Integrated molecular modelling and machine learning strategies for the design of potential therapeutic agents against Neuroblastoma

Abstract

Neuroblastoma is cancer that develops from immature nerve cells found in several areas of the body. It arises from undifferentiated sympathetic-adrenal lineage cells and is mostly intermittent. Intracellular calcium ion concentration is highly maintained in cells as it controls many cellular processes such as ATP synthesis cell cycle apoptosis and much more. Due to the increase of calcium ions, there occurs the disturbance in homeostasis of the calcium apoptosis pathway which leads to neuroblastoma cancer. This rise in Calcium ion due to overexpression of T-type calcium channels in neuroblastoma is our focus of our study. Overexpression of T type Calcium Channels is because of duplication of gene responsible of encoding T type Calcium Channel i.e., CACNA1G which encode for alpha- pore forming domain of Cav3.1. The detailed study of T-type calcium channel and its elongation time due to mutation in Cav3.1 (CACNA1G) gene which leads to increase in calcium ion concentration and result to trigger proliferation in neuroblastoma cells instead of apoptosis. We will focus on making calcium channel blockers which mat assist to down-regulate the concentration of T-type channels in neuroblastoma hence leading to a decrease in the level of calcium in cells which will ultimately induce apoptosis in neuroblastoma cells. Therefore, in this study calcium channels blockers were identified which will help in closing of calcium channel hence maintain the hemostasis of calcium leading to apoptosis instead of cell proliferation. Calcium channel blockers were designed using pharmacophore model s and out of which 8 hits were also identified which can be used as a repurposed drug. Active site residues like Leu352, Thr353, Glu 354, Ile 380, Ser 383, Phe 384, Asn 952, Tyr 953, Phe 956, Asn 957, Ser 1461, Asn 957, Val 960, Glu 922, Glu 923, Lys 1462, Gln 1816 were identified and seen in molecular docking as well which was verified using PLIF analysis as well. Later on pharmacophore model was built which consists of 9 pharmacophore features out which 2 were hydrogen bond acceptor and 7 were hydrophobic xii interactions. This pharmacophore was screened and lastly 8 potential hits were found which can help us block the activity of calcium channel