开源力场的当前状态 蛋白质 - 连带结合的亲和力预测
David F Hahn1, Vytautas Gapsys1,2, Bert L de Groot2
1Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 2340, Belgium.
Journal of chemical information and modeling
|June 19, 2024
概括
使用分子动力学 (MD) 模拟来准确预测蛋白质-连接体结合亲和力对于药物发现至关重要. 这项研究评估了六个力场,发现OPLS3e优越,但共识方法也产生了高精度.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 结合 afinity 的 in silico 预测对于优先考虑候选药物至关重要.
- 使用分子动力学 (MD) 模拟的化学相对结合自由能量 (RBFE) 计算是准确的亲和度排名的流行的方法.
研究的目的:
- 评估六种不同的小分子力场在预测实验性蛋白质-连接体结合亲缘关系方面的性能.
- 评估力场参数对预测准确性的影响.
主要方法:
- 使用MD模拟进行了RBFE计算.
- 评估了六个小分子力场 (OpenFF ,Sage,GAFF,CGenFF,OPLS3e) 的情况.
- 使用了598个配体和22个蛋白质标的数据集.
主要成果:
- 与其他测试的力场相比,OPLS3e的准确性明显更高.
- 结合Sage,GAFF和CGenFF的共识方法实现了与OPLS3e相比的准确性.
- 强力场参数的改进为特定子集带来了更好的准确性.
- 输入准备和模拟的融合也影响了预测的准确性.
结论:
- 强力场的选择显著影响约束亲和力预测的准确性.
- 一个共识策略可以增强预测能力.
- 处理输入准备和采样趋同对于可靠的in silico预测至关重要.
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