ArticlePloS one2019
Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Assessing Molecular Contacts Using Atom Environments Described by Ranked Lists.Journal of chemical information and modeling · 2026Article
- Efficient global accuracy estimation for protein complex structural models using multi-view representation learning.Cell reports methods · 2026Article
- Integrating Magnetic Bead-Based SELEX withACS omega · 2025Article
- iQDeep: an integrated web server for protein scoring using multiscale deep learning models.Journal of molecular biology · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Deep Local Analysis evaluates protein docking conformations with locally oriented cubes.Bioinformatics (Oxford, England) · 2022Article
- Article
- 3-Dimensional convolutional neural networks for predicting StarCraft Ⅱ results and extracting key game situations.PloS one · 2022Article
- Machine learning to estimate the local quality of protein crystal structures.Scientific reports · 2021Article
- Structure-based protein design with deep learning.Current opinion in chemical biology · 2021Review
- P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features.Bioengineering (Basel, Switzerland) · 2021Article
- Deep Learning for Protein-Protein Interaction Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Decoy selection for protein structure prediction via extreme gradient boosting and ranking.BMC bioinformatics · 2020Article
- Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks.Chemical science · 2020Article
- Using physical features of protein core packing to distinguish real proteins from decoys.Protein science : a publication of the Protein Society · 2020Article
- QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks.Bioinformatics (Oxford, England) · 2020Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.Biomolecules · 2019Article
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2 authors.
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Abstract
In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolutional neural network (3DCNN) is an expansion of the 2DCNN and has been applied in several fields, including object recognition. The 3DCNN is also used for MQA tasks, but the performance is low due to several technical limitations related to protein tertiary structures, such as orientation alignment. We proposed a novel single-model MQA method based on local structure quality evaluation using a deep neural network containing 3DCNN layers. The proposed method first assesses the quality of local structures for each residue and then evaluates the quality of whole structures by integrating estimated local qualities. We analyzed the model using the CASP11, CASP12, and 3D-Robot datasets and compared the performance of the model with that of the previous 3DCNN method based on whole protein structures. The proposed method showed a significant improvement compared to the previous 3DCNN method for multiple evaluation measures. We also compared the proposed method to other state-of-the-art methods. Our method showed better performance than the previous 3DCNN-based method and comparable accuracy as the current best single-model methods; particularly, in CASP11 stage2, our method showed a Pearson coefficient of 0.486, which was better than those of the best single-model methods (0.366-0.405). A standalone version of the proposed method and data files are available at https://github.com/ishidalab-titech/3DCNN_MQA.
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