Context-dependent DNA recognition code for C2H2 zinc-finger transcription factors
Open accessAbstract
Motivation: Modeling and identifying the DNA-protein recognition code is one of the most challenging problems in computational biology. Several quantitative methods have been developed to model DNA-protein interactions with specific focus on the C2H2 zinc-finger proteins, the largest transcription factor family in eukaryotic genomes. In many cases, they performed well. But the overall the predictive accuracy of these methods is still limited. One of the major reasons is all these methods used weight matrix models to represent DNA-protein interactions, assuming all base-amino acid contacts contribute independently to the total free energy of binding. Results: We present a context-dependent model for DNA–zinc-finger protein interactions that allows us to identify inter-positional dependencies in the DNA recognition code for C2H2 zinc-finger proteins. The degree of non-independence was detected by comparing the linear perceptron model with the non-linear neural net (NN) model for their predictions of DNA–zinc-finger protein interactions. This dependency is supported by the complex base-amino acid contacts observed in DNA–zinc-finger interactions from structural analyses. Using extensive published qualitative and quantitative experimental data, we demonstrated that the context-dependent model developed in this study can significantly improves predictions of DNA binding profiles and free energies of binding for both individual zinc fingers and proteins with multiple zinc fingers when comparing to previous positional-independent models. This approach can be extended to other protein families with complex base-amino acid residue interactions that would help to further understand the transcriptional regulation in eukaryotic genomes. Availability:The software implemented as c programs and are available by request. http://ural.wustl.edu/softwares.html Contact: stormo@ural.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 [10]
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- Authors
- Liu, J., & Stormo, G. D.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Stormo, Gary D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-06-27
- Volume
- 24
- Issue
- 17
- Pages
- 1850-1857
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2732218
- OpenAlex
- W2129520866
- Cited by
- 48
- References
- 48
- Citations by year
- 2025: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2018: 2 · 2017: 1 · 2015: 7 · 2014: 5 · 2013: 5 · 2012: 5
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (HG00249, R01HG000249)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- C2H2 zinc finger proteins, transcription factors, protein-DNA recognition, context-dependent models, binding free energy, neural network model
- MeSH terms
- Base Sequence, Binding Sites, Computer Simulation, DNA, DNA-Binding Proteins, Models, Genetic, Molecular Sequence Data, Pattern Recognition, Automated, Protein Binding, Transcription Factors, Zinc Fingers
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