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Bioinformatics Advance Access published online on March 1, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti328
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received November 12, 2004
Revised January 24, 2005
Accepted February 11, 2005

Article

Disulfide connectivity prediction using secondary structure information and diresidue frequencies

F. Ferrè 1 and P. Clote 2*

1 Department of Biology, Boston College, Chestnut Hill, MA (USA) 02467
2 Department of Biology, Boston College, Chestnut Hill, MA (USA) 02467; Department of Computer Science (courtesy appointment), Boston College, Chestnut Hill, MA (USA) 02467

* To whom correspondence should be addressed.
P. Clote, E-mail: clote{at}bc.edu


  Abstract

Motivation: We describe a stand-alone algorithm to predict disulfide bond partners in a protein given only the amino acid sequence, using a novel neural network architecture (the diresidue neural network), given input of symmetric flanking regions of N- and C-terminus half-cystines augmented with residue secondary structure (helix, coil, sheet) as well as evolutionary information. The approach is motivated by the observation of a bias in the secondary structure preferences of free cysteines and half-cystines, and by promising preliminary results we obtained using diresidue position specific scoring matrices.

Results: As calibrated by ROC curves from 4-fold cross-validation, our conditioning on secondary structure allows our novel diresidue neural network to perform as well as, and in some cases better than the current state-of-the-art method. A slight drop in performance is seen when secondary structure is predicted rather than derived from three dimensional protein structures.

Availability: http://clavius.bc.edu/~clotelab/DiANNA.

Supplementary Information: Supplementary Tables and Figures, and the complete list of PDB codes of monomers used, can be found at http://clavius.bc.edu/~clotelab/.


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