Evidence map›Paper›PMID 40466641›Full record

ArticleCell systems2025

Machine-guided dual-objective protein engineering for deimmunization and therapeutic functions.

Eric Wolfsberg, Jean-Sebastien Paul, Josh Tycko, Binbin Chen, Michael C Bassik, Lacramioara Bintu, Ash A Alizadeh, Xiaojing J Gao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Small-molecule control of CAR T cells.Nature reviews. Chemistry · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Eric WolfsbergDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.
Jean-Sebastien PaulDepartment of Biology, California Institute of Technology, Pasadena, CA 91125, USA; Department of Computer Science, California Institute of Technology, Pasadena, CA 91125, USA.
Josh TyckoDepartment of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
Binbin ChenDepartment of Genetics, Stanford University, Stanford, CA 94305, USA; Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Vcreate, Menlo Park, CA 94025, USA.
Michael C BassikDepartment of Genetics, Stanford University, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
Lacramioara BintuDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Ash A AlizadehDivision of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA.
Xiaojing J GaoDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305, USA. Electronic address: xjgao@stanford.edu.

Funding

High-throughput development and characterization of compact tools for transcriptional and chromatin perturbationsR01HG011866 · NHGRI · STANFORD UNIVERSITY · PI MICHAEL C BASSIK, Lacramioara Bintu · 2021 to 2026
$6.9M
A Novel Class of Synthetic Receptors to Empower the Age of mRNA TherapiesDP2EB035891 · NIBIB · STANFORD UNIVERSITY · PI Xiaojing J Gao · 2023 to 2026
$2.3M
Synthetic DNA-free Circuits for “Scarless” Programming of Mammalian CellsR00EB027723 · NIBIB · STANFORD UNIVERSITY · PI GAO, XIAOJING J · 2020 to 2022
$747k
NHGRI NIH HHS R01 HG011866NIBIB NIH HHS DP2 EB035891NIBIB NIH HHS R00 EB027723
6 · The paper itself

Abstract

Cell and gene therapies often express nonhuman proteins, which carry a risk of anti-therapy immunogenicity. An emerging consensus is to instead use modified human protein domains, but these domains include nonhuman peptides around mutated residues and at interdomain junctions, which may also be immunogenic. We present a modular workflow to optimize protein function and minimize immunogenicity by using existing machine learning models that predict protein function and peptide-major histocompatibility complex (MHC) presentation. We first applied this workflow to existing transcriptional activation and RNA-binding domains by removing potentially immunogenic MHC II epitopes. We then generated small-molecule-controllable transcription factors with human-derived DNA-binding domains targeting non-genomic DNA sequences. Finally, we established a workflow for creating deimmunized zinc-finger arrays to target arbitrary DNA sequences and upregulated two therapeutically relevant genes, utrophin (UTRN) and sodium voltage-gated channel alpha subunit 1 (SCN1A), using it. Our modular workflow offers a way to potentially make cell and gene therapies safer and more efficacious using state-of-the-art algorithms.

Indexed as

Protein EngineeringHumansMachine LearningTranscription FactorsZinc FingersTranscription Factorsprotein designsynthetic biology

Identifiers

PMID40466641
PMCPMC12276912

What OpenQuestion holds

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Registered trials

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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.