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Martorano LH, Ribeiro CMR, Valverde AL, Dos Santos Junior FM, Sarotti AM. An Integrated ANN-PRA/DP4+ Tandem Computational Approach Contributing to the Ordering of the Heliannuol Family. J Org Chem 2024; 89:8937-8950. [PMID: 38848463 DOI: 10.1021/acs.joc.4c00852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
Heliannuols are a unique class of sesquiterpenes isolated mostly from Helianthus annuus, commonly known as sunflower. The interesting allelopathic properties, combined with their unprecedented carbon skeletons, have drawn wide attention to phytochemistry and synthetic groups. So far, 14 heliannuols (heliannuols A-N) have been described in the literature, although some of them have not yet been validated by total synthesis. Moreover, the structural proposal of some compounds was based on the similarity of NMR data reported for previously isolated analogues (which in many instances turned out to be incorrect), coupled with little or no stereochemical analysis. Consequently, the structural reassignment is a recurring theme in heliannuol's family. Through a rigorous and comprehensive quantum chemical simulation of NMR parameters, encompassing an integrated ANN-PRA/DP4+ tandem approach, we intended to advance unexplored directions regarding the structure of the entire heliannuol family. Furthermore, we found that the size of the fused ring significantly influences the signals corresponding to the aromatic ring, making this discovery an excellent diagnostic tool for quickly determining the core structure of these compounds.
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Affiliation(s)
- Lucas H Martorano
- Department of Organic Chemistry, Chemistry Institute, Universidade Federal Fluminense (UFF), Outeiro de São João Batista, Niterói, Rio de Janeiro 24020-141, Brazil
| | - Carlos Magno Rocha Ribeiro
- Department of Organic Chemistry, Chemistry Institute, Universidade Federal Fluminense (UFF), Outeiro de São João Batista, Niterói, Rio de Janeiro 24020-141, Brazil
| | - Alessandra L Valverde
- Department of Organic Chemistry, Chemistry Institute, Universidade Federal Fluminense (UFF), Outeiro de São João Batista, Niterói, Rio de Janeiro 24020-141, Brazil
| | - Fernando Martins Dos Santos Junior
- Department of Organic Chemistry, Chemistry Institute, Universidade Federal Fluminense (UFF), Outeiro de São João Batista, Niterói, Rio de Janeiro 24020-141, Brazil
| | - Ariel M Sarotti
- Instituto de Química Rosario (CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, Rosario 2000, Argentina
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2
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Nishizaki Y, Sugimoto N, Miura T, Asakura K, Suematsu T, Korhonen SP, Lehtivarjo J, Niemitz M, Pauli GF. Quantum Mechanical Quantitative Nuclear Magnetic Resonance Enables Digital Reference Standards at All Magnetic Fields and Enhances qNMR Sustainability. Anal Chem 2024; 96:9790-9798. [PMID: 38829167 PMCID: PMC11190874 DOI: 10.1021/acs.analchem.3c05267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/05/2024]
Abstract
Quantum mechanics (QM)-driven 1H iterative functionalized spin analysis produces HifSA profiles, which encode the complete 1H spin parameters ("nuclear genotype") of analytes of interest. HifSA profiles enable the establishment of digital reference standards (dRS) that are portable, FAIR (findable - accessible - interoperable - reusable), and fit for the purpose of quantitative 1H NMR (qHNMR) analysis at any magnetic field. This approach enhances the sustainability of analytical standards. Moreover, the analyte-specific complete chemical shift and J-coupling information in HifSA-based dRS enable computational quantitation of substances in mixtures via QM-total-line-shape fitting (QM-qHNMR). We present the proof of concept for HifSA-based dRS by resolving the highly overlapping NMR resonances in the experimental spectra ("nuclear phenotypes") of the diastereomeric mixture of (2RS, 4RS)- and (2RS, 4SR)-difenoconazole (DFZ), a widely used antifouling food additive. The underlying 1H spin parameters are highly conserved in various solvents, are robust against variation in measurement temperature, and work across a wide range of magnetic fields. QM-qHNMR analysis of DFZ samples at 80, 400, 600, and 800 MHz showed high congruence with metrological reference values. Furthermore, this study introduces QM-qHNMR combined with chiral shift reagents for the analysis of all four DFZ stereoisomers: (2R, 4R)-, (2S, 4S)-, (2R, 4S)-, and (2S, 4R)-DFZ to perform chiral qHNMR measurements.
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Affiliation(s)
- Yuzo Nishizaki
- National
Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan
| | - Naoki Sugimoto
- National
Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan
| | - Toru Miura
- FUJIFILM
Wako Pure Chemical Corporation, Kawagoe, Saitama 350-1101, Japan
| | | | | | | | | | | | - Guido F. Pauli
- University
of Illinois Chicago, Chicago, Illinois 60612, United States
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3
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Pin M, Poynton EF, Jordan T, Kim J, Ledingham B, van Santen JA, Yang V, Maras A, Tavangar P, Gautam V, Peters H, Sajed T, Lee BL, Shreffler HA, Koller JT, Tretter ZM, Cort JR, Sumner LW, Wishart DS, Linington RG. A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data. JOURNAL OF NATURAL PRODUCTS 2023; 86:2554-2561. [PMID: 37935005 PMCID: PMC11119957 DOI: 10.1021/acs.jnatprod.3c00795] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases either are not designed for natural products data or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data.
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Affiliation(s)
- Matthew Pin
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Ella F. Poynton
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Tamara Jordan
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Jonghyeok Kim
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Benjamin Ledingham
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Jeffrey A. van Santen
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
- Unnatural Products, 2161 Delaware Ave. Suite A, Santa Cruz, CA 95060, USA
| | - Vera Yang
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Andrew Maras
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Pegah Tavangar
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian L. Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Hailey A. Shreffler
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, United States
| | - James T. Koller
- Interdisciplinary Plant Group, MU Metabolomics Center, Bond Life Sciences Center, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Zachary M. Tretter
- Interdisciplinary Plant Group, MU Metabolomics Center, Bond Life Sciences Center, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - John R. Cort
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Lloyd W. Sumner
- Interdisciplinary Plant Group, MU Metabolomics Center, Bond Life Sciences Center, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - David S. Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Roger G. Linington
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
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4
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Hu G, Qiu M. Machine learning-assisted structure annotation of natural products based on MS and NMR data. Nat Prod Rep 2023; 40:1735-1753. [PMID: 37519196 DOI: 10.1039/d3np00025g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.
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Affiliation(s)
- Guilin Hu
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China.
- University of the Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Minghua Qiu
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China.
- University of the Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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5
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Cortés I, Sarotti AM. Road Map Toward Computer-Guided Total Synthesis of Natural Products. The Dysiherbol A Case Study: What if Serendipity Hadn't Intervened? J Org Chem 2023; 88:14156-14164. [PMID: 37728229 DOI: 10.1021/acs.joc.3c01738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
We present a computational study inspired by the story of dysiherbol A, a natural product whose putative structure was found incorrect through synthesis by a completely fortuitous event. While the carbon connectivity and chemical environment between both structures remain similar, the real dysiherbol A has a different molecular weight than that reported for the natural product. Had the synthesis groups not been favored by fortune, it could be speculated that a substantial amount of time and effort would have been required to solve the structural puzzle. Within the realm of computer-guided total synthesis of natural products, the question arose whether a synthesis group could have in silico reassigned the structure before embarking on the experimental adventure. To address this query, we evaluated some state-of-the-art computational procedures based on their computational demand and ease of implementation for nonexpert users with basic skills in computational chemistry (including HOSE, CASCADE, ANN-PRA, ML-J-DP4, DP4, and DP4+). While discussing the strengths and limitations of these methods, this case study provides a roadmap of what could be done before venturing into complex and time-demanding total synthesis projects.
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Affiliation(s)
- Iván Cortés
- Instituto de Química Rosario (CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000 Rosario, Argentina
| | - Ariel M Sarotti
- Instituto de Química Rosario (CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000 Rosario, Argentina
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6
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McAlpine JB, Ferreira D, Pauli NE, Gafner S, Pauli GF. The Ethics of Publishing Biomedical and Natural Products Research. JOURNAL OF NATURAL PRODUCTS 2023; 86:2228-2237. [PMID: 37638654 DOI: 10.1021/acs.jnatprod.3c00165] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Given that the essence of Science is a search for the truth, one might expect that those identifying as scientists would be conscientious and observant of the demands this places on them. However, that expectation is not fulfilled universally as, not too surprisingly, egregious examples of unethical behavior appear and are driven by money, personal ambition, performance pressure, and other incentives. The reproducibility-, fact-, and truth-oriented modus operandi of Science has come to face a variety of challenges. Organized into 11 cases, this article outlines examples of compromised integrity from borderline to blatant unethical behavior that disgrace our profession unnecessarily. Considering technological developments in neural networks/artificial intelligence, a host of factors are identified as impacting Good Ethical Practices. The goal is manifold: to raise awareness and offer perspectives for refocusing on Science and true scientific evidence; to trigger discussion and developments that strengthen ethical behavior; to foster the recognition of the beauty, simplicity, and rewarding nature of scientific integrity; and to highlight the originality of intelligence.
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Affiliation(s)
- James B McAlpine
- Pharmacognosy Institute, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois 60612, United States
- Institute for Tuberculosis Research, University of Illinois at Chicago, Chicago, Illinois 60612, United States
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois 60612, United States
| | - Daneel Ferreira
- National Center for Natural Products Research and Department of Biomolecular Sciences, Division of Pharmacognosy, School of Pharmacy, University of Mississippi, University, Mississippi 38677, United States
| | - Neil E Pauli
- Downers Grove South High School, 1436 Norfolk Street, Downers Grove, Illinois 60516, United States
| | - Stefan Gafner
- American Botanical Council, 6200 Manor Road, Austin, Texas 78723, United States
| | - Guido F Pauli
- Pharmacognosy Institute, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois 60612, United States
- Institute for Tuberculosis Research, University of Illinois at Chicago, Chicago, Illinois 60612, United States
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois 60612, United States
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7
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Rodeiro J, Vidaña-Vila E, Navarro J, Mallol R. CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources. J Proteome Res 2023; 22:2540-2547. [PMID: 37428859 PMCID: PMC10857572 DOI: 10.1021/acs.jproteome.2c00602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Indexed: 07/12/2023]
Abstract
The field of metabolomics has witnessed the development of hundreds of computational tools, but only a few have become cornerstones of this field. While MetaboLights and Metabolomics Workbench are two well-established data repositories for metabolomics data sets, Workflows4Metabolomics and MetaboAnalyst are two well-established web-based data analysis platforms for metabolomics. Yet, the raw data stored in the aforementioned repositories lack standardization in terms of the file system format used to store the associated acquisition files. Consequently, it is not straightforward to reuse available data sets as input data in the above-mentioned data analysis resources, especially for non-expert users. This paper presents CloMet, a novel open-source modular software platform that contributes to standardization, reusability, and reproducibility in the metabolomics field. CloMet, which is available through a Docker file, converts raw and NMR-based metabolomics data from MetaboLights and Metabolomics Workbench to a file format that can be used directly either in MetaboAnalyst or in Workflows4Metabolomics. We validated both CloMet and the output data using data sets from these repositories. Overall, CloMet fills the gap between well-established data repositories and web-based statistical platforms and contributes to the consolidation of a data-driven perspective of the metabolomics field by leveraging and connecting existing data and resources.
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Affiliation(s)
- Jordi Rodeiro
- Human
Environment Research, La Salle - Universitat
Ramon Llull, 08022 Barcelona, Spain
| | - Ester Vidaña-Vila
- Human
Environment Research, La Salle - Universitat
Ramon Llull, 08022 Barcelona, Spain
| | - Joan Navarro
- Research
Group on Smart Society, La Salle - Universitat
Ramon Llull, 08022 Barcelona, Spain
| | - Roger Mallol
- Human
Environment Research, La Salle - Universitat
Ramon Llull, 08022 Barcelona, Spain
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8
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DiBello M, Healy AR, Nikolayevskiy H, Xu Z, Herzon SB. Structure Elucidation of Secondary Metabolites: Current Frontiers and Lingering Pitfalls. Acc Chem Res 2023; 56:1656-1668. [PMID: 37220079 PMCID: PMC10468810 DOI: 10.1021/acs.accounts.3c00183] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Analytical methods allow for the structure determination of submilligram quantities of complex secondary metabolites. This has been driven in large part by advances in NMR spectroscopic capabilities, including access to high-field magnets equipped with cryogenic probes. Experimental NMR spectroscopy may now be complemented by remarkably accurate carbon-13 NMR calculations using state-of-the-art DFT software packages. Additionally, microED analysis stands to have a profound effect on structure elucidation by providing X-ray-like images of microcrystalline samples of analytes. Nonetheless, lingering pitfalls in structure elucidation remain, particularly for isolates that are unstable or highly oxidized. In this Account, we discuss three projects from our laboratory that highlight nonoverlapping challenges to the field, with implications for chemical, synthetic, and mechanism of action studies. We first discuss the lomaiviticins, complex unsaturated polyketide natural products disclosed in 2001. The original structures were derived from NMR, HRMS, UV-vis, and IR analysis. Owing to the synthetic challenges presented by their structures and the absence of X-ray crystallographic data, the structure assignments remained untested for nearly two decades. In 2021, the Nelson group at Caltech carried out microED analysis of (-)-lomaiviticin C, leading to the startling discovery that the original structure assignment of the lomaiviticins was incorrect. Acquisition of higher-field (800 MHz 1H, cold probe) NMR data as well as DFT calculations provided insights into the basis for the original misassignment and lent further support to the new structure identified by microED. Reanalysis of the 2001 data set reveals that the two structure assignments are nearly indistinguishable, underscoring the limitations of NMR-based characterization. We then discuss the structure elucidation of colibactin, a complex, nonisolable microbiome metabolite implicated in colorectal cancer. The colibactin biosynthetic gene cluster was detected in 2006, but owing to colibactin's instability and low levels of production, it could not be isolated or characterized. We used a combination of chemical synthesis, mechanism of action studies, and biosynthetic analysis to identify the substructures in colibactin. These studies, coupled with isotope labeling and tandem MS analysis of colibactin-derived DNA interstrand cross-links, ultimately led to a structure assignment for the metabolite. We then discuss the ocimicides, plant secondary metabolites that were studied as agents against drug-resistant P. falciparum. We synthesized the core structure of the ocimicides and found significant discrepancies between our experimental NMR spectroscopic data and that reported for the natural products. We determined the theoretical carbon-13 NMR shifts for 32 diastereomers of the ocimicides. These studies indicated that a revision of the connectivity of the metabolites is likely needed. We end with some thoughts on the frontiers of secondary metabolite structure determination. As modern NMR computational methods are straightforward to execute, we advocate for their systematic use in validating the assignments of novel secondary metabolites.
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Affiliation(s)
- Mikaela DiBello
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Alan R Healy
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Herman Nikolayevskiy
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Zhi Xu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Seth B Herzon
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
- Departments of Pharmacology and Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut 06520, United States
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9
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Nazarski RB. On the Use of Deuterated Organic Solvents without TMS to Report 1H/ 13C NMR Spectral Data of Organic Compounds: Current State of the Method, Its Pitfalls and Benefits, and Related Issues. Molecules 2023; 28:molecules28114369. [PMID: 37298845 DOI: 10.3390/molecules28114369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
The quite popular, simple but imperfect method of referencing NMR spectra to residual 1H and 13C signals of TMS-free deuterated organic solvents (referred to as Method A) is critically discussed for six commonly used NMR solvents with respect to their δH and δC values that exist in the literature. Taking into account the most reliable data, it was possible to recommend 'best' δX values for such secondary internal standards. The position of these reference points on the δ scale strongly depends on the concentration and type of analyte under study and the solvent medium used. For some solvents, chemically induced shifts (CISs) of residual 1H lines were considered, also taking into account the formation of 1:1 molecular complexes (for CDCl3). Typical potential errors that can occur as a result of improper application of Method A are considered in detail. An overview of all found δX values adopted by users of this method revealed a discrepancy of up to 1.9 ppm in δC reported for CDCl3, most likely caused by the CIS mentioned above. The drawbacks of Method A are discussed in relation to the classical use of an internal standard (Method B), two 'instrumental' schemes in which Method A is often implicitly applied, that is, the default Method C using 2H lock frequencies and Method D based on Ξ values, recommended by the IUPAC but only occasionally used for 1H/13C spectra, and external referencing (Method E). Analysis of current needs and opportunities for NMR spectrometers led to the conclusion that, for the most accurate application of Method A, it is necessary to (a) use dilute solutions in a single NMR solvent and (b) to report δX data applied for the reference 1H/13C signals to the nearest 0.001/0.01 ppm to ensure the precise characterization of new synthesized or isolated organic systems, especially those with complex or unexpected structures. However, the use of TMS in Method B is strongly recommended in all such cases.
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Affiliation(s)
- Ryszard B Nazarski
- Theoretical and Structural Chemistry Group, Department of Physical Chemistry, Faculty of Chemistry, University of Lodz, 163/165 Pomorska, 90-236 Łódź, Poland
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10
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Böselt L, Aerts R, Herrebout W, Riniker S. Improving the IR spectra alignment algorithm with spectra deconvolution and combination with Raman or VCD spectroscopy. Phys Chem Chem Phys 2023; 25:2063-2074. [PMID: 36546852 PMCID: PMC9847344 DOI: 10.1039/d2cp04907d] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The relative stereochemistry of organic molecules can be determined by comparing theoretical and experimental infrared (IR) spectra of all isomers and assessing the best match. For this purpose, we have recently developed the IR spectra alignment (IRSA) algorithm for automated optimal alignment. IRSA provides a set of quantitative metrics to identify the candidate structure that agrees best with the experimental spectrum. While the correct diastereomer could be determined for the tested sets of rigid and flexible molecules, two issues were identified with more complex compounds that triggered further development. First, strongly overlapping peaks in the IR spectrum are not treated adequately in the current IRSA implementation. Second, the alignment of multiple spectra from different sources (e.g. IR and VCD or Raman) can be improved. In this study, we present an in-depth discussion of these points, followed by the description of modifications to the IRSA algorithm to address them. In particular, we introduce the concept of deconvolution of the experimental and theoretical spectra with a set of pseudo-Voigt bands. The pseudo-Voigt bands have a set of parameters, which can be employed in the alignment algorithm, leading to improved scoring functions. We test the modified algorithm on two data sets. The first set contains compounds with IR and Raman spectra measured in this study, and the second set contains compounds with IR and VCD spectra available in the literature. We show that the algorithm is able to determine the correct diastereomer in all cases. The results highlight that vibrational spectroscopy can be a valuable alternative or complementary method to inform about the stereochemistry of compounds, and the performance of the updated IRSA algorithm suggests that it is a powerful tool for quantitative-based spectral assignments in academia and industry.
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Affiliation(s)
- Lennard Böselt
- Laboratory of Physical Chemistry, ETH ZürichVladimir-Prelog-Weg 28093 ZürichSwitzerland
| | - Roy Aerts
- Department of Chemistry, University of AntwerpGroenenborgerlaan 171B-2020 AntwerpBelgium
| | - Wouter Herrebout
- Department of Chemistry, University of AntwerpGroenenborgerlaan 171B-2020 AntwerpBelgium
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH ZürichVladimir-Prelog-Weg 28093 ZürichSwitzerland
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11
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Petushkov VN, Vavilov MV, Ivanov IA, Ziganshin RH, Rodionova NS, Yampolsky IV, Tsarkova AS, Dubinnyi MA. Deazaflavin cofactor boosts earthworms Henlea bioluminescence. Org Biomol Chem 2023; 21:415-427. [PMID: 36530053 DOI: 10.1039/d2ob01946a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The bioluminescence of Siberian earthworms Henlea sp. was found to be enhanced by two low molecular weight activators, termed ActH and ActS, found in the hot extracts. The fluorescence emission maximum of the activators matches the bioluminescence spectrum that peaks at 464 nm. We purified 4.3 and 8.8 micrograms of ActH and ActS from 200 worms and explored them using orbitrap HRMS with deep fragmentation and 1D/2D NMR equipped with cryoprobes. Their chemical structures were ascertained using chemical shift prediction services, structure elucidation software and database searches. ActH was identified as the riboflavin analoge archaeal cofactor F0, namely 7,8-didemethyl-8-hydroxy-5-deazariboflavin. ActS is a novel compound, namely ActH sulfated at the 3' ribityl hydroxyl. We designed and implemented a new four step synthesis strategy forActH that outperformed previous synthetic approaches. The synthetic ActH was identical to the natural one and activated Henlea sp. bioluminescence. The bioluminescence enhancement factor X was measured at different ActH concentrations and the Michaelis constant Km = 0.22 ± 0.01 μM was obtained by nonlinear regression. At an excess of synthetic ActH, the factor X was saturated at Xmax = 33.3 ± 0.5, thus opening an avenue to further characterisation of the Henlea sp. bioluminescence system. ActH did not produce bioluminescence without the luciferin with an as yet unknown chemical structure. We propose that ActH and the novel sulfated deazariboflavin ActS either emit the light of the Henlea sp. bioluminescence and/or accept hydride(s) donor upon luciferin oxidation.
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Affiliation(s)
- Valentin N Petushkov
- Institute of Biophysics, Krasnoyarsk Research Center, Siberian Branch, Russian Academy of Sciences, Akademgorodok, 660036, Krasnoyarsk, Russia
| | - Matvey V Vavilov
- Shemyakin-Ovchinnikov Institute of bioorganic chemistry, Russian academy of Sciences GSP-7, Miklukho-Maklaya str., 16/10, 117997, Moscow, Russia.
| | - Igor A Ivanov
- Shemyakin-Ovchinnikov Institute of bioorganic chemistry, Russian academy of Sciences GSP-7, Miklukho-Maklaya str., 16/10, 117997, Moscow, Russia.
| | - Rustam H Ziganshin
- Shemyakin-Ovchinnikov Institute of bioorganic chemistry, Russian academy of Sciences GSP-7, Miklukho-Maklaya str., 16/10, 117997, Moscow, Russia.
| | - Natalia S Rodionova
- Institute of Biophysics, Krasnoyarsk Research Center, Siberian Branch, Russian Academy of Sciences, Akademgorodok, 660036, Krasnoyarsk, Russia
| | - Ilia V Yampolsky
- Shemyakin-Ovchinnikov Institute of bioorganic chemistry, Russian academy of Sciences GSP-7, Miklukho-Maklaya str., 16/10, 117997, Moscow, Russia.
| | - Aleksandra S Tsarkova
- Shemyakin-Ovchinnikov Institute of bioorganic chemistry, Russian academy of Sciences GSP-7, Miklukho-Maklaya str., 16/10, 117997, Moscow, Russia. .,Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Maxim A Dubinnyi
- Shemyakin-Ovchinnikov Institute of bioorganic chemistry, Russian academy of Sciences GSP-7, Miklukho-Maklaya str., 16/10, 117997, Moscow, Russia. .,Moscow Institute of Physics and Technology (State University), 9 Institutskiy per., Dolgoprudny, Moscow Region 141700, Russia
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12
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Wishart DS, Rout M, Lee BL, Berjanskii M, LeVatte M, Lipfert M. Practical Aspects of NMR-Based Metabolomics. Handb Exp Pharmacol 2023; 277:1-41. [PMID: 36271165 DOI: 10.1007/164_2022_613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Marcia LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
- Reference Standard Management & NMR QC, Lonza Group AG, Visp, Switzerland
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13
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Sahayasheela VJ, Lankadasari MB, Dan VM, Dastager SG, Pandian GN, Sugiyama H. Artificial intelligence in microbial natural product drug discovery: current and emerging role. Nat Prod Rep 2022; 39:2215-2230. [PMID: 36017693 PMCID: PMC9931531 DOI: 10.1039/d2np00035k] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.
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Affiliation(s)
- Vinodh J Sahayasheela
- Department of Chemistry, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-Ku, Kyoto 606-8502, Japan.
| | - Manendra B Lankadasari
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Vipin Mohan Dan
- Microbiology Division, Jawaharlal Nehru Tropical Botanic Garden and Research Institute, Thiruvananthapuram, Kerala, India
| | - Syed G Dastager
- NCIM Resource Centre, Division of Biochemical Sciences, CSIR - National Chemical Laboratory, Pune, Maharashtra, India
| | - Ganesh N Pandian
- Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University, Yoshida-Ushinomaecho, Sakyo-Ku, Kyoto 606-8501, Japan
| | - Hiroshi Sugiyama
- Department of Chemistry, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-Ku, Kyoto 606-8502, Japan.
- Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University, Yoshida-Ushinomaecho, Sakyo-Ku, Kyoto 606-8501, Japan
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14
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Zhao M, Tian J. Recent advances on the structure identification of components in food. EFOOD 2022. [DOI: 10.1002/efd2.51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Min Zhao
- College of Food Science, Shenyang Agricultural University, National R&D Professional Center For Berry Processing, National Engineering and Technology of Research Center For Small berry, Key Laboratory of Healthy Food Nutrition and Innovative Manufacturing Liaoning Province Shenyang Liaoning China
| | - Jin‐Long Tian
- College of Food Science, Shenyang Agricultural University, National R&D Professional Center For Berry Processing, National Engineering and Technology of Research Center For Small berry, Key Laboratory of Healthy Food Nutrition and Innovative Manufacturing Liaoning Province Shenyang Liaoning China
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15
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NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures. Cells 2022; 11:cells11213526. [DOI: 10.3390/cells11213526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/29/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.
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16
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Li Q, Yang S, Teng H, Li X, Xie W, Wu Z, Yang G, Xu J, Chen Y. Structural elucidation of two intricate polycyclic polyprenylated acylphloroglucinols using quantum chemical calculations and their hypoglycemic activities. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.104137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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17
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García-Domínguez P, de Lera AR. Puzzling Out the Structure of Novofumigatamide: Total Synthesis of Constitutional Isomers. Part II. J Org Chem 2022; 87:12528-12546. [PMID: 36129245 PMCID: PMC9552235 DOI: 10.1021/acs.joc.2c01228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
![]()
The total synthesis
of several constitutional isomers
showing a
different connectivity of the macrolactam ring with the hexahydropyrrolo[2,3-b]indole core, as well as those arising from the positional
exchange of the valine and the anthranilate units of the structure
originally proposed for (−)-novofumigatamide, has been carried
out. The constitutional isomers with 12-membered ring macrolactam
connected with the pyrroloindoline framework through the indole nitrogen,
and the acetyl group at the pyrrole nitrogen, of endo relative configuration, were prepared through the condensation between
the tryptophan and valine edges derived from l- or d-tryptophan and l-valine amino acids. The corresponding exo products are highly unstable structures difficult to
isolate and characterize. A second group of isomeric structures synthesized
considered the positional exchange between the valine and the anthranilate
residues within the macrolactam ring in the originally proposed macrocyclic
structure. Comparison of the spectroscopic data allowed us to discard
these alternative structures for the natural product.
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18
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Fraga KJ, Huang YJ, Ramelot TA, Swapna GVT, Lashawn Anak Kendary A, Li E, Korf I, Montelione GT. SpecDB: A relational database for archiving biomolecular NMR spectral data. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 342:107268. [PMID: 35930941 PMCID: PMC9922030 DOI: 10.1016/j.jmr.2022.107268] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 06/16/2022] [Accepted: 07/06/2022] [Indexed: 05/11/2023]
Abstract
NMR is a valuable experimental tool in the structural biologist's toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Biomolecular NMR research groups produce large amounts of data, and there is renewed interest in organizing these data to train new, sophisticated machine learning architectures and to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectral Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB.
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Affiliation(s)
- Keith J Fraga
- Department of Molecular and Cellular Biology, University of California, Davis, CA 95616, USA.
| | - Yuanpeng J Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
| | - G V T Swapna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA; Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, Piscataway, NJ 08854, USA.
| | | | - Ethan Li
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
| | - Ian Korf
- Department of Molecular and Cellular Biology, University of California, Davis, CA 95616, USA.
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
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19
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Elyashberg M, Novitskiy IM, Bates RW, Kutateladze AG, Williams CM. Reassignment of Improbable Natural Products Identified through Chemical Principle Screening. European J Org Chem 2022. [DOI: 10.1002/ejoc.202200572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Mikhail Elyashberg
- Advanced Chemistry Development Inc. (ACD/Labs) Toronto ON, M5C 1B5 Canada
| | - Ivan M. Novitskiy
- Department of Chemistry and Biochemistry University of Denver Denver CO 80208 United States
| | - Roderick W. Bates
- Division of Chemistry and Biological Chemistry School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 637371
| | - Andrei G. Kutateladze
- Department of Chemistry and Biochemistry University of Denver Denver CO 80208 United States
| | - Craig M. Williams
- School of Chemistry and Molecular Biosciences University of Queensland Brisbane 4072 Queensland Australia
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20
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Rauh D, Blankenburg C, Fischer TG, Jung N, Kuhn S, Schatzschneider U, Schulze T, Neumann S. Data format standards in analytical chemistry. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2021-3101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.
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Affiliation(s)
- David Rauh
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Claudia Blankenburg
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Tillmann G. Fischer
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Nicole Jung
- Karlsruhe Institute of Technology, Institute for Chemical and Biological Systems (IBCS-FMS) , Hermann von Helmholtz Platz 1 , 76344 Eggenstein-Leopolshafen , Germany
| | - Stefan Kuhn
- School of Computer Science and Informatics , De Montfort University , Leicester , UK
| | - Ulrich Schatzschneider
- Institut für Anorganische Chemie , Julius-Maximilians-Universität Würzburg , Am Hubland , D-97074 Würzburg , Germany
| | - Tobias Schulze
- Department of Effect-Directed Analysis , Helmholtz Centre for Environmental Research – UFZ , Permoserstr. 15, 04318 Leipzig , Germany
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
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21
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Zou D, Chen T, He L, Wang Q, Wang Q, La M, Li Y, Jiang R. Time-Domain-Based Methyl Proton NMR with Absolute Quantitation Ability for Targeted Metabolomics. Anal Chem 2022; 94:10062-10073. [PMID: 35786885 DOI: 10.1021/acs.analchem.2c00599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In high-throughput scenarios of targeted metabolomics, it is a significant challenge to process complex NMR spectra with severely overlapping signals produced by metabolites with similar chemical structures. Traditional frequency-domain NMR is ineffective to some degree due to the low sensitivity and poor resolution, and the precision of quantitation is usually affected by poorly or inconsistently phased and baselined spectra. Here, we established a strategy based on time-domain NMR focusing on methyl protons for targeted metabolomics. The targeted metabolomics focusing on bufadienolides for varietal discrimination of three toad venoms was conducted to demonstrate the practicability of time-domain-based methyl proton NMR. It revealed that the signals could be precisely identified and quantitated with an signal-to-noise ratio (SNR) of about 10 and a resolution of about 1.0 Hz. The sensitivity and resolution improvement make it particularly applicable in targeted metabolomics. The precise and absolute quantitation ability confirmed by triple-quadrupole mass spectrometry (QqQ-MS) could further extend its application range. Importantly, the methyl group is common in metabolites with a relatively wide chemical shift range. Time-domain analysis could be conducted in common NMR software. This technique is very easy and convenient for general researchers when employed as a complementary alternative to traditional frequency-domain NMR, especially in the field of targeted metabolomics.
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Affiliation(s)
- Denglang Zou
- Key Laboratory of Biodiversity Formation Mechanism and Comprehensive Utilization of Qinghai-Tibetan Plateau in Qinghai Province, Academy of Plateau Science and Sustainability, School of Life Science, Qinghai Normal University, Xining 810000, P. R. China.,Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, P. R. China.,College of Pharmacy, Jinan University, Guangzhou 510632, P. R. China
| | - Tao Chen
- Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, P. R. China
| | - Liangliang He
- College of Pharmacy, Jinan University, Guangzhou 510632, P. R. China
| | - Qi Wang
- College of Pharmacy, Jinan University, Guangzhou 510632, P. R. China
| | - Qiqi Wang
- College of Pharmacy, Jinan University, Guangzhou 510632, P. R. China
| | - Mencuo La
- Key Laboratory of Biodiversity Formation Mechanism and Comprehensive Utilization of Qinghai-Tibetan Plateau in Qinghai Province, Academy of Plateau Science and Sustainability, School of Life Science, Qinghai Normal University, Xining 810000, P. R. China
| | - Yulin Li
- Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, P. R. China
| | - Renwang Jiang
- College of Pharmacy, Jinan University, Guangzhou 510632, P. R. China
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22
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Rutz A, Sorokina M, Galgonek J, Mietchen D, Willighagen E, Gaudry A, Graham JG, Stephan R, Page R, Vondrášek J, Steinbeck C, Pauli GF, Wolfender JL, Bisson J, Allard PM. The LOTUS initiative for open knowledge management in natural products research. eLife 2022; 11:e70780. [PMID: 35616633 PMCID: PMC9135406 DOI: 10.7554/elife.70780] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 03/22/2022] [Indexed: 12/17/2022] Open
Abstract
Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.
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Affiliation(s)
- Adriano Rutz
- School of Pharmaceutical Sciences, University of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of GenevaGenevaSwitzerland
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University JenaJenaGermany
| | - Jakub Galgonek
- Institute of Organic Chemistry and Biochemistry of the CASPragueCzech Republic
| | - Daniel Mietchen
- Ronin InstituteMontclairUnited States
- Leibniz Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
- School of Data Science, University of VirginiaCharlottesvilleUnited States
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, Maastricht UniversityMaastrichtNetherlands
| | - Arnaud Gaudry
- School of Pharmaceutical Sciences, University of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of GenevaGenevaSwitzerland
| | - James G Graham
- Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at ChicagoChicagoUnited States
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at ChicagoChicagoUnited States
| | - Ralf Stephan
- Ontario Institute for Cancer Research (OICR), University Ave SuiteTorontoCanada
| | | | - Jiří Vondrášek
- Institute of Organic Chemistry and Biochemistry of the CASPragueCzech Republic
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University JenaJenaGermany
| | - Guido F Pauli
- Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at ChicagoChicagoUnited States
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at ChicagoChicagoUnited States
| | - Jean-Luc Wolfender
- School of Pharmaceutical Sciences, University of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of GenevaGenevaSwitzerland
| | - Jonathan Bisson
- Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at ChicagoChicagoUnited States
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at ChicagoChicagoUnited States
| | - Pierre-Marie Allard
- School of Pharmaceutical Sciences, University of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of GenevaGenevaSwitzerland
- Department of Biology, University of FribourgFribourgSwitzerland
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23
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Jablonka KM, Patiny L, Smit B. Making the collective knowledge of chemistry open and machine actionable. Nat Chem 2022; 14:365-376. [PMID: 35379967 DOI: 10.1038/s41557-022-00910-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 02/10/2022] [Indexed: 11/09/2022]
Abstract
Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.
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Affiliation(s)
- Kevin Maik Jablonka
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland
| | - Luc Patiny
- Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland.
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24
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Pinto BN, Moura GA, Demuner AJ, Alvarenga ES. Structural elucidation of a novel pyrrolizidine alkaloid isolated from Crotalaria retusa L. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.132394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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25
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Achanta PS, Niemitz M, Friesen JB, Tadjimukhamedov FK, Bzhelyansky A, Giancaspro GI, Chen SN, Pauli GF. Pharmaceutical analysis by NMR can accommodate strict impurity thresholds: The case of choline. J Pharm Biomed Anal 2022; 214:114709. [PMID: 35339885 DOI: 10.1016/j.jpba.2022.114709] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/23/2022] [Accepted: 03/03/2022] [Indexed: 11/19/2022]
Abstract
The ICH guidelines recommend reporting thresholds for regular impurities in drug substances at the level of 0.05% or 0.03% (w/w) depending on the maximum daily intake. Therefore, any instrumental method of analysis applicable to the impurity analysis should be able to detect and quantify the analytes at those levels. This investigation was designed to verify the suitability of 1H NMR spectroscopy for the detection of impurities, as a first step in the process before attempting quantification. In order to minimize demand on equipment, this study employed a 400 MHz instrument for structural confirmation and signal assignments of choline (1) and O-(2-hydroxyethyl)choline (2), a known impurity. The limit of detection (LOD) of 2 in 10 mg of 1 was established as 0.01% on a 400 MHz instrument and 2% on a 60 MHz (benchtop) NMR spectrometer. Thus, impurities for which quantification is required are readily detected at 400 MHz or above. These results are in contrast to the widespread belief that 1H NMR sensitivity is insufficient for pharmaceutical impurity analysis. The choice of solvent was recognized as a critical parameter for 1H NMR LOD analysis. Furthermore, publicly available NMR raw data (HMDB) proved to be valuable for unveiling the otherwise cryptic information hidden in complex signal patterns via 1H NMR iterative Full Spin Analysis. Finally, the study uncovered the less noticed, yet characteristic, 14N-1H coupling in the -N+(CH3)3 groups, adding strong arguments for the Raw NMR Data Initiative. Collectively, the data prove that the analytical capabilities of high-field NMR easily fulfill the ICH requirements for detection of impurity in the presence of an actual substance of interest which makes it a step closer to achieving regulatory standards.
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Affiliation(s)
- Prabhakar S Achanta
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA; Pharmacognosy Institute, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Matthias Niemitz
- NMR Solutions Limited, Tulliportinkatu 3 A 8, 70100 Kuopio, Finland
| | - J Brent Friesen
- Pharmacognosy Institute, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA; Center for Natural Product Technologies (CENAPT), College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA; Physical Sciences Department, Rosary College of Arts and Sciences, Dominican University, 7900 West Division Street, River Forest, IL 60305, USA
| | | | - Anton Bzhelyansky
- The United States Pharmacopeial Convention, 12601 Twinbrook Pkwy, Rockville, MD 20852, USA
| | - Gabriel I Giancaspro
- The United States Pharmacopeial Convention, 12601 Twinbrook Pkwy, Rockville, MD 20852, USA
| | - Shao-Nong Chen
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA; Pharmacognosy Institute, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA; Center for Natural Product Technologies (CENAPT), College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Guido F Pauli
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA; Pharmacognosy Institute, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA; Center for Natural Product Technologies (CENAPT), College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA.
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26
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Proteau PJ. Journal of Natural Products 2022: Perspectives, Monthly Cover Art, and More. JOURNAL OF NATURAL PRODUCTS 2022; 85:1-2. [PMID: 35086338 DOI: 10.1021/acs.jnatprod.2c00017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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27
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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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Wishart DS, Sayeeda Z, Budinski Z, Guo A, Lee BL, Berjanskii M, Rout M, Peters H, Dizon R, Mah R, Torres-Calzada C, Hiebert-Giesbrecht M, Varshavi D, Varshavi D, Oler E, Allen D, Cao X, Gautam V, Maras A, Poynton EF, Tavangar P, Yang V, van Santen JA, Ghosh R, Sarma S, Knutson E, Sullivan V, Jystad AM, Renslow R, Sumner LW, Linington RG, Cort JR. NP-MRD: the Natural Products Magnetic Resonance Database. Nucleic Acids Res 2021; 50:D665-D677. [PMID: 34791429 PMCID: PMC8728158 DOI: 10.1093/nar/gkab1052] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 11/15/2022] Open
Abstract
The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.,Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada.,Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - Zinat Sayeeda
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Zachary Budinski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - AnChi Guo
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Raynard Dizon
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Robert Mah
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | | | | | - Dorna Varshavi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorsa Varshavi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dana Allen
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Xuan Cao
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Andrew Maras
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Ella F Poynton
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Pegah Tavangar
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Vera Yang
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | | | - Rajarshi Ghosh
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.,MU Metabolomics Center, University of Missouri, Columbia, MO 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Saurav Sarma
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.,MU Metabolomics Center, University of Missouri, Columbia, MO 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Eleanor Knutson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Victoria Sullivan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Amy M Jystad
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Ryan Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Lloyd W Sumner
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.,MU Metabolomics Center, University of Missouri, Columbia, MO 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - John R Cort
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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Jarmusch SA, van der Hooft JJJ, Dorrestein PC, Jarmusch AK. Advancements in capturing and mining mass spectrometry data are transforming natural products research. Nat Prod Rep 2021; 38:2066-2082. [PMID: 34612288 PMCID: PMC8667781 DOI: 10.1039/d1np00040c] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: 2016 up to 2021Mass spectrometry (MS) is an essential technology in natural products research with MS fragmentation (MS/MS) approaches becoming a key tool. Recent advancements in MS yield dense metabolomics datasets which have been, conventionally, used by individual labs for individual projects; however, a shift is brewing. The movement towards open MS data (and other structural characterization data) and accessible data mining tools is emerging in natural products research. Over the past 5 years, this movement has rapidly expanded and evolved with no slowdown in sight; the capabilities of today vastly exceed those of 5 years ago. Herein, we address the analysis of individual datasets, a situation we are calling the '2021 status quo', and the emergent framework to systematically capture sample information (metadata) and perform repository-scale analyses. We evaluate public data deposition, discuss the challenges of working in the repository scale, highlight the challenges of metadata capture and provide illustrative examples of the power of utilizing repository data and the tools that enable it. We conclude that the advancements in MS data collection must be met with advancements in how we utilize data; therefore, we argue that open data and data mining is the next evolution in obtaining the maximum potential in natural products research.
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Affiliation(s)
- Scott A Jarmusch
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads 221, DK-2800 Kongens Lyngby, Denmark.
| | | | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093-0751, USA
| | - Alan K Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093-0751, USA
- Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA
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Cech NB, Medema MH, Clardy J. Benefiting from big data in natural products: importance of preserving foundational skills and prioritizing data quality. Nat Prod Rep 2021; 38:1947-1953. [PMID: 34734219 PMCID: PMC8597707 DOI: 10.1039/d1np00061f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Indexed: 12/02/2022]
Abstract
Systematic, large-scale, studies at the genomic, metabolomic, and functional level have transformed the natural product sciences. Improvements in technology and reduction in cost for obtaining spectroscopic, chromatographic, and genomic data coupled with the creation of readily accessible curated and functionally annotated data sets have altered the practices of virtually all natural product research laboratories. Gone are the days when the natural products researchers were expected to devote themselves exclusively to the isolation, purification, and structure elucidation of small molecules. We now also engage with big data in taxonomic, genomic, proteomic, and/or metabolomic collections, and use these data to generate and test hypotheses. While the oft stated aim for the use of large-scale -omics data in the natural products sciences is to achieve a rapid increase in the rate of discovery of new drugs, this has not yet come to pass. At the same time, new technologies have provided unexpected opportunities for natural products chemists to ask and answer new and different questions. With this viewpoint, we discuss the evolution of big data as a part of natural products research and provide a few examples of how discoveries have been enabled by access to big data. We also draw attention to some of the limitations in our existing engagement with large datasets and consider what would be necessary to overcome them.
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Affiliation(s)
- Nadja B Cech
- Chemistry, University of North Carolina Greensboro, USA.
| | | | - Jon Clardy
- Biological Chemistry and Molecular Pharmacology, Harvard Medical School, USA.
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31
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Beniddir MA, Kang KB, Genta-Jouve G, Huber F, Rogers S, van der Hooft JJJ. Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches. Nat Prod Rep 2021; 38:1967-1993. [PMID: 34821250 PMCID: PMC8597898 DOI: 10.1039/d1np00023c] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Indexed: 12/13/2022]
Abstract
Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.
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Affiliation(s)
- Mehdi A Beniddir
- Université Paris-Saclay, CNRS, BioCIS, 5 rue J.-B Clément, 92290 Châtenay-Malabry, France
| | - Kyo Bin Kang
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Grégory Genta-Jouve
- Laboratoire de Chimie-Toxicologie Analytique et Cellulaire (C-TAC), UMR CNRS 8038, CiTCoM, Université de Paris, 4, Avenue de l'Observatoire, 75006, Paris, France
- Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEEISA), USR 3456, Université De Guyane, CNRS Guyane, 275 Route de Montabo, 97334 Cayenne, French Guiana, France
| | - Florian Huber
- Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
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32
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Molecular models of three ω-3 fatty acids based on NMR and DFT calculations of 1H NMR chemical shifts. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117460] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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33
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Guan Y, Shree Sowndarya SV, Gallegos LC, St John PC, Paton RS. Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network. Chem Sci 2021; 12:12012-12026. [PMID: 34667567 PMCID: PMC8457395 DOI: 10.1039/d1sc03343c] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 07/19/2021] [Indexed: 11/23/2022] Open
Abstract
Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The in silico prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1H and 13C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed 13C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution. From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.![]()
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Affiliation(s)
- Yanfei Guan
- Department of Chemistry, Colorado State University Fort Collins CO 80523 USA
| | - S V Shree Sowndarya
- Department of Chemistry, Colorado State University Fort Collins CO 80523 USA
| | - Liliana C Gallegos
- Department of Chemistry, Colorado State University Fort Collins CO 80523 USA
| | - Peter C St John
- Biosciences Center, National Renewable Energy Laboratory Golden CO 80401 USA
| | - Robert S Paton
- Department of Chemistry, Colorado State University Fort Collins CO 80523 USA
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Honrao C, Teissier N, Zhang B, Powers R, O’Day EM. Gadolinium-Based Paramagnetic Relaxation Enhancement Agent Enhances Sensitivity for NUS Multidimensional NMR-Based Metabolomics. Molecules 2021; 26:molecules26175115. [PMID: 34500549 PMCID: PMC8433644 DOI: 10.3390/molecules26175115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/16/2021] [Accepted: 08/20/2021] [Indexed: 01/03/2023] Open
Abstract
Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics.
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Affiliation(s)
| | | | - Bo Zhang
- Olaris, Inc., Waltham, MA 02451, USA; (C.H.); (N.T.); (B.Z.)
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
- Correspondence: (R.P.); (E.M.O.)
| | - Elizabeth M. O’Day
- Olaris, Inc., Waltham, MA 02451, USA; (C.H.); (N.T.); (B.Z.)
- Correspondence: (R.P.); (E.M.O.)
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35
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Wang X, Nie XB, Grossman RB, Ji TF, Yang XW. Structural Revision of Hyperibrin B and Hyperscabrones H and I by Biosynthetic Considerations, NMR Analysis, and Chemical Synthesis. JOURNAL OF NATURAL PRODUCTS 2021; 84:2059-2064. [PMID: 34236871 DOI: 10.1021/acs.jnatprod.1c00458] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Previously, Gao et al. reported the isolation and structural determination of three natural products, hyperibrin B (HB), hyperscabrone H (HH), and hyperscabrone I (HI), from Hypericum scabrum. HB and HH had different NMR spectroscopic data, but they were assigned identical structures. Furthermore, these compounds should be derived from bicyclic polyprenylated acylphloroglucinols (BPAPs) via degradation, but the assigned structural features of the prenyl and prenylmethyl groups being cis and meta-substituted on the cyclohexanone core were not consistent with their biosynthetic origin. In this note, we revise the structures of HB, HH, and HI via NMR and MS spectroscopic analyses and biosynthetic considerations. We also complete a total synthesis of the revised structure of HB as well as its analogue, hyperibrin A, to further confirm the revision. The revised structures of HB, HH, and HI have not been reported.
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Affiliation(s)
- Xiaohui Wang
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
- College of Agronomy, Sichuan Agricultural University, Chengdu, Sichuan 611130, People's Republic of China
| | - Xiao-Bin Nie
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
- College of Agronomy, Sichuan Agricultural University, Chengdu, Sichuan 611130, People's Republic of China
| | - Robert B Grossman
- Department of Chemistry, University of Kentucky, Lexington, Kentucky 40506-0055, United States
| | - Teng-Fei Ji
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, People's Republic of China
| | - Xing-Wei Yang
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
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Elyashberg M, Argyropoulos D. Computer Assisted Structure Elucidation (CASE): Current and future perspectives. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2021; 59:669-690. [PMID: 33197069 DOI: 10.1002/mrc.5115] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/31/2020] [Accepted: 11/08/2020] [Indexed: 06/11/2023]
Abstract
The first efforts for the development of methods for Computer-Assisted Structure Elucidation (CASE) were published more than 50 years ago. CASE expert systems based on one-dimensional (1D) and two-dimensional (2D) Nuclear Magnetic Resonance (NMR) data have matured considerably by now. The structures of a great number of complex natural products have been elucidated and/or revised using such programs. In this article, we discuss the most likely directions in which CASE will evolve. We act on the premise that a synergistic interaction exists between CASE, new NMR experiments, and methods of computational chemistry, which are continuously being improved. The new developments in NMR experiments (long-range correlation experiments, pure-shift methods, coupling constants measurement and prediction, residual dipolar couplings [RDCs]), and residual chemical shift anisotropies [RCSAs], evolution of density functional theory (DFT), and machine learning algorithms will have an influence on CASE systems and vice versa. This is true also for new techniques for chemical analysis (Atomic Force Microscopy [AFM], "crystalline sponge" X-ray analysis, and micro-Electron Diffraction [micro-ED]), which will be used in combination with expert systems. We foresee that CASE will be utilized widely and become a routine tool for NMR spectroscopists and analysts in academic and industrial laboratories. We believe that the "golden age" of CASE is still in the future.
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Bragagnolo FS, Funari CS, Ibáñez E, Cifuentes A. Metabolomics as a Tool to Study Underused Soy Parts: In Search of Bioactive Compounds. Foods 2021; 10:foods10061308. [PMID: 34200265 PMCID: PMC8230045 DOI: 10.3390/foods10061308] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/01/2021] [Accepted: 06/03/2021] [Indexed: 12/19/2022] Open
Abstract
The valorization of agri-food by-products is essential from both economic and sustainability perspectives. The large quantity of such materials causes problems for the environment; however, they can also generate new valuable ingredients and products which promote beneficial effects on human health. It is estimated that soybean production, the major oilseed crop worldwide, will leave about 597 million metric tons of branches, leaves, pods, and roots on the ground post-harvesting in 2020/21. An alternative for the use of soy-related by-products arises from the several bioactive compounds found in this plant. Metabolomics studies have already identified isoflavonoids, saponins, and organic and fatty acids, among other metabolites, in all soy organs. The present review aims to show the application of metabolomics for identifying high-added-value compounds in underused parts of the soy plant, listing the main bioactive metabolites identified up to now, as well as the factors affecting their production.
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Affiliation(s)
- Felipe Sanchez Bragagnolo
- School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (F.S.B.); (C.S.F.)
- Laboratory of Foodomics, Institute of Food Science Research (CIAL-CSIC), 28049 Madrid, Spain;
| | - Cristiano Soleo Funari
- School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (F.S.B.); (C.S.F.)
| | - Elena Ibáñez
- Laboratory of Foodomics, Institute of Food Science Research (CIAL-CSIC), 28049 Madrid, Spain;
| | - Alejandro Cifuentes
- Laboratory of Foodomics, Institute of Food Science Research (CIAL-CSIC), 28049 Madrid, Spain;
- Correspondence:
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The Advantage of Automatic Peer-Reviewing of 13C-NMR Reference Data Using the CSEARCH-Protocol. Molecules 2021; 26:molecules26113413. [PMID: 34200052 PMCID: PMC8200238 DOI: 10.3390/molecules26113413] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/25/2021] [Accepted: 05/31/2021] [Indexed: 11/23/2022] Open
Abstract
A systematic investigation of the experimental 13C-NMR spectra published in Molecules during the period of 1996 to 2015 with respect to their quality using CSEARCH-technology is described. It is shown that the systematic application of the CSEARCH-Robot-Referee during the peer-reviewing process prohibits at least the most trivial assignment errors and wrong structure proposals. In many cases, the correction of the assignments/chemical shift values is possible by manual inspection of the published tables; in certain cases, reprocessing of the original experimental data might help to clarify the situation, showing the urgent need for a public domain repository. A comparison of the significant key numbers derived for Molecules against those of other important journals in the field of natural product chemistry shows a quite similar level of quality for all publishers responsible for the six journals under investigation. From the results of this study, general rules for data handling, data storage, and manuscript preparation can be derived, helping to increase the quality of published NMR-data and making these data available as validated reference material.
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Borges R, Colby SM, Das S, Edison AS, Fiehn O, Kind T, Lee J, Merrill AT, Merz KM, Metz TO, Nunez JR, Tantillo DJ, Wang LP, Wang S, Renslow RS. Quantum Chemistry Calculations for Metabolomics. Chem Rev 2021; 121:5633-5670. [PMID: 33979149 PMCID: PMC8161423 DOI: 10.1021/acs.chemrev.0c00901] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Indexed: 02/07/2023]
Abstract
A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.
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Affiliation(s)
- Ricardo
M. Borges
- Walter
Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
| | - Sean M. Colby
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Susanta Das
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Arthur S. Edison
- Departments
of Genetics and Biochemistry and Molecular Biology, Complex Carbohydrate
Research Center and Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
| | - Oliver Fiehn
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Tobias Kind
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Jesi Lee
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Amy T. Merrill
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Thomas O. Metz
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nunez
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Dean J. Tantillo
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Lee-Ping Wang
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Shunyang Wang
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Ryan S. Renslow
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
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40
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Nazarski RB. Summary of DFT calculations coupled with current statistical and/or artificial neural network (ANN) methods to assist experimental NMR data in identifying diastereomeric structures. Tetrahedron Lett 2021. [DOI: 10.1016/j.tetlet.2020.152548] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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41
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Novitskiy IM, Holt TA, Kutateladze AG. Structure revision of ent-kaurane diterpenoids, isoserrins A, B, and D, enabled by DU8+ computation of their NMR spectral data. MENDELEEV COMMUNICATIONS 2021. [DOI: 10.1016/j.mencom.2021.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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42
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Burns DC, Reynolds WF. Minimizing the risk of deducing wrong natural product structures from NMR data. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2021; 59:500-533. [PMID: 33855734 DOI: 10.1002/mrc.4933] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 07/31/2019] [Accepted: 08/01/2019] [Indexed: 06/12/2023]
Abstract
There continues to be a disturbing number of natural products reported in the literature whose structures are incorrect. At least in part, this reflects the fact that many natural product chemists have limited formal nuclear magnetic resonance training. Gaps in training and lack of awareness regarding the challenges and ambiguities associated with two-dimensional nuclear magnetic resonance data interpretation can easily lead to errors in structure elucidation. The purpose of this tutorial is to point out some of these issues, highlight the kinds of errors that have been made and provide specific advice on how to avoid these missteps such that the risk of reporting a wrong structure is minimized.
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Affiliation(s)
- Darcy C Burns
- Department of Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - William F Reynolds
- Department of Chemistry, University of Toronto, Toronto, Ontario, Canada
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43
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Novitskiy IM, Holt TA, Kutateladze AG. Structure revision of ent-kaurane diterpenoids, isoserrins A, B, and D, enabled by DU8+ computation of their NMR spectral data. MENDELEEV COMMUNICATIONS 2021. [DOI: 10.1016/j.mencom.2021.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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44
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Egan JM, van Santen JA, Liu DY, Linington RG. Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures. JOURNAL OF NATURAL PRODUCTS 2021; 84:1044-1055. [PMID: 33750122 PMCID: PMC8330833 DOI: 10.1021/acs.jnatprod.0c01076] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.
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Affiliation(s)
- Joseph M Egan
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada
| | - Jeffrey A van Santen
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada
| | - Dennis Y Liu
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada
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45
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Liu M, Han J, Feng Y, Guymer G, Forster PI, Quinn RJ. Antimicrobial Benzyltetrahydroisoquinoline-Derived Alkaloids from the Leaves of Doryphora aromatica. JOURNAL OF NATURAL PRODUCTS 2021; 84:676-682. [PMID: 33667101 PMCID: PMC8008450 DOI: 10.1021/acs.jnatprod.0c01093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Indexed: 06/12/2023]
Abstract
Four new alkaloids, (R)-nomimantharine trifluoroacetate (2), 12-demethylphaeantharine trifluoroacetate (3), nominanthranal trifluoroacetate (4), and the enolic form of 1-hydroxy-6,7-dimethoxy-2-methylisoquinoline trifluoroacetate (5), together with the known dimeric alkaloid phaeantharine trifluoroacetate (1), have been isolated from the extract of the leaves of the rainforest tree Doryphora aromatica (Monimiaceae). The structures of these compounds were elucidated by HRMS and 1D and 2D NMR data. (R)-Nomimantharine trifluoroacetate (2) contains an ether linkage connecting a benzylisoquinoline unit with a tetrahydroisoquinoline, a novel class of dimeric alkaloid. The absolute configuration of (R)-nomimantharine trifluoroacetate (2) was established via electronic circular dichroism data. The compounds isolated were subjected to in vitro antimicrobial assays against a panel of pathogenic microorganisms, including Mycobacterium smegmatis, M. tuberculosis, Escherichia coli, Staphylococcus aureus (SA), and five clinical isolates of oxacillin/methicillin-resistant S. aureus (MRSA). Phaeantharine trifluoroacetate (1) and (R)-nomimantharine trifluoroacetate (2) showed moderate inhibitory activities against Mycobacteria and MRSA strains.
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Affiliation(s)
- Miaomiao Liu
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia
| | - Jianying Han
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia
| | - Yunjiang Feng
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia
| | - Gordon Guymer
- Queensland
Herbarium, Department of Environment and Science, Brisbane
Botanic Gardens, Brisbane, QLD 4066, Australia
| | - Paul I. Forster
- Queensland
Herbarium, Department of Environment and Science, Brisbane
Botanic Gardens, Brisbane, QLD 4066, Australia
| | - Ronald J. Quinn
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia
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Porras G, Chassagne F, Lyles JT, Marquez L, Dettweiler M, Salam AM, Samarakoon T, Shabih S, Farrokhi DR, Quave CL. Ethnobotany and the Role of Plant Natural Products in Antibiotic Drug Discovery. Chem Rev 2021; 121:3495-3560. [PMID: 33164487 PMCID: PMC8183567 DOI: 10.1021/acs.chemrev.0c00922] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The crisis of antibiotic resistance necessitates creative and innovative approaches, from chemical identification and analysis to the assessment of bioactivity. Plant natural products (NPs) represent a promising source of antibacterial lead compounds that could help fill the drug discovery pipeline in response to the growing antibiotic resistance crisis. The major strength of plant NPs lies in their rich and unique chemodiversity, their worldwide distribution and ease of access, their various antibacterial modes of action, and the proven clinical effectiveness of plant extracts from which they are isolated. While many studies have tried to summarize NPs with antibacterial activities, a comprehensive review with rigorous selection criteria has never been performed. In this work, the literature from 2012 to 2019 was systematically reviewed to highlight plant-derived compounds with antibacterial activity by focusing on their growth inhibitory activity. A total of 459 compounds are included in this Review, of which 50.8% are phenolic derivatives, 26.6% are terpenoids, 5.7% are alkaloids, and 17% are classified as other metabolites. A selection of 183 compounds is further discussed regarding their antibacterial activity, biosynthesis, structure-activity relationship, mechanism of action, and potential as antibiotics. Emerging trends in the field of antibacterial drug discovery from plants are also discussed. This Review brings to the forefront key findings on the antibacterial potential of plant NPs for consideration in future antibiotic discovery and development efforts.
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Affiliation(s)
- Gina Porras
- Center for the Study of Human Health, Emory University, 1557 Dickey Dr., Atlanta, Georgia 30322
| | - François Chassagne
- Center for the Study of Human Health, Emory University, 1557 Dickey Dr., Atlanta, Georgia 30322
| | - James T. Lyles
- Center for the Study of Human Health, Emory University, 1557 Dickey Dr., Atlanta, Georgia 30322
| | - Lewis Marquez
- Molecular and Systems Pharmacology Program, Laney Graduate School, Emory University, 615 Michael St., Whitehead 115, Atlanta, Georgia 30322
| | - Micah Dettweiler
- Department of Dermatology, Emory University, 615 Michael St., Whitehead 105L, Atlanta, Georgia 30322
| | - Akram M. Salam
- Molecular and Systems Pharmacology Program, Laney Graduate School, Emory University, 615 Michael St., Whitehead 115, Atlanta, Georgia 30322
| | - Tharanga Samarakoon
- Emory University Herbarium, Emory University, 1462 Clifton Rd NE, Room 102, Atlanta, Georgia 30322
| | - Sarah Shabih
- Center for the Study of Human Health, Emory University, 1557 Dickey Dr., Atlanta, Georgia 30322
| | - Darya Raschid Farrokhi
- Center for the Study of Human Health, Emory University, 1557 Dickey Dr., Atlanta, Georgia 30322
| | - Cassandra L. Quave
- Center for the Study of Human Health, Emory University, 1557 Dickey Dr., Atlanta, Georgia 30322
- Emory University Herbarium, Emory University, 1462 Clifton Rd NE, Room 102, Atlanta, Georgia 30322
- Department of Dermatology, Emory University, 615 Michael St., Whitehead 105L, Atlanta, Georgia 30322
- Molecular and Systems Pharmacology Program, Laney Graduate School, Emory University, 615 Michael St., Whitehead 115, Atlanta, Georgia 30322
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47
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Cox N, Millard P, Charlier C, Lippens G. Improved NMR Detection of Phospho-Metabolites in a Complex Mixture. Anal Chem 2021; 93:4818-4824. [PMID: 33711235 DOI: 10.1021/acs.analchem.0c04056] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Phosphorylated metabolites are omnipresent in cells, but their analytical characterization faces several technical hurdles. Here, we detail an improved NMR workflow aimed at assigning the high-resolution subspectrum of the phospho-metabolites in a complex mixture. Combining a pure absorption J-resolved spectrum (Pell, A. J.; J. Magn. Reson. 2007, 189 (2), 293-299) with alternate on- and off-switching of the 31P coupling interaction during the t1 evolution with a pure in-phase (PIP) HSQMBC experiment (Castañar, L.; Angew. Chem., Int. Ed. 2014, 53 (32), 8379-8382) without or with total correlation spectroscopy (TOCSY) transfer during the insensitive nuclei enhancement by polarization transfer (INEPT) gives access to selective identification of the individual subspectra of the phosphorylated metabolites. Returning to the initial J-res spectra, we can extract with optimal resolution the full trace for the individual phospho-metabolites, which can then be transposed on the high-resolution quantitative one dimensional spectrum.
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Affiliation(s)
- Neil Cox
- TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
| | - Pierre Millard
- TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
| | - Cyril Charlier
- TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
| | - Guy Lippens
- TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
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48
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van Santen JA, Kautsar SA, Medema MH, Linington RG. Microbial natural product databases: moving forward in the multi-omics era. Nat Prod Rep 2021; 38:264-278. [PMID: 32856641 PMCID: PMC7864863 DOI: 10.1039/d0np00053a] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Covering: 2010-2020The digital revolution is driving significant changes in how people store, distribute, and use information. With the advent of new technologies around linked data, machine learning and large-scale network inference, the natural products research field is beginning to embrace real-time sharing and large-scale analysis of digitized experimental data. Databases play a key role in this, as they allow systematic annotation and storage of data for both basic and advanced applications. The quality of the content, structure, and accessibility of these databases all contribute to their usefulness for the scientific community in practice. This review covers the development of databases relevant for microbial natural product discovery during the past decade (2010-2020), including repositories of chemical structures/properties, metabolomics, and genomic data (biosynthetic gene clusters). It provides an overview of the most important databases and their functionalities, highlights some early meta-analyses using such databases, and discusses basic principles to enable widespread interoperability between databases. Furthermore, it points out conceptual and practical challenges in the curation and usage of natural products databases. Finally, the review closes with a discussion of key action points required for the field moving forward, not only for database developers but for any scientist active in the field.
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49
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Nelson KM, Bisson J, Singh G, Graham JG, Chen SN, Friesen JB, Dahlin JL, Niemitz M, Walters MA, Pauli GF. The Essential Medicinal Chemistry of Cannabidiol (CBD). J Med Chem 2020; 63:12137-12155. [PMID: 32804502 PMCID: PMC7666069 DOI: 10.1021/acs.jmedchem.0c00724] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This Perspective of the published essential medicinal chemistry of cannabidiol (CBD) provides evidence that the popularization of CBD-fortified or CBD-labeled health products and CBD-associated health claims lacks a rigorous scientific foundation. CBD's reputation as a cure-all puts it in the same class as other "natural" panaceas, where valid ethnobotanicals are reduced to single, purportedly active ingredients. Such reductionist approaches oversimplify useful, chemically complex mixtures in an attempt to rationalize the commercial utility of natural compounds and exploit the "natural" label. Literature evidence associates CBD with certain semiubiquitous, broadly screened, primarily plant-based substances of undocumented purity that interfere with bioassays and have a low likelihood of becoming therapeutic agents. Widespread health challenges and pandemic crises such as SARS-CoV-2 create circumstances under which scientists must be particularly vigilant about healing claims that lack solid foundational data. Herein, we offer a critical review of the published medicinal chemistry properties of CBD, as well as precise definitions of CBD-containing substances and products, distilled to reveal the essential factors that impact its development as a therapeutic agent.
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Affiliation(s)
- Kathryn M. Nelson
- Department of Medicinal Chemistry, Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, Minnesota 55414, United States
| | - Jonathan Bisson
- Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), and Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - Gurpreet Singh
- Department of Medicinal Chemistry, Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, Minnesota 55414, United States
| | - James G. Graham
- Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), and Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - Shao-Nong Chen
- Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), and Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - J. Brent Friesen
- Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), and Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - Jayme L. Dahlin
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts 02115, United States
- Harvard Medical School, Boston, Massachusetts 02115, United States
| | | | - Michael A. Walters
- Department of Medicinal Chemistry, Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, Minnesota 55414, United States
| | - Guido F. Pauli
- Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), and Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
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50
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Jia W, Yang Z, Yang M, Cheng L, Lei Z, Wang X. Machine Learning Enhanced Spectrum Recognition Based on Computer Vision (SRCV) for Intelligent NMR Data Extraction. J Chem Inf Model 2020; 61:21-25. [PMID: 33170690 DOI: 10.1021/acs.jcim.0c01046] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A machine learning enhanced spectrum recognition system called spectrum recognition based on computer vision (SRCV) for data extraction from previously analyzed 13C and 1H NMR spectra has been developed. The intelligent system was designed with four function modules to extract data from three areas of NMR images, including 13C and 1H chemical shifts, the integral, and the range of the shift values. During this study, three machine learning models were pretrained for number recognition, which is the key procedure for NMR data extraction. The k nearest neighbor (kNN) method was selected with optimized k (k = 4), which displayed a 100% recognition rate. Subsequently, the performance of SRCV was tested and validated to have high accuracy with a short processing time (11-21 s) for each NMR spectral image. Our spectrum recognizer enables high-throughput 13C and 1H NMR data extraction from abundant spectra in the literature and has the potential to be used for spectral database construction. In addition, the system may be applicable to be developed for data import to computer-assisted structure elucidation systems, which would automate this procedure significantly. SRCV can be accessed in GitHub (https://github.com/WJmodels/SRCV).
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Affiliation(s)
- Wenqiang Jia
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Department of Medicinal Chemistry, Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Zhuo Yang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Department of Medicinal Chemistry, Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Minjian Yang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Department of Medicinal Chemistry, Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Liang Cheng
- Golden Intelligence Cloud Co., Ltd., Guangzhou 510000, China
| | - Zengrong Lei
- Guangzhou Fermion Technology Co., Ltd., Guangzhou 510000, China
| | - Xiaojian Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Department of Medicinal Chemistry, Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
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