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Bioengineering Seminar Series: Amarda Shehu
Friday, February 10, 2012
11:00 a.m.-12:00 p.m.
Room 1200 Jeong H. Kim Engineering Bldg.
For More Information:
Professor Silvia Muro
muro@umd.edu

Novel Algorithmic Frameworks for Protein Conformational Search

Amarda Shehu
Assistant Professor
Department of Computer Science
George Mason University

Many search and optimization problems in computer science exhibit complex high-dimensional non-linear solution spaces. Protein systems are ubiquitous biological molecules characterized by such spaces. A fundamental issue in our understanding of biology and treatment of disease concerns elucidating the structures and motions that proteins employ for their biological function. Understanding proteins in silico involves searching a vast high-dimensional conformational space of inherently flexible systems with numerous inter-related degrees of freedom and complex geometry.

This talk will present our recent work on novel and powerful computational frameworks that enhance the sampling of biologically-active protein conformations. Novel probabilistic search algorithms inspired from robotics and evolutionary computing are introduced to handle the high-dimensionality of the conformational space and ruggedness of the protein energy surface. In particular, a robotics-inspired framework is proposed that employs information on the space it has explored so the search can adaptively focus itself and further resources to relevant regions of the conformational space. Another framework exploits evolutionary search strategies to explicitly sample local minima in the protein energy surface. The two are combined to reveal a powerful search framework that enhances sampling of the protein conformational space.

Extensive applications on a growing diverse list of proteins suggest the proposed efforts greatly enhance the sampling of the protein conformational space. Comparisons with well-known methods in protein structure prediction show that the proposed algorithms push the state of the art and efficiently recover functionally-relevant conformations. Interesting insight is obtained on how to tackle the dimensionality challenge both in protein chains and robotic articulated mechanisms and extend applicability to proteins with diverse functional states and assembly of rigid protein units into oligomeric complexes.

About the Speaker
Amarda Shehu is an Assistant Professor in the Department of Computer Science at George Mason University. She holds affiliated appointments in the Department of Bioinformatics and Computational Biology and the Department of Bioengineering at George Mason University. She received her B.S. in Computer Science and Mathematics from Clarkson University in Potsdam, NY and her Ph.D. in Computer Science from Rice University in Houston, TX, where she was an NIH fellow of the Nanobiology Training Program of the Gulf Coast Consortia. Shehu's research contributions are in computational structural biology, biophysics, and bioinformatics with a focus on issues concerning the relationship between sequence, structure, dynamics, and function in biological molecules. Shehu is the recent recipient of an NSF CAREER award for her research on probabilistic search algorithms for protein conformational spaces.

This Event is For: Graduate • Faculty • Post-Docs

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