Mnova StereoFitter (User Manual)

Significance of StereoFitter in NMR-Based Structural Analysis

StereoFitter is a specialized software module within the Mnova environment designed to deconvolute NMR observables into three-dimensional structural information. By integrating multiple NMR parameters—NOE-derived distances, scalar J-couplings, chemical shifts, residual dipolar couplings (RDCs), residual chemical shift anisotropies (RCSAs) and pseudocontact shifts (PCSs)—it provides a robust platform for determining molecular conformations and configurations in solution.

Objectives and Overview of the Software

This user manual describes StereoFitter version 1.1.6 and its workflow: loading molecular models or generating conformers, importing and processing experimental NMR data, fitting parameters against theoretical predictions, and ranking solutions by fit quality and statistical parsimony (Akaike Information Criterion). The goal is to identify the most probable 3D conformer or stereoisomer ensemble that matches the experimental data within defined uncertainties.

Methodology and Instrumentation

StereoFitter applies a discrete‐state model to approximate time-averaged NMR observables as weighted sums of individual conformer properties. Key methodological elements include:

• Conformer generation: via integrated Balloon (distance geometry) and GMMX (MMFF94/MMFF94s/MMX force fields, optional implicit solvation, dispersion corrections).
• Back-calculation of observables: NOE distances from volumes (PANIC‐corrected), Karplus-type J-couplings (Haasnoot–Altona and other parameter sets), DFT-scaled chemical shifts, RDC and RCSA tensors from singular value decomposition, PCS from paramagnetic tags.
• Fitting algorithms: Levenberg–Marquardt or non-negative linear least squares to optimize conformer weights. Optionally include Boltzmann populations from conformer energies and DFT energies.
• Model selection: iterative AIC minimization to avoid overfitting by balancing fit quality and ensemble complexity.
• Interactive visualization: OpenGL 3D viewer for distance, angle, dihedral measurements and overlay of predicted vs. experimental restraints.

Main Results and Discussion

Applying StereoFitter to typical NMR data sets yields:

• Conformational mixtures: identification of the dominant conformers and their populations (e.g., two conformers at 80 %/20 % yields χ² and absolute fit metrics).
• Configurational assignments: ranking stereoisomers by relative probability based on combined RDC and chemical shift fitting, including graphical histograms of AIC vs. isomer label.
• Integration of multi-parametric restraints: demonstration that combining NOE, J-couplings, shifts and anisotropic data improves structural discrimination beyond any single data type.

Benefits and Practical Applications

StereoFitter offers:

• Multitechnique synergy: leverages complementary NMR observables in a single fitting framework.
• Automated workflows: from data import through spectrum integration to model selection, minimizing manual intervention.
• Customization and scalability: supports custom input files, adjustable uncertainties, solvent and tensor corrections, and DFT‐driven predictions via MestReLab servers.
• Quality control: built-in audit trail, warnings for data mismatches, and visualization of restraint violations on 3D structures.

Future Trends and Potential Developments

Emerging directions include:

• Expanded NMR parameter integration: incorporation of new anisotropic measurements (e.g., RCSA under varied alignment conditions, paramagnetic relaxation enhancements).
• Machine-learning acceleration: training models on large structural databases to propose initial conformer ensembles and speed up fitting.
• Enhanced DFT interfaces: on-the-fly quantum calculations for J-couplings and shifts with advanced functionals and solvent models.
• High-throughput screening: batch analysis of compound libraries for rapid 3D structure validation in drug discovery.

Conclusion

StereoFitter 1.1.6 provides a comprehensive, statistically rigorous platform for extracting precise 3D structural information from diverse NMR data. Its integrated conformer generation, multi-parameter fitting, model selection by AIC, and interactive visualization constitute an effective toolset for analytical chemists in academic, industrial, and quality-control laboratories.

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