芳香烃受体
交易激励
生物信息学
计算生物学
虚拟筛选
背景(考古学)
同源建模
对接(动物)
数量结构-活动关系
生物
化学
药物发现
生物信息学
转录因子
生物化学
医学
基因
酶
古生物学
护理部
作者
Farag E.S. Mosa,Ayman O.S. El‐Kadi,Khaled Barakat
出处
期刊:IntechOpen eBooks
[IntechOpen]
日期:2022-05-25
被引量:1
标识
DOI:10.5772/intechopen.99228
摘要
Aryl hydrocarbon receptor (AhR) is a biological sensor that integrates environmental, metabolic, and endogenous signals to control complex cellular responses in physiological and pathophysiological functions. The full-length AhR encompasses various domains, including a bHLH, a PAS A, a PAS B, and transactivation domains. With the exception of the PAS B and transactivation domains, the available 3D structures of AhR revealed structural details of its subdomains interactions as well as its interaction with other protein partners. Towards screening for novel AhR modulators homology modeling was employed to develop AhR-PAS B domain models. These models were validated using molecular dynamics simulations and binding site identification methods. Furthermore, docking of well-known AhR ligands assisted in confirming these binding pockets and discovering critical residues to host these ligands. In this context, virtual screening utilizing both ligand-based and structure-based methods screened large databases of small molecules to identify novel AhR agonists or antagonists and suggest hits from these screens for validation in an experimental biological test. Recently, machine-learning algorithms are being explored as a tool to enhance the screening process of AhR modulators and to minimize the errors associated with structure-based methods. This chapter reviews all in silico screening that were focused on identifying AhR modulators and discusses future perspectives towards this goal.
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