Book of Abstracts: Albany 2003
June 17-21 2003
Active Site Directed Drug Design In Silico: A Case Study of COX-2 Inhibitors
The challenges and opportunities offered by the growing genomic data and the concomitant progresses in structural genomics endeavors are many fold. A particular case is the possibility of automation of lead compound design in silico given the structure of the protein target and a definition of its active site. We are working on a comprehensive software for active site directed drug design. The methodology consists of (i) design of a library of templates now adding up to 129 chemical moieties consisting of unique rings, side chains and linkers spanning nearly the entire chemical space, (ii) template stitching for the trial structure generation of candidate molecules, (iii) screening trial compounds for drug-like properties using Lipinski?s rule of five and other filters, (iv) parameter development for energy evaluation via quantum mechanical calculations, (v) Monte Carlo docking in the active site for structure determination of the complex and further selection of the trial compounds, (vi) energy optimization of the resultant protein-drug complex structures, (vii) binding affinity computations on resultant structures and ranking of candidate molecules, (viii) molecular dynamics simulations and free energy component analyses (1) on a limited set of good candidates and (ix) determination of kinetic properties (2) associated with the diffusion of lead compounds to active site via Brownian dynamics simulations.
The above protocols are being tested for the design of inhibitors with COX-2 (Cyclooxygenase-2) as a test case. Preliminary results indicate that some of the known NSAIDS are recovered automatically at step (iii) and their IC50 values correlate well with the interaction energies at step (v) itself i.e. after Monte Carlo docking. Also, aspirin was found to diffuse to active site of COX-2 following Langevin?s equations of motion in step (ix). Results of these studies will be presented.
1Department of Chemistry & Supercomputing Facility
for Bioinformatics & Computational Biology