ABSTRACT

Sigma-2 (σ₂) receptor is a transmembrane protein shown to be linked with neurodegenerative diseases and cancer development. Thus, it emerges as a potential biological target for the advancement of anticancer and anti-Alzheimer’s agents. The current study was aimed to identify potential σ₂ receptor ligands using integrated computational approaches including homology modelling, combined pharmacophore- and docking-based virtual screening, and molecular dynamics (MD) simulation. Pharmacophore-based screening was conducted against a database composed of 20,523 small natural and natural-like products. In total, 1200 structures were found to satisfy the required pharmacophore features and were then exposed to docking-based screening against the generated homology model of σ₂ receptor. On the basis of the pharmacophore fit scores, docking scores, and mechanism of binding interaction, 20 potential hits were retained. Five promising candidates were selected (SR84, SR823, SR300, SR413, and SR530) on the basis of their binding score and interaction. Further, in silico ADMET profiling of these compounds showed that the selected compounds possess favourable ADME properties with low toxicity risk. The mechanism of interaction of these compounds with σ₂ receptor as well as their binding stability were characterized by MD simulation.

摘要

σ-2（σ ₂）受体是显示出与神经变性疾病和癌症的发展被链接的跨膜蛋白。因此，它成为抗癌和抗阿尔茨海默氏病发展的潜在生物学靶标。目前研究的目的是确定潜在的σ ₂使用集成的计算方法，包括同源性建模，结合pharmacophore-和基于对接的虚拟筛选，和分子动力学（MD）模拟受体配体。针对由20,523种天然和天然小产品组成的数据库进行了基于药理学的筛选。总共发现1200层结构来满足所需的药效特征和随后暴露于对接基于筛选针对的σ所生成同源性模型₂受体。根据药效团拟合分数，对接分数和结合相互作用的机制，保留了20个潜在的命中值。根据它们的结合分数和相互作用，选择了五个有希望的候选物（SR84，SR823，SR300，SR413和SR530）。此外，对这些化合物进行计算机模拟ADMET分析表明，所选化合物具有良好的ADME特性，且毒性风险低。这些化合物与σ的相互作用的机制₂由MD模拟受体以及它们的结合稳定性进行表征。