Cargando…
BIOPHYSICAL PREDICTION OF PROTEIN-PEPTIDE INTERACTIONS AND SIGNALING NETWORKS USING MACHINE LEARNING
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 are known), low binding affinities, and sensitivit...
Autores principales: | Cunningham, Joseph M., Koytiger, Grigoriy, Sorger, Peter K., AlQuraishi, Mohammed |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004877/ https://www.ncbi.nlm.nih.gov/pubmed/31907444 http://dx.doi.org/10.1038/s41592-019-0687-1 |
Ejemplares similares
-
A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks
por: AlQuraishi, Mohammed, et al.
Publicado: (2014) -
ProteinNet: a standardized data set for machine learning of protein structure
por: AlQuraishi, Mohammed
Publicado: (2019) -
Protein structure prediction by AlphaFold2: are attention and symmetries all you need?
por: Bouatta, Nazim, et al.
Publicado: (2021) -
High-throughput deep learning variant effect prediction with Sequence UNET
por: Dunham, Alistair S., et al.
Publicado: (2023) -
An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system
por: AlQuraishi, Mohammed, et al.
Publicado: (2015)