Department of Chemistry, University of Southern California, Los Angeles, CA 90089-1062
Department of Chemistry, University of Southern California, Los Angeles, CA 90089-1062.
Proc Natl Acad Sci U S A. 2022 Feb 15;119(7). doi: 10.1073/pnas.2122355119.
Although computational enzyme design is of great importance, the advances utilizing physics-based approaches have been slow, and further progress is urgently needed. One promising direction is using machine learning, but such strategies have not been established as effective tools for predicting the catalytic power of enzymes. Here, we show that the statistical energy inferred from homologous sequences with the maximum entropy (MaxEnt) principle significantly correlates with enzyme catalysis and stability at the active site region and the more distant region, respectively. This finding decodes enzyme architecture and offers a connection between enzyme evolution and the physical chemistry of enzyme catalysis, and it deepens our understanding of the stability-activity trade-off hypothesis for enzymes. Overall, the strong correlations found here provide a powerful way of guiding enzyme design.
虽然计算酶设计具有重要意义，但利用基于物理的方法的进展一直缓慢，迫切需要进一步的进展。一个有前途的方向是使用机器学习，但这些策略尚未被确立为预测酶催化能力的有效工具。在这里，我们表明，最大熵（MaxEnt）原理从同源序列推断出的统计能量与活性位点区域和更远区域的酶催化和稳定性分别显著相关。这一发现解码了酶的结构，并在酶进化与酶催化的物理化学之间建立了联系，加深了我们对酶的稳定性-活性权衡假设的理解。总的来说，这里发现的强相关性为指导酶设计提供了一种强大的方法。
