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      Natural product drug discovery in the genomic era: realities, conjectures, misconceptions, and opportunities

      Journal of Industrial Microbiology & Biotechnology
      Springer Science and Business Media LLC

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          Abstract

          Natural product discovery from microorganisms provided important sources for antibiotics, anti-cancer agents, immune-modulators, anthelminthic agents, and insecticides during a span of 50 years starting in the 1940s, then became less productive because of rediscovery issues, low throughput, and lack of relevant new technologies to unveil less abundant or not easily detected drug-like natural products. In the early 2000s, it was observed from genome sequencing that Streptomyces species encode about ten times as many secondary metabolites as predicted from known secondary metabolomes. This gave rise to a new discovery approach-microbial genome mining. As the cost of genome sequencing dropped, the numbers of sequenced bacteria, fungi and archaea expanded dramatically, and bioinformatic methods were developed to rapidly scan whole genomes for the numbers, types, and novelty of secondary metabolite biosynthetic gene clusters. This methodology enabled the identification of microbial taxa gifted for the biosynthesis of drug-like secondary metabolites. As genome sequencing technology progressed, the realities relevant to drug discovery have emerged, the conjectures and misconceptions have been clarified, and opportunities to reinvigorate microbial drug discovery have crystallized. This perspective addresses these critical issues for drug discovery.

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          Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2).

          Streptomyces coelicolor is a representative of the group of soil-dwelling, filamentous bacteria responsible for producing most natural antibiotics used in human and veterinary medicine. Here we report the 8,667,507 base pair linear chromosome of this organism, containing the largest number of genes so far discovered in a bacterium. The 7,825 predicted genes include more than 20 clusters coding for known or predicted secondary metabolites. The genome contains an unprecedented proportion of regulatory genes, predominantly those likely to be involved in responses to external stimuli and stresses, and many duplicated gene sets that may represent 'tissue-specific' isoforms operating in different phases of colonial development, a unique situation for a bacterium. An ancient synteny was revealed between the central 'core' of the chromosome and the whole chromosome of pathogens Mycobacterium tuberculosis and Corynebacterium diphtheriae. The genome sequence will greatly increase our understanding of microbial life in the soil as well as aiding the generation of new drug candidates by genetic engineering.
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            Drugs for bad bugs: confronting the challenges of antibacterial discovery.

            The sequencing of the first complete bacterial genome in 1995 heralded a new era of hope for antibacterial drug discoverers, who now had the tools to search entire genomes for new antibacterial targets. Several companies, including GlaxoSmithKline, moved back into the antibacterials area and embraced a genomics-derived, target-based approach to screen for new classes of drugs with novel modes of action. Here, we share our experience of evaluating more than 300 genes and 70 high-throughput screening campaigns over a period of 7 years, and look at what we learned and how that has influenced GlaxoSmithKline's antibacterials strategy going forward.
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              Is Open Access

              antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification

              Abstract Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.
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                Author and article information

                Journal
                Journal of Industrial Microbiology & Biotechnology
                J Ind Microbiol Biotechnol
                Springer Science and Business Media LLC
                1367-5435
                1476-5535
                March 2019
                November 27 2018
                March 2019
                : 46
                : 3-4
                : 281-299
                Article
                10.1007/s10295-018-2115-4
                30484124
                059cd07f-425e-4a73-8a35-20fc36d5139f
                © 2019

                http://www.springer.com/tdm

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