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<p>Iron–sulfur (Fe–S) clusters are ubiquitous cofactors in metalloproteins, supporting essential biological functions such as electron transfer, enzymatic catalysis, and metabolic regulation. Despite their importance, large-scale identification of Fe–S proteins remains challenging due to limitations of experimental methods and inconsistent annotations in existing databases. To address this, we introduced FeSseqdb, a curated sequence-level database derived from the Protein Data Bank (PDB), in which Fe–S cluster-containing chains are systematically verified using atomic coordinates. By standardizing diverse ligand annotations, FeSseqdb provides a unified and reliable resource for Fe–S protein research. Building on this foundation, a machine learning framework was developed to predict Fe–S proteins using only sequence-derived features, including amino acid composition, cysteine-related metrics, and sequence length. Among the classifiers evaluated, the random forest model trained on data resampled with SVMSMOTE achieved the highest predictive performance, highlighting the discriminative power of simple sequence features. To elucidate the biological relevance of these features, explainable AI methods were applied to identify key sequence characteristics associated with Fe–S proteins. Cysteine frequency and spatial distribution, along with proline content, emerged as primary contributors, which is consistent with their known structural roles in cluster coordination. Additionally, serine, glutamic acid, and arginine were identified as secondary determinants, in line with their reported roles in redox and electrostatic environments surrounding metal cofactors. The inclusion of these biologically relevant features demonstrates the potential of sequence-based models not only for accurate prediction but also for uncovering functional insights that align with known biochemical principles. This approach provides a foundation for large-scale, sequence-based discovery of Fe–S proteins and supports future investigations into their functional diversity across proteomes.</p> Graphical abstract <p></p>

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FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron–sulfur proteins

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Jiyeon Min,
  • Bernard R. Brooks,
  • Muhamed Amin

摘要

Iron–sulfur (Fe–S) clusters are ubiquitous cofactors in metalloproteins, supporting essential biological functions such as electron transfer, enzymatic catalysis, and metabolic regulation. Despite their importance, large-scale identification of Fe–S proteins remains challenging due to limitations of experimental methods and inconsistent annotations in existing databases. To address this, we introduced FeSseqdb, a curated sequence-level database derived from the Protein Data Bank (PDB), in which Fe–S cluster-containing chains are systematically verified using atomic coordinates. By standardizing diverse ligand annotations, FeSseqdb provides a unified and reliable resource for Fe–S protein research. Building on this foundation, a machine learning framework was developed to predict Fe–S proteins using only sequence-derived features, including amino acid composition, cysteine-related metrics, and sequence length. Among the classifiers evaluated, the random forest model trained on data resampled with SVMSMOTE achieved the highest predictive performance, highlighting the discriminative power of simple sequence features. To elucidate the biological relevance of these features, explainable AI methods were applied to identify key sequence characteristics associated with Fe–S proteins. Cysteine frequency and spatial distribution, along with proline content, emerged as primary contributors, which is consistent with their known structural roles in cluster coordination. Additionally, serine, glutamic acid, and arginine were identified as secondary determinants, in line with their reported roles in redox and electrostatic environments surrounding metal cofactors. The inclusion of these biologically relevant features demonstrates the potential of sequence-based models not only for accurate prediction but also for uncovering functional insights that align with known biochemical principles. This approach provides a foundation for large-scale, sequence-based discovery of Fe–S proteins and supports future investigations into their functional diversity across proteomes.

Graphical abstract