Investigating the Bioactive Conformation of Angiotensin II Using Markov State Modeling Revisited with Web-Scale Clustering

Emmanouil Christoforou (1, 2) , Hari Leontiadou (3) , Frank Noé (4) , Jannis Samios (1) , Ioannis Z. Emiris (1, 5) , Zoe Cournia (3)

1 NKUA - National and Kapodistrian University of Athens
2 AROMATH - AlgebRe, geOmetrie, Modelisation et AlgoriTHmes
3 BRFAA - Biomedical Research Foundation of the Academy of Athens
4 Freie Universität Berlin
5 ARC - Athena Research and Innovation Centre

Molecular dynamics simulation is a powerful technique for studying the structure and dynamics of biomolecules in atomic-level detail by sampling their various conformations in real time. Because of 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 (MSMs) are efficient computational techniques that can be used to extract dominant conformational states and to connect those with kinetic information. In this work, we perform Molecular Dynamics simulations to investigate the free energy landscape of Angiotensin II (AngII) in order to unravel its bioactive conformations using different clustering techniques and Markov state modeling. AngII is an octapeptide hormone, which binds to the AT1 transmembrane receptor, and plays a vital role in the regulation of blood pressure, conservation of total blood volume, and salt homeostasis. To mimic the water–membrane interface as AngII approaches the AT1 receptor and to compare our findings with available experimental results, the simulations were performed in water as well as in water–ethanol mixtures. 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, 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 molecular dynamics simulations with reasonable trade-offs between time and accuracy. Finally, we extract MSMs using various clustering techniques for the generation of microstates and macrostates and for the selection of the macrostate representatives.

Mots clés: structural bioinformatics, simulation, molecular dynamics, clustering

Domaines: Bio-informatique [q-bio.QM]; Modélisation et simulation; Biochimie, Biologie Moléculaire

Emmanouil Christoforou, Hari Leontiadou, Frank Noé, Jannis Samios, Ioannis Z. Emiris, et al.. Investigating the Bioactive Conformation of Angiotensin II Using Markov State Modeling Revisited with Web-Scale Clustering. Journal of Chemical Theory and Computation, 2022, 18 (9), pp.5636-5648. ⟨10.1021/acs.jctc.1c00881⟩. ⟨hal-03895590⟩