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 and...

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Published in:BMC bioinformatics Vol. 18; no. Suppl 16; pp. 569 - 220
Main Authors: Deng, Lei, Fan, Chao, Zeng, Zhiwen
Format: Journal Article
Language:English
Published: London BioMed Central 28.12.2017
BioMed Central Ltd
BMC
Subjects:
Algorithms
Amino acids
Analysis
Artificial neural networks
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Contact number
Deep neural network
Life Sciences
Machine Learning
Microarrays
Models, Molecular
Neural Networks, Computer
Proteins
Proteins - chemistry
Sequence-derived features
Solvent accessibility
Solvents - chemistry
Deep neural network
Sequence-derived features
Contact number
Solvent accessibility
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-017-1971-7