结核菌菌FAS-II路径的目标是基于集成深度学习的潜在抗结核剂的识别
Animesh Chaurasia1,2, Mohd Mustkim Ansari3, Gunjan Tripathi3,2
1Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
结核病药物发现面临抗药性挑战. 这项研究以机器学习和分子对接为目标,针对Mycobacterium结核病脂肪酸合成酶II (FAS-II) 途径,确定具有高达90%抑制活性的新型化合物.
科学领域:
- 药用化学和药物发现
- 计算生物学 计算生物学
- 微生物学 微生物学
背景情况:
- 由Mycobacterium tuberculosis (Mtb) 引起的结核病 (TB) 仍然是一个全球卫生危机,由广泛的抗菌素耐药性加剧.
- 现有的抗结核病药物因耐药性而面临局限性,需要开发新的治疗策略.
- 脂肪酸合成酶II (FAS-II) 途径对Mtb生存至关重要,缺乏人类同类,为新药开发提供了一个有希望的目标.
研究的目的:
- 通过准Mtb FAS-II通路中的关键酶来识别新型抗菌菌剂.
- 采用综合计算和实验方法,以有效地发现药物.
- 制定一个多目标战略,以克服潜在的抗生素耐药性机制.
主要方法:
- 使用各种机器学习模型,对特定数据集的策划和探索性数据分析.
- 使用人工神经网络 (ANN) 和分子对接,复合库的虚拟选.
- 在实验室验证已识别的化合物和分子动力学 (MD) 模拟,以获得机械洞察力.
主要成果:
- 识别在Mtb FAS-II路径中针对关键酶 (KasA,KasB,FabH) 的新型化合物.
- 化合物显示出显著的抑制活性,高达90%与阳性对照相比.
- 分子动力学模拟证实了多个目标中识别的化合物的稳定性和潜力.
结论:
- 该研究成功地确定了针对FAS-II途径的抗结核病药物开发的有希望的新支架.
- 多目标方法显示了抗生素耐药性的弹性.
- 这些化合物的进一步药用化学优化可能会导致强大的抗菌素剂.
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