The bryostatins are protein kinase C modulators with unique structural features and potential anticancer and neurological activities. These complex polyketides were isolated from the marine bryozoan Bugula neritina, but recent studies indicate that they are produced by the uncultured symbiotic bacterium "Candidatus Endobugula sertula" ("E. sertula"). Here we present the putative biosynthetic genes: five modular polyketide synthase (PKS) genes, a discrete acyltransferase, a beta-ketosynthase, a hydroxy-methyl-glutaryl CoA synthase (HMG-CS), and a methyltransferase. The cluster was sequenced in two closely related "E. sertula" strains from different host species. In one strain the gene cluster is contiguous, while in the other strain it is split into two loci, with one locus containing the PKS genes and the other containing the accessory genes. Here, we propose a hypothesis for the biosynthesis of the bryostatins. Thirteen PKS modules form the core macrolactone ring, and the pendent methyl ester groups are added by the HMG-CS gene cassette. The resulting hypothetical compound bryostatin 0 is the common basis for the 20 known bryostatins. As "E. sertula" is to date uncultured, heterologous expression of this biosynthetic gene cluster has the potential of producing the bioactive bryostatins in large enough quantities for development into a pharmaceutical.
Exploring the virus infection mechanisms is significant for defending against virus infection and providing a basis for studying endocytosis mechanisms. Single-particle tracking technique is a powerful tool to monitor virus infection in real time for obtaining dynamic information. In this study, we reported a quantum-dot-based single-particle tracking technique to efficiently and globally research the virus infection behaviors in individual cells. It was observed that many influenza viruses were moving rapidly, converging to the microtubule organizing center (MTOC), interacting with acidic endosomes, and finally entering the target endosomes for genome release, which provides a vivid portrayal of the five-stage virus infection process. This report settles a long-pending question of how viruses move and interact with acidic endosomes before genome release in the perinuclear region and also finds that influenza virus infection is likely to be a "MTOC rescue" model for genome release. The systemic technique developed in this report is expected to be widely used for studying the mechanisms of virus infection and uncovering the secrets of endocytosis.
Target identification of the known bioactive compounds and novel synthetic analogs is a very important research field in medicinal chemistry, biochemistry, and pharmacology. It is also a challenging and costly step towards chemical biology and phenotypic screening. In silico identification of potential biological targets for chemical compounds offers an alternative avenue for the exploration of ligand-target interactions and biochemical mechanisms, as well as for investigation of drug repurposing. Computational target fishing mines biologically annotated chemical databases and then maps compound structures into chemogenomical space in order to predict the biological targets. We summarize the recent advances and applications in computational target fishing, such as chemical similarity searching, data mining/machine learning, panel docking, and the bioactivity spectral analysis for target identification. We then described in detail a new web-based target prediction tool, TargetHunter (http://www.cbligand.org/TargetHunter). This web portal implements a novel in silico target prediction algorithm, the Targets Associated with its MOst SImilar Counterparts, by exploring the largest chemogenomical databases, ChEMBL. Prediction accuracy reached 91.1% from the top 3 guesses on a subset of high-potency compounds from the ChEMBL database, which outperformed a published algorithm, multiple-category models. TargetHunter also features an embedded geography tool, BioassayGeoMap, developed to allow the user easily to search for potential collaborators that can experimentally validate the predicted biological target(s) or off target(s). TargetHunter therefore provides a promising alternative to bridge the knowledge gap between biology and chemistry, and significantly boost the productivity of chemogenomics researchers for in silico drug design and discovery.
Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific chemogenomics knowledgebase, AlzPlatform () with cloud computing and sourcing functions. AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioactivities from 38 284 corresponding bioassays and 10 050 references. Furthermore, we have demonstrated the application of the AlzPlatform in three case studies for identification of multitargets and polypharmacology analysis of FDA-approved drugs and also for screening and prediction of new AD active small chemical molecules and potential novel AD drug targets by our established TargetHunter and/or HTDocking programs. The predictions were confirmed by reported bioactivity data and our in vitro experimental validation. Overall, AlzPlatform will enrich our knowledge for AD target identification, drug discovery, and polypharmacology analyses and, also, facilitate the chemogenomics data sharing and information exchange/communications in aid of new anti-AD drug discovery and development.
Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unstructured medical text which is growing exponentially. This calls for developing text mining techniques for identifying DDIs. The state-of-the-art DDI extraction methods use Support Vector Machines (SVMs) with non-linear composite kernels to explore diverse contexts in literature. While computationally less expensive, linear kernel-based systems have not achieved a comparable performance in DDI extraction tasks. In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information. The proposed approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that when equipped with a rich set of lexical and syntactic features, a linear SVM classifier is able to achieve a competitive performance in detecting DDIs. In addition, the one-against-one strategy proves vital for addressing an imbalance issue in DDI type classification. Applied to the DDIExtraction 2013 corpus, our system achieves an F1 score of 0.670, as compared to 0.651 and 0.609 reported by the top two participating teams in the DDIExtraction 2013 challenge, both based on non-linear kernel methods.
Infectious hematopoietic necrosis virus (IHNV)is an important fish pathogen that infects both wild and cultured salmonids. As a species of the genus Novirhabdovirus, IHNV is a valuable model system for exploring the host entry mechanisms of rhabdoviruses. In this study, quantum dots (QDs) were used as fluorescent labels for sensitive, long-term tracking of IHNV entry. Using live-cell fluorescence microscopy, we found that IHNV is internalized through clathrin-coated pits after the virus binds to host cell membranes. Pretreatment of host cells with chlorpromazine, a drug that blocks clathrin-mediated endocytosis, and clathrin light chain (LCa) depletion using RNA interference both resulted in a marked reduction in viral entry. We also visualized transport of the virus via the cytoskeleton (i.e., actin filaments and microtubules) in real time. Actin polymerization is involved in the transport of endocytic vesicles into the cytosol, whereas microtubules are required for the trafficking of clathrin-coated vesicles to early endosomes, late endosomes, and lysosomes. Disrupting the host cell cytoskeleton with cytochalasin D or nocodazole significantly impaired IHNV infectivity. Furthermore, infection was significantly affected by pretreating the host cells with bafilomycin A1, a compound that inhibits the acidification of endosomes and lysosomes. Strong colocalizations of IHNV with endosomes indicated that the virus is internalized into these membrane-bound compartments. This is the first report in which QD labeling is used to visualize the dynamic interactions between viruses and endocytic structures; the results presented demonstrate that IHNV enters host cells via clathrin-mediated endocytic, cytoskeleton-dependent, and low-pH-dependent pathways.
"Candidatus Endobugula sertula," the uncultivated bacterial symbiont of Bugula neritina, is the proposed source of the bryostatin family of anticancer compounds. We cloned a large modular polyketide synthase (PKS) gene complex from "Candidatus Endobugula sertula" and characterized one gene, bryA, which we propose is responsible for the initial steps of bryostatin biosynthesis. Typical PKS domains are present. However, acyltransferase domains are lacking in bryA, and beta-ketoacyl synthase domains of bryA cluster with those of PKSs with discrete, rather than integral, acyltransferases. We propose a model for biosynthesis of the bryostatin D-lactate starter unit by the bryA loading module, utilizing atypical domains homologous to FkbH, KR, and DH. The bryA gene product is proposed to synthesize a portion of the pharmacologically active part of bryostatin and may be useful in semisynthesis of clinically useful bryostatin analogs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
334 Leonard St
Brooklyn, NY 11211
Copyright © 2023 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.