ArticleProteins2021
DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.
Article in Proteins, 2021. 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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16 citing papers in PubMed, 39 citations in OpenAlex.
- Post-processing enhances protein secondary structure prediction with second order deep learning and embeddings.Computational and structural biotechnology journal · 2025Article
- Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses.Computational and structural biotechnology journal · 2024Article
- Article
- AlphaFun: Structural-Alignment-Based Proteome Annotation Reveals why the Functionally Unknown Proteins (uPE1) Are So Understudied.Journal of proteome research · 2024Article
- Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.Molecules (Basel, Switzerland) · 2023Article
- DIPS-Plus: The enhanced database of interacting protein structures for interface prediction.Scientific data · 2023Article
- S-Pred: protein structural property prediction using MSA transformer.Scientific reports · 2022Article
- Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.BMC bioinformatics · 2022Article
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
- Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module.Frontiers in bioengineering and biotechnology · 2022Article
- Multistage Combination Classifier Augmented Model for Protein Secondary Structure Prediction.Frontiers in genetics · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- Protein Secondary Structure Prediction With a Reductive Deep Learning Method.Frontiers in bioengineering and biotechnology · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (a) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict 3-state and 8-state secondary structure, and (b) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on independent test data sets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the protein targets of 2018 CASP13 experiment and achieved the Q3 score of 81.62%, SOV score of 72.19%, and Q8 score of 73.28%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.
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