Metabolite Structure Assignment Using <i>in silico</i> NMR and Collision Cross Section (CCS) Techniques

A major challenge for Metablomic analyses is obtaining a comprehensive and unambiguous identification of detected metabolites. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful and generally applicable spectroscopic tool that can be used to deduce the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using NMR techniques depends on how much conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with the standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF) based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time yet matches experimental structural observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for large number of conformationally flexible metabolites. Time permotting we will discuss recent work computing collisional cross section (CCS) values obtained from ion mobility coupled to mass spectrometry (IM - MS) studies using a related in silico protocol.