基于衍生设计和机器学习的结核分枝杆菌PptT-ACP复合物抑制剂结构设计。

IF 3.1 4区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Badriyah Shadid Alotaibi, Vivek Dhar Dwivedi, Mohammad Amjad Kamal
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引用次数: 0

摘要

由结核分枝杆菌(MTB)引起的结核病(TB)仍然是一个重大的全球卫生挑战,特别是随着耐多药菌株(MDR)的增加。本研究采用综合计算方法来鉴定和优化靶向PptT-ACP复合物的抑制剂,PptT-ACP复合物是结核分枝杆菌脂质生物合成的关键酶。通过对fda批准化合物的虚拟筛选,Mk3207被确定为有希望的候选药物。通过分子动力学(MD)模拟进行稳定性分析,验证了其在衍生设计中的选择。确定了三个适合进行结构修饰的化学可处理区域,由于其良好的结构性质和结合相互作用,选择了第二个区域进行衍生物设计。使用ADMEopt设计了100个衍生品并进行了虚拟筛选,最终选择了三个顶级衍生品与Mk3207一起进行进一步评估。所有化合物都进行了三次200 ns MD模拟,其中Compound_36的结合稳定性最高,具有较低的均方根偏差(RMSD)和均方根波动(RMSF)值,其次是Compound_98和Compound_60。自由能分析(FEL)和主成分分析(PCA)证实了这些衍生物的热力学稳定性。使用机器学习(随机森林回归)预测的生物活性显示,Compound_36、Compound_98、Compound_60和Mk3207的pIC50值分别为25.64、22.43、22.32和26.26。这项研究证明了衍生设计和机器学习在设计强效MTB抑制剂方面的潜力,为有效对抗耐药结核病的实验验证提供了强有力的候选药物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structure-based design of Mycobacterium tuberculosis PptT-ACP complex inhibitors using derivative design and machine learning.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a critical global health challenge, particularly with the rise of multidrug-resistant (MDR) strains. This study employed a comprehensive computational approach to identify and optimize inhibitors targeting the PptT-ACP complex, a key enzyme in MTB lipid biosynthesis. Virtual screening of FDA-approved compounds identified Mk3207 as a promising candidate. Stability analysis through molecular dynamics (MD) simulations validated its selection for derivative design. Three chemically tractable regions suitable for structural modification were identified, and the second region was selected for derivative design due to its favorable structural properties and binding interactions. One hundred derivatives were designed using ADMEopt and screened virtually, resulting in three top derivatives selected alongside Mk3207 for further evaluation. All compounds underwent 200 ns MD simulations in triplicate, with Compound_36 exhibiting the highest binding stability, as indicated by low root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values, followed by Compound_98 and Compound_60. Free energy landscape (FEL) and principal component analysis (PCA) confirmed the thermodynamic stability of these derivatives. Predicted biological activity using machine learning (Random Forest Regression) indicated pIC50 values of 25.64, 22.43, 22.32, and 26.26 for Compound_36, Compound_98, Compound_60, and Mk3207, respectively. This study demonstrates the potential of derivative design and machine learning in designing potent MTB inhibitors, providing strong candidates for experimental validation to combat drug-resistant TB effectively.

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来源期刊
Folia microbiologica
Folia microbiologica 工程技术-生物工程与应用微生物
CiteScore
5.80
自引率
0.00%
发文量
82
审稿时长
6-12 weeks
期刊介绍: Unlike journals which specialize ever more narrowly, Folia Microbiologica (FM) takes an open approach that spans general, soil, medical and industrial microbiology, plus some branches of immunology. This English-language journal publishes original papers, reviews and mini-reviews, short communications and book reviews. The coverage includes cutting-edge methods and promising new topics, as well as studies using established methods that exhibit promise in practical applications such as medicine, animal husbandry and more. The coverage of FM is expanding beyond Central and Eastern Europe, with a growing proportion of its contents contributed by international authors.
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