Cargando…
PIQLE: protein–protein interface quality estimation by deep graph learning of multimeric interaction geometries
MOTIVATION: Accurate modeling of protein–protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contribut...
Autores principales: | Shuvo, Md Hossain, Karim, Mohimenul, Roche, Rahmatullah, Bhattacharya, Debswapna |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281963/ https://www.ncbi.nlm.nih.gov/pubmed/37351310 http://dx.doi.org/10.1093/bioadv/vbad070 |
Ejemplares similares
-
PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries
por: Shuvo, Md Hossain, et al.
Publicado: (2023) -
E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction
por: Roche, Rahmatullah, et al.
Publicado: (2023) -
EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks
por: Roche, Rahmatullah, et al.
Publicado: (2023) -
Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading
por: Bhattacharya, Sutanu, et al.
Publicado: (2021) -
QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
por: Shuvo, Md Hossain, et al.
Publicado: (2020)