酶从头设计的机制规则
Mechanistic rules for de novo design of enzymes
AI总结：
提出热力学一致模型描述燃料驱动酶的非平衡构象变化与催化机制，通过动量守恒与耗散耦合实现酶功能分岔涌现，可补充机器学习酶设计策略。
摘要：
酶是纳米尺度的机器，经过进化能够在正确的时间和地点驱动化学反应偏离平衡。鉴于酶功能的复杂性和特异性，酶的从头设计是一项艰巨的任务，远比制造具有特定结合亲和力的被动分子或构建纳米尺度的机械活性装置更具挑战性。我们提出了一个热力学一致的模型来描述这种受燃料驱动的酶的运作，该酶利用有利反应的能量进行非平衡构象变化，进而催化附着底物分子上的化学反应。我们表明，通过对酶低维描述的有效反应坐标适当实施动量守恒，并借助普遍存在的耗散耦合，酶功能可以通过分岔涌现。我们的结果可以补充最近基于机器学习方法开发的从头酶设计策略。
英文摘要：
Enzymes are nano-scale machines that have evolved to drive chemical reactions out of equilibrium in the right place at the right time. Given the complexity and specificity of enzymatic function, bottom-up design of enzymes presents a daunting task that is far more challenging than making passive molecules with specific binding affinities or building nano-scale mechanically active devices. We present a thermodynamically-consistent model for the operation of such a fuelled enzyme, which uses the energy from a favourable reaction to undergo non-equilibrium conformational changes that in turn catalyze a chemical reaction on an attached substrate molecule. We show that enzymatic function can emerge through a bifurcation upon appropriate implementation of momentum conservation on the effective reaction coordinates of the low dimensional description of the enzyme, and thanks to a generically present dissipative coupling. Our results can complement the recently developed strategies for de novo enzyme design based on machine learning approaches.
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