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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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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Summary: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, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm. Conclusions An efficient method for predicting Mg 2+ and Ca 2+ ligand binding sites was presented.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04250-0