Template matching and machine learning for cryo-electron tomography

IF 6.1 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Antonio Martinez-Sanchez
{"title":"Template matching and machine learning for cryo-electron tomography","authors":"Antonio Martinez-Sanchez","doi":"10.1016/j.sbi.2025.103058","DOIUrl":null,"url":null,"abstract":"<div><div>Cryo-electron tomography is the best-suited imaging technique for visual proteomics. Recent advances have increased the number, quality, and resolution of tomograms. However, object detection is the bottleneck task of the analysis workflow because, so far, only a few molecules can be detected by computer methods for pattern recognition. This article introduces the major challenges in detecting molecular complexes for cryo-electron tomography. This paper also identifies the limitations of the current methods. Finally, it describes the approaches proposed to overcome these limitations.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"93 ","pages":"Article 103058"},"PeriodicalIF":6.1000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in structural biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959440X25000764","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Cryo-electron tomography is the best-suited imaging technique for visual proteomics. Recent advances have increased the number, quality, and resolution of tomograms. However, object detection is the bottleneck task of the analysis workflow because, so far, only a few molecules can be detected by computer methods for pattern recognition. This article introduces the major challenges in detecting molecular complexes for cryo-electron tomography. This paper also identifies the limitations of the current methods. Finally, it describes the approaches proposed to overcome these limitations.
低温电子断层扫描的模板匹配与机器学习
低温电子断层扫描是视觉蛋白质组学最适合的成像技术。最近的进展提高了断层摄影的数量、质量和分辨率。然而,目标检测是分析工作流程的瓶颈任务,因为到目前为止,只有少数分子可以通过计算机方法检测到模式识别。本文介绍了低温电子断层扫描检测分子复合物的主要挑战。本文还指出了现有方法的局限性。最后,介绍了克服这些限制的方法。
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
Current opinion in structural biology
Current opinion in structural biology 生物-生化与分子生物学
CiteScore
12.20
自引率
2.90%
发文量
179
审稿时长
6-12 weeks
期刊介绍: Current Opinion in Structural Biology (COSB) aims to stimulate scientifically grounded, interdisciplinary, multi-scale debate and exchange of ideas. It contains polished, concise and timely reviews and opinions, with particular emphasis on those articles published in the past two years. In addition to describing recent trends, the authors are encouraged to give their subjective opinion of the topics discussed. In COSB, we help the reader by providing in a systematic manner: 1. The views of experts on current advances in their field in a clear and readable form. 2. Evaluations of the most interesting papers, annotated by experts, from the great wealth of original publications. [...] The subject of Structural Biology is divided into twelve themed sections, each of which is reviewed once a year. Each issue contains two sections, and the amount of space devoted to each section is related to its importance. -Folding and Binding- Nucleic acids and their protein complexes- Macromolecular Machines- Theory and Simulation- Sequences and Topology- New constructs and expression of proteins- Membranes- Engineering and Design- Carbohydrate-protein interactions and glycosylation- Biophysical and molecular biological methods- Multi-protein assemblies in signalling- Catalysis and Regulation
×
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:604180095
Book学术官方微信