1
|
Chi J, Shu J, Li M, Mudappathi R, Jin Y, Lewis F, Boon A, Qin X, Liu L, Gu H. Artificial Intelligence in Metabolomics: A Current Review. Trends Analyt Chem 2024; 178:117852. [PMID: 39071116 PMCID: PMC11271759 DOI: 10.1016/j.trac.2024.117852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.
Collapse
Affiliation(s)
- Jinhua Chi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Jingmin Shu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Ming Li
- Phoenix VA Health Care System, Phoenix, AZ 85012, USA
- University of Arizona College of Medicine, Phoenix, AZ 85004, USA
| | - Rekha Mudappathi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Yan Jin
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Freeman Lewis
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Alexandria Boon
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Xiaoyan Qin
- College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Haiwei Gu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| |
Collapse
|
2
|
Zhang Y, Liu F, Li XQ, Gao Y, Li KC, Zhang QH. Retention time dataset for heterogeneous molecules in reversed-phase liquid chromatography. Sci Data 2024; 11:946. [PMID: 39209861 PMCID: PMC11362277 DOI: 10.1038/s41597-024-03780-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024] Open
Abstract
Quantitative structure-property relationships have been extensively studied in the field of predicting retention times in liquid chromatography (LC). However, making transferable predictions is inherently complex because retention times are influenced by both the structure of the molecule and the chromatographic method used. Despite decades of development and numerous published machine learning models, the practical application of predicting small molecule retention time remains limited. The resulting models are typically limited to specific chromatographic conditions and the molecules used in their training and evaluation. Here, we have developed a comprehensive dataset comprising over 10,000 experimental retention times. These times were derived from 30 different reversed-phase liquid chromatography methods and pertain to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules. They offer a wide range of examples for modeling retention time with different LC setups.
Collapse
Affiliation(s)
- Yan Zhang
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Fei Liu
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China.
| | - Xiu Qin Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Yan Gao
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Kang Cong Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Qing He Zhang
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China.
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China.
| |
Collapse
|
3
|
Ianni A, Ihling CH, Vranka T, Matoušek V, Sinz A, Iacobucci C. Evaluating Imide-Based Mass Spectrometry-Cleavable Cross-Linkers for Structural Proteomics Studies. JACS AU 2024; 4:2936-2943. [PMID: 39211594 PMCID: PMC11350583 DOI: 10.1021/jacsau.4c00282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 07/01/2024] [Accepted: 07/01/2024] [Indexed: 09/04/2024]
Abstract
Disuccinimidyl dibutyric urea (DSBU) is a mass spectrometry (MS)-cleavable cross-linker that has multiple applications in structural biology, ranging from isolated protein complexes to comprehensive system-wide interactomics. DSBU facilitates a rapid and reliable identification of cross-links through the dissociation of its urea group in the gas phase. In this study, we further advance the structural capabilities of DSBU by remodeling the urea group into an imide, thus introducing a novel class of cross-linkers. This modification preserves the MS cleavability of the amide bond, granted by the two acyl groups of the imide function. The central nitrogen atom enables the introduction of affinity purification tags. Here, we introduce disuccinimidyl disuccinic imide (DSSI) as a prototype of this class of cross-linkers. It features a phosphonate handle for immobilized metal ion affinity chromatography enrichment. We detail DSSI synthesis and describe its behavior in solution and in the gas phase while cross-linking isolated proteins and human cell lysates. DSSI and DSBU cross-links are compared at the same enrichment depth to bridge these two cross-linker classes. We validate DSSI cross-links by mapping them in high-resolution structures of large protein assemblies. The cross-links observed yield insights into the morphology of intrinsically disordered proteins and their complexes. The DSSI linker might spearhead a novel class of MS-cleavable and enrichable cross-linkers.
Collapse
Affiliation(s)
- Alessio
Di Ianni
- Department
of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
- Center
for Structural Mass Spectrometry, Martin
Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
| | - Christian H. Ihling
- Department
of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
- Center
for Structural Mass Spectrometry, Martin
Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
| | - Tomáš Vranka
- CF
Plus Chemicals s.r.o., Karásek 1767/1, Brno-Řečkovice 621 00, Czechia
| | - Václav Matoušek
- CF
Plus Chemicals s.r.o., Karásek 1767/1, Brno-Řečkovice 621 00, Czechia
| | - Andrea Sinz
- Department
of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
- Center
for Structural Mass Spectrometry, Martin
Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
| | - Claudio Iacobucci
- Department
of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
- Center
for Structural Mass Spectrometry, Martin
Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, Halle/Saale D-01620, Germany
- Department
of Physical and Chemical Sciences, University
of L’Aquila, Via Vetoio, Coppito II 67100, L’Aquila, Italy
| |
Collapse
|
4
|
Botticelli L, Bakhtina AA, Kaiser NK, Keller A, McNutt S, Bruce JE, Chu F. Chemical cross-linking and mass spectrometry enabled systems-level structural biology. Curr Opin Struct Biol 2024; 87:102872. [PMID: 38936319 PMCID: PMC11283951 DOI: 10.1016/j.sbi.2024.102872] [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: 04/06/2024] [Revised: 05/22/2024] [Accepted: 06/04/2024] [Indexed: 06/29/2024]
Abstract
Structural information on protein-protein interactions (PPIs) is essential for improved understanding of regulatory interactome networks that confer various physiological and pathological responses. Additionally, maladaptive PPIs constitute desirable therapeutic targets due to inherently high disease state specificity. Recent advances in chemical cross-linking strategies coupled with mass spectrometry (XL-MS) have positioned XL-MS as a promising technology to not only elucidate the molecular architecture of individual protein assemblies, but also to characterize proteome-wide PPI networks. Moreover, quantitative in vivo XL-MS provides a new capability for the visualization of cellular interactome dynamics elicited by drug treatments, disease states, or aging effects. The emerging field of XL-MS based complexomics enables unique insights on protein moonlighting and protein complex remodeling. These techniques provide complimentary information necessary for in-depth structural interactome studies to better comprehend how PPIs mediate function in living systems.
Collapse
Affiliation(s)
- Luke Botticelli
- Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, NH, USA
| | - Anna A Bakhtina
- Department of Genome Sciences, University of Washington, Seattle WA, USA
| | - Nathan K Kaiser
- Department of Genome Sciences, University of Washington, Seattle WA, USA
| | - Andrew Keller
- Department of Genome Sciences, University of Washington, Seattle WA, USA
| | - Seth McNutt
- Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, NH, USA
| | - James E Bruce
- Department of Genome Sciences, University of Washington, Seattle WA, USA.
| | - Feixia Chu
- Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, NH, USA.
| |
Collapse
|
5
|
Kalhor M, Lapin J, Picciani M, Wilhelm M. Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification. Mol Cell Proteomics 2024; 23:100798. [PMID: 38871251 PMCID: PMC11269915 DOI: 10.1016/j.mcpro.2024.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/26/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
Collapse
Affiliation(s)
- Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Joel Lapin
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany.
| |
Collapse
|
6
|
Beck A, Muhoberac M, Randolph CE, Beveridge CH, Wijewardhane PR, Kenttämaa HI, Chopra G. Recent Developments in Machine Learning for Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:233-246. [PMID: 38910862 PMCID: PMC11191731 DOI: 10.1021/acsmeasuresciau.3c00060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/27/2023] [Accepted: 01/22/2024] [Indexed: 06/25/2024]
Abstract
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.
Collapse
Affiliation(s)
- Armen
G. Beck
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Matthew Muhoberac
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Caitlin E. Randolph
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Connor H. Beveridge
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Prageeth R. Wijewardhane
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Hilkka I. Kenttämaa
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Gaurav Chopra
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
- Department
of Computer Science (by courtesy), Purdue University, West Lafayette, Indiana 47907, United States
- Purdue
Institute for Drug Discovery, Purdue Institute for Cancer Research,
Regenstrief Center for Healthcare Engineering, Purdue Institute for
Inflammation, Immunology and Infectious Disease, Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana 47907 United States
| |
Collapse
|
7
|
Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| |
Collapse
|
8
|
Villacrés C, Mizero B, Spicer V, Viner R, Saba J, Patel B, Snovida S, Jensen P, Huhmer A, Krokhin OV. Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity. J Proteome Res 2024; 23:1488-1494. [PMID: 38530092 DOI: 10.1021/acs.jproteome.4c00018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
We studied the effect of the column temperature on the selectivity of reversed-phase peptide separation in bottom-up proteomics. The number of peptide identifications from 2 h liquid chromatography with tandem mass spectrometry (LC-MS/MS) acquisitions reaches a plateau at 45-55 °C, driven simultaneously by improved separation efficiency, a gradual decrease in peptide retention, and possible on-column degradation of peptides at elevated temperatures. Performing 2D LC-MS/MS acquisitions at 25, 35, 45, and 55 °C resulted in the identification of ∼100,000 and ∼120,000 unique peptides for nonmodified and tandem mass tags (TMT)-labeled samples, respectively. These peptide collections were used to investigate the temperature-driven retention features. The latter is governed by the specific temperature response of individual residues, peptide hydrophobicity and length, and amphipathic helicity. On average, peptide retention decreased by 0.56 and 0.5% acetonitrile for each 10 °C increase for label-free and TMT-labeled peptides, respectively. This generally linear response of retention shifts allowed the extrapolation of predictive models beyond the studied temperature range. Thus, (trap) column cooling from room temperature to 0 °C will allow the retention of an additional 3% of detectable tryptic peptides. Meanwhile, the application of 90 °C would result in the loss of ∼20% of tryptic peptides that were amenable to MS/MS-based identification.
Collapse
Affiliation(s)
- Carina Villacrés
- Manitoba Centre for Proteomics and Systems Biology, Winnipeg R3E 3P4, Canada
| | - Benilde Mizero
- Department of Chemistry, University of Manitoba, Winnipeg R3T 2N2, Canada
| | - Victor Spicer
- Manitoba Centre for Proteomics and Systems Biology, Winnipeg R3E 3P4, Canada
| | - Rosa Viner
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Julian Saba
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | - Sergei Snovida
- Thermo Fisher Scientific, Rockford, Illinois 61101, United States
| | - Penny Jensen
- Thermo Fisher Scientific, Rockford, Illinois 61101, United States
| | - Andreas Huhmer
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Oleg V Krokhin
- Manitoba Centre for Proteomics and Systems Biology, Winnipeg R3E 3P4, Canada
- Department of Chemistry, University of Manitoba, Winnipeg R3T 2N2, Canada
- Department of Internal Medicine, University of Manitoba, Winnipeg R3E 3P4, Canada
| |
Collapse
|
9
|
Trepte P, Secker C, Olivet J, Blavier J, Kostova S, Maseko SB, Minia I, Silva Ramos E, Cassonnet P, Golusik S, Zenkner M, Beetz S, Liebich MJ, Scharek N, Schütz A, Sperling M, Lisurek M, Wang Y, Spirohn K, Hao T, Calderwood MA, Hill DE, Landthaler M, Choi SG, Twizere JC, Vidal M, Wanker EE. AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor. Mol Syst Biol 2024; 20:428-457. [PMID: 38467836 PMCID: PMC10987651 DOI: 10.1038/s44320-024-00019-8] [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: 02/09/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/13/2024] Open
Abstract
Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
Collapse
Affiliation(s)
- Philipp Trepte
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Brain Development and Disease, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, 1030, Vienna, Austria.
| | - Christopher Secker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Zuse Institute Berlin, Berlin, Germany.
| | - Julien Olivet
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Structural Biology Unit, Laboratory of Virology and Chemotherapy, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit Leuven, 3000, Leuven, Belgium
| | - Jeremy Blavier
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Simona Kostova
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Sibusiso B Maseko
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Igor Minia
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
| | - Eduardo Silva Ramos
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Patricia Cassonnet
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | - Sabrina Golusik
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Martina Zenkner
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Stephanie Beetz
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Mara J Liebich
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Nadine Scharek
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Anja Schütz
- Protein Production & Characterization, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Marcel Sperling
- Multifunctional Colloids and Coating, Fraunhofer Institute for Applied Polymer Research (IAP), 14476, Potsdam-Golm, Germany
| | - Michael Lisurek
- Structural Chemistry and Computational Biophysics, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Markus Landthaler
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
- Institute of Biology, Humboldt-Universität zu Berlin, 13125, Berlin, Germany
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030, Gembloux, Belgium.
- Laboratory of Algal Synthetic and Systems Biology, Division of Science and Math, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
| | - Erich E Wanker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
| |
Collapse
|
10
|
Zhang Y, Liu F, Li XQ, Gao Y, Li KC, Zhang QH. Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration. Commun Chem 2024; 7:54. [PMID: 38459241 PMCID: PMC10923921 DOI: 10.1038/s42004-024-01135-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024] Open
Abstract
Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.
Collapse
Affiliation(s)
- Yan Zhang
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Fei Liu
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China.
| | - Xiu Qin Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Yan Gao
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Kang Cong Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Qing He Zhang
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China.
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China.
| |
Collapse
|
11
|
Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
Collapse
Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| |
Collapse
|
12
|
Kang Q, Fang P, Zhang S, Qiu H, Lan Z. Deep graph convolutional network for small-molecule retention time prediction. J Chromatogr A 2023; 1711:464439. [PMID: 37865024 DOI: 10.1016/j.chroma.2023.464439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/23/2023]
Abstract
The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-based molecule identification. Recent research shows that graph neural networks (GNNs) outperform traditional machine learning algorithms in RT prediction. However, all of these models use relatively shallow GNNs. This study for the first time investigates how depth affects GNNs' performance on RT prediction. The results demonstrate that a notable improvement can be achieved by pushing the depth of GNNs to 16 layers by the adoption of residual connection. Additionally, we also find that graph convolutional network (GCN) model benefits from the edge information. The developed deep graph convolutional network, DeepGCN-RT, significantly outperforms the previous state-of-the-art method and achieves the lowest mean absolute percentage error (MAPE) of 3.3% and the lowest mean absolute error (MAE) of 26.55 s on the SMRT test set. We also finetune DeepGCN-RT on seven datasets with various chromatographic conditions. The mean MAE of the seven datasets largely decreases 30% compared to previous state-of-the-art method. On the RIKEN-PlaSMA dataset, we also test the effectiveness of DeepGCN-RT in assisting molecular structure identification. By 30% lessening the number of potential structures, DeepGCN-RT is able to improve top-1 accuracy by about 11%.
Collapse
Affiliation(s)
- Qiyue Kang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China.
| | - Pengfei Fang
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Shuai Zhang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China
| | - Huachuan Qiu
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China
| | - Zhenzhong Lan
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China.
| |
Collapse
|
13
|
Cox J. Prediction of peptide mass spectral libraries with machine learning. Nat Biotechnol 2023; 41:33-43. [PMID: 36008611 DOI: 10.1038/s41587-022-01424-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/11/2022] [Indexed: 01/21/2023]
Abstract
The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.
Collapse
Affiliation(s)
- Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
| |
Collapse
|
14
|
Cormican JA, Horokhovskyi Y, Soh WT, Mishto M, Liepe J. inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction. Mol Cell Proteomics 2022; 21:100432. [PMID: 36280141 PMCID: PMC9720494 DOI: 10.1016/j.mcpro.2022.100432] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.
Collapse
Affiliation(s)
- John A Cormican
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Yehor Horokhovskyi
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Wai Tuck Soh
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, London, United Kingdom; The Francis Crick Institute, London, United Kingdom.
| | - Juliane Liepe
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany.
| |
Collapse
|
15
|
Jia W, Wu X, Zhang R, Wang X, Shi L. Novel insight into the resilient drivers of bioaccumulation perchlorate on lipid nutrients alterations in goat milk by spatial multi-omics. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
16
|
Kolbowski L, Lenz S, Fischer L, Sinn LR, O’Reilly FJ, Rappsilber J. Improved Peptide Backbone Fragmentation Is the Primary Advantage of MS-Cleavable Crosslinkers. Anal Chem 2022; 94:7779-7786. [PMID: 35613060 PMCID: PMC9178559 DOI: 10.1021/acs.analchem.1c05266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 04/06/2022] [Indexed: 12/05/2022]
Abstract
Proteome-wide crosslinking mass spectrometry studies have coincided with the advent of mass spectrometry (MS)-cleavable crosslinkers that can reveal the individual masses of the two crosslinked peptides. However, recently, such studies have also been published with noncleavable crosslinkers, suggesting that MS-cleavability is not essential. We therefore examined in detail the advantages and disadvantages of using the commonly used MS-cleavable crosslinker, disuccinimidyl sulfoxide (DSSO). Indeed, DSSO gave rise to signature peptide fragments with a distinct mass difference (doublet) for nearly all identified crosslinked peptides. Surprisingly, we could show that it was not these peptide masses that proved the main advantage of MS cleavability of the crosslinker, but improved peptide backbone fragmentation which reduces the ambiguity of peptide identifications. This also holds true for another commonly used MS-cleavable crosslinker, DSBU. We show furthermore that the more intricate MS3-based data acquisition approaches lack sensitivity and specificity, causing them to be outperformed by the simpler and faster stepped higher-energy collisional dissociation (HCD) method. This understanding will guide future developments and applications of proteome-wide crosslinking mass spectrometry.
Collapse
Affiliation(s)
- Lars Kolbowski
- Technische
Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany
| | - Swantje Lenz
- Technische
Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany
| | - Lutz Fischer
- Technische
Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany
| | - Ludwig R. Sinn
- Technische
Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany
| | | | - Juri Rappsilber
- Technische
Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany
- University
of Edinburgh, Wellcome Centre
for Cell Biology, Edinburgh EH9 3BF, U.K.
| |
Collapse
|
17
|
Altenburg T, Giese SH, Wang S, Muth T, Renard BY. Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00467-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
AbstractMass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.
Collapse
|
18
|
Kamedulska A, Kubik Ł, Wiczling P. Statistical analysis of isocratic chromatographic data using Bayesian modeling. Anal Bioanal Chem 2022; 414:3471-3481. [DOI: 10.1007/s00216-022-03968-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/28/2022] [Accepted: 02/08/2022] [Indexed: 11/01/2022]
|
19
|
Jiao F, Yu C, Wheat A, Wang X, Rychnovsky SD, Huang L. Two-Dimensional Fractionation Method for Proteome-Wide Cross-Linking Mass Spectrometry Analysis. Anal Chem 2022; 94:4236-4242. [PMID: 35235311 DOI: 10.1021/acs.analchem.1c04485] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Cross-linking mass spectrometry (XL-MS) is an emergent technology for studying protein-protein interactions (PPIs) and elucidating architectures of protein complexes. The development of various MS-cleavable cross-linkers has facilitated the identification of cross-linked peptides, enabling XL-MS studies at the systems level. However, the scope and depth of cellular networks revealed by current XL-MS technologies remain limited. Due to the inherently broad dynamic range and complexity of proteomes, interference from highly abundant proteins impedes the identification of low-abundance cross-linked peptides in complex samples. Thus, peptide enrichment prior to MS analysis is necessary to enhance cross-link identification for proteome-wide studies. Although chromatographic techniques including size exclusion (SEC) and strong cation exchange (SCX) have been successful in isolating cross-linked peptides, new fractionation methods are still needed to further improve the depth of PPI mapping. Here, we present a two-dimensional (2D) separation strategy by integrating peptide SEC with tip-based high pH reverse-phase (HpHt) fractionation to expand the coverage of proteome-wide XL-MS analyses. Combined with the MS-cleavable cross-linker DSSO, we have successfully mapped in vitro PPIs from HEK293 cell lysates with improved identification of cross-linked peptides compared to existing approaches. The method developed here is effective and can be generalized for cross-linking studies of complex samples.
Collapse
Affiliation(s)
- Fenglong Jiao
- Department of Physiology & Biophysics, University of California, Irvine, Irvine, California 92694, United States
| | - Clinton Yu
- Department of Physiology & Biophysics, University of California, Irvine, Irvine, California 92694, United States
| | - Andrew Wheat
- Department of Physiology & Biophysics, University of California, Irvine, Irvine, California 92694, United States
| | - Xiaorong Wang
- Department of Physiology & Biophysics, University of California, Irvine, Irvine, California 92694, United States
| | - Scott D Rychnovsky
- Department of Chemistry, University of California, Irvine, Irvine, California 92694, United States
| | - Lan Huang
- Department of Physiology & Biophysics, University of California, Irvine, Irvine, California 92694, United States
| |
Collapse
|
20
|
Klykov O, Kopylov M, Carragher B, Heck AJR, Noble AJ, Scheltema RA. Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology. Mol Cell 2022; 82:285-303. [PMID: 35063097 PMCID: PMC8842845 DOI: 10.1016/j.molcel.2021.12.027] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 01/22/2023]
Abstract
Combining diverse experimental structural and interactomic methods allows for the construction of comprehensible molecular encyclopedias of biological systems. Typically, this involves merging several independent approaches that provide complementary structural and functional information from multiple perspectives and at different resolution ranges. A particularly potent combination lies in coupling structural information from cryoelectron microscopy or tomography (cryo-EM or cryo-ET) with interactomic and structural information from mass spectrometry (MS)-based structural proteomics. Cryo-EM/ET allows for sub-nanometer visualization of biological specimens in purified and near-native states, while MS provides bioanalytical information for proteins and protein complexes without introducing additional labels. Here we highlight recent achievements in protein structure and interactome determination using cryo-EM/ET that benefit from additional MS analysis. We also give our perspective on how combining cryo-EM/ET and MS will continue bridging gaps between molecular and cellular studies by capturing and describing 3D snapshots of proteomes and interactomes.
Collapse
Affiliation(s)
- Oleg Klykov
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Mykhailo Kopylov
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Bridget Carragher
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 CH Utrecht, the Netherlands; Netherlands Proteomics Center, 3584 CH Utrecht, the Netherlands
| | - Alex J Noble
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA.
| | - Richard A Scheltema
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 CH Utrecht, the Netherlands; Netherlands Proteomics Center, 3584 CH Utrecht, the Netherlands.
| |
Collapse
|
21
|
Huang R, Zhu W, Xu Z, Chen J, Jiang B, Chen H, Chen W. Accurate Retention Time Prediction Based on Monolinked Peptide Information to Confidently Identify Cross-Linked Peptides. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2410-2416. [PMID: 34320809 DOI: 10.1021/jasms.1c00120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cross-linking mass spectrometry methods have not been successfully applied to protein-protein interaction discovery at a proteome-wide level mainly due to the computation complexity (O (n2)) issue. In a previous report, we proposed a decision tree searching strategy (DTSS), which can reduce complexity by orders of magnitude. In this study, we further found that the monolinked peptides carry out the information on the retention time of the corresponding cross-linked pairs; therefore, the retention time of cross-linked peptide pairs can be predicted accurately. By utilizing the retention time as an extra filter, the false positive rate can be reduced by around 86% with a sensitivity loss of 10%. The method combined with DTSS (T-DTSS) not only benefits improving identification confidence but also leads to lower cutoff scores and facilitates substantially increasing inter-cross-link identification. T-DTSS was successfully applied to the identification of inter-cross-links obtained from Escherichia coli cell lysate cross-linked by a newly synthesized enrichable cross-linker, pDSBE. The approach can be applicable to both cleavable and noncleavable methods.
Collapse
Affiliation(s)
- Rong Huang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Wei Zhu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Zili Xu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Jiakang Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Biao Jiang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Hongli Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Wenzhang Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| |
Collapse
|