ReviewTrends in biotechnology2026
The evolution of display technologies for antibody drug discovery.
Julian Rojo-Gallegos, Jason Q Tang, Mark Yarmarkovich, Brandon J DeKosky
Abstract readReview
In one paragraphReview in Trends in biotechnology, 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
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0citing papers in PubMed
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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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
4 authors.
Julian Rojo-GallegosThe Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Jason Q TangPerlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY, USA.
Mark YarmarkovichPerlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY, USA. Electronic address: mark.yarmarkovich@nyulangone.org.
Brandon J DeKoskyThe Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute for Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: dekosky@mit.edu.
Funding
Dissecting the mechanisms of HIV resistance in vivo to broadly neutralizing antibodiesU01AI169587 · NIAID · UNIVERSITY OF MINNESOTA · PI Priyamvada Acharya, Brandon James DeKosky · 2022 to 2026
$7.8MPotent broadly neutralizing antibody development against the HIV-1 fusion peptide epitopeR01AI181684 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Brandon James DeKosky · 2023 to 2026
$3.0MPotent Antibody Protection Against P. falciparum and P. vivax MalariaR01AI192975 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Brandon James DeKosky · 2025 to 2026
$1.6MExpanding CAR T cell applications through high-throughput forward- and reverse immune engineeringDP2CA301080 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI YARMARKOVICH, MARK · 2024 to 2024
$1.5MMATCHMAKERS - Solving T-cell receptor recognition and design via integrated high-throughput screening and structural, functional and computational approachesOT2CA297514 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Brandon James DeKosky · 2024 to 2026
$964kHigh-throughput identification and transcriptional analysis of autoreactive T cells in individuals with membranous nephropathy.R21AI178021 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CRAVEDI, PAOLO · 2023 to 2024
$479kNext Generation T cell therapies for childhood cancers (NexTGen)OT2CA290738 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Mark Yarmarkovich · 2023 to 2026
$420kNCI NIH HHS DP2 CA301080NCI NIH HHS OT2 CA290738NCI NIH HHS OT2 CA297514NIAID NIH HHS R01 AI181684NIAID NIH HHS R01 AI192975NIAID NIH HHS R21 AI178021NIAID NIH HHS U01 AI169587
6 · The paper itselfAbstract
Display technologies are a class of experimental methods that enable a direct association between protein function and nucleic acid sequence. Since the introduction of phage display in 1985, antibody display technologies have evolved along multiple branches and enabled new capabilities for drug discovery. Recent advances build on these foundations to accommodate new antibody modalities, improve library size, accelerate functional discovery, and leverage experimental data through computational analysis and structural predictions. Here, we discuss how recent advances in protein display are being applied to solve a broad range of problems in antibody drug discovery, immunology, and biotechnology.
Indexed as
antibody discoverycomputational designdirected evolutiondisplay technologyengineered antibody formatshigh-throughput screening
Identifiers
PMID42185131
PMCPMC13325415
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