Faculty Recruitment, Jian Peng, "Unraveling the mechanism of chaperone-mediated protein folding"
Signup Schedule: http://apps.cs.utexas.edu/talkschedules/cgi/list_events.cgi
Talk Audience: UTCS Faculty, Grads, Undergrads, Other Interested Parties
Host: Chandra Bajaj
Talk Abstract: Chaperones are special proteins that aid in the folding, unfolding, assembly and disassembly of other proteins. Chaperones rely on a large and diverse set of co-chaperones that regulate their specificity and function. How these co-chaperones regulate protein folding and whether they have chaperone-independent biological functions is largely unknown. In this talk, I will first present novel experimental and computational approaches to study the chaperone/co-chaperone/client interaction network in a systematic way. We delineated the relationship between the Hsp70 and Hsp90 chaperone systems, uncovered novel co-chaperones and clients, and established a surprisingly distinct network of protein-protein interactions for co-chaperones. Our results provided a rich resource for exploring how this network changes in the context of development and disease. Finally, I will discuss a case study, a previously unresolved puzzle, on how Hsp90, a molecular chaperone, regulates the folding of protein kinases, possibly the most important drug targets for cancer therapy. Using computational graphical modeling and robust sparse regression methods, we identified striking associations between the binding specificity and a structural motif that includes deeply-buried hydrophobic residues in the kinase core region. Computation-guided mutagenesis validated the role of this motif in binding and suggested that Hsp90 recognizes intermediate kinase conformations by sensing the thermostability of the kinase core region. We anticipate our new results will advance the understanding of the role of Hsp90 in cancer drug development.
Speaker Bio: Jian Peng is a postdoctoral researcher in the Department of Mathematics and the Computer Science and Artificial Intelligence Lab at Massachusetts Institute of Technology. Working with Bonnie Berger, Jian is developing computational and statistical approaches for analyzing massive datasets in genomics, systems biology and molecular biology. Jian obtained his PhD in Computer Science from Toyota Technological Institute at Chicago in 2013. His doctoral research is on statistical inference for protein structure prediction. His prediction program RaptorX was ranked at the very top in the recent community-wide protein structure prediction competitions (CASP). Jian received the Microsoft PhD Research Fellowship in 2010 and Young Investigator Award in the Conference on Retroviruses and Opportunistic Infections in 2011.
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