Protein Function Prediction with Pretrained ProtT5 Embeddings and Gradient Boosting (opens in new tab)

Protein function prediction remains a central challenge in computational biology due to the extreme sparsity and long-tail distribution of Gene Ontology (GO) [1] annotations. Advances in protein language models enable the extraction of dense, fixed-length representations from amino acid sequences, offering a scalable alternative to hand-picked features such as physicochemical properties. In this work, we evaluate a transformer-based embedding approach using ProtT5-XL combined with classical a...