Unknown Molecules Research Articles

A Glossary for Chemical Approaches towards Unlocking the Trove of Metabolic Treasures in Actinomycetes.

Actinobacterial natural products showed a critical basis for the discovery of new antibiotics as well as other lead secondary metabolites. Varied environmental and physiological signals touch the antibiotic machinery that faced a serious decline in the last decades. The reason was exposed by genomic sequencing data, which revealed that Actinomycetes harbor a large portion of silent biosynthetic gene clusters in their genomes that encrypt for secondary metabolites. These gene clusters are linked with a great reservoir of yet unknown molecules, and arranging them is considered a major challenge for biotechnology approaches. In the present paper, we discuss the recent strategies that have been taken to augment the yield of secondary metabolites via awakening these cryptic genes in Actinomycetes with emphasis on chemical signaling molecules used to induce the antibiotics biosynthesis. The rationale, types, applications and mechanisms are discussed in detail, to reveal the productive path for the unearthing of new metabolites, covering the literature until the end of 2020.

Prediction of chemical shift in NMR: A review.

Calculation of solution-state NMR parameters, including chemical shift values and scalar coupling constants, is often a crucial step for unambiguous structure assignment. Data-driven (sometimes called empirical) methods leverage databases of known parameter values to estimate parameters for unknown or novel molecules. This is in contrast to popular ab initio techniques that use detailed quantum computational chemistry calculations to arrive at parameter estimates. Data-driven methods have the potential to be considerably faster than ab inito techniques and have been the subject of renewed interest over the past decade with the rise of high-quality databases of NMR parameters and novel machine learning methods. Here, we review these methods, their strengths and pitfalls, and the databases they are built on.

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy

Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure–Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed. These models were built using in total 10,505 molecules that were extracted from the ChEMBL, ZINC and Reaxys® databases, and recent literature. Three machine learning (ML) techniques e.g., Random Forest, Support Vector Machine, Convolutional Neural Network were explored to build models for p53 inhibitor prediction. The performances of the models were successfully evaluated by internal and external validation. Moreover, based on the best in silico p53 model, a virtual screening campaign was carried out using 1443 FDA-approved drugs that were extracted from the ZINC database. A list of virtual screening hits was assented on base of some limits established in this approach, such as: (1) probability of being active against p53; (2) applicability domain; (3) prediction of the affinity between the p53, and ligands, through molecular docking. The most promising according to the limits established above was dihydroergocristine. This compound revealed cytotoxic activity against a p53-expressing CRC cell line with an IC50 of 56.8 µM. This study demonstrated that the computer-aided drug design approach can be used to identify previously unknown molecules for targeting p53 protein with anti-cancer activity and thus pave the way for the study of a therapeutic solution for CRC.

Computational Search to Find Efficient Red/Near‐Infrared Emitting Organic Molecules Based on Thermally Activated Delayed Fluorescence for Organic Light‐Emitting Diodes

AbstractComputational screening is highly fruitful for initial studies of unknown molecules due to the capability of this tool in molecular discovery and the continuous improvement of computational methods. This article reports how computational studies on more than 300 organic molecules can identify thermally activated delayed fluorescent (TADF) emitters that emit light within the range of red up to near‐infrared (NIR) with high TADF rates. According to time‐dependent density functional theory (TD‐DFT) computations, the best compounds exhibit a low singlet‐triplet energy gap of 0.03 eV and oscillator strength of 0.0425, leading to a high rate of delayed fluorescence decay of 1.4483 μs−1. Additionally, an experimental‐theory calibration approach is used as an adjunct to experimental research in the future, and emission wavelengths in promising charge‐transfer compounds are estimated as large as 689 nm.

Real time, in-line optical mapping of single molecules of DNA

DNA optical mapping in nanochannels allows studying intact molecules and analyzing their long-range structure at the single-molecule level. Recent efforts have demonstrated that optical mapping can be used for various biomedical applications, such as bacteria identification, analysis of tumor cells, or whole-genome mapping. However, techniques for optical mapping are still slow and restricted to specialized labs. Here, we show a complete methodology for real-time DNA optical mapping on-chip, which is simple and offers high throughput. It does not require a microscope nor a high sensitivity camera to read the barcode, nor the use of external forces (like electrophoresis) to drive the molecules into the nanochannels. The DNA molecules are labelled with different methods, which allows barcoding known and unknown molecules. The barcoded DNA sample is analyzed in single-use, plastic nanoimprinted fluidic devices, which are versatile platforms to manipulate and stretch the molecules in nanochannels. And the fluorescent signal of the molecules is recorded in-line, in real time, with a laser system and a photon counter. With this methodology, we obtained barcodes of molecules with periodic sequences, where we marked one site per period. Furthermore, we barcoded the DNA of bacteriophages (Lambda and T4) and of a tumor virus (the Kaposi Sarcoma Herpesvirus, KSHV) by competitive binding, and obtained their unique fingerprints. Interestingly, this method succeeds in the correct detection (length and number) of highly repetitive structures such as the terminal repeat region of KSHV. These results show the versatility of the proposed methodology for fast (few milliseconds per molecule), low cost, high throughput (tens of molecules per minute) DNA analysis on-demand for biomedical applications. In particular, it can be used to analyze DNA with repeated sequences complementing other commercial techniques.

Design and Antioxidant Properties of Bifunctional 2H-Imidazole-Derived Phenolic Compounds-A New Family of Effective Inhibitors for Oxidative Stress-Associated Destructive Processes.

The synthesis of inhibitors for oxidative stress-associated destructive processes based on 2H-imidazole-derived phenolic compounds affording the bifunctional 2H-imidazole-derived phenolic compounds in good-to-excellent yields was reported. In particular, a series of bifunctional organic molecules of the 5-aryl-2H-imidazole family of various architectures bearing both electron-donating and electron-withdrawing substituents in the aryl fragment along with the different arrangements of the hydroxy groups in the polyphenol moiety, namely derivatives of phloroglucinol, pyrogallol, hydroxyquinol, including previously unknown water-soluble molecules, were studied. The structural and antioxidant properties of these bifunctional 5-aryl-2H-imidazoles were comprehensively studied. The redox transformations of the synthesized compounds were carried out. The integrated approach based on single and mixed mechanisms of antioxidant action, namely the AOC, ARC, Folin, and DPPH assays, were applied to estimate antioxidant activities. The relationship “structure-antioxidant properties” was established for each of the antioxidant action mechanisms. The conjugation effect was shown to result in a decrease in the mobility of the hydrogen atom, thus complicating the process of electron transfer in nearly all cases. On the contrary, the conjugation in imidazolyl substituted phloroglucinols was found to enhance their activity through the hydrogen transfer mechanism. Imidazole-derived polyphenolic compounds bearing the most electron-withdrawing functionality, namely the nitro group, were established to possess the higher values for both antioxidant and antiradical capacities. It was demonstrated that in the case of phloroglucinol derivatives, the conjugation effect resulted in a significant increase in the antiradical capacity (ARC) for a whole family of the considered 2H-imidazole-derived phenolic compounds in comparison with the corresponding unsubstituted phenols. Particularly, conjugation of the polyphenolic subunit with 2,2-dimethyl-5-(4-nitrophenyl)-2H-imidazol-4-yl fragment was shown to increase ARC from 2.26 to 5.16 (104 mol-eq/L). This means that the considered family of compounds is capable of exhibiting an antioxidant activity via transferring a hydrogen atom, exceeding the activity of known natural polyphenolic compounds.

When the Frequencies of Sensitization and Elicitation of Allergic Reaction Do Not Correlate-The Case of Apple Gibberellin-Regulated Protein Tested in an Italian Population.

Background: The literature reports describing allergic symptoms against apples in the patients sensitized to the gibberellin-regulated proteins (GRPs) suggested the presence of an allergenic GRP in this fruit.Objective: This study aimed to assess the presence of a GRP protein in apples and investigate its allergenicity.Methods: The protein was isolated and identified by the classical biochemical methods. The bioinformatics tools were used for similar searches and molecular modeling. The immunological features were investigated using the multiplex FABER test. Clinical data were collected by the allergy specialists.Results: A GRP was detected in the apple peel and pulp and it was named applemaclein. This protein displays 94% of sequence identity with peamaclein, Pru p 7, representing the prototype of this allergen family. The applemaclein molecular model shows a very irregular surface with grooves/clefts that may potentially accommodate small molecular ligands. In a population of 4,721 patients in Italy, 187 (4.0%) were sensitized to any allergenic GPR. Of those, 115 (61.5%), 61 (32.6%), 30 (16.0%), and 99 (52.9%) had immunoglobulin E (IgE) to apple, peach, pomegranate, and cypress GRP, respectively. However, in a cohort of the patients in Italy, most individuals IgE positive to the apple GRP did not report allergic reactions against this fruit.Conclusion: Compared with the peach Pru p 7, applemaclein shows some different structural features and higher sensitization frequency, which is often not associated with allergic reactions against apple. Further studies are needed to understand a possible correlation between the applemaclein structural properties, the interaction with still unknown molecules, and immunological behavior.

Antibacterial, Antifungal and Anticancer Activities of Compounds Produced by Newly Isolated Streptomyces Strains from the Szczelina Chochołowska Cave (Tatra Mountains, Poland).

Resistance of bacteria, fungi and cancer cells to antibiotics and other drugs is recognized as one of the major problems in current medicine. Therefore, a search for new biologically active compounds able to either kill pathogenic cells or inhibit their growth is mandatory. Hard-to-reach habitats appear to be unexplored sources of microorganisms producing previously unknown antibiotics and other molecules revealing potentially therapeutic properties. Caves belong to such habitats, and Actinobacteria are a predominant group of microorganisms occurring there. This group of bacteria are known for production of many antibiotics and other bioactive compounds. Interestingly, it was demonstrated previously that infection with bacteriophages might enhance production of antibiotics by them. Here, we describe a series of newly isolated strains of Actinobacteria that were found in caves from the Tatra Mountains (Poland). Phage induction tests indicated that some of them may bear active prophages able to produce virions upon treatment with mitomycin C or UV irradiation. Among all the examined bacteria, two newly isolated Streptomyces sp. strains were further characterized to demonstrate their ability to inhibit the growth of pathogenic bacteria (strains of Staphylococcus aureus, Salmonella enterica, Enterococcus sp., Escherichia coli, and Pseudomonas aeruginosa) and fungi (different species and strains from the genus Candida). Moreover, extracts from these Streptomyces strains reduced viability of the breast-cancer cell line T47D. Chemical analyses of these extracts indicated the presence of isomers of dichloranthrabenzoxocinone and 4,10- or 10,12-dichloro-3-O-methylanthrabenzoxocinone, which are putative antimicrobial compounds. Moreover, various previously unknown (unclassified) molecules were also detected using liquid chromatography–mass spectrometry, suggesting that tested Streptomyces strains may synthesize a battery of bioactive compounds with antibacterial, antifungal, and anticancer activities. These results indicate that further studies on the newly isolated Actinobacteria might be a promising approach to develop novel antibacterial, antifungal, and/or anticancer drugs.

Predicting scalar coupling constants by graph angle-attention neural network

Scalar coupling constant (SCC), directly measured by nuclear magnetic resonance (NMR) spectroscopy, is a key parameter for molecular structure analysis, and widely used to predict unknown molecular structure. Restricted by the high cost of NMR experiments, it is impossible to measure the SCC of unknown molecules on a large scale. Using density functional theory (DFT) to theoretically calculate the SCC of molecules is incredibly challenging, due to the cost of substantial computational time and space. Graph neural networks (GNN) of artificial intelligence (AI) have great potential in constructing molecular-like topology models, which endows them the ability to rapidly predict SCC through data-driven machine learning methods, and avoiding time-consuming quantum chemical calculations. With a priori knowledge of angles, we propose a graph angle-attention neural network (GAANN) model to predict SCC by means of some easily accessible related information. GAANN, with a multilayer message-passing network and a self-attention mechanism, can accurately simulate the molecular-like topological structure and predict molecular properties. Our simulations show that the prediction accuracy by GAANN, with the log(MAE) = −2.52, is close to that by DFT calculations. Different from conventional AI methods, GAANN combining the AI method with quantum chemistry theory (Karplus equation) has a strong physicochemical interpretability about angles. From an AI perspective, we find that bond angle has the highest correlation with the SCC among all angle features (dihedral angle, bond angle, geometric angles) about multiple coupling types in the small molecule datasets.

Crystal structure of the new palladium complexes tetra-kis-(1,3-di-methyl-imidazolium-2-yl-idene)palladium(II) hexa-deca-carbonyl-tetra-rhenium diethyl ether disolvate and octa-μ-carbonyl-di-carbonyl-tetra-kis-(tri-phenyl-phosphane)palladium-dirhenium (unknown solvate).

The investigation of the coordination chemistry of heterometallic transition-metal complexes of palladium (Pd) and rhenium (Re) led to the isolation and crystallographic characterization of tetra-kis-(1,3-di-methyl-imidazolium-2-yl-idene)palladium(II) hexa-deca-carbonyl-tetra-rhenium diethyl ether disolvate, [Pd(C5H8N2)4][Re4(CO)16]·2C4H10O or [Pd(IMe)4][Re4(CO)16]·2C4H10O, (1), and octa-μ-carbonyl-di-carbonyl-tetra-kis-(tri-phenyl-phosphane)palladium-dirhenium, [Pd4Re2(C18H15P)4(CO)10] or Pd4Re2(PPh3)4(μ-CO)8(CO)2, (2), from the reaction of Pd(PPh3)4 with 1,3-di-methyl-imidazolium-2-carboxyl-ate and Re2(CO)10 in a toluene-aceto-nitrile mixture. In complex 1 the Re-Re bond lengths [2.9767 (3)-3.0133 (2) Å] are close to double the covalent Re radii (1.51 Å). The palladium-rhenium carbonyl cluster 2 has not been structurally characterized previously; the Pd-Re bond lengths [2.7582 (2)-2.7796 (2) Å] are about 0.1 Å shorter than the sum of the covalent Pd and Re radii (1.39+1.51 = 2.90 Å). One carbene ligand and a diethyl ether mol-ecule are disordered over two positions with occupancy ratios of 0.5:0.5 and 0.625 (15):0.375 (15) in 1. An unidentified solvent is present in compound 2. The given chemical formula and other crystal data do not take into account the unknown solvent mol-ecule(s). The SQUEEZE routine [Spek (2015 ▸). Acta Cryst. C71, 9-18] in PLATON was used to remove the contribution of the electron density in the solvent region from the intensity data and the solvent-free model was employed for the final refinement. The cavity with a volume of ca 311 Å3 contains approximately 98 electrons.

Climate change influences chlorophylls and bacteriochlorophylls metabolism in hypersaline microbial mat

This study aimed to determine the effect of the climatic change on the phototrophic communities of hypersaline microbial mats. Ocean acidification and warming were simulated alone and together on microbial mats placed into mesocosms. As expected, the temperature in the warming treatments increased by 4 °C from the initial temperature. Surprisingly, no significance difference was observed between the water pH of the different treatments despite of a decrease of 0.4 unit pH in the water reserves of acidification treatments. The salinity increased on the warming treatments and the dissolved oxygen concentration increased and was higher on the acidification treatments. A total of 37 pigments were identified belonging to chlorophylls, carotenes and xanthophylls families. The higher abundance of unknown chlorophyll molecules called chlorophyll derivatives was observed in the acidification alone treatment with a decrease in chlorophyll a abundance. This change in pigmentary composition was accompanied by a higher production of bound extracellular carbohydrates but didn't affect the photosynthetic efficiency of the microbial mats. A careful analysis of the absorption properties of these molecules indicated that these chlorophyll derivatives were likely bacteriochlorophyll c contained in the chlorosomes of green anoxygenic phototroph bacteria. Two hypotheses can be drawn from these results: 1/ the phototrophic communities of the microbial mats were modified under acidification treatment leading to a higher relative abundance of green anoxygenic bacteria, or 2/ the highest availability of CO2 in the environment has led to a shift in the metabolism of green anoxygenic bacteria being more competitive than other phototrophs.

Crystal structure of bis-[cis-di-aqua-bis-(phen-an-thro-line)cobalt(II)] bis-(citrato)germanate(IV) dinitrate.

The asymmetric unit of the title compound, [Co(C12H8N2)2(H2O)2]2[Ge(C6H5O7)2](NO3)2, features two complex [(C12H8N2)2(H2O)2Co]2+ cations, two NO3 - anions as well as one centrosymmetric [(C6H5O7)2Ge]2- anion. Two HCit ligands (Cit = citrate, C6H4O7) each coordinate via three different oxygen atoms (hy-droxy-late, α-carboxyl-ate, β-carboxyl-ate) to the Ge atom, forming a slightly distorted octa-hedron. The coordination polyhedron of the Co atom is also octa-hedral, formed by coordination of four nitro-gen atoms from two phenanthroline mol-ecules and two water oxygen atoms. In the crystal, the cations and anions are linked by hydrogen bonds and form layers parallel to the bc plane. The structure exhibits disorder of the NO3 - anion [disorder ratio 0.688 (9) to 0.312 (9)]. There are also highly disordered solvent mol-ecules (presumably water and/or ethanol) in the crystal structure; explicit refinement of these mol-ecules was not possible, and the content of the voids was instead taken into account using reverse Fourier transform methods [SQUEEZE procedure in PLATON; Spek (2015 ▸). Acta Cryst. C71, 9-18]. The given chemical formula and other crystal data do not take into account the unknown solvent mol-ecule(s).

Molecular Commerce on Coral Reefs: Using Metabolomics to Reveal Biochemical Exchanges Underlying Holobiont Biology and the Ecology of Coastal Ecosystems

The rapidly advancing field of metabolomics encompasses a diverse suite of powerful analytical and bioinformatic tools that can help to reveal the diversity and activity of chemical compounds in individual organisms, species interactions, and entire ecosystems. In this perspective we use examples from studies of coral reefs to illustrate ways in which metabolomics has been and can be applied to understand coastal ecosystems. Examples of new insights that can be provided by metabolomics include resolving metabolite exchange between plants, animals and their microbiota, identifying the relevant metabolite exchanges associated with the onset and maintenance of diverse, microbial mutualisms characterizing unknown molecules that act as cues in coral, reproduction, or defining the suites of compounds involved in coral-algal competition and microbialization of algal-dominated ecosystems. Here we outline sampling, analytical and informatic methods that marine biologists and ecologists can apply to understand the role of chemical processes in ecosystems, with a focus on open access data analysis workflows and democratized databases. Finally, we demonstrate how these metabolomics tools and bioinformatics approaches can provide scientists the opportunity to map detailed metabolic inventories and dynamics for a holistic view of the relationships among reef organisms, their symbionts and their surrounding marine environment.

Reproducible determination of dissolved organic matter photosensitivity

Abstract. Dissolved organic matter (DOM) connects aquatic and terrestrial ecosystems, plays an important role in carbon (C) and nitrogen (N) cycles, and supports aquatic food webs. Understanding DOM chemical composition and reactivity is key for predicting its ecological role, but characterization is difficult as natural DOM is comprised of a large but unknown number of distinct molecules. Photochemistry is one of the environmental processes responsible for changing the molecular composition of DOM, and DOM composition also defines its susceptibility to photochemical alteration. Reliably differentiating the photosensitivity of DOM from different sources can improve our knowledge of how DOM composition is shaped by photochemical alteration and aid research into photochemistry's role in various DOM transformation processes. Here we describe an approach for measuring and comparing DOM photosensitivity consistently, based on the kinetics of changes in DOM fluorescence during 20 h photodegradation experiments. We identify several methodological choices that affect photosensitivity measurements and offer guidelines for adopting our methods, including the use of reference material, precise control of conditions affecting photon dose, leveraging actinometry to estimate photon dose instead of expressing results as a function of exposure time, and frequent (every 20 min) fluorescence and absorbance measurements during exposure to artificial sunlight. We then show that our approach can generate photosensitivity metrics across several sources of DOM, including freshwater wetlands, a stream, an estuary, and Sargassum sp. leachate and observed differences in these metrics that may help identify or explain differences in their composition. Finally, we offer an example of applying our approach to compare DOM photosensitivity in two adjacent freshwater wetlands as seasonal hydrologic changes alter their DOM sources.

Supervised topic modeling for predicting molecular substructure from mass spectrometry

Small-molecule metabolites are principal actors in myriad phenomena across biochemistry and serve as an important source of biomarkers and drug candidates. Given a sample of unknown composition, identifying the metabolites present is difficult given the large number of small molecules both known and yet to be discovered. Even for biofluids such as human blood, building reliable ways of identifying biomarkers is challenging. A workhorse method for characterizing individual molecules in such untargeted metabolomics studies is tandem mass spectrometry (MS/MS). MS/MS spectra provide rich information about chemical composition. However, structural characterization from spectra corresponding to unknown molecules remains a bottleneck in metabolomics. Current methods often rely on matching to pre-existing databases in one form or another. Here we develop a preprocessing scheme and supervised topic modeling approach to identify modular groups of spectrum fragments and neutral losses corresponding to chemical substructures using labeled latent Dirichlet allocation (LLDA) to map spectrum features to known chemical structures. These structures appear in new unknown spectra and can be predicted. We find that LLDA is an interpretable and reliable method for structure prediction from MS/MS spectra. Specifically, the LLDA approach has the following advantages: (a) molecular topics are interpretable; (b) A practitioner can select any set of chemical structure labels relevant to their problem; (c ) LLDA performs well and can exceed the performance of other methods in predicting substructures in novel contexts.

Scalable Fabrication of Kevlar/Ti3C2Tx MXene Intelligent Wearable Fabrics with Multiple Sensory Capabilities.

Fiber-based wearable electronics are highly desirable for wearable devices that are expected to be lightweight, easily prepared, durable, flexible, washable, and conformable. However, developing fiber-based fabric electronics to simulate human perceptual systems or even transcend the sensory capabilities of natural creatures is still a pivotal challenge. Herein, we present a Kevlar/MXene (KM) intelligent wearable fabric with multiple sensory capabilities using an ingenious strategy of continuous wet-spinning. The KM fibers can be washed, knitted, sewed, and fabricated into smart KM fabric sensory systems. An intelligent KM sensory mask is prepared to monitor human breathing in time to detect respiratory problems with high accuracy and portability. It provides an important reference for judging diseases and achieving remote diagnosis. Additionally, a smart temperature-responsive sensory glove is developed to help people make proper behavioral prejudgments and prevent potential injuries by sensing surrounding hazards beforehand. Moreover, this sensory system allows soft robotics to make a rough identification about the basic properties of unknown liquid molecules. Overall, by the virtue of the ultrafast responsiveness (90 ms), resilience (110 ms), and ultrasensitive capability in pressure responding, this KM sensory system offers a gentle approach for wireless detection in information encryption, transmission, and preservation by touching the sensory system with variable pressing time on the basis of the International Morse code principles, establishing a competitive and promising candidate for next generation wearable flexible fabric electronics.

Aspergillus flavus Growth Inhibition and Aflatoxin B1 Decontamination by Streptomyces Isolates and Their Metabolites.

Aflatoxin B1 is a potent carcinogen produced by Aspergillus flavus, mainly during grain storage. As pre-harvest methods are insufficient to avoid mycotoxin presence during storage, diverse curative techniques are being investigated for the inhibition of fungal growth and aflatoxin detoxification. Streptomyces spp. represent an alternative as they are a promising source of detoxifying enzymes. Fifty-nine Streptomyces isolates and a Streptomyces griseoviridis strain from the commercial product Mycostop®, evaluated against Penicillium verrucosum and ochratoxin A during previous work, were screened for their ability to inhibit Aspergillus flavus growth and decrease the aflatoxin amount. The activities of bacterial cells and cell-free extracts (CFEs) from liquid cultures were also evaluated. Fifty-eight isolates were able to inhibit fungal growth during dual culture assays, with a maximal reduction going down to 13% of the control. Aflatoxin-specific production was decreased by all isolates to at least 54% of the control. CFEs were less effective in decreasing fungal growth (down to 40% and 55% for unheated and heated CFEs, respectively) and aflatoxin-specific production, with a few CFEs causing an overproduction of mycotoxins. Nearly all Streptomyces isolates were able to degrade AFB1 when growing in solid and liquid media. A total degradation of AFB1 was achieved by Mycostop® on solid medium, as well as an almost complete degradation by IX20 in liquid medium (6% of the control). CFE maximal degradation went down to 37% of the control for isolate IX09. The search for degradation by-products indicated the presence of a few unknown molecules. The evaluation of residual toxicity of the tested isolates by the SOS chromotest indicated a detoxification of at least 68% of AFB1’s genotoxicity.

Total synthesis and biological evaluation of oscillatoxins D, E, and F.

Oscillatoxins (OTXs) and aplysiatoxins are biosynthetically related polyketides produced by marine cyanobacteria. We previously developed a synthetic route to phenolic O-methyl analogs of OTX-D and 30-methyl-OTX-D during collective synthesis of these natural products. According to our synthetic strategy, we achieved total synthesis of OTX-D, 30-methyl-OTX-D, OTX-E, and OTX-F by deprotecting the O-methyl group in an earlier intermediate, and determined their biological activities. Although OTX-D and 30-methyl-OTX-D have been reported to show antileukemic activity against L1210 cell line, we found that their cytotoxicity in vitro against this cell line is relatively weak (IC50: 29-52 µm). In contrast, OTX-F demonstrated cell line-selective antiproliferative activity against DMS-114 lung cancer cells, which implies that OTXs target as yet unknown target molecules as part of this unique activity.

Psychonauts’ psychedelics: A systematic, multilingual, web-crawling exercise

Psychedelics alter the perception of reality through agonist or partial agonist interaction with the 2A serotoninergic receptor. They are classified as phenethylamines, tryptamines and lysergamides. These classes, according to the United Nations Office on Drugs and Crime (UNODC) and European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), account for an important percentage of the new psychoactive substances (NPS) current scenario.The paper aimed at: a) identifying and categorising psychedelic molecules from a list of psychonaut websites and NPS online resources; and b) comparing the NPSfinderⓇ results with those from the European and United Nations databases. A crawling software (i.e. ‘NPSfinderⓇ’) was created to automatically scan, 24/7, a list of URLs and to extract a range of information (chemical/street names, chemical formulae, etc.) to facilitate NPS identification. Data collected were manually analysed and compared with the EMCDDA and UNODC databases.The overall number of psychedelic NPS detected by NPSfinderⓇ (November 2017-February 2020) was 1344, almost ten-times higher than that reported by the UNODC and EMCDDA combined. Of these, 994 previously unknown molecules were identified as (potential) novel psychedelics, suggesting a strong discrepancy between online and real-world NPS scenarios. The results show the interest of psychonauts, and maybe of the much larger community of ‘recreational’ drug users, towards psychedelics. Moreover, examining online scenario may help in assessing the availability in the real world of psychedelic NPS; understanding drug trends; and in possibly predicting future drug scenarios

SteroidXtract: Deep Learning-Based Pattern Recognition Enables Comprehensive and Rapid Extraction of Steroid-Like Metabolic Features for Automated Biology-Driven Metabolomics.

Despite the vast amount of metabolic information that can be captured in untargeted metabolomics, many biological applications are looking for a biology-driven metabolomics platform that targets a set of metabolites that are relevant to the given biological question. Steroids are a class of important molecules that play critical roles in many physiological systems and diseases. Besides known steroids, there are a large number of unknown steroids that have not been reported in the literature. The ability to rapidly detect and quantify both known and unknown steroid molecules in a biological sample can greatly accelerate a broad range of steroid-focused life science research. This work describes the development and application of SteroidXtract, a convolutional neural network (CNN)-based bioinformatics tool that can recognize steroid molecules in mass spectrometry (MS)-based untargeted metabolomics using their unique tandem MS (MS2) spectral patterns. SteroidXtract was trained using a comprehensive set of standard MS2 spectra from MassBank of North America (MoNA) and an in-house steroid library. Data augmentation strategies, including intensity thresholding and Gaussian noise addition, were created and applied to minimize data overfitting caused by the limited number of standard steroid MS2 spectra. The CNN model embedded in SteroidXtract was further compared with random forest and XGBoost using nested cross-validations to demonstrate its performance. Finally, SteroidXtract was applied in several metabolomics studies to demonstrate its sensitivity, specificity, and robustness. Compared to conventional statistics-driven metabolomics data interpretation, our work offers a novel automated biology-driven approach to interpreting untargeted metabolomics data, prioritizing biologically important molecules with high throughput and sensitivity.