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Coevolution-based prediction of protein-protein interactions in polyketide biosynthetic assembly lines.

Authors
  • Wang, Yan1
  • Correa Marrero, Miguel1
  • Medema, Marnix H1
  • van Dijk, Aalt D J1, 2
  • 1 Bioinformatics Group.
  • 2 Department of Plant Sciences Biometris, Wageningen University & Research, 6708 PB Wageningen, The Netherlands. , (Netherlands)
Type
Published Article
Journal
Bioinformatics (Oxford, England)
Publication Date
Dec 08, 2020
Volume
36
Issue
19
Pages
4846–4853
Identifiers
DOI: 10.1093/bioinformatics/btaa595
PMID: 32592463
Source
Medline
Language
English
License
Unknown

Abstract

Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein-protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains. The code is available on http://www.bif.wur.nl/ (under 'Software'). Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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