A simplified approach to disulfide connectivity prediction from protein sequences

We introduce new methods for predicting disulfide bridges from protein sequences. The methods take advantage of two new decomposition kernels for measuring the similarity between protein sequences according to the amino acid environments around cysteines. Disulfide connectivity is predicted in two passes. First, a binary classifier is trained to predict whether a given protein chain has at least one intra-chain disulfide bridge. Second, a multiclass classifier (plemented by 1-nearest neighbor) is trained to predict connectivity patterns. The two passes can be easily cascaded to obtain connectivity prediction from sequence alone. We report an extensive experimental comparison on several data sets that have been previously employed in the literature to assess the accuracy of cysteine bonding state and disulfide connectivity predictors.We reach state-of-the-art results on bonding state prediction with a simple method that classifies chains rather than individual residues. The prediction accuracy reached by our connectivity prediction method compares favorably with respect to all but the most complex other approaches. On the other hand, our method does not need any model selection or hyperparameter tuning, a property that makes it less prone to overfitting and prediction accuracy overestimation.Some interesting structural, functional, and evolutionary properties of proteins can be inferred from knowledge about the existence and the precise location of disulfide bridges. Since most of the proteins inferred from genomic sequencing lack this structural information, the ab-initio prediction of disulfide bridges from protein sequences can be very useful in several molecular biology studies. This computational problem has received significant attention during the last few years and a number of prediction servers have been recently developed [1-5].Typical approaches predict disulfide bridges by solving two separate sub-problems. First, cysteines are partitioned into two groups