Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery: Bridging Experiments and Computations - September 10-14, 2014, Istanbul, Turkey

Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery Session VI Abstracts

Data Mining Large-Scale Bioactivity Datasets to Find Patterns in Ligand Recognition John Overington . EMBL-EBI, Hinxton, United Kingdom. We have built a large-scale open data resource, ChEMBL; this contains in excess of 1.4 million compound structures, and over 10 million associated bioactivities. Where possible the data is linked to molecular targets, and further annotation performed to provide deeper indexing and organisation of the data. This has become an important resource for the community in developing data-driven approaches to a number of important problems in drug design and safety, including predicting targets and 'off-targets' for novel compounds, quantifying the drug-likeness of compounds, and in the design of novel bioactive molecules. This data has been recently complemented by a new resource, SureChEMBL, containing automatically text-mined data from patent sources, signficantly increasing coverage of chemical and target space and diversity. The presentation will present the principles in the organisation and features of ChEMBL, and then some approaches addressing the difference between allosteric and non-allosteric ligands, target predictions from phenotypic data, and finally analysis of polypharmacology - the binding of a ligand to many components of a cell. Combating Drug Resistance: Lessons from the Viral Proteases of HIV and HCV Celia Schiffer . UMASS Medical School, Worcester, MA, USA. Drug resistance negatively impacts the lives of millions of patients and costs our society billions of dollars by limiting the longevity of many of our most potent drugs. Drug resistance can be caused by a change in the balance of molecular recognition events that selectively weakens inhibitor binding but maintains the biological function of the target. To reduce the likelihood of drug resistance, a detailed understanding of the target’s function is necessary. Both structure at atomic resolution and evolutionarily constraints on its variation is required. “Resilient” targets are less susceptible to drug resistance due to their key location in a particular pathway. This rationale was derived from our lab’s experience with substrate recognition and drug resistance in HIV-1 protease and Hepatitis C (HCV) NS3/4A. Both HIV-1 protease and HCV NS3/4A protease are potentially “resilient” targets where resistant mutations occur outside of the substrate binding site. These principals are likely more generally applicable to other quickly evolving diseases where drug resistance is quickly evolving.

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