Accelerating and focusing protein-protein docking correlations using multi-dimensional rotational FFT generating functions.
BACKGROUND
Predicting how proteins interact at the molecular level is a computationally intensive task. Many protein docking algorithms begin by using fast Fourier transform (FFT) correlation techniques to find putative rigid body docking orientations. Most such approaches use 3D Cartesian grids and are therefore limited to computing three dimensional (3D) translational correlations. However, translational FFTs can speed up the calculation in only three of the six rigid body degrees of freedom, and they cannot easily incorporate prior knowledge about a complex to focus and hence further accelerate the calculation. Furthemore, several groups have developed multi-term interaction potentials and others use multi-copy approaches to simulate protein flexibility, which both add to the computational cost of FFT-based docking algorithms. Hence there is a need to develop more powerful and more versatile FFT docking techniques.


RESULTS
This article presents a closed-form 6D spherical polar Fourier correlation expression from which arbitrary multi-dimensional multi-property multi-resolution FFT correlations may be generated. The approach is demonstrated by calculating 1D, 3D and 5D rotational correlations of 3D shape and electrostatic expansions up to polynomial order L=30 on a 2 GB personal computer. As expected, 3D correlations are found to be considerably faster than 1D correlations but, surprisingly, 5D correlations are often slower than 3D correlations. Nonetheless, we show that 5D correlations will be advantageous when calculating multi-term knowledge-based interaction potentials. When docking the 84 complexes of the Protein Docking Benchmark, blind 3D shape plus electrostatic correlations take around 30 minutes on a contemporary personal computer and find acceptable solutions within the top 20 in 16 cases. Applying a simple angular constraint to focus the calculation around the receptor binding site produces acceptable solutions within the top 20 in 28 cases. Further constraining the search to the ligand binding site gives up to 48 solutions within the top 20, with calculation times of just a few minutes per complex. Hence the approach described provides a practical and fast tool for rigid body protein-protein docking, especially when prior knowledge about one or both binding sites is available.
Ritchie
David W
DW
Department of Computing Science, University of Aberdeen, Aberdeen, Scotland, UK. d.w.ritchie@abdn.ac.uk
Kozakov
Dima
D
Vajda
Sandor
S
D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D005583:Fourier Analysis; D008956:Models, Chemical; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D012399:Rotation; D013223:Statistics as Topic
D011506:Proteins