School of Chemistry and The Sackler Center for Computational Molecular and Materials Science, Tel Aviv University, 6997801, Tel Aviv, Israel.
Department of Systems Biology, HMS, Harvard University, 200 Longwood Avenue, Boston, MA, 02115, USA.
Nat Commun. 2018 Feb 22;9(1):779. doi: 10.1038/s41467-018-02995-6.
The classical theory of enzymatic inhibition takes a deterministic, bulk based approach to quantitatively describe how inhibitors affect the progression of enzymatic reactions. Catalysis at the single-enzyme level is, however, inherently stochastic which could lead to strong deviations from classical predictions. To explore this, we take the single-enzyme perspective and rebuild the theory of enzymatic inhibition from the bottom up. We find that accounting for multi-conformational enzyme structure and intrinsic randomness should strongly change our view on the uncompetitive and mixed modes of inhibition. There, stochastic fluctuations at the single-enzyme level could make inhibitors act as activators; and we state-in terms of experimentally measurable quantities-a mathematical condition for the emergence of this surprising phenomenon. Our findings could explain why certain molecules that inhibit enzymatic activity when substrate concentrations are high, elicit a non-monotonic dose response when substrate concentrations are low.
经典的酶抑制理论采用确定性、基于整体的方法来定量描述抑制剂如何影响酶反应的进程。然而，单酶水平的催化本质上是随机的，这可能导致与经典预测的强烈偏差。为了探索这一点，我们从单酶的角度出发，从底层重新构建酶抑制的理论。我们发现，考虑到多构象酶结构和内在随机性，应该会强烈改变我们对非竞争和混合抑制模式的看法。在这种情况下，单酶水平的随机波动可能使抑制剂表现为激活剂；我们用实验可测量的量来表示，出现这种惊人现象的数学条件。我们的发现可以解释为什么当底物浓度高时，某些抑制酶活性的分子在底物浓度低时会产生非单调的剂量反应。
