Evidence map›Paper›PMID 41159032›Full record

ArticleFrontiers in immunology2025

Directed evolution of drug-like Aβ conformation-specific antibodies.

Alec A Desai, Matthew D Smith, Jennifer M Zupancic, Emily K Makowski, Yulei Zhang, Julia E Gerson, Shannon J Moore, Alexandra B Sutter, Sean P Ferris, Magdalena I Ivanova and 3 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Alec A Desai *Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, United States.
Matthew D Smith *Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, United States.
Jennifer M ZupancicDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI, United States.
Emily K MakowskiBiointerfaces Institute, University of Michigan, Ann Arbor, MI, United States.
Yulei ZhangDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI, United States.
Julia E GersonDepartment of Neurology, University of Michigan, Ann Arbor, MI, United States.
Shannon J MooreProtein Folding Disease Initiative, University of Michigan, Ann Arbor, MI, United States.
Alexandra B SutterDepartment of Neurology, University of Michigan, Ann Arbor, MI, United States.
Sean P FerrisDepartment of Pathology, University of Michigan, Ann Arbor, MI, United States.
Magdalena I IvanovaDepartment of Neurology, University of Michigan, Ann Arbor, MI, United States.
Geoffrey G MurphyProtein Folding Disease Initiative, University of Michigan, Ann Arbor, MI, United States.
Henry L PaulsonDepartment of Neurology, University of Michigan, Ann Arbor, MI, United States.
Peter M TessierDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI, United States.

Funding

Research Education ComponentP30AG072931 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ana Marie Daugherty · 2021 to 2026
$26.5M
Mechanisms of neurodegenerative diseases: intersections with ubiquitin pathwaysR35NS122302 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Henry L Paulson · 2021 to 2026
$6.4M
CD98hc Brain Shuttles for Delivering Off-the-shelf Neuroprotective Antibodies in Alzheimer's DiseaseR01AG080016 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Colin Fred Greineder, Peter M Tessier · 2023 to 2026
$3.2M
Design and Evolution of Polyvalent Domain Antibodies Specific for Tau AggregatesRF1AG059723 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KANE, RAVI S., TESSIER, PETER M · 2018 to 2022
$2.4M
Structure-guided antibody targeting of pre-selected epitopes in amyloidogenic aggregatesR35GM136300 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI TESSIER, PETER M · 2020 to 2024
$1.5M
NIA NIH HHS P30 AG072931NIA NIH HHS R01 AG080016NIA NIH HHS RF1 AG059723NIGMS NIH HHS R35 GM136300NINDS NIH HHS R35 NS122302
6 · The paper itself

Abstract

Monoclonal antibodies that recognize conformational epitopes in protein aggregates are important for research, diagnostic, and therapeutic applications related to neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Unfortunately, it remains challenging to discover and engineer high-quality conformational antibodies that are specific for protein aggregates and possess optimal combinations of three key binding properties, namely high affinity, high conformational specificity, and low off-target binding. Here we report a directed evolution approach for generating high-quality conformational antibodies against Alzheimer's Aβ fibrils in the native IgG format. Our directed evolution approach uses targeted mutagenesis, yeast surface display, cell sorting, and deep sequencing to identify antibody candidates with optimized binding properties. Notably, we find that this approach yields robust isolation of IgGs with higher affinity, higher conformational specificity, and lower off-target binding than multiple clinical-stage Aβ antibodies, including aducanumab and crenezumab. This antibody engineering platform can be readily applied to generate conformational antibodies against diverse types of peptide and protein aggregates linked to human diseases.

Indexed as

Amyloid beta-PeptidesAntibodies, MonoclonalDirected Molecular EvolutionAlzheimer DiseaseAntibodies, Monoclonal, HumanizedAntibody AffinityAntibody SpecificityEpitopesHumansImmunoglobulin GProtein ConformationProtein EngineeringAmyloid beta-PeptidesAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedEpitopesImmunoglobulin Gaffinity maturationaggregationAlzheimer’s diseaseamyloidantibodyconformationalfibrilmAb

Identifiers

PMID41159032
PMCPMC12554751

What OpenQuestion holds

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LicenceCC BY
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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.