ArticleMethods in molecular biology (Clifton, N.J.)2017
EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.
Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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.
Who cites it
19 citing papers in PubMed, 40 citations in OpenAlex.
- Eradication of MRSA biofilm-associated implant infections by low-immunogenic sustained-release lysostaphin fused to thermosensitive polypeptides.Nature communications · 2026Article
- Emerging Technologies Tackling Adeno-Associated Viruses (AAV) Immunogenicity in Gene Therapy Applications.Pharmaceutics · 2025Review
- Tuning ProteinMPNN to reduce protein visibility via MHC Class I through direct preference optimization.Protein engineering, design & selection : PEDS · 2025Article
- Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.Briefings in bioinformatics · 2024Article
- Assessing the predictive ability of computational epitope prediction methods on Fel d 1 and other allergens.PloS one · 2024Article
- Functional Deimmunization of Botulinum Neurotoxin Protease Domain via Computationally Driven Library Design and Ultrahigh-Throughput Screening.ACS synthetic biology · 2023Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.Nature communications · 2022Article
- Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy.Peptides · 2021Article
- MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform.Journal of chemical information and modeling · 2021Article
- Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion.Computational and structural biotechnology journal · 2021Review
- Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering.Computational and structural biotechnology journal · 2021Article
- Rational Design of SARS-CoV-2 Spike Glycoproteins To Increase Immunogenicity By T Cell Epitope Engineering.bioRxiv : the preprint server for biology · 2020Article
- Development of a strategy and computational application to select candidate protein analogues with reduced HLA binding and immunogenicity.Immunology · 2018Article
- Article
- Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Pareto Optimization of Combinatorial Mutagenesis Libraries.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics render them subject to immune surveillance within the patient's body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity.To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure-based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates.
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Registered trials
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.