Improving Prediction Accuracy of Protein-DNA Docking with GPU Computing

Protein-DNA docking is a very challenging problem in bioinformatics and has important implications in a number of applications (e.g. rational drug design). This paper presents a computational approach to improve the prediction accuracy of protein-DNA docking. One of the major difficulties in protein-DNA docking is the high cost of sampling the conformational space. To address this problem, we develop a graphics processing unit (GPU)-based approach to accelerate the sampling process, and thereby improving the quality of conformational space sampling. The effectiveness of the our approach is validated against a non-redundant set of 75 protein-DNA complexes, and the results demonstrate improved performance in finding near-native protein-DNA complex structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem.