Disulfide connectivity prediction using secondary structure information and diresidue frequencies

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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. 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/