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325 papers
It is argued that formation of a tumour-intrinsic niche is a prerequisite for BRAF-mutant CRC seeding to distant organs and that interference with niche formation may help avoid metastatic relapse.
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.
This review systematically delineates the mechanisms underlying bidirectional immune rejection and comprehensively summarizes state-of-the-art mitigation strategies and highlights the development of alternative, inherently hypoimmunogenic cell sources as a fundamental approach to circumventing these immunological barriers.
This review highlights the transformative impact of gene editing on cancer medicine, advancing toward more accurate, effective, and personalized therapeutic approaches.
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This work presents CRISPRLungo, a computational pipeline specifically designed for long-read amplicon sequencing of gene edited samples that incorporates unique molecular identifier-based error correction and statistical filtering to distinguish true editing events from background noise, enabling robust detection of small indels and structural variants.
expression modules suggest that Zm00001eb403030 (RTCL1) and auxin-associated regulators modulate post-embryonic root initiation and branching, and the current investigation outlines a stress-responsive maize RSA network and identifies targets for functional validation, genome editing, and breeding climate-resilient cultivars.
Current preclinical and clinical evidence from 2019 to 2026 for gene-edited hypoimmune islets is critically evaluated, highlighting key immunological vulnerabilities that may emerge over time and whether these long-term challenges can be overcome.
ABSTRACT The auxin-inducible degron (AID) technology is a convenient and powerful tool for protein functional characterization in a broad array of eukaryotic species. We recently demonstrated that the original AID and improved AID2 systems are very effective at rapid protein depletion in Candida albicans, and described a limited set of reagents for their use in certain auxotrophic lab strains. With an eye toward broader applicability with improved flexibility, we report here a new series of template vectors suitable for employing AID2 technology in prototrophic C. albicans strains, such as clinical isolates and the reference strain SC5314. We adapted a common recyclable antibiotic marker system for the required genome editing steps, and developed a strategy for simultaneous CRISPR/Cas9-mediated tagging of both target alleles. We also developed a composite all-in-one tagging cassette that combines the degron tag and the OsTIR1F74A gene for single-step strain engineering. We added a fluorescent protein tag option and designed and validated an approach for N-terminal tagging that retains natural promoter control. We also compared the effectiveness of the two commonly used synthetic auxins, 5-phenyl-indole-3-acetic acid and 5-adamantyl-indole-3-acetic acid, and the two common OsTIR1 variants, F74A and F74G, and provide guidelines for using the new AID2 system. Finally, using the novel all-in-one cassette, we demonstrate that the AID2 system also works in Candida auris, albeit less effectively under some conditions. The new reagents should enhance the convenience and accessibility of the AID2 system for the Candida research community. IMPORTANCE Invasive fungal infections, including those caused by Candida species, are a persistent global health problem, and their treatment is hindered by limited antifungal options and the emergence of drug resistance. There is an urgent need for tools and methods to accelerate the discovery of novel therapeutic targets. The expanded and optimized auxin-inducible degron system described herein provides a versatile platform for characterizing protein function and dissecting pathways governing important traits like virulence, stress tolerance, and antifungal resistance. The new reagents make AID technology applicable to any strain. Ultimately, this enhanced toolkit has the potential to help identify and validate new high‑value drug targets and deepen our understanding of molecular mechanisms that drive pathogenicity of Candida and other fungal pathogen species. Invasive fungal infections, including those caused by Candida species, are a persistent global health problem, and their treatment is hindered by limited antifungal options and the emergence of drug resistance. There is an urgent need for tools and methods to accelerate the discovery of novel therapeutic targets. The expanded and optimized auxin-inducible degron system described herein provides a versatile platform for characterizing protein function and dissecting pathways governing important traits like virulence, stress tolerance, and antifungal resistance. The new reagents make AID technology applicable to any strain. Ultimately, this enhanced toolkit has the potential to help identify and validate new high‑value drug targets and deepen our understanding of molecular mechanisms that drive pathogenicity of Candida and other fungal pathogen species.
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.
Hemoglobinopathies provide the first clinically validated delivery model for CRISPR therapeutics, and offers a donor-independent alternative to allogeneic hematopoietic stem-cell transplantation without graft rejection or graft-versus-host disease.
A pH-gated surface-mediated hydrogel patch for infection-responsive delivery of CRISPR-Cas9 ribonucleoprotein (RNP) complexes establishes a novel paradigm for localized, precision gene therapy and holds strong translational promise for the treatment of chronic inflammatory wounds.
Human-Computer Interaction and Visualization
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
Generative AI
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value — and why innovation resists simple formulas.
General Science
