Parallelized Clustering of Protein Structures on CUDA-Enabled GPUs

Estimation of the pose in which two given molecules might bind together to form a potential complex is a crucial task in structural biology. To solve this so-called "docking problem", most algorithms initially generate large numbers of candidate poses (or decoys) which are then clustered to allow for subsequent computationally expensive evaluations of reasonable representatives. Since the number of such candidates ranges from thousands to millions, performing the clustering on standard CPUs is highly time consuming. In this paper we analyze and evaluate different approaches to parallelize the nearest neighbor chain algorithm to perform hierarchical Ward clustering of protein structures using both atom-based root mean square deviation (RMSD) and rigid-based RMSD molecular distances on a GPU. This leads to a speedup of around one order-of-magnitude of our CUDA implementation on a GeForce Titan GPU compared to a multi-threaded CPU implementation on a Core-i7 2700.}, keywords={biology computing;graphics processing units;parallel architectures;pose estimation;CUDA-enabled GPU;GeForce Titan GPU;atom-based root mean square deviation;docking problem;nearest neighbor chain algorithm;pose estimation;protein structures parallelized clustering;rigid-based RMSD molecular distance;structural biology;Clustering algorithms;Graphics processing units;Indexes;Instruction sets;Proteins;Symmetric matrices;Vectors;CUDA;hierarchical clustering;parallel computing;protein docking;protein structures