用IDPFold精确生成内在无序蛋白质的合规组合
Junjie Zhu1, Zhengxin Li1, Zhuoqi Zheng1
1State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 13, 2025
概括
IDPFold直接从序列中生成蛋白质结构,克服了研究内在无序蛋白质 (IDP) 的局限性. 这种方法有助于理解蛋白质序列,疾病和癌症等疾病的功能之间的联系.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 内在无序蛋白 (IDP) 对于生物功能至关重要,但与癌症和阿尔茨海默氏症等疾病有关.
- 由于它们的动态结构,有限的实验数据和分子动力学 (MD) 模拟等传统方法的计算成本,研究IDP具有挑战性.
- 目前的结构预测方法因序列保存不良和实验性特征稀缺而与IDP扎.
研究的目的:
- 引入IDPFold,一种用于直接从它们的氨基酸序列中生成IDP的构造组合的新方法.
- 克服对多重序列对齐 (MSA) 或实验数据的依赖,以预测IDP结构.
- 为了能够更全面地描述IDP集合中的结构特征.
主要方法:
- 利用微调的扩散模型来预测IDP形态组合.
- 开发了IDPFold,可以直接处理蛋白序列,而不需要MSA或实验数据.
- 评估了27个不同的IDP系统的IDPFold性能.
主要成果:
- 在Cα二次化学转移上,IDPFold实现了0.06的半径转移 (Rg) 误差和0.65ppm的RMSD,与实验值相比.
- 与用于IDP结构预测的现有生成型深度学习方法相比,表现显著优越.
- 成功生成了IDP合奏的详细结构特征.
结论:
- IDPFold提供了一种强大的,基于序列的方法来表征IDP形状集.
- 该方法有效地解决了传统模拟和预测技术对IDP的局限性.
- IDPFold促进了对序列 - 障碍 - 功能范式的更深入的洞察,这对于理解与IDP相关的疾病至关重要.
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