ArticleBMC bioinformatics2006
Predicting DNA-binding sites of proteins from amino acid sequence.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.Nucleic acids research · 2024Article
- OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.Frontiers in genetics · 2023Article
- DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features.Applied bionics and biomechanics · 2022Article
- DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.International journal of molecular sciences · 2021Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
- PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.Molecules (Basel, Switzerland) · 2019Article
- Article
- Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.Scientific reports · 2019Article
- An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences.PloS one · 2019Article
- PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.BMC bioinformatics · 2018Article
- Identification of DNA-protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information.Molecules (Basel, Switzerland) · 2017Article
- EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.BMC bioinformatics · 2017Article
- Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.BMC bioinformatics · 2017Article
- DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues.Nucleic acids research · 2017Article
- HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.BioMed research international · 2017Article
- An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA.BMC genomics · 2016Article
- The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease.PLoS computational biology · 2016Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
backgroundUnderstanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.
resultsWe start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions.
conclusionNaïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs.
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