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      Combinatorial metabolic pathway assembly approaches and toolkits for modular assembly

      , , ,
      Metabolic Engineering
      Elsevier BV

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          Enzymatic assembly of DNA molecules up to several hundred kilobases.

          We describe an isothermal, single-reaction method for assembling multiple overlapping DNA molecules by the concerted action of a 5' exonuclease, a DNA polymerase and a DNA ligase. First we recessed DNA fragments, yielding single-stranded DNA overhangs that specifically annealed, and then covalently joined them. This assembly method can be used to seamlessly construct synthetic and natural genes, genetic pathways and entire genomes, and could be a useful molecular engineering tool.
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            Is Open Access

            antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline

            Abstract Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, β-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.
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              A One Pot, One Step, Precision Cloning Method with High Throughput Capability

              Current cloning technologies based on site-specific recombination are efficient, simple to use, and flexible, but have the drawback of leaving recombination site sequences in the final construct, adding an extra 8 to 13 amino acids to the expressed protein. We have devised a simple and rapid subcloning strategy to transfer any DNA fragment of interest from an entry clone into an expression vector, without this shortcoming. The strategy is based on the use of type IIs restriction enzymes, which cut outside of their recognition sequence. With proper design of the cleavage sites, two fragments cut by type IIs restriction enzymes can be ligated into a product lacking the original restriction site. Based on this property, a cloning strategy called ‘Golden Gate’ cloning was devised that allows to obtain in one tube and one step close to one hundred percent correct recombinant plasmids after just a 5 minute restriction-ligation. This method is therefore as efficient as currently used recombination-based cloning technologies but yields recombinant plasmids that do not contain unwanted sequences in the final construct, thus providing precision for this fundamental process of genetic manipulation.
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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Metabolic Engineering
                Metabolic Engineering
                Elsevier BV
                10967176
                January 2021
                January 2021
                : 63
                : 81-101
                Article
                10.1016/j.ymben.2020.12.001
                33301873
                d717e70f-55b4-4f42-bf78-4ba6145e4678
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

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