ArticleJournal of computational chemistry2012
SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles.
Article in Journal of computational chemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 1 of them a synthesis that pooled it.
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Who cites it
104 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Sequence-Based Prediction for Protein Solvent Accessibility.International journal of molecular sciences · 2025Article
- AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Parameterized hypercomplex convolutional network for accurate protein backbone torsion angle prediction.Scientific reports · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- 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
- HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines.International journal of molecular sciences · 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
- ProFeatX: A parallelized protein feature extraction suite for machine learning.Computational and structural biotechnology journal · 2023Article
- DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system.RSC advances · 2022Article
- Secondary structure specific simpler prediction models for protein backbone angles.BMC bioinformatics · 2022Article
- How sticky are our proteins? Quantifying hydrophobicity of the human proteome.Bioinformatics advances · 2022Article
- Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins.Computational and mathematical methods in medicine · 2022Article
- Prediction of functional microexons by transfer learning.BMC genomics · 2021Article
- Discovering the Ultimate Limits of Protein Secondary Structure Prediction.Biomolecules · 2021Article
- A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.Journal of proteome research · 2021Article
- Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.International journal of molecular sciences · 2021Review
- A large-scale comparative study on peptide encodings for biomedical classification.NAR genomics and bioinformatics · 2021Article
- DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.Proteins · 2021Article
44 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/
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