The proposed work develops further the statistical energy landscape approach to protein folding dynamics and structural prediction.
The specific aims are: 1) To elucidate the mechanism of in vitro and in vivo folding of membrane proteins and to improve our ability to predict their structure 2) To quantify the role of folding in the evolution by comparing the physical energy landscape to co-evolutionary sequence analysis in folding and to use this knowledge to develop algorithms to predict the location of binding sites and allosteric initiation regions in proteins. 3) To understand the role of frustration and self-recognition in the initiation of protein aggregation. To use the tools of energy landscape analysis to predict and classify the structures of oligomers of A peptide and study their binding to ApoE, a genetic marker for Alzheimer's disease.
Folding is a key part of translating genomic data into function. Our work using the energy landscape theory of membrane protein folding will help predict protein structures for drug targets. We will also elucidate the misfolding events important for diseases such as Alzheimer's.
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