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      Challenges and advances in genome mining of ribosomally synthesized and post-translationally modified peptides (RiPPs)

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          Abstract

          Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a class of cyclic or linear peptidic natural products with remarkable structural and functional diversity. Recent advances in genomics and synthetic biology, are facilitating us to discover a large number of new ribosomal natural products, including lanthipeptides, lasso peptides, sactipeptides, thiopeptides, microviridins, cyanobactins, linear thiazole/oxazole-containing peptides and so on. In this review, we summarize bioinformatic strategies that have been developed to identify and prioritize biosynthetic gene clusters (BGCs) encoding RiPPs, and the genome mining-guided discovery of novel RiPPs. We also prospectively provide a vision of what genomics-guided discovery of RiPPs may look like in the future, especially the discovery of RiPPs from dominant but uncultivated microbes, which will be promoted by the combinational use of synthetic biology and metagenome mining strategies.

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

          Kai Blin, Thomas Wolf, Marc Chevrette … (2017)
          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.
          Article 0 comments Cited 540 times – based on 0 reviews     Review now
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            A computational framework to explore large-scale biosynthetic diversity

            Jorge C. Navarro-Munoz, Nelly Selem-Mojica, Michael W. Mullowney … (2019)
            Genome mining has become a key technology to exploit natural product diversity. While initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. Here, we provide a streamlined computational workflow consisting of two new software tools: The ‘Biosynthetic Gene Similarity Clustering And Prospecting Engine’ (BiG-SCAPE) facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families. ‘CORe Analysis of Syntenic Orthologues to prioritize Natural product gene clusters’ (CORASON) elucidates phylogenetic relationships within and across these families. We validate BiG-SCAPE by correlating its output to metabolomic data across 363 actinobacterial strains and demonstrate the discovery potential of CORASON by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven novel analogues.
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              The evolution of genome mining in microbes - a review.

              Nadine Ziemert, Mohammad Alanjary, Tilmann Weber (2016)
              Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.
              Article 0 comments Cited 230 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Yong-Xin Li
                Journal
                Journal ID (nlm-ta): Synth Syst Biotechnol
                Journal ID (iso-abbrev): Synth Syst Biotechnol
                Title: Synthetic and Systems Biotechnology
                Publisher: KeAi Publishing
                ISSN (Electronic): 2405-805X
                Publication date PMC-release: 24 June 2020
                Publication date Collection: September 2020
                Publication date (Electronic): 24 June 2020
                Volume: 5
                Issue: 3
                Pages: 155-172
                Affiliations
                [a ]Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong SAR, China
                [b ]Department of Chemistry and Biochemistry, University of South Carolina, Columbia, USA
                [c ]The Swire Institute of Marine Science, The University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong SAR, China
                [d ]Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), China
                Author notes
                [∗ ]Corresponding author. Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong SAR, China. yxpli@ 123456hku.hk
                [1]

                These authors contributed equally: Zheng Zhong, Beibei He.

                Article
                Publisher ID: S2405-805X(20)30029-6
                DOI: 10.1016/j.synbio.2020.06.002
                PMC ID: 7327761
                PubMed ID: 32637669
                SO-VID: 7f4beba9-19e2-46c1-8ad6-c76c1092cd20
                Copyright © © 2020 KeAi Communications Co.(+) Ltd
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 30 April 2020
                Date revision received : 4 June 2020
                Date accepted : 5 June 2020
                Categories
                Subject: Article

                Keywords: natural products,ribosomally synthesized and post-translationally modified peptides,ripps, genome mining,metagenome mining,synthetic biology
                Data availability:
                Keywords: natural products, ribosomally synthesized and post-translationally modified peptides, ripps, genome mining, metagenome mining, synthetic biology

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