Exploring Angular Distance in Protein-Protein Docking Algorithms

We present a two-stage hybrid-resolution approach for rigid-body protein-protein docking. The first stage is carried out at low-resolution (15 degrees) angular sampling. In the second stage, we sample promising regions from the first stage at a higher resolution of 6 degrees. The hybrid-resolution approach produces the same results as a 6 degrees uniform sampling docking run, but uses only 17% of the computational time. We also show that the angular distance can be used successfully in clustering and pruning algorithms, as well as the characterization of energy funnels. Traditionally the root-mean-square-distance is used in these algorithms, but the evaluation is computationally expensive as it depends on both the rotational and translational parameters of the docking solutions. In contrast, the angular distances only depend on the rotational parameters, which are generally fixed for all docking runs. Hence the angular distances can be pre-computed, and do not add computational time to the post-processing of rigid-body docking results.