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CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks
BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possib...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3634008/ https://www.ncbi.nlm.nih.gov/pubmed/23626696 http://dx.doi.org/10.1371/journal.pone.0061533 |
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author | Ding, Wang Xie, Jiang Dai, Dongbo Zhang, Huiran Xie, Hao Zhang, Wu |
author_facet | Ding, Wang Xie, Jiang Dai, Dongbo Zhang, Huiran Xie, Hao Zhang, Wu |
author_sort | Ding, Wang |
collection | PubMed |
description | BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence) alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective prediction of long length proteins could be possible by the CNNcon. |
format | Online Article Text |
id | pubmed-3634008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36340082013-04-26 CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks Ding, Wang Xie, Jiang Dai, Dongbo Zhang, Huiran Xie, Hao Zhang, Wu PLoS One Research Article BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence) alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective prediction of long length proteins could be possible by the CNNcon. Public Library of Science 2013-04-23 /pmc/articles/PMC3634008/ /pubmed/23626696 http://dx.doi.org/10.1371/journal.pone.0061533 Text en © 2013 Ding et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Ding, Wang Xie, Jiang Dai, Dongbo Zhang, Huiran Xie, Hao Zhang, Wu CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks |
title | CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks |
title_full | CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks |
title_fullStr | CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks |
title_full_unstemmed | CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks |
title_short | CNNcon: Improved Protein Contact Maps Prediction Using Cascaded Neural Networks |
title_sort | cnncon: improved protein contact maps prediction using cascaded neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3634008/ https://www.ncbi.nlm.nih.gov/pubmed/23626696 http://dx.doi.org/10.1371/journal.pone.0061533 |
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