1
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Engler Hart C, Preto AJ, Chanana S, Healey D, Kind T, Domingo-Fernández D. Evaluating the generalizability of graph neural networks for predicting collision cross section. J Cheminform 2024; 16:105. [PMID: 39210378 PMCID: PMC11363525 DOI: 10.1186/s13321-024-00899-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
Ion Mobility coupled with Mass Spectrometry (IM-MS) is a promising analytical technique that enhances molecular characterization by measuring collision cross-section (CCS) values, which are indicative of the molecular size and shape. However, the effective application of CCS values in structural analysis is still constrained by the limited availability of experimental data, necessitating the development of accurate machine learning (ML) models for in silico predictions. In this study, we evaluated state-of-the-art Graph Neural Networks (GNNs), trained to predict CCS values using the largest publicly available dataset to date. Although our results confirm the high accuracy of these models within chemical spaces similar to their training environments, their performance significantly declines when applied to structurally novel regions. This discrepancy raises concerns about the reliability of in silico CCS predictions and underscores the need for releasing further publicly available CCS datasets. To mitigate this, we introduce Mol2CCS which demonstrates how generalization can be partially improved by extending models to account for additional features such as molecular fingerprints, descriptors, and the molecule types. Lastly, we also show how confidence models can support by enhancing the reliability of the CCS estimates.Scientific contributionWe have benchmarked state-of-the-art graph neural networks for predicting collision cross section. Our work highlights the accuracy of these models when trained and predicted in similar chemical spaces, but also how their accuracy drops when evaluated in structurally novel regions. Lastly, we conclude by presenting potential approaches to mitigate this issue.
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Affiliation(s)
- Chloe Engler Hart
- Enveda Biosciences, Inc., 5700 Flatiron Pkwy, Boulder, CO, 80301, USA
| | | | - Shaurya Chanana
- Enveda Biosciences, Inc., 5700 Flatiron Pkwy, Boulder, CO, 80301, USA
| | - David Healey
- Enveda Biosciences, Inc., 5700 Flatiron Pkwy, Boulder, CO, 80301, USA
| | - Tobias Kind
- Enveda Biosciences, Inc., 5700 Flatiron Pkwy, Boulder, CO, 80301, USA
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2
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Xu S, Zhu Z, Delafield DG, Rigby MJ, Lu G, Braun M, Puglielli L, Li L. Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics. Nat Commun 2024; 15:6252. [PMID: 39048572 PMCID: PMC11269705 DOI: 10.1038/s41467-024-50299-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 07/08/2024] [Indexed: 07/27/2024] Open
Abstract
Dysregulated glycerophospholipid (GP) metabolism in the brain is associated with the progression of neurodegenerative diseases including Alzheimer's disease (AD). Routine liquid chromatography-mass spectrometry (LC-MS)-based large-scale lipidomic methods often fail to elucidate subtle yet important structural features such as sn-position, hindering the precise interrogation of GP molecules. Leveraging high-resolution demultiplexing (HRdm) ion mobility spectrometry (IMS), we develop a four-dimensional (4D) lipidomic strategy to resolve GP sn-position isomers. We further construct a comprehensive experimental 4D GP database of 498 GPs identified from the mouse brain and an in-depth extended 4D library of 2500 GPs predicted by machine learning, enabling automated profiling of GPs with detailed acyl chain sn-position assignment. Analyzing three mouse brain regions (hippocampus, cerebellum, and cortex), we successfully identify a total of 592 GPs including 130 pairs of sn-position isomers. Further temporal GPs analysis in the three functional brain regions illustrates their metabolic alterations in AD progression.
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Affiliation(s)
- Shuling Xu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Zhijun Zhu
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Daniel G Delafield
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Michael J Rigby
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Gaoyuan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Megan Braun
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Luigi Puglielli
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Geriatric Research Education Clinical Center, Veterans Affairs Medical Center, Madison, WI, 53705, USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin- Madison, Madison, WI, 53705, USA.
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3
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de Cripan SM, Arora T, Olomí A, Canela N, Siuzdak G, Domingo-Almenara X. Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules. Anal Chem 2024; 96:9088-9096. [PMID: 38783786 PMCID: PMC11154685 DOI: 10.1021/acs.analchem.4c00630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.
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Affiliation(s)
- Sara M. de Cripan
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
- Department
of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
| | - Trisha Arora
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
- Department
of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
| | - Adrià Olomí
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
| | - Núria Canela
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
| | - Gary Siuzdak
- Scripps
Center of Metabolomics and Mass Spectrometry, Department of Chemistry,
Molecular and Computational Biology, Scripps
Research Institute, La Jolla, California 92037, United States
| | - Xavier Domingo-Almenara
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
- Department
of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
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4
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Cormanich RA, da Silva GD. Autobench V1.0: Benchmarking Automation for Electronic Structure Calculations. J Chem Inf Model 2024; 64:3322-3331. [PMID: 38536765 DOI: 10.1021/acs.jcim.4c00250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
This work reports on new software for automatic conformer energy benchmarking calculations for flexible molecules. The software workflow consists of four parts: conformational search, preoptimization, optimization, and frequency calculations at a higher level and last calculations using several theoretical levels. The software was written to be user-friendly and versatile to be used by nonexperts in computational chemistry. Any theoretical levels available in either Gaussian 16 or ORCA 5 may be applied in the benchmarking study. The workflow will automatically run conformational search calculations and deal with conformers that converge to the same minimum and those that show a negative frequency. At the end of the workflow, the user will have the mean absolute deviations and the most accurate method/DFT functional and basis set in comparison to the benchmark to be applied for the molecular system of interest. Case examples are given at the end of the paper that may help users to get insight into the software's main features.
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Affiliation(s)
- Rodrigo A Cormanich
- Instituto de Química, Departamento de Química Orgânica, Universidade Estadual de Campinas, PO Box 6154, Campinas 13083-970, São Paulo, Brazil
| | - Gabriel D da Silva
- Instituto de Química, Departamento de Química Orgânica, Universidade Estadual de Campinas, PO Box 6154, Campinas 13083-970, São Paulo, Brazil
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5
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Das S, Merz KM. Molecular Gas-Phase Conformational Ensembles. J Chem Inf Model 2024; 64:749-760. [PMID: 38206321 DOI: 10.1021/acs.jcim.3c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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6
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Folmsbee D, Koes DR, Hutchison GR. Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences. J Chem Inf Model 2023; 63:7401-7411. [PMID: 38000780 PMCID: PMC10716907 DOI: 10.1021/acs.jcim.3c01278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semiempirical methods are suitable priors for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data with small deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from the limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling and propose an alternative to the widely used "experimental torsion and knowledge distance geometry" (ETKDG) method using quantum torsion-derived distance geometry (QTDG) methods.
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Affiliation(s)
- Dakota
L. Folmsbee
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
- Department
of Anesthesiology & Perioperative Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - David R. Koes
- Department
of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Geoffrey R. Hutchison
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemical & Petroleum Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
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7
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Kartowikromo KY, Olajide OE, Hamid AM. Collision cross section measurement and prediction methods in omics. JOURNAL OF MASS SPECTROMETRY : JMS 2023; 58:e4973. [PMID: 37620034 PMCID: PMC10530098 DOI: 10.1002/jms.4973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/26/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023]
Abstract
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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Affiliation(s)
| | - Orobola E Olajide
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama, USA
| | - Ahmed M Hamid
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama, USA
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8
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Das S, Dinpazhoh L, Tanemura KA, Merz KM. Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input. J Chem Inf Model 2023; 63:4995-5000. [PMID: 37548575 DOI: 10.1021/acs.jcim.3c00890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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9
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Cajahuaringa S, Caetano DLZ, Zanotto LN, Araujo G, Skaf MS. MassCCS: A High-Performance Collision Cross-Section Software for Large Macromolecular Assemblies. J Chem Inf Model 2023; 63:3557-3566. [PMID: 37184925 PMCID: PMC10269586 DOI: 10.1021/acs.jcim.3c00405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Indexed: 05/16/2023]
Abstract
Ion mobility mass spectrometry (IM-MS) techniques have become highly valued as a tool for structural characterization of biomolecular systems since they yield accurate measurements of the rotationally averaged collision cross-section (CCS) against a buffer gas. Despite its enormous potential, IM-MS data interpretation is often challenging due to the conformational isomerism of metabolites, lipids, proteins, and other biomolecules in the gas phase. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS data. In this work, we present MassCCS, a parallelized open-source code for computing CCS of molecules ranging from small organic compounds to massive protein assemblies at the trajectory method level of description using atomic and molecular buffer gas particles. The performance of the code is comparable to other available software for small molecules and proteins but is significantly faster for larger macromolecular assemblies. We performed extensive tests regarding accuracy, performance, and scalability with system size and number of CPU cores. MassCCS has proven highly accurate and efficient, with execution times under a few minutes, even for large (84.87 MDa) virus capsid assemblies with very modest computational resources. MassCCS is freely available at https://github.com/cces-cepid/massccs.
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Affiliation(s)
- Samuel Cajahuaringa
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Daniel L. Z. Caetano
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
- Institute
of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Leandro N. Zanotto
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Guido Araujo
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Munir S. Skaf
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
- Institute
of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
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10
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Li X, Wang H, Jiang M, Ding M, Xu X, Xu B, Zou Y, Yu Y, Yang W. Collision Cross Section Prediction Based on Machine Learning. Molecules 2023; 28:molecules28104050. [PMID: 37241791 DOI: 10.3390/molecules28104050] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.
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Affiliation(s)
- Xiaohang Li
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiting Jiang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiang Ding
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Bei Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yadan Zou
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yuetong Yu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Wenzhi Yang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
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11
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Rainey MA, Watson CA, Asef CK, Foster MR, Baker ES, Fernández FM. CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics. Anal Chem 2022; 94:17456-17466. [PMID: 36473057 PMCID: PMC9772062 DOI: 10.1021/acs.analchem.2c03491] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
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 ΔCCS threshold of 2.8% and a mass error of 10 ppm.
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Affiliation(s)
- Markace A. Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Chandler A. Watson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Carter K. Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Makayla R. Foster
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Erin S. Baker
- Department of Chemistry and Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States; Petit Institute of Bioengineering and Biotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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12
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Shaver AO, Garcia BM, Gouveia GJ, Morse AM, Liu Z, Asef CK, Borges RM, Leach FE, Andersen EC, Amster IJ, Fernández FM, Edison AS, McIntyre LM. An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics. Front Mol Biosci 2022; 9:930204. [PMID: 36438654 PMCID: PMC9682135 DOI: 10.3389/fmolb.2022.930204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 10/10/2022] [Indexed: 11/27/2022] Open
Abstract
Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.
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Affiliation(s)
- Amanda O. Shaver
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States
| | - Brianna M. Garcia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Goncalo J. Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Alison M. Morse
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Zihao Liu
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Carter K. Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Ricardo M. Borges
- Walter Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Franklin E. Leach
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Environmental Health Science, University of Georgia, Athens, GA, United States
| | - Erik C. Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, United States
| | - I. Jonathan Amster
- Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Arthur S. Edison
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Lauren M. McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States,University of Florida Genetics Institute, University of Florida, Gainesville, FL, United States,*Correspondence: Lauren M. McIntyre,
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13
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Feuerstein ML, Hernández-Mesa M, Kiehne A, Le Bizec B, Hann S, Dervilly G, Causon T. Comparability of Steroid Collision Cross Sections Using Three Different IM-HRMS Technologies: An Interplatform Study. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1951-1959. [PMID: 36047677 PMCID: PMC9545150 DOI: 10.1021/jasms.2c00196] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/16/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Steroids play key roles in various biological processes and are characterized by many isomeric variants, which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from different commercial IM-MS instruments. CCS is seen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlaboratory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS), and trapped ion mobility (TIM-MS) platforms using a set of 87 steroids was undertaken. Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1.5%) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different instrument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyzers and implications on the applicability for nontargeted analysis are critically discussed. To the best of our knowledge, this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general.
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Affiliation(s)
- Max L. Feuerstein
- Department
of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | | | - Andrea Kiehne
- Bruker
Daltonics GmbH & Co. KG, 28359 Bremen, Germany
| | | | - Stephan Hann
- Department
of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | | | - Tim Causon
- Department
of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
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14
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Manathunga M, Götz AW, Merz KM. Computer-aided drug design, quantum-mechanical methods for biological problems. Curr Opin Struct Biol 2022; 75:102417. [PMID: 35779437 DOI: 10.1016/j.sbi.2022.102417] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/14/2022] [Accepted: 05/16/2022] [Indexed: 11/28/2022]
Abstract
Quantum chemistry enables to study systems with chemical accuracy (<1 kcal/mol from experiment) but is restricted to a handful of atoms due to its computational expense. This has led to ongoing interest to optimize and simplify these methods while retaining accuracy. Implementing quantum mechanical (QM) methods on modern hardware such as multiple-GPUs is one example of how the field is optimizing performance. Multiscale approaches like the so-called QM/molecular mechanical method are gaining popularity in drug discovery because they focus the application of QM methods on the region of choice (e.g., the binding site), while using efficient MM models to represent less relevant areas. The creation of simplified QM methods is another example, including the use of machine learning to create ultra-fast and accurate QM models. Herein, we summarize recent advancements in the development of optimized QM methods that enhance our ability to use these methods in computer aided drug discovery.
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Affiliation(s)
- Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States. https://twitter.com/@MaduManathunga
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, United States. https://twitter.com/@awgoetz
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States.
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