MedChemExpress化合物通过基于物理和知识的方法阻止神经氨基酶N1
Quynh Mai Thai1,2, Trung Hai Nguyen1,2, Huong Thi Thu Phung3
1Laboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University Ho Chi Minh City Vietnam ngosontung@tdtu.edu.vn.
RSC advances
|June 14, 2024
概括
机器学习确定了五种抑制流感A病毒神经aminidase的化合物. 原子模拟证实了它们的结合,揭示了参与抑制过程的关键残留物.
科学领域:
- 病毒学 病毒学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- A型流感病毒对全球健康构成重大威胁.
- 开发有效的神经氨基酶抑制剂对于流感治疗至关重要.
研究的目的:
- 为了识别新型的神经氨基酶抑制剂的流感A病毒.
- 阐明潜在抑制剂的结合机制.
主要方法:
- 机器学习模型用于选约10,000种化合物以检测神经氨基酶抑制潜力.
- 原子模拟 (分子对接和动力学) 来确认结合亲和力.
- 对联结体-神经氨基酶相互作用的物理见解的分析.
主要成果:
- 五种化合物 (micronomicin,didesmethyl cariprazine, argatroban,Kgp-IN-1,AY 9944) 被确定为潜在的神经氨基酶N1抑制剂. 这些化合物中包括:
- 通过分子对接和动力学模拟证实了结合亲和力.
- 十个关键残留物 (Glu119,Asp151,Arg152,Trp179,Gln228,Glu277,Glu278,Arg293,Asn295,Tyr402) 被确定为关键的联体结合.
结论:
- 该研究成功地确定了具有抑制流感A病毒神经aminidase的潜力的新型化合物.
- 计算方法为抗病毒开发提供了对药物向相互作用的宝贵见解.
- 特定的氨基酸残留在抑制剂与神经aminidase N1.1 的结合中起着关键作用.
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