Cargando…
Generative power of a protein language model trained on multiple sequence alignments
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 novel sequences belonging to protein families....
Autores principales: | Sgarbossa, Damiano, Lupo, Umberto, Bitbol, Anne-Florence |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10038667/ https://www.ncbi.nlm.nih.gov/pubmed/36734516 http://dx.doi.org/10.7554/eLife.79854 |
Ejemplares similares
-
Protein language models trained on multiple sequence alignments learn phylogenetic relationships
por: Lupo, Umberto, et al.
Publicado: (2022) -
Impact of phylogeny on structural contact inference from protein sequence data
por: Dietler, Nicola, et al.
Publicado: (2023) -
Deep generative models for T cell receptor protein sequences
por: Davidsen, Kristian, et al.
Publicado: (2019) -
Multiple alignment of protein sequences with repeats and rearrangements
por: Phuong, Tu Minh, et al.
Publicado: (2006) -
Protein multiple sequence alignment by hybrid bio-inspired algorithms
por: Cutello, Vincenzo, et al.
Publicado: (2011)