Function-Based Classification of Carbohydrate-Active Enzymes by Recognition of Short, Conserved Peptide Motifs
Function-Based Classification of Carbohydrate-Active Enzymes by Recognition of Short, Conserved Peptide Motifs
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DOI：
10.1128/aem.03803-12
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发表时间：
2013-06-01
影响因子：
4.4
通讯作者：
Lange, Lene
中科院分区：
文献类型：
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作者：
Busk, Peter Kamp;Lange, Lene
碳水化合物活性酶的功能预测是困难的，由于低序列同一性。然而，类似的酶通常共享一些短基序，例如，在活性位点周围，即使整体序列非常不同。为了利用这一概念进行碳水化合物活性酶的功能预测，我们开发了一种简单的算法，肽模式识别（PPR），可以将蛋白质分成共享一组短保守序列的序列组。当这种方法被用于118糖苷水解酶5蛋白与9%的平均成对的身份，并代表四个特征的酶功能，97%的蛋白质被分类到与它们的酶活性相关的组。此外，我们分析了8，138种糖苷水解酶13蛋白，包括204种具有28种不同功能的实验表征酶。组与酶活性之间的相关性为91%。这些结果表明，碳水化合物活性酶的功能，可以预测与高精度通过找到短，保守的基序在它们的序列。糖苷水解酶61家族对于真菌生物质转化是重要的，但该家族中只有少数蛋白质具有功能特征。有趣的是，PPR将743种糖苷水解酶61蛋白分为16个亚家族，用于有针对性地研究这些蛋白的功能，并确定了3个保守的基序，这些基序对酶活性具有假定的重要性。此外，保守序列是有用的克隆新的，亚家族特异性糖苷水解酶61蛋白从14种真菌。总之，保守序列基序的鉴定是一种新的序列分析方法，可以预测碳水化合物活性酶的功能与高精度。
Functional prediction of carbohydrate-active enzymes is difficult due to low sequence identity. However, similar enzymes often share a few short motifs, e.g., around the active site, even when the overall sequences are very different. To exploit this notion for functional prediction of carbohydrate-active enzymes, we developed a simple algorithm, peptide pattern recognition (PPR), that can divide proteins into groups of sequences that share a set of short conserved sequences. When this method was used on 118 glycoside hydrolase 5 proteins with 9% average pairwise identity and representing four characterized enzymatic functions, 97% of the proteins were sorted into groups correlating with their enzymatic activity. Furthermore, we analyzed 8,138 glycoside hydrolase 13 proteins including 204 experimentally characterized enzymes with 28 different functions. There was a 91% correlation between group and enzyme activity. These results indicate that the function of carbohydrate-active enzymes can be predicted with high precision by finding short, conserved motifs in their sequences. The glycoside hydrolase 61 family is important for fungal biomass conversion, but only a few proteins of this family have been functionally characterized. Interestingly, PPR divided 743 glycoside hydrolase 61 proteins into 16 subfamilies useful for targeted investigation of the function of these proteins and pinpointed three conserved motifs with putative importance for enzyme activity. Furthermore, the conserved sequences were useful for cloning of new, subfamily-specific glycoside hydrolase 61 proteins from 14 fungi. In conclusion, identification of conserved sequence motifs is a new approach to sequence analysis that can predict carbohydrate-active enzyme functions with high precision.