ArticleBriefings in bioinformatics2023
Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- Protein-nucleic acid binding site prediction using interpretable Kolmogorov-Arnold networks with hypergraph representation learning.Bioinformatics (Oxford, England) · 2026Article
- Integrative single-cell transcriptomics and co-expression network analysis identify SIMALR as a prognostic immune-related lncRNA in breast cancer: in silico analysis and validation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Predicting protein-nucleic acid interactions via protein language models with biophysical and evolutionary priors.iScience · 2026Article
- Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.Frontiers in artificial intelligence · 2026Article
- DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.Bioinformatics advances · 2026Article
- Antimicrobial peptide prediction based on contrastive learning and gated convolutional neural network.Scientific reports · 2025Article
- MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs.Bioinformatics (Oxford, England) · 2025Article
- Artificial intelligence in bioinformatics: a survey.Briefings in bioinformatics · 2025Review
- A novel prediction method for protein-DNA binding sites based on protein language model fusion features with SE-connection pyramidal network and ensemble learning.BMC genomics · 2025Article
- Predicting nucleic acid binding sites by attention map-guided graph convolutional network with protein language embeddings and physicochemical information.Briefings in bioinformatics · 2025Article
- MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model.Briefings in bioinformatics · 2025Article
- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- Article
- Ovarian transcriptome analyses indicate that weak juvenile hormone signaling underlies the molecular basis of oogenesis deficiencies in mosquitoes.BMC biology · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Review
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Authors and funding
2 authors.
Funding
Abstract
Protein-DNA interaction is critical for life activities such as replication, transcription and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called Contrastive Learning And Pre-trained Encoder (CLAPE), which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the area under ROC curve values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.