Abstract
BackgroundCalculation of the root mean square deviation (RMSD) between the atomic coordinates of two optimally superposed structures is a basic component of structural comparison techniques. We describe a quaternion based method, GPU-Q-J, that is stable with single precision calculations and suitable for graphics processor units (GPUs). The application was implemented on an ATI 4770 graphics card in C/C++ and Brook+ in Linux where it was 260 to 760 times faster than existing unoptimized CPU methods. Source code is available from the Compbio website http://software.compbio.washington.edu/misc/downloads/st_gpu_fit/ or from the author LHH.FindingsThe Nutritious Rice for the World Project (NRW) on World Community Grid predicted de novo, the structures of over 62,000 small proteins and protein domains returning a total of 10 billion candidate structures. Clustering ensembles of structures on this scale requires calculation of large similarity matrices consisting of RMSDs between each pair of structures in the set. As a real-world test, we calculated the matrices for 6 different ensembles from NRW. The GPU method was 260 times faster that the fastest existing CPU based method and over 500 times faster than the method that had been previously used.ConclusionsGPU-Q-J is a significant advance over previous CPU methods. It relieves a major bottleneck in the clustering of large numbers of structures for NRW. It also has applications in structure comparison methods that involve multiple superposition and RMSD determination steps, particularly when such methods are applied on a proteome and genome wide scale.
Highlights
Calculation of the root mean square deviation (RMSD) between the atomic coordinates of two optimally superposed structures is a basic component of structural comparison techniques
graphics processor units (GPUs)-Q-J is a significant advance over previous CPU methods
It relieves a major bottleneck in the clustering of large numbers of structures for Nutritious Rice for the World Project (NRW)
Summary
The speed of current CPU implementations of RMSD is not an issue. GPU-Q-J is a stable and very fast method Applications such as iterative density [12] and clustering [19] that require a complete pairwise similarity matrix will benefit significantly from the acceleration of the RMSD calculations. Projects such as NRW create datasets so large that clustering using RMSD or other structural similarity metric is cumbersome with existing methods. Our new RMSD method can be applied to the structural comparison methods used in the annotation step as well This will further accelerate the analysis of the 10 billions structures returned by NRW which we hope will allow us to help better understand rice genome and to potentially develop better strains of rice. In addition the times required to read in the torsional coordinates and convert them to Cartesian coordinates are indicated
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