Over the last few decades, molecular dynamics (MD) simulations have been immensely successful in providing atomistic insights into various complex biological phenomena. Development of better compute power and resources have shifted the bottleneck from generating simulation data to their subsequent analysis. While Markov state models (MSM) is extremely useful in obtaining kinetic properties from macromolecular trajectories, temporal information is essential. However, most commonly used enhanced sampling methods such as replica exchange MD/MC methods lack temporal information and hence MSM is ineffective. This work tries to address the question, ‘Given just an equilibrium sampling of a system’s configurations with no temporal connections between the configurations, is it possible to extract the most reactive path between any two given configurations?’. We propose a probabilistic algorithm, borrowing concepts from graph theory and machine learning, to extract reactive pathways from molecular trajectories in the absence of temporal data. A suitable vector representation was chosen to represent each frame in the macromolecular trajectory and dimensionality reduction was performed using principal component analysis (PCA). The trajectory was then clustered using a density-based clustering algorithm, where each cluster represents a metastable state on the potential energy surface (PES) of the biomolecule under study. A graph was created with these clusters as nodes with the edges learnt using an iterative expectation maximization algorithm. The most reactive path is conceived as the widest path along this graph. We have tested our method on RNA hairpin unfolding trajectory in aqueous urea solution. Our method makes the understanding of the mechanism of unfolding in RNA hairpin molecule more tractable. As this method doesn’t rely on temporal data it can be used to analyze trajectories from Monte Carlo sampling techniques and replica exchange molecular dynamics (REMD).<br/>

Probabilistic framework for constructing temporal relations in replica exchange molecular trajectories using machine learning and graph theory

256th National Meeting (2018)

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