Damiano Sgarbossa

RAG-ESM improves pretrained protein language models by incorporating homology information through retrieval-augmentation, achieving better performance in sequence prediction tasks.

ProtMamba is a homology-aware, alignment-free protein language model that outperforms state-of-the-art models in homolog-conditioned sequence generation.

DiffPaSS improves pairing of interacting protein sequences using differentiable optimization, outperforming existing methods on benchmark datasets.

This paper introduces DiffPALM, a method to pair interacting protein sequences using masked language modeling, improving structure prediction of protein complexes.

The paper demonstrates that MSA Transformer can generate protein sequences that perform as well as natural sequences in homology, coevolution, and structure-based measures, outperforming Potts models in some cases.

This paper shows that protein language models using MSA column attentions capture phylogenetic relationships and distinguish coevolutionary signals from historical contingency.