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Updated: Dec 13, 2025

Generation of Escape Variants of Neutralizing Influenza Virus Monoclonal Antibodies

Generation of Escape Variants of Neutralizing Influenza Virus Monoclonal Antibodies

Published on: August 29, 2017

Optimizing immunization protocols to elicit broadly neutralizing antibodies.

Kayla G Sprenger¹, Joy E Louveau², Pranav M Murugan¹

¹Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America

|August 5, 2020

Summary

This summary is machine-generated.

Sequential immunization protocols can effectively generate broadly neutralizing antibodies (bnAbs) against mutable viruses like HIV. This strategy optimizes immune pressure to target conserved viral sites, enhancing protective antibody evolution.

Keywords:

broadly neutralizing antibodies evolutionary biology highly mutable pathogens sequential vaccination statistical mechanics

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Last Updated: Dec 13, 2025

Generation of Escape Variants of Neutralizing Influenza Virus Monoclonal Antibodies

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Published on: August 29, 2017

Optimization of a Quantitative Micro-neutralization Assay

Optimization of a Quantitative Micro-neutralization Assay

Published on: December 14, 2016

An Optimized Hemagglutination Inhibition HI Assay to Quantify Influenza-specific Antibody Titers

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Published on: December 1, 2017

Area of Science:

Immunology
Virology
Computational Biology

Background:

Pathogen infections and vaccines induce antibodies via affinity maturation for protection.
Highly mutable viruses like HIV and influenza evade strain-specific antibodies through mutation.
Broadly neutralizing antibodies (bnAbs) offer potential protection against diverse viral strains.

Purpose of the Study:

To investigate mechanisms of broadly neutralizing antibody (bnAb) emergence.
To evaluate various vaccination strategies for maximizing bnAb evolution.
To identify optimal protocols for eliciting protective antibodies against mutable viruses.

Main Methods:

Utilized a computational model simulating affinity maturation.
Analyzed a wide range of vaccination strategies.
Focused on immune system dynamics under sequential immunization.

Main Results:

A sequential immunization protocol is proposed as an effective strategy for bnAb evolution.
Each immunization step optimally increases pressure on conserved antigenic sites.
This approach drives the immune system effectively away from steady state, promoting breadth.

Conclusions:

Sequential immunization can be optimized to maximize the evolution of bnAbs.
This strategy facilitates diverse evolutionary pathways for B cells to become bnAbs.
The findings provide a mechanism for enhancing vaccine efficacy against highly mutable pathogens.