ArticleBioinformatics (Oxford, England)2018
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- NEFFy: a versatile tool for computing the number of effective sequences.Bioinformatics (Oxford, England) · 2026Article
- Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16.Communications biology · 2025Article
- APDCA: An accurate and effective method for predicting associations between RBPs and AS-events during epithelial-mesenchymal transition.PLoS computational biology · 2025Article
- Emerging frontiers in protein structure prediction following the AlphaFold revolution.Journal of the Royal Society, Interface · 2025Review
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- When will RNA get its AlphaFold moment?Nucleic acids research · 2023Article
- Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes.Nature communications · 2023Article
- Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.The Journal of chemical physics · 2023Article
- Immunogenicity and efficacy of a novel multi-patch SARS-CoV-2/COVID-19 vaccine candidate.Frontiers in immunology · 2023Article
- PROFASA-a web-based protein fragment and structure analysis workstation.Frontiers in bioengineering and biotechnology · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.Nature communications · 2022Article
- Collectively encoding protein properties enriches protein language models.BMC bioinformatics · 2022Article
- Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).The protein journal · 2022Article
- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
16 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Motivation: Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction. Results: In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length. Availability and implementation: The web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11 and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/. Contact: chengji@missouri.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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