Hidden neural networks for transmembrane protein topology prediction
Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purpos...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021004712 |
| _version_ | 1818979537452531712 |
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| author | Ioannis A. Tamposis Dimitra Sarantopoulou Margarita C. Theodoropoulou Evangelia A. Stasi Panagiota I. Kontou Konstantinos D. Tsirigos Pantelis G. Bagos |
| author_facet | Ioannis A. Tamposis Dimitra Sarantopoulou Margarita C. Theodoropoulou Evangelia A. Stasi Panagiota I. Kontou Konstantinos D. Tsirigos Pantelis G. Bagos |
| author_sort | Ioannis A. Tamposis |
| collection | DOAJ |
| description | Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org. |
| first_indexed | 2024-12-20T17:01:06Z |
| format | Article |
| id | doaj.art-1826dcfedd9741849193e7818f992144 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-20T17:01:06Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-1826dcfedd9741849193e7818f9921442022-12-21T19:32:31ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011960906097Hidden neural networks for transmembrane protein topology predictionIoannis A. Tamposis0Dimitra Sarantopoulou1Margarita C. Theodoropoulou2Evangelia A. Stasi3Panagiota I. Kontou4Konstantinos D. Tsirigos5Pantelis G. Bagos6Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, GreeceInstitute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USADepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, GreeceDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, GreeceDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, GreeceEMBL-EBI, Wellcome Genome Campus, Cambridge, United KingdomDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, Greece; Corresponding author.Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.http://www.sciencedirect.com/science/article/pii/S2001037021004712Hidden Markov ModelsHidden Neural NetworksMembrane proteinsSequence analysisNeural NetworksProtein structure prediction |
| spellingShingle | Ioannis A. Tamposis Dimitra Sarantopoulou Margarita C. Theodoropoulou Evangelia A. Stasi Panagiota I. Kontou Konstantinos D. Tsirigos Pantelis G. Bagos Hidden neural networks for transmembrane protein topology prediction Computational and Structural Biotechnology Journal Hidden Markov Models Hidden Neural Networks Membrane proteins Sequence analysis Neural Networks Protein structure prediction |
| title | Hidden neural networks for transmembrane protein topology prediction |
| title_full | Hidden neural networks for transmembrane protein topology prediction |
| title_fullStr | Hidden neural networks for transmembrane protein topology prediction |
| title_full_unstemmed | Hidden neural networks for transmembrane protein topology prediction |
| title_short | Hidden neural networks for transmembrane protein topology prediction |
| title_sort | hidden neural networks for transmembrane protein topology prediction |
| topic | Hidden Markov Models Hidden Neural Networks Membrane proteins Sequence analysis Neural Networks Protein structure prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2001037021004712 |
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