Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
Peptide ligands of G protein-coupled receptors constitute valuable natural lead structures for the development of highly selective drugs and high-affinity tools to probe ligand-receptor interaction. Currently, pharmacological and metabolic modification of natural peptides involves either an iterativ...
Guardado en:
Autores principales: | Sebastian Bandholtz, Jörg Wichard, Ronald Kühne, Carsten Grötzinger |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2012
|
Materias: | |
Acceso en línea: | https://doaj.org/article/0805680bab184e97b215ad6c2f0cb4f2 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Improved GPCR ligands from nanobody tethering
por: Ross W. Cheloha, et al.
Publicado: (2020) -
GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
por: Marawan Ahmed, et al.
Publicado: (2021) -
Structure-guided development of heterodimer-selective GPCR ligands
por: Harald Hübner, et al.
Publicado: (2016) -
A scalable peptide-GPCR language for engineering multicellular communication
por: Sonja Billerbeck, et al.
Publicado: (2018) -
Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response
por: Besma Benredjem, et al.
Publicado: (2019)