Network-based prediction of anti-cancer drug combinations.

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes.

TAIGET: A small-molecule target identification and annotation web server.

Background: Accurate target identification of small molecules and downstream target annotation are important in pharmaceutical research and drug development. Methods: We present TAIGET, a friendly and easy to operate graphical web interface, which consists of a docking module based on AutoDock Vina and LeDock, a target screen module based on a Bayesian-Gaussian mixture model (BGMM), and a target annotation module derived from >14,000 cancer-related literature works. Results: TAIGET produces binding poses by selecting ≤5 proteins at a time from the UniProt ID-PDB network and submitting ≤3 ligands at a time with the SMILES format. Once the identification process of binding poses is complete, TAIGET then screens potential targets based on the BGMM. In addition, three medical experts and 10 medical students curated associations among drugs, genes, gene regulation, cancer outcome phenotype, 2,170 cancer cell types, and 73 cancer types from the PubMed literature, with the aim to construct a target annotation module. A target-related PPI network can be visualized by an interactive interface. Conclusion: This online tool significantly lowers the entry barrier of virtual identification of targets for users who are not experts in the technical aspects of virtual drug discovery. The web server is available free of charge at http://www.taiget.cn/.

Botanical drugs: a new strategy for structure-based target prediction.

Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand-protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.

Arabidopsis cryptochrome 1 controls photomorphogenesis through regulation of H2A.Z deposition.

Light is a key environmental cue that fundamentally regulates plant growth and development, which is mediated by the multiple photoreceptors including the blue light (BL) photoreceptor cryptochrome 1 (CRY1). The signaling mechanism of Arabidopsis thaliana CRY1 involves direct interactions with CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1)/SUPPRESSOR OF PHYA-105 1 and stabilization of COP1 substrate ELONGATED HYPOCOTYL 5 (HY5). H2A.Z is an evolutionarily conserved histone variant, which plays a critical role in transcriptional regulation through its deposition in chromatin catalyzed by SWR1 complex. Here we show that CRY1 physically interacts with SWC6 and ARP6, the SWR1 complex core subunits that are essential for mediating H2A.Z deposition, in a BL-dependent manner, and that BL-activated CRY1 enhances the interaction of SWC6 with ARP6. Moreover, HY5 physically interacts with SWC6 and ARP6 to direct the recruitment of SWR1 complex to HY5 target loci. Based on previous studies and our findings, we propose that CRY1 promotes H2A.Z deposition to regulate HY5 target gene expression and photomorphogenesis in BL through the enhancement of both SWR1 complex activity and HY5 recruitment of SWR1 complex to HY5 target loci, which is likely mediated by interactions of CRY1 with SWC6 and ARP6, and CRY1 stabilization of HY5, respectively.