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<p>The nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase Sirtuin 2 (SIRT2) plays a regulatory function in diverse cellular processes and has been linked to aging and the development of neurodegenerative and cancerous diseases. Consequently, targeting SIRT2 has emerged as a promising anticancer therapeutic strategy; however, currently available SIRT2 inhibitors and modulators often exhibit limited potency and suboptimal selectivity. Herein, the NCI database, containing more than 230,000 compounds, was systematically screened using an optimized AttentiveFP model to identify small molecules with potential SIRT2-inhibitory activity. The trained model predicted 23,238 NCI compounds as potentially active, which were subsequently subjected to docking computations against SIRT2. Upon docking estimations, the top-ranked NCI compounds bound to SIRT2 were advanced for molecular dynamics simulations (MDS) throughout 300 ns, along with binding energy (Δ<i>G</i><sub>binding</sub>) computations utilizing the MM-GBSA approach. Among these, NCI243049, NCI407129, and NCI248613 unveiled superior binding affinities toward SIRT2 over 300 ns MDS compared to the reference inhibitor SirReal2, with Δ<i>G</i><sub>binding</sub> values of −74.3, −73.1, −71.5, and −47.8&#xa0;kcal/mol, respectively. Post-MD analyses consistently supported the promising stability and binding profiles of the identified NCI compounds bound to SIRT2 throughout 300 ns MDS. The physicochemical and ADMET features of the identified NCI compounds were predicted, indicating their favorable oral bioavailability and pharmacokinetic profiles. Eventually, DFT computations were executed to assess the chemical reactivity of the identified NCI compounds. Collectively, these findings highlighted NCI243049, NCI407129, and NCI248613 as promising SIRT2 inhibitors, meriting further validation through experimental assays for cancer therapy.</p>

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Insights into SIRT2 inhibition from machine learning-assisted multi-level screening of the NCI database

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Laila Abdulmohsen Jaragh-Alhadad,
  • Alaa H. M. Abdelrahman,
  • Peter A. Sidhom,
  • Moustafa S. Moustafa,
  • Mahmoud A. A. Ibrahim

摘要

The nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase Sirtuin 2 (SIRT2) plays a regulatory function in diverse cellular processes and has been linked to aging and the development of neurodegenerative and cancerous diseases. Consequently, targeting SIRT2 has emerged as a promising anticancer therapeutic strategy; however, currently available SIRT2 inhibitors and modulators often exhibit limited potency and suboptimal selectivity. Herein, the NCI database, containing more than 230,000 compounds, was systematically screened using an optimized AttentiveFP model to identify small molecules with potential SIRT2-inhibitory activity. The trained model predicted 23,238 NCI compounds as potentially active, which were subsequently subjected to docking computations against SIRT2. Upon docking estimations, the top-ranked NCI compounds bound to SIRT2 were advanced for molecular dynamics simulations (MDS) throughout 300 ns, along with binding energy (ΔGbinding) computations utilizing the MM-GBSA approach. Among these, NCI243049, NCI407129, and NCI248613 unveiled superior binding affinities toward SIRT2 over 300 ns MDS compared to the reference inhibitor SirReal2, with ΔGbinding values of −74.3, −73.1, −71.5, and −47.8 kcal/mol, respectively. Post-MD analyses consistently supported the promising stability and binding profiles of the identified NCI compounds bound to SIRT2 throughout 300 ns MDS. The physicochemical and ADMET features of the identified NCI compounds were predicted, indicating their favorable oral bioavailability and pharmacokinetic profiles. Eventually, DFT computations were executed to assess the chemical reactivity of the identified NCI compounds. Collectively, these findings highlighted NCI243049, NCI407129, and NCI248613 as promising SIRT2 inhibitors, meriting further validation through experimental assays for cancer therapy.