ArticleBMC bioinformatics2018
RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Protein structure generation via folding diffusion.Nature communications · 2024Article
- DCMA: faster protein backbone dihedral angle prediction using a dilated convolutional attention-based neural network.Frontiers in bioinformatics · 2024Article
- Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.Molecules (Basel, Switzerland) · 2023Article
- SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.Bioinformatics advances · 2023Article
- Deep Learning in Diverse Intelligent Sensor Based Systems.Sensors (Basel, Switzerland) · 2022Review
- Secondary Structures of Proteins Follow Menzerath-Altmann Law.International journal of molecular sciences · 2022Article
- Secondary structure specific simpler prediction models for protein backbone angles.BMC bioinformatics · 2022Article
- Mimetic Neural Networks: A Unified Framework for Protein Design and Folding.Frontiers in bioinformatics · 2022Article
- Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.Molecular diversity · 2021Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
- Deep Learning in Protein Structural Modeling and Design.Patterns (New York, N.Y.) · 2020Review
- A specific small-molecule inhibitor of protein kinase CδI activity improves metabolic dysfunction in human adipocytes from obese individuals.The Journal of biological chemistry · 2019Article
- Distance-based protein folding powered by deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- End-to-End Differentiable Learning of Protein Structure.Cell systems · 2019Article
- ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank.Database : the journal of biological databases and curation · 2019Article
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4 authors.
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Abstract
backgroundProtein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging.
resultsIn this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds.
conclusionsOur study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.
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