# Protein secondary structure prediction using deep convolutional neural fields

arXiv: https://arxiv.org/abs/1512.00843 · HTML: https://mlsanity.com/paper/1512.00843/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2015.

## Built on by (most cited first, 12 of 12)

- [DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing](https://mlsanity.com/paper/2306.01794/) (2023)
- [Modeling Protein Using Large-scale Pretrain Language Model](https://mlsanity.com/paper/2108.07435/) (2021)
- [Protein Language Models and Structure Prediction: Connection and Progression](https://mlsanity.com/paper/2211.16742/) (2022)
- [Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks](https://mlsanity.com/paper/2108.04240/) (2021)
- [Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules](https://mlsanity.com/paper/2406.01622/) (2024)
- [Adaptive Residue-wise Profile Fusion for Low Homologous Protein SecondaryStructure Prediction Using External Knowledge](https://mlsanity.com/paper/2108.04176/) (2021)
- [Learning Sequence Representations by Non-local Recurrent Neural Memory](https://mlsanity.com/paper/2207.09710/) (2022)
- [The divergence time of protein structures modelled by Markov matrices and its relation to the divergence of sequences](https://mlsanity.com/paper/2308.06292/) (2023)
- [CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation](https://mlsanity.com/paper/2410.15592/) (2024)
- [Regular-pattern-sensitive CRFs for Distant Label Interactions](https://mlsanity.com/paper/2411.12484/) (2024)
- [Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers](https://mlsanity.com/paper/2511.13685/) (2025)
- [Protein Secondary Structure Prediction Using Transformers](https://mlsanity.com/paper/2512.08613/) (2025)

## Abstract (arXiv)

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.