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NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
Published on: June 4, 2021
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Efficiently driving protein-based fragment screening and lead discovery using two-dimensional NMR
Chen Peng1, Andrew T Namanja2, Eva Munoz3
1Mestrelab Research, S.L, Feliciano Barrera 9B - Baixo, 15706, Santiago de Compostela, Spain. chen.peng@mestrelab.com.
Journal of Biomolecular NMR
|December 13, 2022
Summary
This study introduces new software tools for analyzing 2D NMR data in fragment-based drug discovery (FBDD). These tools automate the identification and validation of small molecule binders, accelerating early-stage drug development.
Area of Science:
- Biochemistry and Structural Biology
- Computational Chemistry and Cheminformatics
- Pharmacology and Drug Discovery
Background:
- Fragment-based drug discovery (FBDD) relies on identifying weak small molecule binders using NMR spectroscopy.
- Protein-observed 2D NMR methods provide insights into ligand binding mechanisms and affinity, complementing 1D NMR techniques.
- Automated analysis of NMR data is crucial for efficient screening and validation in early drug discovery.
Purpose of the Study:
- To develop and present a suite of software tools within the MestReNova (Mnova) package for analyzing 2D NMR data in FBDD.
- To enhance the efficiency and accuracy of identifying and validating small molecule binders.
- To enable automated derivation of binding affinities (KD) from NMR titration experiments.
Main Methods:
- Development of algorithms for unsupervised data profiling, batch processing of spectra, and analysis of titration series.
- Implementation of automated peak tracing, spectral binning, and principal component analysis (PCA) for variance analysis.
- Introduction of a novel tool for spectral data intensity comparison using ECHOS (Enhanced Chemical Shift Observation).
Main Results:
- The Mnova software package provides tools for profiling raw 2D NMR data, identifying outlier points.
- Automated batch processing enables efficient screening and ranking of potential binders based on chemical shift perturbations or intensity changes.
- The tools accurately and rapidly determine binding affinities (KD) from titration data, as demonstrated on MCL-1 inhibitor development.
Conclusions:
- The developed Mnova software tools significantly streamline the analysis of 2D NMR data for FBDD and hit validation.
- These tools facilitate automated identification, ranking, and affinity determination of small molecule binders.
- The software accelerates the early stages of drug discovery by improving the efficiency of NMR-based screening and validation processes.
