使用基于结构的in silico方法预测治疗抗体中的除氧化和异构化位点
David Hoffmann1,2, Joschka Bauer1,2, Markus Kossner3
1Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany.
mAbs
|March 28, 2024
概括
预测抗体降解对于生物制药开发至关重要. 本研究引入了一种使用定量结构-活性关系 (QSAR) 模型的in silico方法,用于预测治疗性抗体中的阿斯巴拉金脱和酸异构化部位.
科学领域:
- 生物制药开发 生物制药开发
- 蛋白质化学 蛋白质化学
- 计算生物学 计算生物学
背景情况:
- 阿斯巴拉金 (Asn) 脱胺和阿斯巴拉酸 (Asp) 异构是影响治疗抗体稳定性的关键降解途径.
- 这些修改在生物制药开发中带来了重大挑战,需要早期选择稳定的单克隆抗体 (mAbs).
研究的目的:
- 开发和验证一种新的in silico方法,用于预测治疗性抗体中的除化和异构化部位.
- 加强早期工程和选择化学稳定的mAbs.
主要方法:
- 通过分析Asn和Asp残留物的结构环境,开发了一种定量结构-活性关系 (QSAR) 模型.
- 该模型是在57个临床阶段mAbs的强制降解数据上训练的,并使用各种蛋白质结构状态进行评估,包括分子动力学 (MD) 模拟.
- 最有效的QSAR模型结合了可访问的表面积 (ASA),骨干胺 pKa 和根平均平方偏差 (RMSD),进一步增强了结合顺序和位置信息的决策树.
主要成果:
- 该研究评估了静态模型,短时间的MD运行,具有质子重新分配的MD和全原子的MD模拟的预测准确性.
- 最准确的QSAR模型确定了影响残留物反应性的关键结构特征.
- 与决策树集成显著改善了预测性能.
结论:
- 已经开发出一种强大的in silico方法来预测抗体降解部位.
- 这种方法有助于合理设计和选择稳定的治疗抗体.
- 该模型作为一个用户友好的MOE插件来实现,以促进其在生物制药研究中的应用.
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