| Autor: |
Kato Tsuyoshi, Nagano Nozomi |
| Jazyk: |
angličtina |
| Rok vydání: |
2011 |
| Předmět: |
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| Zdroj: |
BMC Bioinformatics, Vol 12, Iss Suppl 1, p S49 (2011) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2105 |
| DOI: |
10.1186/1471-2105-12-S1-S49 |
| Popis: |
Abstract Background Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. Results This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. Conclusions This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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