Prediction of the disulphide bridges in proteins using SVM
International Journal of Bioinformatics Research and Applications
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
WSEAS Transactions on Computers
On Lattice Protein Structure Prediction Revisited
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Information Sciences: an International Journal
Computers in Biology and Medicine
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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@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/