在小蛋白域中的聚合决定因素的全球分析
Cydney M Martell1,2, Kamil K Gebis3, Han My Van3
1Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL USA.
bioRxiv : the preprint server for biology
|November 26, 2025
概括
预测蛋白质聚合是很困难的,但这项研究使用了机器学习对小型蛋白质域的大数据集来识别关键特征. 这项工作提高了我们为生物技术和治疗设计抗聚合蛋白的能力.
科学领域:
- 生物化学 生物化学
- 蛋白质工程是指蛋白质工程.
- 计算生物学 计算生物学
背景情况:
- 蛋白质聚合阻碍了用于生物技术和治疗的重组蛋白质的开发.
- 预测蛋白质聚合是复杂的,受到序列,结构,环境和压力的影响.
研究的目的:
- 了解蛋白质聚合的决定因素,并提高预测的准确性.
- 在压力条件下量化小蛋白域的聚合倾向.
主要方法:
- 使用高通量,基于质谱的方法来量化18987个小蛋白域 (40-72个氨基酸) 中的不溶性聚合物.
- 应用机器学习来识别与聚合相关的序列和结构特征.
- 微调了 SaProt 蛋白质语言模型.
主要成果:
- 在蛋白质域中观察到多样化,压力依赖的聚合表型.
- 经验证的高通量混合池聚合数据与单个蛋白质测量 (皮尔森r = 0.65-0.79) 相比.
- 微调的SaProt模型解释了独立测试组中的43-55%的聚合变化.
结论:
- 确定了影响蛋白质聚合的序列和结构特征.
- 开发的模型显示了设计抗聚合蛋白质的潜力.
- 生成的数据集是未来蛋白质聚合研究的宝贵资源.
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