Abstract
The Type Three Secretion System (T3SS) is a protein complex used by many bacterial genera such as Salmonella, Shigella, and Yersinia by facilitating the transfer of effector proteins into host cells. This research focuses on the T3SS of Yersinia pestis, the causative agent of the bubonic plague. The T3SS of Y. pestis includes a protein called YscF, in its polymerized form this protein makes the needle structure of the T3SS and allows effector proteins to pass through it prior to being injected into the host cell. This protein has never been modeled in its polymerized form, but homologous structures do exist in Salmonella and Shigella that have been structurally elucidated by Cryo-EM. The process of elucidating proteins in a laboratory environment continues to be extremely time consuming and requires knowledge of specialized equipment such as cryo electron microscopes. This research project used tools at the forefront of computational biology such as a supercomputer provided by the ACCESS grant and AlphaFold, artificial intelligence (AI) that specializes in elucidating protein structures in silico. This research utilized the processing power of the supercomputer as well as the vast knowledge of AlphaFold to generate a structure of YscF polymers. The structure was generated using high throughput methods using an increasing number of subunits for the YscF polymer. This allowed for the assembly of the polymer to be seen, the number of subunits to be recorded, and the size of the polymer to be measured. In order to verify the quality of the models, predicted aligned error (PAE) plots and predicted local distance difference tests (pLDDT) were performed. This allowed for physiologically relevant quantitative analysis of the YscF models generated. Additionally, there are mutants of YscF, leading to constitutive secreting (CS) and non-secreting (NS) phenotypes that were modeled using the same high throughput methods. Looking at the differences between the wild type YscF and the mutants provides valuable insight into how secretion is regulated which could assist in developing novel treatments that target the T3SS.
| Original language | English |
|---|---|
| Article number | 108986 |
| Number of pages | 1 |
| Journal | Journal of Biological Chemistry |
| Volume | 301 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2025 |
| Event | ASBMB Annual Meeting - Chicago, United States Duration: Apr 12 2025 → Apr 15 2025 https://www.sciencedirect.com/journal/journal-of-biological-chemistry/vol/301/issue/5/suppl/S |
Funding
This research was made possible due to the ACCESS grant through the NSF which provided access to the ACCESS supercomputer.
Keywords
- Artificial Intelligence
- Computational Biology
- Protein Modeling
- Python
- Type Three Secretion System
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