FIRST MERCURY CONFERENCE IN
Sunday, July 21 to Tuesday, July 23 2002
Chris Cramer, Continuum Solvation Methods, University of Minnesota, Chemistry Department.
Heather Carlson, Drug Design, University of Michigan, College of Pharmacy.
Harold Scheraga, Protein Folding, Cornell University, Deptartment of Chemistry.
Wilma Olson, DNA Structure and Function, Rutgers University, Department of Chemistry.
Barbara Garrison, Surface Science, Penn State, Chemistry Department.
Michael Gilson, Molecular Recognition, University of Maryland Biotechnology Institute, Center for Advanced Research in Biotechnology.
Roberto Gomperts, Getting the Most out of Gaussian, Principal Scientist Life Sciences, SGI.
“Protein flexibility and drug design: How to hit a moving target”
In structure-based drug design, studies are usually based on crystal structures of ligand-protein complexes, and hit lists can be restricted to the size and shape of the receptor model. It is important to explore new conformational and chemical space for the potential inhibitors, but it is difficult to predict the plasticity of the binding site. Many improvements that accommodate protein flexibility in computer-aided drug design are being developed. These methods are reviewed with the focus being techniques that move beyond the rotation of side chains. The use of multiple protein structures is emerging as the best choice for including more realistic changes in protein conformation, but the optimal way to using these structures is still unclear.
(1) HA Carlson, JA McCammon. Accommodating protein flexibility in Computational Drug Design. Mol. Pharmacol. 2000, 57, 213-218.
(2) HA Carlson, KM Masukawa, K Rubins, FD Bushman, WL Jorgensen, RD Lins, JM Briggs, JA McCammon. Developing a dynamic pharmacophore model for HIV-1 integrase. J. Med. Chem. 2000, 43, 2100-2114.
(3) HA Carlson, KM Masukawa, JA McCammon. Method for including the dynamic fluctuations of a protein in computer-aided drug design. J. Phys. Chem. A 1999, 103, 10213-10219.
“Modeling Drug Bioavailability, Environmental Fate Constants, Organic Structure and Reactivity, and Other Solvation-Dependent Phenomena”
Continuum solvent models provide a particularly efficient means for including the effects of surrounding condensed phases in quantum mechanical calculations. Amongst the most successful of the available continuum models for this purpose are the so-called SMx series of solvation models from the Minnesota Solvation Group. The functional form of the SMx models, with an emphasis on the intuitive aspects underlying the continuum approximation, will be presented. Application of the SMx models to a wide variety of processes will also be discussed. Some of the particular case studies presented will be modeling solvent effects on (i) conformational equilibria, (ii) reaction paths, and (iii) partitioning of organic molecules between water and chloroform, phospholipid bilayers, and organic-carbon-content-normalized soil.
(1) Winget, P.; Cramer, C. J.; Truhlar, D. G. “Prediction of Soil Sorption Coefficients Using a Universal Solvation Model” Environ. Sci. Technol. 2000, 34, 4733.
(2) Patterson, E. V.; Cramer, C. J.; Truhlar, D. G. “Reductive Dechlorination of Hexachloroethane in the Environment. Mechanistic Studies via Computational Electrochemistry” J. Am. Chem. Soc. 2001, 123, 2025.
(3) Only for the truly dedicated: Cramer, C. J.; Truhlar, D. G. “Implicit Solvation Models: Equilibria, Structure, Spectra, and Dynamics” Chem. Rev. 1999, 99, 2160.
“Cell Imaging, Tattoo Removal, LASIX, Erosion of the Moons of Saturn and Mass Spectrometry: A Common Denominator”
Fast energy deposition of energy at surfaces leads to the removal of material. The energy source can either be an ion beam in which case the phenomenon is called sputtering or secondary ion mass spectrometry (SIMS) or it can be a laser in which case there is laser ablation. Both processes have applications in high weight mass spectrometry. I will present some of the basics of the protocol and challenges for modeling these fast energy deposition events as well as results from the simulation.
(1) Molecular Dynamics Simulations of Surface Chemical Reactions, B. J. Garrison, Chem. Soc. Reviews, Vol. 21, 155-162 (1992).
(2) Molecule Liftoff from Surfaces, B. J. Garrison, A. Delcorte and K. D. Krantzman, Accts. Chem. Res., 33, 69-77 (2000).
(3) A Microscopic View of Laser Ablation, L. V. Zhigilei, P. B. S. Kodali and B. J. Garrison, J. Phys. Chem. B, Feature Article, 102, 2845-2853 (1998).
“Getting the Most out of Gaussian: Tips, Tricks, and Hints for Using Gaussian98 for Large-Scale Ab-Initio Calculations”
As researchers in Computational Chemistry strive to complete very large Ab-Initio calculations using Gaussian, they reach instinctively to two parameters in Gaussian98 that when increased, are expected to shorten the execution time. These are the number of processors and the amount of memory requested to perform the calculation. The improvements achieved by altering these variables are often disappointing, and in a fair amount of cases they even can cause a slowdown of the program. This talk will address some of these counterintuitive effects. Several Tips, Tricks and Hints for running Gaussian efficiently will be given.
“A New ‘Dimension’ to DNA Sequence Analysis”
In addition to the genetic message, DNA base sequence carries structural and energetic signals that are related to its biological function. We have extracted “knowledge-based” energy functions from high-resolution crystal structures to study the sequence-dependent recognition and folding of the long, threadlike molecule. The deformations of individual base-pair steps are described by six independent “step” parameters: three angular variables (Tilt, Roll, Twist) and three variables (Shift, Slide, Rise) with dimensions of distance. The talk will focus on sequence contexts which underlie the looping of DNA which is implicated in the regulation of transcription and the organization of chromatin.
(1) Westcott, Timothy P., Tobias, Irwin, and Olson, Wilma K. “Elasticity Theory and Numerical Analysis of DNA Supercoiling: An Application to DNA Looping,” J. Phys. Chem. 99, 17926, 17935 (1995).
(2) Olson, Wilma K., Gorin, Andrey A., Lu, Xiang, Jun, Hock, Lynette M., and Zhurkin, Victor B., “DNA Sequence Dependent Deformability Deduced from Protein, DNA Crystal Complexes,” Proc. Natl. Acad. Sci., USA 95,
11163, 11168 (1998).
HAROLD A. SCHERAGA
“Ab initio prediction of protein structure”
Application of a physics-based potential and an efficient procedure to search conformational space to locate the global minimum, without use of ancillary aids such as secondary structure prediction, homology modeling, threading, or fragment coupling, can provide an understanding of how inter-residue interactions lead to the folded three-dimensional structure of a protein. With present computer resources, it is not possible to carry out this search with an all-atom representation of a protein. To circumvent this difficulty, we use a hierarchical approach in which the polypeptide chain is first represented at the united-residue level as a virtual-bond chain (a string of _-carbons) with attached side chains represented as ellipsoids. The corresponding united-residue potential is explored with a conformational space annealing technique to locate the region of the global minimum. The resulting few low-energy united-residue chains are then converted to an all-atom level, and the refinement of these structures is carried out. In blind tests of this physics-based approach, with inclusion of multi-body (cooperative) interactions, in the CASP3 exercise, this procedure performed very well on largely _-helical targets. With the introduction of higher multi-body interactions, from a Kubo-type cumulant expansion of the free energy, it became possible to identify _-structure portions of _/_ proteins in the CASP4 exercise. The computational procedures will be described, and the computed structures, and their comparison with experiment, will be presented.
(1) J. Pillardy, C. Czaplewski, A. Liwo, J. Lee, D.R. Ripoll, R. Kazmierkiewicz, S. Oldziej, W. J. Wedemeyer, K.D. Gibson, Y.A. Arnautova, J. Saunders, Y.-J. Ye and H.A. Scheraga – Recent improvements in prediction of protein structure by global optimization of a potential energy function, Proc. Natl. Acad. Sci., U.S.A., 98, 2329-2333 (2001).
(2) H.A. Scheraga, J. Pillardy, A. Liwo, J. Lee, C. Czaplewski, D.R. Ripoll, W.J. Wedemeyer and Y.A. Arnautova – Evolution of physics-based methodology for exploring the conformational energy landscape of proteins, J. Comput. Chem., 23, 28-34 (2002).