Quantitative Structure–Activity Relationships and Docking Studies of Calcitonin Gene‐Related Peptide Antagonists

Abstract

Defining the role of calcitonin gene-related peptide in migraine pathogenesis could lead to the application of calcitonin gene-related peptide antagonists as novel migraine therapeutics. In this work, quantitative structure-activity relationship modeling of biological activities of a large range of calcitonin gene-related peptide antagonists was performed using a panel of physicochemical descriptors. The computational studies evaluated different variable selection techniques and demonstrated shuffling stepwise multiple linear regression to be superior over genetic algorithm-multiple linear regression. The linear quantitative structure-activity relationship model revealed better statistical parameters of cross-validation in comparison with the non-linear support vector regression technique. Implementing only five peptide descriptors into this linear quantitative structure-activity relationship model resulted in an extremely robust and highly predictive model with calibration, leave-one-out and leave-20-out validation R 2 of 0.9194, 0.9103, and 0.9214, respectively. We performed docking of the most potent calcitonin gene-related peptide antagonists with the calcitonin gene-related peptide receptor and demonstrated that peptide antagonists act by blocking access to the peptide-binding cleft. We also demonstrated the direct contact of residues 28-37 of the calcitonin gene-related peptide antagonists with the receptor. These results are in agreement with the conclusions drawn from the quantitative structure-activity relationship model, indicating that both electrostatic and steric factors should be taken into account when designing novel calcitonin gene-related peptide antagonists.Key words: calcitonin gene-related peptide antagonists, calcitonin gene-related peptide receptor, docking of peptide-protein, multiple linear regression, quantitative structure-activity relationship Calcitonin gene-related peptide (CGRP) is a potent 37 amino acid neuropeptide generated by alternative tissue-specific splicing of the primary transcript of the calcitonin gene. CGRP was first discovered when alternative processing of RNA transcripts from the calcitonin gene was shown to result in the production of distinct CGRP-encoding mRNAs (1). Calcitonin gene-related peptide signals through a seven-transmembrane G-protein-coupled receptor that belongs to the family of secretin receptors. The CGRP receptor is a heterodimer of the G-protein-coupled calcitonin receptor-like receptor (CLR) and receptor activity modifying protein 1 (RAMP1) as an accessory protein. Receptor activity modifying protein 1 has a role in facilitating the expression of CLR on the cell surface and is also necessary for the ligand-binding activity of the receptor (2). Calcitonin gene-related peptide is broadly expressed throughout the central and peripheral nervous systems (3) as well as in the heart, blood vessels, pituitary gland, thyroid gland, lungs and gastrointestinal tract. Although CGRP has a general range of physiological and biological effects including neuromo...

Fluorescent Analogues of Human α-Calcitonin Gene-Related Peptide with Potent Vasodilator Activity

“…Targeting CGRP receptors may be a useful approach for therapeutic intervention [6], and CGRP-neutralizing antibodies have recently been developed for the prevention of migraine [7]. Furthermore, h-αCGRP (8-37)-amide antagonistic peptides have proved to be interesting therapeutic ligands [8], and hence numerous analogues have been reported [9]-. Human α-CGRP linked to a fatty acid with improved half-life has also been described [10].…”

Development of QSAR Models on the Fouling-Release Performance of Silicone Oil-modified Siloxane Polyurethane Coatings

“…The other descriptors in the QSAR model are the MoR28m and Mor31m descriptors included in the Moran autocorrelation descriptors family, and these particular ones capture the autocorrelation properties of molecular properties or structural features within a molecule. The Moran autocorrelation descriptors are based on spatial autocorrelation analysis, which measures the similarity between the values of a molecular property at different positions within a molecule[58].MoR28m and Mor31m specifically capture different signals(28 and 31) within the 3D-MoRSE framework and use atomic mass weighting schemes[63],[64],[65].3D-MoRSE descriptors in the model are valuable descriptors because they consider the precise 3D arrangement of atoms in molecules, avoiding any uncertainties that may arise from chemical graphs [66].…”