Alteration of Polyketide Stereochemistry from anti to syn by a Ketoreductase Domain Exchange in a Type I Modular Polyketide Synthase Subunit

Biochemistry ◽  
2016 ◽  
Vol 55 (12) ◽  
pp. 1677-1680 ◽  
Author(s):  
Clara H. Eng ◽  
Satoshi Yuzawa ◽  
George Wang ◽  
Edward E. K. Baidoo ◽  
Leonard Katz ◽  
...  
2014 ◽  
Vol 61 (1) ◽  
Author(s):  
Magdalena Kotowska ◽  
Jarosław Ciekot ◽  
Krzysztof Pawlik

Type II thioesterases were shown to maintain efficiency of modular type I polyketide synthases and nonribosomal peptide synthetases by removing acyl residues blocking extension modules. We found that thioesterase ScoT from Streptomyces coelicolor A3(2) is required for the production of the yellow-pigmented coelimycin by the modular polyketide synthase Cpk. No production of coelimycin was observed in cultures of scoT disruption mutant. Polyketide production was restored upon complementation with an intact copy of the scoT gene. An enzymatic assay showed that ScoT thioesterase can hydrolyse a 12-carbon acyl chain but the activity is too low to play a role in product release from the polyketide synthase. We conclude that ScoT is an editing enzyme necessary to maintain the activity of polyketide synthase Cpk. We provide a HPLC based method to measure the amount of coelimycin P2 in a culture medium.


2017 ◽  
Vol 46 (D1) ◽  
pp. D509-D515 ◽  
Author(s):  
Clara H Eng ◽  
Tyler W H Backman ◽  
Constance B Bailey ◽  
Christophe Magnan ◽  
Héctor García Martín ◽  
...  

2019 ◽  
Author(s):  
Tingyang Li ◽  
Ashootosh Tripathi ◽  
Fengan Yu ◽  
David H. Sherman ◽  
Arvind Rao

AbstractSummaryDDAP is a tool for predicting the biosynthetic pathways of the products of type I modular polyketide synthase (PKS) with the focus on providing a more accurate prediction of the ordering of proteins and substrates in the pathway. In this study, the module docking domain (DD) affinity prediction performance on a hold-out testing data set reached AUC = 0.88; the MRR of pathway prediction reached 0.67. DDAP has advantages compared to previous informatics tools in several aspects: (i) it does not rely on large databases, making it a high efficiency tool, (ii) the predicted DD affinity is represented by a probability (0 to 1), which is more intuitive than raw scores, (iii) its performance is competitive compared to the current popular rule-based algorithm. To the best of our knowledge, DDAP is so far the first machine learning based algorithm for type I PKS pathway prediction. We also established the first database of type I modular PKSs, featuring a comprehensive annotation of available docking domains information in bacterial biosynthetic pathways.Availability and implementationThe DDAP database is available at https://tylii.github.io/ddap. The prediction algorithm DDAP is freely available on GitHub (https://github.com/tylii/ddap) and released under the MIT [email protected]


2008 ◽  
Vol 74 (17) ◽  
pp. 5571-5574 ◽  
Author(s):  
Hisayuki Komaki ◽  
Ryosuke Fudou ◽  
Takashi Iizuka ◽  
Daisuke Nakajima ◽  
Koei Okazaki ◽  
...  

ABSTRACT The diversity of type I modular polyketide synthase (PKS) was explored by PCR amplification of DNA encoding ketosynthase and acyltransferase domains in myxobacteria. The sequencing of the amplicons revealed that many PKS genes were distantly related to the published sequences. Thus, myxobacteria may be excellent resources for novel and diverse polyketides.


2019 ◽  
Author(s):  
Tingyang Li ◽  
Ashootosh Tripathi ◽  
Fengan Yu ◽  
David H Sherman ◽  
Arvind Rao

Abstract Summary DDAP is a tool for predicting the biosynthetic pathways of the products of type I modular polyketide synthase (PKS) with the focus on providing a more accurate prediction of the ordering of proteins and substrates in the pathway. In this study, the module docking domain (DD) affinity prediction performance on a hold-out testing dataset reached 0.88 as measured by the area under the receiver operating characteristic (ROC) curve (AUC); the Mean Reciprocal Ranking (MRR) of pathway prediction reached 0.67. DDAP has advantages compared to previous informatics tools in several aspects: (i) it does not rely on large databases, making it a high efficiency tool, (ii) the predicted DD affinity is represented by a probability (0–1), which is more intuitive than raw scores, (iii) its performance is competitive compared to the current popular rule-based algorithm. DDAP is so far the first machine learning based algorithm for type I PKS DD affinity and pathway prediction. We also established the first database of type I modular PKSs, featuring a comprehensive annotation of available docking domains information in bacterial biosynthetic pathways. Availability and implementation The DDAP database is available at https://tylii.github.io/ddap. The prediction algorithm DDAP is freely available on GitHub (https://github.com/tylii/ddap) and released under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.


2013 ◽  
Vol 57 (8) ◽  
pp. 3836-3842 ◽  
Author(s):  
Hoang-Chuong Nguyen ◽  
Emmanuelle Darbon ◽  
Robert Thai ◽  
Jean-Luc Pernodet ◽  
Sylvie Lautru

ABSTRACTSpiramycins are clinically important 16-member macrolide antibiotics produced byStreptomyces ambofaciens. Biosynthetic studies have established that the earliest lactonic intermediate in spiramycin biosynthesis, the macrolactone platenolide I, is synthesized by a type I modular polyketide synthase (PKS). Platenolide I then undergoes a series of post-PKS tailoring reactions yielding the final products, spiramycins I, II, and III. We recently characterized the post-PKS glycosylation steps of spiramycin biosynthesis inS. ambofaciens. We showed that three glycosyltransferases, Srm5, Srm29, and Srm38, catalyze the successive attachment of the three carbohydrates mycaminose, forosamine, and mycarose, respectively, with the help of two auxiliary proteins, Srm6 and Srm28. However, the enzymes responsible for the other tailoring steps, namely, the C-19 methyl group oxidation, the C-9 keto group reduction, and the C-3 hydroxyl group acylation, as well as the timing of the post-PKS tailoring reactions, remained to be established. In this study, we show that Srm13, a cytochrome P450, catalyzes the oxidation of the C-19 methyl group into a formyl group and that Srm26 catalyzes the reduction of the C-9 keto group, and we propose a timeline for spiramycin-biosynthetic post-PKS tailoring reactions.


2021 ◽  
Author(s):  
Li Su ◽  
Laurence Hotel ◽  
Cédric Paris ◽  
Alexander Brachmann ◽  
Jörn Piel ◽  
...  

Abstract The modular organization of the type I polyketide synthases (PKSs) would seem propitious for rational engineering of desirable analogous. However, despite decades of efforts, such experiments remain largely inefficient. Here, we combined multiple, state-of-the-art approaches including modification of docking domains, use of modules of varying domain composition, alternative interdomain fusion sites, and targeted adaptation of key domain-domain interfaces, to reprogram the stambomycin PKS by deleting seven internal modules – the most substantial modification to an intact system reported to date. One such system produced the target 37-membered mini-stambomycin metabolites, a reduction in chain length of 14 carbons relative to the 51-membered parental compounds, but also substantial quantities of shunt metabolites released from the multienzyme subunit upstream of the newly-installed junction. Our data also provide evidence for an unprecedented off-loading mechanism of such stalled intermediates involving the C-terminal thioesterase domain acting on chains located four modules upstream. The yields of all metabolites were substantially reduced compared to the wild type compounds, likely reflecting the poor tolerance to the non-native substrates of the modules downstream of the introduced interfaces. Taken together, our data demonstrate that even ‘optimized’ PKS engineering strategies remain inadequate for efficient production of target polyketide derivatives, and highlight several areas for future investigation.


2018 ◽  
Vol 13 (11) ◽  
pp. 3072-3077 ◽  
Author(s):  
Zilong Wang ◽  
Saket R. Bagde ◽  
Gerardo Zavala ◽  
Tsutomu Matsui ◽  
Xi Chen ◽  
...  

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