Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm

Background Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues. Results In this paper, Mg 2+ and Ca 2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences,...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	BMC bioinformatics Jg. 22; H. Suppl 12; S. 1 - 12
Hauptverfasser:	Sun, Kai, Hu, Xiuzhen, Feng, Zhenxing, Wang, Hongbin, Lv, Haotian, Wang, Ziyang, Zhang, Gaimei, Xu, Shuang, You, Xiaoxiao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 20.01.2022 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Alkaline earth metals Amino acids Analysis Artificial neural networks Binding residue Binding sites Binding sites (Biochemistry) Bioinformatics Biomedical and Life Sciences Calcium ions Calcium-binding protein Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data processing Datasets Deep learning Deep learning algorithm Forecasts and trends Life Sciences Ligand binding (Biochemistry) Ligands Machine learning Magnesium Metal ion ligand Metal ions Microarrays Natural language Neural networks Parameters Protein Proteins Solvents Support vector machines Zipf's Law China Binding residue Metal ion ligand Protein Deep learning algorithm
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!