Category
334 papers
Biochemical oxidase and reductase assays on PDI showed that punicalagin inhibits both activities with micromolar potency, thereby extending earlier work that described disulfide reductase inhibition.
Overall, iLYTAC converts non-neutralizing antibodies into functional degraders of viral proteins, enabling effective suppression of infection and providing a potential platform for broad-spectrum antiviral therapeutics.
Reused liquid batter systems in retail food-establishment represent a potential yet poorly studied source of seafood allergen cross-contact. This study quantified the accumulation of shrimp and cod proteins in a liquid batter across 20 sequential preparation batches, each representing a single portion (20-25 g), and evaluated their transfer to subsequently prepared chicken, mushroom, and zucchini, both before and after deep frying at 180°C. Allergen concentrations were measured using four commercial ELISA kits (two kits per species). Shrimp protein concentrations increased about 7-fold between batches 5 and 20, while cod protein concentrations increased approximately 3-fold over the same interval. Overall, cod residues accumulated in the batter at substantially higher levels than shrimp across all batches. Shrimp and cod proteins were detected in all three non-seafood items following preparation in the reused batter. Frying reduced detectable allergen levels by 1-67% for shrimp and 74-88% for cod. Consumer exposure estimates for shrimp did not exceed the 200 mg reference dose in any food type before or after frying, whereas cod exposure exceeded the 5 mg fish-protein reference dose even at the 25th percentile of consumption across all food types. These findings demonstrate that seafood allergens accumulate and transfer readily in reused liquid batter, posing a potential allergic risk in cross-contacted products even after frying.
EMS applied during LPS-induced systemic inflammation exacerbated skeletal muscle atrophy and was associated with activation of IL-6/STAT3-C/EBPδ signaling and proteolytic pathways.
Reach audiences
Advertise in front of researchers, engineers, and readers.
Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff (Explicit Interaction-Prompted Diffusion), an architecture featuring a novel explicit interaction-prompt embedding mechanism that is better suited for real-world target-specific drug design. This architecture replaces biased implicit learning with explicit, residue-level biological guidance, thereby promoting more fine-grained geometric fidelity and more precise interaction-aware conditioning. To fully realize the capabilities of EIP-Diff and provide a reliable basis for performance evaluation, we further constructed CrystalData set, which provides higher-fidelity and less-biased structural supervision than existing data sets. This explicit architecture markedly improves distribution consistency: even when trained on the crossdocked data set, EIP-Diff achieves the highest alignment with authentic pharmacological distributions among evaluated models. Training on CrystalData set further enhances this alignment and improves 3D geometric accuracy, while retaining strong controllability, high chemical space coverage, and near-perfect uniqueness. In addition, target-based validation on KAT6A and YTHDC1 confirmed that EIP-Diff accurately recapitulates native-like binding modes. Furthermore, in a real-world drug design task against IDO1, we successfully designed a novel lead compound with nanomolar potency (IC50 = 0.31 nM). These results demonstrate that the EIP-Diff architecture can explicitly leverage experimentally derived structural data and biologically meaningful interaction information for target-specific molecular generation, thereby enabling its effective application to real-world structure-based drug design.
A review of recent advances in the structural diversity, evolutionary distribution, and functional specialization of DnaJ proteins across model plants and crops highlights DnaJ proteins as promising molecular targets for crop improvement and climate‐smart agriculture aimed at increasing productivity, stress tolerance, and postharvest performance under changing environmental conditions.
This paper introduces Caduceus, a family of MoE-enhanced foundation models built with a hierarchical pre-training paradigm to jointly integrate biological and natural language, and incorporates a multi-task instruction tuning phase, enabling robust protein parsing and natural language question answering.
This work believes this is the first evidence demonstrating an association between these clinically relevant biomarkers of Alzheimer’s disease and phenotypes of brain aging in nonhuman primates, underscoring their importance as models of aging and neurodegenerative disease.
These findings highlight benzimidazole derivatives, particularly 16a and 17b and their nanoparticle formulations, as promising anticancer candidates, driven primarily by strong cellular potency and favorable safety, substantiating their potential as lead candidates for further optimization and therapeutic development.
Data indicate that platelets support greater activity of FVIII than PLV through stabilization against dissociation of the A2 domain and protection from degradation by APC.
It is shown that cellulosomal principles can operate in highly reduced cohesin‐dockerin systems, and two additional candidate interacting partners for ClcC appear to bind via a non‐canonical interface.
HydroGym is introduced, a solver-independent reinforcement learning platform providing more than 60 validated, openly available flow control environments spanning from canonical laminar flows to complex turbulent flows, with systematic progression in the Reynolds number up to Re = 4 × 105, and Mach number variations in two and three dimensions.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops.
Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.
AlphaFold has revealed the structure of a key protein behind heart disease