Papers › Protein secondary structure prediction using deep convolutional neural fields
Protein secondary structure prediction using deep convolutional neural fields
Sheng Wang, Jian Peng, Jianzhu Ma, Jinbo Xu
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Protein Secondary Structure Prediction | CB513 | ACNN | Q8 | 0.697 | #8 of 10 | Archive leaderboard | report |
| Protein Secondary Structure Prediction | CB513 | LucaAngioloni-WindowCNN | Q8 | 0.684 | #9 of 10 | Archive leaderboard | report |
| Protein Secondary Structure Prediction | CullPDB | LucaAngioloni-WindowCNN | Q8 | 0.721522 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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