Protein Structure Predictions, Atomic Model Building, and Validation Using a Cryo-EM Density Map from Hepatitis B Virus Spherical Subviral Particle.

Nadia DiNunno, Emily N Bianchini, Haitao Liu, Joseph Che-Yen Wang
{"title":"Protein Structure Predictions, Atomic Model Building, and Validation Using a Cryo-EM Density Map from Hepatitis B Virus Spherical Subviral Particle.","authors":"Nadia DiNunno, Emily N Bianchini, Haitao Liu, Joseph Che-Yen Wang","doi":"10.21769/BioProtoc.4751","DOIUrl":null,"url":null,"abstract":"<p><p>Hepatitis B virus (HBV) infection is a global public health concern. During chronic infection, the HBV small-surface antigen is expressed in large excess as non-infectious spherical subviral particles (SVPs), which possess strong immunogenicity. To date, attempts at understanding the structure of HBV spherical SVP have been restricted to 12-30 Å with contradictory conclusions regarding its architecture. We have used cryo-electron microscopy (cryo-EM) and 3D image reconstruction to solve the HBV spherical SVP to 6.3 Å. Here, we present an extended protocol on combining AlphaFold2 prediction with a moderate-resolution cryo-EM density map to build a reliable 3D model. This protocol utilizes multiple software packages that are routinely used in the cryo-EM community. The workflow includes 3D model prediction, model evaluation, rigid-body fitting, flexible fitting, real-space refinement, model validation, and model adjustment. Finally, the described protocol can also be applied to high-resolution cryo-EM datasets (2-4 Å).</p>","PeriodicalId":8938,"journal":{"name":"Bio-protocol","volume":"13 14","pages":"e4751"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d3/87/BioProtoc-13-14-4751.PMC10367000.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bio-protocol","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21769/BioProtoc.4751","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Hepatitis B virus (HBV) infection is a global public health concern. During chronic infection, the HBV small-surface antigen is expressed in large excess as non-infectious spherical subviral particles (SVPs), which possess strong immunogenicity. To date, attempts at understanding the structure of HBV spherical SVP have been restricted to 12-30 Å with contradictory conclusions regarding its architecture. We have used cryo-electron microscopy (cryo-EM) and 3D image reconstruction to solve the HBV spherical SVP to 6.3 Å. Here, we present an extended protocol on combining AlphaFold2 prediction with a moderate-resolution cryo-EM density map to build a reliable 3D model. This protocol utilizes multiple software packages that are routinely used in the cryo-EM community. The workflow includes 3D model prediction, model evaluation, rigid-body fitting, flexible fitting, real-space refinement, model validation, and model adjustment. Finally, the described protocol can also be applied to high-resolution cryo-EM datasets (2-4 Å).

Abstract Image

Abstract Image

Abstract Image

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
利用乙型肝炎病毒球形亚病毒粒子的低温电子显微镜密度图进行蛋白质结构预测、原子模型构建和验证。
乙型肝炎病毒(HBV)感染是一个全球性的公共卫生问题。在慢性感染过程中,HBV 小表面抗原以非感染性球形亚病毒颗粒(SVPs)的形式大量表达,具有很强的免疫原性。迄今为止,人们对 HBV 球形亚病毒颗粒结构的了解仅限于 12-30 Å 的范围,对其结构得出的结论相互矛盾。我们利用低温电子显微镜(cryo-EM)和三维图像重建技术将 HBV 球形 SVP 的结构解析到了 6.3 Å。在此,我们介绍了一种将 AlphaFold2 预测与中等分辨率低温电子显微镜密度图相结合以建立可靠三维模型的扩展方案。该方案利用了冷冻电镜界常用的多个软件包。工作流程包括三维模型预测、模型评估、刚体拟合、柔性拟合、实空间细化、模型验证和模型调整。最后,所述方案还可应用于高分辨率冷冻电镜数据集(2-4 Å)。
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
自引率
0.00%
发文量
0
期刊最新文献
A Simple Immunofluorescence Method to Characterize Neurodegeneration and Tyrosine Hydroxylase Reduction in Whole Brain of a Drosophila Model of Parkinson’s Disease Unlocking Bio-Instructive Polymers: A Novel Multi-Well Screening Platform Based on Secretome Sampling A Versatile Pipeline for High-fidelity Imaging and Analysis of Vascular Networks Across the Body Generation of Human Induced Pluripotent Stem Cell (hiPSC)-Derived Astrocytes for Amyotrophic Lateral Sclerosis and Other Neurodegenerative Disease Studies CoCoNat: A Deep Learning–Based Tool for the Prediction of Coiled-coil Domains in Protein Sequences
×
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