We are developing a new computational technique to predict conformationally switching elements in proteins from their amino acid sequences. The method, called ASP (Ambivalent Structure Predictor) analyzes results from a secondary structure prediction algorithm to identify regions of conformational ambivalence. ASP identifies ambivalent regions in test protein sequences for which function involves substantial backbone rearrangements. Sites previously described as conformational switches are correctly predicted to be part of structurally ambivalent regions. ASP can also identify putative pathways of allosteric communication between the nucleotide, actin binding and fulcrum sites of myosin. The facilities at the Computer Graphics Laboratory are used to acquire sequence data and secondary structure predictions. Molecular graphics are integral to data analysis, as we map predictions of structural ambivalence onto the 3D crystal structures of the proteins. Further development of our algorithm may provide a tool for guiding experimental studies on protein function and motion in the absence of detailed three-dimensional structural data.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR001081-24
Application #
6456732
Study Section
Project Start
2001-07-01
Project End
2003-08-31
Budget Start
Budget End
Support Year
24
Fiscal Year
2001
Total Cost
$273,230
Indirect Cost
Name
University of California San Francisco
Department
Type
DUNS #
073133571
City
San Francisco
State
CA
Country
United States
Zip Code
94143
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