Cargando…
Structure-based neural network protein–carbohydrate interaction predictions at the residue level
Carbohydrates dynamically and transiently interact with proteins for cell–cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential...
Autores principales: | Canner, Samuel W., Shanker, Sudhanshu, Gray, Jeffrey J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318439/ https://www.ncbi.nlm.nih.gov/pubmed/37409346 http://dx.doi.org/10.3389/fbinf.2023.1186531 |
Ejemplares similares
-
Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level
por: Canner, Samuel W., et al.
Publicado: (2023) -
De novo prediction of RNA–protein interactions with graph neural networks
por: Arora, Viplove, et al.
Publicado: (2022) -
Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
por: Brysbaert, Guillaume, et al.
Publicado: (2021) -
RIP-MD: a tool to study residue interaction networks in protein molecular dynamics
por: Contreras-Riquelme, Sebastián, et al.
Publicado: (2018) -
DeepNC: a framework for drug-target interaction prediction with graph neural networks
por: Tran, Huu Ngoc Tran, et al.
Publicado: (2022)