natural product discovery
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Metabolites ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 785
Author(s):  
Junyang Wang ◽  
Jens Nielsen ◽  
Zihe Liu

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.


2021 ◽  
pp. 197-218
Author(s):  
Sergi Herve Akone ◽  
Cong-Dat Pham ◽  
Huiqin Chen ◽  
Antonius R. B. Ola ◽  
Fidele Ntie-Kang ◽  
...  

2021 ◽  
Author(s):  
Lee Ling Tan ◽  
Elena Heng ◽  
Nadiah Zulkarnain ◽  
Chuang Yan Leong ◽  
Veronica Ng ◽  
...  

In recent years, CRISPR-Cas toolboxes for Streptomyces editing have rapidly accelerated natural product discovery and engineering. However, Cas efficiencies are also oftentimes strain dependent, subsequently a variety of Cas proteins would allow for flexibility and enable genetic manipulation within a wider range of Streptomyces strains. In this work, we have further expanded the Cas toolbox by presenting the first example of Cas12j mediated editing in Streptomyces sp. A34053. In our study, we have also observed significantly improved editing efficiencies with Acidaminococcus sp. Cas12j compared to Cas12a, Francisella tularensis subsp. novicida U112's type V-A Cas (FnCpf1).


2021 ◽  
Author(s):  
Tiago F. Leao ◽  
Mingxun Wang ◽  
Ricardo da Silva ◽  
Justin J.J. van der Hooft ◽  
Anelize Bauermeister ◽  
...  

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.


Antibiotics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 873
Author(s):  
Balasubramanian Cibichakravarthy ◽  
Polapass Arul Jose

Streptomyces are the most prolific source of structurally diverse microbial natural products. Advancing genome-based analysis reveals the previously unseen potential of Streptomyces to produce numerous novel secondary metabolites, which allows us to take natural product discovery to the next phase. However, at present there is a huge disproportion between the rate of genome reports and discovery of new compounds. From this perspective of harnessing the enduring importance of Streptomyces, we discuss the recent genome-directed advancements inspired by hidden biosynthetic wealth that provide hope for future antibiotics.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhijun Miao ◽  
Jinwei Bai ◽  
Li Shen ◽  
Rajeev K. Singla

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.


Author(s):  
Markus Oberpaul ◽  
Stephan Brinkmann ◽  
Michael Marner ◽  
Sanja Mihajlovic ◽  
Benedikt Leis ◽  
...  

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