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

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/.

Identification of cyclosporin C from Amphichorda felina using a Cryptococcus neoformans differential temperature sensitivity assay

“…Similar NRPS A-domains retrieved by the BLAST search that have been linked to known chemical structures were aligned to the CsC and CsA A-domains. The AAs were predicted from the A-domain by four different methods (Stachelhaus codes, NRPSpredictor2, HMM, and SEQL) (Khayatt et al 2013; Knudsen et al 2016; Röttig et al 2011; Stachelhaus et al 1999) and compared to the actual AAs in the CsA and CsC peptides.…”

Minimum Information about a Biosynthetic Gene cluster

“…Substrate specificities of PKS acyltransferase domains and NRPS adenylation domains, as well as their evidence codes, will be registered automatically for all gene clusters. This will enable automated updating of the training sets for key chemistry prediction algorithms 19,20,21 , which can then be curated by the degree of evidence available, increasing the accuracy of predictions of core peptide and polyketide scaffolds. Also, because groups of genes associated with the biosynthesis of specific chemical moieties (such as sugars and nonproteinogenic amino acids) will be registered consistently, a continuously growing dataset of such sub-clusters will be available to use as a basis for chemical structure predictions.…”

The Crystal Structure of the Adenylation Enzyme VinN Reveals a Unique β-Amino Acid Recognition Mechanism

“…The other eight specificity-conferring code residues are involved in the recognition of the side chain of the ␣-amino acid substrate. This specificity-conferring code rule can be applied to various NRPS-type adenylation enzymes because information on the relationship between the sequence and substrate specificity of these enzymes has been accumulated (3)(4)(5)(6)(7). This rule allows us to predict the substrate specificity of many biochemically uncharacterized adenylation enzymes from their amino acid sequences and enables rational alteration of their substrate specificities (3, 8 -12).…”

“…2B). Other ␣-amino acid-activating enzymes generally have small aliphatic residues at the positions corresponding to Ser 299 and Lys 330 in VinN (3)(4)(5)(6)(7). In addition, they contain neither Arg nor Lys at the position equivalent to Arg 331 in VinN, although they have various types of residues, including Asp and His, at this position.…”

“…These polar and basic residues might be specific for the recognition of dicarboxylic ␤-amino acids. Even adenylation enzymes using dicarboxylic ␣-amino acid substrates, including L-aspartate and L-glutamate, have no basic residues at corresponding positions (7). These enzymes have a basic or polar residue at a different position such as the 234 or 278 position in PheA numbering for recognition of a carboxyl group that is not adenylated.…”