Ligand-Guided Receptor Optimization
ABSTRACT Receptor models generated by homology or even obtained by crystallography often have their binding pockets suboptimal for ligand docking and virtual screening applications due to insufficient accuracy or induced fit bias. Knowledge of previously discovered receptor ligands provides key information that can be used for improving docking and screening performance of the receptor. Here, we present a comprehensive ligand-guided receptor optimization (LiBERO) algorithm that exploits ligand information for selecting the best performing protein models from an ensemble. The energetically feasible protein conformers are generated through normal mode analysis and Monte Carlo conformational sampling. The algorithm allows iteration of the conformer generation and selection steps until convergence of a specially developed fitness function which quantifies the conformer's ability to select known ligands from decoys in a small-scale virtual screening test. Because of the requirement for a large number of computationally intensive docking calculations, the automated algorithm has been implemented to use Linux clusters allowing easy parallel scaling. Here, we will discuss the setup of LiBERO calculations, selection of parameters, and a range of possible uses of the algorithm which has already proven itself in several practical applications to binding pocket optimization and prospective virtual ligand screening.
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ABSTRACT: Despite recent progress in structural coverage of the G-protein-coupled receptor (GPCR) family, high plasticity of these membrane proteins poses additional challenges for crystallographic studies of their complexes with different classes of ligands, especially agonists. The ability to predict computationally the binding of natural and clinically relevant agonists and corresponding changes in the receptor pocket, starting from inactive GPCR structures, is therefore of great interest for understanding GPCR biology and drug action. Comparison of computational models published in 2009 and 2010 with recently determined agonist-bound structures of β-adrenergic and adenosine A(2A) receptors reveals high accuracy of the predicted agonist binding poses (0.8 Å and 1.7 Å respectively) and receptor interactions. In the case of the β(2)AR, energy-based models with limited backbone flexibility have also allowed characterization of side-chain rotations and a finite backbone shift in the pocket region as determinants of full, partial or inverse agonism. Development of accurate models of agonist binding for other GPCRs will be instrumental for functional and pharmacological studies, complementing biochemical and crystallographic techniques.Trends in Pharmacological Sciences 09/2011; 32(11):637-43. DOI:10.1016/j.tips.2011.08.001 · 9.99 Impact Factor
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ABSTRACT: The ghrelin receptor displays a high constitutive activity suggested to be involved in the regulation of appetite and food intake. Here, we have created peptides with small changes in the core binding motif -wFw- of the hexapeptide KwFwLL-NH(2) that can swap the peptide behavior from inverse agonism to agonism, indicating the importance of this sequence. Introduction of β-(3-benzothienyl)-d-alanine (d-Bth), 3,3-diphenyl-d-alanine (d-Dip) and 1-naphthyl-d-alanine (d-1-Nal) at position 2 resulted in highly potent and efficient inverse agonists, whereas the substitution of d-tryptophane at position 4 with 1-naphthyl-d-alanine (d-1-Nal) and 2-naphthyl-d-alanine (d-2-Nal) induces agonism in functional assays. Competitive binding studies showed a high affinity of the inverse agonist K-(d-1-Nal)-FwLL-NH(2) at the ghrelin receptor. Moreover, mutagenesis studies of the receptor revealed key positions for the switch between inverse agonist and agonist response. Hence, only minor changes in the peptide sequence can decide between agonism and inverse agonism and have a major impact on the biological activity.Journal of Medicinal Chemistry 08/2012; 55(17):7437-49. DOI:10.1021/jm300414b · 5.48 Impact Factor
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ABSTRACT: Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal-mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha.Journal of Chemical Information and Modeling 09/2012; 52(10):2705-14. DOI:10.1021/ci3001088 · 4.07 Impact Factor