论文标题
基于聚类的偏见蒙特卡洛方法蛋白滴定曲线预测
A clustering-based biased Monte Carlo approach to protein titration curve prediction
论文作者
论文摘要
在这项工作中，我们开发了一种有效的方法来计算实体之间具有成对加节的能量相互作用的系统中的整体平均值。涉及完整枚举配置空间的方法导致指数复杂性。已经提出了采样方法，例如马尔可夫链蒙特卡洛（MCMC）算法来解决这些问题的指数复杂性。但是，在某些情况下，在实体之间存在明显的能量耦合的某些情况下，这种算法的效率可以降低。我们使用策略来通过利用相互作用能量矩阵中的群集结构来提高MCMC的效率，从而偏向采样。我们为有偏见的MCMC运行而采取了两种不同的方案，并表明它们是有效的MCMC计划。与常规MCMC方法相比，我们使用合成的和现实世界的系统来显示我们有偏见的MCMC方法的性能。特别是，我们将这些算法应用于估计蛋白质中残基的质子化集成平均值和滴定曲线的问题。
In this work, we developed an efficient approach to compute ensemble averages in systems with pairwise-additive energetic interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems; however, in certain scenarios where significant energetic coupling exists between the entities, the efficiency of the such algorithms can be diminished. We used a strategy to improve the efficiency of MCMC by taking advantage of the cluster structure in the interaction energy matrix to bias the sampling. We pursued two different schemes for the biased MCMC runs and show that they are valid MCMC schemes. We used both synthesized and real-world systems to show the improved performance of our biased MCMC methods when compared to the regular MCMC method. In particular, we applied these algorithms to the problem of estimating protonation ensemble averages and titration curves of residues in a protein.
