Ligand Screening & Binding
Streamlined Ligand Screening, From Design to Discovery
Ligand discovery is complex and multi-step, often requiring multiple analytical techniques to confirm both what a ligand is, and how it interacts with its target. Scientists face challenges at every stage—from designing experiments and assembling mixtures with minimal peak overlap, to handling large datasets and deriving reliable dissociation constants (Kd) across expanding compound libraries. Manual processing and transcription slow progress, introduce errors, and add unnecessary workload.
SciY simplifies this entire workflow with an integrated suite of Mnova tools tailored for ligand discovery. It supports library QC and mixture design to ensure compounds are correctly identified, automates large-scale, multi-technique data analysis, and accelerates binding studies with automated Kd calculations. By consolidating identity confirmation, binding assessment, and data interpretation in one vendor-agnostic platform, SciY helps researchers verify hits with confidence, save time and protein, and focus on advancing their science.
What can Mnova do for Ligand & Binding Screening?
High-Throughput Ligand Screening by 1D NMR
Speed up 1D NMR-based ligand screening with Mnova Screen. The software helps you create compound mixtures with minimal peak overlap and supports a range of experiments, including ¹H or ¹⁹F, single compounds or mixtures, and multiple spectra types (STD, T1ρ, WaterLOGSY, CPMG). It streamlines data handling and reporting, making it easy to analyze results and accelerate hit identification.
Advanced 2D NMR Screening
Process protein-observed 2D NMR spectra (¹H-¹⁵N, ¹H-¹³C, HSQC, HMQC) using Mnova Screen 2D to rapidly identify binding ligands within a batch. The software automatically detects and matches peaks, calculates chemical shift perturbation (CSP) scores, and ranks ligands based on these scores. An interactive display presents batch processing results, allowing comprehensive analysis as well as manual review and correction when needed.
Affinity Selection Mass Spectrometry (AS-MS)
Simplify and accelerate AS-MS analysis using Affinity Screen. The software automates data processing and binding assessment, removing bottlenecks and enabling the screening of hundreds of thousands of compounds with a single click. Wells with no binding or irrelevant data are filtered out, while positive hits are clearly highlighted, making it easy to review results and quickly identify promising leads.
Quantitative Binding Analysis by NMR
Determine dissociation constants (Kd) from NMR-based titrations quickly and efficiently. Mnova Binding processes 2D HSQC spectra, tracks peak movements, calculates Kd values, and performs statistical analyses. It also integrates with third-party tools like AFFINImeter for advanced binding isotherm analysis using multiple models.
Higher-Order Structure Analysis for Biologics and Biosimilars
Assess the higher-order structure of biologics and biosimilars using Mnova BioHOS. The software analyzes 1D and 2D NMR spectra to compare reference and test samples through fingerprinting, 1D Profile analysis, peak-based metrics such as CCSD, global spectral comparison with ECHOS, and advanced chemometric models including PCA, SIMCA, and PLS. Interactive visualization links statistical results directly to spectral regions, enabling objective similarity assessment, outlier detection, and confident decision making throughout development and manufacturing.
Publications
- Fast and Efficient Fragment-Based Lead Generation by Fully Automated Processing and Analysis of Ligand-Observed NMR Binding Data. J. Med. Chem. 2016, 59 (7), 3303–3310. https://doi.org/10.1021/acs.jmedchem.6b00019
- Efficiently Driving Protein-Based Fragment Screening and Lead Discovery Using Two-Dimensional NMR. J. Biomol. NMR 2023, 77 (1–2), 39–53. https://doi.org/10.1007/s10858-022-00410-3
- NMR Characterization of the Interaction between Bcl-xL and the BH3-like Motif of Hepatitis B Virus X Protein. Biochem. Biophys. Res. Commun. 2019, 518 (3), 445–450. https://doi.org/10.1016/j.bbrc.2019.08.036
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