相关实验视频
Updated: Jun 16, 2025
07:04
Identifying PD-1/PD-L1 Inhibitors with Surface Plasmon Resonance Technology
Published on: May 2, 2025
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将动态网络分析与人工智能集成为PD-L1中增强的表皮预测:表皮相互作用
Diego E B Gomes1, Byeongseon Yang2,3, Rosario Vanella2,3
1Department of Physics, Auburn University, Auburn, Alabama 36849, United States.
Journal of the American Chemical Society
|August 15, 2024
概括
这项研究引入了一种动态网络分析方法,准确预测蛋白质-蛋白质相互作用表位,优于AlphaFold3等AI模型. 这种方法提高了治疗目标的计算结构预测的可靠性.
科学领域:
- 计算结构生物学
- 生物物理
- 蛋白与蛋白的相互作用
背景情况:
- 确定蛋白与蛋白相互作用 (PPI) 结合表位对药物发现和生物医学至关重要.
- 实验性表位测定方法通常是低吞吐量和昂贵的.
- 目前的人工智能 (AI) 方法,包括AlphaFold2,难以预测新型绑定域的表征.
研究的目的:
- 开发一种集成的计算方法来准确预测蛋白质结合表位.
- 解开PD-L1:Affibody复合体的结构和结合表位.
- 评估人工智能模型和传统方法在预测PPI接口方面的性能.
主要方法:
- 在多个分子动态 (MD) 轨迹上进行综合的基于关联的通用动态网络分析 (GCCA-DyNA).
- 从AlphaFold2Multimer预测的结构开始MD模拟.
- 使用实验性表位图绘制技术进行验证:交联质谱 (XL-MS) 和深度突变扫描 (DMS).
主要成果:
- 综合GCCA-DyNA方法成功地区分了PD-L1:Affibody复合体的平行和垂直结合模式,确定垂直模式更稳定.
- 实验验证证了垂直结合姿势的计算预测.
- AlphaFold3未能预测正确的结合姿势,突出了当前AI结构预测新型相互作用的局限性.
结论:
- 与人工智能预测结构集成的动态网络分析为准确的表征识别提供了强大的方法.
- 这些发现挑战了对人工智能产生的蛋白质结构的不批判的接受,并强调了对补充计算策略的需求.
- 这种综合方法提高了预测蛋白质-蛋白质相互作用界面的可靠性,对治疗开发有影响.
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