Recognizing ion ligand binding sites by SMO algorithm

被引：12

作者：

Wang, Shan ^{[1
]}

Hu, Xiuzhen ^{[1
]}

Feng, Zhenxing ^{[1
]}

Zhang, Xiaojin ^{[1
]}

Liu, Liu ^{[1
]}

Sun, Kai ^{[1
]}

Xu, Shuang ^{[1
]}

机构：

[1] Inner Mongolia Univ Technol, Coll Sci, Hohhot 010051, Peoples R China

来源：

BMC MOLECULAR AND CELL BIOLOGY | 2019年 / 20卷 / Suppl 3期

基金：

中国国家自然科学基金;

关键词：

Ion ligand; SMO algorithm; Binding site; Sequence information; ATP SULFURYLASE; GENE-EXPRESSION; IDENTIFICATION; CLASSIFICATION; PREDICTION; PROTEINS; METAL;

D O I：

10.1186/s12860-019-0237-9

中图分类号：

Q2 [细胞生物学];

学科分类号：

071009 ; 090102 ;

摘要：

Background: In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results: In this study, four acid radical ion ligands (NO2-,CO32-,SO42-,PO43-) and ten metal ion ligands (Zn2+,Cu2+, Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions: An efficient method for predicting ion ligand binding sites was presented.

引用

页数：9

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