金属酸盐的计算设计
Donghyo Kim1,2, Seth M Woodbury1,2,3, Woody Ahern1,2,4
1Department of Biochemistry, University of Washington, Seattle, WA, USA.
Nature
|December 3, 2025
概括
我们开发了RFdiffusion2,这是一种用于设计新型酶的AI方法. 这种方法成功地产生了具有前所未有的催化效率的高度活性金属酸酶,证明了精确的de novo酶设计.
科学领域:
- 生物化学和分子生物学
- 计算生物学和生物信息学
- 合成生物学 合成生物学
背景情况:
- 新的酶设计旨在创建具有针对特定化学反应优化的活性位点的蛋白质.
- 现有的生成人工智能方法,如射频扩散,需要精确指定催化残留位置和骨干坐标,限制设计灵活性.
- 需要更高效,更灵活的计算工具来设计具有高催化活性的新型酶.
研究的目的:
- 引入RFdiffusion2,一个改进的生成AI方法,用于新的酶设计.
- 使用RFdiffusion2设计新的金属化工酶,从量子化学衍生的活性位点几何学开始.
- 为了证明RFdiffusion2能够产生高度活跃和精确的酶设计的能力.
主要方法:
- 利用RFdiffusion2,一个生成AI工具,消除了指定催化残留位置和骨干坐标的需求.
- 通过将量子化学衍生的活性位点几何学与RFdiffusion2算法集成而设计的金属化工酶.
- 在实验中测试了通过两轮RF扩散2.2生成的192个de novo酶设计.
主要成果:
- 设计的最活跃的酶具有16000M^-1s^-1的催化效率 (kcat/KM),明显超过以前设计的金属化酶.
- 第二轮设计产生了三个额外的高活性酶,kcat/KM高达53,000M^-1s^-1和kcat高达1.5s^-1.
- 最活跃设计的晶体结构与其计算模型密切匹配,验证了RFdiffusion2方法的准确性.
结论:
- RFdiffusion2可以在没有实验优化的情况下直接计算生成高度活跃的酶.
- 设计的酶具有预先组织的活性位点,可促进基质结合和催化.
- 这一进步为新一代强大,计算机设计的生物催化剂铺平了道路.
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