ArticleBMC bioinformatics2017
A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.BMC bioinformatics · 2021Article
- Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique.Bioinformatics (Oxford, England) · 2019Article
- Diagnosis of mesothelioma with deep learning.Oncology letters · 2019Article
- A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.Frontiers in pharmacology · 2019Article
- Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.Molecules (Basel, Switzerland) · 2018Review
- A bioinformatics potpourri.BMC genomics · 2018Article
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3 authors.
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Abstract
backgroundDirect 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.
resultsIn 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.
conclusionsWe have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.
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