Format

Send to

Choose Destination
Bioinformatics. 2005 May 15;21(10):2336-46. Epub 2005 Mar 1.

Disulfide connectivity prediction using secondary structure information and diresidue frequencies.

Author information

1
Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.

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), 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.

PMID:
15741247
DOI:
10.1093/bioinformatics/bti328
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center