Unveiling Interpretable Features in Protein Language Models through Sparse Autoencoders

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Protein language models (PLMs) have advanced the prediction of protein structure and function by analyzing amino acid sequences. Despite their progress, their internal mechanisms remain unclear. Sparse Autoencoders (SAEs) provide a method to uncover and interpret the features learned by PLMs, revealing meaningful biological patterns. Researchers from Stanford have developed a framework using SAEs to analyze PLMs like ESM-2, identifying thousands of latent features correlating with known biological concepts. Their tool, InterPLM, maps these features to annotations, offering a deeper understanding of protein structures and functions, and enhancing protein databases by identifying annotation gaps.