Evidence map›Paper›PMID 42607673›Full record

ArticleCell systems2026

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.

Claire J LeBlanc, Pooja Agarwal, Jackson E Demaray, Gean Hu, Marissa A Zintel, Angelica W Y Lam, Joel Enrique Castro Hernandez, Max V Staller

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Claire J LeBlancDepartment of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA.
Pooja AgarwalDepartment of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA; Department of Electrical Engineering & Computer Science, University of California Berkeley, Berkeley, CA 94720, USA.
Jackson E DemarayCenter for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA.
Gean HuCenter for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA; Department of Electrical Engineering & Computer Science, University of California Berkeley, Berkeley, CA 94720, USA; Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA.
Marissa A ZintelDepartment of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA.
Angelica W Y LamDepartment of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA.
Joel Enrique Castro HernandezCenter for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA; Department of Electrical Engineering & Computer Science, University of California Berkeley, Berkeley, CA 94720, USA.
Max V StallerDepartment of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California Berkeley, Berkeley, CA 94720, USA; Biohub, San Francisco, CA 94158, USA. Electronic address: mstaller@berkeley.edu.

Funding

Molecular Biology Across Scales Training ProgramT32GM148378 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI David Bilder, Elcin Unal · 2023 to 2026
$7.3M
Defining the protein sequence features that control transcriptional activation domain functionR35GM150813 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI Max Valentin Staller · 2023 to 2026
$1.5M
NIGMS NIH HHS R35 GM150813NIGMS NIH HHS T32 GM148378
6 · The paper itself

Abstract

Deep neural networks have improved many difficult prediction tasks in biology, but it remains challenging to interpret these networks and learn the molecular mechanisms. Here, we address interpretation challenges by building biophysical neural networks for predicting activation domains, the regions within transcription factors (TFs) that recruit coactivators to drive gene expression. Deep neural networks can now accurately predict acidic activation domains from protein sequences, but these predictors are difficult to interpret. We designed shallow neural networks that incorporated biophysical models and visualized the parameters directly. We found two ways that the arrangement of residues (i.e., sequence grammar) controls function: (1) C-terminal hydrophobic residues increase coactivator binding and decrease protein abundance, and (2) acidic residues at the N terminus promote coactivator binding, while acidic residues at the C terminus promote TF abundance. We demonstrate how combining biophysical and deep neural networks maximizes prediction accuracy and interpretability, revealing biological mechanisms across datasets. A record of this paper's transparent peer review process is included in the supplemental information.

Indexed as

Neural Networks, ComputerTranscriptional ActivationHumansProtein BindingProtein DomainsTranscription FactorsTranscription Factorsbiophysical neural networksconvolutional neural networksintrinsically disordered proteinsintrinsically disordered regionsmachine-learning interpretabilityneural networksprotein abundancetranscriptiontranscriptional activation domainstranscription factors

Identifiers

PMID42607673
PMCPMC13596073

What OpenQuestion holds

Textmetadata
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.