Kamble Subodh A, Barale Sagar S, Mohammed Ali Abdulmawjood, Paymal Sneha B, Naik Nitin M, Sonawane Kailas D
Structural Bioinformatics Unit, Department of Biochemistry, Shivaji University, Kolhapur, M.S., 416004, India.
Department of Microbiology, Shivaji University, 416004, M.S., Kolhapur, India.
Amino Acids. 2024 Apr 22;56(1):33. doi: 10.1007/s00726-023-03367-1.
Alzheimer's disease (AD) is the most prevalent type of dementia caused by the accumulation of amyloid beta (Aβ) peptides. The extracellular deposition of Aβ peptides in human AD brain causes neuronal death. Therefore, it has been found that Aβ peptide degradation is a possible therapeutic target for AD. CathD has been known to breakdown amyloid beta peptides. However, the structural role of CathD is not yet clear. Hence, for the purpose of gaining a deeper comprehension of the structure of CathD, the present computational investigation was performed using virtual screening technique to predict CathD's active site residues and substrate binding mode. Ligand-based virtual screening was implemented on small molecules from ZINC database against crystal structure of CathD. Further, molecular docking was utilised to investigate the binding mechanism of CathD with substrates and virtually screened inhibitors. Localised compounds obtained through screening performed by PyRx and AutoDock 4.2 with CathD receptor and the compounds having highest binding affinities were picked as; ZINC00601317, ZINC04214975 and ZINCC12500925 as our top choices. The hydrophobic residues Viz. Gly35, Val31, Thr34, Gly128, Ile124 and Ala13 help stabilising the CathD-ligand complexes, which in turn emphasises substrate and inhibitor selectivity. Further, MM-GBSA approach has been used to calculate binding free energy between CathD and selected compounds. Therefore, it would be beneficial to understand the active site pocket of CathD with the assistance of these discoveries. Thus, the present study would be helpful to identify active site pocket of CathD, which could be beneficial to develop novel therapeutic strategies for the AD.
阿尔茨海默病（AD）是由β淀粉样蛋白（Aβ）肽积累引起的最常见的痴呆类型。Aβ肽在人类AD大脑中的细胞外沉积会导致神经元死亡。因此，已发现Aβ肽降解是AD的一个可能治疗靶点。组织蛋白酶D（CathD）已知可分解β淀粉样蛋白肽。然而，CathD的结构作用尚不清楚。因此，为了更深入地理解CathD的结构，本计算研究使用虚拟筛选技术来预测CathD的活性位点残基和底物结合模式。基于配体的虚拟筛选是针对来自ZINC数据库的小分子对CathD的晶体结构进行的。此外，利用分子对接来研究CathD与底物和虚拟筛选的抑制剂的结合机制。通过PyRx和AutoDock 4.2与CathD受体进行筛选获得的定位化合物以及具有最高结合亲和力的化合物被选为：ZINC00601317、ZINC04214975和ZINCC12500925作为我们的首选。疏水残基即甘氨酸35、缬氨酸31、苏氨酸34、甘氨酸128、异亮氨酸124和丙氨酸13有助于稳定CathD - 配体复合物，这反过来强调了底物和抑制剂的选择性。此外，MM - GBSA方法已用于计算CathD与选定化合物之间的结合自由能。因此，借助这些发现来了解CathD的活性位点口袋将是有益的。因此，本研究将有助于识别CathD的活性位点口袋，这可能有利于开发针对AD的新型治疗策略。
