Abstract:

The global rise of antimicrobial resistance has reawakened concern over re-emerging pathogens such as Yersinia pestis, the bacterium responsible for plague. A critical virulence factor in Y. pestis is the Type III Secretion System (T3SS), which enables the injection of effector proteins directly into host cells. The needle-like structure responsible for translocation is formed by polymerization of the YscF protein, yet its complete oligomeric structure and the effects of function-disrupting mutations remain poorly understood.

In this study, we used AlphaFold 3 to model oligomeric assemblies of key mutations in the YscF protein. The mutants analyzed were found to have a large effect on needle formation and regulation during previous in-vitro studies. These include single, R73A double, D28A/D46A and multi-site mutants. Specifically, the constant secretion (CS) mutant (I13A, D17A, D28A, D46A) and the non-secretion (NS) mutant (N31A, V34A, D77A, I82A). Models were constructed for 21- and 24-subunit oligomers and evaluated using PyMOL and quantitative structural analysis.Comparative assessments using helical alignment, inter-subunit distances, and interface contact areas revealed that R73A and D28A/D46A mutants preserved elongation and symmetry, consistent with functional secretion. In contrast, CS and NS mutants produced structurally compacted or distorted assemblies, correlating with aberrant secretion phenotypes.

This data offers insight into the structural basis of T3SS assembly and underscore how specific amino acid substitutions can disrupt higher-order oligomerization. This work not only enhances our mechanistic understanding of secretion system architecture but also demonstrates the utility of deep learning models in predicting complex macromolecular behavior supporting future structural studies.

Speaker:

Stephanie Bellido, Nova Southeastern University

Stephanie Bellido, Lukah Varghese, & Dev Patel are undergraduate students from Nova Southeastern University and will be presenting on Deep Learning–Based Structural Modeling of YscF Mutants.