ArticleBioinformatics (Oxford, England)2012
Recognition models to predict DNA-binding specificities of homeodomain proteins.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
- Plant mobile domain protein-DNA motif modules counteract Polycomb silencing to stabilize gene expression.Nature plants · 2025Article
- Predicting the DNA binding specificity of transcription factor mutants using family-level biophysically interpretable machine learning.Nucleic acids research · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- Geometric deep learning of protein-DNA binding specificity.Nature methods · 2024Article
- Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity inNAR genomics and bioinformatics · 2024Article
- DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues.Nature communications · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- The Arabidopsis Nodulin Homeobox Factor AtNDX Interacts with AtRING1A/B and Negatively Regulates Abscisic Acid Signaling.The Plant cell · 2020Article
- Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination.Nucleic acids research · 2020Article
- Extreme divergence between one-to-one orthologs: the structure of N15 Cro bound to operator DNA and its relationship to the λ Cro complex.Nucleic acids research · 2019Article
- Article
- Similarity regression predicts evolution of transcription factor sequence specificity.Nature genetics · 2019Article
- Diversification of transcription factor-DNA interactions and the evolution of gene regulatory networks.Wiley interdisciplinary reviews. Systems biology and medicine · 2018Review
- Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data.Nucleic acids research · 2017Article
- Sex-specific chromatin landscapes in an ultra-compact chordate genome.Epigenetics & chromatin · 2017Article
- Past Roadblocks and New Opportunities in Transcription Factor Network Mapping.Trends in genetics : TIG · 2016Review
- Computational learning on specificity-determining residue-nucleotide interactions.Nucleic acids research · 2015Article
- Affinity regression predicts the recognition code of nucleic acid-binding proteins.Nature biotechnology · 2015Article
- Determination of specificity influencing residues for key transcription factor families.Quantitative biology (Beijing, China) · 2015Article
- The Evolutionarily Conserved LIM Homeodomain Protein LIM-4/LHX6 Specifies the Terminal Identity of a Cholinergic and Peptidergic C. elegans Sensory/Inter/Motor Neuron-Type.PLoS genetics · 2015Article
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6 authors.
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Abstract
motivationRecognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C(2)H(2) zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes.
resultsUsing extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests (RFs) can produce recognition models for HD proteins that are significant improvements over KNN-based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting position frequency matrices from protein sequence using a RF-based model.
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