Confidence assessment of protein-DNA complex models

Protein-DNA docking is an important computational technique for generating native or near-native complex models. A docking program typically generates a number of complex conformations and predicts the docking solution based on interaction energies. However, incomplete sampling and energy function deficiencies can result in false positive protein-DNA complex models, which hampers its application in biology or medicine. Built upon our investigation of structural features for binding specificity between protein and DNA molecules, we present here a Support Vector Machine (SVM)-based approach for quality assessment of the docked transcription factor-DNA complex models by combining structural features and a knowledge-based protein-DNA interaction potential. Our results show that the SVM scoring model greatly improves the prediction accuracy by successfully identifying the false positive cases, in which the docking algorithm fails to produce any near-native complex models.