Session S1 - DCMP/DBP/DCOMP Prize Session.
INVITED session, Wednesday afternoon, March 24
517A, Palais des Congres
Peter G. Wolynes Center for Theoretical Biological Physics Department of Chemistry and Biochemistry and Physics University of California, San Diego La Jolla, CA 92093-0371
Fifteen years ago, how proteins folded into organized structures on the basis of their sequence was a great mystery. By characterizing the energy landscapes of proteins with tools from the statistical mechanics of disordered systems like spin glasses, a “new view” of the folding process became possible. Energy landscape theory provided an incentive to pursue heroic new experiments and to carry out difficult computer simulations addressing protein folding mechanisms.
Many aspects of folding kinetics revealed by these studies can be quantitatively understood using the simple idea that the topography of the energy landscape is that of a “rugged funnel”.
Energy landscape theory provided a quantitative means of characterizing which amino acid sequences can rapidly fold. Algorithms based on energy landscape theory have been used to successfully design novel sequences that fold to a given structure in the laboratory.
Energy landscape ideas have begun to transform the prediction of protein structure from sequence data from being an art to being a science. The success of energy landscape- based algorithms in predicting protein structure from sequence will be highlighted. While there is still much to learn about folding mechanisms and much work to do achieving universally reliable structure prediction, many parts of what used to be called “the protein folding problem” can now be considered solved.