在酶活性位点中的一个组合完整的表皮性健身景观
Kadina E Johnston1, Patrick J Almhjell2, Ella J Watkins-Dulaney1
1Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125.
概括
蛋白质工程面临的挑战是预测由于表观症而产生的组合氨基酸效应. 这项研究绘制了一个大型的酶适应性景观,揭示了表观阻碍定向进化,并突出了基于酶工程的保护预测的局限性.
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:
- 蛋白质工程通常侧重于活跃部位,在这些部位上,表皮体 (非添加性相互作用) 会使预测氨基酸替代的组合效应变得复杂.
- 大规模的序列适应性数据集,特别是用于酶催化,很少,阻碍了以模型为指导的酶工程.
- 了解表观酶对于设计具有改善催化活性和稳定性的酶至关重要.
研究的目的:
- 为酶的活性部位构建一个全面的健身景观,以在规模上研究表皮质.
- 评估不同的定向进化策略在导航复杂的,经验丰富的景观中的有效性.
- 为了对酶工程的计算和机器学习方法进行基准测试.
主要方法:
- 创建了一个组合完整的,16万个变体的健身景观,跨越了四个热稳定的托合成酶 (TrpB) 的β子单元的活性部位残留物.
- 在非原生环境中测试TrpB的原生反应,以测量变体适应性.
- 模拟定向进化方法在生成的健身景观上.
主要成果:
- TrpB健身景观表现出显著的表现和众多的本地最佳状态,阻碍了向全球最佳状态的高效定向进化.
- 定向进化方法的可变性有效性为计算方法提供了实验基准.
- 最适合的变种包含了进化保护无法预测的替代,这表明了基于保护的方法的局限性.
结论:
- 酶活性部位的表观性对预测变体适应性和实现所需的蛋白质功能提出了重大挑战.
- 当前以模型为指导的酶工程方法,包括使用进化数据的方法,难以识别表现最佳的变体.
- 机器学习和物理建模方面的进步是必要的,以改善复杂的导航,进发性健身景观的酶工程.
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