非自然和循环折叠景观的高分辨率调整与使用马尔科夫模型和贝叶斯推断对合规群体的NMR测量
Thi Dung Nguyen1, Robert M Raddi1, Vincent A Voelz1
1Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States.
Journal of chemical theory and computation
|June 9, 2025
概括
这项研究使用马尔科夫模型与NMR数据重量化,以准确预测折叠景观. 这种方法通过了解微妙的化学修饰效应,使得稳定,非原生和循环的合理设计成为可能.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 分子动力学分子动力学
背景情况:
- 在没有详细的结构洞察的情况下,设计稳定,预先组织的很困难.
- 了解折叠景观对于预测稳定性和功能至关重要.
研究的目的:
- 开发一种准确建模折叠景观的方法.
- 使用计算和实验数据,复制化学修改对折叠稳定性的影响.
主要方法:
- 从模拟的轨迹中构建马尔科夫模型,使用通用力场.
- 对实验性核磁共振 (NMR) 测量进行重权模型.
- 使用 Bayesian Conformational Populations (BICePs) 的推理算法来改进参数和重量集.
主要成果:
- 重新加权的马尔科夫模型准确地捕获实验性NMR可观测值 (NOE距离,化学转移,标量合).
- 由BICeP重量调整的折叠景观与实验趋势保持一致,改进了以前的方法,如NAMFIS.
- 微妙的化学修改被发现会改变折叠稳定性,最高可达2kJmol-1.1.
结论:
- 重量化方法为设计可折叠的非天然和循环提供了强大而可靠的途径.
- 通过整合分子模拟和实验性NMR数据,可以实现精确的折叠景观.
- 这种方法提供了一个强大的工具,用于在各种科学应用中推进体设计.
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