用于深度学习和结构预测的冷EM进行蛋白质结构建模的综合协议
Genki Terashi1, Xiao Wang2, Devashish Prasad2
1Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA.
bioRxiv : the preprint server for biology
|October 31, 2023
概括
DeepMainmast使用深度学习来在冷电子显微镜 (cryo-EM) 地图中追踪蛋白质主链. 将其与AlphaFold2结合起来,可以提高准确性,并识别同型多元链.
科学领域:
- 结构生物学是结构生物学.
- 生物物理学的生物物理.
- 计算生物学是一种计算生物学.
背景情况:
- 低温电子显微镜 (cryo-EM) 对于确定蛋白质结构至关重要.
- 即使在高分辨率下,在冷电磁图中准确地追踪蛋白质主链仍然是一个挑战.
结论:
- DeepMainmast提供了一个强大的解决方案,用于挑战冷-EM地图解释.
- 与AlphaFold2的协同方法提高了蛋白质结构建模的准确性.
- 该协议改善了从冷EM数据中分析同型多重蛋白质结构的分析.
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