Protein Function Prediction with Contrastive Alignment

Predicting protein function from sequence is a central challenge in computational biology. While existing methods rely heavily on structured ontologies or similaritybased techniques, they often lack the flexibility to express structure-free functional descriptions and novel biological functions. In this work, we introduce Prot2TextV2, a novel multimodal sequence-to-text model that generates free-form natural language descriptions of protein function directly from amino acid sequences. Our method combines a protein language model as a sequence encoder (ESM-3B) and a decoder-only language model (LLaMA-3.1-8B-Instruct)