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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Podrobná bibliografia
Vydané v:	BMC bioinformatics Ročník 22; číslo Suppl 12; s. 1 - 12
Hlavní autori:	Sun, Kai, Hu, Xiuzhen, Feng, Zhenxing, Wang, Hongbin, Lv, Haotian, Wang, Ziyang, Zhang, Gaimei, Xu, Shuang, You, Xiaoxiao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 20.01.2022 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	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
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-021-04250-0