A new study published on arXiv reveals that protein language models (pLMs) fail to accurately predict protein contacts when the amino acid alphabet is arbitrarily relabeled. While classical methods like Potts models remain invariant to this transformation, pLMs across various architectures and scales, including ESM-2 and MSA Transformer, show significant degradation in contact map accuracy. This suggests that pLMs rely on specific residue identities rather than purely coevolutionary statistics, highlighting a difference in how these models compute contacts and a potential bias in their representations. AI
IMPACT Highlights potential biases in protein language models, suggesting a need for more robust evaluation methods beyond standard benchmarks.
RANK_REASON Academic paper detailing a new diagnostic framework for evaluating protein language models. [lever_c_demoted from research: ic=1 ai=1.0]
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