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. 2026 Feb 24;19:151. doi: 10.1186/s13104-026-07742-5

A comprehensive dataset of homologous and non-homologous isofunctional enzymes across the tree of life

Fernanda Cristina de Oliveira 1, Emanuel Umbelino 2, Sérgio Lifschitz 2, Edward Hermann Haeusler 2, Ana Carolina Ramos Guimarães 1, Antonio Basílio de Miranda 1, Marcos Catanho 1,✉
PMCID: PMC13041283  PMID: 41736073

Abstract

Background

Convergent evolution, the independent emergence of similar traits, is increasingly recognized as a pervasive force shaping molecular and metabolic diversity. A striking manifestation of convergence at the molecular level is represented by non-homologous isofunctional enzymes (NISE), distinct proteins with no detectable common ancestry that catalyze identical biochemical reactions. Despite their conceptual and practical relevance, NISE are often treated as exceptional cases, and no large-scale, systematically curated resource has been available to explore their distribution and properties across all domains of life.

Data description

Here we present a curated dataset of homologous and non-homologous isofunctional enzymes (HISE and NISE) derived from UniProtKB release 2025_01, encompassing both reviewed (Swiss-Prot) and unreviewed (TrEMBL) entries. Using Enzyme Commission (EC) numbers to define catalytic equivalence and SUPERFAMILY (SCOP structure superfamily) annotations to infer evolutionary relationships, we implemented a transparent and reproducible pipeline to classify enzymes into homologous and non-homologous functional groups. The dataset comprises over 200,000 Swiss-Prot and 27 million TrEMBL enzymes with complete EC and SUPERFAMILY annotations, organized by domain of life, enzyme class, and structural domain composition. Multiple output files, including presence/absence matrices, clustered enzyme groups, phyloprofiles, and full annotation tables, are provided to facilitate downstream evolutionary, functional, and comparative analyses. This resource offers a global view of molecular convergence and divergence in enzymatic functions, highlighting the widespread nature of NISE across taxa and enzyme classes. It provides a foundation for studying metabolic evolution, functional redundancy, drug target discovery, and the evolutionary constraints shaping biochemical solutions.

Keywords: Convergent evolution, Non-homologous isofunctional enzymes, Enzyme evolution, Functional analogy, Metabolic evolution, SCOP, SUPERFAMILY, UniProtKB, Enzyme commission

Objective

Convergent evolution reflects the constrained nature of biochemical solutions to life’s functional demands and is increasingly recognized as a widespread feature of molecular evolution [23]. One of its most striking manifestations is the existence of non-homologous isofunctional enzymes (NISE), distinct proteins that catalyze the same biochemical reaction despite lacking detectable common ancestry. Previous studies have demonstrated that NISE are involved in diverse biological processes [15, 17, 24], antimicrobial resistance [9, 12, 19], pathogenicity [10, 18], and drug target discovery to treat infeccious deseases [5, 6, 11, 13] or to control phytopathogens (da Silva et al., 7).

The objective of this work was to generate a comprehensive and reusable dataset of homologous and non-homologous isofunctional enzymes across the tree of life by integrating functional and evolutionary annotations from UniProtKB [22]. Enzyme Commission (EC) numbers were used to define catalytic equivalence, while SUPERFAMILY phylogenetic assignments [16] of SCOP protein structure superfamilies [3] were employed to infer shared or independent evolutionary origins. This strategy has been shown to effectively discriminate between homologous and non-homologous enzymes even in the absence of experimentally determined three-dimensional structures [17].

The resulting dataset enables large-scale analyses of enzymatic convergence and divergence, supports comparative and evolutionary genomics studies, and provides a resource for identifying alternative biochemical solutions to the same metabolic reactions. We are currently preparing a manuscript exploring this dataset to support our hypotesis that enzymatic activity, rather than gene lineage, serves as a unit of selection, with homologous and non-homologous isofunctional enzymes behaving analogously to alleles within microbial populations. We identified numerous enzymatic activities performed by structurally unrelated enzymes often coexisting in phylogenetically related taxa. These enzyme variants demonstrate population-level distributions, phylogenetic biases, and expression divergence consistent with selection acting on catalytic function, independent of molecular ancestry. Our results extend prior work on biased horizontal gene transfer and functional replacement [1, 2], and provide a mechanistic basis for the persistence of functional traits via convergent molecular solutions, aligning with recent theoretical proposals that emphasize differential persistence over differential reproduction in microbial evolution [4, 8]. This framework supports a pluralistic view of evolutionary units, where enzyme activity plays the role of an adaptive trait and gene variants - homologous or non-homologous - serve as its alleles.

Data description

The dataset is available through an open-access data repository doi:10.5281/zenodo.18008936 and consists of multiple tab-delimited text files and matrices derived from UniProtKB release 2025_01 < https://www.uniprot.org/release-notes/2025-02-05-release%3E. An overview of the data files is provided in Table 1.

Table 1.

Overview of data files deposited in the repository

Labels Name of data file/data set File types (extension) Data repository and identifier (DOI or accession number)
Data set 1 SwissProt_NISE.tsv.gz TSV (.tsv) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_NISE.tsv.gz [14]
Data set 2 SwissProt_NISE.mtx.gz TSV (.mtx) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_NISE.mtx.gz [14]
Data set 3 SwissProt_NISE.clust.gz TXT (.clust) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_NISE.clust.gz [14]
Data set 4 SwissProt_NISE.bnf.gz TXT (.bnf) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_NISE.bnf.gz [14]
Data set 5 SwissProt_NISE.multi.gz TXT (.multi) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_NISE.multi.gz [14]
Data set 6 SwissProt_NISE.log.gz TSV (.log) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_NISE.log.gz [14]
Data set 7 SwissProt_HISE.tsv.gz TSV (.tsv) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_HISE.tsv.gz [14]
Data set 8 SwissProt_HISE.bnf.gz TXT (.bnf) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_HISE.bnf.gz [14]
Data set 9 SwissProt_HISE.multi.gz TXT (.multi) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_HISE.multi.gz [14]
Data set 10 SwissProt_HISE.sgl.gz TXT (sgl) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_HISE.sgl.gz [14]
Data set 11 SwissProt_PhyloProfiles.tar.gz TSV (.phyprof) Zenodo: https://zenodo.org/records/18008936/files/SwissProt_PhyloProfiles.tar.gz
Data set 12 TrEMBL_NISE.tsv.gz TSV (.tsv) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_NISE.tsv.gz [14]
Data set 13 TrEMBL_NISE.mtx.gz TSV (.mtx) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_NISE.mtx.gz [14]
Data set 14 TrEMBL_NISE.clust.gz TXT (.clust) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_NISE.clust.gz [14]
Data set 15 TrEMBL_NISE.bnf.gz TXT (.bnf) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_NISE.bnf.gz [14]
Data set 16 TrEMBL_NISE.multi.gz TXT (.multi) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_NISE.multi.gz [14]
Data set 17 TrEMBL_NISE.log.gz TSV (.log) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_NISE.log.gz [14]
Data set 18 TrEMBL_HISE.tsv.gz TSV (.tsv) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_HISE.tsv.gz [14]
Data set 19 TrEMBL_HISE.bnf.gz TXT (.bnf) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_HISE.bnf.gz [14]
Data set 20 TrEMBL_HISE.multi.gz TXT (.multi) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_HISE.multi.gz [14]
Data set 21 TrEMBL_HISE.sgl.gz TXT (sgl) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_HISE.sgl.gz [14]
Data set 22 TrEMBL_PhyloProfiles.tar.gz TSV (.phyprof) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL_PhyloProfiles.tar.gz [14]
Data set 23 Stats.txt TXT (.txt) Zenodo: https://zenodo.org/records/18008936/files/Stats.txt [14]
Data set 24 SwissProt.png PNG (.png) Zenodo: https://zenodo.org/records/18008936/files/SwissProt.png [14]
Data set 25 TrEMBL.png PNG (.png) Zenodo: https://zenodo.org/records/18008936/files/TrEMBL.png [14]
Data set 26 Flowchart.png PNG (.png) Zenodo: https://zenodo.org/records/18008936/files/Flowchart.png [14]
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Primary annotation tables: Two groups of comprehensive tables (Swiss-Prot/TrEMBL_HISE.tsv and Swiss-Prot/TrEMBL_NISE.tsv) provide full annotation for homologous (SwissProt_HISE.tsv.gz <https://zenodo.org/records/18008936/files/SwissProt_HISE.tsv.gz%3E and TrEMBL_HISE.tsv.gz <https://zenodo.org/records/18008936/files/TrEMBL_HISE.tsv.gz%3E [14]) and non-homologous (SwissProt_NISE.tsv.gz > < https://zenodo.org/records/18008936/files/SwissProt_NISE.tsv.gz%3E and TrEMBL_NISE.tsv.gz <https://zenodo.org/records/18008936/files/TrEMBL_NISE.tsv.gz%3E [14]) isofunctional enzymes, respectively. Each row corresponds to a UniProtKB entry and includes accession, protein and gene names, EC number, SUPERFAMILY annotation(s), organism name, and taxonomic classification from domain to genus.

Presence/absence matrix: SUPERFAMILY presence/absence matrices summarize domain usage across enzymes sharing the same EC number (SwissProt_NISE.mtx <https://zenodo.org/records/18008936/files/SwissProt_NISE.mtx.gz%3E and TrEMBL_NISE.mtx <https://zenodo.org/records/18008936/files/TrEMBL_NISE.mtx.gz%3E [14]).

Clustered enzyme files: Enzymes are grouped by EC number and clustered by SUPERFAMILY profiles into tables of homologous or non-homologous isofunctional enzymes (SwissProt_NISE.clust <https://zenodo.org/records/18008936/files/SwissProt_NISE.clust.gz%3E and TrEMBL_NISE.clust <https://zenodo.org/records/18008936/files/TrEMBL_NISE.clust.gz%3E [14]). Separate files are provided for bona fide monomeric (SwissProt_NISE.bnf <https://zenodo.org/records/18008936/files/SwissProt_NISE.bnf.gz%3E, SwissProt_HISE.bnf <https://zenodo.org/records/18008936/files/SwissProt_HISE.bnf.gz%3E,

TrEMBL_NISE.bnf <https://zenodo.org/records/18008936/files/TrEMBL_NISE.bnf.gz%3E, and TrEMBL_HISE.bnf <https://zenodo.org/records/18008936/files/TrEMBL_HISE.bnf.gz%3E [14]), multimeric (SwissProt_NISE.multi.gz <https://zenodo.org/records/18008936/files/SwissProt_NISE.multi.gz%3E, TrEMBL_NISE.multi.gz <https://zenodo.org/records/18008936/files/TrEMBL_NISE.multi.gz%3E, SwissProt_HISE.multi.gz <https://zenodo.org/records/18008936/files/SwissProt_HISE.multi.gz%3E, and TrEMBL_HISE.multi.gz <https://zenodo.org/records/18008936/files/TrEMBL_HISE.multi.gz%3E [14] ), and singleton (SwissProt_HISE.sgl.gz <https://zenodo.org/records/18008936/files/SwissProt_HISE.sgl.gz%3E and TrEMBL_HISE.sgl.gz <https://zenodo.org/records/18008936/files/TrEMBL_HISE.sgl.gz%3E [14]) enzymes, enabling fine-grained analyses of domain architecture and functional convergence.

Phyloprofile matrices: For each EC number, SUPERFAMILY presence/absence profiles describe the taxonomic distribution of NISE and HISE across domains, kingdoms, and lower taxonomic ranks. These files (*.phyprof) - packed and compressed in files SwissProt_PhyloProfiles.tar.gz <https://zenodo.org/records/18008936/files/SwissProt_PhyloProfiles.tar.gz%3E and TrEMBL_PhyloProfiles.tar.gz <https://zenodo.org/records/18008936/files/TrEMBL_PhyloProfiles.tar.gz%3E [14] - are compatible with the PhyloProfile software <https://github.com/BIONF/PhyloProfile%3E. PhyloProfile is a bioinformatics tool designed for comparative genomics and phylogenetic profiling [20, 21]. Its core functionality is to assess patterns of presence and absence of features (genes, domains, pathways) across multiple taxa and to visualize these patterns in a phylogenetic or taxonomic context. The software might adds biological insight, placing NISE/HISE taxonomic profiles into a comparative evolutionary framework, in which: enzyme families distributed across domains of life (Archaea, Bacteria, Eukarya, Viruses) can be visualized; patterns of conservation, gain, loss, or convergence can be detected; whether certain domain architectures correlate with specific lineages, metabolic capabilities, or evolutionary events can be investigated.

Methodological summary

Proteins annotated with catalytic activity were filtered to retain sequences with a single, complete EC number and at least one SUPERFAMILY annotation. For each EC, enzymes were classified as homologous or non-homologous isofunctional based on whether they shared identical or distinct SUPERFAMILY profiles. A detailed, step-by-step description of the filtering, clustering, and classification pipeline is provided in the repository documentation.

Limitations

This dataset has several limitations that should be considered when interpreting or reusing the data.

First, reliance on EC numbers (even full four digits assignment) might obscure functional nuances, such as moonlighting (a single protein performing two or more distinct, physiologically relevant biological functions) or condition-dependent activity, particularly for enzymes grouped under broad EC categories. Although promiscuous enzymes were excluded to reduce ambiguity, some functional diversity within EC classes might remain unaccounted for.

Second, structural homology is inferred from SUPERFAMILY annotations rather than experimentally determined protein structures. While SUPERFAMILY has proven effective for discriminating homologous and non-homologous enzymes even in the absence of three-dimensional structures [6], very distant evolutionary relationships might not be detected.

Finally, viral enzymes are comparatively underrepresented, reflecting current annotation biases rather than true biological absence. As protein annotation and structural coverage improve, future updates of the dataset may expand and refine these classifications.

Acknowledgements

We acknowledge the UniProtKB Consortium and the developers of the SUPERFAMILY and SCOP databases for providing essential resources that made this work possible.

Author contributions

M.C., A.B.M., AC.R.G.: study conception, experimental designM.C., F.C.O., E.U.: investigation, computationM.C., F.C.O., E.U.: formal analysisM.C.: manuscript writingS.L., E.H.H.: manuscript critical reviewF.C.O., E.U: contributed equally to this work.

Funding

This research did not receive funding.

Data availability

All data supporting the conclusions of this article are available at the Zenodo open-access data repository doi:10.5281/zenodo.18008936: SwissProt_NISE.tsv.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.tsv.gz> [14], SwissProt_NISE.mtx.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.mtx.gz> [14], SwissProt_NISE.clust.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.clust.gz> [14], SwissProt_NISE.bnf.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.bnf.gz> [14], SwissProt_NISE.multi.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.multi.gz> [14], SwissProt_NISE.log.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.log.gz> [14], SwissProt_HISE.tsv.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.tsv.gz> [14], SwissProt_HISE.bnf.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.bnf.gz> [14], SwissProt_HISE.multi.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.multi.gz> [14], SwissProt_HISE.sgl.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.sgl.gz> [14], SwissProt_PhyloProfiles.tar.gz < https://zenodo.org/records/18008936/files/SwissProt_PhyloProfiles.tar.gz, TrEMBL_NISE.tsv.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.tsv.gz> [14], TrEMBL_NISE.mtx.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.mtx.gz> [14], TrEMBL_NISE.clust.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.clust.gz> [14], TrEMBL_NISE.bnf.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.bnf.gz> [14], TrEMBL_NISE.multi.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.multi.gz> [14], TrEMBL_NISE.log.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.log.gz> [14], TrEMBL_HISE.tsv.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.tsv.gz> [14], TrEMBL_HISE.bnf.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.bnf.gz> [14], TrEMBL_HISE.multi.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.multi.gz> [14], TrEMBL_HISE.sgl.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.sgl.gz> [14], TrEMBL_PhyloProfiles.tar.gz < https://zenodo.org/records/18008936/files/TrEMBL_PhyloProfiles.tar.gz> [14], Stats.txt < https://zenodo.org/records/18008936/files/Stats.txt> [14], SwissProt.png < https://zenodo.org/records/18008936/files/SwissProt.png> [14], TrEMBL.png < https://zenodo.org/records/18008936/files/TrEMBL.png> [14], Flowchart.png < https://zenodo.org/records/18008936/files/Flowchart.png> [14]. See Table 1 for details.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Andam CP, Williams D, Gogarten JP. Biased gene transfer mimics patterns created through shared ancestry. Proc Natl Acad Sci U S A. 2010;107(23):10679–84. 10.1073/pnas.1001418107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Andam CP, Gogarten JP. Biased gene transfer and its implications for the concept of lineage. Biol Direct. 2011;6:47. 10.1186/1745-6150-6-47 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Andreeva A, Kulesha E, Gough J, Murzin AG. The SCOP database in 2020: expanded classification of representative family and superfamily domains of known protein structures. Nucleic Acids Res. 2020;48(D1):D376-D382. 10.1093/nar/gkz1064 [DOI] [PMC free article] [PubMed]
  • 4.Bapteste E, Papale F. Modeling the evolution of interconnected processes: it is the song and the singers: tracking units of selection with interaction networks. BioEssays. 2021;43(1):e2000077. 10.1002/bies.202000077 [DOI] [PubMed] [Google Scholar]
  • 5.Capriles PV, Baptista LP, Guedes IA, Guimarães AC, Custódio FL, Alves-Ferreira M, Dardenne LE. Structural modeling and docking studies of ribose 5-phosphate isomerase from Leishmania major and Homo sapiens: a comparative analysis for Leishmaniasis treatment. J Mol Graph Model. 2015;55:134 – 47. 10.1016/j.jmgm.2014.11.002. [DOI] [PubMed]
  • 6.Capriles PV, Guimarães AC, Otto TD, Miranda AB, Dardenne LE, Degrave WM. Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas’ disease treatment. BMC Genom. 2010;11:610. 10.1186/1471-2164-11-610 [DOI] [PMC free article] [PubMed]
  • 7.da Silva RA, Pereira LM, Silveira MC, Jardim R, de Miranda AB. Mining of potential drug targets through the identification of essential and analogous enzymes in the genomes of pathogens of Glycine max, Zea mays and Solanum lycopersicum. PLoS One. 2018 May 25;13(5):e0197511. 10.1371/journal.pone.0197511 [DOI] [PMC free article] [PubMed]
  • 8.Doolittle WF, Inkpen SA. Processes and patterns of interaction as units of selection: an introduction to ITSNTS thinking. Proc Natl Acad Sci U S A. 2018;115(16):4006–14. 10.1073/pnas.1722232115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Fröhlich C, Chen JZ, Gholipour S, Erdogan AN, Tokuriki N. Evolution of β-lactamases and enzyme promiscuity. Protein Eng Des Sel. 2021;34:gzab013. 10.1093/protein/gzab013 [DOI] [PubMed]
  • 10.Garcia YM, Barwinska-Sendra A, Tarrant E, Skaar EP, Waldron KJ, Kehl-Fie TE. A Superoxide dismutase capable of functioning with iron or manganese promotes the resistance of staphylococcus aureus to calprotectin and nutritional immunity. PLoS Pathog. 2017;13(1):e1006125. 10.1371/journal.ppat.1006125 [DOI] [PMC free article] [PubMed]
  • 11.Gomes MR, Guimarães AC, de Miranda AB. Specific and nonhomologous isofunctional enzymes of the genetic information processing pathways as potential therapeutical targets for tritryps. Enzyme Res. 2011;2011:543912. 10.4061/2011/543912 [DOI] [PMC free article] [PubMed]
  • 12.Keshri V, Arbuckle K, Chabrol O, Rolain JM, Raoult D, Pontarotti P. The functional convergence of antibiotic resistance in β-lactamases is not conferred by a simple convergent substitution of amino acid. Evol Appl. 2019;12(9):1812–1822. 10.1111/eva.12835 [DOI] [PMC free article] [PubMed]
  • 13.Lima CR, Carels N, Guimaraes AC, Tufféry P, Derreumaux P. In silico structural characterization of protein targets for drug development against Trypanosoma cruzi. J Mol Model. 2016;22(10):244. 10.1007/s00894-016-3115-9 [DOI] [PubMed]
  • 14.de Oliveira FC, Umbelino E, Lifschitz S, Haeusler E, Ramos Guimarães AC, de Miranda A, Catanho M. A comprehensive dataset of homologous and non-homologous isofunctional enzymes across the tree of life (1.0.0). Zenodo. 2025. 10.5281/zenodo.18008936. [DOI] [PMC free article] [PubMed]
  • 15.Piergiorge RM. Analogia funcional no metabolismo humano: enzimas com distintos papéis biológicos ou redundância funcional?. Rio de Janeiro: Instituto Oswaldo Cruz, Fundação Oswaldo Cruz; 2017. https://arca.fiocruz.br/handle/icict/42671
  • 16.Pandurangan AP, Stahlhacke J, Oates ME, Smithers B, Gough J. The SUPERFAMILY 2.0 database: a significant proteome update and a new webserver. Nucleic Acids Res. 2019;47(D1):D490-D494. 10.1093/nar/gky1130 [DOI] [PMC free article] [PubMed]
  • 17.Piergiorge RM, de Miranda AB, Guimarães AC, Catanho M. Functional analogy in human metabolism: enzymes with different biological roles or functional redundancy? Genome Biol Evol. 2017;9(6):1624–1636. 10.1093/gbe/evx119 [DOI] [PMC free article] [PubMed]
  • 18.Schatzman SS, Culotta VC. Chemical warfare at the microorganismal level: a closer look at the superoxide dismutase enzymes of pathogens. ACS Infect Dis. 2018;4(6):893–903. 10.1021/acsinfecdis.8b00026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Silveira MC, Azevedo da Silva R, Faria da Mota F, Catanho M, Jardim R, R Guimarães AC, de Miranda AB. Systematic identification and classification of β-lactamases based on sequence similarity criteria: β-lactamase annotation. Evol Bioinform Online. 2018;14:1176934318797351. 10.1177/1176934318797351 [DOI] [PMC free article] [PubMed]
  • 20.Tran NV, Greshake Tzovaras B, Ebersberger I. PhyloProfile: dynamic visualization and exploration of multi-layered phylogenetic profiles. Bioinformatics. 2018;34(17):3041–3. 10.1093/bioinformatics/bty225. [DOI] [PubMed] [Google Scholar]
  • 21.Tran V, Ebersberger I. PhyloProfile v2: scalable exploration of multilayered phylogenetic profiles via dimensionality reduction. arXiv. 2025. 10.48550/arXiv.2504.19710.41532042 [Google Scholar]
  • 22.UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Res. 2025;53(D1):D609-D617. 10.1093/nar/gkae1010 [DOI] [PMC free article] [PubMed]
  • 23.Wu CI, Wang GD, Xu S. Convergent adaptive evolution-how common, or how rare? Natl Sci Rev. 2020;7(6):945–946. 10.1093/nsr/nwaa081 [DOI] [PMC free article] [PubMed]
  • 24.Zhang J, Wang H, Huang Q, Zhang Y, Zhao L, Liu F, Wang G. Four superoxide dismutases of Bacillus cereus 0–9 are non-redundant and perform different functions in diverse living conditions. World J Microbiol Biotechnol. 2020;36(1):12. 10.1007/s11274-019-2786-7 [DOI] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. de Oliveira FC, Umbelino E, Lifschitz S, Haeusler E, Ramos Guimarães AC, de Miranda A, Catanho M. A comprehensive dataset of homologous and non-homologous isofunctional enzymes across the tree of life (1.0.0). Zenodo. 2025. 10.5281/zenodo.18008936. [DOI] [PMC free article] [PubMed]

Data Availability Statement

All data supporting the conclusions of this article are available at the Zenodo open-access data repository doi:10.5281/zenodo.18008936: SwissProt_NISE.tsv.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.tsv.gz> [14], SwissProt_NISE.mtx.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.mtx.gz> [14], SwissProt_NISE.clust.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.clust.gz> [14], SwissProt_NISE.bnf.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.bnf.gz> [14], SwissProt_NISE.multi.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.multi.gz> [14], SwissProt_NISE.log.gz < https://zenodo.org/records/18008936/files/SwissProt_NISE.log.gz> [14], SwissProt_HISE.tsv.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.tsv.gz> [14], SwissProt_HISE.bnf.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.bnf.gz> [14], SwissProt_HISE.multi.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.multi.gz> [14], SwissProt_HISE.sgl.gz < https://zenodo.org/records/18008936/files/SwissProt_HISE.sgl.gz> [14], SwissProt_PhyloProfiles.tar.gz < https://zenodo.org/records/18008936/files/SwissProt_PhyloProfiles.tar.gz, TrEMBL_NISE.tsv.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.tsv.gz> [14], TrEMBL_NISE.mtx.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.mtx.gz> [14], TrEMBL_NISE.clust.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.clust.gz> [14], TrEMBL_NISE.bnf.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.bnf.gz> [14], TrEMBL_NISE.multi.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.multi.gz> [14], TrEMBL_NISE.log.gz < https://zenodo.org/records/18008936/files/TrEMBL_NISE.log.gz> [14], TrEMBL_HISE.tsv.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.tsv.gz> [14], TrEMBL_HISE.bnf.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.bnf.gz> [14], TrEMBL_HISE.multi.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.multi.gz> [14], TrEMBL_HISE.sgl.gz < https://zenodo.org/records/18008936/files/TrEMBL_HISE.sgl.gz> [14], TrEMBL_PhyloProfiles.tar.gz < https://zenodo.org/records/18008936/files/TrEMBL_PhyloProfiles.tar.gz> [14], Stats.txt < https://zenodo.org/records/18008936/files/Stats.txt> [14], SwissProt.png < https://zenodo.org/records/18008936/files/SwissProt.png> [14], TrEMBL.png < https://zenodo.org/records/18008936/files/TrEMBL.png> [14], Flowchart.png < https://zenodo.org/records/18008936/files/Flowchart.png> [14]. See Table 1 for details.

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