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      Nonribosomal biosynthesis of backbone-modified peptides

      , , , , ,
      Nature Chemistry
      Springer Nature

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          Abstract

          Biosynthetic modification of nonribosomal peptide backbones represents a potentially powerful strategy to modulate the structure and properties of an important class of therapeutics. Using a high-throughput assay for catalytic activity, we show here that an L-Phe-specific module of an archetypal nonribosomal peptide synthetase can be reprogrammed to accept and process the backbone-modified amino acid (S)-β-Phe with near-native specificity and efficiency. A co-crystal structure with a non-hydrolysable aminoacyl-AMP analogue reveals the origins of the 40,000-fold α/β-specificity switch, illuminating subtle but precise remodelling of the active site. When the engineered catalyst was paired with downstream module(s), (S)-β-Phe-containing peptides were produced at preparative scale in vitro (~1 mmol) and high titres in vivo (~100 mg l-1), highlighting the potential of biosynthetic pathway engineering for the construction of novel nonribosomal β-frameworks.

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          Most cited references38

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          Assembly-line enzymology for polyketide and nonribosomal Peptide antibiotics: logic, machinery, and mechanisms.

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            Nonribosomal Peptide Synthesis-Principles and Prospects

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              NRPSpredictor2—a web server for predicting NRPS adenylation domain specificity

              The products of many bacterial non-ribosomal peptide synthetases (NRPS) are highly important secondary metabolites, including vancomycin and other antibiotics. The ability to predict substrate specificity of newly detected NRPS Adenylation (A-) domains by genome sequencing efforts is of great importance to identify and annotate new gene clusters that produce secondary metabolites. Prediction of A-domain specificity based on the sequence alone can be achieved through sequence signatures or, more accurately, through machine learning methods. We present an improved predictor, based on previous work (NRPSpredictor), that predicts A-domain specificity using Support Vector Machines on four hierarchical levels, ranging from gross physicochemical properties of an A-domain’s substrates down to single amino acid substrates. The three more general levels are predicted with an F-measure better than 0.89 and the most detailed level with an average F-measure of 0.80. We also modeled the applicability domain of our predictor to estimate for new A-domains whether they lie in the applicability domain. Finally, since there are also NRPS that play an important role in natural products chemistry of fungi, such as peptaibols and cephalosporins, we added a predictor for fungal A-domains, which predicts gross physicochemical properties with an F-measure of 0.84. The service is available at http://nrps.informatik.uni-tuebingen.de/.
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                Author and article information

                Journal
                Nature Chemistry
                Nature Chem
                Springer Nature
                1755-4330
                1755-4349
                November 20 2017
                November 20 2017
                :
                :
                Article
                10.1038/nchem.2891
                29461527
                6f4abc31-61bc-4de7-ae99-c11b2ddea6f8
                © 2017
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