来自连续蛋白质载体嵌入的增强病毒感染性的纤维的反向设计
Kübra Kaygisiz1, Arghya Dutta2, Lena Rauch-Wirth3
1Department Synthesis of Macromolecules, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany. weil@mpip-mainz.mpg.de.
Biomaterials science
|June 21, 2023
概括
机器学习预测了新的短,可以增强病毒基因传递. 这种方法有效地发现了用于治疗应用的新功能序列.
科学领域:
- 生物技术是生物技术.
- 材料科学 材料科学 材料科学
- 计算生物学 计算生物学
背景情况:
- 自组合形成像粉样样的纳米纤维,可以增强病毒基因转移.
- 发现新的序通常涉及选大型库或修改已知的活性.
- 由于复杂的依赖关系,预测 *de novo* 的结构-活性关系具有挑战性.
研究的目的:
- 开发一种机器学习 (ML) 方法来预测*de novo*增强病毒感染性的序列.
- 识别用于治疗性病毒基因传递应用的新短序列.
- 克服合理设计功能性 *de novo* 的局限性.
主要方法:
- 使用自然语言处理和163个已知的的连续向量表示训练了一种ML模型.
- 采样了序列空间以确定有前途的6氨基酸候选者 (*de novo* 6-mers).
- 选候选的电荷和聚合倾向,然后进行实验测试.
主要成果:
- 确定了16种新的活跃的 *de novo* 6 - 位子,其命中率为25%.
- 这些*de novo*是传染性增强的最短报告的序列,与训练集无关.
- 发现了第一个具有中度负面电荷的疏水性纤维,可以增强病毒感染力.
结论:
- ML策略提供了一种时间和成本高效的方法,用于发现功能性的*de novo*自组装.
- 这种方法扩大了短功能的序列空间,特别是用于治疗性病毒基因传递.
- 这些发现证明了ML在加速发现新型基生物材料方面的力量.
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