Enhancing computational enzyme design by a maximum entropy strategy.
Enhancing computational enzyme design by a maximum entropy strategy.
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DOI：
10.1073/pnas.2122355119
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发表时间：
2022-02-15
影响因子：
11.1
通讯作者：
Warshel A
中科院分区：
文献类型：
--
作者：
Xie WJ;Asadi M;Warshel A
设计高效的酶可以为可持续发展的未来做出贡献。目前的计算方法，包括基于物理和基于机器学习的设计，还没有导致一个强大的酶设计。预测酶的催化能力是酶设计的关键步骤。在这里，我们发现酶的特性与它们的进化信息以一种非平凡的方式相关。对于活性位点区域和更远的区域，酶同源物的最大熵模型得到的统计能量分别与酶的催化能力和稳定性强相关。这里的发现可以用来理解酶的催化和进化。将目前的方法与基于物理的计算机建模相结合，可以为酶的设计提供有力的工具。尽管计算酶设计非常重要，但利用基于物理的方法的进展缓慢，迫切需要进一步的进展。一个有希望的方向是使用机器学习，但这种策略尚未被建立为预测酶催化能力的有效工具。本研究表明，根据最大熵（MaxEnt）原理从同源序列推断出的统计能量分别与活性位点区域和更远区域的酶催化和稳定性显著相关。这一发现破译了酶的结构，并提供了酶进化与酶催化物理化学之间的联系，加深了我们对酶的稳定性-活性权衡假说的理解。总的来说，这里发现的强相关性为指导酶的设计提供了一种强有力的方法。
Designing efficient enzymes could contribute to a sustainable future. Current computational approaches, including physics-based and machine learning–based design, have not led to a robust enzyme design. Predicting enzyme catalytic power is the crucial step for enzyme design. Here, we found that the properties of enzymes are correlated in a nontrivial way with their evolutionary information. For the active site region and the more distant region, the statistical energy obtained from the maximum entropy model for enzyme homologs is strongly correlated with enzyme catalytic power and stability, respectively. The findings here could be used to understand enzyme catalysis and evolution. Combining the present approach with physics-based computer modeling can provide a potent tool for enzyme design. 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.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
48
作者：
Fowler, Douglas M.;Fields, Stanley
通讯作者：
Fields, Stanley
DOI：
10.1073/pnas.1605237113
发表时间：
2016-07-12
影响因子：
11.1
作者：
Isaksen, Geir Villy;Aqvist, Johan;Brandsdal, Bjorn Olav
通讯作者：
Brandsdal, Bjorn Olav
影响因子：
7.7
作者：
Aalbers, Friso S.;Furst, Maximilian J. L. J.;Fraaije, Marco W.
通讯作者：
Fraaije, Marco W.
影响因子：
12.9
作者：
Mondal D;Kolev V;Warshel A
通讯作者：
Warshel A