论文标题
使用顺序的蒙特卡洛估算蛋白质结构量的玻尔兹曼平均值
Estimating Boltzmann Averages for Protein Structural Quantities Using Sequential Monte Carlo
论文作者
论文摘要
顺序蒙特卡洛（SMC）方法被广泛用于从棘手的目标分布中绘制样品。当目标分布受到高度限制或多模式时，粒子退化性可能会阻碍SMC的使用。作为一种激励性的应用，我们考虑了从玻尔兹曼分布中抽样蛋白质结构的问题。本文提出了一种通用的SMC方法，该方法可以传播每个粒子的多个后代，然后重新采样以维持所需的颗粒数。仿真研究证明了解决蛋白质采样问题的方法的功效。作为一个真实的数据示例，我们使用我们的方法来估计SARS-COV-2病毒尖峰蛋白的关键段的原子接触次数。
Sequential Monte Carlo (SMC) methods are widely used to draw samples from intractable target distributions. Particle degeneracy can hinder the use of SMC when the target distribution is highly constrained or multimodal. As a motivating application, we consider the problem of sampling protein structures from the Boltzmann distribution. This paper proposes a general SMC method that propagates multiple descendants for each particle, followed by resampling to maintain the desired number of particles. Simulation studies demonstrate the efficacy of the method for tackling the protein sampling problem. As a real data example, we use our method to estimate the number of atomic contacts for a key segment of the SARS-CoV-2 viral spike protein.
