Journal:SistematX, an online web-based cheminformatics tool for data management of secondary metabolites

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Full article title SistematX, an online web-based cheminformatics tool for data management of secondary metabolites
Journal Molecules
Author(s) Scotti, Marcus T.; Herrera-Acevedo, Chonny; Oliveira, Tiago B.; Costa, Renan P.O.; de Oliveira Santos, Silas Y.K.;
Rodrigues, Ricardo P.; Scotti, Luciana; Da-Costa, Fernando B.
Author affiliation(s) Federal University of Paraíba, Federal University of Sergipe, University of São Paulo
Primary contact Phone: +55-83-99869-0415
Year published 2018
Volume and issue 23(1)
Page(s) 103
DOI 10.3390/molecules23010103
ISSN 1420-3049
Distribution license Creative Commons Attribution 4.0 International
Website http://www.mdpi.com/1420-3049/23/1/103/htm
Download http://www.mdpi.com/1420-3049/23/1/103/pdf (PDF)

Abstract

The traditional work of a natural products researcher consists in large part of time-consuming experimental work, collecting biota to prepare, extracts to analyze, and innovative metabolites to identify. However, along this long scientific path, much information is lost or restricted to a specific niche. The large amounts of data already produced and the science of metabolomics reveal new questions: Are these compounds known or new? How fast can this information be obtained? To answer these and other relevant questions, an appropriate procedure to correctly store information on the data retrieved from the discovered metabolites is necessary. The SistematX (http://sistematx.ufpb.br) interface is implemented considering the following aspects: (a) the ability to search by structure, SMILES (Simplified Molecular-Input Line-Entry System) code, compound name, and species; (b) the ability to save chemical structures found by searching; (c) the ability to display compound data results, including important characteristics for natural products chemistry; and (d) the user's ability to find specific information for taxonomic rank (from family to species) of the plant from which the compound was isolated, the searched-for molecule, and the bibliographic reference and Global Positioning System (GPS) coordinates. The SistematX homepage allows the user to log into the data management area using a login name and password and gain access to administration pages. In this article, we introduce a modern and innovative web interface for the management of a secondary metabolite database. With its multi-platform design, it is able to be properly consulted via the internet and managed from any accredited computer. The interface provided by SistematX contains a wealth of useful information for the scientific community about natural products, highlighting the locations of species from which compounds are isolated.

Keywords: SistematX, secondary metabolites, data management, online web-based tool

Introduction

The traditional work of a natural products researcher can be summarized as the collection of biological samples, preparation of extracts for biological screening or bioassay-guided fractionation, and isolation and purification of (bioactive or not) compounds. However, the first question that may arise is the following: are these compounds known or new? In addition, metabolomics studies have introduced a new question: how fast can this information be obtained?[1]

The stage of dereplication, a process known as the rapid characterization of previously known compounds in mixtures without their prior purification, has become a strategically important area for natural products research involved in screening programs in several commercial and non-commercial databases.[2][3][4] These databases can be searched with minimal information, such as structural chemical and biological data from compounds; however, dereplication now requires additional information, such as biogeographical and taxonomic information, or the presence of a certain compound (new or known) in other individuals of the same species, genus, subfamily, and family. This information can also help to reduce the number of hits during chemical identification by dereplication.

Large structure-based data collections, such as ChemSpider[5], PubChem[6], ChEBI[7], and ZINC[8] can be used for this purpose.[9][10] However, these databases are not specialized in secondary metabolite information that is valuable to the natural products researchers, for example, botanical occurrence and geographical localization. For this reason, a number of specialized natural products databases were developed that are commercially or freely available and only contain restricted information, for example, the Dictionary of Natural Products (DNP)[11], NAPRALERT[12], Marinlit for marine natural products[13], and Antibase for microorganisms and higher fungi materials. Nevertheless, none of these provide structural collections in a format that can be rapidly integrated into software such as ACD/Structure Elucidator and others.[9]

Other natural products databases provide natural products extracted from various resources and contain various associated information such as toxicity prediction, but so far, little or nothing is known about these resources, for example, SUPER NATURAL II.[14] Natural products databases exhibit a huge range of structural complexity and thus are expected to contribute to the ability of such databases to provide positive hits.[2][15] These structures are available in regional databases, for example NeBBEDB[16], SANCDB[17], TM-CM[18], TCM-Database@Taiwan[19], NANPDB[20], and TCMID.[21] Many have been used in virtual screening research studies. In addition to the database information described above that uses two-dimensional (2D) structures, several databases have selected methods and tools for generating three-dimensional (3D) structures of small organic molecules, often for use in structure-based drug design.

In addition, databases of natural products with a focus on metabolomic studies with relationships between species-metabolites include the KNApSAcK Family[22], TIPdb-3D[23], and AsterDB[24], which enable searches for chemical structures by plant species names and other taxonomic information. Nevertheless, some data are still lacking for the purpose of exact dereplication. Information such as exact mass and geographic data can be very important for this type of study.[25][26][27]

It is not enough simply to focus on the information contained in a database. A clean and user-friendly interface, fast search, and consistency between currently available operating systems (Microsoft Windows, Mac, and Linux) can be just as important. For this purpose, the SistematX software was developed to provide the abovementioned information for chemosystematics studies, dereplication, and botanical correlations.

Results and discussion

Utility and discussion

The SistematX homepage is shown in Figure 1A. After the user enters the website (http://sistematx.ufpb.br), the “Structure search” option is seen with the MarvinJS API (Application Programming Interface) at the top of the screen. Another three search options can be exhibited in the interface. The initial screen of the system also shows the SMILES (Simplified Molecular-Input Line-Entry System) code (Figure 1B), compound name (Figure 1C) and plant species search modes (Figure 1D).


Fig1 Scotti Molecules2018 23-1.png

Figure 1. SistematX homepage with different search options: (A) by structure; (B) by Simplified Molecular-Input Line-Entry System (SMILES); (C) by compound name; and (D) by plant species

In the first option, the user can perform the search using the drawn full structure or molecular skeleton, fragments, or substructures, which is important in cases when the user only knows a structural characteristic of the structure such as functional groups or when studies require structural similarity, structural groups, or families of compounds. It is possible to use the similarity search option that is currently available in SistematX. The search results page shows all compounds that correspond to the value above the cut off provided by the user in the decreasing order of similarity, showing the similarity values on the top. A substructure and similarity search is performed using a hashed fingerprint. Special molecular features are present in the query (e.g., stereochemistry, charge), only those targets match that also contain the feature. However, if a feature is missing from the query, it is not required to be missing.

In addition, it is possible to search by SMILES code, a chemical notation system capable of representing even the most complex organic compounds using a simple grammar that is very well known to organic chemistry researchers; for this reason we add this option separately from the structure search using the MarvinJS API, being friendly for one just to copy and paste the SMILES code (Figure 1B); by common (usual) name or IUPAC (International Union of Pure and Applied Chemistry) name (or part of one of these); and by species, although in this option, it is necessary to first insert the name of the genus (which presents an autocompletion option). After being selected, the system presents all species available for the user to select for the search.

When performing a search, the mechanism generates a search results page (six results per page), using common names; if the compound does not have one, it shows the IUPAC name (Figure 2). The user can set the number of structure results per page. When a result is selected, the user has access to the data for that molecule, which are classified into six different groups (Figure 3).


Fig2 Scotti Molecules2018 23-1.png

Figure 2. SistematX results page

Fig3 Scotti Molecules2018 23-1.png

Figure 3. SistematX screen for molecular data

References

  1. Blunt, J.W.; Munro, M.H.G. (2014). "22. Is There an Ideal Database for Natural Products Research?". In Osbourn, A.; Goss, R.J.; Carter, G.T.. Natural Products: Discourse, Diversity, and Design. John Wiley & Sons, Inc. pp. 413–431. doi:10.1002/9781118794623.ch22. ISBN 9781118794623. 
  2. 2.0 2.1 Harvey, A.L.; Edrada-Ebel, R.; Quinn, R.J. (2015). "The re-emergence of natural products for drug discovery in the genomics era". Nature Reviews Drug Discovery 14 (2): 111–29. doi:10.1038/nrd4510. PMID 25614221. 
  3. Corley, D.G.; Durley, R.C. (1994). "Strategies for Database Dereplication of Natural Products". Journal of Natural Products 57 (11): 1484–1490. doi:10.1021/np50113a002. 
  4. Oliveira, T.; Chagas-Paula, D.; Rosa, A. et al. (2013). "Temporal characteristics of a natural products in-house database". Planta Medica 79: 1113–1114. doi:10.1055/s-0033-1351852. 
  5. Pence, H.E.; Williams, A. (2010). "ChemSpider: An Online Chemical Information Resource". Journal of Chemical Education 87 (11): 1123–1124. doi:10.1021/ed100697w. 
  6. Bolton, E.E.; Wang, Y.; Thiessen, P.A.; Bryant, S.H. (2008). "Chapter 12 - PubChem: Integrated Platform of Small Molecules and Biological Activities". In Wheeler, R.A.; Spellmeyer, D.C.. Annual Reports in Computational Chemistry. 4. Elsevier Ltd. pp. 217-241. doi:10.1016/S1574-1400(08)00012-1. ISBN 9780444532503. 
  7. Degtyarenko, K.; de Matos, P.; Ennis, M. et al. (2008). "ChEBI: A database and ontology for chemical entities of biological interest". Nucleic Acids Research 36 (DB1): D344-50. doi:10.1093/nar/gkm791. PMC PMC2238832. PMID 17932057. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2238832. 
  8. Irwin, J.J.; Shoichet, B.K. (2005). "ZINC: A free database of commercially available compounds for virtual screening". Journal of Chemical Information and Modeling 45 (1): 177–82. doi:10.1021/ci049714+. PMC PMC1360656. PMID 15667143. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1360656. 
  9. 9.0 9.1 Williams, R.B.; O'Neil-Johnson, M.; Williams, A.J. et al. (2015). "Dereplication of natural products using minimal NMR data inputs". Organic & Biomolecular Chemistry 13 (39): 9957–62. doi:10.1039/c5ob01713k. PMID 26381222. 
  10. Eugster, P.J.; Boccard, J.; Debrus, B. et al. (2014). "Retention time prediction for dereplication of natural products (CxHyOz) in LC-MS metabolite profiling". Phytochemistry 108: 196–207. doi:10.1016/j.phytochem.2014.10.005. PMID 25457501. 
  11. "Dictionary of Natural Products". CRC Press. http://dnp.chemnetbase.com/. Retrieved 04 March 2017. 
  12. Graham, J.G.; Farnsworth, N.R. (2010). "The NAPRALERT database as an aid for discovery of novel bioactive compounds". Comprehensive Natural Products II: Chemistry and Biology. 3. Elsevier Ltd. pp. 81–94. ISBN 9780080453828. 
  13. Dabb, S.; Blunt, J.; Munro, M. (2014). "MarinLit: Database and essential tools for the marine natural products community". Proceedings of the 248th National Meeting of the American-Chemical-Society (ACS) 248. 
  14. Banerjee, P.; Erehman, J.; Gohlke, B.O. et al. (2015). "Super Natural II:A database of natural products". Nucleic Acids Research 43 (DB1): D935–9. doi:10.1093/nar/gku886. PMC PMC4384003. PMID 25300487. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4384003. 
  15. Drewry, D.H.; Macarron, R. (2010). "Enhancements of screening collections to address areas of unmet medical need: An industry perspective". Current Opinions in Chemical Biology 14 (3): 289–98. doi:10.1016/j.cbpa.2010.03.024. PMID 20413343. 
  16. Valli, M.; dos Santos, R.N.; Figueira, L.D. et al. (2013). "Development of a natural products database from the biodiversity of Brazil". Journal of Natural Products 76 (3): 439-44. doi:10.1021/np3006875. PMID 23330984. 
  17. Hatherley, R.; Brown, D.K.; Musyoka, T.M. et al. (2015). "SANCDB: A South African natural compound database". Journal of Cheminformatics 7: 29. doi:10.1186/s13321-015-0080-8. PMC PMC4471313. PMID 26097510. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4471313. 
  18. Kim, S.K.; Nam, S.; Jang, H. et al. (2015). "TM-MC: A database of medicinal materials and chemical compounds in Northeast Asian traditional medicine". BMC Complementary and Alternative Medicine 15: 218. doi:10.1186/s12906-015-0758-5. PMC PMC4495939. PMID 26156871. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495939. 
  19. Chen, C.Y. (2011). "TCM Database@Taiwan: The world's largest traditional Chinese medicine database for drug screening in silico". PLoS One 6 (1): e15939. doi:10.1371/journal.pone.0015939. PMC PMC3017089. PMID 21253603. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3017089. 
  20. Ntie-Kang, F.; Telukunta, K.K.; Döring, K. et al. (2017). "NANPDB: A Resource for Natural Products from Northern African Sources". Journal of Natural Products 80 (7): 2067-2076. doi:10.1021/acs.jnatprod.7b00283. PMID 28641017. 
  21. Xue, R.; Fang, Z.; Zhang, M. et al. (2013). "TCMID: Traditional Chinese Medicine integrative database for herb molecular mechanism analysis". Nucleic Acids Research 41 (DB1): D1089-95. doi:10.1093/nar/gks1100. PMC PMC3531123. PMID 23203875. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531123. 
  22. Afendi, F.M.; Okada, T.; Yamazaki, M. et al. (2012). "KNApSAcK family databases: Integrated metabolite-plant species databases for multifaceted plant research". Plsnt & Cell Physiology 53 (2): e1. doi:10.1093/pcp/pcr165. PMID 22123792. 
  23. Tung, C.W.; Lin, Y.C.; Chang, H.S. et al. (2014). "TIPdb-3D: The three-dimensional structure database of phytochemicals from Taiwan indigenous plants". Database 2014: bau055. doi:10.1093/database/bau055. PMC PMC4057645. PMID 24930145. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4057645. 
  24. "AsterDB". AsterBioChem. http://www.asterbiochem.org/asterdb. Retrieved 04 March 2017. 
  25. Sampaio, B.L.; Edrada-Ebel, R.; Da Costa, F.B. (2016). "Effect of the environment on the secondary metabolic profile of Tithonia diversifolia: A model for environmental metabolomics of plants". Scientific Reports 6: 29265. doi:10.1038/srep29265. PMC PMC4935878. PMID 27383265. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4935878. 
  26. Schmidt, T.J.; Rzeppa, S.; Kaiser, M.; Brun, R. (2012). "Larrea tridentata—Absolute configuration of its epoxylignans and investigations on its antiprotozoal activity". Phytochemistry Letters 5 (3): 632-638. doi:10.1016/j.phytol.2012.06.011. 
  27. Gaquerel, E.; Kuhl, C.; Neumann, S. (2013). "Computational annotation of plant metabolomics profiles via a novel network-assisted approach". Metabolomics 9 (4): 904–918. doi:10.1007/s11306-013-0504-2. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.