Predicting DNA-binding locations and orientation on proteins using knowledge-based learning of geometric properties

DNA-binding proteins perform their functions through specific or non-specific sequence recognition. Although many sequence-based approaches have been proposed to identify DNA-binding residues on proteins or protein-binding sites on DNA sequences with satisfied performance, it remains a challenging task to unveil the exact mechanism of protein-DNA interactions without crystal complex structures. Without information from complexes, the linkages between DNA-binding proteins and their binding sites on DNA are still missing. While it is still difficult to acquire co-crystallized structures in an efficient way, this study proposes a knowledge-based learning method to effectively predict DNA orientation and base locations around the protein's DNA-binding sites when given a protein structure. First, the functionally important residues of a query protein are predicted by a sequential pattern mining tool. After that, surface residues within the functional regions are determined based on the given structure. These residues are then clustered based on their spatial coordinates and the resultant clusters are ranked by a proposed DNA-binding propensity function. Clusters with high DNA-binding propensities are treated as DNA-binding units (DBUs) and each DBU is analyzed by principal component analysis (PCA) to predict potential orientation of DNA grooves. This paper proposes a knowledge-based learning procedure to determine the location of the bound groove by considering geometric propensity between protein side chains and DNA bases. The 11 test cases used in this study reveal that the location and orientation of the DNA groove around a selected DBU can be predicted with satisfied errors. How the proposed method can be incorporated with protein-DNA docking tools to investigate protein-DNA interactions deserve further studies in the near future.