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Ruth Nussinov, Ph.D.

CCR Nanobiology Program Head, Computational Structural Biology Group Senior Investigator (Contr) NCI at Frederick P.O. Box B Bldg. 469, Rm. 149 Frederick, MD 21702-1201 Phone:   301-846-5579 Fax:   301-846-5598 E-Mail:   ruthn@ncifcrf.gov Link: Other Homepage

Biography

Dr. Ruth Nussinov is a Professor in the Department of Human Genetics, School of Medicine, Tel Aviv University, Tel Aviv, 69978 Israel, and a Senior Principal Scientist and Principal Investigator at the National Cancer Institute. She has received her B. Sc degree in Microbiology from the University of Washington (Seattle, Washington) and her Ph D in Biochemistry from Rutgers University (NJ). She was a Fellow at the Weizmann Institute, and a visiting scientist at the chemistry department at Berkeley and at the Biochemistry department at Harvard. She joined the Medical School at Tel Aviv in 1985 as an Associate Professor. In 1990 she became a Full Professor. Her association with the NIH started in 1983, first with the NICHHD and since 1985 with the NCI. Currently, she has a large group of graduate students in Tel Aviv, in collaboration with Prof. H. Wolfson, from the School of Computer Science. Additionally, she has a group at the NCI. She is an author of over 260 scientific papers. Dr. Nussinov was the developer of the dynamic programming algorithm for RNA secondary structure prediction, first by maximizing the number of base pairs (SIAM, 35: 68-82, 1978) and later introducing 'energy rules' into the algorithm (Proc Natl Acad Sci U S A. 77: 6903-13, 1980; See also Biological sequence analysis (Durbin, Eddy, Krogh & Mitchison, Cambridge University Press, 1998). Dr. Nussinov was also among the pioneers in DNA sequence analysis, with numerous publications on recurrence of nucleotide patterns already in the early-mid 1980's. Until 1990 her papers addressed RNA and DNA sequence and structure and nucleic acid-protein interactions. In 1990 she switched to proteins. Currently her research focuses on computational studies of protein folding, binding and protein function. She addresses Systems Biology and most recently applications of computational biology concepts and strategies toward nanobiology, in nanostructure design. Her research is inter-disciplinary, with a strong component of computer science.

Research

Computational Structural Biology: Protein Structure, Function and Protein-Protein Interactions

Our research is computational. We focus on protein structures and their associations. In particular, we address the following areas: Protein folding, protein binding, and the inter-relationship between structure and function. We have developed the building block folding model and are currently developing approaches to use it toward a reduction of the computational complexity of protein folding. We study protein stability. In parallel, we investigate amyloid formation, the sequence-dependence of amyloid conformations and the mechanism of amyloid toxicity. We use both short peptide model systems for which there are experimental data and entire proteins. We are intensely interested in protein binding. We have derived nonredundant datasets of protein-protein interfaces and these are used to decipher the determinants of protein-protein associations. We are extremely interested in protein flexibility and its role in binding mechanisms and in protein function. Our studies of protein-protein interactions and protein function have led us to structure-based Systems Biology.

The group has a strong component of method development. It has developed extremely efficient algorithms with unique capabilities for structural comparisons, motif detection and docking. These algorithms are used for prediction of binding sites on protein surfaces, detection of residue hot spots, protein classification and protein-protein and protein-small ligand docking. Development of new algorithms is on- going.

Our research is carried out with the notion pioneered by the group, that protein folding and binding are similar processes with similar underlying mechanisms. Both involve molecular recognition. This conceptual similarity leads us to develop and apply similar biophysical and algorithmic approaches. Currently, we are further applying our concepts and expertise toward nanostructure design.

This page was last updated on 6/11/2008.