Sequence networks, capacity, and flow between protein structures
November 11, Tue 2008
1:00 pm, MRB 200 Conference Room
Dr. Ron Elber
Professor of Chemistry and Biochemistry and W.A. "Tex" Moncrief Chair in Computational Life Sciences and Biology, University of Texas
Sequence-structure relationships in proteins are highly asymmetric because many sequences fold into relatively few structures. What is the number of sequences that fold into a particular protein structure? Is it possible to switch between stable protein folds by point mutations? To address these questions, we compute a directed graph of sequences and structures of proteins, which is based on 2,060 experimentally determined protein shapes from the Protein Data Bank. The directed graph is highly connected at native energies with “sinks” that attract many sequences from other folds. The sinks are rich in -sheets. The number of sequences that transition between folds is significantly smaller than the number of sequences retained by their fold. The sequence flow into a particular protein shape from other proteins correlates with the number of sequences that matches this shape in empirically determined genomes. Properties of strongly connected components of the graph are correlated with protein length and secondary structure.
Ron Elber, Ph.D., Professor, in the Department of Chemistry, University of Texas at Austin, and W.A. "Tex" Moncrief Chair in Computational Life Sciences and Biology in the Institute of Computational Engineering and Sciences (ICES) as of July 1, 2007. Ron Elber obtained a bachelor's degree in chemistry and physics in 1981 and a Ph.D. theoretical chemistry in 1984 at the Hebrew University of Jerusalem. He was a postdoctoral fellow in theoretical biophysics from 1984 to 1987 at Harvard University. Ron was on the chemistry faculty of the University of Illinois (1987-1992) and on the chemistry and biology faculty at Hebrew University (1992-1999). From 1999-2007, he has been on the computer science faculty at Cornell where he was a full professor. Ron's research is in computational biology and bioinformatics. His group is developing novel tools (MOIL) to simulate dynamics of biological macromolecules. His current research focuses on algorithms to extend the time scales of simulations and to study complex processes, such as the kinetics of protein folding. Ron's techniques for path following and enhanced sampling are in wide use and motivated the development of related algorithms. His bioinformatic investigations focus on protein annotation using sequence-to structure matches (LOOPP). He currently investigates the network created by sequence structure relationship in proteins.