Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, India.
J Biomol Struct Dyn. 2024 Oct;42(17):8831-8853. doi: 10.1080/07391102.2023.2248509. Epub 2023 Aug 23.
HDAC3 is an emerging target for the identification and discovery of novel drug candidates against several disease conditions including cancer. Here, a fragment-based non-linear machine learning (ML) method along with chemical space exploration followed by a structure-based binding mode of interaction analysis study was carried out on some HDAC3 inhibitors to obtain the key structural features modulating HDAC3 inhibition. Both the ML and chemical space analysis identified several physicochemical and structural properties namely lipophilicity, polar and relative polar surface area, arylcarboxamide moiety, bulky fused aromatic group, -alkyl, and cinnamoyl moieties, the higher number of oxygen atoms, π-electrons for the substituted tetrahydrofuronaphthodioxolone moiety favorable for higher HDAC3 inhibition. Moreover, hydrogen bond forming capabilities, the length and substitution position of the linker moiety, the importance of phenyl ring in the linker motif, the contribution of heterocyclic cap moieties for effective inhibitor binding at the HDAC3 catalytic site that correspondingly affects the HDAC3 inhibitory potency. Again, macrocyclic ring structure and cyclohexyl cap moiety are responsible for lower HDAC3 inhibition. The MD simulation study of selected compounds explained strong binding patterns at the HDAC3 active site as evidenced by the lower RMSD and RMSF values. Nevertheless, it also explained the importance of the crucial structural fragments derived from the fragment-based analysis during ligand-enzyme interactions. Therefore, the outcomes of this current structural analysis will be a useful tool for fragment-based drug discovery of effective HDAC3 inhibitors for clinical therapeutics in the future.Communicated by Ramaswamy H. Sarma.
HDAC3 是鉴定和发现针对多种疾病状况（包括癌症）的新型药物候选物的新兴靶标。在这里，我们对一些 HDAC3 抑制剂进行了基于片段的非线性机器学习 (ML) 方法和化学空间探索，以及基于结构的结合模式相互作用分析研究，以获得调节 HDAC3 抑制的关键结构特征。ML 和化学空间分析都确定了几个物理化学和结构性质，包括疏水性、极性和相对极性表面积、芳基羧酰胺部分、大的稠合芳基基团、-烷基和肉桂酰部分、更多的氧原子、取代的四氢呋喃并萘二恶酮部分的π电子有利于更高的 HDAC3 抑制。此外，氢键形成能力、连接子部分的长度和取代位置、连接子模体中苯基环的重要性、杂环帽部分对于在 HDAC3 催化位点有效结合抑制剂的贡献，相应地影响 HDAC3 抑制效力。此外，大环结构和环己基帽部分负责较低的 HDAC3 抑制。所选化合物的 MD 模拟研究解释了在 HDAC3 活性位点的强结合模式，这一点可以从较低的 RMSD 和 RMSF 值得到证明。尽管如此，它还解释了在配体-酶相互作用过程中来自基于片段分析的关键结构片段的重要性。因此，当前结构分析的结果将成为未来用于临床治疗的有效 HDAC3 抑制剂的基于片段的药物发现的有用工具。由 Ramaswamy H. Sarma 传达。
