Learning from pre-pandemic data to design and test future-proof therapeutics

Effective pandemic preparedness relies on predicting immune-evasive viral mutations to enable early detection of variants of concern and design vaccines and therapeutics that are resilient to future viral evolution. However, current strategies for viral evolution prediction are not available early in a pandemic and have limited predictive power – experimental approaches require host polyclonal antibodies and existing computational methods draw heavily from current strain prevalence. In addition, vaccines and therapeutics have been designed with an eye towards past or circulating variants, not towards future evolution. To address these challenges, we developed EVEscape, a generalizable framework that integrates fitness predictions from a deep generative model of evolutionary sequences with biophysical and structural information. EVEscape quantifies the immune escape potential of viral strains at scale and is applicable before surveillance sequencing, experimental scans, or 3D structures of antibody complexes are available. We demonstrate that EVEscape, trained on sequences available prior to 2020, performs as accurately as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including Influenza A virus, HIV, and understudied viruses with pandemic potential such as Lassa and Nipah. We investigate both alignment-based and protein language models to explore the best model of mutation effects across pandemic-threat viral families. We demonstrate the utility of EVEscape in three critical applications: (1) Surveillance efforts flagging high escape SARS-CoV-2 variants from their first appearance (2) Design of panels of viral antigens that mimic future viral variants for early, proactive evaluation of the future protection of vaccines and therapeutic; and (3) Design of a pan-sarbecovirus nanoparticle-based vaccine capable of eliciting broad, long-lasting protection against sarbecoviruses, including future variants. This three-pronged approach represents a paradigm shift in pandemic preparedness, offering a novel strategy to preemptively address viral families with pandemic potential and significantly bolster global prevention efforts.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Gurev, Sarah

Advisor dc:contributor.advisor

• Marks, Debora

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164504

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164504