A deep learning framework using protein language models to identify candidate allergenic serine proteases across gut and oral microbiome gene catalogs.
Thurimella, K., Wu, E., Li, C., Graham, D. B., Owens, R. M., Plichta, D. R., Sokol, C. L., Xavier, R. J., & Bacallado, S. (2026). Identifying microbial protease allergens through protein language model-guided homology. Cell Systems, 0, 101510.
CAZyLingua is the first annotation tool to use protein language models for accurate classification of carbohydrate-active enzyme families and subfamilies in metagenomics.
Thurimella, K., Mohamed, A. M., Li, C., Vatanen, T., Graham, D. B., Owens, R. M., La Rosa, S. L., Plichta, D. R., Bacallado, S., & Xavier, R. J. (2025). Protein language models uncover carbohydrate-active enzyme function in metagenomics. BMC Bioinformatics, 26(285).
SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features.
Shaffer, M.†, Thurimella, K.†, Sterrett, J. D., & Lozupone, C. A. (2023). SCNIC: Sparse Correlation Network Investigation for Compositional Data. Molecular Ecology Resources, 23(1), 312–325. †Co-first authors
AMON is an open-source bioinformatics application that annotates which compounds in the metabolome could have been produced by bacteria or the host, evaluates pathway enrichment, and visualizes metabolite origins in KEGG pathway maps.
Shaffer, M., Thurimella, K., Quinn, K., Doenges, K., Zhang, X., Bokatzian, S., Reisdorph, N., & Lozupone, C. A. (2019). AMON: Annotation of metabolite origins via networks to integrate microbiome and metabolome data. BMC Bioinformatics, 20(1), 1–11.
Gordon Research Conference, 2026 · Ventura, CA
Keystone Symposia: Microbiota and Cancer Immunity, 2024 · Taipei, Taiwan
Western Medical Research Conference, 2020 · Carmel, CA
A novel in vitro bioelectronic platform for studying human gut barrier absorption in nutritional research.
Stoeger, V., Strauss, M., Thurimella, K., Elias-Kirma, S., Niewczas, I., Parlar, E., Gerner, C., & Owens, R. M. (2026). Bioelectronic technology for nutritional research—a novel in vitro platform for a better understanding of human gut barrier absorption. Advanced Biology, 10(2), e00409.
A systematic review and meta-analysis evaluating supercharged pedicled jejunal interposition for esophageal reconstruction in adult and pediatric patients.
Haas, E. J., Thurimella, K., Baghshomali, Y., Egan, K., Kaoutzanis, C., Irwin, T., McCarter, M., David, E. A., Mathes, D. W., & Yu, J. W. (2026). Supercharged pedicled jejunal interposition for esophageal reconstruction: A systematic review and meta-analysis of adult and pediatric patients. Journal of Reconstructive Microsurgery.
Reveals how antiretroviral drugs modulate systemic inflammation through direct interactions with gut microbial strains.
Jabbar, K. S., Priya, S., Xu, J., Das Adhikari, U., Pishchany, G., Johansen, J., Thurimella, K., ..., Plichta, D. R., Kwon, D. S., & Xavier, R. J. (2025). Antiretroviral drugs modulate systemic inflammation through direct interactions with gut microbial strains. Nature Microbiology.
Decodes host-environment chemosensory dialogues through intestinal G protein-coupled receptors.
Kadoki, M., Nanki, K., Szamosvári, D., Stražar, M., Thurimella, K., ..., Graham, D. B., & Xavier, R. J. (2025). Decoding host–environment chemosensory dialogues through intestinal G protein-coupled receptors. Cell Host & Microbe (Under Review).
Uses protein language models and allele coverage-aware prioritization to unlock immunogenic microbial epitopes.
Hung, Y.-M., Stražar, M., Hyun, Y.-S., Pust, M.-M., Thurimella, K., Pishchany, G., Graham, D. B., & Xavier, R. J. (2025). Unlocking immunogenic microbial epitopes with protein language models and allele coverage–aware prioritization. Immunity (Submitted).
Gut microbiome and metabolome profiling in the Framingham Heart Study reveals cholesterol-metabolizing bacteria linked with lower cardiovascular risk.
Li, C., Stražar, M., Mohamed, A. M. T., Pacheco, J. A., Walker, R. L., Lebar, T., Zhao, S., Lockart, J., Dame, A., Thurimella, K., ..., Plichta, D. R., & Xavier, R. J. (2024). Gut microbiome and metabolome profiling in Framingham Heart Study reveals cholesterol-metabolizing bacteria linked with lower cardiovascular risk. Cell.
Develops regularised canonical correlation analysis methods using graphical lasso and biplots.
Wells, L., Thurimella, K., & Bacallado, S. (2023). Regularised canonical correlation analysis: Graphical lasso, biplots and beyond. Journal of the American Statistical Association (Under Review).