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Background <p>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.</p> Data description <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Fernanda Cristina de Oliveira,
  • Emanuel Umbelino,
  • Sérgio Lifschitz,
  • Edward Hermann Haeusler,
  • Ana Carolina Ramos Guimarães,
  • Antonio Basílio de Miranda,
  • Marcos Catanho

摘要

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.