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.

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.

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.