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High end GPCR design: crafted ligand design and druggability analysis using protein structure, lipophilic hotspots and explicit water networks

ABSTRACT: Purpose G Protein-Coupled Receptors (GPCRs) are a large family of therapeutically important proteins and as diverse X-ray structures become available it is increasingly possible to leverage structural information for rational drug design. We present herein approaches that use explicit water networks combined with energetic surveys of the binding site (GRID), providing an enhanced druggability and ligand design approach, with structural understanding of ligand binding, including a ‘magic’ methyl and binding site mutations, and a fast new approach to generate and score waters. Methods The GRID program was used to identify lipophilic and hydrogen bonding hotspots. Explicit full water networks were generated and scored for (pseudo)apo structures and ligand-protein complexes using a new approach, WaterFLAP (Molecular Discovery), together with WaterMap (Schrödinger) for (pseudo)apo structures. A scoring function (MetaScore) was developed using a fast computational protocol based on several short adiabatic biased MD simulations followed by multiple short well-tempered metadynamics runs. Results Analysis of diverse ligands binding to the adenosine A2A receptor together with new structures for the ?/?/? opioid and CCR5 receptors confirmed the key role of lipophilic hotspots in driving ligand binding and thus design; the displacement of ‘unhappy’ waters generally found in these regions provides a key binding energy component. Complete explicit water networks could be robustly generated for protein-ligand complexes using a WaterFLAP based approach. They provide a structural understanding of structure-activity relationships such as a ‘magic methyl’ effect and with the metadynamics approach a useful estimation of the binding energy changes resulting from active site mutations. Conclusions The promise of full structure-based drug design (SBDD) for GPCRs is now possible using a combination of advanced experimental and computational data. The conformational thermostabilisation of StaR® proteins provide the ability to easily generate biophysical screening data (binding including fragments, kinetics) and to get crystal structures with both potent and weak ligands. Explicit water networks for apo and ligand-complex structures are a critical ‘third dimension’ for SBDD and are key for understanding ligand binding energies and kinetics. GRID lipophilic hotspots are found to be key drivers for binding. In this context ‘high end’ GPCR ligand design is now enabled. Electronic supplementary material The online version of this article (doi:10.1186/2193-9616-1-23) contains supplementary material, which is available to authorized users.

SUBMITTER: Mason J

PROVIDER: S-EPMC4796210 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Project description:BackgroundFragment-based ligand design is used for the development of novel ligands that target macromolecules, most notably proteins. Central to its success is the identification of fragment binding sites that are spatially adjacent such that fragments occupying those sites may be linked to create drug-like ligands. Current experimental and computational approaches that address this problem typically identify only a limited number of sites as well as use a limited number of fragment types.MethodsThe site-identification by ligand competitive saturation (SILCS) approach is extended to the identification of fragment bindings sites, with the method termed SILCS-Hotspots. The approach involves precomputation of the SILCS FragMaps following which the identification of Hotspots, performed by identifying of all possible fragment binding sites on the full 3D structure of the protein followed by spatial clustering.ResultsThe SILCS-Hotspots approach identifies a large number of sites on the target protein, including many sites not accessible in experimental structures due to low binding affinities and binding sites on the protein interior. The identified sites are shown to recapitulate the location of known drug-like molecules in both allosteric and orthosteric binding sites on seven proteins including the androgen receptor, the CDK2 and Erk5 kinases, PTP1B phosphatase and three GPCRs; the β2-adrenergic, GPR40 fatty-acid binding and M2-muscarinic receptors. Analysis indicates the importance of considering all possible fragment binding sites, and not just those accessible to experimental methods, when identifying novel binding sites and performing ligand design versus just considering the most favorable sites. The approach is shown to identify a larger number of known binding sites of drug-like molecules versus the commonly used FTMap and Fpocket methods.General significanceThe present results indicate the potential utility of the SILCS-Hotspots approach for fragment-based rational design of ligands, including allosteric modulators.

Project description:G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

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High end GPCR design: crafted ligand design and druggability analysis using protein structure, lipophilic hotspots and explicit water networks

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