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Predicting conformational switches in proteins

Published online by Cambridge University Press:  01 September 1999

MALIN YOUNG
Affiliation:
Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143-0446 Current address: Sandia National Laboratories, Livermore, California 94550.
KENT KIRSHENBAUM
Affiliation:
Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143-0446 Current address: Department of Chemistry and Chemical Engineering, Mail Code 210-41, California Institute of Technology, Pasadena, California 91125.
KEN A. DILL
Affiliation:
Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143-0446
STEFAN HIGHSMITH
Affiliation:
Department of Biochemistry, University of the Pacific School of Dentistry, San Francisco, California 94115-2399
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Abstract

We describe 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 16 test protein sequences for which function involves substantial backbone rearrangements. In the test set, all sites previously described as conformational switches are correctly predicted to be structurally ambivalent regions. No such regions are predicted in three negative control protein sequences. ASP may be useful as a guide for experimental studies on protein function and motion in the absence of detailed three-dimensional structural data.

Type
Research Article
Copyright
© 1999 The Protein Society

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