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Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions.

Nathalie Lagarde1, Alessandra Carbone2,3, Sophie Sacquin-Mora1

  • 1Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie, Paris, 75005, France.

Proteins
|April 18, 2018
PubMed
Summary

Computational methods predict protein binding sites, but accuracy varies. This study reveals that seemingly incorrect predictions often identify novel interaction sites with previously unknown protein or nucleic acid partners.

Keywords:
alternate partnersbinding site predictionsdockingmultiple binding sitesprotein-protein interfaces

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Area of Science:

  • Structural biology
  • Computational biology
  • Bioinformatics

Background:

  • Protein-protein interactions (PPIs) are crucial for biological processes.
  • In silico methods, like cross-docking, aid in identifying PPIs and predicting binding sites.
  • Predicting binding sites can be challenging due to multiple interaction partners and surface sites.

Purpose of the Study:

  • To evaluate the accuracy of protein binding site predictions using cross-docking simulations.
  • To investigate the reasons behind low-quality binding site predictions.
  • To identify novel protein and nucleic acid interaction partners.

Main Methods:

  • Performed complete cross-docking simulations for 358 proteins.
  • Utilized two scoring schemes that account for multiple binding sites.
  • Analyzed cases with low prediction quality (AUC < 0.5) to identify alternate binding sites.

Main Results:

  • Overall good performance in binding site prediction.
  • Identified 68 cases with low prediction quality, often due to alternate binding site predictions.
  • Demonstrated that these alternate sites frequently represent interactions with unincluded partners (proteins and nucleic acids).

Conclusions:

  • Cross-docking is a valuable tool for predicting protein binding sites, even with multiple partners.
  • Low-quality predictions can uncover previously unknown interaction sites and partners.
  • Understanding structural determinants of multi-site interactions is key for accurate prediction.