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Approaching Tyrosine Phosphorylation as a Design Problem

The Naegle Lab seeks to understand the regulation and function of tyrosine phosphorylation in complex networks. We combine systems biology, computation, and experiments to test predict and test the function of tyrosine phosphorylation in proteins and protein networks. We hope to tackle the outsized challenge of understanding the role of 46,000 phosphotyrosines in the human proteome, while developing molecular and computational tools that are open source and broadly applicable to other fields of molecular research.

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Selected Publications

  • Crowl, S., Jordan, B., Ma, C., & Naegle, K.M (2021). KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data.ABS Nature Communications
  • Ronan, T., Anastasio, S., Qi, Z., Tavares, P. H. da S., Sloutsky, R., & Naegle, K. M. (2018). OpenEnsembles : A Python Resource for Ensemble Clustering. Journal of Machine Learning Research, 26, 1–6.
  • Matlock, M. K., Holehouse, A. S., & Naegle, K. M. (2015). ProteomeScout: a repository and analysis resource for post-translational modifications and proteins.ABS Nucleic Acids Research, 43(D1), D521–D530.
  • Schaberg, K. E., Shirure, V. S., Worley, E. A., George, S. C., & Naegle, K. M. (2017). Ensemble clustering of phosphoproteomic data identifies differences in protein interactions and cell-cell junction integrity of HER2-overexpressing cells.ABS Integr. Biol., 9, 539–547.
  • Ronan, T., Qi, Z., & Naegle, K. M. (2016). Avoiding common pitfalls when clustering biological data. Sci. Signal, 9(432), 1–12.

More About Us

  • 4

    first author publications per PhD student

  • 5

    open source software packages

  • 25

    undergrads mentored since 2012

  • 2

    active NIH R21 awards