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

The marine sponge-derived fungus Stachylidium bicolor 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 N-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 (1) and F (2). We showed that endolide F (but not E) is a moderate antagonist of the arginine vasopressin V1A receptor, a member of the G protein-coupled receptor superfamily.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions.

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

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. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

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. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

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. A native metabolomics screen for protease inhibitors from an environmental cyanobacteria community reveals 30 chymotrypsin-binding cyclodepsipeptides. Guided by the native metabolomics results, we select and purify five of these compounds for full structure elucidation via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy as well as evaluation of their biological activities. These results identify rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as a promising approach for drug discovery, chemical ecology, and chemical biology studies.

Heterologous Expression in Anabaena of the Columbamide Pathway from the Cyanobacterium Moorena bouillonii 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 Moorena bouillonii 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. Here, we report the heterologous expression in Anabaena (Nostoc) PCC 7120 of the 28.5 kb BGC that encodes for columbamide biosynthesis. The production of columbamides in Anabaena is investigated under several different culture conditions, and several new columbamide analogs are identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR). In addition to previously characterized columbamides A, B, and C, new columbamides I-M are produced in these experiments, and the structure of the most abundant monochlorinated analog, columbamide K (11), is fully characterized. The other new columbamide analogs are produced in only small quantities, and structures are proposed based on high-resolution-MS, MS/MS, and 1H NMR data. Overexpression of the pathway's predicted halogenases resulted in increased productions of di- and trichlorinated compounds. The most significant change in production of columbamides in Anabaena is correlated with the concentration of NaCl in the medium.

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. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

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. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.

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

Both the soil bacterium Chromobacterium vaccinii and the bacterial endosymbiont Candidatus Burkholderia crenata of the plant Ardisia crenata are producers of FR900359 (FR). This cyclic depsipeptide is a potent and selective Gq 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). As a result, 30 previously unknown FR derivatives were identified, one-third being unique to C. vaccinii. Guided by MS, a novel FR derivative, FR-6 (compound 1), was isolated, and its structure unambiguously established. In a whole-cell biosensing assay based on detection of dynamic mass redistribution (DMR) as readout for Gq inhibition, FR-6 suppressed Gq signaling with micromolar potency (pIC50 = 5.56). This functional activity was confirmed in radioligand binding assays (pKi = 7.50). This work demonstrates the power of molecular networking, guiding the way to a novel Gq-inhibiting FR derivative and underlining the potency of FR as a Gq inhibitor.

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., reports are often focused on an individual natural product and its biosynthesis). This study focuses on describing the natural product genetic potential as well as the expressed natural product molecules in benthic tropical cyanobacteria. We collected from several sites around the world and sequenced the genomes of 24 tropical filamentous marine cyanobacteria. The informatics program antiSMASH was used to annotate the major classes of gene clusters. BiG-SCAPE phylum-wide analysis revealed the most promising strains for natural product discovery among these cyanobacteria. LCMS/MS-based metabolomics highlighted the most abundant molecules and molecular classes among 10 of these marine cyanobacterial samples. We observed that despite many genes encoding for peptidic natural products, peptides were not as abundant as lipids and lipopeptides in the chemical extracts. Our results highlight a number of highly interesting biosynthetic gene clusters for genome mining among these cyanobacterial samples.

Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra.

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.

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. Here we report a set of genetic tools for the heterologous expression of biosynthetic gene clusters in the cyanobacteria Synechococcus elongatus PCC 7942 and Anabaena (Nostoc) PCC 7120. To facilitate the transfer of gene clusters in both strains, we engineered a strain of Anabaena that contains S. elongatus homologous sequences for chromosomal recombination at a neutral site and devised a CRISPR-based strategy to efficiently obtain segregated double recombinant clones of Anabaena. These genetic tools were used to express the large 28.7 kb cryptomaldamide biosynthetic gene cluster from the marine cyanobacterium Moorena (Moorea) producens JHB in both model strains. S. elongatus did not produce cryptomaldamide; however, high-titer production of cryptomaldamide was obtained in Anabaena. The methods developed in this study will facilitate the heterologous expression of biosynthetic gene clusters isolated from marine cyanobacteria and complex metagenomic samples.

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. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

Cell-permeable high-affinity tracers for Gq 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 Gq 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. Gq inhibition may be an efficient therapeutic strategy constituting a new level of intervention. However, diagnostic tools and therapeutic drugs for Gq proteins are lacking. EXPERIMENTAL APPROACH: We have now developed Gq -specific, cell-permeable 3 H-labelled high-affinity probes based on the macrocyclic depsipeptides FR900359 (FR) and YM-254890 (YM). The tracers served to specifically label and quantify Gq proteins in their native conformation in cells and tissues with high accuracy. KEY RESULTS: FR and YM displayed low nanomolar affinity for Gαq , Gα11 and Gα14 expressed in CRISPR/Cas9 Gαq -knockout cells, but not for Gα15 . The two structurally very similar tracers showed strikingly different dissociation kinetics, which is predicted to result in divergent biological effects. Computational studies suggested a "dowel" effect of the pseudoirreversibly binding FR. A high-throughput binding assay led to the discovery of novel Gq inhibitors, which inhibited Gq signalling in recombinant cells and primary murine brown adipocytes, resulting in enhanced differentiation. CONCLUSIONS AND IMPLICATIONS: The Gq protein inhibitors YM and FR are pharmacologically different despite similar structures. The new versatile tools and powerful assays will contribute to the advancement of the rising field of G protein research.

Design of Gallinamide A Analogs as Potent Inhibitors of the Cysteine Proteases Human Cathepsin L and Trypanosoma cruzi 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 (10, Ki = 0.0937 ± 0.01 nM and kinact/Ki = 8 730 000). From a protein structure and substrate preference perspective, cruzain, an essential Trypanosoma cruzi cysteine protease, is highly homologous. Our investigations revealed that gallinamide and its analogs potently inhibit cruzain and are exquisitely toxic toward T. cruzi in the intracellular amastigote stage. The most active compound, 5, had an IC50 = 5.1 ± 1.4 nM, but was relatively inactive to both the epimastigote (insect stage) and the host cell, and thus represents a new candidate for the treatment of Chagas disease.

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 Gq/11 proteins and can be achieved by the two natural products FR900359 (FR) and YM-254890 (YM). Unfortunately, recent rational-design-based approaches to address G proteins other than Gq/11/14 subtypes were not successful mainly due to the conformational complexity of these new modalities-like compounds. Here, we report the water-derived NMR structure of YM, which strongly differs from the conformation of Gq-bound YM as found in the crystal structure. Reanalysis of the crystal structure suggests that the water-derived NMR structure of YM also represents a valid solution of the electron density. Extensive molecular dynamic simulations unveiled much higher binding affinities of the water-derived NMR structure compared to the original YM conformation of pdb 3ah8 . Employing a in-silico-designed, fast activating G protein conformation molecular dynamics data ultimately show how the inhibitor impairs the domain motion of the G protein necessary to hinder nucleotide exchange.

Direct targeting of Gαq and Gα11 oncoproteins in cancer cells.

Somatic gain-of-function mutations of GNAQ and GNA11, which encode α subunits of heterotrimeric Gαq/11 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 Gαq/11 proteins is their reduced intrinsic rate of hydrolysis of guanosine triphosphate (GTP), which results in their accumulation in the GTP-bound, active state. Here, we report that the cyclic depsipeptide FR900359 (FR) directly interacted with GTPase-deficient Gαq/11 proteins and preferentially inhibited mitogenic ERK signaling rather than canonical phospholipase Cß (PLCß) signaling driven by these oncogenes. Thereby, FR suppressed the proliferation of melanoma cells in culture and inhibited the growth of Gαq-driven UM mouse xenografts in vivo. In contrast, FR did not affect tumor growth when xenografts carried mutated B-RafV600E as the oncogenic driver. Because FR enabled suppression of malignant traits in cancer cells that are driven by activating mutations at codon 209 in Gαq/11 proteins, we envision that similar approaches could be taken to blunt the signaling of non-Gαq/11 G proteins.

Rational design of a heterotrimeric G protein α subunit with artificial inhibitor sensitivity.

Transmembrane signals initiated by a range of extracellular stimuli converge on members of the Gq family of heterotrimeric G proteins, which relay these signals in target cells. Gq family G proteins comprise Gq, G11, G14, and G16, which upon activation mediate their cellular effects via inositol lipid-dependent and -independent signaling to control fundamental processes in mammalian physiology. To date, highly specific inhibition of Gq/11/14 signaling can be achieved only with FR900359 (FR) and YM-254890 (YM), two naturally occurring cyclic depsipeptides. To further development of FR or YM mimics for other Gα subunits, we here set out to rationally design Gα16 proteins with artificial FR/YM sensitivity by introducing an engineered depsipeptide-binding site. Thereby we permit control of G16 function through ligands that are inactive on the WT protein. Using CRISPR/Cas9-generated Gαq/Gα11-null cells and loss- and gain-of-function mutagenesis along with label-free whole-cell biosensing, we determined the molecular coordinates for FR/YM inhibition of Gq and transplanted these to FR/YM-insensitive G16. Intriguingly, despite having close structural similarity, FR and YM yielded biologically distinct activities: it was more difficult to perturb Gq inhibition by FR and easier to install FR inhibition onto G16 than perturb or install inhibition with YM. A unique hydrophobic network utilized by FR accounted for these unexpected discrepancies. Our results suggest that non-Gq/11/14 proteins should be amenable to inhibition by FR scaffold-based inhibitors, provided that these inhibitors mimic the interaction of FR with Gα proteins harboring engineered FR-binding sites.

Applying Molecular Networking for the Detection of Natural Sources and Analogues of the Selective Gq Protein Inhibitor FR900359.

The cyclic depsipeptide FR900359 (FR), isolated from the traditional Chinese medicine plant Ardisia crenata, is a potent Gq protein inhibitor and thus a valuable tool to study Gq-mediated signaling of G protein-coupled receptors. Two new FR analogues (3 and 4) were isolated from A. crenata together with the known analogues 1 and 2. The structures of compounds 3 and 4 were established by NMR spectroscopic data and MS-based molecular networking followed by in-depth LCMS2 analysis. The latter approach led to the annotation of further FR analogues 5-9. Comparative bioactivity tests of compounds 1-4 along with the parent molecule FR showed high-affinity binding to Gq proteins in the low nanomolar range (IC50 = 2.3-16.8 nM) for all analogues as well as equipotent inhibition of Gq signaling, which gives important SAR insights into this valuable natural product. Additionally, FR was detected from leaves of five other Ardisia species, among them the non-nodulated leaves of Ardisia lucida, implying a much broader distribution of FR than originally anticipated.

Deciphering Specificity Determinants for FR900359-Derived Gq α Inhibitors Based on Computational and Structure-Activity Studies.

Direct targeting of intracellular Gα subunits of G protein-coupled receptors by chemical tools is a challenging task in current pharmacological studies and in the development of novel therapeutic approaches. In this study we analyzed novel FR900359-based analogs from natural sources, synthetic cyclic peptides, as well as all so-far known Gq α inhibitors in a comprehensive study to devise a strategy for the elucidation of characteristics that determine interactions with and inhibition of Gq in the specific FR/YM-binding pocket. Using 2D NMR spectroscopy and molecular docking we identified unique features in the macrocyclic structures responsible for binding to the target protein correlating with inhibitory activity. While all novel compounds were devoid of effects on Gi and Gs proteins, no inhibitor surpassed the biological activity of FR. This raises the question of whether depsipeptides such as FR already represent valuable chemical tools for specific inhibition of Gq and, at the same time, are suitable natural lead structures for the development of novel compounds to target Gα subunits other than Gq .