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.
The term "personalized medicine" was coined at the turn of the century, but most medicines are currently prescribed based on disease categories and occasionally racial demographics, but not personalized attributes. In cardiovascular medicine, the personalization of medication is minimal; however, it is accepted that not all patients respond equally to common cardiovascular medications. Here we chose one prominent cardiovascular drug target, the angiotensin receptor, and, using computer modeling, created preliminary models of over 100 known alterations to the angiotensin receptor to determine if the alterations changed the ability of clinically used drugs to interact with the angiotensin receptor. The strength of interaction was compared to the unaltered angiotensin receptor, generating a map predicting which alteration affected each drug. It is expected that in the future, a patient's receptors can be sequenced, and maps, such as the one presented here, can be used to select the optimum medication based on the patient's genetics. Such a process would allow for the personalization of current medication therapy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.