In silico prediction of ARB resistance: A first step in creating personalized ARB therapy
| Autor: | Isaac Nies, Garima Sharma, Tik Hang Soong, Tomomi Kurita, Jae Kyung Yeon, Bradley T. Andresen, Asna Tabassum, Andrew Chandler, Yin Win Thu, Shane D. Anderson, Wesley M. Botello-Smith, Priscilla Santos, Yun Luo, Abdelaziz Alsamarah, Rhye-Samuel Kanassatega |
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| Rok vydání: | 2020 |
| Předmět: |
Angiotensin receptor
Protein Data Bank (RCSB PDB) Tetrazoles Molecular Dynamics Molecular Docking Simulation Biochemistry Computational Chemistry Biochemical Simulations Medicine and Health Sciences Biology (General) Precision Medicine Mammals Crystallography Ecology Chemistry Physics Imidazoles Eukaryota Software Engineering Ruminants AutoDock Condensed Matter Physics Computational Theory and Mathematics Modeling and Simulation Physical Sciences Vertebrates Crystal Structure Engineering and Technology Olmesartan medicine.drug Research Article Signal Transduction Chemical Elements Computer and Information Sciences Transmembrane Receptors QH301-705.5 In silico Single-nucleotide polymorphism Computational biology Molecular Dynamics Simulation Polymorphism Single Nucleotide Receptor Angiotensin Type 1 Computer Software Cellular and Molecular Neuroscience DOCK Genetics medicine Humans Solid State Physics Animals Molecular Biology Ecology Evolution Behavior and Systematics Clinical Genetics business.industry Deer Sodium Organisms Personalized Medicine Reproducibility of Results Biology and Life Sciences Proteins Computational Biology Cell Biology Angiotensin II Docking (molecular) Amniotes Personalized medicine business G Protein Coupled Receptors Angiotensin II Type 1 Receptor Blockers Zoology |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 16, Iss 11, p e1007719 (2020) |
| ISSN: | 1553-7358 |
| Popis: | 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. Author summary The term "personalized medicine" was coined at the turn of the century, but most medicines currently prescribed are based on disease categories and occasionally racial demographics, not personalized attributes. In cardiovascular medicine, the personalization of medication is minimal, despite the fact 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 wild-type angiotensin receptor, generating a map predicting which alteration affected which drug(s). It is expected that in the future, sequencing of drug targets can be used to compare a patient’s result to a map similar to what is provided in this manuscript to choose the optimal medication based on the patient’s genetics. Such a process has the potential to facilitate the personalization of current medication therapy. |
| Databáze: | OpenAIRE |
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