Abstract
Fragment-based drug discovery (FBDD) and validation of small molecule binders using NMR spectroscopy is an established and widely used method in the early stages of drug discovery. Starting from a library of small compounds, ligand- or protein-observed NMR methods are employed to detect binders, typically weak, that become the starting points for structure–activity relationships (SAR) by NMR. Unlike the more frequently used ligand-observed 1D NMR techniques, protein-observed 2D 1H-15N or 1H-13C heteronuclear correlation (HSQC or HMQC) methods offer insights that include the mechanism of ligand engagement on the target and direct binding affinity measurements in addition to routine screening. We hereby present the development of a set of software tools within the MestReNova (Mnova) package for analyzing 2D NMR for FBDD and hit validation purposes. The package covers three main tasks: (1) unsupervised profiling of raw data to identify outlier data points to exclude in subsequent analyses; (2) batch processing of single-point spectra to identify and rank binders based on chemical shift perturbations or spectral peak intensity changes; and (3) batch processing of multiple titration series to derive binding affinities (KD) by tracing the changes in peak locations or measuring global spectral changes. Toward this end, we implemented and evaluated a set of algorithms for automated peak tracing, spectral binning, and variance analysis by PCA, and a new tool for spectral data intensity comparison using ECHOS. The accuracy and speed of the tools are demonstrated on 2D NMR binding data collected on ligands used in the development of potential inhibitors of the anti-apoptotic MCL-1 protein.
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The datasets generated and analyzed for the current study are not publicly available but may be available from the AbbVie corresponding author pending AbbVie legal review and approval of request.
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Acknowledgements
Dr. Eva Munoz wishes to acknowledge Xunta de Galicia for co-funding her contract within the framework of the “Talento Senior” program (Persoal cofinanciado pola Xunta de Galicia ao abeiro da Resolución da Axencia Galega de Innovación do 29 de maio de 2019, Programa Talento Sénior). Dr. Chen Peng wishes to thank Dr. Christian Fischer at Bruker Inc. for helpful discussions on PCA and ECHOS.
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A.T.N. and C.P. contributed equally. The manuscript was written through contributions of all authors. A.T.N., C.P., E.M., H.W., T.E.F., C.S., and A.M.P. designed the study and wrote the manuscript; A.T.N. and Q.S. prepared samples for the study. A.T.N. collected NMR data. M.M., I.F., E.M., C.P., and C.C. developed the software; A.T.N., C.P., and E.M. analyzed the data. All the authors have approved the final version of the manuscript.
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A.T.N., H.W., T.E.F, Q.S., C.S., and A.M.P. are employees of AbbVie and may own AbbVie stock. AbbVie participated in the interpretation of data, review, and approval of the publication. The authors declare the following competing financial interest(s): C.P., E.M., M.M., I.F., and C.C. are employees of Mestrelab Research.
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Peng, C., Namanja, A.T., Munoz, E. et al. Efficiently driving protein-based fragment screening and lead discovery using two-dimensional NMR. J Biomol NMR 77, 39–53 (2023). https://doi.org/10.1007/s10858-022-00410-3
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DOI: https://doi.org/10.1007/s10858-022-00410-3