ArticleScientific reports2015
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers.
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117 citing papers in PubMed, 364 citations in OpenAlex.
- Deep learning architectures achieve state-of-the-art (SOTA) accuracy in protein secondary structure prediction.Molecular diversity · 2026Review
- MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.BMC biology · 2025Article
- Sequence-Based Prediction for Protein Solvent Accessibility.International journal of molecular sciences · 2025Article
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- CNN-Meth: A Tool to Accurately Predict Lysine Methylation Sites Using Evolutionary Information-Based Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2025Article
- MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction.International journal of molecular sciences · 2024Article
- Parameterized hypercomplex convolutional network for accurate protein backbone torsion angle prediction.Scientific reports · 2024Article
- Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs.Biomolecules · 2024Review
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
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- Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.Database : the journal of biological databases and curation · 2024Review
- Using the power of innate immunoprofiling to understand vaccine design, infection, and immunity.Human vaccines & immunotherapeutics · 2023Review
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- Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.International journal of molecular sciences · 2023Article
- Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.Molecules (Basel, Switzerland) · 2023Article
- Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.Bioinformatics advances · 2023Article
- Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
57 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.