ArticleBMC bioinformatics2018
Prediction of 8-state protein secondary structures by a novel deep learning architecture.
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 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 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
- ConforFold recovers alternative protein conformations beyond MSA subsampling.Protein science : a publication of the Protein Society · 2026Article
- Review
- Combining knowledge distillation and neural networks to predict protein secondary structure.Scientific reports · 2025Article
- Molecular modelling and gene expression analysis to probe the GT-γ trihelix transcription factors in Solanum tuberosum under drought stress.Scientific reports · 2025Article
- Prediction of Protein Secondary Structures Based on Substructural Descriptors of Molecular Fragments.International journal of molecular sciences · 2024Article
- Impact of Multi-Factor Features on Protein Secondary Structure Prediction.Biomolecules · 2024Article
- Structure based functional identification of an uncharacterized protein from Coxiella burnetii involved in adipogenesis.Scientific reports · 2024Article
- ProtAgents: protein discoveryDigital discovery · 2024Article
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- Insights into the inner workings of transformer models for protein function prediction.Bioinformatics (Oxford, England) · 2024Article
- ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model.Science advances · 2024Article
- DCMA: faster protein backbone dihedral angle prediction using a dilated convolutional attention-based neural network.Frontiers in bioinformatics · 2024Article
- 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
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- Generative design ofChem · 2023Article
- Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory.Frontiers in bioengineering and biotechnology · 2023Article
- Plant catalase in silico characterization and phylogenetic analysis with structural modeling.Journal, genetic engineering & biotechnology · 2022Article
- S-Pred: protein structural property prediction using MSA transformer.Scientific reports · 2022Article
Corrections and comments
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure-based properties analysis.
resultsWe present a novel deep learning architecture which exploits an integrative synergy of prediction by a convolutional neural network, residual network, and bidirectional recurrent neural network to improve the performance of protein secondary structure prediction. A local block comprised of convolutional filters and original input is designed for capturing local sequence features. The subsequent bidirectional recurrent neural network consisting of gated recurrent units can capture global context features. Furthermore, the residual network can improve the information flow between the hidden layers and the cascaded recurrent neural network. Our proposed deep network achieved 71.4% accuracy on the benchmark CB513 dataset for the 8-state prediction; and the ensemble learning by our model achieved 74% accuracy. Our model generalization capability is also evaluated on other three independent datasets CASP10, CASP11 and CASP12 for both 8- and 3-state prediction. These prediction performances are superior to the state-of-the-art methods.
conclusionOur experiment demonstrates that it is a valuable method for predicting protein secondary structure, and capturing local and global features concurrently is very useful in deep learning.
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