蒙特卡洛普森采样引导的设计用于抗体工程
Taro Kakuzaki1, Hikaru Koga1, Shuuki Takizawa1
1Research Division, Chugai Pharmaceutical Co., Ltd., Yokohama, Japan.
mAbs
|August 22, 2023
概括
蒙特卡洛普森采样 (MTS) 通过改善勘探-开发平衡来增强抗体工程. 这种机器学习方法有效地发现了优异的抗体候选人,特别是当初始数据有限时.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 抗体是关键的治疗药物,但优化它们的特性,如亲和力和稳定性是具有挑战性的.
- 探索广的抗体序列空间以改善特征是由于组合复杂性的计算密集型.
- 机器学习,特别是Thompson采样 (TS) 的贝叶斯优化,已被用于高效的蛋白质工程.
研究的目的:
- 为了解决Thompson采样 (TS) 中的过度探索问题,当初始数据有偏差时,用于抗体工程.
- 引入和评估蒙特卡洛普森采样 (MTS) 以在大型抗体库中平衡探索-利用.
- 证明MTS在发现高质量的抗体候选人的有效性比TS更有效.
主要方法:
- 开发了蒙特卡罗普森采样 (MTS) 以使用蒙特卡罗方法来定义后位分布.
- 将MTS应用于具有众多突变的大型虚拟抗体库.
- 在抗体工程实验中比较了MTS与标准普森采样 (TS) 的性能.
主要成果:
- MTS有效地平衡了勘探开发的权衡,减轻了在TS中看到的过度勘探问题.
- 在识别理想的抗体候选人方面,MTS显著优于TS,特别是在早期的实验阶段.
- 该研究表明,在处理有限的实验回合和有偏见的初始数据集时,MTS的性能优越.
结论:
- 蒙特卡洛普森采样 (MTS) 是蛋白质工程的强大进步,特别是在抗体发现方面.
- 与传统的TS相比,MTS提供了一种更有效的方法来识别具有所需特征的抗体.
- 当实验资源有限时,这种技术对于优化抗体特别有价值.
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