Characterizing the interactions of the B-box E3 ligase with E2 conjugating enzyme: Learning about protein ubiquitination.
Michael Massiah (George Washington University)
Dr. Massiah will present recent work on the B-box domain from the human MID1 protein. The B-box domain may represent a new member of the ubiquitin E3 ligase, with similar fold as the RING-type E3 ligase. However, it has weaker activity. Despite this, it is require for the targeting and polyubiquitination of protein phosphatase 2A (a molecular master switch), alpha4, and the fused kinase. To understand how the B-box domain functions, NMR and fluorescence spectroscopy were used to characterize its interaction with the E2 enzyme, UbcH5, and mutagenesis to identify structural features that are important for ligase activity, and to engineer a more active E3 ligase. He will also discuss interesting observation how RNA affect MID1 E3 ligase activity.
Computational approaches to designing safer chemicals
Jakub Kostal (George Washington University)
Designing chemicals for targeted biological activity is a time-consuming and technologically challenging process. Computational methods have revolutionized drug discovery by being both fast, virtually screening vast chemical libraries to find drug candidates against biological targets of therapeutic interest, and accurate, providing state-of-the-art tools to optimize said candidates for greater activity. In developing pharmaceuticals, we seek to impart specific biological activity to a molecule but also to minimize any side effects caused by unintended activity. The latter is true for all commercial chemicals as our society has grown increasingly aware of the adverse effects chemicals can have on human and environmental health. Regrettably, to test every new chemical on animals to ensure its safety has been both economically and ethically unfeasible. In vitro and in silico methods offer a promising alternative; however, they generally lack the accuracy and robustness needed to replace animal tests. Inspired by the successes of computer-aided drug discovery, our group has focused on transforming said techniques to aid in safer chemical design. Mimicking Lipinski’s rules for druglikeness, we have developed broad, property-based guidelines that inform design of chemicals with minimal ecotoxicity. More recently, we have transformed statistical free energy perturbation calculations used in drug lead optimization to afford redesign of existing toxicants for increased safety.