The Genome of the Plant-Associated Lactic Acid Bacterium Lactococcus lactis KF147 Harbors a Hybrid NRPS-PKS System Conserved in Strains of the Dental Cariogenic Streptococcus mutans.

Lactococcus lactis subsp. lactis KF147 as a non-dairy strain from lactic acid bacteria (LAB) can inhabit plant tissues. It can grow on complex carbohydrates derived from plant cell walls. Its genome size is one of the largest among the sequenced lactococcal strains, possessing many genes that do not have homologues in the published genome sequences of dairy-associated L. lactis strains. In silico analysis has identified a gene cluster encoding a hybrid NRPS-PKS system (composed of non-ribosomal peptide synthetases and polyketide synthases) in the L. lactis KF147 genome, as first example of a LAB possessing such hybrid mega-enzymes. Hybrid systems produce hybrid NRP-PK secondary metabolites (natural products) in a wide variety of bacteria, fungi, and plants. In the hybrid NRPS-PKS system of L. lactis KF147, a total of 21 NRPS and 8 PKS domains were identified that are arranged into 6 NRPS modules, 3 PKS modules, and two single functional domains (trans-acyl-transferase "transAT" and thioesterase). We found homologous hybrid systems having similar gene, module, and domain organization in six other L. lactis strains and 25 strains of the dental cariogenic Streptococcus mutans. This study mainly aimed to predict the structure and function of the hybrid NRP-PK product of L. lactis KF147 using comparative genomics techniques, and included a detailed analysis of the regulatory system. Various bioinformatical approaches were used to predict the substrate specificity of the six A domains and the iterative transAT domain. Functional conservation of the A domains within different-niche-associated strains supported the prediction of the primary core structure of the putative hybrid natural product to be Leu-DLeu-Asp-DAsn-Gly-MC-MC-MC-DAsp (MC = Malonyl-CoA). Oxidative stress resistance and biofilm formation are the most probable functions of this hybrid system. The need for such a system in two different niches is argued, as an adaptation of L. lactis and S. mutans to adhere to plant tissues and human teeth, respectively, in an oxidative environment.

Classification of the adenylation and acyl-transferase activity of NRPS and PKS systems using ensembles of substrate specific hidden Markov models.

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/.