基于深度学习的结构建模揭示了β-solenoid折叠空间未知区域的结构和功能
Shahram Mesdaghi1, Rebecca M Price2, Jillian Madine2
1The University of Liverpool, Institute of Systems, Molecular & Integrative Biology, Biosciences Building, Crown Street, Liverpool L69 7ZB, United Kingdom; Computational Biology Facility, MerseyBio, University of Liverpool, Crown Street, Liverpool L69 7ZB, United Kingdom.
Journal of structural biology
|August 6, 2023
概括
深度学习模型识别了新的β-solenid蛋白质结构,揭示了它们在粘附蛋白,粘膜蛋白和功能性粉样蛋白中的多样性功能.
科学领域:
- 结构生物学是结构生物学.
- 计算生物学是一种计算生物学.
- 蛋白质科学是一种蛋白质科学.
背景情况:
- 重复的蛋白质无处不在,具有不同的功能和结构模式.
- 作为一种重复蛋白质的类别,β-solenids具有独特的结构特征,如手性和扭曲.
- 由于建模和结晶的挑战,许多在毒性因子和功能性粉样蛋白中至关重要的β-粉样蛋白缺乏实验性结构.
研究的目的:
- 通过深度学习发现新的β-胺蛋白结构.
- 为了识别结构邻居,并推断预测β-solenoids的潜在功能.
- 为了探索β-solenid折叠的结构多样性和功能影响.
主要方法:
- 应用各种基于深度学习的结构建模技术.
- 使用结构数据库搜索来寻找同源蛋白质.
- 将预测的结构与潜在的生物功能联系起来.
主要成果:
- 发现了具有β-solenoid折叠的新型真核和 prokaryotic 粘附体.
- 鉴定异常长,平坦的β-solenid结构,可能形成粘素串联重复.
- 描述了前所未有的小β-基结构和一个新的FapC希腊键β-基折叠.
结论:
- 深度学习有效地预测了各种β-烯结构及其功能.
- 证实了β-solenid折叠和粘附功能之间的联系.
- 突出了素结构和Pseudomonas功能性粉样纤维中的潜在作用.
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