ArticleGenetics2023
Clusters of acidic and hydrophobic residues can predict acidic transcriptional activation domains from protein sequence.
Article in Genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- How do low-complexity domain interactions control transcription?Biochemical Society transactions · 2026Review
- A transcription factor by any other name - function beyond DNA binding and transactivation.EMBO reports · 2026Review
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.Cell systems · 2026Article
- Acidic transcription factors position the genome at nuclear speckles through transcription-dependent and -independent mechanisms.Molecular cell · 2026Article
- Article
- Article
- Isoform-specific cofactor recruitment through the intrinsically disordered N-terminus of p63 underlies differential transcriptional activities.bioRxiv : the preprint server for biology · 2026Article
- A SWI/SNF-specific Ig-like domain, SWIFT, is a transcription factor binding platform.Science (New York, N.Y.) · 2026Article
- Oligomerization-competent PIF4 drives thermomorphogenesis through functional redundancy in transactivation and DNA binding.Nature communications · 2026Article
- Evolutionary turnover of key amino acids explains conservation of function without conservation of sequence in transcriptional activation domains.PLoS genetics · 2026Article
- Evolutionary discovery and characterization of fungal transcriptional activators using active learning.bioRxiv : the preprint server for biology · 2025Article
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.bioRxiv : the preprint server for biology · 2025Article
- PIF4-mediated thermomorphogenesis relies on its oligomerization ability, not DNA-binding or transactivation activity.Research square · 2025Article
- Hierarchy in regulator interactions with distant transcriptional activation domains empowers rheostatic regulation.Protein science : a publication of the Protein Society · 2025Article
- Boosting transcriptional activities by employing repeated activation domains in transcription factors.The Plant cell · 2025Article
- The potent PHL4 transcription factor effector domain contains significant disorder.Protein science : a publication of the Protein Society · 2024Article
- Article
- High-throughput affinity measurements of direct interactions between activation domains and co-activators.bioRxiv : the preprint server for biology · 2024Article
- Article
- The Potent PHL4 Transcription Factor Effector Domain Contains Significant Disorder.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Transcription factors activate gene expression in development, homeostasis, and stress with DNA binding domains and activation domains. Although there exist excellent computational models for predicting DNA binding domains from protein sequence, models for predicting activation domains from protein sequence have lagged, particularly in metazoans. We recently developed a simple and accurate predictor of acidic activation domains on human transcription factors. Here, we show how the accuracy of this human predictor arises from the clustering of aromatic, leucine, and acidic residues, which together are necessary for acidic activation domain function. When we combine our predictor with the predictions of convolutional neural network (CNN) models trained in yeast, the intersection is more accurate than individual models, emphasizing that each approach carries orthogonal information. We synthesize these findings into a new set of activation domain predictions on human transcription factors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.