Parametrization of a specific free energy function for automated docking against RNA targets using neural networks

A set of 8 RNA–drug complexes was extracted from the NDB database and used to determine new parameters of the empirical free energy model implemented in Autodock software. 248 docking experiments were performed with different values for the contributions of van der Waals, electrostatic, hydrogen bon...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems 2006-05, Vol.82 (1), p.269-275
Main Authors: Barbault, Florent, Zhang, Liangren, Zhang, Lihe, Fan, Bo Tao
Format: Article
Language:English
Subjects:
Aminoglycoside
Autodock software
Automated docking
Empirical free energy function
Neural network
RNA
RNA complex
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Summary:A set of 8 RNA–drug complexes was extracted from the NDB database and used to determine new parameters of the empirical free energy model implemented in Autodock software. 248 docking experiments were performed with different values for the contributions of van der Waals, electrostatic, hydrogen bonding, torsion and desolvation, respectively. These parameters were correlated with both RMSD and Δ G bind for all docking computations using a layered neural network with back-propagation algorithm (BP-NN). The model obtained from the correlation has allowed us to adjust the parameters. The most important differences between new and the default values were observed for the electrostatic, the torsion angle loss of entropy and desolvation, while the others' terms are comparable with default data. This new set of parameters could be used specifically for virtual screening against RNA targets.
ISSN:0169-7439
1873-3239
DOI:10.1016/j.chemolab.2005.05.014