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20112026
most citedProtein language models trained on multiple sequence alignments learn phylogenetic relationships

75 citations · 408 across the 27 of their papers we have counts for

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11 papers · 1 filter

q-bio.BM2026

Out-of-equilibrium selection pressure enhances inference from protein sequence data

Nicola Dietler, Cyril Malbranke, Anne-Florence Bitbol

Homologous proteins have similar three-dimensional structures and biological functions that shape their sequences. The resulting coevolution-driven correlations underlie methods fr…

q-bio.BM2024★ 3 cited

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

Umberto Lupo, Damiano Sgarbossa, Martina Milighetti +1

Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due…

q-bio.BM2023

Pairing interacting protein sequences using masked language modeling

Umberto Lupo, Damiano Sgarbossa, Anne-Florence Bitbol

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 p…

q-bio.BM2022★ 9 cited

Impact of phylogeny on structural contact inference from protein sequence data

Nicola Dietler, Umberto Lupo, Anne-Florence Bitbol

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino-aci…

q-bio.BM2022★ 14 cited

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Carlos A. Gandarilla-Perez, Sergio Pinilla, Anne-Florence Bitbol +1

Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequ…

q-bio.BM2022★ 55 cited

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

Damiano Sgarbossa, Umberto Lupo, Anne-Florence Bitbol

Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to prot…