Bayesian data mining of protein domains gives an efficient predictive algorithm and new insight

Abstract

Identification of structural domains in uncharacterized protein sequences is important in the prediction of protein tertiary folds and functional sites, and hence in designing biologically active molecules. We present a new predictive computational method of classifying a protein into single, two continuous or two discontinuous domains using Bayesian Data Mining. The algorithm requires only the primary sequence and computer-predicted secondary structure. It incorporates correlation patterns between certain 3-dimensional motifs and some local helical folds found conserved in the vicinity of protein domains with high statistical confidence. The prediction of domain-class by this computationally simple and fast method shows good accuracy of prediction-average accuracies 83.3% for single domain, 60% for two continuous and 65.7% for two discontinuous domain proteins. Experiments on the large validation sample show its performance to be significantly better than that of DGS and DomSSEA. Computations of Bayesian probabilities show important features in terms of correlation of certain conserved patterns of secondary folds and tertiary motifs and give new insight. Applications for improved accuracy of predicting domain boundary points relevant to protein structural and functional modeling are also highlighted.