ArticlePLoS computational biology2017
Inherent limitations of probabilistic models for protein-DNA binding specificity.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Generation of aptamers for the selective detection and neutralization of soluble lymphotoxin alpha.Molecular therapy. Nucleic acids · 2026Article
- Quantifying transcription factor specificity with advanced DNA universal microarrays featuring long and modified binding sites.Nucleic acids research · 2025Article
- TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.Genome biology · 2024Article
- Eukaryotic gene regulation at equilibrium, or non?Current opinion in systems biology · 2022Article
- Modeling binding specificities of transcription factor pairs with random forests.BMC bioinformatics · 2022Article
- Diffusion of DNA-Binding Species in the Nucleus: A Transient Anomalous Subdiffusion Model.Biophysical journal · 2020Article
- Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters.Nucleic acids research · 2020Article
- Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination.Nucleic acids research · 2020Article
- ChIPulate: A comprehensive ChIP-seq simulation pipeline.PLoS computational biology · 2019Article
- Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity.PLoS genetics · 2018Article
- Comparison of discriminative motif optimization using matrix and DNA shape-based models.BMC bioinformatics · 2018Article
- SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site.Genome research · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible.
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