This project analyzes and classifies the bacterial antiviral protein, Tiamat, to identify its homologs across the Tree of Life. The study involves clustering sequences, setting filtering parameters, translating data for analysis, generating statistical data, domain annotation, and 3D modeling to understand Tiamat's domains, structure, and mechanism of action.

Initially, sequences were clustered based on percentage identity similarity. As the percentage identity increased, fewer sequences were similar enough to cluster, resulting in more representative sequences. Filtering parameters were applied to exclude single-sequence clusters, refining the data and determining identity parameters for each domain across Archaea, Bacteria, or Eukaryote.

Domain annotation was performed using normalized positions to pinpoint the start and end points of various prominent domains within Tiamat sequences. In visual representations, blue dots indicated start points, and red dots marked endpoints.

Cluster analysis showed that higher percentage identities led to fewer clusters, increasing the number of representative sequences. The domain localization results revealed a conserved structure of three domains (ATPase, Duf3684, and No-Vein) in bacterial sequences, suggesting their crucial role in Tiamat's function.

3D modeling using AlphaFold predicted the Tiamat protein's structure, aiding in detailed domain analysis and annotation. This comprehensive approach provided a deeper understanding of Tiamat's functional domains.

This study's computational tools allowed for a detailed analysis of Tiamat sequences, enabling the investigation of their prevalence and mechanisms of action across the Tree of Life. The findings enhance our understanding of their biological roles and potential applications, contributing to the broader knowledge of bacterial antiviral mechanisms.