# PALM

Protein sequence databases are growing far faster than curated annotation, and existing language models learn only from sequence likelihood, so InterPro domain and functional-site knowledge stays indirect. PALM builds on ESM2 with LoRA adaptation, learned layer aggregation and a bidirectional LSTM encoder, and uses a soft-target loss that lets one residue keep several valid labels. On 2,309,606 held-out proteins it reached domain- and site-occurrence F1 of 0.941 and 0.877, and still 0.857 and 0.828 in the subset below 20% sequence identity.

Protein sequence databases are growing far faster than curated annotation, and existing language models learn only from sequence likelihood, so InterPro domain and functional-site knowledge stays indirect. PALM builds on ESM2 with LoRA adaptation, learned layer aggregation and a bidirectional LSTM encoder, and uses a soft-target loss that lets one residue keep several valid labels. On 2,309,606 held-out proteins it reached domain- and site-occurrence F1 of 0.941 and 0.877, and still 0.857 and 0.828 in the subset below 20% sequence identity.