Revealing the differences in collision cross section values of small organic molecules acquired by different instrumental designs and prediction models

Belova, Lidia; Celma, Alberto; Haesendonck, Glenn Van; Lemière, Filip; Sancho, J.; Nuijs, Alexander L.N. van; Bijlsma, Lubertus

doi:10.1016/j.aca.2022.340361

Cited by 18 publications

(17 citation statements)

References 48 publications

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“…It is possible that the minor mass effect on the TWIM CCS values shown in Figure c was a result of the divergence of the TWIM CCS values from their DTIM values (2.34% mean relative error (MRE)) (Supplementary Figure 7a) owing to the additional ion separation in the TWIM chamber. This deviation in CCS values between DTIM and TWIM has been reported in several studies. , …”

Section: Resultssupporting

confidence: 78%

“…This deviation in CCS values between DTIM and TWIM has been reported in several studies. 16,45 Accurate CCS Prediction by Polarizability and m/z Parameters. By examining the experimental data accessible on CCSbase, 7 we found that experimental CCS values from different laboratories and experimental setups vary substantially (Supplementary Figure 7b), as also mentioned by previous studies.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…By examining the experimental data accessible on CCSbase, 7 we found that experimental CCS values from different laboratories and experimental setups vary substantially (Supplementary Figure 7b), as also mentioned by previous studies. 3,[16][17][18]45 Thus, it is beneficial for each laboratory to construct an in-house database of predicted CCS values to achieve the most accurate compound identification. To build an accurate model for CCS prediction based on a limited number of reference standards available inhouse, a prediction model must be built using the relevant input parameters.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data

Wisanpitayakorn,

Sartyoungkul,

Kurilung

et al. 2024

J. Chem. Inf. Model.

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The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion's polarizability and mass-to-charge ratio (m/z) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR_CCSPrediction under the GNU General Public License (GPL) version 3.

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Section: Resultssupporting

confidence: 78%

Section: ■ Results and Discussionmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data

Wisanpitayakorn,

Sartyoungkul,

Kurilung

et al. 2024

J. Chem. Inf. Model.

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“…Both were trained by TWIM data, and the relative error was within 6% for 95% of the chemicals. Belova et al [ 68 ] compared experimental DTIM measured CCS values to predicted CCS values by the ANN-based and MARS-based predictors. A total of 95% of the protonated and deprotonated ions observed the relative error under 6.7%.…”

Section: Resultsmentioning

confidence: 99%

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

et al. 2022

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High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals’ super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.

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“…This method was applied to spinach samples of pesticides and showed a difference of less than 6% between the experimental and predicted CCS values of protonated pesticides. Furthermore, other ANN methods have been described, such as the study by Belova et al, 183 who demonstrated the differences between experimental CCS values from different instrumental designs and predicted CCS values using the ANN model and MARS. Similar to the regression model, the ANN model has been used to create some CCS prediction methods that also include prediction in another dimension, such as RT and MS 2 intensity.…”

Section: Ccs Modelsmentioning

confidence: 99%

Collision cross section measurement and prediction methods in omics

Kartowikromo,

Olajide,

Hamid

2023

J Mass Spectrom

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Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant‐independent and calibrant‐dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

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Revealing the differences in collision cross section values of small organic molecules acquired by different instrumental designs and prediction models

Cited by 18 publications

References 48 publications

Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data

Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

Collision cross section measurement and prediction methods in omics

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