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增强组蛋白去乙酰化酶抑制剂筛选:解决对接研究中的锌参数化和配体质子化问题。

Enhancing HDAC Inhibitor Screening: Addressing Zinc Parameterization and Ligand Protonation in Docking Studies.

作者信息

Buccheri Rocco, Coco Alessandro, Pasquinucci Lorella, Amata Emanuele, Marrazzo Agostino, Rescifina Antonio

机构信息

Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy.

出版信息

Int J Mol Sci. 2025 Jan 20;26(2):850. doi: 10.3390/ijms26020850.

Abstract

Precise binding free-energy predictions for ligands targeting metalloproteins, especially zinc-containing histone deacetylase (HDAC) enzymes, require specialized computational approaches due to the unique interactions at metal-binding sites. This study evaluates a docking algorithm optimized for zinc coordination to determine whether it could accurately differentiate between protonated and deprotonated states of hydroxamic acid ligands, a key functional group in HDAC inhibitors (HDACi). By systematically analyzing both protonation states, we sought to identify which state produces docking poses and binding energy estimates most closely aligned with experimental values. The docking algorithm was applied across HDAC 2, 4, and 8, comparing protonated and deprotonated ligand correlations to experimental data. The results demonstrate that the deprotonated state consistently yielded stronger correlations with experimental data, with R values for deprotonated ligands outperforming protonated counterparts in all HDAC targets (average R = 0.80 compared to the protonated form where R = 0.67). These findings emphasize the significance of proper ligand protonation in molecular docking studies of zinc-binding enzymes, particularly HDACs, and suggest that deprotonation enhances predictive accuracy. The study's methodology provides a robust foundation for improved virtual screening protocols to evaluate large ligand libraries efficiently. This approach supports the streamlined discovery of high-affinity, zinc-binding HDACi, advancing therapeutic exploration of metalloprotein targets. A comprehensive, step-by-step tutorial is provided to facilitate a thorough understanding of the methodology and enable reproducibility of the results.

摘要

由于金属结合位点存在独特的相互作用,针对金属蛋白(尤其是含锌组蛋白脱乙酰酶(HDAC))的配体进行精确的结合自由能预测需要专门的计算方法。本研究评估了一种针对锌配位优化的对接算法,以确定其是否能够准确区分异羟肟酸配体(HDAC抑制剂(HDACi)中的关键官能团)的质子化和去质子化状态。通过系统地分析这两种质子化状态,我们试图确定哪种状态产生的对接构象和结合能估计值与实验值最为吻合。该对接算法应用于HDAC 2、4和8,比较质子化和去质子化配体与实验数据的相关性。结果表明,去质子化状态始终与实验数据具有更强的相关性,在所有HDAC靶点中,去质子化配体的R值均优于质子化配体(平均R = 0.80,而质子化形式的R = 0.67)。这些发现强调了在锌结合酶(特别是HDAC)的分子对接研究中适当配体质子化的重要性,并表明去质子化可提高预测准确性。该研究方法为改进虚拟筛选方案以有效评估大型配体库提供了坚实的基础。这种方法支持高效发现高亲和力的锌结合HDACi,推动金属蛋白靶点的治疗探索。本文提供了一份全面的分步教程,以促进对该方法的深入理解并实现结果的可重复性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93da/11766394/60a255129650/ijms-26-00850-g001.jpg

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