ArticleScientific reports2019
Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Deep learning architectures achieve state-of-the-art (SOTA) accuracy in protein secondary structure prediction.Molecular diversity · 2026Review
- Self-amplifying mRNA vaccine cocktail against Staphylococcus aureus: A multi-epitope immunoinformatics and structural simulation framework.World journal of microbiology & biotechnology · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction.Scientific reports · 2025Review
- Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design.Briefings in bioinformatics · 2025Article
- Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.Proteins · 2025Article
- PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs).Biomolecules · 2025Article
- Integration of proteomics and bioinformatics in traumatic brain injury biomarker discovery.Biotechnologia · 2025Article
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- DeepPredict: a state-of-the-art web server for protein secondary structure and relative solvent accessibility prediction.Frontiers in bioinformatics · 2025Article
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs).International journal of molecular sciences · 2024Article
- SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.Computational and structural biotechnology journal · 2024Article
- SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.International journal of molecular sciences · 2024Article
- Characterizing the flavodoxin landscape inMicrobiology spectrum · 2024Article
- NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes.Nucleic acids research · 2024Article
- Article
- The Role of Phase-Separated Condensates in Fusion Oncoprotein-Driven Cancers.Annual review of cancer biology · 2023Article
- Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks.International journal of molecular sciences · 2023Article
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3 authors.
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Abstract
Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88-90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes. In this study we present tests on different models trained both on single sequence and evolutionary profile-based inputs and develop a new state-of-the-art system with Porter 5. Porter 5 is composed of ensembles of cascaded Bidirectional Recurrent Neural Networks and Convolutional Neural Networks, incorporates new input encoding techniques and is trained on a large set of protein structures. Porter 5 achieves 84% accuracy (81% SOV) when tested on 3 classes and 73% accuracy (70% SOV) on 8 classes on a large independent set. In our tests Porter 5 is 2% more accurate than its previous version and outperforms or matches the most recent predictors of secondary structure we tested. When Porter 5 is retrained on SCOPe based sets that eliminate homology between training/testing samples we obtain similar results. Porter is available as a web server and standalone program at http://distilldeep.ucd.ie/porter/ alongside all the datasets and alignments.
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