Predicted 3D structures of olfactory receptors with details of odorant binding to OR1G1

Kim, Soo‐Kyung; Goddard, William A.

doi:10.1007/s10822-014-9793-4

Cited by 18 publications

(18 citation statements)

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“…Here, we analyze the accuracy of the alignment by translating it into atomic‐level models. Five models of the human OR1G1 are built either with Modeller using different receptor structures as templates (Bovine Rhodopsin, Human β2‐adrenergic, Human Chemokine‐1, and a combination of them three) or by means of the ab initio GEnSeMBLE (GPCR Ensemble of Structures in Membrane BiLayer Environment) complete sampling method …”

Section: Resultsmentioning

confidence: 99%

“…Five models of the human OR1G1 are built either with Modeller 19 using different receptor structures as templates (Bovine Rhodopsin, Human b2-adrenergic, Human Chemokine-1, and a combination of them three) or by means of the ab initio GEnSeMBLE (GPCR Ensemble of Structures in Membrane BiLayer Environment) complete sampling method. 3,20,21 Figure 2 gathers information inferred from these models. Focusing on the helical TM domains, all structures are similar with Ca Root Mean Square deviations (RMSd) lower than 3 Å [see Fig.…”

Section: Three-dimensional Structure and Comparison With Experimentalmentioning

confidence: 99%

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G protein‐coupled odorant receptors: From sequence to structure

et al. 2015

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Odorant receptors (ORs) are the largest subfamily within class A G protein-coupled receptors (GPCRs). No experimental structural data of any OR is available to date and atomic-level insights are likely to be obtained by means of molecular modeling. In this article, we critically align sequences of ORs with those GPCRs for which a structure is available. Here, an alignment consistent with available site-directed mutagenesis data on various ORs is proposed. Using this alignment, the choice of the template is deemed rather minor for identifying residues that constitute the wall of the binding cavity or those involved in G protein recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Three-dimensional Structure and Comparison With Experimentalmentioning

confidence: 99%

G protein‐coupled odorant receptors: From sequence to structure

et al. 2015

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“…While most studies have shown a preferred binding region for ORs is bound by TM helices 3, 4, 5 and 6, 25,27,31,[33][34][35] another less preferable binding region has also been identified, bound by helices 1, 2, 6 and 7. 28 A computational study showed that docking in this region is likely to be inhibitory, while binding in the preferred region is likely to lead to OR excitation.…”

Section: Odorant Dockingmentioning

confidence: 99%

“…Amino acid residues within specified (say, hydrogen-bonding) distances were determined as crucial to binding-and any subsequent OR activation. 25,[31][32][33]35 Later, studies of dynamic OR-odorant interactions were published. 15,27,28,34 These studies were driven largely by the availability of molecular dynamics simulation software as well high performance computing facilities.…”

Section: Or-odorant Bindingmentioning

confidence: 99%

Novel Paradigm for Odorant-Olfactory Receptor Interactions

Crasto¹

2016

MOJPB

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Abbreviations: GPCR, g (TP-binding) protein coupledreceptors; HMM, hidden markov models; OR, olfactory receptor(s); NMR, nuclear magnetic resonance; TM, transmembrane; RMSD, root mean square displacement; RMSF, root mean square fluctuations OpinionThe publication of the discovery of olfactory receptors (OR) in 1991 1 sparked burgeoning research efforts in the domain of chemoreception. The need to study the role of olfactory receptors in olfaction became imperative, following the publication of the initial drafts of the human genome. Olfactory or olfactory-like receptors mined from the human genome constituted its largest family, possessing between 350 and 400 functional ORs genes. [2][3][4] Interestingly, the functional genes in the OR super family were a little more than third of the approximately, 1000 olfactory receptor repertoire mined from the genome the remaining are non-functional or pseudogenes. This raises very important questions as to how the sense of smell in human beings has evolved. Rodent 5,6 and canine 7 OR repertoires (consisting of about 1200 OR genes), when mined from their genomes, showed, that approximately, half of these genes were pseudogenic, or otherwise, non-functional. More recently, the OR repertoire from the African elephant genome listed 5000 ORs, of which, 40% are functional. 8One of the primary challenges in the study of ORs is elucidating the mechanism(s) by which a few hundred ORs in mammals discriminate several thousand odorants from the environment, which singly or in combination produce distinct and identifiable odors. OR-odorant interactions are promiscuous: one OR is known to bind multiple odorants; odorants, in turn, are known to interact with more than one OR.Research in deorphanizing ORs-identifying odorants or odors that are likely to bind an OR-has adopted a two-pronged approach. The primary approach is experimental, where excitatory responses are identified following the exposure of olfactory receptor neurons (one receptor corresponds to one neuron) to a panel of odorants. [9][10][11][12] Excitatory responses that arise from these interactions over a range of concentrations of odorants (typically organic chemical compounds) are then measured. This opinion paper is primarily concerned with the secondarycomputational-approach. This approach has often been a companion to the experimental functional analysis approach. 13,14 But it is increasingly coming into its own as an independent sub-domain of study. Computational studies involve studying the interactions between ORs and odorants, in silico. This paper will culminate in the recommendation to revisit the established paradigms related to the computational approaches for deorphanizing ORs. The challenges facing in silico approachesORs are membrane bound proteins. They belong to a class of fairly ubiquitous proteins called G(TP-binding) Protein Coupled Receptors (GPCRs). These receptors, following excitatory responses to stimuli (an interaction with an odorant or odor) bind to a G-protein and initiate a cytoplasmic signal...

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“…To begin to address this problem, we previously developed the GEnSeMBLE (2) method to predict the ensemble of lowenergy (stable) 3D structures of GPCRs. This method has successfully predicted the structures for several class A and class B GPCRs: C-C chemokine receptor type 5 (CCR5) (3), adenosine A3 receptor (AA 3 R) (4), cannabinoid receptor type 1 (CB1) (5), taste receptor type 2 member 38 (Tas2R38) (6), olfactory receptor 1G1 (OR1G1) (7), glucagon-like peptide 1 receptor (GLP1R) (8), and prostaglandin D2 receptor (DP) (9). Most of the predictions are for inactive-state structures, but we were able to predict and validate active-state structures of AA 3 R (4) and CB1 (5).…”

Section: Introductionmentioning

confidence: 99%

Conformational and Thermodynamic Landscape of GPCR Activation from Theory and Computation

Dong

Goddard

Abrol

2016

Biophysical Journal

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We present a hybrid computational methodology to predict multiple energetically accessible conformations for G protein-coupled receptors (GPCRs) that might play a role in binding to ligands and different signaling partners. To our knowledge, this method, termed ActiveGEnSeMBLE, enables the first quantitative energy profile for GPCR activation that is consistent with the qualitative profile deduced from experiments. ActiveGEnSeMBLE starts with a systematic coarse grid sampling of helix tilts/rotations (∼13 trillion transmembrane-domain conformations) and selects the conformational landscape based on energy. This profile identifies multiple potential active-state energy wells, with the TM3-TM6 intracellular distance as an approximate activation coordinate. These energy wells are then sampled locally using a finer grid to find locally minimized conformation in each energy well. We validate this strategy using the inactive and active experimental structures of β2 adrenergic receptor (hβ2AR) and M2 muscarinic acetylcholine receptor. Structures of membrane-embedded hβ2AR along its activation coordinate are subjected to molecular-dynamics simulations for relaxation and interaction energy analysis to generate a quantitative energy landscape for hβ2AR activation. This landscape reveals several metastable states along this coordinate, indicating that for hβ2AR, the agonist alone is not enough to stabilize the active state and that the G protein is necessary, consistent with experimental observations. The method's application to somatostatin receptor SSTR5 (no experimental structure available) shows that to predict an active conformation it is better to start from an inactive structure template based on a close homolog than to start from an active template based on a distant homolog. The energy landscape for hSSTR5 activation is consistent with hβ2AR in the role of the G protein. These results demonstrate the utility of the ActiveGEnSeMBLE method for predicting multiple conformations along the pathways for activating GPCRs and the corresponding energy landscapes, thereby providing detailed structural insights into the initial molecular events of GPCR function that are not easily accessible by experiments.

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Predicted 3D structures of olfactory receptors with details of odorant binding to OR1G1

Cited by 18 publications

References 58 publications

G protein‐coupled odorant receptors: From sequence to structure

G protein‐coupled odorant receptors: From sequence to structure

Novel Paradigm for Odorant-Olfactory Receptor Interactions

Conformational and Thermodynamic Landscape of GPCR Activation from Theory and Computation

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