arXiv:2608. 12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology.
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Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on large corpora of protein sequence data, are widely used to translate amino acid sequences into latent-space embeddings, ready for use in diverse downstream tasks (DTs).
The paper introduces a method that uses orthogonal projection to remove the influence of known biochemical features from protein language model (PLM) embeddings, allowing the authors to assess how much these features contribute to protein fitness predictions. By applying this technique to high‑order and interaction effects, they demonstrate that eliminating these interpretable features reduces downstream classifier performance, indicating that PLM embeddings encode patterns correlated with biochemical properties. The authors also show that these biochemical features explain a substantial portion of the variance in the classifier’s predictions, suggesting that PLM embeddings capture biologically relevant information.
The paper introduces Murmur2Vec, a lightweight, alignment‑free embedding that uses k‑mer counts hashed with MurmurHash to create a compact representation for biological sequences. It provides a full theoretical analysis, including bias/variance formulas, a Johnson–Lindenstrauss‑style concentration bound, and an excess‑risk bound that clarifies the trade‑off between hash‑table size and classifier performance. Empirically, Murmur2Vec matches or surpasses a fine‑tuned 650M‑parameter ESM‑2 protein language model across several classification tasks, including SARS‑CoV‑2 spike lineage and HIV‑1 Env subtype identification.
The paper introduces a scalable method to interpret sparse autoencoder (SAE) features in the ESM-2 protein language model by leveraging geometrically inspired features of the protein α‑carbon backbone. Across 8M layers of ESM-2, a false discovery rate–controlled analysis shows that local geometry is significantly associated with many SAE features, revealing substructure within known biological labels and enabling annotation of unannotated metagenomic proteins. Ablation experiments demonstrate that removing these geometric features shifts ESM-2’s predicted contact maps toward the descriptor, linking mechanistic interpretability with structural biology.
MT-ProtBERT is a multi‑task extension of ProtBERT designed for classifying intrinsically disordered proteins (IDPs) in low‑data settings. It combines Dynamic Window Masking, a Multi‑Scale 1D Convolutional classifier, and auxiliary biochemistry‑informed objectives to jointly optimize masked language modeling and domain‑specific tasks. In experiments on phosphorylation site prediction and protein compaction prediction, MT‑ProtBERT outperforms the RNN‑based IDP model PARROT across all limited‑data tasks.
arXiv:2603.18908v5 Announce Type: replace Abstract: Independently trained language models often learn compatible late-stage representations, despite differences in training objectives, architectures,...