The field of structural genomics - the study of the three dimensional geometric structures of proteins - is complicated by vast amounts of data, expensive experiments and cumbersome methods of analysis.
Computer Science Professor Bruce Randall Donald and his students are working to ease this burden by developing techniques that simultaneously minimize the number of experiments and accelerate the data analysis involved in determining the structure of proteins.
Learning about protein structure is especially relevant for treating illnesses that alter protein function, such as cancer.
Published in consecutive months of the Journal of Biomolecular NMR (nuclear magnetic resonance), Donald, a graduate student and a post-doctoral fellow present new algorithms that interpret NMR data to reveal a protein's shape and molecular architecture. NMR surveys a protein's molecular structure and uses tiny, spectroscopic protractors and rulers to generate a network of geometric measurements.
"In these papers, we discuss a new framework for thinking about how to solve these problems, and our algorithms are highly accurate," says Donald, the Joan P. and Edward J. Foley Jr. 1933 Professor of Computer Science and an Adjunct Professor of Chemistry and of Biological Sciences.