A clustering-based biased Monte Carlo approach to protein titration curve prediction.

ABSTRACT: 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 scheme

SUBMITTER: Sathanur AV 

PROVIDER: S-EPMC8513769 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature