NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins
<p>Abstract</p> <p>Background</p> <p>Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted pr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2011-01-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/12/21 |
| _version_ | 1818021610528964608 |
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| author | Pino Camilo Restrepo-Montoya Daniel Nino Luis F Patarroyo Manuel E Patarroyo Manuel A |
| author_facet | Pino Camilo Restrepo-Montoya Daniel Nino Luis F Patarroyo Manuel E Patarroyo Manuel A |
| author_sort | Pino Camilo |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.</p> <p>Results</p> <p>Several feature-based classifiers were trained using different sequence transformation vectors (frequencies, dipeptides, physicochemical factors and PSSM) and Support Vector Machines (SVMs) with Linear, Polynomial and Gaussian kernel functions. Nested <it>k</it>-fold cross-validation (CV) was applied to select the best models, using the inner CV loop to tune the model parameters and the outer CV group to compute the error. The parameters and Kernel functions and the combinations between all possible feature vectors were optimized using grid search.</p> <p>Conclusions</p> <p>The final model was tested against an independent set not previously seen by the model, obtaining better predictive performance compared to SecretomeP V2.0 and SecretPV2.0 for the identification of non-classically secreted proteins. NClassG+ is freely available on the web at <url>http://www.biolisi.unal.edu.co/web-servers/nclassgpositive/</url></p> |
| first_indexed | 2024-04-14T08:21:20Z |
| format | Article |
| id | doaj.art-d1332a74495c47ae9349729aac6327ca |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-14T08:21:20Z |
| publishDate | 2011-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d1332a74495c47ae9349729aac6327ca2022-12-22T02:04:12ZengBMCBMC Bioinformatics1471-21052011-01-011212110.1186/1471-2105-12-21NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteinsPino CamiloRestrepo-Montoya DanielNino Luis FPatarroyo Manuel EPatarroyo Manuel A<p>Abstract</p> <p>Background</p> <p>Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.</p> <p>Results</p> <p>Several feature-based classifiers were trained using different sequence transformation vectors (frequencies, dipeptides, physicochemical factors and PSSM) and Support Vector Machines (SVMs) with Linear, Polynomial and Gaussian kernel functions. Nested <it>k</it>-fold cross-validation (CV) was applied to select the best models, using the inner CV loop to tune the model parameters and the outer CV group to compute the error. The parameters and Kernel functions and the combinations between all possible feature vectors were optimized using grid search.</p> <p>Conclusions</p> <p>The final model was tested against an independent set not previously seen by the model, obtaining better predictive performance compared to SecretomeP V2.0 and SecretPV2.0 for the identification of non-classically secreted proteins. NClassG+ is freely available on the web at <url>http://www.biolisi.unal.edu.co/web-servers/nclassgpositive/</url></p>http://www.biomedcentral.com/1471-2105/12/21 |
| spellingShingle | Pino Camilo Restrepo-Montoya Daniel Nino Luis F Patarroyo Manuel E Patarroyo Manuel A NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins BMC Bioinformatics |
| title | NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins |
| title_full | NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins |
| title_fullStr | NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins |
| title_full_unstemmed | NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins |
| title_short | NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins |
| title_sort | nclassg a classifier for non classically secreted gram positive bacterial proteins |
| url | http://www.biomedcentral.com/1471-2105/12/21 |
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