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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

Haiqing Zhao1, Donald Petrey1, Diana Murray1

  • 1Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032.

Proceedings of the National Academy of Sciences of the United States of America
|May 14, 2024
PubMed
Summary

We developed ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a new framework for assessing protein complex models using evolutionary data. ZEPPI enhances protein-protein interaction predictions by incorporating evolutionary information into structural model evaluations.

Keywords:
coevolutionprotein complex predictionprotein structureprotein-protein interactions

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Evaluating structural models of protein complexes is crucial for understanding biological functions.
  • Existing methods often focus on model quality rather than direct interaction evidence.
  • Incorporating evolutionary information can improve the accuracy of protein-protein interaction (PPI) predictions.

Purpose of the Study:

  • To introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a novel framework for evaluating structural models of protein complexes.
  • To leverage sequence coevolution and conservation data from interfacial residues to assess PPIs.
  • To enhance existing PPI prediction databases like PrePPI with evolutionary insights.

Main Methods:

  • ZEPPI calculates a score by comparing interface metrics from structural models to those of random residues.
  • It utilizes species-paired multiple sequence alignments, focusing on interfacial residues to enable use of shallow alignments.
  • The framework is applied proteome-wide to millions of dimeric complex models in the E. coli and human interactomes from the PrePPI database.

Main Results:

  • ZEPPI successfully incorporates evolutionary information into the PrePPI scoring function.
  • Performance evaluation on CASP-CAPRI decoys and experimental complexes demonstrates ZEPPI's utility.
  • ZEPPI identifies weak PPI signals missed by standard CAPRI scores and low-confidence PPIs from PrePPI.

Conclusions:

  • ZEPPI provides a robust method for evaluating structural models of protein complexes using evolutionary data.
  • The integration of ZEPPI with PrePPI offers a powerful tool for generating functional hypotheses about protein interactions.
  • ZEPPI's ability to detect subtle evolutionary signals enhances the prediction and validation of protein-protein interactions.