levels. For sequence refinement, PocketGen integrates a structural adapter into the protein language model, ensuring that structure-based predictions align with sequence-based predictions. PocketGen can generate high-fidelity protein pockets with enhanced binding affinity and structural validity. It operates ten times faster than physics-based methods and achieves a 97% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets. Additionally, it attains an amino acid recovery rate exceeding 63%. 

Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability. 

The mixed tin (Sn) and lead (Pb) perovskite compositions have shown great potential in perovskite photovoltaic devices due to the significantly enhanced material stability and prolonged carrier lifetime, compared to the pure Sn halide perovskites. In spite of the increasing interest, the behaviors of photo-generated charges and of the intrinsic point defects, such as the metal cation vacancies (V Sn and V Pb ) and the interstitial halogen (i I ), have not been well understood in this class of materials. We report first-principles density functional theory (DFT) calculations combined with ab initio non-adiabatic molecular dynamics (NAMD) simulations on the static and dynamic structures of MA 2 SnPbI 6 with and without these intrinsic defects. We discuss the nature of the defect states and unveil the influence of the intrinsic point defects on the structure, optoelectronic properties, and charge carrier dynamics of MA 2 SnPbI 6 . The i I defect significantly shortens the carrier lifetime by creating mid-gap states that provide new recombination pathways. In comparison, the vacancy defects have much weaker influence on the carrier lifetime. Both V Sn and V Pb produce the defect states just below the valence band maxima (VBMs), and do not alter the band gap. They affect the carrier lifetime through changing the energy dispersions of VBMs and the conduction band minima (CBMs). We suggest that excess cations should be used in the synthesis of perovskites to avoid the appearance of interstitial halogen defects.