Cargando…
Embeddings from deep learning transfer GO annotations beyond homology
Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation tr...
Autores principales: | Littmann, Maria, Heinzinger, Michael, Dallago, Christian, Olenyi, Tobias, Rost, Burkhard |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806674/ https://www.ncbi.nlm.nih.gov/pubmed/33441905 http://dx.doi.org/10.1038/s41598-020-80786-0 |
Ejemplares similares
-
Protein embeddings and deep learning predict binding residues for various ligand classes
por: Littmann, Maria, et al.
Publicado: (2021) -
Embeddings from protein language models predict conservation and variant effects
por: Marquet, Céline, et al.
Publicado: (2021) -
Clustering FunFams using sequence embeddings improves EC purity
por: Littmann, Maria, et al.
Publicado: (2021) -
Contrastive learning on protein embeddings enlightens midnight zone
por: Heinzinger, Michael, et al.
Publicado: (2022) -
LambdaPP: Fast and accessible protein‐specific phenotype predictions
por: Olenyi, Tobias, et al.
Publicado: (2023)