具有广泛化学修饰的多肽和蛋白质等电点值的预测

Vladlen S. Skvortsov, A. Voronina, Y. Ivanova, A. Rybina
{"title":"具有广泛化学修饰的多肽和蛋白质等电点值的预测","authors":"Vladlen S. Skvortsov, A. Voronina, Y. Ivanova, A. Rybina","doi":"10.18097/bmcrm00161","DOIUrl":null,"url":null,"abstract":"The scale of virtual pKa values for calculating the isoelectric point of peptides and proteins with chemical and post-translational modifications (PTM) is presented. The learning set of pKa values is based on data from 25 experiments of isoelectric focusing of peptides with subsequent mass spectrometric identification (ProteomeXchange accession codes: PXD000065, PXD005410, PXD006291, PXD010006 and PXD017201). In order to enrich the resulting sets with peptides containing modifications the identification of peptides was repeated using raw mass spectrometry data of all datasets. In the final learning set have included peptides satisfying the following conditions: the peptide was found in the fraction with scoring function maximum and maximum peptide abundance; the peptide was found in more than one experiment, and differences of the pI value between experiments was less than 0.15 pH unit. Two variants of the scales were created. In the first variant, pKa values depended only on the residue position relative to the ends of the sequence (N- or C-terminal residue or inside the chain). In the second variant, the effect of neighboring residues was also taken into account. The prediction accuracy of the second variant was higher. The comparison with other methods of pI prediction was carried out. Although the scale was calculated from set containing only peptides, it would be applicable for pI prediction of proteins with and without PTM. The software for prediction of pI values using the resulting pKa scales is available at http://pIPredict3.ibmc.msk.ru.","PeriodicalId":286037,"journal":{"name":"Biomedical Chemistry: Research and Methods","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Prediction of the Isoelectric Point Value of Peptides and Proteins with a Wide Range of Chemical Modifications\",\"authors\":\"Vladlen S. Skvortsov, A. Voronina, Y. Ivanova, A. Rybina\",\"doi\":\"10.18097/bmcrm00161\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The scale of virtual pKa values for calculating the isoelectric point of peptides and proteins with chemical and post-translational modifications (PTM) is presented. The learning set of pKa values is based on data from 25 experiments of isoelectric focusing of peptides with subsequent mass spectrometric identification (ProteomeXchange accession codes: PXD000065, PXD005410, PXD006291, PXD010006 and PXD017201). In order to enrich the resulting sets with peptides containing modifications the identification of peptides was repeated using raw mass spectrometry data of all datasets. In the final learning set have included peptides satisfying the following conditions: the peptide was found in the fraction with scoring function maximum and maximum peptide abundance; the peptide was found in more than one experiment, and differences of the pI value between experiments was less than 0.15 pH unit. Two variants of the scales were created. In the first variant, pKa values depended only on the residue position relative to the ends of the sequence (N- or C-terminal residue or inside the chain). In the second variant, the effect of neighboring residues was also taken into account. The prediction accuracy of the second variant was higher. The comparison with other methods of pI prediction was carried out. Although the scale was calculated from set containing only peptides, it would be applicable for pI prediction of proteins with and without PTM. The software for prediction of pI values using the resulting pKa scales is available at http://pIPredict3.ibmc.msk.ru.\",\"PeriodicalId\":286037,\"journal\":{\"name\":\"Biomedical Chemistry: Research and Methods\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Chemistry: Research and Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18097/bmcrm00161\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Chemistry: Research and Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18097/bmcrm00161","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

提出了用于计算具有化学修饰和翻译后修饰(PTM)的肽和蛋白质等电点的虚拟pKa值的尺度。pKa值的学习集基于25个肽等电聚焦实验的数据,随后进行质谱鉴定(ProteomeXchange登录代码:PXD000065, PXD005410, PXD006291, PXD010006和PXD017201)。为了丰富含有修饰肽的结果集,使用所有数据集的原始质谱数据重复鉴定肽。在最终的学习集中包含满足以下条件的肽:该肽存在于评分函数最大和肽丰度最大的分数中;该肽存在于多个实验中,且实验间pI值差异小于0.15 pH单位。两种不同的尺度被创造出来。在第一种变体中,pKa值仅取决于相对于序列末端的残基位置(N端或c端残基或链内)。在第二种变体中,也考虑了邻近残基的影响。第二种变异的预测精度较高。并与其他pI预测方法进行了比较。虽然这个尺度是由只含有多肽的集合计算出来的,但它适用于有或没有PTM的蛋白质的pI预测。使用由此产生的pKa尺度预测pI值的软件可在http://pIPredict3.ibmc.msk.ru上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Prediction of the Isoelectric Point Value of Peptides and Proteins with a Wide Range of Chemical Modifications
The scale of virtual pKa values for calculating the isoelectric point of peptides and proteins with chemical and post-translational modifications (PTM) is presented. The learning set of pKa values is based on data from 25 experiments of isoelectric focusing of peptides with subsequent mass spectrometric identification (ProteomeXchange accession codes: PXD000065, PXD005410, PXD006291, PXD010006 and PXD017201). In order to enrich the resulting sets with peptides containing modifications the identification of peptides was repeated using raw mass spectrometry data of all datasets. In the final learning set have included peptides satisfying the following conditions: the peptide was found in the fraction with scoring function maximum and maximum peptide abundance; the peptide was found in more than one experiment, and differences of the pI value between experiments was less than 0.15 pH unit. Two variants of the scales were created. In the first variant, pKa values depended only on the residue position relative to the ends of the sequence (N- or C-terminal residue or inside the chain). In the second variant, the effect of neighboring residues was also taken into account. The prediction accuracy of the second variant was higher. The comparison with other methods of pI prediction was carried out. Although the scale was calculated from set containing only peptides, it would be applicable for pI prediction of proteins with and without PTM. The software for prediction of pI values using the resulting pKa scales is available at http://pIPredict3.ibmc.msk.ru.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Measurement of Breast Tissue Estrogens by Liquid Chromatography-tandem Mass Spectrometry Determination of Cholesterol and Triglyceride Concentrations in Serum Extracellular Vesicles Using Commercial Kits Optimization of Conditions for Human Bacterial Preparation for Biological Correction of Intestinal Microflora Proteoliposomes as a Method of Membrane Protein Immobilization for SPR-analysis with the Human CYP3A4 and CYB5A Interaction as an Example Development of a Method for the Extraction of the Total Proteome of Bacillus anthracis Spores
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1