In Silico Collision Cross Section Calculations to Aid Metabolite Annotation.
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ABSTRACT:
SUBMITTER: Das S
PROVIDER: S-EPMC9277703 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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 curate an ion mobility CCS atlas, namely AllCCS, and develop 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 demonstrate 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 facilitates multi-dimensional match and substantially improves 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.
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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.
| S-EPMC7423765 | biostudies-literature
Project description:High-throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and environmental samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent technological advances, metabolomics studies still result in the detection of a disproportionate number of features that cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics, the reliance on reference libraries constructed by analysis of authentic reference materials with limited commercial availability. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e., conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs first-principles simulation, distinguished by the use of molecular dynamics, quantum chemistry, and ion mobility calculations, to generate structures and chemical property libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over 2 orders of magnitude. Calculated CCS values were validated against 1983 experimentally measured CCS values and compared to previously reported CCS calculation approaches. Average calculated CCS error for the validation set is 3.2% using standard parameters, outperforming other density functional theory (DFT)-based methods and machine learning methods (e.g., MetCCS). An online database is introduced for sharing both calculated and experimental CCS values ( metabolomics.pnnl.gov ), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including providing evidence for the presence of an environmental degradation product, the separation of molecular isomers, and an initial characterization of complex blinded mixtures of exposure chemicals. This work represents a method to address the limitations of small molecule identification and offers an alternative to generating chemical identification libraries experimentally by analyzing authentic reference materials. All code is available at github.com/pnnl .
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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>
2020-07-28 | MTBLS1622 | MetaboLights
Project description: Not available
| S-EPMC10843661 | biostudies-literature
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>Known Metabolite Annotation in Different Biological Samples assays</strong> are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS1693' rel='noopener noreferrer' target='_blank'><strong>MTBLS1693</strong></a>.</p><p><strong>Unknown Metabolite Annotation in Mouse Aging assay</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS1622' rel='noopener noreferrer' target='_blank'><strong>MTBLS1622</strong></a>.</p>
2020-07-28 | MTBLS1693 | MetaboLights
Project description:The present study investigates the ion mobility characteristics of synthetic peptides carrying 22 different post-translational modifications by Ion Mobility-Mass Spectrometry. Samples were kindly provided by the ProteomeTools project.
2023-08-25 | PXD042416 | Pride
Project description:Rotationally averaged collision cross section (CCS) values for a series of proteins and protein complexes ranging in size from 8.6 to 810 kDa are reported. The CCSs were obtained using a native electrospray ionization drift tube ion mobility-Orbitrap mass spectrometer specifically designed to enhance sensitivity while having high-resolution ion mobility and mass capabilities. Periodic focusing (PF)-drift tube (DT)-ion mobility (IM) provides first-principles determination of the CCS of large biomolecules that can then be used as CCS calibrants. The experimental, first-principles CCS values are compared to previously reported experimentally determined and computationally calculated CCS using projected superposition approximation (PSA), the Ion Mobility Projection Approximation Calculation Tool (IMPACT), and Collidoscope. Experimental CCS values are generally in agreement with previously reported CCSs, with values falling within ∼5.5%. In addition, an ion mobility resolution (CCS centroid divided by CCS fwhm) of ∼60 is obtained for pyruvate kinase (MW ∼ 233 kDa); however, ion mobility resolution for bovine serum albumin (MW ∼ 68 kDa) is less than ∼20, which arises from sample impurities and underscores the importance of sample quality. The high resolution afforded by the ion mobility-Orbitrap mass analyzer provides new opportunities to understand the intricate details of protein complexes such as the impact of post-translational modifications (PTMs), stoichiometry, and conformational changes induced by ligand binding.
| S-EPMC7967297 | biostudies-literature
Project description:Ion mobility mass spectrometry (IM-MS) expands the analyte coverage of existing multi-omic workflows by providing an additional separation dimension as well as a parameter for characterization and identification of molecules - the collision cross section (CCS). This work presents a large, Unified CCS compendium of >3800 experimentally acquired CCS values obtained from traceable molecular standards and measured with drift tube ion mobility-mass spectrometers. An interactive visualization of this compendium along with data analytic tools have been made openly accessible. Represented in the compendium are 14 structurally-based chemical super classes, consisting of a total of 80 classes and 157 subclasses. Using this large data set, regression fitting and predictive statistics have been performed to describe mass-CCS correlations specific to each chemical ontology. These structural trends provide a rapid and effective filtering method in the traditional untargeted workflow for identification of unknown biochemical species. The utility of the approach is illustrated by an application to metabolites in human serum, quantified trends of which were used to assess the probability of an unknown compound belonging to a given class. CCS-based filtering narrowed the chemical search space by 60% while increasing the confidence in the remaining isomeric identifications from a single class, thus demonstrating the value of integrating predictive analyses into untargeted experiments to assist in identification workflows. The predictive abilities of this compendium will improve in specificity and expand to more chemical classes as additional data from the IM-MS community is contributed. Instructions for data submission to the compendium and criteria for inclusion are provided.
| S-EPMC6349024 | biostudies-literature
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.
| S-EPMC9261268 | biostudies-literature