2026-09-18T20:14:48Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/300932022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Bonnie Berger.
Menke, Matthew Ewald, 1978-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-03-24T18:18:48Z
2006-03-24T18:18:48Z
2004
2004
http://hdl.handle.net/1721.1/30093
55675401
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 45-47).
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.
by Matthew Ewald Menke.
S.M.
47 p.
1848213 bytes
1848021 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Predicting the beta-trefoil fold from protein sequence data
Thesis
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<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.</Abstract>
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