ArticleBMC bioinformatics2017
Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Article
- iDLDDG: predicting protein stability changes from missense mutations in DNA-binding proteins using integrated deep learning features.Briefings in bioinformatics · 2026Article
- Accurate prediction of nucleic acid binding proteins using protein language model.Bioinformatics advances · 2025Article
- Improved prediction of DNA and RNA binding proteins with deep learning models.Briefings in bioinformatics · 2024Article
- Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches.Biomolecules · 2022Review
- Prediction of DNA-Binding Protein-Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature.Frontiers in bioengineering and biotechnology · 2022Article
- PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.Molecules (Basel, Switzerland) · 2019Article
- On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach.PloS one · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity. In this study, we systematically consider a variety of schemes to represent protein sequences: OAAC (overall amino acid composition) features, dipeptide compositions, PSSM (position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs.
resultsOur results suggest that some sequence features can significantly differentiate DSBs and SSBs. Evaluated by 10 fold cross-validation on the benchmark datasets, our prediction method can achieve the accuracy of 88.7% and AUC (area under the curve) of 0.919. Moreover, our method has good performance in independent testing.
conclusionsUsing various sequence-derived features, a novel method is proposed to distinguish DSBs and SSBs accurately. The method also explores novel features, which could be helpful to discover the binding specificity of DNA-binding proteins.
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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.