|
|
|
|
|
|
| About David Case (Rutgers University) |
|
David Case's research interests cover fairly broad areas of theoretical and computational chemistry, especially in areas related to biochemistry. He received a B.S. degree in Chemistry from Michigan State, and Masters and Ph.D degrees from Harvard in Chemical Physics. His academic career has included stints at the University of California, Davis, The Scripps Research
Institute, and (starting in 2008) Rutgers University. Dr. Case's research focuses on computer simulations of proteins and nucleic acids, and he is the lead developer of the Amber suite of biomolecular simulation programs. In recent years, he has been most active in the development of implicit solvent techniques (based mainly on the generalized Born model); in the analysis of
parameters derived from biomolecular NMR spectroscopy (including chemical shifts, coupling constants, and relaxation times); and in studies of the electronic structures of active sites of metalloenzymes (with an emphasis on iron-sulfur and heme proteins).
|
|
Scoring and Re-Scoring Ligand Binding Energies using Implicit Solvent Models
David A. Case, Dept. of Chemistry and Chemical Biology, and BioMaPS Institute for Quantitative Biology, Rutgers University
Molecular docking computationally screens thousands to millions of organic molecules against protein structures, looking for those with complementary fits. Many approximations are made, often resulting in low hit rates. A strategy to overcome these approximations is to rescore top-ranked docked molecules using a better but slower method. One such class of methods include
mechanics generalized Born surface area (MM-GBSA) techniques. These more physically realistic methods have improved models for solvation and electrostatic interactions and conformational change compared to most docking programs. In a collaboration with Matt Jacobson and Brian Shoichet at UCSF, we recently re-ranked docking hit lists in three small buried sites: a hydrophobic cavity that binds apolar ligands, a slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an anionic cavity that binds cationic ligands. In retrospective calculations, MM-GBSA techniques with binding-site minimization better distinguished the known ligands for each cavity from the known decoys compared to the docking calculation alone. A total of 33 molecules highly ranked by MM-GBSA for the three cavities were tested experimentally. Of these, 23 were observed to bind: these are docking false negatives rescued by rescoring. X-ray crystal structures were determined for 21 of these 23 molecules. In many cases, the geometry prediction by MM-GBSA improved the initial docking pose and more closely resembled the crystallographic result; yet in several cases, the rescored geometry failed to capture large conformational changes in the protein. I will discuss what we know about the origins of these successes and failures, and prospects for rescoring in biologically relevant targets.
|
|
|
|
|
|
|
|
|
