Researchers at MIT applied sparse autoencoders to protein language models (PLMs) to clarify how these AI systems predict protein function from amino acid sequences. This approach enhances interpretability, revealing the patterns PLMs detect about protein families and their roles. Published in the Proceedings of the National Academy of Sciences, the study addresses trust issues by providing insight into what protein-centric AI models 'understand,' distinct from structural predictors like AlphaFold.