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Machine Learning Approaches for Predicting Protein Complex Similarity.

Roshanak Farhoodi1, Bahar Akbal-Delibas2, Nurit Haspel1

  • 11 Department of Computer Science, University of Massachusetts Boston , Boston, Massachusetts.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 18, 2016
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts protein complex structures, reducing false positives in protein-protein docking. This approach estimates structural similarity, improving the prediction of native-like protein interactions.

Keywords:
RMSD predictionmachine learningneural networksprotein docking and refinementscoring functions

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in structural biology

Background:

  • Protein-protein docking aims to predict complex structures but struggles with discriminating native-like conformations from false positives.
  • Current docking scoring functions often fail due to limitations in accurately capturing the complex relationship between intermolecular forces and structural similarity.
  • State-of-the-art methods still produce numerous false positives, hindering accurate prediction of protein binding.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting the similarity of docked protein complex structures to their native conformations.
  • To improve the accuracy of protein-protein docking by directly predicting structural deviations (RMSD) rather than relying solely on ranking candidate structures.
  • To assess the performance of different neural network architectures, including two-layer neural networks, multilayer neural networks, and Restricted Boltzmann Machines.

Main Methods:

  • Trained various machine learning models, including neural networks and Restricted Boltzmann Machines, using extensive datasets of unbound protein complexes generated by RosettaDock and PyDock.
  • Validated the predictive models using a set of refinement candidate structures.
  • Focused on predicting the root mean squared deviation (RMSD) between predicted and native complex structures.

Main Results:

  • Achieved highly accurate predictions of protein complex RMSDs, with error margins often below 1.5 Å for structures with RMSD values up to 7 Å.
  • Demonstrated robust performance even with protein samples having larger RMSD values (up to 27 Å), maintaining a relatively small average prediction error.
  • Machine learning models showed potential in ranking candidate structures and predicting their similarity to native conformations.

Conclusions:

  • Machine learning approaches offer a promising avenue for improving the accuracy of protein-protein docking by directly predicting structural similarity.
  • The developed models can effectively discriminate native-like structures from false positives, addressing a key challenge in the field.
  • This work highlights the potential of advanced computational methods to enhance our understanding and prediction of protein-protein interactions.