Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models.

ABSTRACT: Protein language models (PLMs) are often assumed to capture evolutionary information by training on large protein sequence datasets. Yet it remains unclear whether PLMs can reason about evolution-that is, infer evolutionary relationships between sequences. We test this capability by evaluating whether standard PLM usage, frozen or fine-tuned embeddings with distance-based comparison, supports evolutionary reasoning. Existing PLMs consistently fail to recover phylogenetic structure, despite strong performance on sequence-level tasks such as masked-token and contact prediction. We present Phyla, a hybrid state-space and transformer model that jointly processes multiple sequences and is trained using a tree-based objective across 3,000 phylogenies spanning diverse protein families. Phyla outp

SUBMITTER: Ektefaie Y 

PROVIDER: S-EPMC11785049 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature