Evidence map›Paper›PMID 40407693›Full record

ArticleAntibodies (Basel, Switzerland)2025

Generative Deep Learning Design of Single-Domain Antibodies Against Venezuelan Equine Encephalitis Virus.

Jinny L Liu, Gabrielle C Bayacal, Jerome Anthony E Alvarez, Lisa C Shriver-Lake, Ellen R Goldman, Scott N Dean

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Antibodies (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Jinny L LiuCenter for Bio/Molecular Science and Engineering, US Naval Research Laboratory, Washington, DC 20375, USA.
Gabrielle C BayacalNaval Research Enterprise Internship Program, US Naval Research Laboratory, Washington, DC 20375, USA.
Jerome Anthony E AlvarezCenter for Bio/Molecular Science and Engineering, US Naval Research Laboratory, Washington, DC 20375, USA.ORCID 0009-0001-2061-972X
Lisa C Shriver-LakeCenter for Bio/Molecular Science and Engineering, US Naval Research Laboratory, Washington, DC 20375, USA.ORCID 0000-0002-9894-6640
Ellen R GoldmanCenter for Bio/Molecular Science and Engineering, US Naval Research Laboratory, Washington, DC 20375, USA.
Scott N DeanCenter for Bio/Molecular Science and Engineering, US Naval Research Laboratory, Washington, DC 20375, USA.ORCID 0000-0002-3394-6164

Funding

Defense Threat Reduction Agency CB10869Office of Naval Research 61A1P4
6 · The paper itself

Abstract

BACKGROUND/

objectivesVenezuelan equine encephalitis virus (VEEV) represents a significant biothreat with no FDA-approved vaccine currently available, highlighting the need for alternative therapeutic strategies. Single-domain antibodies (sdAbs) present a potential alternative to conventional antibodies, due to their small size and ability to recognize cryptic epitopes.

methodsThis research describes the development and preliminary evaluation of VEEV-binding sdAbs generated using a generative artificial intelligence (AI) platform. Using a dataset of known alphavirus-binding sdAbs, the AI model produced sequences with predicted affinity for the E2 glycoprotein of VEEV. These candidate sdAbs were expressed in a bacterial periplasmic system and purified for initial assessment.

resultsEnzyme-linked immunosorbent assays (ELISAs) indicated binding activity of the sdAbs to VEEV antigens. In vitro neutralization tests suggested inhibition of VEEV infection in cultured cells for some of the candidates.

conclusionsThis study demonstrates how generative AI can expedite antiviral therapeutic development and establishes a framework for quick responses to emerging viral threats when extensive example databases are unavailable. Additional refinement and validation of AI-generated sdAbs could establish effective VEEV therapeutics.

Indexed as

generative AInanobodiesviruses

Identifiers

PMID40407693
PMCPMC12101358

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