# A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration

arXiv: https://arxiv.org/abs/2402.19095 · HTML: https://mlsanity.com/paper/2402.19095/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach](https://mlsanity.com/paper/2401.00534/) (2023)

## Built on by (most cited first, 4 of 4)

- [Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning](https://mlsanity.com/paper/2403.19345/) (2024)
- [Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis](https://mlsanity.com/paper/2403.17549/) (2024)
- [RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models](https://mlsanity.com/paper/2405.06655/) (2024)
- [Transformers in Protein: A Survey](https://mlsanity.com/paper/2505.20098/) (2025)

## Abstract (arXiv)

Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and folding of the secondary structure. Therefore, the study of protein secondary structure is very helpful to the overall understanding of protein structure. Although the accuracy of protein secondary structure prediction has continuously improved with the development of machine learning and deep learning, progress in the field of protein structure prediction, unfortunately, remains insufficient to meet the large demand for protein information. Therefore, based on the advantages of deep learning-based methods in feature extraction and learning ability, this paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization.