In silico prediction of ARB resistance: A first step in creating personalized ARB therapy

by Shane D. Anderson, Asna Tabassum, Jae Kyung Yeon, Garima Sharma, Priscilla Santos, Tik Hang Soong, Yin Win Thu, Isaac Nies, Tomomi Kurita, Andrew Chandler, Abdelaziz Alsamarah, Rhye-Samuel Kanassatega, Yun L. Luo, Wesley M. Botello-Smith, Bradley T. Andresen

Angiotensin II type 1 receptor (AT1R) blockers (ARBs) are among the most prescribed drugs. However, ARB effectiveness varies widely, which may be due to non-synonymous single nucleotide polymorphisms (nsSNPs) within the AT1R gene. The AT1R coding sequence contains over 100 nsSNPs; therefore, this study embarked on determining which nsSNPs may abrogate the binding of selective ARBs. The crystal structure of olmesartan-bound human AT1R (PDB:4ZUD) served as a template to create an inactive apo-AT1R via molecular dynamics simulation (n = 3). All simulations resulted in a water accessible ligand-binding pocket that lacked sodium ions. The model remained inactive displaying little movement in the receptor core; however, helix 8 showed considerable flexibility. A single frame representing the average stable AT1R was used as a template to dock Olmesartan via AutoDock 4.2, MOE, and AutoDock Vina to obtain predicted binding poses and mean Boltzmann weighted average affinity. The docking results did not match the known pose and affinity of Olmesartan. Thus, an optimization protocol was initiated using AutoDock 4.2 that provided more accurate poses and affinity for Olmesartan (n = 6). Atomic models of 103 of the known human AT1R polymorphisms were constructed using the molecular dynamics equilibrated apo-AT1R. Each of the eight ARBs was then docked, using ARB-optimized parameters, to each polymorphic AT1R (n = 6). Although each nsSNP has a negligible effect on the global AT1R structure, most nsSNPs drastically alter a sub-set of ARBs affinity to the AT1R. Alterations within N298 –L314 strongly effected predicted ARB affinity, which aligns with early mutagenesis studies. The current study demonstrates the potential of utilizing in silico approaches towards personalized ARB therapy. The results presented here will guide further biochemical studies and refinement of the model to increase the accuracy of the prediction of ARB resistance in order to increase overall ARB effectiveness.

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