Mutation-sensitive prediction of transcription factor-DNA interactions in plants
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Project related publications:
Jiang, S., Su, Z., Bloodworth, N., Liu, Y., Martina, C. E., Harrison, D. G., & Meiler, J. (2025). Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201. PloS One, 20(6), e0314833. https://doi.org/10.1371/journal.pone.0314833
Lin, X., Su, Z., Liu, Y. L., Liu, J., Kuang, X., Cummings, P. T., Spencer-Smith, J., & Meiler, J. (2025). Supermetal: A generative AI framework for rapid and precise metal ion location prediction in proteins. Journal of Cheminformatics, 17(1), 107. https://doi.org/10.1186/s13321-025-01038-9
Schermeng, T., Fürll, A., Liessmann, F., Bredow, L. von, Stichel, J., Weaver, C. D., Tretbar, M., Meiler, J., & Beck-Sickinger, A. G. (2025). Similar Binding Mode of a 5-Sulfonylthiouracil Derivative Antagonist at Chemerin Receptors CMKLR1 and GPR1. Journal of Medicinal Chemistry, 68(11), 11149–11173. https://doi.org/10.1021/acs.jmedchem.5c00135