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

Wang, Yiquan

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

Description

Title

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

Author(s)

Wang, Yiquan

Issue Date

2025-12-02

Director of Research (if dissertation) or Advisor (if thesis)

Wu, Nicholas

Doctoral Committee Chair(s)

Wu, Nicholas

Committee Member(s)

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

Department of Study

Biochemistry

Discipline

Biochemistry

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

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

Abstract

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.

Graduation Semester

2025-12

Type of Resource

Thesis

Handle URL

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

Copyright and License Information

© 2025 Yiquan Wang. All rights reserved

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