Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.

IF 4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Proteomes Pub Date : 2023-05-02 DOI:10.3390/proteomes11020016
Neha Varshney, Abhinava K Mishra
{"title":"Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.","authors":"Neha Varshney,&nbsp;Abhinava K Mishra","doi":"10.3390/proteomes11020016","DOIUrl":null,"url":null,"abstract":"<p><p>Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.</p>","PeriodicalId":20877,"journal":{"name":"Proteomes","volume":"11 2","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204361/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proteomes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/proteomes11020016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

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
磷蛋白质组学中的深度学习:在癌症药物发现中的方法和应用。
蛋白磷酸化是一个关键的翻译后修饰(PTM),是许多细胞信号通路的中心调控机制。几种蛋白激酶和磷酸酶精确地控制着这一生化过程。这些蛋白质的功能缺陷与许多疾病有关,包括癌症。基于质谱(MS)的生物样品分析提供了磷蛋白质组的深入覆盖。公共资源库中大量的质谱数据已经揭开了磷酸化蛋白质组学领域的大数据面纱。为了解决与处理大数据和扩大磷酸化位点预测的信心相关的挑战,近年来许多计算算法和基于机器学习的方法的发展势头强劲。高分辨率、高灵敏度的实验方法和数据挖掘算法的出现,为定量蛋白质组学提供了强大的分析平台。在这篇综述中,我们收集了用于预测磷酸化位点的生物信息学资源,以及它们在癌症背景下的潜在治疗应用。
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 去求助
来源期刊
Proteomes
Proteomes Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
6.50
自引率
3.00%
发文量
37
审稿时长
11 weeks
期刊介绍: Proteomes (ISSN 2227-7382) is an open access, peer reviewed journal on all aspects of proteome science. Proteomes covers the multi-disciplinary topics of structural and functional biology, protein chemistry, cell biology, methodology used for protein analysis, including mass spectrometry, protein arrays, bioinformatics, HTS assays, etc. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on the length of papers. Scope: -whole proteome analysis of any organism -disease/pharmaceutical studies -comparative proteomics -protein-ligand/protein interactions -structure/functional proteomics -gene expression -methodology -bioinformatics -applications of proteomics
期刊最新文献
The Non-Linear Profile of Aging: U-Shaped Expression of Myostatin, Follistatin and Intermediate Signals in a Longitudinal In Vitro Murine Cell Sarcopenia Model. Assessment of Data-Independent Acquisition Mass Spectrometry (DIA-MS) for the Identification of Single Amino Acid Variants. Transcriptomics Revealed Differentially Expressed Transcription Factors and MicroRNAs in Human Diabetic Foot Ulcers. Comparative Proteome-Wide Abundance Profiling of Yeast Strains Deleted for Cdc48 Adaptors. Multiple Reaction Monitoring-Mass Spectrometric Immunoassay Analysis of Parathyroid Hormone Fragments with Vitamin D Deficiency in Patients with Diabetes Mellitus.
×
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