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,...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 22; no. Suppl 12; pp. 1 - 12
Main Authors:	Sun, Kai, Hu, Xiuzhen, Feng, Zhenxing, Wang, Hongbin, Lv, Haotian, Wang, Ziyang, Zhang, Gaimei, Xu, Shuang, You, Xiaoxiao
Format:	Journal Article
Language:	English
Published:	London BioMed Central 20.01.2022 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	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 Access:	Get full text
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