University of Illinois at Urbana-Champaign
AlphaFold2 reveals structural patterns of seasonal haplotype diversification in SARS-CoV-2 structural protein variants
Abstract
dc:descriptionThe COVID-19 pandemic showcases the impact of mitigation and elimination strategies across the globe, including the development of effective vaccines, antiviral drugs and diagnostic tools. However, the virus changes rapidly over time. Consequently, control strategies have been limited by time-consuming experimental acquisition of three-dimensional atomic protein structures of the fast-developing mutant ‘variants’ of the virus, which remains an unviable strategy for fast and effective disease control. Here, we use AlphaFold2 to model the atomic structure of the ever-changing SARS-CoV-2 structural proteins in silico. AlphaFold2 is an artificial intelligence (AI) deep learning computational tool capable of producing models at experimental resolution in only a few hours. Structural models for major Variants of Concern (Alpha, Delta, and Omicron) and latitude-delimited haplotypes, sets of genetically linked and highly prevalent mutations that impact the epidemic calendar of the virus, were compared to the structure of the reference Wuhan strain. We find that patterns of structural change triggered by seasonal haplotype diversification could help predict the changing face of the virus, understand seasonal behavior, and develop more resilient vaccines and drugs.
Degree
thesis:*- Name thesis:degree_name
- M.S.
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Bioinformatics
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Ali, Muhammad Asif
- Contributors dc:contributor
-
- Caetano-Anollés, Gustavo
- Rodriguez-Zas, Sandra L
- Villamil, Maria B
Subjects
dc:subject × 12Rights
dc:rights- Statement dc:rights
-
- Copyright 2024 Muhammad Asif Ali
- Language dc:language
- en, eng
Identifiers
dc:identifier.*- Handle dc:identifier
- https://hdl.handle.net/2142/124433