基于聚类的有偏蒙特卡罗方法预测蛋白质滴定曲线
A clustering-based biased Monte Carlo approach to protein titration curve prediction
摘要：
在这项工作中，我们开发了一种有效的方法来计算系统中的整体平均值与实体之间的成对添加剂充满活力的相互作用。涉及配置空间的完全枚举的方法导致指数复杂性。采样方法，如马尔可夫链蒙特卡罗（MCMC）算法已被提出来解决这些问题的指数复杂性，然而，在某些情况下，显着的能量耦合实体之间存在，这种算法的效率可能会降低。我们使用了一种策略，以提高MCMC的效率，利用集团结构的相互作用能矩阵偏置的采样。我们追求两个不同的计划有偏见的MCMC运行，并表明它们是有效的MCMC计划。我们使用合成和真实世界的系统，以显示我们的偏MCMC方法相比，定期MCMC方法的性能改善。特别是，我们将这些算法的问题估计质子化系综平均值和滴定曲线的蛋白质中的残基。
英文摘要：
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.
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