The class A G-protein-coupled receptors (GPCRs) Orexin-1 (OX1) and Orexin-2 (OX2) are located predominantly in the brain and are linked to a range of different physiological functions, including the control of feeding, energy metabolism, modulation of neuro-endocrine function, and regulation of the sleep–wake cycle. The natural agonists for OX1 and OX2 are two neuropeptides, Orexin-A and Orexin-B, which have activity at both receptors. Site-directed mutagenesis (SDM) has been reported on both the receptors and the peptides and has provided important insight into key features responsible for agonist activity. However, the structural interpretation of how these data are linked together is still lacking. In this work, we produced and used SDM data, homology modeling followed by MD simulation, and ensemble-flexible docking to generate binding poses of the Orexin peptides in the OX receptors to rationalize the SDM data. We also developed a protein pairwise similarity comparing method (ProS) and a GPCR-likeness assessment score (GLAS) to explore the structural data generated within a molecular dynamics simulation and to help distinguish between different GPCR substates. The results demonstrate how these newly developed methods of structural assessment for GPCRs can be used to provide a working model of neuropeptide–Orexin receptor interaction.

The class A G-protein-coupled receptors (GPCRs) Orexin-1 (OX1) and Orexin-2 (OX2) are located predominantly in the brain and are linked to a range of different physiological functions, including the control of feeding, energy metabolism, modulation of neuro-endocrine function, and regulation of the sleep-wake cycle. Site-directed mutagenesis (SDM) and domain exchange (chimera) studies have provided important insight into key features of the OX1 and OX2 binding sites. However, the precise determinants of antagonist binding and selectivity are still not fully known. In this work, we used homology modeling of OX receptors to direct further SDM studies. These SDM studies were followed by molecular dynamics (MD) simulations to rationalize the full scope of the SDM data and to explain the role of each mutated residue in the binding and selectivity of a set of OX antagonists: Almorexant (dual OX1 and OX2 antagonist), SB-674042 (OX1 selective antagonist), EMPA (OX2 selective antagonist), and others. Our primary interest was focused on transmembrane helix 3 (TM3), which is identified as being of great importance for the selectivity of OX antagonists. These studies revealed conformational differences between the TM3 helices of OX1 and OX2, resulting from differences in amino acid sequences of the OX receptors that affect key interhelical interactions formed between TM3 and neighboring TM domains. The MD simulation protocol used here, which was followed by flexible docking studies, went beyond the use of static models and allowed for a more detailed exploration of the OX structures. In this work, we have demonstrated how even small differences in the amino acid sequences of GPCRs can lead to significant differences in structure, antagonist binding affinity, and selectivity of these receptors. The MD simulations allowed refinement of the OX receptor models to a degree that was not possible with static homology modeling alone and provided a deeper rationalization of the SDM data obtained. To validate these findings and to demonstrate that they can be usefully applied to the design of novel, very selective OX antagonists, we show here two examples of antagonists designed in house: EP-109-0092 (OX1 selective) and EP-009-0513 (OX2 selective).

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