Pep2Path: Automated Mass Spectrometry-Guided Genome Mining of Peptidic Natural Products

Abstract

Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X.

Indexing the Pseudomonas specialized metabolome enabled the discovery of poaeamide B and the bananamides

“…16, 48 None were found. Therefore the genomes of P. synxantha CR32 and P. fluorescens BW11P2 were sequenced and subjected to antiSMASH analysis.…”

Computational approaches to natural product discovery

“…An alternative method, Pep2Path 62 , uses a probabilistic framework to predict the likelihood that each NRPS module selects every possible amino acid as a substrate, and then calculates combined probabilities for all possible NRPS assembly lines to match a mass spectrometry-derived mass shift sequence tag: a sequence of fragment molecular weight differences that is representative for the amino acid sequence of the peptide under study. Even though Pep2Path is based on the same algorithm for substrate specificity prediction as NRPquest (i.e., NRPSPredictor2), the advantage of this approach is that the algorithm will not fail to predicted a peptide-BGC link if a few modules are slightly mispredicted: e.g., if a module is specific for tyrosine, and a phenylalanine is observed, the probability of the module to be responsible for the observed amino acid will still be high.…”

Mass spectrometry tools and workflows for revealing microbial chemistry

“…New approaches such as peptidogenomics and glycogenomics have demonstrated the potential of MS genome mining to connect MS/MS sequences to predicted genomic-based structures following biosynthetic logic of ribosomal peptides (RiPPs), nonribosomal peptides (NRP) and glycosylated NPs 170, 182, 189, 190 . Recently several tools were introduced for the automation of MS based microbial genome mining such as “Pep2Path” 181 for peptidic NPs, “RiPPquest” 182 for RiPPs and “NRPquest” for NRPs 183 (Table 2). Furthermore, following MS genome mining approach, molecular networking has been used to characterize novel molecular families of unsequenced microbes by using previously discovered NP’s from sequenced organisms 128 .…”