Evidence map›Paper›PMID 32416068›Full record

ArticleMolecular cell2020

A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning.

Ariel Erijman, Lukasz Kozlowski, Salma Sohrabi-Jahromi, James Fishburn, Linda Warfield, Jacob Schreiber, William S Noble, Johannes Söding, Steven Hahn

Erratum issuedOpen access · bronzeAbstract read
In one paragraph

Article in Molecular cell, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed
6.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

74 citing papers in PubMed, 135 citations in OpenAlex.

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14 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Ariel ErijmanFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Lukasz KozlowskiQuantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
Salma Sohrabi-JahromiQuantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
James FishburnFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Linda WarfieldFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Jacob SchreiberPaul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
William S NobleDepartment of Genome Sciences, University of Washington, Seattle, WA, USA; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Johannes SödingQuantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany. Electronic address: soeding@mpibpc.mpg.de.
Steven HahnFred Hutchinson Cancer Research Center, Seattle, WA, USA. Electronic address: shahn@fredhutch.org.
Fred Hutch Cancer Center · USMax Planck Institute for Biophysical Chemistry · DEUniversity of Washington · US

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI YATES III, JOHN R · 2012 to 2021
$20.7M
Mechanisms of eukaryotic transcription activationR01GM075114 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HAHN, STEVEN M · 2005 to 2020
$7.9M
Encoding genomic architecture in the encyclopedia: linking DNA elements, chromatin state, and gene expression in 3DU01HG009395 · NHGRI · SLOAN-KETTERING INST CAN RESEARCH · PI LESLIE, CHRISTINA S · 2017 to 2021
$3.7M
NCI NIH HHS P30 CA015704NHGRI NIH HHS U01 HG009395NIGMS NIH HHS P41 GM103533NIGMS NIH HHS R01 GM075114
6 · The paper itself

Abstract

Acidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, "fuzzy" interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.

Indexed as

Amino Acid SequenceBasic-Leucine Zipper Transcription FactorsDeep LearningDNA-Binding ProteinsHigh-Throughput Screening AssaysProtein BindingProtein DomainsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsTrans-ActivatorsTranscriptional ActivationTranscription FactorsBasic-Leucine Zipper Transcription FactorsDNA-Binding ProteinsSaccharomyces cerevisiae ProteinsTrans-ActivatorsTranscription Factorsactivatorallovalencyaviditycoactivatordeep learningenhancerintrinsically disordered proteinmachine learningtranscription activationtranscriptional regulation

Identifiers

PMID32416068
PMCPMC7275923
OpenAlexW3024091520

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.