ArticleScientific reports2017
iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
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- Root-associated protein prediction using a protein large language model and hypergraph convolutional networks.Scientific reports · 2026Article
- PLysPTM-HGNN: predicting lysine PTM sites of proteins using hybrid graph neural networks.BMC bioinformatics · 2026Article
- SLiMs prediction method based on enhanced attention mechanism and feature fusion.Bioinformatics advances · 2026Article
- GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.Protein science : a publication of the Protein Society · 2025Article
- Accurate prediction of nucleic acid binding proteins using protein language model.Bioinformatics advances · 2025Article
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein.Nature communications · 2024Article
- Improved prediction of DNA and RNA binding proteins with deep learning models.Briefings in bioinformatics · 2024Article
- StackDPP: a stacking ensemble based DNA-binding protein prediction model.BMC bioinformatics · 2024Article
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.Scientific reports · 2024Article
- LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning.Frontiers in genetics · 2024Article
- Phonon-assisted nearly pure spin current in DNA molecular chains: a multifractal analysis.Scientific reports · 2023Article
- Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins.Computational and mathematical methods in medicine · 2022Article
- Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework.Frontiers in plant science · 2022Article
- IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.Journal of computer-aided molecular design · 2021Article
- Article
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3 authors.
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Abstract
DNA-binding proteins play a very important role in the structural composition of the DNA. In addition, they regulate and effect various cellular processes like transcription, DNA replication, DNA recombination, repair and modification. The experimental methods used to identify DNA-binding proteins are expensive and time consuming and thus attracted researchers from computational field to address the problem. In this paper, we present iDNAProt-ES, a DNA-binding protein prediction method that utilizes both sequence based evolutionary and structure based features of proteins to identify their DNA-binding functionality. We used recursive feature elimination to extract an optimal set of features and train them using Support Vector Machine (SVM) with linear kernel to select the final model. Our proposed method significantly outperforms the existing state-of-the-art predictors on standard benchmark dataset. The accuracy of the predictor is 90.18% using jack knife test and 88.87% using 10-fold cross validation on the benchmark dataset. The accuracy of the predictor on the independent dataset is 80.64% which is also significantly better than the state-of-the-art methods. iDNAProt-ES is a novel prediction method that uses evolutionary and structural based features. We believe the superior performance of iDNAProt-ES will motivate the researchers to use this method to identify DNA-binding proteins. iDNAProt-ES is publicly available as a web server at: http://brl.uiu.ac.bd/iDNAProt-ES/ .
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