Cargando…
Prediction of metal ion ligand binding residues by adding disorder value and propensity factors based on deep learning algorithm
Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge. In this study, we selected Zn(2+), Cu(2+), Fe(2+), Fe(3+), Co(2+), Mn(2+), Ca(2+) a...
Autores principales: | Hao, Sixi, Hu, Xiuzhen, Feng, Zhenxing, Sun, Kai, You, Xiaoxiao, Wang, Ziyang, Yang, Caiyun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402973/ https://www.ncbi.nlm.nih.gov/pubmed/36035120 http://dx.doi.org/10.3389/fgene.2022.969412 |
Ejemplares similares
-
Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors
por: Xu, Shuang, et al.
Publicado: (2022) -
The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility
por: Hu, Xiuzhen, et al.
Publicado: (2020) -
Predicting Ca(2+) and Mg(2+) ligand binding sites by deep neural network algorithm
por: Sun, Kai, et al.
Publicado: (2022) -
Recognizing Ion Ligand–Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle
por: Liu, Liu, et al.
Publicado: (2020) -
Recognizing ion ligand binding sites by SMO algorithm
por: Wang, Shan, et al.
Publicado: (2019)