Beyond Modeling: All-Atom Olfactory Receptor Model Simulations

Lai, Peter; Crasto, Chiquito J.

doi:10.3389/fgene.2012.00061

Cited by 25 publications

(30 citation statements)

References 49 publications

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“…Alternatively, each OR TM helix that results from homology modeling is "relaxed" from its template-structure artifacts using molecular dynamics simulation in a hydrated environment before being reinserted into the OR helical scaffold. 27,28 Once the helical model is built, the helices have to be rotated such that the hydrophilic side of the helix is pointed inwards towards the putative binding region, while the hydrophobic side is pointed outwards. Different approaches have been used to rationalize these helical rotations.…”

Section: Or Protein Model Buildingmentioning

confidence: 99%

“…Loop positioning from homology modeling, and especially, after helix rotations, results in unnatural strains. 27 During the helical scaffold building stage and the helix-rotation stages of model-building, the loop regions are removed from the protein sequences. These are then re-attached using loopbuilding strategies, identifying cysteine disulfide bridges if they exist in proximate loops.…”

Section: Or Protein Model Buildingmentioning

confidence: 99%

“…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%

“…This is a well characterized OR, functionally, and from some of our previous studies, computationally. 27 The odorant ligand in this study is the isoamylacetate (C 7 H 14 O 2 ). Functional studies have established this odorant as strongly excitatory for the hOR17-209 receptor.…”

Section: Re-addressing the Or-odorant Interaction Paradigmmentioning

confidence: 99%

“…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.Studies of dynamic interactions are advantageous to static docking and represent a major advancement in the field of computational interactions. Indeed, static docking becomes the starting point or t=0 of a molecular dynamic simulations.…”

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confidence: 99%

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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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Section: Or Protein Model Buildingmentioning

confidence: 99%

Section: Or Protein Model Buildingmentioning

confidence: 99%

Section: Odorant Dockingmentioning

confidence: 99%

Section: Re-addressing the Or-odorant Interaction Paradigmmentioning

confidence: 99%

mentioning

confidence: 99%

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Novel Paradigm for Odorant-Olfactory Receptor Interactions

Crasto¹

2016

MOJPB

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Discrimination between Olfactory Receptor Agonists and Non‐agonists

Topin

March

Charlier

et al. 2014

Chemistry A European J

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A joint approach combining free-energy calculations and calcium-imaging assays on the broadly tuned human 1G1 olfactory receptor is reported. The free energy of binding of ten odorants was computed by means of molecular-dynamics simulations. This state function allows separating the experimentally determined eight agonists from the two non-agonists. This study constitutes a proof-of-principle for the computational deorphanization of olfactory receptors.

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Computational Studies of Smell and Taste Receptors

Pizio

Niv

2014

Israel Journal of Chemistry

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Smell and taste are among the basic senses with which we perceive the world around us. In addition to enabling recognition of chemical moieties that provide social or nutritional clues, taste and smell receptors are expressed in many extraoral tissues, including the gastrointestinal, respiratory, and reproductive systems. It is, therefore, likely that taste and smell receptors have additional physiological roles, which are currently under intensive study. Most of the taste modalities, as well as olfaction, are mediated by G‐protein coupled receptors (GPCRs). Recent breakthroughs in crystallography and signaling studies of GPCRs (celebrated by the 2012 Nobel Prize in Chemistry to Robert Lefkowitz and Brian Kobilka) provide excellent opportunities for applying this information towards furthering our understanding of taste and smell signaling. No crystal structures of odorant or taste receptors are currently available. However, computational techniques, many of which stem from the pioneering contributions of the 2013 Nobel Prize in Chemistry laureates, Martin Karplus, Michael Levitt, and Arieh Warshel, can shed light on the function of taste and olfactory GPCRs. In this review, we highlight examples of iterative combinations of simulation and experiment that were successfully applied toward delineating binding modes of tastants and odorants and toward predicting additional ligands. Further studies are required in order to answer remaining questions regarding receptor promiscuity versus selectivity, the details of receptor coupling to G‐proteins, and the roles of oligomerization and of allosteric modulation in taste and smell transduction.

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Beyond Modeling: All-Atom Olfactory Receptor Model Simulations

Cited by 25 publications

References 49 publications

Novel Paradigm for Odorant-Olfactory Receptor Interactions

Novel Paradigm for Odorant-Olfactory Receptor Interactions

Discrimination between Olfactory Receptor Agonists and Non‐agonists

Computational Studies of Smell and Taste Receptors

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