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Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system

Quantum.Ligand.Dock (protein–ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein–ligand interactions via high-performance docking code. The main flavour of our approach is a combination of...

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Detalles Bibliográficos
Autor principal: Kantardjiev, Alexander A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394274/
https://www.ncbi.nlm.nih.gov/pubmed/22669908
http://dx.doi.org/10.1093/nar/gks515
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author Kantardjiev, Alexander A.
author_facet Kantardjiev, Alexander A.
author_sort Kantardjiev, Alexander A.
collection PubMed
description Quantum.Ligand.Dock (protein–ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein–ligand interactions via high-performance docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of docking partners. On the other hand, Quantum.Ligand.Dock is the the only docking server offering such a subtle supplement to protein docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments—the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein–ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.
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spelling pubmed-33942742012-07-30 Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system Kantardjiev, Alexander A. Nucleic Acids Res Articles Quantum.Ligand.Dock (protein–ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein–ligand interactions via high-performance docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of docking partners. On the other hand, Quantum.Ligand.Dock is the the only docking server offering such a subtle supplement to protein docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments—the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein–ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html. Oxford University Press 2012-07 2012-06-04 /pmc/articles/PMC3394274/ /pubmed/22669908 http://dx.doi.org/10.1093/nar/gks515 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Kantardjiev, Alexander A.
Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system
title Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system
title_full Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system
title_fullStr Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system
title_full_unstemmed Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system
title_short Quantum.Ligand.Dock: protein–ligand docking with quantum entanglement refinement on a GPU system
title_sort quantum.ligand.dock: protein–ligand docking with quantum entanglement refinement on a gpu system
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394274/
https://www.ncbi.nlm.nih.gov/pubmed/22669908
http://dx.doi.org/10.1093/nar/gks515
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