In silico Analysis for Potential SIRT2 Inhibitors

SIRT2 is positioned second in class III HDAC family of sirtuins and NAD+ (nicotinamide adenine dinucleotide)dependent in nature. SIRT2 plays a crucial role in neurodegeneration leading to diseases like Parkinson’s disease (PD). Therefore, the need to discover SIRT2 inhibitors aligns itself with potential therapeutic developments for treating PD. As a result of SIRT2 inhibition, $\alpha$-synuclein ($\alpha$-Syn) induced toxicity decreases leading to reduction in cell death. SirReal2 is a native SIRT2 inhibitor. It is situated in the selectivity pocket which is hydrophobic in nature present near the zinc binding domain (ZBD). SirReal2 was taken as the reference ligand for this study. The primary objective was to shortlist better suited compounds for SIRT2 inhibition as potential drug components for Parkinson’s therapy in comparison to what was deduced in previous such studies. Here, a large number of compounds were shortlisted on the basis of structural similarity to SirReal2 which were filtered by ADME analysis. As the next step docking studies were performed to generate binding affinities. This investigation revealed seven such compounds with affinities higher than SirReal2. Extended ADME analysis strengthened the fact that these drug like commercial compounds from ZINC database displayed greater viability for advancement in the pharmacological field. In contrast with the previous studies on this matter, these probable inhibitors were in fact capable of crossing the blood brain barrier making them a better choice. In silico analysis incorporates artificial intelligence (AI) via machine learning (ML) as a part of various computational tools used in the study. Such analysis of compounds can significantly reduce the time and cost of wet lab research. Conclusively, results from this study strongly advocate for the analysis of these probable inhibitors inside living systems.