Mapping the glycosyltransferase fold landscape using interpretable deep learning
Glycosyltransferases (GT) are proteins that display extensive sequence and functional variation on a subset of 3D folds. Here, the authors use interpretable deep learning to predict 3D folds from sequence without the need for sequence alignment, which also enables the prediction of GTs with new fold...
Guardado en:
Autores principales: | Rahil Taujale, Zhongliang Zhou, Wayland Yeung, Kelley W. Moremen, Sheng Li, Natarajan Kannan |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f5c17c58d05e4400857aec9c85fdb18c |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Mapping the kinetic barriers of a Large RNA molecule's folding landscape.
por: Jörg C Schlatterer, et al.
Publicado: (2014) -
A <italic toggle="yes">Toxoplasma</italic> Prolyl Hydroxylase Mediates Oxygen Stress Responses by Regulating Translation Elongation
por: Celia Florimond, et al.
Publicado: (2019) -
Infusing theory into deep learning for interpretable reactivity prediction
por: Shih-Han Wang, et al.
Publicado: (2021) -
Interpretable survival prediction for colorectal cancer using deep learning
por: Ellery Wulczyn, et al.
Publicado: (2021) -
Bacterial glycosyltransferase-mediated cell-surface chemoenzymatic glycan modification
por: Senlian Hong, et al.
Publicado: (2019)