Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this dissertation, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function and an efficient pentamer algorithm. The integrative energy function combines a multibody (MB) knowledge-based potential and atomic energy terms (hydrogen bond and π-interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. A pentamer algorithm is developed to address the computational complexity issue due to the exponential increase of the number of DNA sequences for longer binding sites that need to be evaluated. Test results show that the new energy function improves the prediction accuracy over the knowledge-based, statistical potentials based on a non-redundant dataset that consists of TF-DNA complexes from 12 different families. The pentamer algorithm improves TFBS prediction accuracy while greatly reducing the time complexity for long binding sites.