ChemFlow─From 2D Chemical Libraries to Protein-Ligand Binding Free Energies.

The accurate prediction of protein-ligand binding affinities is a fundamental problem for the rational design of new drug entities. Current computational approaches are either too expensive or inaccurate to be effectively used in virtual high-throughput screening campaigns. In addition, the most sophisticated methods, e.g., those based on configurational sampling by molecular dynamics, require significant pre- and postprocessing to provide a final ranking, which hinders straightforward applications by nonexpert users. We present a novel computational platform named ChemFlow to bridge the gap between 2D chemical libraries and estimated protein-ligand binding affinities. The software is designed to prepare a library of compounds provided in SMILES or SDF format, dock them into the protein binding site, and rescore the poses by simplified free energy calculations. Using a data set of 626 protein-ligand complexes and GPU computing, we demonstrate that ChemFlow provides relative binding free energies with an RMSE < 2 kcal/mol at a rate of 1000 ligands per day on a midsize computer cluster. The software is publicly available at https://github.com/IFMlab/ChemFlow.

Functional molecular switches of mammalian G protein-coupled bitter-taste receptors.

Bitter taste receptors (TAS2Rs) are a poorly understood subgroup of G protein-coupled receptors (GPCRs). The experimental structure of these receptors has yet to be determined, and key-residues controlling their function remain mostly unknown. We designed an integrative approach to improve comparative modeling of TAS2Rs. Using current knowledge on class A GPCRs and existing experimental data in the literature as constraints, we pinpointed conserved motifs to entirely re-align the amino-acid sequences of TAS2Rs. We constructed accurate homology models of human TAS2Rs. As a test case, we examined the accuracy of the TAS2R16 model with site-directed mutagenesis and in vitro functional assays. This combination of in silico and in vitro results clarifies sequence-function relationships and proposes functional molecular switches that encode agonist sensing and downstream signaling mechanisms within mammalian TAS2Rs sequences.

ProLIF: a library to encode molecular interactions as fingerprints.

Interaction fingerprints are vector representations that summarize the three-dimensional nature of interactions in molecular complexes, typically formed between a protein and a ligand. This kind of encoding has found many applications in drug-discovery projects, from structure-based virtual-screening to machine-learning. Here, we present ProLIF, a Python library designed to generate interaction fingerprints for molecular complexes extracted from molecular dynamics trajectories, experimental structures, and docking simulations. It can handle complexes formed of any combination of ligand, protein, DNA, or RNA molecules. The available interaction types can be fully reparametrized or extended by user-defined ones. Several tutorials that cover typical use-case scenarios are available, and the documentation is accompanied with code snippets showcasing the integration with other data-analysis libraries for a more seamless user-experience. The library can be freely installed from our GitHub repository ( https://github.com/chemosim-lab/ProLIF ).

Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists.

The concept of reverse chemical ecology (exploitation of molecular knowledge for chemical ecology) has recently emerged in conservation biology and human health. Here, we extend this concept to crop protection. Targeting odorant receptors from a crop pest insect, the noctuid moth Spodoptera littoralis, we demonstrate that reverse chemical ecology has the potential to accelerate the discovery of novel crop pest insect attractants and repellents. Using machine learning, we first predicted novel natural ligands for two odorant receptors, SlitOR24 and 25. Then, electrophysiological validation proved in silico predictions to be highly sensitive, as 93% and 67% of predicted agonists triggered a response in Drosophila olfactory neurons expressing SlitOR24 and SlitOR25, respectively, despite a lack of specificity. Last, when tested in Y-maze behavioral assays, the most active novel ligands of the receptors were attractive to caterpillars. This work provides a template for rational design of new eco-friendly semiochemicals to manage crop pest populations.

Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms.

In a preregistered, cross-sectional study, we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n = 4148) or negative (C19-; n = 546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean ± SD, C19+: -82.5 ± 27.2 points; C19-: -59.8 ± 37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC = 0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.

The best COVID-19 predictor is recent smell loss: a cross-sectional study.

BACKGROUND: COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19. METHODS: This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery. RESULTS: Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing no significant model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ~50% of participants and was best predicted by time since illness onset. CONCLUSIONS: As smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (10

More Than Smell-COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis.

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments, such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, and 8 others, aged 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste, and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change ±100) revealed a mean reduction of smell (-79.7 ± 28.7, mean ± standard deviation), taste (-69.0 ± 32.6), and chemesthetic (-37.3 ± 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell but also affects taste and chemesthesis. The multimodal impact of COVID-19 and the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

Novel scaffold of natural compound eliciting sweet taste revealed by machine learning.

Sugar replacement is still an active issue in the food industry. The use of structure-taste relationships remains one of the most rational strategy to expand the chemical space associated to sweet taste. A new machine learning model has been setup based on an update of the SweetenersDB and on open-source molecular features. It has been implemented on a freely accessible webserver. Cellular functional assays show that the sweet taste receptor is activated in vitro by a new scaffold of natural compounds identified by the in silico protocol. The newly identified sweetener belongs to the lignan chemical family and opens a new chemical space to explore.

Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor.

Odorant receptors expressed at the peripheral olfactory organs are key proteins for animal volatile sensing. Although they determine the odor space of a given species, their functional characterization is a long process and remains limited. To date, machine learning virtual screening has been used to predict new ligands for such receptors in both mammals and insects, using chemical features of known ligands. In insects, such approach is yet limited to Diptera, whereas insect odorant receptors are known to be highly divergent between orders. Here, we extend this strategy to a Lepidoptera receptor, SlitOR25, involved in the recognition of attractive odorants in the crop pest Spodoptera littoralis larvae. Virtual screening of 3 million molecules predicted 32 purchasable ones whose function has been systematically tested on SlitOR25, revealing 11 novel agonists with a success rate of 28%. Our results show that Support Vector Machine optimizes the discovery of novel agonists and expands the chemical space of a Lepidoptera OR. More, it opens up structure-function relationship analyses through a comparison of the agonist chemical structures. This proof-of-concept in a crop pest could ultimately enable the identification of OR agonists or antagonists, capable of modifying olfactory behaviors in a context of biocontrol.

Metal Ions Activate the Human Taste Receptor TAS2R7.

Divalent and trivalent salts exhibit a complex taste profile. They are perceived as being astringent/drying, sour, bitter, and metallic. We hypothesized that human bitter-taste receptors may mediate some taste attributes of these salts. Using a cell-based functional assay, we found that TAS2R7 responds to a broad range of divalent and trivalent salts, including zinc, calcium, magnesium, copper, manganese, and aluminum, but not to potassium, suggesting TAS2R7 may act as a metal cation receptor mediating bitterness of divalent and trivalent salts. Molecular modeling and mutagenesis analysis identified 2 residues, H943.37 and E2647.32, in TAS2R7 that appear to be responsible for the interaction of TAS2R7 with metallic ions. Taste receptors are found in both oral and extraoral tissues. The responsiveness of TAS2R7 to various mineral salts suggests it may act as a broad sensor, similar to the calcium-sensing receptor, for biologically relevant metal cations in both oral and extraoral tissues.