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Int. J. Mol. Sci. 2014, 15(1), 798-816; doi:10.3390/ijms15010798
Article

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

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Received: 14 November 2013; in revised form: 17 December 2013 / Accepted: 23 December 2013 / Published: 9 January 2014
(This article belongs to the Special Issue Xenobiotic Metabolism)
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Abstract: Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.
Keywords: Automated binding free energy calculation; iterative LIE method; CYP 2D6; aryloxypropanolamines Automated binding free energy calculation; iterative LIE method; CYP 2D6; aryloxypropanolamines
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Vosmeer, C.R.; Pool, R.; van Stee, M.F.; Perić-Hassler, L.; Vermeulen, N.P.E.; Geerke, D.P. Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach. Int. J. Mol. Sci. 2014, 15, 798-816.

AMA Style

Vosmeer CR, Pool R, van Stee MF, Perić-Hassler L, Vermeulen NPE, Geerke DP. Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach. International Journal of Molecular Sciences. 2014; 15(1):798-816.

Chicago/Turabian Style

Vosmeer, C. Ruben; Pool, René; van Stee, Mariël F.; Perić-Hassler, Lovorka; Vermeulen, Nico P.E.; Geerke, Daan P. 2014. "Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach." Int. J. Mol. Sci. 15, no. 1: 798-816.



Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert