Metabolite Structure Assignment Using In Silico NMR Techniques.

Area of Science:

• Computational chemistry
• Metabolomics
• Machine learning

Background:

• Unambiguous metabolite identification is crucial for metabolomic analysis.
• Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for structure elucidation.
• Accurate computational NMR prediction requires comprehensive conformational sampling, which is computationally expensive.

Purpose of the Study:

• To develop an efficient protocol for calculating NMR chemical shifts.
• To reduce the computational cost associated with predicting metabolite structures.
• To improve the tractability of NMR chemical shift calculations for conformationally flexible metabolites.

Main Methods:

• A pipeline combining force field (FF)-based conformation generation with machine learning (ML) filtering (ASE-ANI) was employed.
• Chemically unique conformations were identified through clustering.
• Density Functional Theory (DFT) was used for structural optimization and NMR chemical shift calculation of unique conformations.

Main Results:

• The developed protocol reduces computational time by two orders of magnitude compared to fully ab initio methods.
• The protocol demonstrates good agreement with experimental NMR chemical shift data.
• The method enables tractable chemical shift computation for a large number of flexible metabolites.

Conclusions:

• The ML-assisted DFT protocol provides an efficient and accurate approach for NMR chemical shift prediction.
• This advancement facilitates unambiguous metabolite identification in metabolomic analyses.
• The protocol enhances the utility of NMR spectroscopy in structural biology and chemistry.