Computationally mapping olfactory receptors to odor percepts using docking energy scores

Mapping the olfactory factors directly to perceived smells has broad implications for neuroscience, chemistry, and medicine. Since the discovery of olfactory genes in 1991, the completion of the olfactory code has been hindered by two obstacles: the unknown combinatorial principles by which ∼400 receptors collectively encode thousands of perceptually distinct odors, and the absence of a complete functional map from the olfactory receptors to conscious perception. We investigated this problem by using the binding energy scores of odorants with the olfactory receptors. We first showed that using only docking scores, we can predict the smell percepts with an accuracy similar to a full set of chemical fingerprints, combining the two resulted in even better performance. This supports there is a direct relationship between olfactory receptors and specific smells. Next, we turned on the olfactory receptors one by one by iterative training and simulation, and produced corresponding specific perception profiles for each olfactory receptor. The generated matrix is sparse, with only 1-2 smell types activated for each olfactory receptors. Despite the limitation of the size of the training data, it suggests the possibility of a low-dimensional combinatorial principle underlying thousands of smells that humans can perceive. We confirmed the prediction by a list of well-known olfactory receptors. We applied the model trained on single chemicals to mixtures, confirming competitive binding was the driving force for smell specificity. This strong performance, surprisingly, is established on the simplest modeling of the binding scores on hundreds of chemicals, and we believe the mapping can become more accurate if more complicated structural modeling techniques and more data are used.