ArticleProteins2021
Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.
Article in Proteins, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.Nature communications · 2026Review
- AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation.Journal of imaging informatics in medicine · 2026Article
- Paying attention to attention: High attention sites as indicators of protein family and function in language models.PLoS computational biology · 2025Article
- Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.International journal of molecular sciences · 2024Review
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
- Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.Briefings in bioinformatics · 2022Review
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.Computational and structural biotechnology journal · 2022Article
- High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments · 2021Article
- AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function.Journal of chemical information and modeling · 2021Article
- Improving deep learning-based protein distance prediction in CASP14.Bioinformatics (Oxford, England) · 2021Article
- MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction.Scientific reports · 2021Article
- Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.Proteins · 2021Article
- Deep Learning-Based Advances in Protein Structure Prediction.International journal of molecular sciences · 2021Review
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Authors and funding
4 authors.
Funding
Abstract
Deep learning has emerged as a revolutionary technology for protein residue-residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning-based contact predictions have been achieved since then. However, little effort has been put into interpreting the black-box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention-based convolutional neural network for protein contact prediction, which consists of two attention mechanism-based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free-modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold-determining residues in proteins. We expect the attention-based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap.
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