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Title: Clusters of acidic and hydrophobic residues can predict acidic transcriptional activation domains from protein sequence
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.  more » « less
Award ID(s):
2112057
PAR ID:
10510795
Author(s) / Creator(s):
;
Editor(s):
Kaplan, C
Publisher / Repository:
GENETICS
Date Published:
Journal Name:
GENETICS
Volume:
225
Issue:
2
ISSN:
1943-2631
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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