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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Related Experiment Video

Updated: Jul 2, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

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Published on: November 15, 2017

Probe-based identification of metal-binding sites using deep learning representations.

Shijie Xu1,2, Akira Onoda3,4

  • 1Graduate School of Environmental Science, Hokkaido University, Sapporo, Japan.

Nature Communications
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

We developed PRIME, a deep learning tool that accurately predicts metal-binding sites in proteins. This method combines evolutionary and structural data for improved metalloprotein identification.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Metalloproteins are crucial for cellular functions, utilizing metal ions as cofactors.
  • Identifying metal-binding sites is challenging due to protein complexity and metal ion promiscuity.
  • Existing computational methods lack accuracy and sufficient data.

Purpose of the Study:

  • To introduce PRIME, a novel hybrid deep learning framework for accurate and efficient prediction of metal-binding sites.
  • To overcome limitations of current computational approaches in metalloprotein research.

Main Methods:

  • PRIME integrates protein language models and pre-trained structure models for sequence and structure analysis.
  • A probe generation algorithm bridges sequence- and structure-based predictions.
  • The framework combines evolutionary and structural signals for enhanced prediction.

Main Results:

  • PRIME demonstrates superior performance compared to existing methods for various metal ions (e.g., Zn, Ca, K, Na).
  • Ablation studies confirm that pre-trained structure models significantly improve prediction accuracy.
  • Case studies using AlphaFold2 models highlight PRIME's potential for high-throughput metalloproteomics.

Conclusions:

  • PRIME offers a significant advancement in predicting metal-binding sites, addressing key challenges in the field.
  • The framework's ability to leverage both sequence and structural information provides a powerful tool for metalloprotein research.
  • PRIME shows promise for large-scale metalloproteomics studies and understanding protein-metal interactions.