FY2010 - Oak Ridge National Laboratory

Director’s R&D Fund—
Ultrascale Computing and Data Science
enzyme beta-ketoacyl-acyl carrier protein reductase (FabG) has important implications in antibacterial
activity. DHFR is a classic drug target and widely studied enzyme both theoretically and experimentally.
FabG reduces the beta keto-acyl acyl carrier protein to a beta hydroxy intermediate in the fatty acid
synthesis system. Our methodology for the identification of the allosteric sites is based on modeling the
rate-limiting reactivity catalyzed by these enzymes. In both enzymes the rate-limiting step is the hydride
transfer from cofactor nicotinamide adenosine dinucleotide. The identification of the allosteric sites was
achieved by characterizing the reaction-coupled protein flexibility and gradually mapping the network of
residues involved in promoting the reaction. Note, in addition to identification of the allosteric sites, our
computational studies also enabled us to obtained detailed insights into the mechanism of catalysis for the
target enzymes.
The high-throughput docking infrastructure has been developed for determining the best binding pose of
the ligands with the enzyme targets given the receptor binding (allosteric) sites. The infrastructure has
been deployed on ORNL’s Jaguar supercomputer. In the first phase, a test compound library constituting
1010 compounds was docked on the set on the allosteric sites. Based on the free energy of binding and the
corresponding scores, the top compounds and best poses of the ligands on the allosteric sites were
identified. The computational screening was then deployed to handle large chemical libraries.
In the second phase of this project, a full compound library (with about 625,000 drug-like compounds)
has been screened against the two medical targets. For FabG, a list of 400 compounds has been prepared
(100 compounds each for 4 allosteric sites) based on the use of the high-throughput screening
methodology. This list is currently being refined using semi-empirical electronic structure methods. For
this phase, we are interacting with the structural biologist and computational chemists at St. Jude’s
Medical Research Center. Further, the results are being validated against an experimental screening
(performed by our collaborators). The correlation between the experimental (wet-lab) screening and the
computationally predicted results is being used to further refine the high-throughput screening
methodology. Once the optimization of the computational screening methodology is achieved, for the top
results based our screening, we will proceed to perform crystallographic studies of the enzyme in
presence of the ligands.
Information Shared
Ramanathan, A., P. K. Agarwal, M. Kurnikova, and C. J. Langmead. 2010. “An Online Approach for
Mining Collective Behaviors from Molecular Dynamics Simulations.” Journal of Computational
Biology 17(3), 309–324.
Kamath, G., E. E. Howell, and P. K. Agarwal. 2010. “The Tail Wagging the Dog: Insights into Catalysis
in R67 Dihydrofolate Reductase.” Biochemistry 49(42), 9078–9088.
Ramanathan, A., and P. K. Agarwal. 2010. “Evolutionarily conserved linkage between enzyme fold,
flexibility, and catalysis.” PLoS Biology, under review.
Ramanathan, A., A. Savol, C. J. Langmead, P. K. Agarwal, and C. S. Chennubhotla. 2010. “Organizing
conformational diversity relevant to protein function.”PLoS One, under review.
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