AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability

Autor: Arnaud Renard, Jean-Hugues Renault, Sandie Escotte-Binet, Stéphanie Baud, Laurence Voutquenne-Nazabadioko, Luiz Angelo Steffenel, Dominique Aubert, Pierre Darme, Isabelle Villena, Manuel Dauchez
Přispěvatelé: Institut de Chimie Moléculaire de Reims - UMR 7312 (ICMR), SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Epidémiosurveillance de protozooses à transmission alimentaire et vectorielle (ESCAPE), Université de Reims Champagne-Ardenne (URCA)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Matrice extracellulaire et dynamique cellulaire - UMR 7369 (MEDyC), Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Plateau Technique de Modélisation Moléculaire Multi-échelle (P3M), Laboratoire d'Informatique en Calcul Intensif et Image pour la Simulation (LICIIS), Université de Reims Champagne-Ardenne (URCA), Centre National de référence de la Toxoplasmose (CNRT)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Computer science
GPU
Ligands
01 natural sciences
Molecular Docking Simulation
Workflow
Reduction (complexity)
[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases
Biology (General)
Throughput (business)
Spectroscopy
0303 health sciences
010304 chemical physics
General Medicine
AutoDock
Supercomputer
Computer Science Applications
Chemistry
Pharmaceutical Preparations
[INFO.INFO-DC]Computer Science [cs]/Distributed
Parallel
and Cluster Computing [cs.DC]
Algorithms
[CHIM.CHEM]Chemical Sciences/Cheminformatics
QH301-705.5
Graphics processing unit
Toxoplasma gondii
Catalysis
Article
Computational science
Inorganic Chemistry
03 medical and health sciences
parallelization
0103 physical sciences
Computer Graphics
Humans
[CHIM]Chemical Sciences
Physical and Theoretical Chemistry
Molecular Biology
QD1-999
030304 developmental biology
screening
Organic Chemistry
Proteins
Reproducibility of Results
molecular docking
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
High-Throughput Screening Assays
high performance computing
Docking (molecular)
Software
Zdroj: International Journal of Molecular Sciences, Vol 22, Iss 7489, p 7489 (2021)
International Journal of Molecular Sciences
International Journal of Molecular Sciences, MDPI, 2021, 22, ⟨10.3390/ijms22147489⟩
Volume 22
Issue 14
ISSN: 1661-6596
1422-0067
DOI: 10.3390/ijms22147489⟩
Popis: 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.
Databáze: OpenAIRE
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