Integrative approaches to decipher influenza evolution, antibody responses, and AI-driven specificity prediction

The ongoing threat of viral pathogens, such as SARS-CoV-2 and influenza Viruses, highlights the urgent need to understand immune responses and viral evolution to guide therapeutic and vaccine development. This dissertation integrates high-throughput experimental techniques and artificial intelligence (AI) to address key questions in virus-immunity interactions through three interconnected research areas: (1) deep mutational scanning (DMS) to map sequence–function relationships in influenza viral proteins; (2) large-scale analysis of antibody responses to SARS-CoV-2 and influenza; and (3) development of AI models to predict antibody specificity. Chapter 1 introduces the rapid advancement of high-throughput and AI methodologies for studying immune responses and viral evolution. Chapter 2 presents a robust DMS pipeline that reveals high N-terminal tolerance in the nuclear export protein (NEP) and identifies charge-driven epistasis as a constraint on neuraminidase (NA) antigenic evolution. Chapter 3 describes large-scale profiling of antibody repertoires across viral pathogens, identifying critical residues in IGHV1-69 broadly neutralizing antibodies that target the hemagglutinin (HA) stem. Chapter 4 showcases AI-driven models for predicting antibody specificity and highlights their promise for therapeutic design. Chapter 5 synthesizes key findings and outlines future directions. By combining high-throughput experimentation with AI, this dissertation advances our understanding of host–pathogen interactions and provides new tools for vaccine design and immunotherapy.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biochemistry

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Wang, Yiquan

Contributors dc:contributor

• Wu, Nicholas
• Brooke, Christopher
• Stadtmueller, Beth
• Tajkhorshid, Emad

• Deep learning
• Antibody Recognition
• Influenza Virus
• Artificial Intelligence

Statement dc:rights

• © 2025 Yiquan Wang. All rights reserved

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132547

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132547