Nandi Rajat, Sharma Anupama, Priya Ananya, Kumar Diwakar
Deparment of Microbiology, Assam University, Silchar, 788011, Assam, India.
IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India.
Mol Divers. 2024 Dec 10. doi: 10.1007/s11030-024-11070-w.
Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current drug therapies have limitations, necessitating the exploration of new, cost-effective treatments. This study utilized a comprehensive computational workflow, integrating traditional 2D-QSAR, q-RASAR, and molecular docking to identify novel anti-leishmanial compounds, with a focus on Glycyl-tRNA Synthetase (LdGlyRS) as a promising drug target. A feature selection process combining Genetic Function Approximation (GFA)-Lasso with Multiple Linear Regression (MLR) was used to characterize 99 azole compounds across ten structural classes. The baseline MLR model (MOD1), containing seven simple and interpretable 2D features, exhibited robust predictive capabilities, achieving an R value of 0.82 and an R value of 0.87. To further enhance prediction accuracy, three qualified single models (two MLR and one q-RASAR) were used to construct three consensus models (CMs), with CM2 (MAE = 0.127) demonstrating significantly higher prediction accuracy for test compounds than the MOD1. Subsequently, Support Vector Regression (SVR) and Boosting yielded 0.88 (R), 0.86 (R), 0.92 (R), and 0.82 (R), respectively. Molecular docking highlighted interactions of potent azoles within the QSAR dataset with critical residues in the LdGlyRS active site (Arg226 and Glu350), emphasizing their inhibitory potential. Furthermore, the pIC50 values of an accurate external set of 2000 azole compounds from the ZINC20 database were simultaneously predicted by CM2 + SVR + Boosting models and docked against the LdGlyRS, which identified Bazedoxifene, Talmetacin, Pyrvinium, Enzastaurin as leading FDA candidates, whereas three novel compounds with the database code ZINC000001153734, ZINC000011934652, and ZINC000009942262 displayed stable docked interactions and favourable ADMET assessments. Subsequently, Molecular Dynamics (MD) simulations for 100 ns were conducted to validate the findings further, offering enhanced insights into the stability and dynamic behaviour of the ligand-protein complexes. The integrated approach of this study underscores the efficacy of 2D-QSAR modelling. It identifies LdGlyRS as a promising leishmaniasis target, offering a robust strategy for discovering and optimizing anti-leishmanial compounds to address the critical need for improved treatments.
利什曼病是一种由多种利什曼原虫引起的被忽视的热带疾病，对全球健康构成重大挑战，尤其是在资源有限的地区。内脏利什曼病（VL）在其严重表现中较为突出，目前的药物治疗存在局限性，因此需要探索新的、具有成本效益的治疗方法。本研究采用了一种综合计算工作流程，整合了传统的二维定量构效关系（2D-QSAR）、定量构效关系随机森林回归（q-RASAR）和分子对接，以识别新型抗利什曼化合物，重点关注甘氨酰-tRNA合成酶（LdGlyRS）作为一个有前景的药物靶点。使用结合了遗传函数逼近（GFA）-套索与多元线性回归（MLR）的特征选择过程，对十个结构类别的99种唑类化合物进行表征。包含七个简单且可解释的二维特征的基线MLR模型（MOD1）表现出强大的预测能力，R值达到0.82，交叉验证R值为0.87。为了进一步提高预测准确性，使用三个合格的单模型（两个MLR和一个q-RASAR）构建了三个共识模型（CMs），其中CM2（平均绝对误差 = 0.127）对测试化合物的预测准确性明显高于MOD1。随后，支持向量回归（SVR）和Boosting分别得到0.88（R）、0.86（R）、0.92（R）和0.82（R）。分子对接突出了QSAR数据集中强效唑类与LdGlyRS活性位点中的关键残基（Arg226和Glu350）之间的相互作用，强调了它们的抑制潜力。此外，通过CM2 + SVR + Boosting模型同时预测了来自ZINC20数据库的一组准确的2000种唑类化合物的半数抑制浓度负对数（pIC50）值，并将其与LdGlyRS进行对接，确定巴多昔芬、他美辛、吡维铵、恩扎妥林为主要的美国食品药品监督管理局（FDA）候选药物，而三种具有数据库代码ZINC000001153734、ZINC000011934652和ZINC000009942262的新型化合物表现出稳定的对接相互作用和良好的药物代谢动力学、药物代谢和毒性（ADMET）评估。随后，进行了100纳秒的分子动力学（MD）模拟以进一步验证研究结果，从而更深入地了解配体-蛋白质复合物的稳定性和动态行为。本研究的综合方法强调了二维定量构效关系建模（2D-QSAR）的有效性。它将LdGlyRS确定为一个有前景的利什曼病靶点，为发现和优化抗利什曼化合物提供了一个强有力的策略，以满足改善治疗的迫切需求。
