NRPSpredictor2-a web server for predicting NRPS adenylation domain specificity

The products of many bacterial non-ribosomal peptide synthetases (NRPS) are highly important secondary metabolites, including vancomycin and other antibiotics. The ability to predict substrate specificity of newly detected NRPS Adenylation (A-) domains by genome sequencing efforts is of great importance to identify and annotate new gene clusters that produce secondary metabolites. Prediction of A-domain specificity based on the sequence alone can be achieved through sequence signatures or, more accurately, through machine learning methods. We present an improved predictor, based on previous work (NRPSpredictor), that predicts A-domain specificity using Support Vector Machines on four hierarchical levels, ranging from gross physicochemical properties of an A-domain's substrates down to single amino acid substrates. The three more general levels are predicted with an F-measure better than 0.89 and the most detailed level with an average F-measure of 0.80. We also modeled the applicability domain of our predictor to estimate for new A-domains whether they lie in the applicability domain. Finally, since there are also NRPS that play an important role in natural products chemistry of fungi, such as peptaibols and cephalosporins, we added a predictor for fungal A-domains, which predicts gross physicochemical properties with an F-measure of 0.84. The service is available at http://nrps.informatik.uni-tuebingen.de/

Pattern recognition methods for the prediction of chemical structures of fungal secondary metabolites

Non-Ribosomal Peptide Synthetases (NRPS) are mega synthetases that are predominantly found in bacteria and fungi. They produce small peptides that serve numerous biological functions and crucial ecological roles. Adenylation (A) domains of NRPSs catalyze ATP dependent activation of substrates harboring carboxy terminus. A-domain substrates include not only natural amino acids (D and L forms) but also non-proteinogenic amino acids. As the substrate repertoire is large and specificity rules for fungi are not established well, there is a difficulty in predicting substrates for fungal A-domains. In bacteria, ten amino acid residues were established as NRPS code, which determine specificity of A-domains. To study relationships between fungal A-domains and their specificity, the cluster analysis of NRPS code residues was done. NRPS code residues were encoded by physicochemical properties essential for binding small molecules and these residues were clustered. Cluster analysis showed similar NRPS codes for α-amino adipic acid, and tryptophan, etc. between bacteria and fungi. Fungal NRPS codes for substrates such as tyrosine, and proline, did not cluster together with bacteria, which indicates an independent evolution of substrate specificity in fungi. This emphasizes the need for the development of a fungus-specific prediction tool. Currently available A-domain substrate specificity prediction tools accurately identify substrates for bacteria but fail to provide correct predictions for fungi. A novel approach for fungal A-domain substrate specificity prediction is presented here. Neural Network based A-domain substrate specificity classifier (NNassc) was developed using Keras with TensorFlow backend. NNassc was trained solely using fungal NRPS codes and combines physicochemical and structural features for specificity predictions. Internal and external validation datasets of experimentally verified NRPS codes were used to assess the performance of NNassc

Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery.

Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs

NRPSsp: non-ribosomal peptide synthase substrate predictor

ABSTRACT Summary: Non-Ribosomal Peptide Synthetases (NRPSs) are multimodular enzymes which biosynthesize many important peptide compounds produced by bacteria and fungi. Some studies have revealed that an individual domain within the NRPSs shows significant substrate selectivity. The discovery and characterisation of nonribosomal peptides are of great interest for the biotechnological industries. We have applied computational mining methods in order to build a database of NRPSs modules which bind to specific substrates. We have used this database to build an HMM predictor of substrates which bind to a given NRPS. Availability: The database and the predictor are freely available on an easy-to-use website at www.nrpssp.com

Systematic analysis of the kalimantacin assembly line NRPS module using an adapted targeted mutagenesis approach

Kalimantacin is an antimicrobial compound with strong antistaphylococcal activity that is produced by a hybrid trans-acyltransferase polyketide synthase/nonribosomal peptide synthetase system in Pseudomonas fluorescens BCCM_ID9359. We here present a systematic analysis of the substrate specificity of the glycine-incorporating adenylation domain from the kalimantacin biosynthetic assembly line by a targeted mutagenesis approach. The specificity-conferring code was adapted for use in Pseudomonas and mutated adenylation domain active site sequences were introduced in the kalimantacin gene cluster, using a newly adapted ligation independent cloning method. Antimicrobial activity screens and LC-MS analyses revealed that the production of the kalimantacin analogues in the mutated strains was abolished. These results support the idea that further insight in the specificity of downstream domains in nonribosomal peptide synthetases and polyketide synthases is required to efficiently engineer these strains in vivo

A Machine Learning Approach for Discovery of Novel Non-Ribosomal Peptide Synthetases (NRPS) in genomes of Plant Growth Promoting Pseudomonas Spp

ABSTRACT: Non-ribosomal peptide synthetases (NRPSs) are multi-modular megasynthasespossessing the ability to catalyze biosynthesis of small bioactive peptides through a thiotemplate mechanismwhich is independent of ribosomes. These enzymes are invovled in production of a wide range of chemical products of broad structural and biological activity. The present study was performed with an aim to develop a gene prediction tool using a machine learning work bench called WEKA (Waikato Environment for Knowledge Analysis) for NRPS in plant growth promoting Pseudomonas spp.First, a model was developed using the training data which was generated using many classifiers. The trained model was then used for the prediction of NRPS in a given set of unknown sequences. Cross-validation results showed that the 'Logisticof Functions' was the best classifier when compared to others, showing high accuracy and performance in classifying the instances. We hope that the tool will aid in discovering of novel NRPS by predicting them from sequence data obtained by whole genome sequencing of bacteria or metagenomics

A non-canonical NRPS is involved in the synthesis of fungisporin and related hydrophobic cyclic tetrapeptides in Penicillium chrysogenum.

The filamentous fungus Penicillium chrysogenum harbors an astonishing variety of nonribosomal peptide synthetase genes, which encode proteins known to produce complex bioactive metabolites from simple building blocks. Here we report a novel non-canonical tetra-modular nonribosomal peptide synthetase (NRPS) with microheterogenicity of all involved adenylation domains towards their respective substrates. By deleting the putative gene in combination with comparative metabolite profiling various unique cyclic and derived linear tetrapeptides were identified which were associated with this NRPS, including fungisporin. In combination with substrate predictions for each module, we propose a mechanism for a 'trans-acting' adenylation domain