KinasePhos 3.0:激酶特异性磷酸化位点预测的重新设计和扩展

IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Genomics, Proteomics & Bioinformatics Pub Date : 2023-02-01 DOI:10.1016/j.gpb.2022.06.004
Renfei Ma , Shangfu Li , Wenshuo Li , Lantian Yao , Hsien-Da Huang , Tzong-Yi Lee
{"title":"KinasePhos 3.0:激酶特异性磷酸化位点预测的重新设计和扩展","authors":"Renfei Ma ,&nbsp;Shangfu Li ,&nbsp;Wenshuo Li ,&nbsp;Lantian Yao ,&nbsp;Hsien-Da Huang ,&nbsp;Tzong-Yi Lee","doi":"10.1016/j.gpb.2022.06.004","DOIUrl":null,"url":null,"abstract":"<div><p>The purpose of this work is to enhance KinasePhos, a machine learning-based <strong>kinase-specific phosphorylation site prediction</strong> tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at <span>https://awi.cuhk.edu.cn/KinasePhos/download.html</span><svg><path></path></svg> or <span>https://github.com/tom-209/KinasePhos-3.0-executable-file</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 1","pages":"Pages 228-241"},"PeriodicalIF":11.5000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373160/pdf/","citationCount":"11","resultStr":"{\"title\":\"KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites\",\"authors\":\"Renfei Ma ,&nbsp;Shangfu Li ,&nbsp;Wenshuo Li ,&nbsp;Lantian Yao ,&nbsp;Hsien-Da Huang ,&nbsp;Tzong-Yi Lee\",\"doi\":\"10.1016/j.gpb.2022.06.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The purpose of this work is to enhance KinasePhos, a machine learning-based <strong>kinase-specific phosphorylation site prediction</strong> tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at <span>https://awi.cuhk.edu.cn/KinasePhos/download.html</span><svg><path></path></svg> or <span>https://github.com/tom-209/KinasePhos-3.0-executable-file</span><svg><path></path></svg>.</p></div>\",\"PeriodicalId\":12528,\"journal\":{\"name\":\"Genomics, Proteomics & Bioinformatics\",\"volume\":\"21 1\",\"pages\":\"Pages 228-241\"},\"PeriodicalIF\":11.5000,\"publicationDate\":\"2023-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373160/pdf/\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genomics, Proteomics & Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S167202292200081X\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics, Proteomics & Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S167202292200081X","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 11

摘要

这项工作的目的是增强KinasePhos,一种基于机器学习的激酶特异性磷酸化位点预测工具。从PhosphoSitePlus、UniProtKB、GPS 5.0和Phospho.ELM收集了实验验证的激酶特异性磷酸化数据。总共鉴定了41421个实验验证的磷酸化位点。共鉴定出1380种独特的激酶,其中753种具有来自KinBase的现有分类信息,其余627种通过构建系统发育树进行注释。基于这种激酶分类,使用阳性训练数据集中至少15个实验验证的底物位点,在个体、家族和组水平上总共建立了771个预测模型。与其他预测工具相比,改进后的模型显示了其有效性。例如,蛋白激酶B、酪蛋白激酶2和蛋白激酶A家族磷酸化位点的预测准确率分别为94.5%、92.5%和90.0%。771个模型的平均预测准确率为87.2%。为了提高可解释性,采用了SHapley加性预测(SHAP)方法来评估特征重要性。KinasePhos 3.0的网络界面已被重新设计,以提供多种蛋白质上激酶特异性磷酸化位点的全面注释。此外,考虑到大规模的磷酸蛋白质组学数据,可下载的预测工具可在https://awi.cuhk.edu.cn/KinasePhos/download.html或https://github.com/tom-209/KinasePhos-3.0-executable-file.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

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
KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

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
Genomics, Proteomics & Bioinformatics
Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
14.30
自引率
4.20%
发文量
844
审稿时长
61 days
期刊介绍: Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.
期刊最新文献
Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data. Identification of highly repetitive barley enhancers with long-range regulation potential via STARR-seq CpG island definition and methylation mapping of the T2T-YAO genome Pindel-TD: a tandem duplication detector based on a pattern growth approach SMARTdb: An Integrated Database for Exploring Single-cell Multi-omics Data of Reproductive Medicine
×
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