Transport features predict if a molecule is odorous.

In studies of vision and audition, stimuli can be chosen to span the visible or audible spectrum; in olfaction, the axes and boundaries defining the analogous odorous space are unknown. As a result, the population of olfactory space is likewise unknown, and anecdotal estimates of 10,000 odorants have endured. The journey a molecule must take to reach olfactory receptors (ORs) and produce an odor percept suggests some chemical criteria for odorants: a molecule must 1) be volatile enough to enter the air phase, 2) be nonvolatile and hydrophilic enough to sorb into the mucous layer coating the olfactory epithelium, 3) be hydrophobic enough to enter an OR binding pocket, and 4) activate at least one OR. Here, we develop a simple and interpretable quantitative model that reliably predicts whether a molecule is odorous or odorless based solely on the first three criteria. Applying our model to a database of all possible small organic molecules, we estimate that at least 40 billion possible compounds are odorous, six orders of magnitude larger than current estimates of 10,000. With this model in hand, we can define the boundaries of olfactory space in terms of molecular volatility and hydrophobicity, enabling representative sampling of olfactory stimulus space.

Genetic analysis of impaired trimethylamine metabolism using whole exome sequencing.

BACKGROUND: Trimethylaminuria (TMAU) is a genetic disorder whereby people cannot convert trimethylamine (TMA) to its oxidized form (TMAO), a process that requires the liver enzyme FMO3. Loss-of-function variants in the FMO3 gene are a known cause of TMAU. In addition to the inability to metabolize TMA precursors like choline, patients often emit a characteristic odor because while TMAO is odorless, TMA has a fishy smell. The Monell Chemical Senses Center is a research institute with a program to evaluate people with odor complaints for TMAU. METHODS: Here we evaluated ten subjects by (1) odor evaluation by a trained sensory panel, (2) analysis of their urine concentration of TMA relative to TMAO before and after choline ingestion, and (3) whole exome sequencing as well as subsequent variant analysis of all ten samples to investigate the genetics of TMAU. RESULTS: While all subjects reported they often emitted a fish-like odor, none had this malodor during sensory evaluation. However, all were impaired in their ability to produce >90% TMAO/TMA in their urine and thus met the criteria for TMAU. To probe for genetic causes, the exome of each subject was sequenced, and variants were filtered by genes with a known (FMO3) or expected effect on TMA metabolism function (other oxidoreductases). We filtered the remaining variants by allele frequency and predicated functional effects. We identified one subject that had a rare loss-of-function FMO3 variant and six with more common decreased-function variants. In other oxidoreductases genes, five subjects had four novel rare single-nucleotide polymorphisms as well as one rare insertion/deletion. Novel in this context means no investigators have previously linked these variants to TMAU although they are in dbSNP. CONCLUSIONS: Thus, variants in genes other than FMO3 may cause TMAU and the genetic variants identified here serve as a starting point for future studies of impaired TMA metabolism.

Predicting human olfactory perception from chemical features of odor molecules.

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.