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Cited by 6Plant biochemistry and biosynthesisMicrobial Natural Products and BiosynthesisBiochemical and biochemical processes

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MARTS-DB: a database of mechanisms and reactions of terpene synthases

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Engst, Martin, Brokeš, Martin, Čalounová, Tereza, Samusevich, Raman, Bushuiev, Roman, Bushuiev, Anton, Chatpatanasiri, Ratthachat, Tajovská, Adéla, Akmeşe, Safa Mert, Perković, Milana, Soldát, Matouš, Sivic, Josef, Pluskal, Tomáš·2025·BMC Bioinformatics
DOI 10.1186/s12859-025-06341-8PMC PMC12797696

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Abstract

Background: Terpene synthases (TPSs) are enzymes that catalyze some of the most complex reactions in nature—the cyclizations of terpenes, which form the carbon backbones to the largest group of natural products, the terpenoids. On average, more than half of the carbon atoms in a terpene scaffold undergo a change in connectivity or configuration during these enzymatic cascades. Understanding TPS reaction mechanisms remains challenging, often requiring intricate computational modeling and isotopic labelling studies. Moreover, the relationship between TPS sequence and catalytic function is difficult to decipher, while data-driven approaches remain limited due to a lack of comprehensive, high-quality data sources. Main: We introduce the Mechanisms And Reactions of Terpene Synthases DataBase (MARTS-DB)—a manually curated, structured, and searchable database that integrates TPS enzymes, the terpenes they produce, and their detailed reaction mechanisms. MARTS-DB includes over 2850 reactions catalyzed by 1432 annotated enzymes from across all domains of life, with reaction mechanisms mapped as stepwise cascades for more than 500 terpenes. Accessible at https://www.marts-db.org , the database provides advanced search functionality and supports full dataset downloads in machine-readable formats. It also encourages community contributions to promote continuous growth. Conclusion: User-friendly and comprehensive, MARTS-DB enables the systematic exploration of TPS catalysis, opening new avenues for computational analysis and machine learning, as recently demonstrated in the prediction of novel TPSs.

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Fig. 1 Number of terpenes and terpene synthases by type and taxonomic kingdom. A Number of TPSs catalyzing the production of each terpene type, coloured according to the taxon of their source species. TPSs catalyzing reactions of multiple terpene types are counted in each relevant category. B Distribution of taxonomic kingdoms for all 1434 enzymes in MARTS-DB (data from November 2025). C Number of unique terpene products and unique carbon scaffolds of those products, grouped by terpene type
Fig. 1 Number of terpenes and terpene synthases by type and taxonomic kingdom. A Number of TPSs catalyzing the production of each terpene type, coloured according to the taxon of their source species. TPSs catalyzing reactions of multiple terpene types are counted in each relevant category. B Distribution of taxonomic kingdoms for all 1434 enzymes in MARTS-DB (data from November 2025). C Number of unique terpene products and unique carbon scaffolds of those products, grouped by terpene typeCC BY · PubMed Central
Fig. 2 The schematic structure of MARTS-DB: The core of the database are Enzymes, Molecules and Reaction mechanisms, which can all be found by their persistent identifier called MARTS ID. Enzymes catalyze Reactions, where Molecules serve as either products or substrates. Mechanisms are composed of steps, whereas molecules again serve as either “products” or “substrates” of those steps. Each Enzyme-Reaction pair can be assigned a specific Mechanism. All Reaction–Enzyme and Reaction-Enzyme-Mechanism associations in the database are supported by one or more references to the primary literature, shown here by a book icon
Fig. 2 The schematic structure of MARTS-DB: The core of the database are Enzymes, Molecules and Reaction mechanisms, which can all be found by their persistent identifier called MARTS ID. Enzymes catalyze Reactions, where Molecules serve as either products or substrates. Mechanisms are composed of steps, whereas molecules again serve as either “products” or “substrates” of those steps. Each Enzyme-Reaction pair can be assigned a specific Mechanism. All Reaction–Enzyme and Reaction-Enzyme-Mechanism associations in the database are supported by one or more references to the primary literature, shown here by a book iconCC BY · PubMed Central
Fig. 3 The MARTS-DB home page also serves as a quick search results page. Search results are displayed as cards for each enzyme, showing the reactions they catalyze
Fig. 3 The MARTS-DB home page also serves as a quick search results page. Search results are displayed as cards for each enzyme, showing the reactions they catalyzeCC BY · PubMed Central
Fig. 4 Mechanism representation in MARTS-DB. A Mechanisms are represented as individual steps of the reaction cascade. Each step approximates a change in a single bond. Each step is assigned a type and evidence sourced from the literature. B Example of the network representation of mechanisms within MARTS-DB web page. Final products are highlighted with green backgrounds, with yellow backgrounds indicating the molecular structure queried by the user
Fig. 4 Mechanism representation in MARTS-DB. A Mechanisms are represented as individual steps of the reaction cascade. Each step approximates a change in a single bond. Each step is assigned a type and evidence sourced from the literature. B Example of the network representation of mechanisms within MARTS-DB web page. Final products are highlighted with green backgrounds, with yellow backgrounds indicating the molecular structure queried by the userCC BY · PubMed Central

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Research works printed but not linked: 46

  1. Christianson DW. Structural and chemical biology of terpenoid cyclases. Chem Rev. 2017;117:11570–648. · DOI
  2. Whitehead JN, Leferink NGH, Johannissen LO, Hay S, Scrutton NS. Decoding catalysis by terpene synthases. ACS Catal. 2023;13:12774–802. · DOI
  3. Beran F, Rahfeld P, Luck K, Nagel R, Vogel H, Wielsch N, et al. Novel family of terpene synthases evolved from trans-isoprenyl diphosphate synthases in a flea beetle. Proc Natl Acad Sci U S A. 2016;113:2922–7. · DOI
  4. Baker CH, Matsuda SP, Liu DR, Corey EJ. Molecular-cloning of the human gene encoding lanosterol synthase from a liver cDNA library. Biochem Biophys Res Commun. 1995;213:154–60. · DOI
  5. Karunanithi PS, Zerbe P. Terpene synthases as metabolic gatekeepers in the evolution of plant terpenoid chemical diversity. Front Plant Sci. 2019;10:1166. · DOI
  6. Burkhardt I, de Rond T, Chen PY-T, Moore BS. Ancient plant-like terpene biosynthesis in corals. Nat Chem Biol. 2022;18:664–9. · DOI
  7. Chen C-C, Malwal SR, Han X, Liu W, Ma L, Zhai C, et al. Terpene cyclases and prenyltransferases: structures and mechanisms of action. ACS Catal. 2021. https://doi.org/10.1021/acscatal.0c04710. · DOI
  8. Tarshis LC, Yan M, Poulter CD, Sacchettini JC. Crystal structure of recombinant farnesyl diphosphate synthase at 2.6-A resolution. Biochemistry. 1994;33:10871–7. · DOI
  9. Pan X, Rudolf JD, Dong L-B. Class II terpene cyclases: structures, mechanisms, and engineering. Nat Prod Rep. 2024;41:402–33. · DOI
  10. Cane DE. Enzymic formation of sesquiterpenes. Chem Rev. 1990;90:1089–103. · DOI
  11. Croteau R. Biosynthesis and catabolism of monoterpenoids. Chem Rev. 1987;87:929–54. · DOI
  12. Rabe P, Barra L, Rinkel J, Riclea R, Citron CA, Klapschinski TA, et al. Conformational analysis, thermal rearrangement, and EI-MS fragmentation mechanism of (1 (10) E, 4E, 6S, 7R)-germacradien-6-ol by 13C-labeling experiments. Angew Chem Int Ed. 2015. https://doi.org/10.1002/anie.201507615. · DOI
  13. Rabe P, Rinkel J, Klapschinski TA, Barra L, Dickschat JS. A method for investigating the stereochemical course of terpene cyclisations. Org Biomol Chem. 2016;14:158–64. · DOI
  14. Rinkel J, Dickschat JS. Recent highlights in biosynthesis research using stable isotopes. Beilstein J Org Chem. 2015;11:2493–508. · DOI
  15. Tantillo DJ. Biosynthesis via carbocations: theoretical studies on terpene formation. Nat Prod Rep. 2011;28:1035–53. · DOI
  16. Tantillo DJ. Importance of inherent substrate reactivity in enzyme-promoted carbocation cyclization/rearrangements. Angew Chem Int Ed Engl. 2017;56:10040–5. · DOI
  17. Major DT, Freud Y, Weitman M. Catalytic control in terpenoid cyclases: multiscale modeling of thermodynamic, kinetic, and dynamic effects. Curr Opin Chem Biol. 2014;21:25–33. · DOI
  18. González Requena V, Srivastava PL, Miller DJ, Allemann RK. Single point mutation abolishes water capture in germacradien‐4‐ol synthase. ChemBioChem. 2024;25:e202400290. · DOI
  19. Hayes T, Rao R, Akin H, Sofroniew NJ, Oktay D, Lin Z, et al. Simulating 500 million years of evolution with a language model. Science. 2025;387:850–8. · DOI · Paper page
  20. Yang J, Li F-Z, Arnold FH. Opportunities and challenges for machine learning-assisted enzyme engineering. ACS Cent Sci. 2024;10:226–41. · DOI
  21. Yang J, Mora A, Liu S, Wittmann BJ, Anandkumar A, Arnold FH, et al. CARE: a benchmark suite for the classification and retrieval of enzymes. In: Globerson A, Mackey L, Belgrave D, Fan A, Paquet U, Tomczak J, et al., editors. Advances in neural information processing systems. Red Hook: Curran Associates, Inc.; 2024. p. 3094–121.
  22. UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Res. 2025;53:D609–17. · DOI · Paper page
  23. Wang H, Wang Q, Liu Y, Liao X, Chu H, Chang H, et al. PCPD: plant cytochrome P450 database and web-based tools for structural construction and ligand docking. Synth Syst Biotechnol. 2021;6:102–9. · DOI
  24. Wu H, Li Z, Zhong Z, Guo Y, He L, Xu X, et al. Insect cytochrome P450 database: an integrated resource of genetic diversity, evolution and function. Mol Ecol Resour. 2025;25(4):e14070. · DOI
  25. Hansen CC, Nelson DR, Møller BL, Werck-Reichhart D. Plant cytochrome P450 plasticity and evolution. Mol Plant. 2021;14:1772. · DOI
  26. Herrera LPT, Andreassen SN, Caroli J, Rodríguez-Espigares I, Kermani AA, Keserű GM, et al. GPCRdb in 2025: adding odorant receptors, data mapper, structure similarity search and models of physiological ligand complexes. Nucleic Acids Res. 2025;53:D425–35. · DOI
  27. Zeng T, Liu Z, Zhuang J, Jiang Y, He W, Diao H, et al. TeroKit: a database-driven web server for terpenome research. J Chem Inf Model. 2020;60:2082–90. · DOI
  28. Bretaudeau A, Coste F, Humily F, Garczarek L, Le Corguillé G, Six C, et al. CyanoLyase: a database of phycobilin lyase sequences, motifs and functions. Nucleic Acids Res. 2013;41:D396-401. · DOI
  29. Durairaj J, Di Girolamo A, Bouwmeester HJ, de Ridder D, Beekwilder J, van Dijk AD. An analysis of characterized plant sesquiterpene synthases. Phytochemistry. 2019;158:157–65. · DOI
  30. Durairaj J, Melillo E, Bouwmeester HJ, Beekwilder J, de Ridr D, van Dijk ADJ. Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases. PLoS Comput Biol. 2021;17:e1008197. · DOI
  31. Priya P, Yadav A, Chand J, Yadav G. Terzyme: a tool for identification and analysis of the plant terpenome. Plant Methods. 2018;14:4. · DOI
  32. Paysan-Lafosse T, Andreeva A, Blum M, Chuguransky SR, Grego T, Pinto BL, et al. The Pfam protein families database: embracing AI/ML. Nucleic Acids Res. 2025;53:D523–34. · DOI
  33. Jung Y, Mitsuhashi T, Sato S, Senda M, Senda T, Fujita M. Function and structure of a terpene synthase encoded in a giant virus genome. J Am Chem Soc. 2023;145:25966–70. · DOI
  34. Tachibana A, Yano Y, Otani S, Nomura N, Sako Y, Taniguchi M. Novel prenyltransferase gene encoding farnesylgeranyl diphosphate synthase from a hyperthermophilic archaeon, Aeropyrum pernix. Molecularevolution with alteration in product specificity. Eur J Biochem. 2000;267:321–8. · DOI
  35. Pichersky E, Raguso RA. Why do plants produce so many terpenoid compounds? New Phytol. 2018;220:692–702. · DOI
  36. Jiang S-Y, Jin J, Sarojam R, Ramachandran S. A comprehensive survey on the terpene synthase gene family provides new insight into its evolutionary patterns. Genome Biol Evol. 2019;11:2078–98. · DOI
  37. Zhou F, Pichersky E. The complete functional characterisation of the terpene synthase family in tomato. New Phytol. 2020;226:1341–60. · DOI
  38. Pan X, Du W, Zhang X, Lin X, Li F-R, Yang Q, et al. Discovery, structure, and mechanism of a class II sesquiterpene cyclase. J Am Chem Soc. 2022;144:22067–74. · DOI
  39. Garms S, Köllner TG, Boland W. A multiproduct terpene synthase from Medicago truncatula generates cadalane sesquiterpenes via two different mechanisms. J Org Chem. 2010;75:5590–600. · DOI
  40. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. · DOI · Paper page
  41. Sayers EW, Beck J, Bolton EE, Brister JR, Chan J, Connor R, et al. Database resources of the National Center for Biotechnology Information in 2025. Nucleic Acids Res. 2025;53:D20–9. · DOI
  42. Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, et al. ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res. 2016;44:D1214–9. · DOI · Paper page
  43. Samusevich R, Hebra T, Bushuiev R, Engst M, Kulhánek J, Bushuiev A, et al. Structure-enabled enzyme function prediction unveils elusive terpenoid biosynthesis in archaea. bioRxiv. 2025. https://doi.org/10.1101/2024.01.29.577750. · DOI
  44. Mikhael PG, Chinn I, Barzilay R. CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes. arXiv [q-bio.QM]. 2024.
  45. Bushuiev A, Bushuiev R, Zadorozhny N, Samusevich R, Stärk H, Sedlar J, et al. Training on test proteins improves fitness, structure, and function prediction. arXiv [cs.LG]. 2024.
  46. Leferink NGH, Scrutton NS. Predictive engineering of class I terpene synthases using experimental and computational approaches. ChemBioChem. 2022;23:e202100484. · DOI

Metadata

Authors
Engst, M., Brokeš, M., Čalounová, T., Samusevich, R., Bushuiev, R., Bushuiev, A., Chatpatanasiri, R., Tajovská, A., Akmeşe, S. M., Perković, M., Soldát, M., Sivic, J., & Pluskal, T.
Affiliations
  • Czech Academy of Sciences (CZ)
  • Czech Technical University in Prague (CZ)
Corresponding author
Pluskal, Tomáš
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2025-12-08
Volume
27
Issue
1
Pages
10-10
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-025-06341-8
PMC
PMC12797696
OpenAlex
W4417105682
Cited by
6
References
5
Citations by year
2026: 4 · 2025: 2
FWCI
1.80
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • HAL (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Vysoká Škola Chemicko-technologická v Praze (A2_FCHT_2025_011)
  • HORIZON EUROPE European Research Council (101097822, 101170268)
  • Grantová Agentura České Republiky (21-11563M)
  • HORIZON EUROPE Marie Sklodowska-Curie Actions (101168583)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Plant biochemistry and biosynthesis
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Microbial Natural Products and Biosynthesis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Biochemical and biochemical processes
Keywords
terpene synthase, terpene cyclization, reaction mechanism, terpenoids, enzyme catalysis, reaction cascade, enzyme annotation, enzyme function prediction
MeSH terms
Terpenes, Alkyl and Aryl Transferases, Databases, Protein

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CoChat. (2026). Citation check: MARTS-DB: a database of mechanisms and reactions of terpene synthases. https://app.cochat.ai/papers/10.1186/s12859-025-06341-8
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Engst, M., Brokeš, M., Čalounová, T., Samusevich, R., Bushuiev, R., Bushuiev, A., Chatpatanasiri, R., Tajovská, A., Akmeşe, S. M., Perković, M., Soldát, M., Sivic, J., & Pluskal, T. (2025). MARTS-DB: a database of mechanisms and reactions of terpene synthases. BMC Bioinformatics. https://doi.org/10.1186/s12859-025-06341-8