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Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

Csilla Várnai1, Nikolas S Burkoff1, David L Wild1

  • 1Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom.

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|February 7, 2017
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Summary
This summary is machine-generated.

This study introduces a new interface scoring method using correlated mutation measures (CMMs) for protein complexes. The approach effectively re-ranks docking decoys, improving predictions even with limited evolutionary data.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Evolutionary information from multiple sequence alignments (MSAs) aids in identifying protein complex interfaces through co-conservation or co-mutation analysis.
  • Correlated mutation measures (CMMs), like direct information, have emerged to pinpoint interacting residue pairs, but typically require large, high-quality MSAs.
  • Previous methods focused on ideal MSAs, leaving a gap in analyzing typical, smaller datasets.

Purpose of the Study:

  • To develop and evaluate an interface-level CMM score for re-ranking protein complex docking decoys using typical MSAs (fewer than 400 sequences).
  • To assess the performance of the developed CMM score against existing methods like the complementarity trace score.
  • To investigate the synergistic effect of combining co-mutation and co-conservation information for enhanced prediction accuracy.

Main Methods:

  • Applied a maximum entropy-based CMM at the residue level to a dataset of 79 intramolecular protein complexes.
  • Developed an interface-level CMM score to re-rank docking decoys.
  • Integrated the CMM score with other features including interface residue count, knowledge-based potentials, and site variability scores.

Main Results:

  • The interface-level CMM score demonstrated favorable performance when combined with other scoring terms, outperforming the complementarity trace score in certain contexts.
  • Combining CMM co-mutation information with complementarity trace co-conservation information yielded the best prediction performance.
  • This combined approach successfully predicted near-native structures as the top prediction for 41% of the dataset.
  • The method's effectiveness was shown even with smaller, more typical MSAs.

Conclusions:

  • The developed interface-level CMM score is a valuable tool for re-ranking docking decoys, particularly for protein complexes with limited MSAs.
  • Combining orthogonal evolutionary information from co-mutation (CMM) and co-conservation (complementarity trace) significantly enhances prediction accuracy.
  • The method shows promise for improving protein complex interface prediction across various MSA qualities and sizes.