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<p>Accurate classification of amino acids is fundamental to protein engineering and structural biology. Traditional schemes often rely on single-dimensional properties, potentially overlooking complex structure–function relationships. In this study, we present an automated, AI-driven hierarchical clustering pipeline utilizing 22 novel graph-theoretic molecular descriptors to uncover high-dimensional biochemical associations. Using average linkage with Manhattan distance, our approach achieved a high cophenetic correlation (0.847), significantly outperforming K-means and DBSCAN in both cluster quality (Silhouette score: 0.573 vs. 0.548 and 0.412, respectively) and biological interpretability. The analysis revealed two dominant clusters: one comprising aromatic (Trp, Phe, Tyr) and positively charged residues (Arg, His, Lys), and a second encompassing aliphatic, polar, and acidic residues. Notably, we identified a robust, high-stability association between Arginine and aromatic residues (consensus &gt; 0.85), suggesting a functional basis in cation–π interactions often missed by classical hydrophobicity scales. Conversely, Glycine and Proline emerged as distinct structural outliers with low co-clustering probabilities (&lt; 0.3). Sensitivity analysis demonstrated remarkable robustness, with core clusters persisting across 98.6% of descriptor subsets. These findings validate graph-theoretic descriptors as a powerful tool for refining amino acid alphabets, offering a robust framework for predicting mutation effects and guiding de novo protein design.</p>

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Unraveling protein secrets: machine learning unveils novel biologically significant associations among amino acids

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月13日

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  • Samuel Kakraba,
  • Aayire C. Yadem,
  • Kuukua E. Abraham,
  • Farhana S. Chaudhry,
  • Edmund F. Agyemang

摘要

Accurate classification of amino acids is fundamental to protein engineering and structural biology. Traditional schemes often rely on single-dimensional properties, potentially overlooking complex structure–function relationships. In this study, we present an automated, AI-driven hierarchical clustering pipeline utilizing 22 novel graph-theoretic molecular descriptors to uncover high-dimensional biochemical associations. Using average linkage with Manhattan distance, our approach achieved a high cophenetic correlation (0.847), significantly outperforming K-means and DBSCAN in both cluster quality (Silhouette score: 0.573 vs. 0.548 and 0.412, respectively) and biological interpretability. The analysis revealed two dominant clusters: one comprising aromatic (Trp, Phe, Tyr) and positively charged residues (Arg, His, Lys), and a second encompassing aliphatic, polar, and acidic residues. Notably, we identified a robust, high-stability association between Arginine and aromatic residues (consensus > 0.85), suggesting a functional basis in cation–π interactions often missed by classical hydrophobicity scales. Conversely, Glycine and Proline emerged as distinct structural outliers with low co-clustering probabilities (< 0.3). Sensitivity analysis demonstrated remarkable robustness, with core clusters persisting across 98.6% of descriptor subsets. These findings validate graph-theoretic descriptors as a powerful tool for refining amino acid alphabets, offering a robust framework for predicting mutation effects and guiding de novo protein design.