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Machine learning models using protein sequence embeddings can accurately distinguish catalytic from structural zinc sites. This sequence-based method offers a powerful new tool for large-scale metalloproteome annotation.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Zinc ions play critical roles in protein function, acting as catalytic cofactors or structural elements.
  • Differentiating these zinc site functions based solely on amino acid sequence is difficult due to similar coordination geometries.

Purpose of the Study:

  • To investigate if protein sequence embeddings can accurately classify zinc sites as catalytic or structural.
  • To develop a sequence-only computational method for annotating zinc metalloproteomes.

Main Methods:

  • Utilized ESM-2 embeddings, a deep learning model for protein sequences.
  • Trained machine learning classifiers on sequence-diverse zinc proteins.
  • Employed attention analysis to interpret model predictions and identify key residue interactions.

Main Results:

  • Achieved high accuracy (ROC-AUC 0.93-0.97) in classifying zinc sites using ESM-2 embeddings.
  • Significantly outperformed a traditional motif-based approach (AUC = 0.759).
  • Attention analysis revealed specific interactions between catalytic histidine ligands and second-shell carboxylate residues.

Conclusions:

  • Evolutionary sequence patterns encode information about the extended hydrogen-bonding networks that differentiate catalytic and structural zinc sites.
  • A sequence-only approach provides a complementary and scalable method to structure-based techniques for metalloproteome annotation.