ArticleMethods in molecular biology (Clifton, N.J.)2023
Computational Epitope Prediction and Design for Antibody Development and Detection.
Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Adaptive Disorder as the Hallmark of Nanobodies Antigen-Binding Loops.Journal of chemical information and modeling · 2026Article
- N-Glycans modulate tilting of HIV-1 envelope glycoprotein.Nature communications · 2026Article
- Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.Frontiers in cellular and infection microbiology · 2024Review
- Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.Journal of chemical theory and computation · 2023Review
- SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: A Computational Model of Epitope Loss in Variants of Concern.Journal of chemical information and modeling · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The design of optimized protein antigens is a fundamental step in the development of new vaccine candidates and in the detection of therapeutic antibodies. A fundamental prerequisite is the identification of antigenic regions that are most prone to interact with antibodies, namely, B-cell epitopes. Here, we describe an efficient structure-based computational method for epitope prediction, called MLCE. In this approach, all that is required is the 3D structure of the antigen of interest. MLCE can be applied to glycosylated proteins, facilitating the identification of immunoreactive versus immune-shielding carbohydrates.
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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.