BACKGROUND: Protein-DNA docking is a very challenging problem in structural 
bioinformatics and has important implications in a number of applications, such 
as structure-based prediction of transcription factor binding sites and rational 
drug design. Protein-DNA docking is very computational demanding due to the high 
cost of energy calculation and the statistical nature of conformational sampling 
algorithms. More importantly, experiments show that the docking quality depends 
on the coverage of the conformational sampling space. It is therefore desirable 
to accelerate the computation of the docking algorithm, not only to reduce 
computing time, but also to improve docking quality.
METHODS: In an attempt to accelerate the sampling process and to improve the 
docking performance, we developed a graphics processing unit (GPU)-based 
protein-DNA docking algorithm. The algorithm employs a potential-based energy 
function to describe the binding affinity of a protein-DNA pair, and integrates 
Monte-Carlo simulation and a simulated annealing method to search through the 
conformational space. Algorithmic techniques were developed to improve the 
computation efficiency and scalability on GPU-based high performance computing 
systems.
RESULTS: The effectiveness of our approach is tested on a non-redundant set of 
75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We 
demonstrated that the GPU-based docking algorithm can significantly accelerate 
the simulation process and thereby improving the chance of finding near-native 
TF-DNA complex structures. This study also suggests that further improvement in 
protein-DNA docking research would require efforts from two integral aspects: 
improvement in computation efficiency and energy function design.
CONCLUSIONS: We present a high performance computing approach for improving the 
prediction accuracy of protein-DNA docking. The GPU-based docking algorithm 
accelerates the search of the conformational space and thus increases the chance 
of finding more near-native 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.