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Topsy-Turvy: integrating a global view into sequence-based PPI prediction.

Rohit Singh1, Kapil Devkota2, Samuel Sledzieski1

  • 1Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Bioinformatics (Oxford, England)
|June 27, 2022
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Summary

Topsy-Turvy is a novel deep-learning method for predicting protein-protein interactions (PPIs) using only sequence data. It achieves state-of-the-art performance across species, enabling genome-scale predictions for non-model organisms.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Systems biology

Background:

  • Protein-protein interactions (PPIs) are crucial for cellular functions.
  • Existing computational methods for PPI prediction are either sequence-based ('bottom-up') or network-based ('top-down').
  • Integrating global network insights into sequence-based prediction remains a challenge.

Purpose of the Study:

  • To introduce Topsy-Turvy, a novel deep-learning method for PPI prediction.
  • To develop a hybrid model (TT-Hybrid) for enhanced prediction accuracy in species with existing PPI data.
  • To enable accurate, interpretable, and genome-scale PPI prediction, particularly for non-model organisms.

Main Methods:

  • Topsy-Turvy utilizes a sequence-based, multi-scale deep-learning architecture.
  • It employs a transfer-learning approach during training, incorporating global and molecular-level interaction patterns.
  • TT-Hybrid integrates Topsy-Turvy with a network-based link prediction model.

Main Results:

  • Topsy-Turvy achieves state-of-the-art performance in cross-species PPI prediction.
  • TT-Hybrid outperforms constituent models and other methods for both well- and sparsely-characterized proteins.
  • Both methods demonstrate genome-scale feasibility and scalability, outperforming methods like AlphaFold-Multimer.

Conclusions:

  • Topsy-Turvy and TT-Hybrid offer accurate, generalizable, and scalable solutions for PPI prediction.
  • These methods facilitate comprehensive mapping of protein interactions in both model and non-model organisms.
  • The approach unlocks new possibilities for understanding protein organization and function at a genome-wide scale.