1. Neuroscience
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Expanding the olfactory code by in silico decoding of odor-receptor chemical space

  1. Sean Michael Boyle
  2. Shane McInally
  3. Anandasankar Ray  Is a corresponding author
  1. University of California, Riverside, United States
Research Article
Cite this article as: eLife 2013;2:e01120 doi: 10.7554/eLife.01120
6 figures, 2 tables and 1 additional file


Figure 1 with 2 supplements
A receptor-optimized molecular descriptor approach has strong predictive power to find new ligands.

(A) Schematic of the cheminfomatics pipeline used to identify novel ligands from a larger chemical space. (B) Plot of mean APoA values for 19 Drosophila Ors calculated using various methods including a previously identified set (Haddad et al., 2008). (C) Receiver-operating-characteristic curve (ROC) representing computational validation of ligand predictive ability of the Or-optimization approach. (D) Hierarchical cluster analysis of the 109 odorants of the training set using Or-specific optimized descriptor sets to calculate distances in chemical space for odorant receptors with strong activators (green), and odorant receptors with no strong activators (yellow).

Figure 1—figure supplement 1
Analysis of APoA curves for individual odor receptors.

Plots of the mean APoA values obtained from various molecular descriptor methods demonstrates that optimized descriptor subsets generate highest values. Previous = 32 Dragon descriptors selected in Haddad et al. (2008). Molecular descriptor methods were compared using the 109 compounds that were previously tested in (Hallem and Carlson, 2006).

Figure 1—figure supplement 2
Pharmacophores of active compounds for individual Ors.

Hierarchical cluster identical to Figure 1D. Known odorant activity scale is indicated using independent color gradient scales. Horizontal black bars underneath cluster indicate part of active cluster, a subset of which were used to generate pharmacophores using the Ligand Scout program (shown underneath each Or in two orientations). Yellow = hydrophobic region, red = Hydrogen-bond acceptor, green/red = Hydrogen-bond donor or acceptor depending upon pH.

Electrophysiology validates that odorant receptor-optimized molecular descriptors can successfully identify new ligands for Drosophila.

Mean increase in response of neurons to 0.5-s stimulus of indicated odors (10−2 dilution) predicted for each associated Or. Dashed lines indicate the activator threshold (50 spikes/s). ΔH: Or85b (ab3B) = flies lack expression of Or22a in neighboring neuron, thus all observed neuron activation is unambiguously caused by Or85b. N = 3, error bars = s.e.m.

Predicted receptor–odor interactions are highly specific.

(A) Plot of activity (Top) for electrophysiologically tested receptor-odor interactions. (Bottom) Plot indicating locations of predicted receptor-odor combinations (green) and same odorants tested in non-target receptor-odor combinations (gray). (B) Plot of percentage of activating odors (>50 spikes/s) considering all activating or inactive odors (>0 spikes/s) across ranking bins for all odors tested using electrophysiology.

Analysis of receptor–odor relationships and breadth of tuning.

(A) Hierarchical clusters created from Euclidean distance values between Drosophila Ors calculated using: (left to right) shared optimized descriptors; known activity to training set odors (Hallem and Carlson, 2006); overlap across top 500 predicted ligands; and Phylogenic tree of receptors (Hallem and Carlson, 2006). Sub clusters shaded with colors or bars. (B) Frequency distribution of compounds from the >240K library within the top 15% distance from highest active plotted to generate predicted breadth of tuning curves. Green arrows indicate relative distance of the furthest known activating compound determined by electrophysiology.

Analysis of predicted natural odor sources and cross activation.

(A) (Left) The numbers of compounds present in the collected volatile library according to source. (Right) The numbers and sources of predicted ligands for the 19 Drosophila odor receptors/neurons within the top 500 predicted compounds. (B) Comparison of plots for percentage of receptors that are: (top left) activated by percentage of known odors from training set (Hallem and Carlson, 2006); (bottom left) predicted to be activated by Natural compound library; (top right) predicted to be activated from >240K library; and (bottom right) activated by ligands for 10 shared Ors in this study vs activated by comparable actives previously tested (Hallem and Carlson, 2006).

Predicted odor space and network view of odor coding.

(A) Expansion of the peripheral olfactory code in this study: large increase in numbers of identified activators and inhibitors. The different sized circles represent the approximate ratio of numbers of previously known ligands (top circles), predicted ligands based on a cutoff of the top 500 predicted compounds per receptor and corrected to the validation success rate (lower, diffuse circles). (B) Drosophila receptor–odor network. Each known interaction (>50 spikes/s) from this and previous studies (Hallem and Carlson, 2006) is linked by a purple edge. Predicted receptor–odor network (top 500 hits) are linked by light-grey edges. All compounds are represented as small black circles and Ors are represented as large colored circles matching the colors used in (Figure 4A).



Table 1

Optimized molecular descriptor set compositions

Descriptor class type counts for all Ors
 GETAWAY descriptors75
 3D-MoRSE descriptors66
 2D autocorrelations44
 Edge adjacency indices44
 2D binary fingerprints44
 Functional group counts43
 Atom-centred fragments37
 WHIM descriptors36
 Topological charge indices24
 Atomtypes (Cerius2)23
 Burden eigenvalues23
 Molecular properties23
 Topological descriptors22
 Geometrical descriptors18
 2D frequency fingerprints11
 RDF descriptors8
 Walk and path counts6
 Connectivity indices5
 Information indices5
 Topological (Cerius2)4
 Constitutional descriptors3
 Structural (Cerius2)2
 Randic molecular profiles2
Optimized descriptor analysis
 Average descriptor overlap between Ors13%
 Average number of descriptors per Or29.9
 Average number 3D descriptors per Or10.8
 Average number 2D descriptors per Or12.2
 Average number 1D descriptors per Or6.6
 Average number 0D descriptors per Or0.3
Descriptor dimensionality counts
 Number three dimensional descriptors205
 Number two dimensional descriptors232
 Number one dimensional descriptors126
 Number zero dimensional descriptors5
Descriptor Origin
 Number Dragon descriptors539
 Number Cerius descriptors29
  1. Breakdowns of the molecular descriptor class type, dimensionality, origin, and average overlap for all optimized molecular descriptors selected for each Or.

Table 2

Predicted receptor–odor interactions validated as highly accurate using electrophysiology

Ligands (%)883186392791928710071
Agonists (>50 spikes/s) (%)63318133186469709258
Agonists (>100 spikes/s) (%)3113621194548486737
Inverse agonists (%)250569252317813
  1. Summary of prediction accuracy percentages obtained by electrophysiology validation. Ligands = Agonists (≥50 spikes/s) + Inverse agonists (>50% reduction from baseline activity).

Additional files

Supplementary file 1

(A) Optimized descriptor sets for each Drosophila Or. Optimized descriptors occurrences, symbol, brief description, class, and dimensionality are listed. A summary of the total number of descriptors selected for the receptor repertoire is provided at the beginning. Descriptors are listed in ascending order of when they were selected into the optimized set, such that the descriptors selected first are more important. Weights indicate the number of times a descriptor was selected in an optimized descriptor set. (B) Top 100 predicted compounds for each Drosophila Or. Chemical name or Pubchem compound ID (CIDs), SMILES strings, and distances, of the top ∼100 predicted compounds for each Or. All distances represent the minimum distance based on optimized descriptors to the previously known strongest active compound listed in the gray cells for that particular Or.


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