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A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction
BACKGROUND: Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding an...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751690/ https://www.ncbi.nlm.nih.gov/pubmed/29297299 http://dx.doi.org/10.1186/s12859-017-1971-7 |
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author | Deng, Lei Fan, Chao Zeng, Zhiwen |
author_facet | Deng, Lei Fan, Chao Zeng, Zhiwen |
author_sort | Deng, Lei |
collection | PubMed |
description | BACKGROUND: Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. RESULTS: In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. CONCLUSIONS: We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach. |
format | Online Article Text |
id | pubmed-5751690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57516902018-01-05 A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction Deng, Lei Fan, Chao Zeng, Zhiwen BMC Bioinformatics Research BACKGROUND: Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. RESULTS: In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. CONCLUSIONS: We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach. BioMed Central 2017-12-28 /pmc/articles/PMC5751690/ /pubmed/29297299 http://dx.doi.org/10.1186/s12859-017-1971-7 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Deng, Lei Fan, Chao Zeng, Zhiwen A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction |
title | A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction |
title_full | A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction |
title_fullStr | A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction |
title_full_unstemmed | A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction |
title_short | A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction |
title_sort | sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751690/ https://www.ncbi.nlm.nih.gov/pubmed/29297299 http://dx.doi.org/10.1186/s12859-017-1971-7 |
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