DIpro is a cysteine disulfide bond predictor based on 2D recurrent neural network, support vector machine, graph matching and regression algorithms. It can predict if the sequence has disulfide bonds or not, estimate the number of disulfide bonds, and predict the bonding state of each cysteine and the bonded pairs. It yields the best accuracy on the benchmark dataset Sp39. It can handle any number of disulfide bonds where most of methods available so far only can handle less than 6 disulfide bonds.
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J. Cheng, H. Saigo, & P. Baldi.
Large-Scale Prediction of Disulphide Bridges Using Kernel Methods, Two-Dimensional Recursive Neural Networks, and Weighted Graph Matching.
Proteins, vol. 62, no. 3, pp. 617-629, (2006)