Part of the book: DNA Repair
Part of the book: New Research Directions in DNA Repair
Voltage-gated calcium channels (VGCCs) manage the electrical signaling of cells by allowing the selective-diffusion of calcium ions in response to the changes in the cellular membrane potential. Among the different VGCCs, the long-lasting or the L-type calcium channels (LTCCs) are prevalently expressed in a variety of cells, such as skeletal muscle, ventricular myocytes, smooth muscles and dendritic cells and forms the largest family of the VGCCs. Their wide expression pattern and significant role in diverse cellular events, including neurotransmission, cell cycle, muscular contraction, cardiac action potential and gene expression, has made these channels the major targets for drug development. In this book chapter, we aim to provide a comprehensive overview of the different VGCCs and focus on the sequence-structure–function properties of the LTCCs. Our chapter will summarize and review the various experimental and computational analyses performed on the structures of the LTCCs and their implications in drug discovery applications.
Part of the book: Ion Channels in Health and Sickness
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.
Part of the book: High-Throughput Screening for Drug Discovery