Development of docking programs for Lomonosov supercomputer
Abstract
The initial step of the rational drug design pipeline extremely needs an increase in effectiveness. This can be done using molecular modeling: docking and molecular dynamics. Docking programs are popular now due to their simple idea, quickness and ease of use. Nevertheless accuracy of these programs still leaves much to be desired and discovery by chance and experimental screening still play an important role. Docking performs ligand positioning in the target protein and estimates the protein-ligand binding free energy. While in many cases positioning accuracy of docking is satisfactory, the accuracy of binding energy calculations is insufficient to perform the hit-to-lead optimization. The accuracy depends on many approximations which are built into the respective model. We show that all simplifications restricting docking accuracy can be withdrawn and this can be done on the basis of modern supercomputer facilities allowing to perform docking of one ligand using many thousand computing cores. We describe in short the SOL docking program which is used during years for virtual screening of large ligand databases using supercomputer resources of LomonosovMoscow State University. SOL to some extent is organized similarly to popular docking programs and reflects their limitations and advantages. We present our supercomputer docking programs, FLM and SOL-P, developed over the past 5 years for Lomonosov supercomputer of Moscow State University. These programs are free of most important simplifications and their performance shows the road map of the docking accuracy improvement. Some results of their performance for very flexible ligand docking into the rigid protein and docking of flexible ligands into the protein with some moveable protein atoms are presented. The so-called quasi-docking approach combining a force field and quantum chemical methods is described and it is shown that best docking accuracy is reached with the PM7 method and the COSMO solvent model.
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References
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Details
Primary Language
English
Subjects
-
Journal Section
Research Article
Authors
Vladimir Sulimov
*
0000-0002-7102-6107
Russian Federation
İvan Ilin
This is me
0000-0002-3612-393X
Russian Federation
Danil Kutov
This is me
0000-0002-4777-6522
Russian Federation
Alexey Sulimov
This is me
0000-0002-8767-642X
Russian Federation
Publication Date
February 15, 2020
Submission Date
October 17, 2019
Acceptance Date
December 25, 2019
Published in Issue
Year 2020 Volume: 7 Number: 1
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