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The paper introduces ProtoMech, a framework for mechanistic interpretability of protein language models (pLMs) that uses cross-layer transcoders to learn sparse latent representations capturing the model's full computational circuitry. By jointly analyzing representations across layers of ESM2, ProtoMech identifies compressed circuits that retain significant performance on protein family classification and function prediction while using only a small fraction of the latent space. Steering along these identified circuits enables high-fitness protein design, demonstrating the framework's utility in understanding and manipulating pLM behavior.
Uncover the hidden computational circuits within protein language models and steer them to design better proteins.
Protein language models (pLMs) have emerged as powerful predictors of protein structure and function. However, the computational circuits underlying their predictions remain poorly understood. Recent mechanistic interpretability methods decompose pLM representations into interpretable features, but they treat each layer independently and thus fail to capture cross-layer computation, limiting their ability to approximate the full model. We introduce ProtoMech, a framework for discovering computational circuits in pLMs using cross-layer transcoders that learn sparse latent representations jointly across layers to capture the model's full computational circuitry. Applied to the pLM ESM2, ProtoMech recovers 82-89% of the original performance on protein family classification and function prediction tasks. ProtoMech then identifies compressed circuits that use<1% of the latent space while retaining up to 79% of model accuracy, revealing correspondence with structural and functional motifs, including binding, signaling, and stability. Steering along these circuits enables high-fitness protein design, surpassing baseline methods in more than 70% of cases. These results establish ProtoMech as a principled framework for protein circuit tracing.