Title:Computational design of carbohydrate-binding proteins
Speaker: David F. Green Stony Brook University
Time: 2012-10-18 14:00-2012-10-18 15:30
Venue:FIT-1-222

Abstract:

The ability to rationally engineer novel functionality into carbohydrate-binding proteins could find use in a wide range of potential applications, including diagnostic identification of microbes, prevention of retroviral infection, and targeted delivery of protein-therapeutics to specific tissue-types. However, the design of carbohydrate binding interfaces is challenging both due to the intrinsic conformational flexibility of carbohydrate targets and to the highly polar nature of the binding interface, which necessitates accurate treatments of solvation effects. Here, we will highlight some of the methodological advances we have made in order to make the computational design of carbohydrate-binding proteins tractable; these include the use of optimized models for the computational of carbohydrate binding energies, the application of statistically-derived rules for hierarchical screening methods, and the combination of molecular dynamic simulation with the Dead-End Elimination and A* algorithms for discrete conformational search. The applicability of these methods will be presented using examples from the engineering of a number of algal lectins with potent virucidal activity against the Human Immunodeficiency Virus (HIV).



Short Bio:
Originally from Vancouver, Canada, Prof. Green began his scientific career as an undergraduate in Chemistry at Simon Fraser University (BS 1997), where he did work in both organometallic synthesis and in the conformational analysis of carbohydrates. He then moved to Cambridge, MA for graduate school in Biological Chemistry at the Massachusetts Institute of Technology (PhD 2002), where his focus switched to computational biophysics; his thesis work was built around applications of a novel affinity optimization scheme based in continuum electrostatic theory. Following graduation, he remained at M.I.T. as a post-doctoral associate in the Biological Engineering Division and the Computer Science & Artificial Intelligence Lab. His post-doctoral work shifted focus to problems of protein design, particularly to the engineering of protein complexes with desired affinities and specificities. Since 2005, he has been a Professor of Applied Mathematics & Statistics at Stony Brook University (State University of New York) on Long Island. He is a member of the Laufer Center for Quantitative and Physical Biology, and has additional affiliations with the Departments of Chemistry and Physiology & Biophysics and the Graduate Program in Biochemistry and Structural Biology. His current work is targeted in a general sense towards developing methods for the rational engineering of protein complexes for functions in complex biological systems. Two specific areas of interest include methods of modeling and design of mixed protein–carbohydrate systems and developing an understanding of both the mechanisms and consequences of differential specificities in protein complex formation.  His group applies a wide range of approaches: molecular dynamics simulation; continuum electrostatic analysis; computational protein design; dynamic modeling of signaling networks; and experimental protein chemistry.