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Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Sparse autoencoders uncover biologically interpretable features in protein language model representations.

Onkar Gujral1,2, Mihir Bafna2, Eric Alm3,4

  • 1Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America
|August 19, 2025
PubMed
Summary

Sparse autoencoders extract interpretable features from protein language models (PLMs) without supervision. These features reveal biological insights, enhancing the explainability and trust of AI in life sciences.

Keywords:
interpretabilityprotein language modelssparse autoencoderstranscoders

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Area of Science:

  • Computational Biology
  • Artificial Intelligence in Life Sciences
  • Bioinformatics

Background:

  • Protein language models (PLMs) have advanced biological predictions but suffer from a "black-box" nature, limiting transparency and explainability.
  • Interpreting the features learned by PLMs is crucial for human-AI collaboration and understanding biological mechanisms.
  • Existing methods for feature interpretation often require supervision, which can be labor-intensive and may not capture all relevant information.

Purpose of the Study:

  • To develop an unsupervised method for extracting interpretable features from protein-level and amino acid-level representations of PLMs.
  • To assess the biological relevance and interpretability of the extracted sparse features.
  • To enhance the safety, trust, and explainability of PLMs in biological applications.

Main Methods:

  • Leveraged sparse autoencoders (SAEs) and transcoders, adapted from natural language processing, to extract features from the ESM2 PLM.
  • Performed feature extraction in a completely unsupervised manner, without relying on external biological labels or probes.
  • Utilized Anthropic's Claude AI to automate the interpretation of extracted sparse features.

Main Results:

  • SAEs successfully extracted interpretable sparse features from both protein-level and amino acid-level representations of ESM2.
  • Many extracted sparse features showed strong associations with Gene Ontology (GO) terms across all hierarchy levels.
  • Automated interpretation identified features corresponding to specific protein families (e.g., NAD Kinase, PTH), functions (e.g., methyltransferase activity), and sensory perceptions.

Conclusions:

  • SAEs provide a powerful unsupervised approach for disentangling biologically relevant information within PLM representations.
  • The extracted sparse features are more interpretable than raw PLM neurons, aiding biological insight discovery.
  • This work significantly advances the explainability and trustworthiness of PLMs, paving the way for deeper biological understanding.