Jay W. Ponder, Ph.D. Associate Professor Biochemistry and Molecular Biophysics Biomedical Engineering Computational and Molecular Biophysics Program Computational and Systems Biology Program
Office Phone: 314-362-4195 Lab Phone: 314-362-4198 Other Phone: FAX: 314-362-7183 Box: 8231 Lab Address: Center for Computational Biology, 700 S. Euclid Avenue, Room 208 Email: ponder@wustl.edu Website: http://dasher.wustl.edu Keywords: molecular modeling; protein structure; computational chemistry; molecular dynamics simulation; protein engineering Short Research Description: Computational chemistry, protein engineering, theoretical protein structure and folding.
Research Abstract: My group develops and applies computational tools for problems in structural biology and in protein engineering, function and folding. The Ponder Lab produces and distributes software packages ranging from macromolecular mechanics and dynamics simulation (TINKER) to molecular visualization (Force Field Explorer) to empirical packing analysis of protein structure (PROPAK) to sequence analysis and tertiary structure prediction (SLEUTH). Our research focuses on three areas related to biopolymer modeling. First, we have implemented efficient methods for including multipole electrostatics and polarization in simulations as a framework for our next-generation AMOEBA force field. This new energy model enables reliable calculation of structures. It also yields energetics for ligand docking and drug design to within "chemical accuracy"--absolute errors of 0.5 kcal/mol or less. Current AMOEBA applications include elucidation of the role of ions in biology, and refinement of highly accurate homology models. Second, we are exploring a powerful approach to conformational search for flexible biopolymers. Our method transforms the potential energy surface for a molecule by a diffusion equation-based smoothing procedure. This "potential smoothing" paradigm is applicable to a variety of problems including transmembrane helix packing, global optimization, and energy-based clustering of conformations. Third, we use a novel distance geometry algorithm and heuristic rules as a basis for protein structure prediction. Statistical distance distributions and predicted secondary structure constraints generate libraries of candidate folds to be scored with an informatics-based contact function or physics-based effective mean force potential. Ultimately, our interest lies in the "end game" of protein folding--in making a connection between atomic-level protein structures and low-resolution models available from fold recognition algorithms.
Selected Publications: Grossfield A, Ren P, Ponder JW. Ion solvation thermodynamics from simulation with a polarizable force field. J Am Chem Soc 2003 125:15671-15682. Ponder, J. W.; Case, D. A.; Daggett, V. Advances in Protein Chemistry. Vol. 66, Force fields for protein simulation. New York: Academic Press; 2003. p. 27-85. Ren P, Ponder JW. Polarizable atomic multipole water model for molecular mechanics simulation. J Phys Chem B 2003 107:5933-5947. Huang ES, Samudrala R, Ponder JW. Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions. J Mol Biol 1999 290:267-281. Pappu RV, Marshall GR, Ponder JW. A potential smoothing algorithm accurately predicts transmembrane helix packing. Nat Struct Biol 1999 6:50-55.