Abstract
Two Bruker data files are provided that were acquired using three NMR experiments for the determination of KDs of protein-ligand interactions. One used 40uM naproxen and the other 40uM naproxen and 1uM HSA.
The abstract of the associated publication is provided below:
Fragment-Based Drug Discovery (FBDD) is a powerful strategy used in the development of new therapeutics. Molecular fragments are screened against a target protein, where interactions are typically characterized by a low affinity. Nuclear Magnetic Resonance (NMR) spectroscopy is well-suited to detect weak protein-ligand interactions and is therefore often used in FBDD. However, while NMR is very effective in initial screening, follow-up NMR experiments to measure binding affinities (i.e., KDs) are labor-intensive and time-consuming. To address this challenge, we have developed an innovative SHARPER NMR fragment scoring technique. The high sensitivity of SHARPER NMR dramatically reduces the data acquisition times, allowing faster and more accurate quantification of fragment KDs from ligand titration curves. To further accelerate fragment scoring, a machine learning model was developed that accurately ranks fragment affinities from only two SHARPER titration points. The resulting integrated method, termed “ML-boosted 1H LB SHARPER NMR”, produced signifi-cant time savings; using a 600 MHz QCI cryoprobe, KD values of up to 144 ligands in a day could be determined under our conditions, compared with only a handful achievable by traditional approaches. The proposed methodology will shorten the transition from hits to lead compounds, accelerating the drug discovery process by rapidly and reliably evaluating fragment binding, providing informed decision-making in the early stages of FBDD.
The abstract of the associated publication is provided below:
Fragment-Based Drug Discovery (FBDD) is a powerful strategy used in the development of new therapeutics. Molecular fragments are screened against a target protein, where interactions are typically characterized by a low affinity. Nuclear Magnetic Resonance (NMR) spectroscopy is well-suited to detect weak protein-ligand interactions and is therefore often used in FBDD. However, while NMR is very effective in initial screening, follow-up NMR experiments to measure binding affinities (i.e., KDs) are labor-intensive and time-consuming. To address this challenge, we have developed an innovative SHARPER NMR fragment scoring technique. The high sensitivity of SHARPER NMR dramatically reduces the data acquisition times, allowing faster and more accurate quantification of fragment KDs from ligand titration curves. To further accelerate fragment scoring, a machine learning model was developed that accurately ranks fragment affinities from only two SHARPER titration points. The resulting integrated method, termed “ML-boosted 1H LB SHARPER NMR”, produced signifi-cant time savings; using a 600 MHz QCI cryoprobe, KD values of up to 144 ligands in a day could be determined under our conditions, compared with only a handful achievable by traditional approaches. The proposed methodology will shorten the transition from hits to lead compounds, accelerating the drug discovery process by rapidly and reliably evaluating fragment binding, providing informed decision-making in the early stages of FBDD.
| Date made available | 15 Jan 2026 |
|---|---|
| Publisher | Edinburgh DataShare |
| Temporal coverage | Jun 2023 - Jun 2025 |
| Geographical coverage | UNITED KINGDOM,UK |
Research output
- 1 Article
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Fast and Reliable NMR-Based Fragment Scoring for Drug Discovery
Nepravishta, R., Munoz-Garcia, J. C., Cameron, K., Angulo, J. & Uhrin, D., 22 Oct 2025, (E-pub ahead of print) In: Journal of the American Chemical Society.Research output: Contribution to journal › Article › peer-review
Open AccessFile
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