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+ an...

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Bibliographic Details
Published in:Frontiers in genetics Vol. 13; p. 969412
Main Authors: Hao, Sixi, Hu, Xiuzhen, Feng, Zhenxing, Sun, Kai, You, Xiaoxiao, Wang, Ziyang, Yang, Caiyun
Format: Journal Article
Language:English
Published: Frontiers Media S.A 11.08.2022
Subjects:
binding residues
deep neural network algorithm
disorder value
Genetics
metal ion ligand
propensity factors
ISSN:1664-8021, 1664-8021
Online Access:Get full text
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Summary: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+ and Mg 2+ metal ion ligands from the BioLip database as the research objects. Based on the amino acids, the physicochemical properties and predicted structural information, we introduced the disorder value as the feature parameter. In addition, based on the component information, position weight matrix and information entropy, we introduced the propensity factor as prediction parameters. Then, we used the deep neural network algorithm for the prediction. Furtherly, we made an optimization for the hyper-parameters of the deep learning algorithm and obtained improved results than the previous IonSeq method.
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Edited by: Pu-Feng Du, Tianjin University, China
This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
Chunhua Li, Beijing University of Technology, China
Reviewed by: Yongchun Zuo, Inner Mongolia University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2022.969412