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Due to the organism\u2019s slow evolutionary rate, its limited amino acid variation enhances detection of physiologically relevant patterns in plasmid protein composition. In this study, we applied embedding-based analyses and machine learning methods to characterize pXO1 protein modules across diverse B. anthracis lineages. We generated protein sequence embeddings, constructed phylogenies, and compared plasmid content with whole genome variation. While whole genome and plasmid-based phylogenies diverge, the composition of proteins encoded along the pXO1 plasmid revealed lineage specific structure. Association rule mining combined with decision tree classification produced plasmid-encoded targets for assessing anthrax sublineage, which yielded functionally redundant protein modules that reflected geographic and phylogenetic patterns. A conserved DNA replication module exhibited both shared and B. anthracis lineage specific features. These results show that pXO1 plasmid protein modules contain biologically meaningful and evolutionarily informative signatures, exemplifying their value in phylogeographic characterizations of bacterial pathogens. This framework can be extended to study additional virulence plasmids across Bacillus and other environmental pathogens using scalable protein language model tools.<\/jats:p>","DOI":"10.3390\/pathogens14101019","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T08:22:08Z","timestamp":1759911728000},"page":"1019","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Based Characterization of Bacillus anthracis Phenotypes from pXO1 Plasmid Proteins"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-8464","authenticated-orcid":false,"given":"William","family":"Harrigan","sequence":"first","affiliation":[{"name":"Department of Information and Computer Science, University of Hawai\u2019i at M\u0101noa, Honolulu, HI 96822, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thi Hai Au","family":"La","sequence":"additional","affiliation":[{"name":"Pathogen Analysis and Translational Health Group, School of Life Sciences, University of Hawai\u2019i at M\u0101noa, Honolulu, HI 96822, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashant","family":"Dahal","sequence":"additional","affiliation":[{"name":"Pathogen Analysis and Translational Health Group, School of Life Sciences, University of Hawai\u2019i at M\u0101noa, Honolulu, HI 96822, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahdi","family":"Belcaid","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Science, University of Hawai\u2019i at M\u0101noa, Honolulu, HI 96822, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5285-0432","authenticated-orcid":false,"given":"Michael H.","family":"Norris","sequence":"additional","affiliation":[{"name":"Pathogen Analysis and Translational Health Group, School of Life Sciences, University of Hawai\u2019i at M\u0101noa, Honolulu, HI 96822, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1016\/S0140-6736(22)02185-7","article-title":"Global mortality associated with 33 bacterial pathogens in 2019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"400","author":"Ikuta","year":"2022","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1093\/molbev\/msaa278","article-title":"Common Adaptive Strategies Underlie Within-Host Evolution of Bacterial Pathogens","volume":"38","author":"Gatt","year":"2021","journal-title":"Mol. 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