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Research questionHow can protein language models predict mutation effects accurately without the cost of larger dense models?Larger dense Transformer backbones increase computational cost, yet their added capacity does not reliably improve predictions of how amino-acid substitutions affect protein function. The challenge is preserving mutation-sensitive and structural representations under tighter computational budgets.
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Latest papersRecent research connected to this question, newest first.ProtLingo: Efficient Protein Language Modeling via Conditional Memory and Expert RoutingThe source studies ProtLingo, which augments a pretrained single-sequence protein language model with conditional local memory and sparse expert routing. Evidence covers protein fitness prediction, FLIP mutation-effect benchmarks, and supervised contact prediction using a 150M-scale backbone; it does not establish broader deployment or access requirements.research paper · Sep 4, 2026
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