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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Frontiers in genetics Ročník 13; s. 969412
Hlavní autoři: Hao, Sixi, Hu, Xiuzhen, Feng, Zhenxing, Sun, Kai, You, Xiaoxiao, Wang, Ziyang, Yang, Caiyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Frontiers Media S.A 11.08.2022
Témata:
binding residues
deep neural network algorithm
disorder value
Genetics
metal ion ligand
propensity factors
ISSN:1664-8021, 1664-8021
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
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