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.
- SourceAvailable from: Vsevolod Katritch[Show abstract] [Hide abstract]
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
- [Show abstract] [Hide abstract]
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
- [Show abstract] [Hide abstract]
ABSTRACT: Our structural understanding of the superfamily of G-protein coupled receptors, a group of targets of utmost pharmacological importance, has improved dramatically in the last few years. This was directly translated in an increase of both the number and the relevance of computer-assisted drug design efforts devoted to these receptors. The field, which had been greatly influenced by ligand-based methods, has experienced a radical transformation with a number of successful structure-based ligand design and discovery studies. This revolution has been accompanied by the exponential increase of computational resources, and as a result the scenario in GPCR structural and chemical studies is now more complex and richer than ever. Virtual screens, both structure- and ligand-based, co-exist with accurate computational characterizations of the receptor conformational dynamics and of the energy landscapes of receptor-ligand interactions. We here provide an integrated and updated view of the different computational techniques applied to the ligand design of GPCRs. Particular emphasis is put on the studies that take into account the novel structural information of GPCRs, together with those that consider the enormous amount of chemical information accumulated on these receptors in the last decades. Indeed, we propose that proper combinations of the different computational techniques: ligand-based, structure-based and studies taking dynamics into account, should be performed to better integrate all available information whenever possible. With this in mind, a major impact of computational technologies in the ligand design on GPCRs is expected in the forthcoming years.Current pharmaceutical design 09/2012; DOI:10.2174/1381612811319120009 · 3.29 Impact Factor