在发现药物发现的神秘口袋方面的计算进步
Martijn P Bemelmans1, Zoe Cournia2, Kelly L Damm-Ganamet3
1Computer-Aided Drug Design, In Silico Discovery, Therapeutics Discovery, Johnson & Johnson Innovative Medicine, Turnhoutseweg 30, 2340 Beerse, Belgium; School of Pharmaceutical Sciences, University of Geneva, Rue Michel Servet 1, Geneva, 1206, Switzerland.
Current opinion in structural biology
|January 8, 2025
概括
研究人员正在使用计算方法发现"神秘"的蛋白质口袋,以前是"不可抗药"的目标. 这为开发抗挑战性疾病的药物开辟了新的途径.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 由于缺少可结合口袋,许多治疗点蛋白被认为是"不可抗药的".
- 蛋白质动力学揭示了短暂的"密码"口袋,这些口袋在连接物结合时形成.
- 这些口袋提供了一种策略来准困难的蛋白质.
研究的目的:
- 审查用于建模密码口袋的计算方法.
- 突出加密口袋识别中的既定和新兴技术.
- 讨论治疗相关蛋白质的应用.
主要方法:
- 混合溶剂分子动力学模拟.混合溶剂分子动力学模拟.
- 增强采样技术. 提升采样技术.
- 基于人工智能 (AI) 的方法用于口袋预测.
主要成果:
- 像混合溶剂MD这样的既定方法对于建模密码口袋是有效的.
- 增强的采样和人工智能方法在识别短暂结合位点方面表现有前途.
- 这些计算策略正在应用于具有挑战性的治疗目标.
结论:
- 密码口袋建模是一种可行的策略,用于准以前无法药物处理的蛋白质.
- 计算方法的进步正在扩大可用药物的蛋白质组.
- 本综述提供了对药物发现的当前和未来方向的见解,目标是加密口袋.
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