Protein secondary structure prediction using periodic-quadratic-logistic models: statistical and theoretical issues

We extend logistic discriminant function methodology to compete effectively with neural networks and "information theory" methods in prediction of protein secondary structure. Unlike "black-box" methods, our model produces 400 pairwise interaction parameters which are interpretable from a molecular standpoint. Under optimal conditions, our model can produce up to 65.9% crossvalidated prediction accuracy on three states. A broad family of models is searched using a semi-parametric (penalized) approach combined with stepwise parameter selection. We show that optimal models have about 800 effective parameters for this data set. The highest prediction accuracy is concentrated in a fraction of the total residues, and the confidence of a prediction can be easily calculated. Such high-confidence predictions may be useful as the basis for prediction of the complete structure of the protein.<
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