自动蛋白链分离从3D冷冻电镜数据和体积比较工具

Michael Nissenson, Dong Si
{"title":"自动蛋白链分离从3D冷冻电镜数据和体积比较工具","authors":"Michael Nissenson, Dong Si","doi":"10.1145/3107411.3107500","DOIUrl":null,"url":null,"abstract":"In electron cryo-microscopy (cryo-EM), manual isolation of volumetric protein density map data surrounding known protein structures is a time-consuming process that requires constant expert attention for multiple hours. This paper presents a tool, Volume Cut, and an algorithm to automatically isolate the volumetric data surrounding individual protein chains from the entire macro-molecular complex that runs in just minutes. This tool can be used in the data collection and data pre-processing steps to generate good training datasets of single chain volume-structure pairs, which can be further used for the study of protein structure prediction from experimental 3D cryo-EM density maps using data mining and machine learning. Additionally, an application of this tool was explored in depth that compares the cut experimental cryo-EM data with simulated data in an attempt to find irregularities of experimental data for the purpose of validation. The source for both tools can be found at https://github.com/nissensonm/VolumeCut/.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Protein Chain Isolation from 3D Cryo-EM Data and Volume Comparison Tool\",\"authors\":\"Michael Nissenson, Dong Si\",\"doi\":\"10.1145/3107411.3107500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In electron cryo-microscopy (cryo-EM), manual isolation of volumetric protein density map data surrounding known protein structures is a time-consuming process that requires constant expert attention for multiple hours. This paper presents a tool, Volume Cut, and an algorithm to automatically isolate the volumetric data surrounding individual protein chains from the entire macro-molecular complex that runs in just minutes. This tool can be used in the data collection and data pre-processing steps to generate good training datasets of single chain volume-structure pairs, which can be further used for the study of protein structure prediction from experimental 3D cryo-EM density maps using data mining and machine learning. Additionally, an application of this tool was explored in depth that compares the cut experimental cryo-EM data with simulated data in an attempt to find irregularities of experimental data for the purpose of validation. The source for both tools can be found at https://github.com/nissensonm/VolumeCut/.\",\"PeriodicalId\":246388,\"journal\":{\"name\":\"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3107411.3107500\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3107411.3107500","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

在电子冷冻显微镜(cryo-EM)中,人工分离已知蛋白质结构周围的体积蛋白质密度图数据是一个耗时的过程,需要持续数小时的专家关注。本文介绍了一种工具,Volume Cut和一种算法,可以在几分钟内自动从整个大分子复合物中分离出单个蛋白质链周围的体积数据。该工具可用于数据收集和数据预处理步骤,生成单链体积-结构对的良好训练数据集,可进一步用于利用数据挖掘和机器学习从实验三维冷冻电镜密度图中预测蛋白质结构的研究。此外,还深入探讨了该工具的应用,将切割的实验低温电镜数据与模拟数据进行比较,试图找到实验数据的不规则性,以进行验证。这两个工具的源代码可以在https://github.com/nissensonm/VolumeCut/上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Protein Chain Isolation from 3D Cryo-EM Data and Volume Comparison Tool
In electron cryo-microscopy (cryo-EM), manual isolation of volumetric protein density map data surrounding known protein structures is a time-consuming process that requires constant expert attention for multiple hours. This paper presents a tool, Volume Cut, and an algorithm to automatically isolate the volumetric data surrounding individual protein chains from the entire macro-molecular complex that runs in just minutes. This tool can be used in the data collection and data pre-processing steps to generate good training datasets of single chain volume-structure pairs, which can be further used for the study of protein structure prediction from experimental 3D cryo-EM density maps using data mining and machine learning. Additionally, an application of this tool was explored in depth that compares the cut experimental cryo-EM data with simulated data in an attempt to find irregularities of experimental data for the purpose of validation. The source for both tools can be found at https://github.com/nissensonm/VolumeCut/.
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
×
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