GNPS High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites
Ontology highlight
ABSTRACT:
INSTRUMENT(S): SYNAPT G2-Si
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Libin Xu
PROVIDER: MSV000088549 | GNPS | Thu Dec 09 11:58:00 GMT 2021
REPOSITORIES: GNPS
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Project description:High-throughput IM-MS analysis of drug metabolism incubations prepared from the MicroSource Spectrum Discovery Collection using pooled human liver microsomes and S9 fraction
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Project description:Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8-2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.
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Project description:Matching experimental ion mobility-mass spectrometry data to computationally-generated collision cross section (CCS) values enables more confident metabolite identifications. Here, we show for the first time that accurately predicting CCS values with simple models for the largest library of metabolite cross sections is indeed possible, achieving a root mean square error of 7.0 Å2 (median error of ∼2%) using linear methods accesible to most researchers. A comparison on the performance of 2D vs. 3D molecular descriptors for the purposes of CCS prediction is also presented for the first time, enabling CCS prediction without a priori knowledge of the metabolite's energy-minimized structure.
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Project description:<p>The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility – mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curated an ion mobility CCS atlas, namely AllCCS, and developed an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrated the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in-silico MS/MS spectra facilitated multi-dimensional match and substantially improved the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.</p><p><br></p><p><strong>Unknown Metabolite Annotation in Mouse Aging assay</strong> is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS1622' rel='noopener noreferrer' target='_blank'><strong>MTBLS1622</strong></a>.</p><p><strong>Known Metabolite Annotation in Different Biological Samples assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS1693' rel='noopener noreferrer' target='_blank'><strong>MTBLS1693</strong></a>.</p>
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Project description: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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2020-07-28 | MTBLS1693 | MetaboLights
Project description:The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]+ adducts and 95.0% of CCS values for [M + Na]+ adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H]+ and [M + Na]+ adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.
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Project description:The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.
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Project description:Successful drug administration to the central nervous system requires accurate adjustment of the drugs' molecular properties. Therefore, structure-derived descriptors of potential brain therapeutic agents are essential for an early evaluation of pharmacokinetics during drug development. The collision cross section (CCS) of molecules was recently introduced as a novel measurable parameter to describe blood-brain barrier (BBB) permeation. This descriptor combines molecular information about mass, structure, volume, branching and flexibility. As these chemical properties are known to influence cerebral pharmacokinetics, CCS determination of new drug candidates may provide important additional spatial information to support existing models of BBB penetration of drugs. Besides measuring CCS, calculation is also possible; but however, the reliability of computed CCS values for an evaluation of BBB permeation has not yet been fully investigated. In this work, prediction tools based on machine learning were used to compute CCS values of a large number of compounds listed in drug libraries as negative or positive with respect to brain penetration (BBB+ and BBB- compounds). Statistical evaluation of computed CCS and several other descriptors could prove the high value of CCS. Further, CCS-deduced maximum molecular size of BBB+ drugs matched the dimensions of BBB pores. A threshold for transcellular penetration and possible permeation through pore-like openings of cellular tight-junctions is suggested. In sum, CCS evaluation with modern in silico tools shows high potential for its use in the drug development process.
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Project description:In this work we have established a collision cross section (CCS) library of primary metabolites based on analytical standards in the Mass Spectrometry Metabolite Library of Standards (MSMLS). Using a commercially available ion mobility-mass spectrometer (IM-MS, Agilent 6560), from the 554 unique compounds in the MSMLS plate library we obtained a total of 1246 CCS measurements over a wide range of biochemical classes and adduct types. Resulting data analysis showed that the curated CCS library provides broad molecular coverage of metabolic pathways and highlights intrinsic mass/mobility relationships for specific metabolite superclasses. The separation and characterization of isomeric metabolites were assessed as well as an investigation to determine the analytical separation efficiency in both the mass (m/z) and mobility (CCS/ΔCCS) dimension required for untargeted metabolomic analyses. To further demonstrate the analytical utility of CCS as an additional molecular descriptor, a well characterized biological sample of human plasma serum (NIST 1950) was examined by LC-IM-MS and a detailed analysis of carbohydrate isomers by ion mobility is presented.
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