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罗塞塔抗体:抗体可变区同源建模服务器。

RosettaAntibody: antibody variable region homology modeling server.

作者信息

Sircar Aroop, Kim Eric T, Gray Jeffrey J

机构信息

Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.

出版信息

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W474-9. doi: 10.1093/nar/gkp387. Epub 2009 May 20.

DOI:10.1093/nar/gkp387
PMID:19458157
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2703951/
Abstract

The RosettaAntibody server (http://antibody.graylab.jhu.edu) predicts the structure of an antibody variable region given the amino-acid sequences of the respective light and heavy chains. In an initial stage, the server identifies and displays the most sequence homologous template structures for the light and heavy framework regions and each of the complementarity determining region (CDR) loops. Subsequently, the most homologous templates are assembled into a side-chain optimized crude model, and the server returns a picture and coordinate file. For users requesting a high-resolution model, the server executes the full RosettaAntibody protocol which additionally models the hyper-variable CDR H3 loop. The high-resolution protocol also relieves steric clashes by optimizing the CDR backbone torsion angles and by simultaneously perturbing the relative orientation of the light and heavy chains. RosettaAntibody generates 2000 independent structures, and the server returns pictures, coordinate files, and detailed scoring information for the 10 top-scoring models. The 10 models enable users to use rational judgment in choosing the best model or to use the set as an ensemble for further studies such as docking. The high-resolution models generated by RosettaAntibody have been used for the successful prediction of antibody-antigen complex structures.

摘要

罗塞塔抗体服务器(http://antibody.graylab.jhu.edu)可根据相应轻链和重链的氨基酸序列预测抗体可变区的结构。在初始阶段,该服务器会识别并展示轻链和重链框架区以及每个互补决定区(CDR)环的序列同源性最高的模板结构。随后,将序列同源性最高的模板组装成一个侧链优化的粗模型,服务器会返回一张图片和坐标文件。对于请求高分辨率模型的用户,服务器会执行完整的罗塞塔抗体协议,该协议还会对高变CDR H3环进行建模。高分辨率协议还通过优化CDR主链扭转角以及同时扰动轻链和重链的相对取向来消除空间冲突。罗塞塔抗体生成2000个独立结构,服务器会返回10个得分最高模型的图片、坐标文件和详细评分信息。这10个模型可让用户在选择最佳模型时进行合理判断,或者将其作为一个整体用于进一步研究,如对接。罗塞塔抗体生成的高分辨率模型已成功用于预测抗体-抗原复合物结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/417d/2703951/75ea53023b44/gkp387f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/417d/2703951/75ea53023b44/gkp387f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/417d/2703951/75ea53023b44/gkp387f1.jpg

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2
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Proteins. 2009 Feb 1;74(2):497-514. doi: 10.1002/prot.22309.
3
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Proteins. 2025 Jan 20. doi: 10.1002/prot.26801.
4
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Emerg Microbes Infect. 2025 Dec;14(1):2432345. doi: 10.1080/22221751.2024.2432345. Epub 2024 Dec 9.
5
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6
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