The objective of the Structural Biology (StructBiol) Core of the Center to Develop Therapeutic Countermeasures to High-threat bacterial Agents is to provide a unique platform for Center investigators to engage in structure-based lead optimization projects by determining the x-ray crystallographic structures of antibacterial agents bound to target protein. The StructBiol Core will help investigators realize the enormous potential of detailed structural Information on ligand-protein interactions for target validation and lead optimization, thereby speeding the antibiotic discovery process.
The specific aims of the StructBiol Core are:
Aim 1 : To provide services for all stages of structural analysis projects from protein expression and purification, biophysical characterization of ligand-target interactions, crystallization and structure determination to structure analysis and presentation.
Aim 2 : To use knowledge gained from structural studies ofthe inhibitor-target protein interactions to increase the binding affinity of lead compounds toward target and improve their pharmacological and chemical properties.
This research is highly relevant for understanding of the structures of antibiotic target sites at a detailed atomic level, and for ultimate discovery of new antibiotics against resistant bacterial infections.
|Chu, John; Vila-Farres, Xavier; Inoyama, Daigo et al. (2016) Discovery of MRSA active antibiotics using primary sequence from the human microbiome. Nat Chem Biol 12:1004-1006|
|Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean et al. (2016) Predicting Mouse Liver Microsomal Stability with ""Pruned"" Machine Learning Models and Public Data. Pharm Res 33:433-49|
|Ekins, Sean; Perryman, Alexander L; Clark, Alex M et al. (2016) Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015). J Chem Inf Model 56:1332-43|
|Ekins, Sean; Madrid, Peter B; Sarker, Malabika et al. (2015) Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery. PLoS One 10:e0141076|