极星挑战:以数据驱动的Priors改进对接,以预测姿势
Kunyang Sun1, Yingze Wang1, Justin Purnomo1
1Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, California 94720, United States.
Journal of chemical information and modeling
|October 28, 2025
概括
这项研究开发了一个开源的计算工作流来预测药物设计中的联结体结合姿势. 该方法使用碎片衍生信息来提高像SARS-CoV-2和MERS-CoV.等病毒蛋白酶的对接精度.
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:
- 准确预测连接体结合位置对于基于结构的药物设计至关重要.
- 现有的方法在可靠地预测姿势方面面临挑战,特别是对于病毒目标.
研究的目的:
- 开发和评估一个开源的计算工作流来预测连接体结合位置.
- 利用结晶学数据和碎片信息来提高抗病毒药物设计的姿势预测准确性.
主要方法:
- 使用Vina-GPU进行集体对接,并增加了用于姿势生成和得分的fragment-derived priors.
- 当碎片信息不可用时,回归MM/GBSA以进行姿势评分.
- 利用时间分割测试集来评估姿势预测性能.
主要成果:
- 在预测SARS-CoV-2和MERS-CoV蛋白酶标的2 Å RMSD内位的成功率超过50%.
- 碎片信息的复制和姿势生成显著提高了预测准确性.
- 从SARS-CoV-2向MERS-CoV成功转移知识,突出了保存的结合口袋.
结论:
- 基于对接的工作流程加上可转移的碎片知识和基于物理的改进,实现了对姿势预测的竞争性准确性.
- 在相关的病毒蛋白酶之间保存的结合口袋有助于知识转移以进行位预测.
- 开发的工作流是高效的,完全开源的,适用于蛋白质家族.
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