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Wrap-and-pack: a new paradigm for beta structural motif recognition with application to recognizing beta trefoils

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Annual Conference on Research in Computational Molecular Biology archive
Proceedings of the eighth annual international conference on Resaerch in computational molecular biology table of contents

San Diego, California, USA

Pages: 298 - 307

Year of Publication: 2004

ISBN:1-58113-755-9

Authors

Matthew Menke	MIT, Cambridge, MA
Eben Scanlon	MIT, Cambridge, MA
Jonathan King	MIT, Cambridge, MA
Bonnie Berger	MIT, Cambridge, MA
Lenore Cowen	Tufts University, Medford, MA

Sponsors

ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory

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ACM New York, NY, USA

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ABSTRACT

A method is presented that uses β-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.

REFERENCES

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