基于SMO算法的离子配体结合位点识别。

IF 2.4 3区生物学 Q4 CELL BIOLOGY

BMC Molecular and Cell Biology Pub Date : 2019-12-11 DOI:10.1186/s12860-019-0237-9

Shan Wang, Xiuzhen Hu, Zhenxing Feng, Xiaojin Zhang, Liu Liu, Kai Sun, Shuang Xu

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引用次数: 8

摘要

背景:在许多重要的生命活动中，蛋白质功能的执行依赖于蛋白质与配体之间的相互作用。离子配体作为一种重要的蛋白质结合配体，其结合位点的鉴定对蛋白质功能的研究具有重要作用。结果:本研究选取了4种酸性自由基离子配体(NO2-、CO32-、SO42-、PO43-)和10种金属离子配体(Zn2+、Cu2+、Fe2+、Fe3+、Ca2+、Mg2+、Mn2+、Na+、K+、Co2+)作为研究对象，提出了基于序列信息的序贯最小优化(SMO)算法，通过5倍交叉验证获得了较好的预测结果。结论:提出了一种预测离子配体结合位点的有效方法。

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Recognizing ion ligand binding sites by SMO algorithm.

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Recognizing ion ligand binding sites by SMO algorithm.

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^-,CO₃^2-,SO₄^2-,PO₄^3-) 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.