The proposed work develops further the statistical energy landscape approach to protein folding dynamics and structural prediction.
The specific aims are: 1) to elucidate coupling of the folding and functional energy landscapes of allosteric proteins, concentrating on the role of local frustration 2) to elucidate the mechanism of in vitro and in vivo folding of membrane proteins and to improve our ability to predict their structure. Energy landscape theory provides mathematical techniques for characterizing in probabilistic terms the energies of the ensembles of partially folded protein configurations and the dynamics of interconverting between them. We will use analytical and computer simulation approaches that will provide quantitative estimates for the role of minimally frustrated networks of interactions in guiding the protein to its native state and the role that frustration has in impeding that flow. Allosteric proteins are predicted to positively use frustration to sculpt the functional landscape. Mutation in allosteric proteins such as kinases can cause a protein to be oncogenic. Errors in the folding of membrane proteins can lead specifically to diseases such as cystic fibrosis. Advances in understanding the landscape of membrane protein can help predict their structure an important step in designing drugs. PHS 398/2590 (Rev. 11/07) Page Continuation Format Page
Folding is a key step in translating genomic data into function. Our work on the energy landscape theory of folding helps predict protein structures of drug targets. The elucidation of folding mechanism is also important for understanding diseases caused by errors in folding, such as cystic fibrosis and Type II diabetes. PHS 398/2590 (Rev. 11/07) Page Continuation Format Page
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