蛋白质结合的in silico选,用于开发抗生素耐药性的抑制剂
Xianjin Xu1,2,3,4, Wei-Ling Kao2,5,6, Allison Wang2,5,6
1Department of Physics, University of Missouri, Columbia, MO 65211, USA.
PNAS nexus
|December 11, 2024
概括
一种新的计算方法,MDockPeP2_VS,通过整合分子对接和结构保护,使治疗性的大规模片选成为可能. 这加速了药物发现,通过识别TEM-1β-乳酸酶的强有力的抑制剂来证明这一点.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物化学 生物化学
背景情况:
- 治疗性对小分子具有优势,包括提高安全性和选择性.
- 类药物发现的一个主要挑战是缺乏有效的,大规模的蛋白质标选方法.
- 的灵活性和序列多样性使计算方法复杂化.
研究的目的:
- 开发一种新的计算方法,MDockPeP2_VS,用于大规模的,基于结构的治疗的片选.
- 为了解决现有的计算工具在处理的灵活性和序列空间方面的局限性.
- 为了证明MDockPeP2_VS在识别强抑制剂中的实用性.
主要方法:
- 分子对接与蛋白质折叠和结之间的结构性保护原则的整合.
- 利用保存的界面残留物来减少对形状的搜索空间.
- MDockPeP2_VS的应用用于选针对大肠杆菌TEM-1β-lactamase的胺抑制剂.
主要成果:
- MDockPeP2_VS显著减少了计算时间,使得在片选实用.
- 该方法成功识别了针对TEM-1β-乳酸酶的潜在抑制剂.
- 来自in silico屏幕的排名第一的片TF7显示出显著的β-乳酸酶活性抑制,其Ki值为1.37±0.37μM.
结论:
- MDockPeP2_VS是一个完全自动化的,大规模的,基于结构的片选软件.
- 开发的方法加快了治疗性的发现,特别是对于具有挑战性的目标,如抗生素耐药性酶.
- 该软件是免费提供的,促进进一步的研究在基于的药物发现.
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