通过精心调整的MM/PBSA(GBSA) 基方法,提高对蛋白循环复合体的结合 afinities 和 poses 的预测性能
Huifeng Zhao1,2, Jianxiang Huang1,3, Gaoqi Weng1
1College of Pharmaceutical Sciences, Zhejiang University, Yuhangtang Road 866, Hangzhou, Zhejiang 310058, China.
Briefings in bioinformatics
|November 30, 2025
概括
预测蛋白质循环相互作用是具有挑战性的. 这项研究精细调整了分子力学Poisson-Boltzmann表面积 (MM/PBSA) 和泛化Born表面积 (MM/GBSA) 方法,改善了结合亲和力预测.
科学领域:
- 计算化学是一种计算化学.
- 生物制药研究的研究.
- 分子建模分子建模
背景情况:
- 循环是有希望的生物制药支架.
- 分子对接有助于选循环对蛋白质标.
- 使用评分函数准确预测蛋白循环 (P-cp) 相互作用仍然是一个挑战.
研究的目的:
- 综合评估MM / PBSA (GBSA) 的预测能力,以得分结合亲和关系和重新排名P-cp复合物的姿势.
- 为准确的P-cp结合亲缘关系预测提出和验证一个两步工作流.
主要方法:
- 评估MM/PBSA (GBSA) 在50个P-cp复合体用于结合亲和度评分和81个P-cp复合体用于姿势重新排名的数据集上.
- 开发了一个两步工作流程:为顶部姿势选择进行最佳重新排名,然后为亲和度估计进行最佳评分.
- 将工作流与实验值验证并与AutoDock CrankPep进行比较.
主要成果:
- 拟议的工作流实现了绑定亲和度预测的相关系数 (Rp) -0.732,超过了AutoDock CrankPep (Rp = -0.316).
- 工作流需要每次预测仅需3秒.
- 微调的MM/PBSA (GBSA) 方法对于准确的P-cp相互作用预测至关重要.
结论:
- 当MM/PBSA(GBSA) 方法得到适当的微调时,可以可靠地预测P-cp结合的亲缘关系和姿势.
- 拟议的两步工作流提供了一个快速而准确的方法,用于循环的计算选.
- 这项研究强调了优化计算方法对于推进循环药物发现的重要性.
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