scholarly journals Imaging mass spectrometry for natural products discovery: a review of ionization methods

2020 ◽  
Vol 37 (2) ◽  
pp. 150-162 ◽  
Author(s):  
Joseph E. Spraker ◽  
Gordon T. Luu ◽  
Laura M. Sanchez
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Natural Product ◽  
Imaging Mass Spectrometry ◽  
Natural Product Discovery ◽  
Ionization Sources ◽  
Natural Products Discovery ◽  
Ionization Methods

This mini review discusses advantages, limitations, and examples of different mass spectrometry ionization sources applicable to natural product discovery workflows.

scholarly journals The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking

mSystems ◽  
2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Daniela B. B. Trivella ◽  
Rafael de Felicio
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Natural Product ◽  
Biosynthetic Pathway ◽  
Genome Mining ◽  
Chemical Compounds ◽  
Gene Clusters ◽  
Tandem Mass ◽  
Molecular Networking ◽  
Natural Product Discovery

ABSTRACT Natural products are the richest source of chemical compounds for drug discovery. Particularly, bacterial secondary metabolites are in the spotlight due to advances in genome sequencing and mining, as well as for the potential of biosynthetic pathway manipulation to awake silent (cryptic) gene clusters under laboratory cultivation. Further progress in compound detection, such as the development of the tandem mass spectrometry (MS/MS) molecular networking approach, has contributed to the discovery of novel bacterial natural products. The latter can be applied directly to bacterial crude extracts for identifying and dereplicating known compounds, therefore assisting the prioritization of extracts containing novel natural products, for example. In our opinion, these three approaches—genome mining, silent pathway induction, and MS-based molecular networking—compose the tripod for modern bacterial natural product discovery and will be discussed in this perspective.

scholarly journals Coupling Mass Spectral and Genomic Information to Improve Bacterial Natural Product Discovery Workflows

Marine Drugs ◽  
2021 ◽  
Vol 19 (3) ◽  
pp. 142 ◽  
Author(s):  
Max Crüsemann
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Natural Product ◽  
Large Scale ◽  
Genome Mining ◽  
Structural Diversity ◽  
Gene Clusters ◽  
Genomic Information ◽  
Mass Spectral ◽  
Natural Product Discovery

Bacterial natural products possess potent bioactivities and high structural diversity and are typically encoded in biosynthetic gene clusters. Traditional natural product discovery approaches rely on UV- and bioassay-guided fractionation and are limited in terms of dereplication. Recent advances in mass spectrometry, sequencing and bioinformatics have led to large-scale accumulation of genomic and mass spectral data that is increasingly used for signature-based or correlation-based mass spectrometry genome mining approaches that enable rapid linking of metabolomic and genomic information to accelerate and rationalize natural product discovery. In this mini-review, these approaches are presented, and discovery examples provided. Finally, future opportunities and challenges for paired omics-based natural products discovery workflows are discussed.

scholarly journals A supervised fingerprint-based strategy to connect natural product mass spectrometry fragmentation data to their biosynthetic gene clusters

2021 ◽  
Author(s):  
Tiago F. Leao ◽  
Mingxun Wang ◽  
Ricardo da Silva ◽  
Justin J.J. van der Hooft ◽  
Anelize Bauermeister ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Natural Product ◽  
Nearest Neighbor ◽  
Gene Clusters ◽  
Supervised Machine Learning ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Biosynthetic Genes ◽  
Natural Product Discovery

AbstractMicrobial natural products, in particular secondary or specialized metabolites, are an important source and inspiration for many pharmaceutical and biotechnological products. However, bioactivity-guided methods widely employed in natural product discovery programs do not explore the full biosynthetic potential of microorganisms, and they usually miss metabolites that are produced at low titer. As a complementary method, the use of genome-based mining in natural products research has facilitated the charting of many novel natural products in the form of predicted biosynthetic gene clusters that encode for their production. Linking the biosynthetic potential inferred from genomics to the specialized metabolome measured by metabolomics would accelerate natural product discovery programs. Here, we applied a supervised machine learning approach, the K-Nearest Neighbor (KNN) classifier, for systematically connecting metabolite mass spectrometry data to their biosynthetic gene clusters. This pipeline offers a method for annotating the biosynthetic genes for known, analogous to known and cryptic metabolites that are detected via mass spectrometry. We demonstrate this approach by automated linking of six different natural product mass spectra, and their analogs, to their corresponding biosynthetic genes. Our approach can be applied to bacterial, fungal, algal and plant systems where genomes are paired with corresponding MS/MS spectra. Additionally, an approach that connects known metabolites to their biosynthetic genes potentially allows for bulk production via heterologous expression and it is especially useful for cases where the metabolites are produced at low amounts in the original producer.SignificanceThe pace of natural products discovery has remained relatively constant over the last two decades. At the same time, there is an urgent need to find new therapeutics to fight antibiotic resistant bacteria, cancer, tropical parasites, pathogenic viruses, and other severe diseases. To spark the enhanced discovery of structurally novel and bioactive natural products, we here introduce a supervised learning algorithm (K-Nearest Neighbor) that can connect known and analogous to known, as well as MS/MS spectra of yet unknowns to their corresponding biosynthetic gene clusters. Our Natural Products Mixed Omics tool provides access to genomic information for bioactivity prediction, class prediction, substrate predictions, and stereochemistry predictions to prioritize relevant metabolite products and facilitate their structural elucidation.

Applied evolution: phylogeny-based approaches in natural products research

2019 ◽  
Vol 36 (9) ◽  
pp. 1295-1312 ◽  
Author(s):  
Martina Adamek ◽  
Mohammad Alanjary ◽  
Nadine Ziemert
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Structure Elucidation ◽  
Phylogenetic Analyses ◽  
Natural Product Discovery

Here we highlight how phylogenetic analyses can be used to facilitate natural product discovery and structure elucidation.

scholarly journals Modern mass spectrometry for synthetic biology and structure-based discovery of natural products

2016 ◽  
Vol 33 (8) ◽  
pp. 942-950 ◽  
Author(s):  
Matthew T. Henke ◽  
Neil L. Kelleher
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Synthetic Biology ◽  
Modern Mass ◽  
Natural Products Discovery

This highlight serves as a primer for those curious about the abilities of mass spectrometry for natural products discovery and engineering.

scholarly journals The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhijun Miao ◽  
Jinwei Bai ◽  
Li Shen ◽  
Rajeev K. Singla
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Natural Products ◽  
Natural Product ◽  
Neurodegenerative Disorder ◽  
Data Driven ◽  
Natural Product Discovery ◽  
Challenges And Opportunities ◽  
Future Data ◽  
Personalized Diagnosis

Parkinson’s disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.

scholarly journals Multiplexed screening of thousands of natural products for protein-ligand binding in native mass spectrometry

2021 ◽  
Author(s):  
Giang Nguyen ◽  
Jack Bennett ◽  
Sherrie Liu ◽  
Sarah Hancock ◽  
Daniel Winter ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Drug Discovery ◽  
Small Molecules ◽  
Natural Product ◽  
Drug Targets ◽  
Structural Diversity ◽  
Native Mass Spectrometry ◽  
Protein Targets ◽  
Rapid Measurement

The structural diversity of natural products offers unique opportunities for drug discovery, but challenges associated with their isolation and screening can hinder the identification of drug-like molecules from complex natural product extracts. Here we introduce a mass spectrometry-based approach that integrates untargeted metabolomics with multistage, high-resolution native mass spectrometry to rapidly identify natural products that bind to therapeutically relevant protein targets. By directly screening crude natural product extracts containing thousands of drug-like small molecules using a single, rapid measurement, novel natural product ligands of human drug targets could be identified without fractionation. This method should significantly increase the efficiency of target-based natural product drug discovery workflows.

Multiplexed screening of thousands of natural products for protein-ligand binding in native mass spectrometry

2021 ◽  
Author(s):  
Giang Nguyen ◽  
Jack Bennett ◽  
Sherrie Liu ◽  
Sarah Hancock ◽  
Daniel Winter ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Drug Discovery ◽  
Small Molecules ◽  
Natural Product ◽  
Drug Targets ◽  
Structural Diversity ◽  
Native Mass Spectrometry ◽  
Protein Targets ◽  
Rapid Measurement

The structural diversity of natural products offers unique opportunities for drug discovery, but challenges associated with their isolation and screening can hinder the identification of drug-like molecules from complex natural product extracts. Here we introduce a mass spectrometry-based approach that integrates untargeted metabolomics with multistage, high-resolution native mass spectrometry to rapidly identify natural products that bind to therapeutically relevant protein targets. By directly screening crude natural product extracts containing thousands of drug-like small molecules using a single, rapid measurement, novel natural product ligands of human drug targets could be identified without fractionation. This method should significantly increase the efficiency of target-based natural product drug discovery workflows.

scholarly journals Imaging the Unimaginable: Desorption Electrospray Ionization – Imaging Mass Spectrometry (DESI-IMS) in Natural Product Research

Planta Medica ◽  
2018 ◽  
Vol 84 (09/10) ◽  
pp. 584-593 ◽  
Author(s):  
Delphine Parrot ◽  
Stefano Papazian ◽  
Daniel Foil ◽  
Deniz Tasdemir
Keyword(s):  
Mass Spectrometry ◽  
Natural Product ◽  
Electrospray Ionization ◽  
Imaging Mass Spectrometry ◽  
Desorption Electrospray Ionization ◽  
Repeated Measurements ◽  
Chemical Information ◽  
Ambient Conditions ◽  
Chemical Screening ◽  
Natural Product Research

AbstractImaging mass spectrometry (IMS) has recently established itself in the field of “spatial metabolomics.” Merging the sensitivity and fast screening of high-throughput mass spectrometry with spatial and temporal chemical information, IMS visualizes the production, location, and distribution of metabolites in intact biological models. Since metabolite profiling and morphological features are combined in single images, IMS offers an unmatched chemical detail on complex biological and microbiological systems. Thus, IMS-type “spatial metabolomics” emerges as a powerful and complementary approach to genomics, transcriptomics, and classical metabolomics studies. In this review, we summarize the current state-of-the-art IMS methods with a strong focus on desorption electrospray ionization (DESI)-IMS. DESI-IMS utilizes the original principle of electrospray ionization, but in this case solvent droplets are rastered and desorbed directly on the sample surface. The rapid and minimally destructive DESI-IMS chemical screening is achieved at ambient conditions and enables the accurate view of molecules in tissues at the µm-scale resolution. DESI-IMS analysis does not require complex sample preparation and allows repeated measurements on samples from different biological sources, including microorganisms, plants, and animals. Thanks to its easy workflow and versatility, DESI-IMS has successfully been applied to many different research fields, such as clinical analysis, cancer research, environmental sciences, microbiology, chemical ecology, and drug discovery. Herein we discuss the present applications of DESI-IMS in natural product research.

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