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NMR-based fragment screening and lead discovery accelerated by principal component analysis

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Protein-based NMR spectroscopy has proven to be a very robust method for finding fragment leads to protein targets. However, one limitation of protein-based NMR is that the data acquisition and analysis can be time consuming. In order to streamline the scoring of protein-based NMR fragment screening data and the determination of ligand affinities using 2D NMR experiments we have developed a data analysis workflow based on principal component analysis (PCA) within the TREND NMR Pro software package. We illustrate this using four different proteins and sets of ligands which interact with these proteins over a range of affinities. Also, these PCA-based methods can be successfully applied even to systems where ligand binding to target proteins is in intermediate or even slow exchange on the NMR time scale. Finally, these methods will work for scoring of fragment binding data using protein spectra that are either highly overlapped or lower in resolution.

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Andrew T. Namanja, Haihong Wu, Qi Sun, Anup K. Upadhyay, Chaohong Sun, and Andrew M. Petros are employees of AbbVie. The design, study conduct, and financial support for this research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication. Steven R. Van Doren is an employee of the University of Missouri and Jia Xu was an employee of the University of Missouri at the time this research was conducted. Steven R. Van Doren and Jia Xu have no official connection to AbbVie outside of this study and have received no financial support from AbbVie for their participation in this research study.

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Correspondence to Steven R. Van Doren or Andrew M. Petros.

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Namanja, A.T., Xu, J., Wu, H. et al. NMR-based fragment screening and lead discovery accelerated by principal component analysis. J Biomol NMR 73, 675–685 (2019).

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