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PLM-interact: extending protein language models to predict protein-protein interactions.

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Protein language models (PLMs) can now predict protein-protein interactions by jointly encoding protein pairs. This novel approach, PLM-interact, achieves state-of-the-art results across species and mutation effects.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in biology

Background:

  • Protein structure prediction from sequence is accurate, but protein-protein interaction (PPI) prediction remains challenging.
  • Existing methods using protein language models (PLMs) often overlook the physical interaction context.
  • There is a need for advanced computational methods to accurately predict complex biomolecular relationships.

Purpose of the Study:

  • To evaluate and adapt protein language models (PLMs) for predicting protein-protein interactions.
  • To develop a novel approach, PLM-interact, that jointly encodes interacting protein pairs.
  • To assess the model's performance on cross-species interaction prediction, mutation effects, and virus-host interactions.

Main Methods:

  • Retraining protein language models (PLMs) specifically for protein-protein interaction prediction.
  • Developing PLM-interact, a method that jointly encodes protein pairs, inspired by natural language processing's next-sentence prediction.
  • Fine-tuning PLM-interact to predict the impact of mutations on protein interactions.
  • Evaluating performance on a cross-species benchmark dataset and virus-host interaction prediction.

Main Results:

  • PLM-interact achieves state-of-the-art performance on a cross-species protein-protein interaction prediction benchmark.
  • The model demonstrates high accuracy when trained on human data and tested on diverse species like mouse, fly, worm, E. coli, and yeast.
  • A fine-tuning method successfully detects mutation effects on protein interactions.
  • The approach outperforms existing methods in predicting virus-host protein interactions.

Conclusions:

  • Protein language models can be effectively extended to learn complex biomolecular relationships, including protein-protein interactions, directly from sequences.
  • PLM-interact represents a significant advancement in predicting protein-protein interactions and related biological phenomena.
  • This work highlights the potential of large language models in deciphering intricate molecular interactions in biology.