Abstract
Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.
Highlights
Molecular docking consists of modelling the interaction between two molecules at the atomic level
AMIDE version performances were compared by computing the total calculation time of docking of the ligand (4) on the 12 docking boxes of the calcium-dependent protein kinase 1 from T. gondii (TgCDPK1, 6BFA ID in the Protein Data Bank)
AMIDE v2 was evaluated in a double screening process between the T. gondii t6aorfg1e4t dataset (87 proteins) and the database of nine ligands presented in Section 4 to evaluate the potential of the improved AMIDE version for further high throughput inverse screening applications involving tens of proteins and thousands of ligands
Summary
Molecular docking consists of modelling the interaction between two molecules at the atomic level. AMIDE v2 is using GPU based parallelization, allowing time calculation decrease by computing multiple dockings at once (D) AMIDE v1 was only able to perform inverse docking of one ligand at a time, which made tedious its use for High-Throughput inverse docking (multiple ligands, multiple proteins) This limitation could be bypassed by manual intervention at the end of each ligand inverse docking, but was still not competitive for large ligands and receptors databases screening. A new version, called AMIDE v2, was developed to speed up the inverse docking process This framework is based on AutoDock-GPU [15] and has benefited from the total revision of the programming workflow.
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