Recognition models to predict DNA-binding specificities of homeodomain proteins
Open accessAbstract
Motivation: Recognition 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 C2H2 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. Results: Using 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. Contact: stormo@wustl.edu
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In some cases, DNA-binding motifs can be predicted from specific interactions between DNA-contacting amino acid residues and DNA bases.
“in some cases motifs can even be predicted on the basis of specific interactions between DNA-contacting amino acid residues and DNA bases9,10”
From A compendium of RNA-binding motifs for decoding gene regulation (2013) · cites this paper as [9]
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A compendium of RNA-binding motifs for decoding gene regulation
Ray, D., Kazan, H., Cook, K. B., Weirauch, M. T., Najafabadi, H. S., Li, X., Gueroussov, S., Albu, M., Zheng, H., Yang, A., et al. (2013) · Nature · doi:10.1038/nature12311 · Cited by 1,480 · see citing works
- Authors
- Christensen, R. G., Enuameh, M. S., Noyes, M. B., Brodsky, M. H., Wolfe, S. A., & Stormo, G. D.
- Affiliations
- University of Massachusetts Chan Medical School (US)
- Corresponding author
- Stormo, Gary D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-06-09
- Volume
- 28
- Issue
- 12
- Pages
- i84-i89
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3371834
- OpenAlex
- W2069550885
- Cited by
- 42
- References
- 56
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2022: 3 · 2020: 1 · 2019: 4 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 7 · 2014: 5 · 2013: 8 · 2012: 1
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion, cc-by-nc)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (HG00249, R01HG000249, HG004744, R01HG004744)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- homeodomain transcription factor, transcription factors, random forest, support vector machine, k-nearest neighbor comparison, web-based prediction tool, transcriptional regulatory networks
- MeSH terms
- Algorithms, Amino Acid Sequence, Animals, Artificial Intelligence, Binding Sites, DNA, Drosophila, Humans, Transcription Factors, Models, Statistical, Zinc Fingers, Sequence Alignment, Homeodomain Proteins, Computational Biology, Mice, Support Vector Machine
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