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CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation

机译:CMASA:一种检测局部蛋白质结构相似性的准确算法及其在酶催化位点注释中的应用

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Background The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods. Results The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA). Conclusions The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server ( http://159.226.149.45/other1/CMASA/CMASA.htm ).
机译:背景技术结构基因组学的快速发展已导致许多“未知功能”蛋白沉积在蛋白质数据库(PDB)中,因此,这些蛋白的功能预测已成为结构生物信息学的一项挑战。已经开发了几种基于序列和基于结构的方法来预测蛋白质功能,但是这些方法需要进一步改进,例如提高准确性,灵敏度和计算速度。在这里,已经开发出一种精确的算法CMASA(基于接触矩阵的局部结构比对算法),以基于局部蛋白质结构相似性来预测蛋白质的未知功能。通过构建包括164个酶家族的测试集对该算法进行了评估,并将其与其他方法进行了比较。结果对CMASA的评估表明,CMASA的准确度(0.96),敏感度(0.86)和足够快的速度可用于大规模功能注释。与基于序列的方法和基于全局结构的方法相比,CMASA不仅可以找到遥远的同源蛋白,而且可以找到活性位点的融合。与其他基于局部结构比较的方法相比,CMASA可以获得比FFF(使用几何结构预测蛋白质功能的方法)和SPASM(局部结构比对方法)更好的性能。 CMASA比PINTS更为灵敏,并且比JESS更为精确(均为局部结构对齐方法)。 CMASA用于注释非冗余PDB的酶催化位点,并且已提出至少166个推定的催化位点,这些位点无法通过Catalytic Site Atlas(CSA)观察到。结论CMASA是一种检测局部蛋白质结构相似性的准确算法,在预测酶活性位点方面具有多个优势。 CMASA可用于大规模的酶活性位点注释。 CMASA可以通过基于邮件的服务器(http://159.226.149.45/other1/CMASA/CMASA.htm)获得。

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