Fragmenstein:使用严格的基于保存结合的方法学预测来自已知的晶体学碎片碰撞的化合物的蛋白质-连接体结构
Matteo P Ferla1,2,3, Rubén Sánchez-García4, Rachael E Skyner5,6
1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK. matteo.ferla@stats.ox.ac.uk.
Journal of cheminformatics
|January 13, 2025
概括
基于碎片的药物设计 (FBDD) 可以使用Fragmenstein算法进行改进. 这种方法从结构数据中合了连接体原子,以更准确地预测蛋白质-连接体复合体构造.
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:
- 基于碎片的药物设计 (FBDD) 通常在不完全利用3D结构数据的情况下合并或链接最初的成功.
- 现有的方法,如药理受约束的对接,在预测蛋白质-连接体复杂构造方面存在局限性.
研究的目的:
- 在药物设计中引入和验证弗拉格曼斯坦算法,以改进蛋白质 - 连接体复合体形状预测.
- 为了证明利用碎片屏幕的3D结构信息的有效性.
主要方法:
- 开发了弗拉格曼斯坦算法,从晶体学数据中"合"连接体原子.
- 应用Fragmenstein将碎片击中合并为新的虚拟化合物.
- 使用弗拉格曼斯坦来预测现有分子的结合复杂构造,其次是能量最小化.
主要成果:
- 与一般的对接方法相比,Fragmenstein提供了更准确,更可靠的蛋白质-连接体复合体构造的预测.
- 对COVID Moonshot数据的回顾性分析证实了使用已知的绑定器坐标的重要性.
- 在击中到的选中展示了现实世界的应用,实现亚微分子化合物.
结论:
- 弗拉格曼斯坦算法通过有效利用碎片屏幕的3D结构信息来增强药物设计.
- 这种方法对预测衍生分子构造有价值,可以集成到药物设计管道中.
- 弗拉格曼斯坦已经证明了在加速击中到领先优化中的实用性.
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