Philological analysis of ancient Egyptian recipes supported by modern chemical profiling approaches.

Ethnopharmacological Relevance: Ancient Egyptian sickness categories are poorly described, making it a challenge to correlate use of materia medica with sickness experience. Nevertheless, many identified ingredients are reported to have therapeutic potential, often used to support Egyptological interpretations of categories. Crucially, these interpretations fail to consider the impact of ancient processing methods.

A universal language for finding mass spectrometry data patterns.

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns.

Fatuamide A, a Hybrid PKS/NRPS Metallophore from a sp. Marine Cyanobacterium Collected in American Samoa.

A structurally novel metabolite, fatuamide A (), was discovered from a laboratory cultured strain of the marine cyanobacterium sp., collected from Faga'itua Bay, American Samoa. A bioassay-guided approach using NCI-H460 human lung cancer cells directed the isolation of fatuamide A, which was obtained from the most cytotoxic fraction.

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration.

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015.

Multispectral investigation of natural resins.

Resins have been used as remedies since ancient times and various embalming resins have been identified in recent years. In Europe, Mumia vera aegyptiaca, a resinous substance from ancient Egyptian mummies, was even sold in pharmacies as a tonic until the early 20th century. It is difficult to examine the composition of these archeological samples in detail as the well-established analytical techniques, that is, gas chromatography-mass spectrometry or liquid chromatography coupled with tandem mass spectrometry, are destructive and therefore do not allow the analysis of valuable archeological samples.

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides.

The marine sponge-derived fungus 293 K04 is a prolific producer of specialized metabolites, including certain cyclic tetrapeptides called endolides, which are characterized by the presence of the unusual amino acid -methyl-3-(3-furyl)-alanine. This rare feature can be used as bait to detect new endolide-like analogs through customized fragment pattern searches of tandem mass spectrometry data using the Mass Spec Query Language (MassQL). Here, we integrate endolide-specific MassQL queries with molecular networking to obtain substructural information guiding the targeted isolation and structure elucidation of the new proline-containing endolides E () and F ().

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature.

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data.

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra.

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters.

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites.

Native metabolomics identifies the rivulariapeptolide family of protease inhibitors.

The identity and biological activity of most metabolites still remain unknown. A bottleneck in the exploration of metabolite structures and pharmaceutical activities is the compound purification needed for bioactivity assignments and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we develop a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry and detection of protein binding via native mass spectrometry.

Heterologous Expression in of the Columbamide Pathway from the Cyanobacterium and Production of New Analogs.

Columbamides are chlorinated acyl amide natural products, several of which exhibit cannabinomimetic activity. These compounds were originally discovered from a culture of the filamentous marine cyanobacterium PNG5-198 collected from the coastal waters of Papua New Guinea. The columbamide biosynthetic gene cluster (BGC) had been identified using bioinformatics, but not confirmed by experimental evidence.

NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties.

Feature-Based Molecular Networking for the Targeted Identification of G-Inhibiting FR900359 Derivatives.

Both the soil bacterium and the bacterial endosymbiont Burkholderia crenata of the plant are producers of FR900359 (FR). This cyclic depsipeptide is a potent and selective G protein inhibitor used extensively to investigate the intracellular signaling of G protein coupled receptors (GPCRs). In this study, the metabolomes of both FR producers were investigated and compared using feature-based molecular networking (FBMN).

A community resource for paired genomic and metabolomic data mining.

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

A Multi-Omics Characterization of the Natural Product Potential of Tropical Filamentous Marine Cyanobacteria.

Microbial natural products are important for the understanding of microbial interactions, chemical defense and communication, and have also served as an inspirational source for numerous pharmaceutical drugs. Tropical marine cyanobacteria have been highlighted as a great source of new natural products, however, few reports have appeared wherein a multi-omics approach has been used to study their natural products potential (i.e.

Heterologous Expression of Cryptomaldamide in a Cyanobacterial Host.

Filamentous marine cyanobacteria make a variety of bioactive molecules that are produced by polyketide synthases, nonribosomal peptide synthetases, and hybrid pathways that are encoded by large biosynthetic gene clusters. These cyanobacterial natural products represent potential drug leads; however, thorough pharmacological investigations have been impeded by the limited quantity of compound that is typically available from the native organisms. Additionally, investigations of the biosynthetic gene clusters and enzymatic pathways have been difficult due to the inability to conduct genetic manipulations in the native producers.

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products.

Cell-permeable high-affinity tracers for G proteins provide structural insights, reveal distinct binding kinetics and identify small molecule inhibitors.

Background And Purpose: G proteins are intracellular switches that transduce and amplify extracellular signals from GPCRs. The G protein subtypes, which are coupled to PLC activation, can act as oncogenes, and their expression was reported to be up-regulated in cancer and inflammatory diseases. G inhibition may be an efficient therapeutic strategy constituting a new level of intervention.

Structural and Dynamical Basis of G Protein Inhibition by YM-254890 and FR900359: An Inhibitor in Action.

Specific inhibition of G proteins holds a great pharmacological promise to, e.g., target oncogenic G proteins and can be achieved by the two natural products FR900359 (FR) and YM-254890 (YM).

Design of Gallinamide A Analogs as Potent Inhibitors of the Cysteine Proteases Human Cathepsin L and Cruzain.

Gallinamide A, originally isolated with a modest antimalarial activity, was subsequently reisolated and characterized as a potent, selective, and irreversible inhibitor of the human cysteine protease cathepsin L. Molecular docking identified potential modifications to improve binding, which were synthesized as a suite of analogs. Resultingly, this current study produced the most potent gallinamide analog yet tested against cathepsin L (, = 0.

Direct targeting of Gα and Gα oncoproteins in cancer cells.

Somatic gain-of-function mutations of and , which encode α subunits of heterotrimeric Gα proteins, occur in about 85% of cases of uveal melanoma (UM), the most common cancer of the adult eye. Molecular therapies to directly target these oncoproteins are lacking, and current treatment options rely on radiation, surgery, or inhibition of effector molecules downstream of these G proteins. A hallmark feature of oncogenic Gα proteins is their reduced intrinsic rate of hydrolysis of guanosine triphosphate (GTP), which results in their accumulation in the GTP-bound, active state.