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Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

Overview of attention for article published in Journal of Cheminformatics, September 2009
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Title
Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock
Published in
Journal of Cheminformatics, September 2009
DOI 10.1186/1758-2946-1-15
Pubmed ID
Authors

Zsolt Bikadi, Eszter Hazai

Abstract

Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 430 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 <1%
United States 2 <1%
United Kingdom 2 <1%
Indonesia 1 <1%
Pakistan 1 <1%
Portugal 1 <1%
Sweden 1 <1%
Canada 1 <1%
Brazil 1 <1%
Other 4 <1%
Unknown 414 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 76 18%
Student > Ph. D. Student 66 15%
Researcher 51 12%
Student > Master 45 10%
Student > Doctoral Student 21 5%
Other 68 16%
Unknown 103 24%
Readers by discipline Count As %
Chemistry 116 27%
Agricultural and Biological Sciences 59 14%
Biochemistry, Genetics and Molecular Biology 58 13%
Pharmacology, Toxicology and Pharmaceutical Science 26 6%
Medicine and Dentistry 14 3%
Other 42 10%
Unknown 115 27%