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Identifying 14-3-3 interactome binding sites with deep learning.

Laura van Weesep1, Rıza Özçelik1,2,3, Marloes Pennings1,4

  • 1Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology Eindhoven The Netherlands l.brunsveld@tue.nl f.grisoni@tue.nl.

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We developed a deep learning model to predict protein binding sites for 14-3-3 proteins, crucial for cellular signaling. The model achieved 75% accuracy and identified five new experimentally validated binding sites.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Protein-protein interactions are fundamental to biological processes and disease.
  • Identifying protein interaction sites, particularly for intrinsically disordered proteins, remains a significant challenge.
  • 14-3-3 proteins act as central hubs in cellular signaling networks, making their interactions critical to understand.

Purpose of the Study:

  • To develop a deep learning framework for predicting protein binding sites to 14-3-3 proteins.
  • To identify novel 14-3-3 binding sites in medically relevant proteins, including intrinsically disordered ones.
  • To provide a freely accessible web resource for predicting 14-3-3 binding sites.

Main Methods:

  • Systematic testing of various deep learning approaches.
  • Development of an ensemble deep learning model for sequence binding prediction.
  • Prospective application of the model to ~300 medically relevant protein sequences.
  • Experimental validation of top predicted peptide sequences using wet-lab techniques.
  • Structural analysis using X-ray crystallography and molecular dynamics simulations.

Main Results:

  • An ensemble deep learning model achieved 75% balanced accuracy on external sequences for predicting 14-3-3 binding.
  • Experimental validation confirmed binding for five out of eight predicted peptide sequences, with dissociation constants (Kd) ranging from 1.6 ± 0.1 μM to 70 ± 5 μM.
  • Novel binding sites were identified in medically relevant proteins, including those implicated in diseases like Alzheimer's.
  • The study demonstrated deep learning's capability to predict interactions involving intrinsically disordered proteins.

Conclusions:

  • The developed deep learning framework effectively predicts protein binding sites for 14-3-3 proteins.
  • The identified novel binding sites offer new avenues for investigating protein function and therapeutic targeting.
  • The study underscores the power of deep learning in deciphering complex protein-protein interactions.
  • A publicly available web tool was created to facilitate further research on 14-3-3 interactions.