Predicting the beta-trefoil fold from protein sequence data
Name
55675401-MIT.pdf
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Full printable version
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1.65 MB
Format
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Checksum (MD5)
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Author(s)
Menke, Matthew Ewald, 1978-
Advisor(s)
Bonnie Berger.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
A method is presented that uses [beta]-strand interactions at both the sequence and the atomic level, to predict the beta-structural motifs in protein sequences. A program called Wrap-and-Pack implements this method, and is shown to recognize β-trefoils, an important class of globular β-structures, in the Protein Data Bank with 92% specificity and 92.3% sensitivity in cross-validation. It is demonstrated that Wrap-and-Pack learns each of the ten known SCOP β-trefoil families, when trained primarily on β-structures that are not β-trefoils, together with 3D structures of known β-trefoils from outside the family. Wrap-and-Pack also predicts many proteins of unknown structure to be β-trefoils. The computational method used here may generalize to other β-structures for which strand topology and profiles of residue accessibility are well conserved.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 45-47).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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