预测金属结合蛋白质和结构通过融合进化规模和基于物理的建模
Xin Dai1, Max Henderson2, Shinjae Yoo1
1Computational Science Initiative, Brookhaven National Laboratory Upton NY USA.
Journal of molecular biology
|January 26, 2025
概括
我们开发了ESMBind,这是一个计算工作流程,用于识别金属结合蛋白并预测它们的结构. 这种方法提高了我们对基本金属在生物学和疾病中的作用的理解,特别是在真菌病原体中.
科学领域:
- 生物化学 生化学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 金属对蛋白质功能至关重要,参与稳定,催化和调节.
- 识别金属结合蛋白及其结构对于理解生物过程和疾病至关重要.
- 现有的计算方法需要改进,以准确预测和蛋白质金属相互作用的结构建模.
研究的目的:
- 开发和验证一种新的计算工作流程ESMBind,用于预测金属结合蛋白及其复杂结构.
- 为了提高残留水平金属结合预测和3D结构生成的准确性.
- 应用ESMBind工作流程来识别未表征的真菌病原体中的新型金属结合蛋白.
主要方法:
- 利用进化规模建模 (ESM-2和ESM-IF) 来预测残留水平的金属结合概率.
- 开发了一种基于物理的方法,包括金属放置和能量最小化,用于3D结构生成.
- 将基于进化和物理的方法结合到集成的ESMBind工作流中.
主要成果:
- 与现有模型相比,ESMBind工作流在残留水平和3D水平预测方面表现出卓越的性能.
- 成功地将ESMBind应用于142种未表征的真菌病原体蛋白质.
- 确定了潜在的金属结合蛋白质,涉及真菌感染和毒性.
结论:
- ESMBind提供了一种强大而准确的计算工具,用于识别和表征金属结合蛋白.
- 该工作流推进了对金属蛋白的研究,对理解真菌病原发生有重大影响.
- 这种方法有助于在真菌病原体中发现新的治疗点.
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