1
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Mongia M, Yasaka TM, Liu Y, Guler M, Lu L, Bhagwat A, Behsaz B, Wang M, Dorrestein PC, Mohimani H. Fast mass spectrometry search and clustering of untargeted metabolomics data. Nat Biotechnol 2024:10.1038/s41587-023-01985-4. [PMID: 38168990 DOI: 10.1038/s41587-023-01985-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 09/12/2023] [Indexed: 01/05/2024]
Abstract
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.
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Affiliation(s)
- Mihir Mongia
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tyler M Yasaka
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Yudong Liu
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Mustafa Guler
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Liang Lu
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Aditya Bhagwat
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Bahar Behsaz
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Chemia Biosciences Inc., Pittsburgh, PA, USA
| | - Mingxun Wang
- Computer Science and Engineering, University of California Riverside, Riverside, CA, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
- Department of Pharmacology and Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Hosein Mohimani
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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2
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Gomes PWP, de Tralia Medeiros TC, Maimone NM, Leão TF, de Moraes LAB, Bauermeister A. Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:225-248. [PMID: 37843811 DOI: 10.1007/978-3-031-41741-2_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
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.
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Affiliation(s)
- Paulo Wender P Gomes
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Talita Carla de Tralia Medeiros
- Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Naydja Moralles Maimone
- Departamento de Ciências Exatas, Escola Superior de Agricultura 'Luiz de Queiroz', Universidade de São Paulo, Piracicaba, São Paulo, Brazil
| | - Tiago F Leão
- Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, São Paulo, Brazil
| | - Luiz Alberto Beraldo de Moraes
- Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Anelize Bauermeister
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
- Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, Brazil.
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3
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Caesar LK, Montaser R, Keller NP, Kelleher NL. Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities. Nat Prod Rep 2021; 38:2041-2065. [PMID: 34787623 PMCID: PMC8691422 DOI: 10.1039/d1np00036e] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
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.
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Affiliation(s)
- Lindsay K Caesar
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
| | - Rana Montaser
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
| | - Nancy P Keller
- Department of Medical Microbiology and Immunology and Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Neil L Kelleher
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA
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4
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Behsaz B, Bode E, Gurevich A, Shi YN, Grundmann F, Acharya D, Caraballo-Rodríguez AM, Bouslimani A, Panitchpakdi M, Linck A, Guan C, Oh J, Dorrestein PC, Bode HB, Pevzner PA, Mohimani H. Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery. Nat Commun 2021; 12:3225. [PMID: 34050176 PMCID: PMC8163882 DOI: 10.1038/s41467-021-23502-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 05/04/2021] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Bahar Behsaz
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California at San Diego, La Jolla, CA, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Edna Bode
- Molecular Biotechnology, Department of Biosciences, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Alexey Gurevich
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St Petersburg, Russia
| | - Yan-Ni Shi
- Molecular Biotechnology, Department of Biosciences, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Florian Grundmann
- Molecular Biotechnology, Department of Biosciences, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Deepa Acharya
- Tiny Earth Chemistry Hub, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrés Mauricio Caraballo-Rodríguez
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Amina Bouslimani
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Morgan Panitchpakdi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Annabell Linck
- Molecular Biotechnology, Department of Biosciences, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Changhui Guan
- The Jackson Laboratory of Medical Genomics, Farmington, CT, USA
| | - Julia Oh
- The Jackson Laboratory of Medical Genomics, Farmington, CT, USA
| | - Pieter C Dorrestein
- Center for Microbiome Innovation, University of California at San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Helge B Bode
- Molecular Biotechnology, Department of Biosciences, Goethe University Frankfurt, Frankfurt am Main, Germany.
- Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University Frankfurt & Senckenberg Research Institute, Frankfurt am Main, Germany.
- Max-Planck-Institute for Terrestrial Microbiology, Department for Natural Products in Organismic Interactions, Marburg, Germany.
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
| | - Hosein Mohimani
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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5
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Tagirdzhanov AM, Shlemov A, Gurevich A. NPS: scoring and evaluating the statistical significance of peptidic natural product-spectrum matches. Bioinformatics 2020; 35:i315-i323. [PMID: 31510666 PMCID: PMC6612854 DOI: 10.1093/bioinformatics/btz374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
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.
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Affiliation(s)
- Azat M Tagirdzhanov
- Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia.,Department of Higher Mathematics, St. Petersburg Electrotechnical University "LETI", St. Petersburg, Russia
| | - Alexander Shlemov
- Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia
| | - Alexey Gurevich
- Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia
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6
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De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments. Cell Syst 2019; 10:99-108.e5. [PMID: 31864964 DOI: 10.1016/j.cels.2019.11.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 09/18/2019] [Accepted: 11/18/2019] [Indexed: 12/20/2022]
Abstract
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.
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7
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Cao L, Gurevich A, Alexander KL, Naman CB, Leão T, Glukhov E, Luzzatto-Knaan T, Vargas F, Quinn R, Bouslimani A, Nothias LF, Singh NK, Sanders JG, Benitez RAS, Thompson LR, Hamid MN, Morton JT, Mikheenko A, Shlemov A, Korobeynikov A, Friedberg I, Knight R, Venkateswaran K, Gerwick WH, Gerwick L, Dorrestein PC, Pevzner PA, Mohimani H. MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities. Cell Syst 2019; 9:600-608.e4. [PMID: 31629686 DOI: 10.1016/j.cels.2019.09.004] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 04/23/2019] [Accepted: 09/12/2019] [Indexed: 12/22/2022]
Abstract
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.
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Affiliation(s)
- Liu Cao
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alexey Gurevich
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Kelsey L Alexander
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; Department of Chemistry and Biochemistry, University of California, San Diego, San Diego, CA, USA
| | - C Benjamin Naman
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; Li Dak Sum Yip Yio Chin Kenneth Li Marine Biopharmaceutical Research Center, Department of Marine Pharmacy, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China
| | - Tiago Leão
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Evgenia Glukhov
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Tal Luzzatto-Knaan
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Fernando Vargas
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Robby Quinn
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Amina Bouslimani
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Louis Felix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Nitin K Singh
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jon G Sanders
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA
| | - Rodolfo A S Benitez
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA
| | - Luke R Thompson
- Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA; Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, CA, USA
| | - Md-Nafiz Hamid
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA; Interdepartmental program in Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA
| | - James T Morton
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA
| | - Alla Mikheenko
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Alexander Shlemov
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Anton Korobeynikov
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Department of Mathematics and Mechanics, St. Petersburg State University, St. Petersburg, Russia
| | - Iddo Friedberg
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA; Interdepartmental program in Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, San Diego, CA, USA; Department of Bioengineering, University of California, San Diego, San Diego, CA, USA
| | | | - William H Gerwick
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Lena Gerwick
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, San Diego, CA, USA
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, San Diego, CA, USA
| | - Hosein Mohimani
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA.
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8
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Kou Q, Wang Z, Lubeckyj RA, Wu S, Sun L, Liu X. A Markov Chain Monte Carlo Method for Estimating the Statistical Significance of Proteoform Identifications by Top-Down Mass Spectrometry. J Proteome Res 2019; 18:878-889. [PMID: 30638379 DOI: 10.1021/acs.jproteome.8b00562] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
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.
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Affiliation(s)
- Qiang Kou
- Department of BioHealth Informatics , Indiana University-Purdue University Indianapolis , Indianapolis , Indiana 46202 , United States
| | - Zhe Wang
- Department of Chemistry and Biochemistry , The University of Oklahoma , Norman , Oklahoma 73019-5251 , United States
| | - Rachele A Lubeckyj
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824-1332 , United States
| | - Si Wu
- Department of Chemistry and Biochemistry , The University of Oklahoma , Norman , Oklahoma 73019-5251 , United States
| | - Liangliang Sun
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824-1332 , United States
| | - Xiaowen Liu
- Department of BioHealth Informatics , Indiana University-Purdue University Indianapolis , Indianapolis , Indiana 46202 , United States.,Center for Computational Biology and Bioinformatics , Indiana University School of Medicine , Indianapolis , Indiana 46202 , United States
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9
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Mohimani H, Gurevich A, Shlemov A, Mikheenko A, Korobeynikov A, Cao L, Shcherbin E, Nothias LF, Dorrestein PC, Pevzner PA. Dereplication of microbial metabolites through database search of mass spectra. Nat Commun 2018; 9:4035. [PMID: 30279420 PMCID: PMC6168521 DOI: 10.1038/s41467-018-06082-8] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 08/14/2018] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- Hosein Mohimani
- Computational Biology Department, School of Computer Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
| | - Alexey Gurevich
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Alexander Shlemov
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Alla Mikheenko
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Anton Korobeynikov
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
- Department of Statistical Modelling, St. Petersburg State University, St. Petersburg, Russia
| | - Liu Cao
- Computational Biology Department, School of Computer Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Egor Shcherbin
- National Research University Higher School of Economics, St. Petersburg, Russia
| | - Louis-Felix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Department of Pharmacology and Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
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10
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Increased diversity of peptidic natural products revealed by modification-tolerant database search of mass spectra. Nat Microbiol 2018; 3:319-327. [PMID: 29358742 DOI: 10.1038/s41564-017-0094-2] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 12/08/2017] [Indexed: 11/09/2022]
Abstract
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.
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11
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Dereplication of peptidic natural products through database search of mass spectra. Nat Chem Biol 2016; 13:30-37. [PMID: 27820803 PMCID: PMC5409158 DOI: 10.1038/nchembio.2219] [Citation(s) in RCA: 151] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 08/17/2016] [Indexed: 11/08/2022]
Abstract
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.
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Mohimani H, Pevzner PA. Dereplication, sequencing and identification of peptidic natural products: from genome mining to peptidogenomics to spectral networks. Nat Prod Rep 2016; 33:73-86. [PMID: 26497201 PMCID: PMC5590107 DOI: 10.1039/c5np00050e] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
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.
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Affiliation(s)
- Hosein Mohimani
- Department of Computer Science and Engineering, University of California, San Diego, USA.
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California, San Diego, USA.
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Mohimani H, Liu WT, Kersten R, Moore BS, Dorrestein PC, Pevzner PA. NRPquest: Coupling Mass Spectrometry and Genome Mining for Nonribosomal Peptide Discovery. JOURNAL OF NATURAL PRODUCTS 2014; 77:1902-9. [PMID: 25116163 PMCID: PMC4143176 DOI: 10.1021/np500370c] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Indexed: 05/31/2023]
Abstract
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.
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Affiliation(s)
- Hosein Mohimani
- Department of Electrical and Computer
Engineering, Department of Chemistry and Biochemistry, Center for Marine
Biotechnology and Biomedicine, Scripps Institution of Oceanography, Skaggs School of
Pharmacy and Pharmaceutical Sciences, and Department of Computer Science
and Engineering, University of California
San Diego, La Jolla, California 92093, United States
| | - Wei-Ting Liu
- Department of Electrical and Computer
Engineering, Department of Chemistry and Biochemistry, Center for Marine
Biotechnology and Biomedicine, Scripps Institution of Oceanography, Skaggs School of
Pharmacy and Pharmaceutical Sciences, and Department of Computer Science
and Engineering, University of California
San Diego, La Jolla, California 92093, United States
| | - Roland
D. Kersten
- Department of Electrical and Computer
Engineering, Department of Chemistry and Biochemistry, Center for Marine
Biotechnology and Biomedicine, Scripps Institution of Oceanography, Skaggs School of
Pharmacy and Pharmaceutical Sciences, and Department of Computer Science
and Engineering, University of California
San Diego, La Jolla, California 92093, United States
| | - Bradley S. Moore
- Department of Electrical and Computer
Engineering, Department of Chemistry and Biochemistry, Center for Marine
Biotechnology and Biomedicine, Scripps Institution of Oceanography, Skaggs School of
Pharmacy and Pharmaceutical Sciences, and Department of Computer Science
and Engineering, University of California
San Diego, La Jolla, California 92093, United States
| | - Pieter C. Dorrestein
- Department of Electrical and Computer
Engineering, Department of Chemistry and Biochemistry, Center for Marine
Biotechnology and Biomedicine, Scripps Institution of Oceanography, Skaggs School of
Pharmacy and Pharmaceutical Sciences, and Department of Computer Science
and Engineering, University of California
San Diego, La Jolla, California 92093, United States
| | - Pavel A. Pevzner
- Department of Electrical and Computer
Engineering, Department of Chemistry and Biochemistry, Center for Marine
Biotechnology and Biomedicine, Scripps Institution of Oceanography, Skaggs School of
Pharmacy and Pharmaceutical Sciences, and Department of Computer Science
and Engineering, University of California
San Diego, La Jolla, California 92093, United States
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Mohimani H, Kersten RD, Liu WT, Wang M, Purvine SO, Wu S, Brewer HM, Pasa-Tolic L, Bandeira N, Moore BS, Pevzner PA, Dorrestein PC. Automated genome mining of ribosomal peptide natural products. ACS Chem Biol 2014; 9:1545-51. [PMID: 24802639 PMCID: PMC4215869 DOI: 10.1021/cb500199h] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
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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.
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Affiliation(s)
- Hosein Mohimani
- Department
of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Roland D. Kersten
- Center
for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
| | - Wei-Ting Liu
- Department
of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Mingxun Wang
- Department
of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Samuel O. Purvine
- Environmental
Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Si Wu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Heather M. Brewer
- Environmental
Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ljiljana Pasa-Tolic
- Environmental
Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Nuno Bandeira
- Department
of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, United States
- Skaggs School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Bradley S. Moore
- Center
for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
- Skaggs School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Pavel A. Pevzner
- Department
of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Pieter C. Dorrestein
- Department
of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
- Skaggs School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
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Hufsky F, Scheubert K, Böcker S. New kids on the block: novel informatics methods for natural product discovery. Nat Prod Rep 2014; 31:807-17. [DOI: 10.1039/c3np70101h] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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