Discriminative structural approaches for enzyme active-site prediction
<p>Abstract</p> <p>Background</p> <p>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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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BMC
2011-02-01
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| Series: | BMC Bioinformatics |
| _version_ | 1811276902511411200 |
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| author | Kato Tsuyoshi Nagano Nozomi |
| author_facet | Kato Tsuyoshi Nagano Nozomi |
| author_sort | Kato Tsuyoshi |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.</p> |
| first_indexed | 2024-04-13T00:06:07Z |
| format | Article |
| id | doaj.art-b2ee3020795547ed9d88a86513896395 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T00:06:07Z |
| publishDate | 2011-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-b2ee3020795547ed9d88a865138963952022-12-22T03:11:14ZengBMCBMC Bioinformatics1471-21052011-02-0112Suppl 1S4910.1186/1471-2105-12-S1-S49Discriminative structural approaches for enzyme active-site predictionKato TsuyoshiNagano Nozomi<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.</p> |
| spellingShingle | Kato Tsuyoshi Nagano Nozomi Discriminative structural approaches for enzyme active-site prediction BMC Bioinformatics |
| title | Discriminative structural approaches for enzyme active-site prediction |
| title_full | Discriminative structural approaches for enzyme active-site prediction |
| title_fullStr | Discriminative structural approaches for enzyme active-site prediction |
| title_full_unstemmed | Discriminative structural approaches for enzyme active-site prediction |
| title_short | Discriminative structural approaches for enzyme active-site prediction |
| title_sort | discriminative structural approaches for enzyme active site prediction |
| work_keys_str_mv | AT katotsuyoshi discriminativestructuralapproachesforenzymeactivesiteprediction AT naganonozomi discriminativestructuralapproachesforenzymeactivesiteprediction |