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Related Experiment Video

Updated: Sep 6, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Interpretable pairwise distillations for generative protein sequence models.

Christoph Feinauer1,2, Barthelemy Meynard-Piganeau3,4, Carlo Lucibello1,2

  • 1Department of Computing Sciences, Bocconi University, Milan, Italy.

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Summary
This summary is machine-generated.

This study analyzes neural network (NN) models for protein sequences, finding that simpler pairwise models can effectively replicate complex NN performance in tasks like mutational effect prediction.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Generative models for protein sequences are crucial for applications like protein design and predicting mutational effects.
  • Neural network (NN) architectures excel due to their ability to capture complex, higher-order interactions in protein data.

Purpose of the Study:

  • To analyze two different NN models for protein sequences.
  • To assess the proximity of these NN models to simpler pairwise distributions.
  • To develop a method for extracting pairwise models from complex NN models.

Main Methods:

  • Analysis of two distinct neural network (NN) architectures for protein sequence modeling.
  • Application of an energy-based modeling framework to extract pairwise models from NN models.
  • Evaluation of extracted pairwise models against original NN models on tasks such as mutational effect prediction.

Main Results:

  • Extracted pairwise models successfully replicated the energies of the original NN models.
  • Pairwise models demonstrated comparable performance to NN models in predicting mutational effects.
  • Simpler, factorized models also achieved performance close to the original complex NN models.

Conclusions:

  • Complex neural network models for protein sequences can be effectively approximated by simpler pairwise models.
  • The energy-based modeling framework provides a viable method for extracting interpretable pairwise models from deep learning architectures.
  • Simpler models offer a computationally efficient alternative without significant loss of predictive power for certain protein-related tasks.