Exploring details about structure requirements based on antioxidant tripeptide derived from β-Lactoglobulin by in silico approaches

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Fangfang Wang, Menghao Wen, Bo Zhou
{"title":"Exploring details about structure requirements based on antioxidant tripeptide derived from β-Lactoglobulin by in silico approaches","authors":"Fangfang Wang,&nbsp;Menghao Wen,&nbsp;Bo Zhou","doi":"10.1007/s00726-023-03350-w","DOIUrl":null,"url":null,"abstract":"<div><p><i>β</i>-Lactoglobulin is one of the proteins in milk possessing antioxidant activity. The peptides derived from <i>β</i>-Lactoglobulin exhibit higher antioxidant activities than the most commonly used antioxidant. Furthermore, the detailed structure–activity relationship of these antioxidant peptides has not been elucidated. Therefore, in the present work, two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional quantitative structure–activity relationship (3D-QSAR) models were constructed to investigate the structural factors affecting activities and gave information for the rational design of novel antioxidant peptides. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by multiple linear regression (MLR), partial least squares regression (PLSR) and support vector machines (SVM) approaches. The statistical parameters are as follows: R<sup>2</sup> = 0.643, Q<sup>2</sup> = 0.553/MLR, R<sup>2</sup> = 0.612, Q<sup>2</sup> = 0.5278/PLSR, R<sup>2</sup> = 0.7085, Q<sup>2</sup> = 0.6887/SVM, indicating that the SVM model is superior to the MLR and PLSR models. In addition, in the 3D-QSAR models, the Dragon-CoMFA (R<sup>2</sup><sub>cv</sub> = 0.537, R<sup>2</sup><sub>pred</sub> = 0.5201) and Dragon-CoMSIA (R<sup>2</sup><sub>cv</sub> = 0.665, R<sup>2</sup><sub>pred</sub> = 0.6489) methods were conducted to predict the antioxidant activities. Comparison of statistical parameters illustrates that the suitability of Dragon-CoMSIA is superior to the Dragon-CoMFA model. The results show the robustness and excellent prediction of the proposed models, and would be applied for modifying and designing novel and potent antioxidant peptides.</p></div>","PeriodicalId":7810,"journal":{"name":"Amino Acids","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Amino Acids","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s00726-023-03350-w","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

β-Lactoglobulin is one of the proteins in milk possessing antioxidant activity. The peptides derived from β-Lactoglobulin exhibit higher antioxidant activities than the most commonly used antioxidant. Furthermore, the detailed structure–activity relationship of these antioxidant peptides has not been elucidated. Therefore, in the present work, two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional quantitative structure–activity relationship (3D-QSAR) models were constructed to investigate the structural factors affecting activities and gave information for the rational design of novel antioxidant peptides. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by multiple linear regression (MLR), partial least squares regression (PLSR) and support vector machines (SVM) approaches. The statistical parameters are as follows: R2 = 0.643, Q2 = 0.553/MLR, R2 = 0.612, Q2 = 0.5278/PLSR, R2 = 0.7085, Q2 = 0.6887/SVM, indicating that the SVM model is superior to the MLR and PLSR models. In addition, in the 3D-QSAR models, the Dragon-CoMFA (R2cv = 0.537, R2pred = 0.5201) and Dragon-CoMSIA (R2cv = 0.665, R2pred = 0.6489) methods were conducted to predict the antioxidant activities. Comparison of statistical parameters illustrates that the suitability of Dragon-CoMSIA is superior to the Dragon-CoMFA model. The results show the robustness and excellent prediction of the proposed models, and would be applied for modifying and designing novel and potent antioxidant peptides.

Abstract Image

通过计算机模拟方法探索基于β-乳球蛋白的抗氧化三肽的结构要求的细节。
β-乳球蛋白是牛奶中具有抗氧化活性的蛋白质之一。源自β-乳球蛋白的肽比最常用的抗氧化剂表现出更高的抗氧化活性。此外,这些抗氧化肽的详细结构-活性关系尚未阐明。因此,本工作构建了二维定量结构-活性关系(2D-QSAR)和三维定量结构-性能关系(3D-QSAR)模型,以研究影响活性的结构因素,为合理设计新型抗氧化肽提供信息。经过分子描述符的计算和筛选,采用多元线性回归(MLR)、偏最小二乘回归(PLSR)和支持向量机(SVM)方法建立了线性和非线性模型。统计参数如下:R2 = 0.643,Q2 = 0.553/MLR,R2 = 0.612,Q2 = 0.5278/PLSR,R2 = 0.7085,Q2 = 0.6887/SVM,表明SVM模型优于MLR和PLSR模型。此外,在3D-QSAR模型中,Dragon CoMFA(R2cv = 0.537,R2pred = 0.5201)和Dragon CoMSIA(R2cv = 0.665,R2pred = 0.6489)方法预测抗氧化活性。统计参数的比较表明,Dragon-CoMSIA模型的适用性优于Dragon-CoMFA模型。结果表明,所提出的模型具有较强的稳健性和良好的预测性,可用于修饰和设计新型强效抗氧化肽。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Amino Acids
Amino Acids 生物-生化与分子生物学
CiteScore
6.40
自引率
5.70%
发文量
99
审稿时长
2.2 months
期刊介绍: Amino Acids publishes contributions from all fields of amino acid and protein research: analysis, separation, synthesis, biosynthesis, cross linking amino acids, racemization/enantiomers, modification of amino acids as phosphorylation, methylation, acetylation, glycosylation and nonenzymatic glycosylation, new roles for amino acids in physiology and pathophysiology, biology, amino acid analogues and derivatives, polyamines, radiated amino acids, peptides, stable isotopes and isotopes of amino acids. Applications in medicine, food chemistry, nutrition, gastroenterology, nephrology, neurochemistry, pharmacology, excitatory amino acids are just some of the topics covered. Fields of interest include: Biochemistry, food chemistry, nutrition, neurology, psychiatry, pharmacology, nephrology, gastroenterology, microbiology
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信