| Summary: | Sheikh Arslan Sehgal,1–4 Shazia Mannan,1 Sannia Ali1 1Department of Bioscience, COMSATS Institute of Information Technology, Sahiwal, Pakistan; 2State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Zoology, Chinese Academy of Sciences, 3University of Chinese Academy of Sciences, Beijing, People’s Republic of China; 4Department of Bioinformatics and Biotechnology, International Islamic University, Islamabad, Pakistan Abstract: Charcot–Marie–Tooth (CMT) disease is an inherited peripheral neuromuscular disorder characterized by length-dependent and progressive degeneration of peripheral nerves, leading to muscular weakness. Research has shown that mutated HSPB8 may be responsible for depression, neurodegenerative disorders, and improper functioning of peripheral nerves, resulting in neuromuscular disorders like CMT. In the current work, a hybrid approach of virtual screening and molecular docking studies was followed by homology modeling and pharmacophore identification. Detailed screening analyses were carried out by 2-D similarity search against prescribed antidepressant drugs with physicochemical properties. LigandScout was employed to ascertain novel molecules and pharmacophore properties. In this study, we report three novel compounds that showed maximum binding affinity with HSPB8. Docking analysis elucidated that Met37, Ser57, Ser58, Trp60, Thr63, Thr114, Lys115, Asp116, Gly117, Val152, Val154, Leu186, Asp189, Ser190, Gln191, and Glu192 are critical residues for ligand–receptor interactions. Our analyses suggested paroxetine as a potent compound for targeting HSPB8. Selected compounds have more effective energy scores than the selected drug analogs. Additionally, site-directed mutagenesis could be significant for further analysis of the binding pocket. The novel findings based on an in silico approach may be momentous for potent drug design against depression and CMT. Keywords: bioinformatics, computer-aided drug design, HSPB8, heat shock protein (HSP), molecular docking, modeling, pharmacoinformatics, virtual screening, neurodegenerative disorders, antidepressants
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