Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

Abstract

Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect and non-targeted screening. These allow for tentative identification of new compounds, and in-silico predicted reference values are used for improving confidence and filtering false-positive identifications. In this work, predictions of both RT and CCS values are performed with machine learning using artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model was investigated for the first time. The optimized combined RT-CCS model was a four-layered multi-layer perceptron ANN, and the 95th prediction error percentiles were within 2 min RT error and 5% relative CCS error for the external validation set (n = 36) and the full RT-CCS dataset (n = 357). 88.6% (n = 733) of predicted RTs were within 2 min error for the full dataset. Overall, when using 2 min RT error and 5% relative CCS error, 91.9% (n = 328) of compounds were retained, while 99.4% (n = 355) were retained when using at least one of these thresholds. This combined prediction approach can therefore be useful for rapid suspect/non-targeted screening involving HRMS, and will support current workflows.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

“…In the past, a wide variety of retention prediction models have been proposed for HILIC and reversed phase columns based on different machine learning approaches. These included partial least square methods [ 123 , 124 , 125 ], multiple linear regression [ 126 , 127 , 128 ], support vector regression [ 129 , 130 ], random forests [ 131 ] and artificial neural networks [ 132 , 133 , 134 ].…”

“…This opens up the LC-IMS-MS/MS technology for orthogonal filtering approaches utilizing CSS values [ 147 ] (see Figure 3 ) and more importantly for predictive technologies utilizing CCS values in a similar to retention time predictions. Such predictive approaches can include computational and quantum chemical models [ 148 , 149 ] as well as machine learning predictions [ 150 ] such as artificial neural networks [ 132 , 151 ]. Prediction errors as low as 3% have been reported for CCS models [ 152 ].…”

Deep Learning to Generate in Silico Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples

“…CCS is a measure of an ionized molecule's effective interaction surface with a buffer gas from ion mobility spectroscopy separations. Importantly, both properties can be consistently and accurately measured experimentally [38][39][40][41][42][43][44][45][46] , as well as predicted computationally 12,[47][48][49][50] .…”

The role of analytical chemistry in exposure science: Focus on the aquatic environment

“…isomers, some analytes in highly complex matrices). Prediction of CCS has been successfully applied to improve identification of suspects and will surely be an efficient tool in suspect and nontarget screening in the near future (Bijlsma et al, 2017;Mollerup et al, 2018;Zhou et al, 2016).…”

“…The most recent data reported in the literature reveal that screening by LC-HRMS now leads to results similar to those from the regular target monitoring using specific (LC-MS/MS) analytical methods. Still, some challenges have to be faced in the coming years, such as (i) developing a further optimized quality assurance strategy, (ii) optimization of the LC, prediction of retention times (Aalizadeh et al, 2016;Bade et al, 2015;McEachran et al, 2018;Miller et al, 2013;Stanstrup et al, 2015), and prediction of CCS in Ion Mobility MS (Bijlsma et al, 2017;Mollerup et al, 2018;Zhou et al, 2016) (iii) storage and exchange of measurement data e.g. (Wang et al, 2016) and (iv) the identification of unknown compounds.…”