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

Public Health Relevance

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

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Macromolecular Structure and Function D Study Section (MSFD)
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Wehrle, Janna P
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Rice University
Schools of Arts and Sciences
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Wolynes, Peter G (2015) Evolution, energy landscapes and the paradoxes of protein folding. Biochimie 119:218-30
Schafer, N P; Kim, B L; Zheng, W et al. (2014) Learning To Fold Proteins Using Energy Landscape Theory. Isr J Chem 54:1311-1337
Ferreiro, Diego U; Komives, Elizabeth A; Wolynes, Peter G (2014) Frustration in biomolecules. Q Rev Biophys 47:285-363
Morcos, Faruck; Schafer, Nicholas P; Cheng, Ryan R et al. (2014) Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection. Proc Natl Acad Sci U S A 111:12408-13
Kim, Bobby L; Schafer, Nicholas P; Wolynes, Peter G (2014) Predictive energy landscapes for folding ?-helical transmembrane proteins. Proc Natl Acad Sci U S A 111:11031-6
Truong, Ha H; Kim, Bobby L; Schafer, Nicholas P et al. (2013) Funneling and frustration in the energy landscapes of some designed and simplified proteins. J Chem Phys 139:121908
Zheng, Weihua; Schafer, Nicholas P; Wolynes, Peter G (2013) Frustration in the energy landscapes of multidomain protein misfolding. Proc Natl Acad Sci U S A 110:1680-5
Davtyan, Aram; Schafer, Nicholas P; Zheng, Weihua et al. (2012) AWSEM-MD: protein structure prediction using coarse-grained physical potentials and bioinformatically based local structure biasing. J Phys Chem B 116:8494-503
Stevenson, Jacob D; Wolynes, Peter G (2011) The ultimate fate of supercooled liquids. J Phys Chem A 115:3713-9
Li, Wenfei; Wolynes, Peter G; Takada, Shoji (2011) Frustration, specific sequence dependence, and nonlinearity in large-amplitude fluctuations of allosteric proteins. Proc Natl Acad Sci U S A 108:3504-9

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