Disulfide connectivity prediction using secondary structure information and diresidue frequencies.
BACKGROUND
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.
Ferrè
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Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.
Clote
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D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D003545:Cysteine / Q000737:chemistry*; D004220:Disulfides / Q000032:analysis / Q000737:chemistry*; D008956:Models, Chemical; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013329:Structure-Activity Relationship
D004220:Disulfides; D011506:Proteins; D003545:Cysteine