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We derived and implemented a linear classification algorithm for the prediction of a molecule’s odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.

Every cell produces thousands of distinct lipid species, but insight into how lipid chemical diversity contributes to biological signaling is lacking, particularly because of a scarcity of methods for quantitatively studying lipid function in living cells. Using the example of diacylglycerols, prominent second messengers, we here investigate whether lipid chemical diversity can provide a basis for cellular signal specification. We generated photo-caged lipid probes, which allow acute manipulation of distinct diacylglycerol species in the plasma membrane. Combining uncaging experiments with mathematical modeling, we were able to determine binding constants for diacylglycerol–protein interactions, and kinetic parameters for diacylglycerol transbilayer movement and turnover in quantitative live-cell experiments. Strikingly, we find that affinities and kinetics vary by orders of magnitude due to diacylglycerol side-chain composition. These differences are sufficient to explain differential recruitment of diacylglycerol binding proteins and, thus, differing downstream phosphorylation patterns. Our approach represents a generally applicable method for elucidating the biological function of single lipid species on subcellular scales in quantitative live-cell experiments.

Neuronal communication across synapses relies on neurotransmitter release from presynaptic active zones (AZs) followed by postsynaptic transmitter detection. Synaptic plasticity homeostatically maintains functionality during perturbations and enables memory formation. Postsynaptic plasticity targets neurotransmitter receptors, but presynaptic mechanisms regulating the neurotransmitter release apparatus remain largely enigmatic. By studying Drosophila neuromuscular junctions (NMJs) we show that AZs consist of nano-modular release sites and identify a molecular sequence that adds modules within minutes of inducing homeostatic plasticity. This requires cognate transport machinery and specific AZ-scaffolding proteins. Structural remodeling is not required for immediate potentiation of neurotransmitter release, but necessary to sustain potentiation over longer timescales. Finally, mutations in Unc13 disrupting homeostatic plasticity at the NMJ also impair short-term memory when central neurons are targeted, suggesting that both plasticity mechanisms utilize Unc13. Together, while immediate synaptic potentiation capitalizes on available material, it triggers the coincident incorporation of modular release sites to consolidate synaptic potentiation. Synaptic plasticity ensures functionality during perturbations and enables memory formation. Here, the authors describe homeostatic functional and nano-modular active zone modifications for immediate and long-lasting enhancement of neurotransmitter release, and identify Unc13 as a presynaptic molecular target for homeostatic potentiation and learning.

Coupling distances between synaptic vesicles and Ca 2+ channels determine the efficacy of neurotransmission. Böhme et al . find that presynaptic scaffold complexes spatiotemporally control Unc13 isoforms to establish two independent release pathways at subsynaptic active zones: Unc13B defines nascent, loosely coupled synapses whereas Unc13A facilitates release at mature synapses by tight coupling between Ca 2+ channels and synaptic vesicles. Brain function relies on fast and precisely timed synaptic vesicle (SV) release at active zones (AZs). Efficacy of SV release depends on distance from SV to Ca 2+ channel, but molecular mechanisms controlling this are unknown. Here we found that distances can be defined by targeting two unc-13 (Unc13) isoforms to presynaptic AZ subdomains. Super-resolution and intravital imaging of developing Drosophila melanogaster glutamatergic synapses revealed that the Unc13B isoform was recruited to nascent AZs by the scaffolding proteins Syd-1 and Liprin-α, and Unc13A was positioned by Bruchpilot and Rim-binding protein complexes at maturing AZs. Unc13B localized 120 nm away from Ca 2+ channels, whereas Unc13A localized only 70 nm away and was responsible for docking SVs at this distance. Unc13A null mutants suffered from inefficient, delayed and EGTA-supersensitive release. Mathematical modeling suggested that synapses normally operate via two independent release pathways differentially positioned by either isoform. We identified isoform-specific Unc13-AZ scaffold interactions regulating SV-Ca 2+ -channel topology whose developmental tightening optimizes synaptic transmission.

Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures. Due to chemical interactions of these compounds in the olfactory system, assessing or even predicting the olfactory quality of such mixtures is a difficult task, not only for statistical models, but even for trained assessors. Here, we combine fast automated analytical assessment tools with human sensory data of 11 experienced panelists and machine learning algorithms. Using 16 previously analyzed whisky samples (American or Scotch origin), we apply the linear classifier OWSum to distinguish the samples based on their detected molecules and to gain insights into the key molecular structure characteristics and odor descriptors for sample type. Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). The predictions outperform the inter-panelist agreement and thus demonstrate previously impossible data-driven sensory assessment in mixtures. Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures, and assessing or predicting the olfactory qualities of such mixtures is challenging. Here, fast automated analytical assessment tools are combined with the human sensory data of 11 experienced panelists and machine learning algorithms, enabling samples to be distinguished and classified based on their detected molecules, and gaining insights into key molecular structure characteristics and odor descriptors.

Chemical synaptic transmission relies on the Ca2+-induced fusion of transmitter-laden vesicles whose coupling distance to Ca2+ channels determines synaptic release probability and short-term plasticity, the facilitation or depression of repetitive responses. Here, using electron- and super-resolution microscopy at the Drosophila neuromuscular junction we quantitatively map vesicle:Ca2+ channel coupling distances. These are very heterogeneous, resulting in a broad spectrum of vesicular release probabilities within synapses. Stochastic simulations of transmitter release from vesicles placed according to this distribution revealed strong constraints on short-term plasticity; particularly facilitation was difficult to achieve. We show that postulated facilitation mechanisms operating via activity-dependent changes of vesicular release probability (e.g. by a facilitation fusion sensor) generate too little facilitation and too much variance. In contrast, Ca2+-dependent mechanisms rapidly increasing the number of releasable vesicles reliably reproduce short-term plasticity and variance of synaptic responses. We propose activity-dependent inhibition of vesicle un-priming or release site activation as novel facilitation mechanisms. Cells in the nervous system of all animals communicate by releasing and sensing chemicals at contact points named synapses. The ‘talking’ (or pre-synaptic) cell stores the chemicals close to the synapse, in small spheres called vesicles. When the cell is activated, calcium ions flow in and interact with the release-ready vesicles, which then spill the chemicals into the synapse. In turn, the ‘listening’ (or post-synaptic) cell can detect the chemicals and react accordingly. When the pre-synaptic cell is activated many times in a short period, it can release a greater quantity of chemicals, allowing a bigger reaction in the post-synaptic cell. This phenomenon is known as facilitation, but it is still unclear how exactly it can take place. This is especially the case when many of the vesicles are not ready to respond, for example when they are too far from where calcium flows into the cell. Computer simulations have been created to model facilitation but they have assumed that all vesicles are placed at the same distance to the calcium entry point: Kobbersmed et al. now provide evidence that this assumption is incorrect. Two high-resolution imaging techniques were used to measure the actual distances between the vesicles and the calcium source in the pre-synaptic cells of fruit flies: this showed that these distances are quite variable – some vesicles sit much closer to the source than others. This information was then used to create a new computer model to simulate facilitation. The results from this computing work led Kobbersmed et al. to suggest that facilitation may take place because a calcium-based mechanism in the cell increases the number of vesicles ready to release their chemicals. This new model may help researchers to better understand how the cells in the nervous system work. Ultimately, this can guide experiments to investigate what happens when information processing at synapses breaks down, for example in diseases such as epilepsy.

In this paper we present a method that allows leveraging 3D electron density information to train a deep neural network pipeline to segment regions of high, medium and low electronegativity and classify substances as health hazardous or non-hazardous. We show that this can be used for use-cases such as cosmetics and food products. For this purpose, we first generate 3D electron density cubes using semiempirical molecular calculations for a custom European Chemicals Agency (ECHA) subset consisting of substances labelled as hazardous and non-hazardous for cosmetic usage. Together with their 3-class electronegativity maps we train a modified 3D-UNet with electron density cubes to segment reactive sites in molecules and classify substances with an accuracy of 78.1%. We perform the same process on a custom food dataset (CompFood) consisting of hazardous and non-hazardous substances compiled from European Food Safety Authority (EFSA) OpenFoodTox, Food and Drug Administration (FDA) Generally Recognized as Safe (GRAS) and FooDB datasets to achieve a classification accuracy of 64.1%. Our results show that 3D electron densities and particularly masked electron densities, calculated by taking a product of original electron densities and regions of high and low electronegativity can be used to classify molecules for different use-cases and thus serve not only to guide safe-by-design product development but also aid in regulatory decisions. Scientific contribution We aim to contribute to the diverse 3D molecular representations used for training machine learning algorithms by showing that a deep learning network can be trained on 3D electron density representation of molecules. This approach has previously not been used to train machine learning models and it allows utilization of the true spatial domain of the molecule for prediction of properties such as their suitability for usage in cosmetics and food products and in future, to other molecular properties. The data and code used for training is accessible at https://github.com/s-singh-ivv/eDen-Substances .

Synaptic transmission relies on presynaptic neurotransmitter (NT) release from synaptic vesicles (SVs) and on NT detection by postsynaptic receptors. Transmission exists in two principal modes: action-potential (AP) evoked and AP-independent, “spontaneous” transmission. AP-evoked neurotransmission is considered the primary mode of inter-neuronal communication, whereas spontaneous transmission is required for neuronal development, homeostasis, and plasticity. While some synapses appear dedicated to spontaneous transmission only, all AP-responsive synapses also engage spontaneously, but whether this encodes functional information regarding their excitability is unknown. Here we report on functional interdependence of both transmission modes at individual synaptic contacts of Drosophila larval neuromuscular junctions (NMJs) which were identified by the presynaptic scaffolding protein Bruchpilot (BRP) and whose activities were quantified using the genetically encoded Ca 2+ indicator GCaMP. Consistent with the role of BRP in organizing the AP-dependent release machinery (voltage-dependent Ca 2+ channels and SV fusion machinery), most active BRP-positive synapses (>85%) responded to APs. At these synapses, the level of spontaneous activity was a predictor for their responsiveness to AP-stimulation. AP-stimulation resulted in cross-depletion of spontaneous activity and both transmission modes were affected by the non-specific Ca 2+ channel blocker cadmium and engaged overlapping postsynaptic receptors. Thus, by using overlapping machinery, spontaneous transmission is a continuous, stimulus independent predictor for the AP-responsiveness of individual synapses.

The evaluation of aroma properties of beverages, both analytically as well as with human sensory studies, is a challenging task and most often related to time- and cost-intensive analyses. Whisky is a spirit offering a wide variety of aroma impressions caused by a complex mixture of aroma active compounds. In the present study, methods for the efficient evaluation of aroma characteristics are evaluated for 16 whisky samples of different origins (Scotch and American). Rate all that apply (RATA) was applied as a rapid method for the sensory evaluation of whiskies. Sensory evaluation of the samples led to the determination of eight significant aroma attributes: caramel-/cream caramel-/toffee-like, vanilla-like, (canned) peach-like, phenolic, smoky, fruity, flowery and (fermented) apple-/cider-like. Chemical analysis was conducted by stir bar sorptive extraction (SBSE) coupled to gas chromatography-mass spectrometry in combination with an in-house data processing tool for semi-automated analyte detection. Through chemical analysis of the whisky samples and automated compound detection, we report over 200 mostly aroma-active volatiles. To test both approaches for their potential for sample classification, a simple classification problem (Scotch vs. American) was applied. Linear discriminant analysis (LDA) indicates both that sensory evaluation by RATA (97.86%) and the applied analytical procedure (96.94%) are suitable for the distinction between the two whisky types. In both approaches, potential markers were determined for the classification. These investigations build a solid foundation for the implementation of a versatile platform facilitating rapid and efficient aroma evaluation in various foodstuffs and beverages.

Brain function relies on fast and precisely timed synaptic vesicle (SV) release at active zones (AZs). Efficacy of SV release depends on distance from SV to Ca(2+) channel, but molecular mechanisms controlling this are unknown. Here we found that distances can be defined by targeting two unc-13 (Unc13) isoforms to presynaptic AZ subdomains. Super-resolution and intravital imaging of developing Drosophila melanogaster glutamatergic synapses revealed that the Unc13B isoform was recruited to nascent AZs by the scaffolding proteins Syd-1 and Liprin-α, and Unc13A was positioned by Bruchpilot and Rim-binding protein complexes at maturing AZs. Unc13B localized 120 nm away from Ca(2+) channels, whereas Unc13A localized only 70 nm away and was responsible for docking SVs at this distance. Unc13A(null) mutants suffered from inefficient, delayed and EGTA-supersensitive release. Mathematical modeling suggested that synapses normally operate via two independent release pathways differentially positioned by either isoform. We identified isoform-specific Unc13-AZ scaffold interactions regulating SV-Ca(2+)-channel topology whose developmental tightening optimizes synaptic transmission.