From sequence to enzyme mechanism using multi-label machine learning.
From sequence to enzyme mechanism using multi-label machine learning.
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DOI：
10.1186/1471-2105-15-150
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发表时间：
2014-05-19
影响因子：
3
通讯作者：
Mitchell JB
中科院分区：
文献类型：
--
作者：
De Ferrari L;Mitchell JB
在这项工作中，我们预测酶的化学机制的水平上的功能，提供比传统的酶委员会（EC）类更细粒度的注释。因此，我们不仅可以预测新测序的生物体中的假定酶是否有可能进行某种反应，而且可以预测反应如何进行，使用哪些辅因子以及对哪些药物或抑制剂的敏感性，详细说明药物和酶设计的重要后果。基于3D蛋白质结构特征预测酶催化活性的工作将机制的预测限制在已经具有适合同源建模的解析结构或近亲的蛋白质上。在这项研究中，我们评估是否序列同一性，InterPro或催化位点图谱序列签名提供足够的信息，批量预测酶的机制。通过拆分MACIE（Mechanism，Annotation and Classification in Enzymes database）机制标签的粒度更细，其中包括蛋白质链在整个酶复合物中的作用，该方法可以预测96%的准确率（和96%的微观平均精确度，99.9%的宏观平均召回率）MACie中可用的248种蛋白质的MACie机制定义，EzCatDb（酶催化机制数据库）和SFLD（结构功能链接数据库）数据库使用现成的K-最近邻多标记算法。我们发现InterPro签名对于准确预测酶机制至关重要。我们还发现，纳入催化站点地图集属性似乎并没有提供额外的准确性。软件代码（ml 2db）、数据和结果可在http://sourceforge.net/projects/ml2db/在线获得，并作为补充文件。
In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm. We find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.
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影响因子：
14.9
作者：
Haft DH;Selengut JD;Richter RA;Harkins D;Basu MK;Beck E
通讯作者：
Beck E
DOI：
10.1093/database/bas019
发表时间：
2012
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Attwood TK;Coletta A;Muirhead G;Pavlopoulou A;Philippou PB;Popov I;Romá-Mateo C;Theodosiou A;Mitchell AL
通讯作者：
Mitchell AL
影响因子：
14.9
作者：
Bru C;Courcelle E;Carrère S;Beausse Y;Dalmar S;Kahn D
通讯作者：
Kahn D
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：
5.6
作者：
Gough, J;Karplus, K;Chothia, C
通讯作者：
Chothia, C