CoChat
Sign Up
PreprintCited by 6Plant biochemistry and biosynthesisMicrobial Natural Products and BiosynthesisBiochemical and biochemical processes

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

MARTS-DB: A Database of Mechanisms And Reactions of Terpene Synthases

Open access
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 10.1101/2025.05.11.653183PMC PMC12797696

Preprint — published as 10.1186/s12859-025-06341-8 · Paper page

Open this paper in CoChat — free account

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 2,600 reactions catalyzed by 1,334 annotated enzymes from across all domains of life, with reaction mechanisms mapped as stepwise cascades for more than 400 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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2025.05.11.653183

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Figures

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 48

  1. 10.1021/acs.chemrev.7b00287 · DOI
  2. 10.1021/acscatal.3c03047 · DOI
  3. 10.1073/pnas.1523468113 · DOI
  4. 10.1006/bbrc.1995.2110 · DOI
  5. 10.3389/fpls.2019.01166 · DOI
  6. 10.1038/s41589-022-01026-2 · DOI
  7. 10.1021/bi00202a004 · DOI
  8. 10.1039/D3NP00033H · DOI
  9. 10.1021/cr00105a002 · DOI
  10. 10.1021/cr00081a004 · DOI
  11. 10.1039/C5OB01998B · DOI
  12. 10.3762/bjoc.11.271 · DOI
  13. 10.1039/c1np00006c · DOI
  14. 2017, Importance of Inherent Substrate Reactivity in Enzyme-Promoted Carbocation Cyclization/Rearrangements, Angew Chem Int Ed Engl, 56, 10040, 10.1002/anie.201702363 · DOI
  15. 2014, Catalytic control in terpenoid cyclases: multiscale modeling of thermodynamic, kinetic, and dynamic effects, Curr Opin Chem Biol, 21, 25, 10.1016/j.cbpa.2014.03.010 · DOI
  16. 10.1002/cbic.202400290 · DOI
  17. 10.1126/science.ads0018 · DOI · Paper page
  18. 10.1126/science.ade2574 · DOI · Paper page
  19. 10.1038/S41586-024-07487-W · DOI · Paper page
  20. 2024, Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering, ACS Cent Sci, 10, 226, 10.1021/acscentsci.3c01275 · DOI
  21. 10.1126/science.adu2454 · DOI · Paper page
  22. 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. Curran Associates, Inc.; 2024. p. 3094–121. · DOI
  23. 10.1093/nar/gkae1010 · DOI · Paper page
  24. 10.1093/NAR/GKAA1025 · DOI · Paper page
  25. 10.1016/j.synbio.2021.04.004 · DOI
  26. 10.1111/1755-0998.14070 · DOI
  27. 2021, Plant cytochrome P450 plasticity and evolution, Mol Plant, 14, 1772, 10.1016/j.molp.2021.09.013 · DOI
  28. 10.1093/nar/gkae1065 · DOI
  29. 10.1021/acs.jcim.0c00141 · DOI
  30. 10.1093/nar/gks1091 · DOI
  31. 10.1016/j.phytochem.2018.10.020 · DOI
  32. 10.1371/journal.pcbi.1008197 · DOI
  33. 10.1186/s13007-017-0269-0 · DOI
  34. 10.1093/nar/gkae997 · DOI
  35. 10.1021/jacs.3c10603 · DOI
  36. 2000, Novel prenyltransferase gene encoding farnesylgeranyl diphosphate synthase from a hyperthermophilic archaeon, Aeropyrum pernix, Molecularevolution with alteration in product specificity. Eur J Biochem, 267, 321
  37. 10.1111/nph.14178 · DOI
  38. 10.1093/gbe/evz142 · DOI
  39. 10.1111/nph.16431 · DOI
  40. 10.1021/jacs.2c09412 · DOI
  41. 10.1021/jo100917c · DOI
  42. 10.1101/gr.1239303 · DOI · Paper page
  43. 10.1093/nar/gkae979 · DOI
  44. 10.1093/nar/gkv1031 · DOI · Paper page
  45. 10.1101/2024.01.29.577750 · DOI
  46. Mikhael PG , Chinn I , Barzilay R. CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes. arXiv [q-bio.QM]. 2024.
  47. 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.
  48. 2022, Predictive Engineering of Class I Terpene Synthases Using Experimental and Computational Approaches, Chembiochem, 23, e202100484, 10.1002/cbic.202100484 · 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)
  • Czech Institute of Informatics, Robotics and Cybernetics (CIIRC), Czech Technical University, Jugoslávských partyzánů 1580/3, 160 00 Prague, Czech Republic
Corresponding author
Pluskal, Tomáš
ORCID
Venue
BMC Bioinformatics
Publisher
openRxiv
Published
2025-05-15
Volume
27
Issue
1
Pages
10-10
ISSN
1471-2105 (ISSN-L)
DOI
10.1101/2025.05.11.653183
PMC
PMC12797696
OpenAlex
W4417105682
Cited by
6
References
1
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
European Research Council (101170268, 101168583, 101097822, 101120237, 101136607)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review