基于物理和机器学习的组合方法,使用SILCS热点来识别可使用药物的结合部位
Erik B Nordquist1, Mingtian Zhao1, Anmol Kumar1
1Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States.
Journal of chemical information and modeling
|September 16, 2024
概括
我们开发了一种机器学习模型,用SILCS方法识别可用药物的蛋白质结合点. 这种方法通过预测新药类分子的潜在结合口袋来增强药物发现.
科学领域:
- 计算化学和结构生物学
- 机器学习在药物发现中的作用
- 蛋白质 - 配体相互作用
背景情况:
- 识别可用药物的结合部位,特别是神秘或异质的结合部位,至关重要但具有挑战性.
- 现有的方法可能会错过在静态蛋白质结构中不明显的部位.
- 通过连接物竞争和 (SILCS) 方法识别位点使用模拟来捕获蛋白质的灵活性并识别潜在的结合口袋.
研究的目的:
- 开发一种机器学习 (ML) 模型来排列由SILCS识别的蛋白质结合位点.
- 预测这些部位容纳类似药物分子的可能性.
- 推进新型治疗点和候选药物的发现.
主要方法:
- 利用全原子分子模拟 (SILCS) 来建模蛋白质灵活性和溶液分布.
- 开发了一种ML模型,根据药物可用性对SILCS识别的"热点"进行排名.
- 在一组独立的酶和受体上验证了ML模型.
主要成果:
- ML模型成功地回忆了分别在排名前10和前20的热点中67%和89%的已知连接物结合点.
- 该模型的决策函数在预测结合位点和它们对新目标的可用性方面被证明是有效的.
- 识别了被埋藏的绑定口袋,这些口袋被实验结构遗漏了.
结论:
- 用ML增强的SILCS方法显著改善了对orthosteric和allosteric结合位点的识别.
- 这种方法有助于发现药物样分子,这些分子针对以前无法访问的蛋白质位点.
- 开发的工具代表了在药物开发中对联体发现和优化的关键进步.
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