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

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM044557-23
Application #
8471706
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
Project Start
1990-07-01
Project End
2014-05-31
Budget Start
2013-06-01
Budget End
2014-05-31
Support Year
23
Fiscal Year
2013
Total Cost
$285,168
Indirect Cost
$91,804
Name
Rice University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
050299031
City
Houston
State
TX
Country
United States
Zip Code
77005
Chen, Mingchen; Zheng, Weihua; Wolynes, Peter G (2016) Energy landscapes of a mechanical prion and their implications for the molecular mechanism of long-term memory. Proc Natl Acad Sci U S A 113:5006-11
Schafer, Nicholas P; Truong, Ha H; Otzen, Daniel E et al. (2016) Topological constraints and modular structure in the folding and functional motions of GlpG, an intramembrane protease. Proc Natl Acad Sci U S A 113:2098-103
Chen, Mingchen; Lin, Xingcheng; Zheng, Weihua et al. (2016) Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model. J Phys Chem B 120:8557-65
Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D et al. (2016) Electrostatics, structure prediction, and the energy landscapes for protein folding and binding. Protein Sci 25:255-69
Zheng, Weihua; Tsai, Min-Yeh; Chen, Mingchen et al. (2016) Exploring the aggregation free energy landscape of the amyloid-β protein (1-40). Proc Natl Acad Sci U S A 113:11835-11840
Wolynes, Peter G (2015) Evolution, energy landscapes and the paradoxes of protein folding. Biochimie 119:218-30
Sirovetz, Brian J; Schafer, Nicholas P; Wolynes, Peter G (2015) Water Mediated Interactions and the Protein Folding Phase Diagram in the Temperature-Pressure Plane. J Phys Chem B 119:11416-27
Truong, Ha H; Kim, Bobby L; Schafer, Nicholas P et al. (2015) Predictive energy landscapes for folding membrane protein assemblies. J Chem Phys 143:243101
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

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