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Bioinformatics Advance Access originally published online on March 1, 2005
Bioinformatics 2005 21(10):2336-2346; 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{at}oupjournals.org

Disulfide connectivity prediction using secondary structure information and diresidue frequencies

F. Ferrè 1 and P. Clote 1,2,*

1Department of Biology, Boston College Chestnut Hill, MA 02467, USA
2Department of Computer Science, Boston College Chestnut Hill, MA 02467, USA

*To whom correspondence should be addressed.

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), and given input of symmetric flanking regions of N-terminus 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 receiver operating characteristic 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 being derived from three-dimensional protein structures.

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

Contact: clote{at}bc.edu

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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