Reproduce “No Generation without Representation: Efficient Causal Protein Language Models Enable Zero-Shot Fitness Estimation” on OpenResearch

About this paper

Furkan Eris

Proust, a 309M-parameter causal protein language model, unifies high-quality protein fitness estimation and sequence generation by achieving competitive performance with larger masked language models on substitutions and setting a new state-of-the-art for indel prediction, all while utilizing 62-229 times less training compute. The model also offers interpretable insights into protein evolution and structure from sequence data alone.

Key insights

• Causal protein language models, when architecturally optimized, can achieve performance competitive with or superior to masked models for protein fitness prediction, bridging the long-standing divide between representation and generation tasks.
• Specific architectural components and optimization strategies from general large language models (e.g., GQA-S2 attention, Canon layers, Muon optimizer) are highly transferable and effective in the protein domain, enabling significant computational efficiency gains.
• Proust implicitly learns fundamental biological principles such as evolutionary constraint, secondary structure propensity, and functional motif recognition directly from protein sequence data, without explicit structural supervision.
• The standard deviation of per-position entropy within the model serves as a reliable, inexpensive heuristic to predict when expensive retrieval augmentation will be beneficial for fitness estimation, optimizing inference compute.

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