ArticlePLoS computational biology2017
Optimal Sequential Immunization Can Focus Antibody Responses against Diversity Loss and Distraction.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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.
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
24 citing papers in PubMed, 41 citations in OpenAlex.
- Computational Design and Expression of Headless Influenza Hemagglutinin Antigens Toward a Modular Universal Nanoparticle Vaccine.Antibodies (Basel, Switzerland) · 2026Article
- Phenomenological Modeling of Antibody Response from Vaccine Strain Composition.Antibodies (Basel, Switzerland) · 2025Article
- Epistatic hotspots organize antibody fitness landscape and boost evolvability.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Vaccination against rapidly evolving pathogens and the entanglements of memory.Nature immunology · 2024Review
- A multiscale spatial modeling framework for the germinal center response.Frontiers in immunology · 2024Article
- Article
- Germinal centers are permissive to subdominant antibody responses.Frontiers in immunology · 2023Article
- Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV.PLoS computational biology · 2022Article
- A Coarse-Grained Model of Affinity Maturation Indicates the Importance of B-Cell Receptor Avidity in Epitope Subdominance.Frontiers in immunology · 2022Article
- Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models.Frontiers in immunology · 2022Article
- Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies.eLife · 2021Article
- Coevolutionary transitions emerging from flexible molecular recognition and eco-evolutionary feedback.iScience · 2021Article
- Increased B Cell Selection Stringency In Germinal Centers Can Explain Improved COVID-19 Vaccine Efficacies With Low Dose Prime or Delayed Boost.Frontiers in immunology · 2021Article
- Administration of Multivalent Influenza Virus Recombinant Hemagglutinin Vaccine in Combination-Adjuvant Elicits Broad Reactivity Beyond the Vaccine Components.Frontiers in immunology · 2021Article
- Article
- Optimizing immunization protocols to elicit broadly neutralizing antibodies.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Article
- Tuning environmental timescales to evolve and maintain generalists.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- HIV-1 gp120-CD4-Induced Antibody Complex Elicits CD4 Binding Site-Specific Antibody Response in Mice.Journal of immunology (Baltimore, Md. : 1950) · 2020Article
- Evolving generalists in switching rugged landscapes.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Affinity maturation is a Darwinian process in which B lymphocytes evolve potent antibodies to encountered antigens and generate immune memory. Highly mutable complex pathogens present an immense antigenic diversity that continues to challenge natural immunity and vaccine design. Induction of broadly neutralizing antibodies (bnAbs) against this diversity by vaccination likely requires multiple exposures to distinct but related antigen variants, and yet how affinity maturation advances under such complex stimulation remains poorly understood. To fill the gap, we present an in silico model of affinity maturation to examine two realistic new aspects pertinent to vaccine development: loss in B cell diversity across successive immunization periods against different variants, and the presence of distracting epitopes that entropically disfavor the evolution of bnAbs. We find these new factors, which introduce additional selection pressures and constraints, significantly influence antibody breadth development, in a way that depends crucially on the temporal pattern of immunization (or selection forces). Curiously, a less diverse B cell seed may even favor the expansion and dominance of cross-reactive clones, but only when conflicting selection forces are presented in series rather than in a mixture. Moreover, the level of frustration due to evolutionary conflict dictates the degree of distraction. We further describe how antigenic histories select evolutionary paths of B cell lineages and determine the predominant mode of antibody responses. Sequential immunization with mutationally distant variants is shown to robustly induce bnAbs that focus on conserved elements of the target epitope, by thwarting strain-specific and distracted lineages. An optimal range of antigen dose underlies a fine balance between efficient adaptation and persistent reaction. These findings provide mechanistic guides to aid in design of vaccine strategies against fast mutating pathogens.
Indexed as
Identifiers
What OpenQuestion holds
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.