Trajectory data for the publication "Investigating the bioactive conformation of Angiotensin II using Markov State Modeling revisited with web-scale clustering"

This dataset contains the trajectories for the following publication:

ABSTRACT: Molecular Dynamics simulations is a powerful technique for studying the structure and dynamics of biomolecules in atomic-level detail by sampling their various conformations in real time. Due to the long timescales that need to be sampled to study biomolecular processes and the big and complex nature of the corresponding data, relevant analyses of important biophysical phenomena are challenging. Clustering and Markov State Models are efficient computational techniques that can be used to extract dominant conformational states and to connect those with kinetic information. In this work, we investigate the free energy landscape of Angiotensin II (AngII) in water-ethanol mixtures as a  water-membrane model interface mimic in order to unravel its bioactive conformations using clustering techniques and Markov State modeling. AngII is an octapeptide hormone, which plays a vital role in the regulation of blood pressure, in the conservation of total blood volume, and salt homeostasis. AngII binds to the AT1 receptor, which is a transmembrane protein. Here, the simulations were performed in water as well as in water-ethanol mixtures to mimic the water-membrane interface as AngII approaches the AT1 receptor, and compare our findings with available experimental results in this solvent mixture. Our results show that in the water-ethanol environment AngII adopts more compact U-shaped (folded) conformations than in water, which resembles its structure when bound to the AT1 receptor. For clustering of the conformations, we validate the efficiency of an inverted-quantized k-means algorithm (IQ-means), as a fast approximate clustering technique for web-scale data (millions of points into thousands or millions of clusters) compared to k-means, on data from trajectories of MD simulations with reasonable trade-offs between time and accuracy. Finally, we extract Markov State Models using various clustering techniques for the generation of microstates, macrostates and for the selection of the macrostate representatives.