Accurate identification of ligand binding sites (LBS) on a protein structure is critical for understanding protein function and designing structure-based drugs. As the previous pocket-centric methods are usually based on the investigation of pseudo-surface-points outside the protein structure, they cannot fully take advantage of the local connectivity of atoms within the protein, as well as the global 3D geometrical information from all the protein atoms. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom Intersection over Union (atom-IoU) by a large margin. Furthermore, our segmented binding atoms, that is, atoms with high probability predicted by our model can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Besides, we further directly extend PointSite trained on bound proteins for LBS identification on unbound proteins, which demonstrates the superior generalization capacity of PointSite. Through cascaded filter and reranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks, CAMEO hard targets, and unbound proteins in terms of the commonly used DCA criteria.
Nanorobotic manipulation to access subcellular organelles remains unmet due to the challenge in achieving intracellular controlled propulsion. Intracellular organelles, such as mitochondria, are an emerging therapeutic target with selective targeting and curative efficacy. We report an autonomous nanorobot capable of active mitochondria-targeted drug delivery, prepared by facilely encapsulating mitochondriotropic doxorubicin-triphenylphosphonium (DOX-TPP) inside zeolitic imidazolate framework-67 (ZIF-67) nanoparticles. The catalytic ZIF-67 body can decompose bioavailable hydrogen peroxide overexpressed inside tumor cells to generate effective intracellular mitochondriotropic movement in the presence of TPP cation. This nanorobot-enhanced targeted drug delivery induces mitochondria-mediated apoptosis and mitochondrial dysregulation to improve the in vitro anticancer effect and suppression of cancer cell metastasis, further verified by in vivo evaluations in the subcutaneous tumor model and orthotopic breast tumor model. This nanorobot unlocks a fresh field of nanorobot operation with intracellular organelle access, thereby introducing the next generation of robotic medical devices with organelle-level resolution for precision therapy.
Epigenetic dysregulation contributes to bladder cancer tumorigenesis. H3K36me2 demethylase KDM2A functions as an important epigenetic regulator of cell fate in many types of tumors. However, its role in bladder cancer remains unknown. Here, we revealed a positive correlation between KDM2A gene copy number gain and upregulation of KDM2A mRNA expression in bladder cancer. Moreover, a super-enhancer (SE) driving KDM2A transcription was found in high-grade bladder cancer, resulting in a significantly higher expression of KDM2A mRNA compared to that in low-grade bladder tumors. KDM2A knockdown (KD) decreased the proliferation, invasion, and spheroid formation of high-grade bladder cancer cells and inhibited tumor growth in mouse xenograft models. Furthermore, we identified RARRES3 as a key KDM2A target gene. KDM2A suppresses RARRES3 expression via demethylation of H3K36me2 in the RARRES3 promoter. Intriguingly, RARRES3 KD attenuated the inhibitory effects of KDM2A depletion on the malignant phenotypes of high-grade bladder cancer cells. The combination of the KDM2A inhibitor IOX1 and the RARRES3 agonist all-trans retinoic acid (ATRA) synergistically inhibited the proliferation of high-grade bladder cancer cells, suggesting that the KDM2A/RARRES3 axis may be a promising therapeutic target for the treatment of high-grade bladder cancer.
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