Khatun Samima, Dasgupta Indrasis, Sen Sourish, Amin Sk Abdul, Qureshi Insaf Ahmed, Jha Tarun, Gayen Shovanlal
Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India.
Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad 500 046, Telangana, India.
Int J Biol Macromol. 2025 Jan;284(Pt 2):138119. doi: 10.1016/j.ijbiomac.2024.138119. Epub 2024 Nov 26.
Histone deacetylase 8 (HDAC8) inhibitors play a pivotal role in epigenetic regulation. Numerous HDAC8 inhibitors (HDAC8is), that are non-hydroxamates have been identified to date, and a few of them exhibit antiproliferative activity that is on par with hydroxamates. While many non-hydroxamate-based HDAC8is have demonstrated selectivity, hydroxamate-based HDAC8is, like Vorinostat and TSA, have a tendency of non-specificity among the different HDAC isoforms. Moreover, because of the unfavorable toxic side effects, there are significant concerns surrounding the use of hydroxamate derivatives as therapeutic agents in cancer as well as other chronic diseases. Consequently, the research on non-hydroxamate-based HDAC8is is of utmost priority. In the present study, a comprehensive study was presented to unravel the structural requirements of non-hydroxamate-based HDAC8is from a diverse set of 866 compounds. The study utilized Classification-based Quantitative Structure-Activity Relationship (QSAR) analysis, incorporating Bayesian classification, Recursive partitioning, and other machine learning methods to pinpoint the key structural features essential for HDAC8 inhibition. To underscore and gain deeper insights into the identified structural features, molecular docking, and molecular dynamic (MD) simulation studies were conducted. The integration of these computational approaches unveiled key structural motifs essential for potent HDAC8 inhibitory activity, shedding light on the molecular basis of HDAC8 inhibition using non-hydroxamates.
组蛋白去乙酰化酶8（HDAC8）抑制剂在表观遗传调控中起着关键作用。迄今为止，已鉴定出许多非异羟肟酸类的HDAC8抑制剂（HDAC8is），其中一些表现出与异羟肟酸类相当的抗增殖活性。虽然许多基于非异羟肟酸的HDAC8is已显示出选择性，但基于异羟肟酸的HDAC8is，如伏立诺他和曲古抑菌素A，在不同的HDAC亚型中存在非特异性倾向。此外，由于不良的毒副作用，围绕异羟肟酸衍生物作为癌症以及其他慢性疾病治疗药物的使用存在重大担忧。因此，基于非异羟肟酸的HDAC8is的研究至关重要。在本研究中，进行了一项全面的研究，以从866种不同的化合物中揭示基于非异羟肟酸的HDAC8is的结构要求。该研究利用基于分类的定量构效关系（QSAR）分析，结合贝叶斯分类、递归划分和其他机器学习方法，以确定HDAC8抑制所必需的关键结构特征。为了强调并更深入地了解所确定的结构特征，进行了分子对接和分子动力学（MD）模拟研究。这些计算方法的整合揭示了强效HDAC8抑制活性所必需的关键结构基序，阐明了使用非异羟肟酸抑制HDAC8的分子基础。
