Collision Cross Section Calculations Using HPCCS

Heerdt, Gabriel; Zanotto, Leandro; Souza, Paulo C. T.; Araújo, Guido; Skaf, Munir S.

doi:10.1007/978-1-0716-0030-6_19

Cited by 12 publications

(13 citation statements)

References 32 publications

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“…Standard DFT geometry optimization and atomic charge calculation are then performed on a representative conformation from each cluster at the B3LYP/6-31+G(d,p) and B3LYP/6-311++G(d,p) level of theory, respectively, using the Gaussian 16 software package. − The input file for CCS calculation is prepared by extracting the geometry and atomic charges from the DFT computations. Finally, the room-temperature N 2 -based trajectory method is used to calculate the average collisional cross-sectional areas using the HPCCS code developed by Zanotto et al , and predict the structure of the target metabolites by comparing the computed results using a Boltzmann-weighted average over multiple conformers with experimental CCS values. We describe details of each step of the workflow in the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

Das

Tanemura

Dinpazhoh

et al. 2022

J. Am. Soc. Mass Spectrom.

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The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

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Section: Methodsmentioning

confidence: 99%

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

Das

Tanemura

Dinpazhoh

et al. 2022

J. Am. Soc. Mass Spectrom.

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“…In silico prediction of CCS is based on computational modeling of ions in the gas phase (Mesleh et al, 1996;Wyttenbach et al, 1996) and was first applied to large biomolecules such as peptides, proteins, oligonucleotides, and oligosaccharides (Clemmer et al, 1995;Gidden et al, 2001;Lee et al, 1997;Von Helden et al, 1995). In recent years, a growing interest in deriving in silico CCS for small molecules has led to several publications on this topic (Bijlsma et al, 2017;Heerdt et al, 2020;Lapthorn et al, 2015;Menikarachchi et al, 2012;Mollerup et al, 2018;Shrivastav et al, 2017, Soper-Hopper et al, 2017, Zanotto et al 2018.…”

Section: In Silico Prediction Of Ccs Valuesmentioning

confidence: 99%

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Paglia

Smith

Astarita

2021

Mass Spectrometry Reviews

121

100

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Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM‐MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM‐MS provides three main technical advantages over traditional LC‐MS workflows. Firstly, in addition to mass, IM‐MS allows collision cross‐section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM‐MS increases peak capacity and the signal‐to‐noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM‐MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state‐of‐the‐art in IM‐MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

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“…CCS values for molecules as small as metabolites and as large as protein complexes have been predicted computationally for comparison to IM measurements. , However, such predictions require three-dimensional structures by the way of solid-state NMR or X-ray crystallography experimental data. Quantum methods have shown significant promise in the prediction of CCS values but tend to be computationally expensive. − As a faster, less resource-intensive alternative, several efforts involving machine learning (ML) CCS prediction have been reported in the literature. These include support vector regression (SVR) models, such as MetCCS, LipidCCS, and AllCCS, − more sophisticated regression models such as CCSBase, joint prediction of CCS and chromatographic retention times, and deep neural network models such as DeepCCS, among others.…”

Section: Introductionmentioning

confidence: 99%

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

et al. 2022

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Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M − H] − , 155 [M + H] + , and 138 [M + Na] + adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a Δ CCS threshold of 2.8% and a mass error of 10 ppm.

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Collision Cross Section Calculations Using HPCCS

Cited by 12 publications

References 32 publications

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

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