AI 中文总结
本研究开发了基于分子动力学的动力学蒙特卡洛模型，以Trp-cage蛋白为例实现了秒级以上的肽构象动力学模拟，可连接局部柔性与全蛋白动力学。
AI 中文摘要
我们提出了一种动力学蒙特卡洛（KMC）建模方法，用于描述肽分子从纳秒到秒级时间尺度的随机动力学。蛋白质构象变化的动力学在局部层面通过二面角跃迁来解释。以Trp-cage微型蛋白为例，该KMC模型从多条分子动力学（MD）轨迹中“学习”跃迁规律。训练基于局部分治策略，将离散化的主链二面角态识别为构象空间的构建单元，结合相关的二面角翻转跃迁率来描述构象态间的动力学。该方法的关键特征是纳入主链相关性，使跃迁率以局部环境和空间位阻耦合为条件。我们表明，内置相关性的KMC模型与MD结果高度吻合。这种方法在标准台式计算机上仅需数小时CPU时间即可达到秒级时间尺度，且能轻松生成构象动力学的多个随机实现。我们的KMC模型构建方案应普遍适用于多种蛋白质，可用于连接局部柔性与全蛋白动力学。
英文摘要
We present a kinetic Monte Carlo (KMC) modeling approach to describe the stochastic dynamics of a peptide molecule spanning nanosecond to second timescales. The dynamics of protein conformational changes is interpreted at a local level in terms of dihedral transitions. Taking Trp-cage miniprotein as an example, the KMC model "learns" about the transitions from multiple MD trajectories. Training is based on local divide-and-conquer strategy that identifies the discretized backbone dihedral states as building blocks for the conformational space, along with associated transition rates of dihedral flips to describe the conformational state-to-state dynamics. A key feature in our approach is the incorporation of backbone correlations, such that rates are conditioned on the local environment and steric coupling. We show that with the correlations built-in, the KMC model closely matches MD. Such an approach is shown to reach second timescales in a few CPU hours on a standard desktop computer, and can easily yield multiple stochastic realizations of the conformational dynamics. Our KMC model construction scheme should be generally applicable to a wide range of proteins, and can be used for bridging local flexibility to protein-wide dynamics.
Comments31 pages, 9 figures
