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Updated: May 31, 2026

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在CASP16中,CoDock集团对药物和核酸标的联结预测
Ren Kong1, Zunyun Jiang2, Xufeng Lu3
1Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China.
这项研究提出了在药物设计中对联体结合预测的强有力的策略,结合了对接,多重构造和AI评分. 这种方法在蛋白质 - 配体结合的姿势预测方面取得了成功,并确定机器学习在亲和度排名方面优越.
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:
- 在基于结构的药物设计中,对连接体结合的预测至关重要.
- CASP16扩展了挑战,包括蛋白质-配体,核酸-配体结合和亲和度排名.
研究的目的:
- 开发和评估一个复杂的预测策略,用于带结合.
- 解决蛋白质连接体和核酸连接体结合预测和亲和度排名方面的挑战.
主要方法:
- 结合基于模板的对接,多个受体构造和人工智能驱动的得分.
- 使用PDB模板,配体相似性,CoDock-Ligand和AutoDock Vina进行蛋白质-配体姿势预测.
- 采用各种方法,包括机器学习 (SVR_Conjoint) 和基于物理的方法来进行亲和预测.
主要成果:
- 实现了对蛋白质 - 连接体系统的令人满意的连接体姿势预测准确性 (>66%与RMSD<3Å).
- 与SARS-CoV-2 Mpro和Autotaxin等目标取得了成功,同时面临着绑定地点灵活性方面的挑战.
- 核酸-配体预测显示出混合的结果;SVR_Conjoint超越了基于物理学的亲和力预测方法 (肯德尔的Tau = 0.43).
结论:
- 开发的策略在CASP16中显示出对联体结合预测的稳定性.
- 强调需要改进的方法来处理形状动态,并提高得分准确度.
- 提供了一个框架,用于未来的联结结合预测努力在计算药物发现.
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