ArticleNucleic acids research2013
Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins.
Article in Nucleic acids research, 2013. 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, 38 citations in OpenAlex.
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.Frontiers in artificial intelligence · 2026Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features.Methods (San Diego, Calif.) · 2023Article
- SAResNet: self-attention residual network for predicting DNA-protein binding.Briefings in bioinformatics · 2021Article
- PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.Scientific reports · 2020Article
- PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.Molecules (Basel, Switzerland) · 2019Article
- Structural changes in DNA-binding proteins on complexation.Nucleic acids research · 2018Article
- Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.Nucleic acids research · 2018Article
- PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection.Scientific reports · 2017Article
- DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues.Nucleic acids research · 2017Article
- PDBparam: Online Resource for Computing Structural Parameters of Proteins.Bioinformatics and biology insights · 2016Review
- Structure based approach for understanding organism specific recognition of protein-RNA complexes.Biology direct · 2015Article
- SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.PloS one · 2015Article
- Prediction of RNA binding residues: an extensive analysis based on structure and function to select the best predictor.PloS one · 2014Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-DNA complexes play vital roles in many cellular processes by the interactions of amino acids with DNA. Several computational methods have been developed for predicting the interacting residues in DNA-binding proteins using sequence and/or structural information. These methods showed different levels of accuracies, which may depend on the choice of data sets used in training, the feature sets selected for developing a predictive model, the ability of the models to capture information useful for prediction or a combination of these factors. In many cases, different methods are likely to produce similar results, whereas in others, the predictors may return contradictory predictions. In this situation, a priori estimates of prediction performance applicable to the system being investigated would be helpful for biologists to choose the best method for designing their experiments. In this work, we have constructed unbiased, stringent and diverse data sets for DNA-binding proteins based on various biologically relevant considerations: (i) seven structural classes, (ii) 86 folds, (iii) 106 superfamilies, (iv) 194 families, (v) 15 binding motifs, (vi) single/double-stranded DNA, (vii) DNA conformation (A, B, Z, etc.), (viii) three functions and (ix) disordered regions. These data sets were culled as non-redundant with sequence identities of 25 and 40% and used to evaluate the performance of 11 different methods in which online services or standalone programs are available. We observed that the best performing methods for each of the data sets showed significant biases toward the data sets selected for their benchmark. Our analysis revealed important data set features, which could be used to estimate these context-specific biases and hence suggest the best method to be used for a given problem. We have developed a web server, which considers these features on demand and displays the best method that the investigator should use. The web server is freely available at http://www.biotech.iitm.ac.in/DNA-protein/. Further, we have grouped the methods based on their complexity and analyzed the performance. The information gained in this work could be effectively used to select the best method for designing experiments.
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