IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database.
IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database.
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IntenzyDB：一种集成的结构 - 金属酶学数据库。
DOI：
10.1021/acs.jcim.2c01139
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发表时间：
2022-11-28
影响因子：
5.6
通讯作者：
Yang, Zhongyue J.
中科院分区：
文献类型：
--
作者：
Yan, Bailu;Ran, Xinchun;Gollu, Anvita;Cheng, Zihao;Zhou, Xiang;Chen, Yiwen;Yang, Zhongyue J.
数据驱动的建模已经成为生物催化剂设计和发现的新范式。迫切需要整合酶结构和功能数据的生物催化数据库。在这里，我们将IntEnzyDB描述为一个集成的结构动力学数据库，用于简单的统计建模和机器学习。IntEnzyDB采用具有扁平化数据结构的关系数据库架构，这允许快速的数据操作。这种架构也使得IntEnzyDB可以轻松整合更多类型的酶功能数据。IntEnzyDB包含来自六个酶委员会类别的酶动力学和结构数据。使用1050个酶的结构动力学对，我们研究了接近或远离活性位点的突变的效率干扰倾向。统计结果表明，提高效率的突变是全局编码的，有害的突变更可能发生在近端突变比远端突变。最后，我们描述了一个Web界面，允许公众用户访问存储在IntEnzyDB中的酶学数据。IntEnzyDB将为生物催化和分子进化中的数据驱动建模提供计算设施。
Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure–kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure–kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.
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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：
14.9
作者：
Gillespie M;Jassal B;Stephan R;Milacic M;Rothfels K;Senff-Ribeiro A;Griss J;Sevilla C;Matthews L;Gong C;Deng C;Varusai T;Ragueneau E;Haider Y;May B;Shamovsky V;Weiser J;Brunson T;Sanati N;Beckman L;Shao X;Fabregat A;Sidiropoulos K;Murillo J;Viteri G;Cook J;Shorser S;Bader G;Demir E;Sander C;Haw R;Wu G;Stein L;Hermjakob H;D'Eustachio P
通讯作者：
D'Eustachio P
影响因子：
14.9
作者：
Ribeiro, Antonio J. M.;Holliday, Gemma L.;Thornton, Janet M.
通讯作者：
Thornton, Janet M.
影响因子：
14.9
作者：
Holliday GL;Almonacid DE;Bartlett GJ;O'Boyle NM;Torrance JW;Murray-Rust P;Mitchell JB;Thornton JM
通讯作者：
Thornton JM
影响因子：
3.6
作者：
Clouthier, Christopher M.;Kayser, Margaret M.;Reetz, Manfred T.
通讯作者：
Reetz, Manfred T.