Study Unveils Unknown Protein Motifs

By Shawn Hutchins

The task of determining protein structure and function is highly complex. Proteins are made of hundreds and sometimes thousands of atoms strung together that take on different forms and functions, and their sheer number and random variations make experimental approaches time-consuming and costly. Now, however, work by a recent Rice graduate is helping bring the picture into focus.

For the past three years, Drew Bryant ’07 has worked in Lydia Kavraki’s Physical and Biological Computing group with Brian Y. Chen ’06, statistics graduate student Viacheslav Fofanov and professor of statistics Marek Kimmel to develop computational techniques that compare functional information about well-studied proteins with uncharacterized proteins of similar geometric and chemical structure.

“Drew is among the brightest and most dedicated undergraduate students I’ve ever worked with, and he has brought valuable bioengineering perspectives to our projects,” said Kavraki, the Noah Harding Professor of Computer Science and professor of bioengineering. “He used his biological insight to suggest properties that should be incorporated in the motif design and was a key player in the development of algorithmic tools that can identify those properties in the proteins stored in the Protein Data Bank.”

Bryant and Chen orchestrated computational searches to efficiently identify matches between motifs and protein targets with high sensitivity and specificity. While with Kavraki’s group, Bryant also built a Web server designed to make the protein prediction software available to the scientific community.
“We wanted to develop a rigorous computational framework that allows one to design, test and tweak motifs quickly so that the optimal representation of the protein’s function can be found,” said Bryant. “I hope to use these statistics to understand the problem of predicting protein function more accurately.”

This year, Bryant’s cumulative work has received tremendous attention. He is a major contributor to two papers: “Cavity Scaling: Automated Refinement of Cavity-Aware Motifs in Protein Function Prediction,” published in August 2006 by Imperial College Press, and “The MASH Pipeline for Protein Function Prediction and an Algorithm for the Geometric Refinement of 3-D Motifs,” published by the Journal of Computational Biology. Last summer, while working at Genentech in San Francisco, Bryant completed a third paper that investigates different conformations of the same protein structure to define motifs.

Bryant, who will continue his graduate studies with Kavraki, also won the 2007 James S. Waters Creativity Award for his exceptional work in 3-D structural pattern matching for protein function prediction, and his poster, “Integrating Novel Protein Structure Data for Improved Function Prediction Accuracy,” received first-place honors and the 2007 Jenessa Shapiro Award at the Rice Undergraduate Research Symposium.

Bryant’s work has been part of a larger project in Kavraki’s laboratory that is supported partially by a National Science Foundation subcontract from Baylor College of Medicine.