Publications by Damiano Sgarbossa

ProtMamba: a homology-aware but alignment-free protein state space model

Anne-Florence Raphaëlle Bitbol, Damiano Sgarbossa

Motivation Protein language models are enabling advances in elucidating the sequence-to-function mapping, and have important applications in protein design. Models based on multiple sequence alignments efficiently capture the evolutionary information in ho ...

Oxford University Press (OUP)2025

RAG-ESM: Improving Pretrained Protein Language Models via Sequence Retrieval

Anne-Florence Raphaëlle Bitbol, Damiano Sgarbossa

Protein language models are significantly advancing the modeling of sequence-function relationships. However, most of them are not directly informed of homology and evolutionary relationships between protein sequences. Here, we propose a method to make the ...

American Physical Society (APS)2025

Revealing and exploiting coevolution through protein language models

Damiano Sgarbossa

Protein sequences carry rich evolutionary information that reflects both structural and functional constraints, as well as shared ancestry. In this thesis, I explore how protein language models (pLMs) can uncover and leverage these signals, particularly co ...

EPFL2025

Pairing interacting protein sequences using masked language modeling

Anne-Florence Raphaëlle Bitbol, Umberto Lupo, Damiano Sgarbossa

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such ...

National Academy of Sciences2024

DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores

Anne-Florence Raphaëlle Bitbol, Umberto Lupo, Damiano Sgarbossa

MOTIVATION: Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due to their common evolutionary history, and feature correlations i ...

2024

Generative power of a protein language model trained on multiple sequence alignments

Anne-Florence Raphaëlle Bitbol, Umberto Lupo, Damiano Sgarbossa

Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating no ...

eLIFE SCIENCES PUBL LTD2023

Delay effects on the stability of large ecosystems

Damiano Sgarbossa

The common intuition among the ecologists of the midtwentieth century was that large ecosystems should bemore stable than those with a smaller number of species. This view was challenged by Robert May, who found a stability bound for randomly assembled eco ...

National Academy of Sciences2022

Protein language models trained on multiple sequence alignments learn phylogenetic relationships

Anne-Florence Raphaëlle Bitbol, Umberto Lupo, Damiano Sgarbossa

Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoFormer, ...

Nature Portfolio2022