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An Overview of the De Novo Prediction of Enzyme Catalytic Residues (Supplementry file)

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The identification of catalytic residues of an enzyme is one of the most important steps towards understanding its biological roles and exploring its applications. Thus far, a range of catalytic residue prediction methods have been developed, which play an increasingly important role in complementing the experimental characterization of enzymatic functions. The available approaches can be split into two broad categories: i) similarity-based catalytic residue annotation and ii) de novo catalytic residue prediction. In this article, we review the existing research strategies, recently developed bioinformatics tools, and future perspectives in the topic of de novo catalytic residue prediction. In particular, we review the various residue properties that have been used to distinguish catalytic and non-catalytic residues. We also detail how these residue properties can be combined into a prediction system with the assistance of different statistical or machine learning methods. Since in many respects de novo prediction of catalytic residues is still in its infancy, in this review we also propose some hints that are likely to result in novel prediction methods or increased performance.





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Keywords: Bioinformatics; catalytic residues; machine learning methods; prediction

Document Type: Research Article

Publication date: September 1, 2009

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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