Accelerating Molecular Docking Calculations Using Graphics Processing Units

Abstract

The generation of molecular conformations and the evaluation of interaction potentials are common tasks in molecular modeling applications, particularly in protein-ligand or protein-protein docking programs. In this work, we present a GPU-accelerated approach capable of speeding up these tasks considerably. For the evaluation of interaction potentials in the context of rigid protein-protein docking, the GPU-accelerated approach reached speedup factors of up to over 50 compared to an optimized CPU-based implementation. Treating the ligand and donor groups in the protein binding site as flexible, speedup factors of up to 16 can be observed in the evaluation of protein-ligand interaction potentials. Additionally, we introduce a parallel version of our protein-ligand docking algorithm PLANTS that can take advantage of this GPU-accelerated scoring function evaluation. We compared the GPU-accelerated parallel version to the same algorithm running on the CPU and also to the highly optimized sequential CPU-based version. In terms of dependence of the ligand size and the number of rotatable bonds, speedup factors of up to 10 and 7, respectively, can be observed. Finally, a fitness landscape analysis in the context of rigid protein-protein docking was performed. Using a systematic grid-based search methodology, the GPU-accelerated version outperformed the CPU-based version with speedup factors of up to 60.

Applications of High Performance Computing in Bioinformatics, Computational Biology and Computational Chemistry

“…In literature is also available [14] an enhanced version of the PLANTS [15] approach for protein-ligand docking using GPUs. They report speedup factors of up to 50x in their GPU implementation compared to an optimized CPU based implementation for the evaluation of interaction potentials in the context of rigid protein.…”

“…For instance, applications such programs of Molecular Dynamics (MD) [1], employed to analyse the dynamical properties of macromolecules such as folding and allosteric regulations, or software used for solving atom-to-atom interactions for drug discovery, such as AutoDock [2] and FlexScreen [3], could clearly benefit from enhanced computing capabilities and also from some novel algorithms approaches like those inspired by nature such as Genetic algorithms [4] or Ant Colony Optimization techniques [5].…”

Hardware Accelerated Molecular Docking: A Survey

“…The GPU accelerated PLANTS is described in reference [35]. The authors followed the traditional way of GPU programming using OpenGL and the NVIDIA Cg shading language.…”

Recent Advances and Future Trend on the Emerging Role of GPUs as Platforms for Virtual Screening-Based Drug Discovery

“…They also report the importance of an efficient visualization method. Korb et al (2011) enhance the PLANTS (Korb et al, 2006) approach for protein-ligand docking using GPUs. They report speedup factors of up to 50x in their GPU implementation compared to an optimized CPU based implementation for the evaluation of interaction potentials in the context of rigid protein.…”

“…Using this way of programming GPUs, the programming effort is too high, and also some peculiarities of the GPU architecture may be limited. For instance, the authors say that some of the spatial data structures used in the CPU implementation can not directly be mapped to the GPU programming model because of missing support for shared memory operations (Korb et al, 2011). The speedup factors observed, especially for small ligands, are limited by several factors.…”