Molecular Mechanics Demonstrate S-COMT as promising therapeutic receptor when analyzed with secondary plant metabolites

Major depressive disorder (MDD) and other psychiatric conditions are debilitating illnesses affecting millions globally. Catechol-O-methyltransferase (COMT), an enzyme that regulates dopamine and norepinephrine breakdown in the brain, has emerged as a potential therapeutic target for these disorders. This study explores the inhibitory potential of plant secondary metabolites against S-COMT using computational techniques. COMT exists in two isoforms: membrane-bound COMT (MB-COMT), primarily found in brain neurons, and soluble COMT (S-COMT), present in peripheral tissues. S-COMT, particularly in the prefrontal cortex, is crucial for regulating neurotransmitters and maintaining cognitive function. Studies suggest S-COMT variants might be linked to the development of depression, schizophrenia, and other psychiatric disorders. Current COMT inhibitors often suffer from limitations, necessitating the exploration of novel therapeutic strategies. This study employed in-silico methods to investigate plant secondary metabolites as potential S-COMT inhibitors. Here, we describe the S-COMT protein structure retrieval and validation, followed by molecular docking simulations to identify plant compounds with the strongest binding affinity to the receptor's active site. Key amino acid residues involved in these interactions were also analyzed. Furthermore, molecular dynamics simulations were conducted to assess the stability of the top-scoring protein-ligand complexes over a 100-ns timeframe. The results explored the stability of ligand binding within the active site and its impact on the overall conformation of the S-COMT receptor. Our findings highlight promising therapeutic potential for these plant-derived compounds. Further in vitro and in vivo studies are warranted to validate their efficacy and safety for potential clinical applications in treating S-COMT-related disorders.

Subjects

Bioinformatics and Computational Biology, Proteomics, Neurogenerative Diseases.