通过深度学习引导的序列设计,对活跃的非原生蛋白激酶进行组合突变图谱
Kosuke Seki1, Amy B Guo1, Deniz Akpinaroglu1,2
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.
bioRxiv : the preprint server for biology
|August 6, 2025
概括
深度学习能够通过生成具有许多突变的功能序列来重新设计新型蛋白质. 这种方法将蛋白质序列功能景观的探索扩展到进化界限之外.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 绘制蛋白质序列功能关系是具有挑战性的,因为探索各种突变的局限性.
- 现有的方法往往限制探索的小突变步骤或进化保存序列.
研究的目的:
- 为了克服探索蛋白质序列功能景观的局限性.
- 使用深度学习生成具有高度组合突变的新型功能性蛋白质序列.
主要方法:
- 应用深度学习指导的重新设计,以天然的蛋白质氨酸激酶.
- 使用无细胞测试测量了537个重新设计的序列的活性和度.
- 开发了一个回归模型来识别函数的序列决定因素.
主要成果:
- 产生了平均37个突变的功能序列,在85%的变异中保持活性.
- 在酶域的76个位置探索了436个独特的突变.
- 确定了关键序列决定因素,并预测了未见序列的功能.
结论:
- 深度学习引导的重新设计使得高度组合的序列功能景观的功能探索成为可能.
- 综合方法允许以前所未有的突变规模进行探索.
- 这种方法扩大了蛋白质工程和设计的可能性.
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