A new approach to evaluating statistical significance of spectral identifications

Fast mass spectrometry search and clustering of untargeted metabolomics data

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

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.

NPS: scoring and evaluating the statistical significance of peptidic natural product-spectrum matches

MOTIVATION

Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved.

RESULTS

Here we present NPS, a statistical learning-based approach for scoring PNP-spectrum matches. We incorporated NPS into two leading PNP discovery tools and benchmarked them on millions of natural product mass spectra. The results demonstrate more than 45% increase in the number of identified spectra and 20% more found PNPs at a false discovery rate of 1%.

AVAILABILITY AND IMPLEMENTATION

NPS is available as a command line tool and as a web application at http://cab.spbu.ru/software/NPS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments

Cyclic and branch cyclic peptides (cyclopeptides) represent a class of bioactive natural products that include many antibiotics and anti-tumor compounds. Despite the recent advances in metabolomics analysis, still little is known about the cyclopeptides in the human gut and their possible interactions due to a lack of computational analysis pipelines that are applicable to such compounds. Here, we introduce CycloNovo, an algorithm for automated de novo cyclopeptide analysis and sequencing that employs de Bruijn graphs, the workhorse of DNA sequencing algorithms, to identify cyclopeptides in spectral datasets. CycloNovo reconstructed 32 previously unreported cyclopeptides (to the best of our knowledge) in the human gut and reported over a hundred cyclopeptides in other environments represented by various spectra on Global Natural Products Social Molecular Network (GNPS). https://github.com/bbehsaz/cyclonovo.

MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.

A Markov Chain Monte Carlo Method for Estimating the Statistical Significance of Proteoform Identifications by Top-Down Mass Spectrometry

Top-down mass spectrometry is capable of identifying whole proteoform sequences with multiple post-translational modifications because it generates tandem mass spectra directly from intact proteoforms. Many software tools, such as ProSightPC, MSPathFinder, and TopMG, have been proposed for identifying proteoforms with modifications. In these tools, various methods are employed to estimate the statistical significance of identifications. However, most existing methods are designed for proteoform identifications without modifications, and the challenge remains for accurately estimating the statistical significance of proteoform identifications with modifications. Here we propose TopMCMC, a method that combines a Markov chain random walk algorithm and a greedy algorithm for assigning statistical significance to matches between spectra and protein sequences with variable modifications. Experimental results showed that TopMCMC achieved high accuracy in estimating E-values and false discovery rates of identifications in top-down mass spectrometry. Coupled with TopMG, TopMCMC identified more spectra than the generating function method from an MCF-7 top-down mass spectrometry data set.

Dereplication of microbial metabolites through database search of mass spectra

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results.

Increased diversity of peptidic natural products revealed by modification-tolerant database search of mass spectra

Peptidic natural products (PNPs) include many antibiotics and other bioactive compounds. While the recent launch of the Global Natural Products Social (GNPS) molecular networking infrastructure is transforming PNP discovery into a high-throughput technology, PNP identification algorithms are needed to realize the potential of the GNPS project. GNPS relies on the assumption that each connected component of a molecular network (representing related metabolites) illuminates the 'dark matter of metabolomics' as long as it contains a known metabolite present in a database. We reveal a surprising diversity of PNPs produced by related bacteria and show that, contrary to the 'comparative metabolomics' assumption, two related bacteria are unlikely to produce identical PNPs (even though they are likely to produce similar PNPs). Since this observation undermines the utility of GNPS, we developed a PNP identification tool, VarQuest, that illuminates the connected components in a molecular network even if they do not contain known PNPs and only contain their variants. VarQuest reveals an order of magnitude more PNP variants than all previous PNP discovery efforts and demonstrates that GNPS already contains spectra from 41% of the currently known PNP families. The enormous diversity of PNPs suggests that biosynthetic gene clusters in various microorganisms constantly evolve to generate a unique spectrum of PNP variants that differ from PNPs in other species.

Dereplication of peptidic natural products through database search of mass spectra

Peptidic Natural Products (PNPs) are widely used compounds that include many antibiotics and a variety of other bioactive peptides. While recent breakthroughs in PNP discovery raised the challenge of developing new algorithms for their analysis, identification of PNPs via database search of tandem mass spectra remains an open problem. To address this problem, natural product researchers utilize dereplication strategies that identify known PNPs and lead to the discovery of new ones even in cases when the reference spectra are not present in existing spectral libraries. DEREPLICATOR is a new dereplication algorithm that enabled high-throughput PNP identification and that is compatible with large-scale mass spectrometry-based screening platforms for natural product discovery. After searching nearly one hundred million tandem mass spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure, DEREPLICATOR identified an order of magnitude more PNPs (and their new variants) than any previous dereplication efforts.

Dereplication, sequencing and identification of peptidic natural products: from genome mining to peptidogenomics to spectral networks

Covering: 2000 to 2015. While recent breakthroughs in the discovery of peptide antibiotics and other Peptidic Natural Products (PNPs) raise a challenge for developing new algorithms for their analyses, the computational technologies for high-throughput PNP discovery are still lacking. We discuss the computational bottlenecks in analyzing PNPs and review recent advances in genome mining, peptidogenomics, and spectral networks that are now enabling the discovery of new PNPs via mass spectrometry. We further describe the connections between these advances and the new generation of software tools for PNP dereplication, de novo sequencing, and identification.

NRPquest: Coupling Mass Spectrometry and Genome Mining for Nonribosomal Peptide Discovery

Nonribosomal peptides (NRPs) such as vancomycin and daptomycin are among the most effective antibiotics. While NRPs are biomedically important, the computational techniques for sequencing these peptides are still in their infancy. The recent emergence of mass spectrometry techniques for NRP analysis (capable of sequencing an NRP from small amounts of nonpurified material) revealed an enormous diversity of NRPs. However, as many NRPs have nonlinear structure (e.g., cyclic or branched-cyclic peptides), the standard de novo sequencing tools (developed for linear peptides) are not applicable to NRP analysis. Here, we introduce the first NRP identification algorithm, NRPquest, that performs mutation-tolerant and modification-tolerant searches of spectral data sets against a database of putative NRPs. In contrast to previous studies aimed at NRP discovery (that usually report very few NRPs), NRPquest revealed nearly a hundred NRPs (including unknown variants of previously known peptides) in a single study. This result indicates that NRPquest can potentially make MS-based NRP identification as robust as the identification of linear peptides in traditional proteomics.

Automated genome mining of ribosomal peptide natural products

Ribosomally synthesized and posttranslationally modified peptides (RiPPs), especially from microbial sources, are a large group of bioactive natural products that are a promising source of new (bio)chemistry and bioactivity.1 In light of exponentially increasing microbial genome databases and improved mass spectrometry (MS)-based metabolomic platforms, there is a need for computational tools that connect natural product genotypes predicted from microbial genome sequences with their corresponding chemotypes from metabolomic data sets. Here, we introduce RiPPquest, a tandem mass spectrometry database search tool for identification of microbial RiPPs, and apply it to lanthipeptide discovery. RiPPquest uses genomics to limit search space to the vicinity of RiPP biosynthetic genes and proteomics to analyze extensive peptide modifications and compute p-values of peptide-spectrum matches (PSMs). We highlight RiPPquest by connecting multiple RiPPs from extracts of Streptomyces to their gene clusters and by the discovery of a new class III lanthipeptide, informatipeptin, from Streptomyces viridochromogenes DSM 40736 to reflect that it is a natural product that was discovered by mass spectrometry based genome mining using algorithmic tools rather than manual inspection of mass spectrometry data and genetic information. The presented tool is available at cyclo.ucsd.edu.