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.
journal_name
Int J Mol Scijournal_title
International journal of molecular sciencesauthors
Vosmeer CR,Pool R,Van Stee MF,Peric-Hassler L,Vermeulen NP,Geerke DPdoi
10.3390/ijms15010798subject
Has Abstractpub_date
2014-01-09 00:00:00pages
798-816issue
1issn
1422-0067pii
ijms15010798journal_volume
15pub_type
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