Literature DB >> 21387347

Task-parallel message passing interface implementation of Autodock4 for docking of very large databases of compounds using high-performance super-computers.

Barbara Collignon1, Roland Schulz, Jeremy C Smith, Jerome Baudry.   

Abstract

A message passing interface (MPI)-based implementation (Autodock4.lga.MPI) of the grid-based docking program Autodock4 has been developed to allow simultaneous and independent docking of multiple compounds on up to thousands of central processing units (CPUs) using the Lamarkian genetic algorithm. The MPI version reads a single binary file containing precalculated grids that represent the protein-ligand interactions, i.e., van der Waals, electrostatic, and desolvation potentials, and needs only two input parameter files for the entire docking run. In comparison, the serial version of Autodock4 reads ASCII grid files and requires one parameter file per compound. The modifications performed result in significantly reduced input/output activity compared with the serial version. Autodock4.lga.MPI scales up to 8192 CPUs with a maximal overhead of 16.3%, of which two thirds is due to input/output operations and one third originates from MPI operations. The optimal docking strategy, which minimizes docking CPU time without lowering the quality of the database enrichments, comprises the docking of ligands preordered from the most to the least flexible and the assignment of the number of energy evaluations as a function of the number of rotatable bounds. In 24 h, on 8192 high-performance computing CPUs, the present MPI version would allow docking to a rigid protein of about 300K small flexible compounds or 11 million rigid compounds.

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Year:  2010        PMID: 21387347     DOI: 10.1002/jcc.21696

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  8 in total

1.  Accelerating Virtual High-Throughput Ligand Docking: current technology and case study on a petascale supercomputer.

Authors:  Sally R Ellingson; Sivanesan Dakshanamurthy; Milton Brown; Jeremy C Smith; Jerome Baudry
Journal:  Concurr Comput       Date:  2014-04-25       Impact factor: 1.536

2.  Multilevel Parallelization of AutoDock 4.2.

Authors:  Andrew P Norgan; Paul K Coffman; Jean-Pierre A Kocher; David J Katzmann; Carlos P Sosa
Journal:  J Cheminform       Date:  2011-04-28       Impact factor: 5.514

3.  Accessible high-throughput virtual screening molecular docking software for students and educators.

Authors:  Reed B Jacob; Tim Andersen; Owen M McDougal
Journal:  PLoS Comput Biol       Date:  2012-05-31       Impact factor: 4.475

Review 4.  Building a virtual ligand screening pipeline using free software: a survey.

Authors:  Enrico Glaab
Journal:  Brief Bioinform       Date:  2015-06-20       Impact factor: 11.622

5.  A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model.

Authors:  Renata De Paris; Christian Vahl Quevedo; Duncan D Ruiz; Furia Gargano; Osmar Norberto de Souza
Journal:  BMC Bioinformatics       Date:  2018-06-22       Impact factor: 3.169

6.  wFReDoW: a cloud-based web environment to handle molecular docking simulations of a fully flexible receptor model.

Authors:  Renata De Paris; Fábio A Frantz; Osmar Norberto de Souza; Duncan D A Ruiz
Journal:  Biomed Res Int       Date:  2013-04-11       Impact factor: 3.411

7.  Click chemistry, 3D-printing, and omics: the future of drug development.

Authors:  Razelle Kurzrock; David J Stewart
Journal:  Oncotarget       Date:  2016-01-19

8.  Interleukin-26 activates macrophages and facilitates killing of Mycobacterium tuberculosis.

Authors:  Heike C Hawerkamp; Lasse van Geelen; Jan Korte; Jeremy Di Domizio; Marc Swidergall; Afaque A Momin; Francisco J Guzmán-Vega; Stefan T Arold; Joachim Ernst; Michel Gilliet; Rainer Kalscheuer; Bernhard Homey; Stephan Meller
Journal:  Sci Rep       Date:  2020-10-14       Impact factor: 4.379

  8 in total

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