Classification of the Adenylation and Acyl-Transferase Activity of NRPS and PKS Systems Using Ensembles of Substrate Specific Hidden Markov Models

PLoS One. 2013 Apr 18;8(4):e62136. doi: 10.1371/journal.pone.0062136. Print 2013.

Abstract

There is a growing interest in the Non-ribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) of microbes, fungi and plants because they can produce bioactive peptides such as antibiotics. The ability to identify the substrate specificity of the enzyme's adenylation (A) and acyl-transferase (AT) domains is essential to rationally deduce or engineer new products. We here report on a Hidden Markov Model (HMM)-based ensemble method to predict the substrate specificity at high quality. We collected a new reference set of experimentally validated sequences. An initial classification based on alignment and Neighbor Joining was performed in line with most of the previously published prediction methods. We then created and tested single substrate specific HMMs and found that their use improved the correct identification significantly for A as well as for AT domains. A major advantage of the use of HMMs is that it abolishes the dependency on multiple sequence alignment and residue selection that is hampering the alignment-based clustering methods. Using our models we obtained a high prediction quality for the substrate specificity of the A domains similar to two recently published tools that make use of HMMs or Support Vector Machines (NRPSsp and NRPS predictor2, respectively). Moreover, replacement of the single substrate specific HMMs by ensembles of models caused a clear increase in prediction quality. We argue that the superiority of the ensemble over the single model is caused by the way substrate specificity evolves for the studied systems. It is likely that this also holds true for other protein domains. The ensemble predictor has been implemented in a simple web-based tool that is available at http://www.cmbi.ru.nl/NRPS-PKS-substrate-predictor/.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acyltransferases / metabolism*
  • Adenosine Monophosphate / metabolism
  • Catalytic Domain
  • Markov Chains
  • Nucleotidyltransferases / metabolism*
  • Peptide Biosynthesis, Nucleic Acid-Independent / physiology*
  • Polyketide Synthases / chemistry*
  • Polyketide Synthases / metabolism
  • Protein Structure, Tertiary
  • Sequence Alignment
  • Substrate Specificity*
  • Support Vector Machine*

Substances

  • Adenosine Monophosphate
  • Polyketide Synthases
  • Acyltransferases
  • Nucleotidyltransferases

Grant support

This work was supported by the City Nijmegen-The Netherlands (Gemeente Nijmegen). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.