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A paired sequence language model for protein-protein interaction modeling.

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A new Protein Pair Language Model (PPLM) learns protein-protein interactions (PPIs) by analyzing paired sequences. This approach advances PPI prediction, binding affinity, and contact prediction, outperforming existing methods.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Protein structure and function

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular mechanisms and drug development.
  • Current protein language models primarily focus on single protein sequences, limiting their ability to capture interaction dynamics.
  • There is a need for models that can effectively learn from paired protein sequences to understand PPIs.

Purpose of the Study:

  • To introduce a novel Protein Pair Language Model (PPLM) capable of jointly encoding paired protein sequences.
  • To develop specialized PPLM-based tools (PPLM-PPI, PPLM-Affinity, PPLM-Contact) for predicting binary interactions, binding affinity, and interface contacts.
  • To demonstrate the superior performance of PPLM in various PPI prediction tasks.

Main Methods:

  • Development of a Protein Pair Language Model (PPLM) for joint sequence encoding.
  • Application of PPLM to create models for binary interaction (PPLM-PPI), binding affinity (PPLM-Affinity), and contact prediction (PPLM-Contact).
  • Extensive experimental validation on large-scale datasets across different species and complex biological systems.

Main Results:

  • PPLM-PPI achieved state-of-the-art results in binary protein-protein interaction prediction across species.
  • PPLM-Affinity demonstrated superior binding affinity prediction compared to ESM2 and structure-based methods, especially for antibody-antigen and TCR-pMHC complexes.
  • PPLM-Contact outperformed existing predictors in inter-protein contact prediction and interface residue recognition.

Conclusions:

  • Co-represented language models, like PPLM, offer a powerful new paradigm for computational modeling of protein-protein interactions.
  • The PPLM framework significantly advances the prediction accuracy for various aspects of PPIs, including interactions, affinity, and contacts.
  • This work paves the way for improved understanding of cellular processes and accelerated therapeutic discovery through enhanced PPI prediction.