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The Critical Assessment of Predicted Interactions (CAPRI) experiment shows that protein docking and scoring methods remain robust. Automatic servers now rival human performance in predicting protein-protein interactions.

Keywords:
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Area of Science:

  • Computational biology
  • Structural biology
  • Biophysics

Background:

  • The Critical Assessment of Predicted Interactions (CAPRI) is a community-wide experiment evaluating protein-protein interaction prediction methods.
  • The fifth CAPRI evaluation covers 15 diverse targets from 2010-2012, including classical docking and novel challenges.

Purpose of the Study:

  • To assess the performance of docking and scoring methods for protein-protein interactions.
  • To evaluate new challenges in predicting binding affinity and the effects of mutations.
  • To identify areas for improvement in computational modeling and protein design.

Main Methods:

  • Analysis of predictions submitted by the CAPRI community for 15 diverse targets.
  • Evaluation of both "classical" docking and scoring problems.
  • Assessment of performance on new challenges including water molecule positioning, relative binding affinity, and mutation effects.

Main Results:

  • Docking and scoring method performance remained robust across diverse targets.
  • Automatic docking servers showed significantly improved performance, matching human prediction accuracy.
  • Participant performance on novel challenges was mediocre, but yielded insights for future method development.

Conclusions:

  • Computational methods for predicting protein-protein interactions are reliable and improving.
  • Automatic docking servers are becoming increasingly competitive with expert human predictions.
  • Further research into predicting binding affinity and mutation effects is needed to advance protein modeling and design.