机器学习可以从二进制细胞分类数据中预测连续蛋白质特性,并绘制未见的序列空间图
Marshall Case1, Matthew Smith1,2, Jordan Vinh3
1Chemical Engineering, University of Michigan, Ann Arbor, MI 48109.
概括
本研究介绍了一种机器学习框架,用于从定向进化实验中预测蛋白质特性. 该方法使用线性模型有效地识别具有改进功能的优化蛋白质变体.
科学领域:
- 生物化学和分子生物学
- 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:
- 蛋白质是具有不同细胞功能的必不可少的生物分子.
- 蛋白质工程旨在快速进化蛋白质以获得更好的特性.
- 高通量方法增强了定向进化,但数据解释仍然具有挑战性.
研究的目的:
- 从定向进化数据中开发一个用于预测连续蛋白质特性的框架.
- 通过利用可解释的线性机器学习模型来提高蛋白质优化.
- 为了确定具有增强功能的蛋白候选人.
主要方法:
- 开发了一个使用可解释,线性机器学习模型的框架.
- 利用简单,不准确的实验估计蛋白质适应性的数据.
- 应用框架来从细胞分类数据中预测聚合的亲和力和特异性.
- 结合整数线性编程与机器学习的突变得分进行优化.
主要成果:
- 线性机器学习模型从定向进化数据准确地预测连续蛋白质特性.
- 该框架的预测能力接近更精确但昂贵的方法.
- 蛋白质适应性空间是通过序列突变之间的线性关系合理建模的.
- 在前性测试中成功识别了具有改进和共优特性的蛋白质变体.
结论:
- 开发的框架提供了一种多功能工具,用于分析和识别改进的蛋白质变体.
- 从易于获得的深度测序数据中预测连续蛋白质特性是可行的.
- 线性关系有效地模拟蛋白质健身景观,从而实现高效的优化.
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