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Dens C, Adams C, Laukens K, Bittremieux W. Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39074335 DOI: 10.1021/jasms.4c00180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.
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Affiliation(s)
- Ceder Dens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
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2
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Ananth V, Sanders J, Yilmaz M, Wen B, Oh S, Noble WS. A learned score function improves the power of mass spectrometry database search. Bioinformatics 2024; 40:i410-i417. [PMID: 38940129 PMCID: PMC11211853 DOI: 10.1093/bioinformatics/btae218] [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: 06/29/2024] Open
Abstract
MOTIVATION One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools. RESULTS To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.
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Affiliation(s)
- Varun Ananth
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Justin Sanders
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Melih Yilmaz
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Bo Wen
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Sewoong Oh
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - William Stafford Noble
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
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3
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Orsburn BC. Single cell proteomics by mass spectrometry reveals deep epigenetic insight into the actions of an orphan histone deacetylase inhibitor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574437. [PMID: 38260471 PMCID: PMC10802306 DOI: 10.1101/2024.01.05.574437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Epigenetic programming has been shown to play a role in nearly every human system and disease where anyone has thought to look. However, the levels of heterogeneity at which epigenetic or epiproteomic modifications occur at single cell resolution across a population remains elusive. While recent advances in sequencing technology have allowed between 1 and 3 histone post-translational modifications to be analyzed in each single cell, over twenty separate chemical PTMs are known to exist, allowing thousands of possible combinations. Single cell proteomics by mass spectrometry (SCP) is an emerging technology in which hundreds or thousands of proteins can be directly quantified in typical human cells. As the proteins detected and quantified by SCP are heavily biased toward proteins of highest abundance, chromatin proteins are an attractive target for analysis. To this end, I applied SCP to the analysis of cancer cells treated with mocetinostat, a class specific histone deacetylase inhibitor. I find that 16 PTMs can be confidently identified and localized with high site specificity in single cells. In addition, the high abundance of histone proteins allows higher throughput methods to be utilized for SCP than previously described. While quantitative accuracy suffers when analyzing more than 700 cells per day, 9 histone proteins can be measured in single cells analyzed at even 3,500 cells per day, a throughput 10-fold greater than any previous report. In addition, the unbiased global approach utilized herein identifies a previously uncharacterized response to this drug through the S100-A8/S100-A9 protein complex partners. This response is observed in nearly every cell of the over 1,000 analyzed in this study, regardless of the relative throughput of the method utilized. While limitations exist in the methods described herein, current technologies can easily improve upon the results presented here to allow comprehensive analysis of histone PTMs to be performed in any mass spectrometry lab. All raw and processed data described in this study has been made publicly available through the ProteomeXchange/MASSIVE repository system as MSV000093434.
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Lai S, Zhao P, Zhou C, Li N, Yu W. PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications. J Proteome Res 2024. [PMID: 38770571 DOI: 10.1021/acs.jproteome.3c00819] [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: 05/22/2024]
Abstract
Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.
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Affiliation(s)
- Shengzhi Lai
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong, China
| | - Peize Zhao
- Interdisciplinary Programs Office, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong, China
| | - Chen Zhou
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong, China
| | - Ning Li
- Shenzhen-Hong Kong Collaborative Innovation Research Institute, HKUST, Futian, Shenzhen 518000, China
- Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong, China
| | - Weichuan Yu
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong, China
- Shenzhen-Hong Kong Collaborative Innovation Research Institute, HKUST, Futian, Shenzhen 518000, China
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5
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Adams C, Gabriel W, Laukens K, Picciani M, Wilhelm M, Bittremieux W, Boonen K. Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF. Nat Commun 2024; 15:3956. [PMID: 38730277 PMCID: PMC11087512 DOI: 10.1038/s41467-024-48322-0] [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: 07/17/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.
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Affiliation(s)
- Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Kris Laukens
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
- Munich Data Science Institute, Technical University of Munich, 85748, Garching, Germany
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, Antwerp, Belgium.
| | - Kurt Boonen
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Sustainable Health Department, Flemish Institute for Technological Research (VITO), Antwerp, Belgium.
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6
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Battellino T, Yeung D, Neustaeter H, Spicer V, Ogata K, Ishihama Y, Krokhin OV. Retention time prediction for post-translationally modified peptides: Ser, Thr, Tyr-phosphorylation. J Chromatogr A 2024; 1718:464714. [PMID: 38359688 DOI: 10.1016/j.chroma.2024.464714] [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: 10/30/2023] [Revised: 01/22/2024] [Accepted: 02/01/2024] [Indexed: 02/17/2024]
Abstract
The development of a peptide retention prediction model for reversed-phase chromatography applications in proteomics is reported for peptides carrying phosphorylated Ser, Thr and Tyr-residues. The major retention features have been assessed using a collection of over 10,000 phosphorylated/non-phosphorylated peptide pairs identified in a series 1D and 2D LC-MS/MS acquisitions using formic acid as ion pairing modifier. Single modification event on average results in increased peptide retention for phosphorylation of Ser (+ 1.46), Thr (+1.33), Tyr (+0.93% acetonitrile, ACN) on gradient elution scale for Luna C18(2) stationary phase. We established several composition and sequence specific features, which drive deviations from these average values. Thus, single phosphorylation of serine results in retention shifts ranging from -2.4 to 5.5% ACN depending on position of the residue, nature of nearest neighbour residues, peptide length, hydrophobicity and pI value, and its propensity to form amphipathic helical structures. We established that the altered ion-pairing environment upon phosphorylation is detrimental for this variability. Hydrophobicity of ion-pairing modifier directly informs the magnitude of expected shifts: (most hydrophilic) 0.5 % acetic acid (larger positive shift upon phosphorylation) > 0.1 % formic acid (positive) > 0.1 % trifluoroacetic (negative) > 0.1 % heptafluorobutyric acid (larger negative shift). The effect of phosphorylation has been also evaluated for several separation conditions used in the first dimension of 2D LC applications: high pH reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), strong cation- and strong anion exchange separations.
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Affiliation(s)
- Taylor Battellino
- Department of Chemistry, University of Manitoba, 360 Parker Building, 144 Dysart Road, Winnipeg, R3T 2N2, Canada
| | - Darien Yeung
- Department of Biochemistry and Medical Genetics, University of Manitoba, 336 BMSB, 745 Bannatyne Avenue, Winnipeg, R3E 0J9, Canada
| | - Haley Neustaeter
- Department of Chemistry, University of Manitoba, 360 Parker Building, 144 Dysart Road, Winnipeg, R3T 2N2, Canada
| | - Vic Spicer
- Manitoba Centre for Proteomics and Systems Biology, 799 JBRC, 715 McDermot Avenue, Winnipeg, R3E 3P4, Canada
| | - Kosuke Ogata
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan
| | - Oleg V Krokhin
- Manitoba Centre for Proteomics and Systems Biology, 799 JBRC, 715 McDermot Avenue, Winnipeg, R3E 3P4, Canada; Department of Internal Medicine, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg, R3E 3P4, Canada.
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7
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Zhao X, Hu Y, Zhao J, Liu Y, Ma X, Chen H, Xing Y. Role of protein Post-translational modifications in enterovirus infection. Front Microbiol 2024; 15:1341599. [PMID: 38596371 PMCID: PMC11002909 DOI: 10.3389/fmicb.2024.1341599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/18/2024] [Indexed: 04/11/2024] Open
Abstract
Enteroviruses (EVs) are the main cause of a number of neurological diseases. Growing evidence has revealed that successful infection with enteroviruses is highly dependent on the host machinery, therefore, host proteins play a pivotal role in viral infections. Both host and viral proteins can undergo post-translational modification (PTM) which can regulate protein activity, stability, solubility and interactions with other proteins; thereby influencing various biological processes, including cell metabolism, metabolic, signaling pathways, cell death, and cancer development. During viral infection, both host and viral proteins regulate the viral life cycle through various PTMs and different mechanisms, including the regulation of host cell entry, viral protein synthesis, genome replication, and the antiviral immune response. Therefore, protein PTMs play important roles in EV infections. Here, we review the role of various host- and virus-associated PTMs during enterovirus infection.
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Affiliation(s)
- Xiaohui Zhao
- Department of Pathogen Biology, School of Medicine, Qinghai University, Qinghai, China
| | - Yibo Hu
- Department of Orthopaedic Trauma, The Affiliated Hospital of Qinghai University, Qinghai, China
| | - Jun Zhao
- Department of Pathogen Biology, School of Medicine, Qinghai University, Qinghai, China
| | - Yan Liu
- Department of Immunology, School of Medicine, Qinghai, China
| | - Xueman Ma
- Department of Traditional Chinese Medicine, School of Medicine, Qinghai University, Qinghai, China
| | - Hongru Chen
- Department of Public Health, School of Medicine, Qinghai University, Qinghai, China
| | - Yonghua Xing
- Department of Genetics, School of Medicine, Qinghai University, Qinghai, China
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8
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Aleshin VA, Kaehne T, Maslova MV, Graf AV, Bunik VI. Posttranslational Acylations of the Rat Brain Transketolase Discriminate the Enzyme Responses to Inhibitors of ThDP-Dependent Enzymes or Thiamine Transport. Int J Mol Sci 2024; 25:917. [PMID: 38255994 PMCID: PMC10815635 DOI: 10.3390/ijms25020917] [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: 10/24/2023] [Revised: 12/23/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Transketolase (TKT) is an essential thiamine diphosphate (ThDP)-dependent enzyme of the non-oxidative branch of the pentose phosphate pathway, with the glucose-6P flux through the pathway regulated in various medically important conditions. Here, we characterize the brain TKT regulation by acylation in rats with perturbed thiamine-dependent metabolism, known to occur in neurodegenerative diseases. The perturbations are modeled by the administration of oxythiamine inhibiting ThDP-dependent enzymes in vivo or by reduced thiamine availability in the presence of metformin and amprolium, inhibiting intracellular thiamine transporters. Compared to control rats, chronic administration of oxythiamine does not significantly change the modification level of the two detected TKT acetylation sites (K6 and K102) but doubles malonylation of TKT K499, concomitantly decreasing 1.7-fold the level of demalonylase sirtuin 5. The inhibitors of thiamine transporters do not change average levels of TKT acylation or sirtuin 5. TKT structures indicate that the acylated residues are distant from the active sites. The acylations-perturbed electrostatic interactions may be involved in conformational shifts and/or the formation of TKT complexes with other proteins or nucleic acids. Acetylation of K102 may affect the active site entrance/exit and subunit interactions. Correlation analysis reveals that the action of oxythiamine is characterized by significant negative correlations of K499 malonylation or K6 acetylation with TKT activity, not observed upon the action of the inhibitors of thiamine transport. However, the transport inhibitors induce significant negative correlations between the TKT activity and K102 acetylation or TKT expression, absent in the oxythiamine group. Thus, perturbations in the ThDP-dependent catalysis or thiamine transport manifest in the insult-specific patterns of the brain TKT malonylation and acetylations.
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Affiliation(s)
- Vasily A. Aleshin
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, 119234 Moscow, Russia; (V.A.A.); (A.V.G.)
- Department of Biochemistry, Sechenov University, 119048 Moscow, Russia
| | - Thilo Kaehne
- Institute of Experimental Internal Medicine, Otto von Guericke University, 39106 Magdeburg, Germany;
| | - Maria V. Maslova
- Faculty of Biology, Lomonosov Moscow State University, 119234 Moscow, Russia;
| | - Anastasia V. Graf
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, 119234 Moscow, Russia; (V.A.A.); (A.V.G.)
- Faculty of Biology, Lomonosov Moscow State University, 119234 Moscow, Russia;
| | - Victoria I. Bunik
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, 119234 Moscow, Russia; (V.A.A.); (A.V.G.)
- Department of Biochemistry, Sechenov University, 119048 Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234 Moscow, Russia
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9
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Orsburn BC. Analyzing Posttranslational Modifications in Single Cells. Methods Mol Biol 2024; 2817:145-156. [PMID: 38907153 DOI: 10.1007/978-1-0716-3934-4_12] [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: 06/23/2024]
Abstract
With the rapid expansion of capabilities in the analysis of proteins in single cells, we can now identify multiple classes of protein posttranslational modifications on some of these proteins. Each new technology that has increased the number of proteins measured per cell has likewise increased our ability to identify and quantify modified peptides. In this chapter, I will discuss our current capabilities, concerns, and challenges specific to this emerging field of study and the inevitable demand for services, providing a general review of concepts that should be considered.
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Affiliation(s)
- Benjamin C Orsburn
- The Department of Pharmacology and Molecular Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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10
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Chan CMJ, Lam H. Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries. J Proteome Res 2023; 22:3692-3702. [PMID: 37910637 DOI: 10.1021/acs.jproteome.3c00180] [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: 11/03/2023]
Abstract
Spectral libraries are useful resources in proteomic data analysis. Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences. This enables predicted spectral libraries to be compiled, and searching against such libraries has been shown to improve the sensitivity in peptide identification over conventional sequence database searching. However, current prediction models lack support for longer peptides, and thus far, predicted library searching has only been demonstrated for backbone ion-only spectrum prediction methods. Here, we propose a deep learning-based full-spectrum prediction method to generate predicted spectral libraries for peptide identification. We demonstrated the superiority of using full-spectrum libraries over backbone ion-only prediction approaches in spectral library searching. Furthermore, merging spectra from different prediction models, as a form of ensemble learning, can produce improved spectral libraries, in terms of identification sensitivity. We also show that a hybrid library combining predicted and experimental spectra can lead to 20% more confident identifications over experimental library searching or sequence database searching.
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Affiliation(s)
- Chak Ming Jerry Chan
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
| | - Henry Lam
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
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11
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Will A, Oliinyk D, Bleiholder C, Meier F. Peptide collision cross sections of 22 post-translational modifications. Anal Bioanal Chem 2023; 415:6633-6645. [PMID: 37758903 PMCID: PMC10598134 DOI: 10.1007/s00216-023-04957-4] [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: 12/23/2022] [Revised: 07/13/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023]
Abstract
Recent advances have rekindled the interest in ion mobility as an additional dimension of separation in mass spectrometry (MS)-based proteomics. Ion mobility separates ions according to their size and shape in the gas phase. Here, we set out to investigate the effect of 22 different post-translational modifications (PTMs) on the collision cross section (CCS) of peptides. In total, we analyzed ~4300 pairs of matching modified and unmodified peptide ion species by trapped ion mobility spectrometry (TIMS). Linear alignment based on spike-in reference peptides resulted in highly reproducible CCS values with a median coefficient of variation of 0.26%. On a global level, we observed a redistribution in the m/z vs. ion mobility space for modified peptides upon changes in their charge state. Pairwise comparison between modified and unmodified peptides of the same charge state revealed median shifts in CCS between -1.4% (arginine citrullination) and +4.5% (O-GlcNAcylation). In general, increasing modified peptide masses were correlated with higher CCS values, in particular within homologous PTM series. However, investigating the ion populations in more detail, we found that the change in CCS can vary substantially for a given PTM and is partially correlated with the gas phase structure of its unmodified counterpart. In conclusion, our study shows PTM- and sequence-specific effects on the cross section of peptides, which could be further leveraged for proteome-wide PTM analysis.
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Affiliation(s)
- Andreas Will
- Functional Proteomics, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
| | - Denys Oliinyk
- Functional Proteomics, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
| | - Christian Bleiholder
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, 32304, USA
| | - Florian Meier
- Functional Proteomics, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany.
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12
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Neale Q, Prefontaine A, Battellino T, Mizero B, Yeung D, Spicer V, Budisa N, Perreault H, Zahedi RP, Krokhin OV. Compendium of Chromatographic Behavior of Post-translationally and Chemically Modified Peptides in Bottom-Up Proteomic Experiments. Anal Chem 2023; 95:14634-14642. [PMID: 37739932 DOI: 10.1021/acs.analchem.3c02412] [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: 09/24/2023]
Abstract
We have systematically evaluated the chromatographic behavior of post-translationally/chemically modified peptides using data spanning over 70 of the most relevant modifications. These retention properties were measured for standard bottom-up proteomic settings (fully porous C18 separation media, 0.1% formic acid as ion-pairing modifier) using collections of modified/nonmodified peptide pairs. These pairs were generated by spontaneous degradation, chemical or enzymatic treatment, analysis of synthetic peptides, or the cotranslational incorporation of noncanonical proline analogues. In addition, these measurements were validated using external data acquired for synthetic peptides and enzymatically induced citrullination. Working in units of hydrophobicity index (HI, % ACN) and evaluating the average retention shifts (ΔHI) represent the simplest approach to describe the effect of modifications from a didactic point of view. Plotting HI values for modified (y-axis) vs nonmodified (x-axis) counterparts generates unique slope and intercept values for each modification defined by the chemistry of the modifying moiety: its hydrophobicity, size, pKa of ionizable groups, and position of the altered residue. These composition-dependent correlations can be used for coarse incorporation of PTMs into models for prediction of peptide retention. More accurate predictions would require the development of specific sequence-dependent algorithms to predict ΔHI values.
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Affiliation(s)
- Quinn Neale
- Department of Chemistry, University of Manitoba, 360 Parker Building, Winnipeg R3T 2N2, Manitoba, Canada
| | - Alexandre Prefontaine
- Department of Chemistry, University of Manitoba, 360 Parker Building, Winnipeg R3T 2N2, Manitoba, Canada
| | - Taylor Battellino
- Department of Chemistry, University of Manitoba, 360 Parker Building, Winnipeg R3T 2N2, Manitoba, Canada
| | - Benilde Mizero
- Department of Chemistry, University of Manitoba, 360 Parker Building, Winnipeg R3T 2N2, Manitoba, Canada
| | - Darien Yeung
- Department of Biochemistry and Medical Genetics, University of Manitoba, 336 Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg R3E 0J9, Manitoba, Canada
| | - Victor Spicer
- Manitoba Centre for Proteomics and Systems Biology, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg R3E 3P4, Manitoba, Canada
| | - Nediljko Budisa
- Department of Chemistry, University of Manitoba, 360 Parker Building, Winnipeg R3T 2N2, Manitoba, Canada
| | - Helene Perreault
- Department of Chemistry, University of Manitoba, 360 Parker Building, Winnipeg R3T 2N2, Manitoba, Canada
| | - Rene P Zahedi
- Department of Biochemistry and Medical Genetics, University of Manitoba, 336 Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg R3E 0J9, Manitoba, Canada
- Manitoba Centre for Proteomics and Systems Biology, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg R3E 3P4, Manitoba, Canada
- Department of Internal Medicine, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg R3E 3P4, Manitoba, Canada
- CancerCare Manitoba Research Institute, 675 McDermot Avenue, Winnipeg R3E 0 V9, Manitoba, Canada
| | - Oleg V Krokhin
- Department of Biochemistry and Medical Genetics, University of Manitoba, 336 Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg R3E 0J9, Manitoba, Canada
- Manitoba Centre for Proteomics and Systems Biology, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg R3E 3P4, Manitoba, Canada
- Department of Internal Medicine, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg R3E 3P4, Manitoba, Canada
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13
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Nagy K, Gellén G, Papp D, Schlosser G, Révész Á. Optimum collision energies for proteomics: The impact of ion mobility separation. JOURNAL OF MASS SPECTROMETRY : JMS 2023; 58:e4957. [PMID: 37415399 DOI: 10.1002/jms.4957] [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: 12/22/2022] [Revised: 05/28/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
Ion mobility spectrometry (IMS) is a widespread separation technique used in various research fields. It can be coupled to liquid chromatography-mass spectrometry (LC-MS/MS) methods providing an additional separation dimension. During IMS, ions are subjected to multiple collisions with buffer gas, which may cause significant ion heating. The present project addresses this phenomenon from the bottom-up proteomics point of view. We performed LC-MS/MS measurements on a cyclic ion mobility mass spectrometer with varied collision energy (CE) settings both with and without IMS. We investigated the CE dependence of identification score, using Byonic search engine, for more than 1000 tryptic peptides from HeLa digest standard. We determined the optimal CE values-giving the highest identification score-for both setups (i.e., with and without IMS). Results show that lower CE is advantageous when IMS separation is applied, by 6.3 V on average. This value belongs to the one-cycle separation configuration, and multiple cycles may supposedly have even larger impact. The effect of IMS is also reflected in the trends of optimal CE values versus m/z functions. The parameters suggested by the manufacturer were found to be almost optimal for the setup without IMS; on the other hand, they are obviously too high with IMS. Practical consideration on setting up a mass spectrometric platform hyphenated to IMS is also presented. Furthermore, the two CID (collision induced dissociation) fragmentation cells of the instrument-located before and after the IMS cell-were also compared, and we found that CE adjustment is needed when the trap cell is used for activation instead of the transfer cell. Data have been deposited in the MassIVE repository (MSV000090944).
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Affiliation(s)
- Kinga Nagy
- MS Proteomics Research Group, Research Centre for Natural Sciences, Budapest, H-1117, Hungary
- Hevesy György PhD School of Chemistry, Faculty of Science, Institute of Chemistry, Eötvös Loránd University, Budapest, H-1117, Hungary
| | - Gabriella Gellén
- MTA-ELTE Lendület Ion Mobility Mass Spectrometry Research Group, Faculty of Science, Institute of Chemistry, Eötvös Loránd University, Budapest, H-1117, Hungary
| | - Dávid Papp
- Hevesy György PhD School of Chemistry, Faculty of Science, Institute of Chemistry, Eötvös Loránd University, Budapest, H-1117, Hungary
- MTA-ELTE Lendület Ion Mobility Mass Spectrometry Research Group, Faculty of Science, Institute of Chemistry, Eötvös Loránd University, Budapest, H-1117, Hungary
| | - Gitta Schlosser
- MTA-ELTE Lendület Ion Mobility Mass Spectrometry Research Group, Faculty of Science, Institute of Chemistry, Eötvös Loránd University, Budapest, H-1117, Hungary
| | - Ágnes Révész
- MS Proteomics Research Group, Research Centre for Natural Sciences, Budapest, H-1117, Hungary
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14
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Geiszler DJ, Polasky DA, Yu F, Nesvizhskii AI. Detecting diagnostic features in MS/MS spectra of post-translationally modified peptides. Nat Commun 2023; 14:4132. [PMID: 37438360 DOI: 10.1038/s41467-023-39828-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 06/23/2023] [Indexed: 07/14/2023] Open
Abstract
Post-translational modifications are an area of great interest in mass spectrometry-based proteomics, with a surge in methods to detect them in recent years. However, post-translational modifications can introduce complexity into proteomics searches by fragmenting in unexpected ways, ultimately hindering the detection of modified peptides. To address these deficiencies, we present a fully automated method to find diagnostic spectral features for any modification. The features can be incorporated into proteomics search engines to improve modified peptide recovery and localization. We show the utility of this approach by interrogating fragmentation patterns for a cysteine-reactive chemoproteomic probe, RNA-crosslinked peptides, sialic acid-containing glycopeptides, and ADP-ribosylated peptides. We also analyze the interactions between a diagnostic ion's intensity and its statistical properties. This method has been incorporated into the open-search annotation tool PTM-Shepherd and the FragPipe computational platform.
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Affiliation(s)
- Daniel J Geiszler
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
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15
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Révész Á, Hevér H, Steckel A, Schlosser G, Szabó D, Vékey K, Drahos L. Collision energies: Optimization strategies for bottom-up proteomics. MASS SPECTROMETRY REVIEWS 2023; 42:1261-1299. [PMID: 34859467 DOI: 10.1002/mas.21763] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 06/07/2023]
Abstract
Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.
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Affiliation(s)
- Ágnes Révész
- MS Proteomics Research Group, Institute of Organic Chemistry, Research Centre for Natural Sciences, Budapest, Hungary
| | - Helga Hevér
- Chemical Works of Gedeon Richter Plc, Budapest, Hungary
| | - Arnold Steckel
- Department of Analytical Chemistry, MTA-ELTE Lendület Ion Mobility Mass Spectrometry Research Group, Institute of Chemistry, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Gitta Schlosser
- Department of Analytical Chemistry, MTA-ELTE Lendület Ion Mobility Mass Spectrometry Research Group, Institute of Chemistry, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dániel Szabó
- MS Proteomics Research Group, Institute of Organic Chemistry, Research Centre for Natural Sciences, Budapest, Hungary
| | - Károly Vékey
- MS Proteomics Research Group, Institute of Organic Chemistry, Research Centre for Natural Sciences, Budapest, Hungary
| | - László Drahos
- MS Proteomics Research Group, Institute of Organic Chemistry, Research Centre for Natural Sciences, Budapest, Hungary
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16
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Cassidy L, Kaulich PT, Tholey A. Proteoforms expand the world of microproteins and short open reading frame-encoded peptides. iScience 2023; 26:106069. [PMID: 36818287 PMCID: PMC9929600 DOI: 10.1016/j.isci.2023.106069] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Microproteins and short open reading frame-encoded peptides (SEPs) can, like all proteins, carry numerous posttranslational modifications. Together with posttranscriptional processes, this leads to a high number of possible distinct protein molecules, the proteoforms, out of a limited number of genes. The identification, quantification, and molecular characterization of proteoforms possess special challenges to established, mainly bottom-up proteomics (BUP) based analytical approaches. While BUP methods are powerful, proteins have to be inferred rather than directly identified, which hampers the detection of proteoforms. An alternative approach is top-down proteomics (TDP) which allows to identify intact proteoforms. This perspective article provides a brief overview of modified microproteins and SEPs, introduces the proteoform terminology, and compares present BUP and TDP workflows highlighting their major advantages and caveats. Necessary future developments in TDP to fully accentuate its potential for proteoform-centric analytics of microproteins and SEPs will be discussed.
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Affiliation(s)
- Liam Cassidy
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Philipp T. Kaulich
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Andreas Tholey
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany,Corresponding author
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17
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Zeng WF, Zhou XX, Willems S, Ammar C, Wahle M, Bludau I, Voytik E, Strauss MT, Mann M. AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nat Commun 2022; 13:7238. [PMID: 36433986 PMCID: PMC9700817 DOI: 10.1038/s41467-022-34904-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA ).
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Affiliation(s)
- Wen-Feng Zeng
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Xie-Xuan Zhou
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Sander Willems
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Constantin Ammar
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maria Wahle
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Isabell Bludau
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Eugenia Voytik
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maximillian T. Strauss
- grid.5254.60000 0001 0674 042XProteomics Program, NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Matthias Mann
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany ,grid.5254.60000 0001 0674 042XProteomics Program, NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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18
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Bittremieux W, Wang M, Dorrestein PC. The critical role that spectral libraries play in capturing the metabolomics community knowledge. Metabolomics 2022; 18:94. [PMID: 36409434 DOI: 10.1007/s11306-022-01947-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/19/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. AIM OF REVIEW We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. KEY SCIENTIFIC CONCEPTS OF REVIEW This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.
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Affiliation(s)
- Wout Bittremieux
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Mingxun Wang
- Department of Computer Science, University of California Riverside, Riverside, CA, 92507, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA.
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19
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Wan N, Wang N, Yu S, Zhang H, Tang S, Wang D, Lu W, Li H, Delafield DG, Kong Y, Wang X, Shao C, Lv L, Wang G, Tan R, Wang N, Hao H, Ye H. Cyclic immonium ion of lactyllysine reveals widespread lactylation in the human proteome. Nat Methods 2022; 19:854-864. [PMID: 35761067 DOI: 10.1038/s41592-022-01523-1] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/13/2022] [Indexed: 12/18/2022]
Abstract
Lactylation was initially discovered on human histones. Given its nascence, its occurrence on nonhistone proteins and downstream functional consequences remain elusive. Here we report a cyclic immonium ion of lactyllysine formed during tandem mass spectrometry that enables confident protein lactylation assignment. We validated the sensitivity and specificity of this ion for lactylation through affinity-enriched lactylproteome analysis and large-scale informatic assessment of nonlactylated spectral libraries. With this diagnostic ion-based strategy, we confidently determined new lactylation, unveiling a wide landscape beyond histones from not only the enriched lactylproteome but also existing unenriched human proteome resources. Specifically, by mining the public human Meltome Atlas, we found that lactylation is common on glycolytic enzymes and conserved on ALDOA. We also discovered prevalent lactylation on DHRS7 in the draft of the human tissue proteome. We partially demonstrated the functional importance of lactylation: site-specific engineering of lactylation into ALDOA caused enzyme inhibition, suggesting a lactylation-dependent feedback loop in glycolysis.
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Affiliation(s)
- Ning Wan
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Nian Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Siqin Yu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Hanqing Zhang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Shuo Tang
- State Key Laboratory Cultivation Base for TCM Quality and Efficacy, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Dexiang Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Wenjie Lu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.,School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Huanhuan Li
- State Key Laboratory Cultivation Base for TCM Quality and Efficacy, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Daniel G Delafield
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ying Kong
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.,School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xinmiao Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Chang Shao
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.,School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Langlang Lv
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Guangji Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Renxiang Tan
- State Key Laboratory Cultivation Base for TCM Quality and Efficacy, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Nanxi Wang
- State Key Laboratory Cultivation Base for TCM Quality and Efficacy, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China.
| | - Haiping Hao
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China. .,School of Pharmacy, China Pharmaceutical University, Nanjing, China.
| | - Hui Ye
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.
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20
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Abstract
We have developed the underrepresented post-translational modification (PTM) database (urPTMdb), a PTM gene set database to accelerate the discovery of enriched protein modifications in experimental data. urPTMdb provides curated lists of proteins reported to be substrates of underrepresented modifications. Their enrichment in proteomics datasets can reveal unexpected PTM regulations. urPTMdb can be implemented in existing workflows, or used in TeaProt, an online Shiny tool that integrates upstream transcription factor enrichment analysis with downstream pathway analysis through an easy-to-use interactive interface. TeaProt annotates user-uploaded data with drug-gene interactions, subcellular localizations, phenotypic functions, gene-disease associations, and enzyme-gene interactions. TeaProt enables gene set enrichment analysis (GSEA) to discover enrichments in gene sets from various resources, including MSigDB, CHEA, and urPTMdb. We demonstrate the utility of urPTMdb and TeaProt through the analysis of a previously published Western diet-induced remodeling of the tongue proteome, which revealed altered cellular processes associated with energy metabolism, interferon alpha/gamma response, adipogenesis, HMGylation substrate enrichment, and transcription regulation through PPARG and CEBPA. Additionally, we analyzed the interactome of ADP-ribose glycohydrolase TARG1, a key enzyme that removes mono-ADP-ribosylation. This analysis identified an enrichment of ADP-ribosylation, ribosomal proteins, and proteins localized in the nucleoli and endoplasmic reticulum. TeaProt and urPTMdb are accessible at https://tea.coffeeprot.com/.
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Affiliation(s)
- Jeffrey Molendijk
- Department
of Anatomy and Physiology, University of
Melbourne, Melbourne, VIC 3010, Australia,. Phone: +61 401 758 489
| | - Rui Yip
- Department
of Anatomy and Physiology, University of
Melbourne, Melbourne, VIC 3010, Australia
| | - Benjamin L. Parker
- Department
of Anatomy and Physiology, University of
Melbourne, Melbourne, VIC 3010, Australia
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21
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Sigismondo G, Papageorgiou DN, Krijgsveld J. Cracking chromatin with proteomics: From chromatome to histone modifications. Proteomics 2022; 22:e2100206. [PMID: 35633285 DOI: 10.1002/pmic.202100206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 11/10/2022]
Abstract
Chromatin is the assembly of genomic DNA and proteins packaged in the nucleus of eukaryotic cells, which together are crucial in regulating a plethora of cellular processes. Histones may be the best known class of protein constituents in chromatin, which are decorated by a range of post-translational modifications to recruit accessory proteins and protein complexes to execute specific functions, ranging from DNA compaction, repair, transcription and duplication, all in a dynamic fashion and depending on the cellular state. The key role of chromatin in cellular fitness is emphasized by the deregulation of chromatin determinants predisposing to different diseases, including cancer. For this reason, deep investigation of chromatin composition is fundamental to better understand cellular physiology. Proteomic approaches have played a crucial role to understand critical aspects of this complex interplay, benefiting from the ability to identify and quantify proteins and their modifications in an unbiased manner. This review gives an overview of the proteomic approaches that have been developed by combining mass spectrometry-based with tailored biochemical and genetic methods to examine overall protein make-up of chromatin, to characterize chromatin domains, to determine protein interactions, and to decipher the broad spectrum of histone modifications that represent the quintessence of chromatin function. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Gianluca Sigismondo
- German Cancer Research Center (DKFZ), Division of Proteomics of Stem Cells and Cancer, Heidelberg, Germany
| | - Dimitris N Papageorgiou
- German Cancer Research Center (DKFZ), Division of Proteomics of Stem Cells and Cancer, Heidelberg, Germany.,Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Jeroen Krijgsveld
- German Cancer Research Center (DKFZ), Division of Proteomics of Stem Cells and Cancer, Heidelberg, Germany.,Medical Faculty, Heidelberg University, Heidelberg, Germany
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22
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Du Z, Yang Q, Liu Y, Chen S, Zhao H, Bai H, Shao W, Zhang Y, Qin W. A New Strategy for High-Efficient Tandem Enrichment and Simultaneous Profiling of N-Glycopeptides and Phosphopeptides in Lung Cancer Tissue. Front Mol Biosci 2022; 9:923363. [PMID: 35685241 PMCID: PMC9171396 DOI: 10.3389/fmolb.2022.923363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 05/04/2022] [Indexed: 11/21/2022] Open
Abstract
N-glycosylation and phosphorylation, two common posttranslational modifications, play important roles in various biological processes and are extensively studied for biomarker and drug target screening. Because of their low abundance, enrichment of N-glycopeptides and phosphopeptides prior to LC–MS/MS analysis is essential. However, simultaneous characterization of these two types of posttranslational modifications in complex biological samples is still challenging, especially for tiny amount of samples obtained in tissue biopsy. Here, we introduced a new strategy for the highly efficient tandem enrichment of N-glycopeptides and phosphopeptides using HILIC and TiO2 microparticles. The N-glycopeptides and phosphosites obtained by tandem enrichment were 21%–377% and 22%–263% higher than those obtained by enriching the two PTM peptides separately, respectively, using 160–20 μg tryptic digested peptides as the starting material. Under the optimized conditions, 2798 N-glycopeptides from 434 N-glycoproteins and 5130 phosphosites from 1986 phosphoproteins were confidently identified from three technical replicates of HeLa cells by mass spectrometry analysis. Application of this tandem enrichment strategy in a lung cancer study led to simultaneous characterization of the two PTM peptides and discovery of hundreds of differentially expressed N-glycosylated and phosphorylated proteins between cancer and normal tissues, demonstrating the high sensitivity of this strategy for investigation of dysregulated PTMs using very limited clinical samples.
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Affiliation(s)
- Zhuokun Du
- School of Basic Medical Science, Anhui Medical University, Hefei, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing, China
| | - Qianying Yang
- School of Basic Medical Science, Anhui Medical University, Hefei, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing, China
| | - Yuanyuan Liu
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing, China
| | - Sijie Chen
- School of Basic Medical Science, Anhui Medical University, Hefei, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing, China
| | - Hongxian Zhao
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing, China
| | - Haihong Bai
- Phase I Clinical Trial Center, Beijing Shijitan Hospital of Capital Medical University, Beijing, China
| | - Wei Shao
- School of Basic Medical Science, Anhui Medical University, Hefei, China
| | - Yangjun Zhang
- School of Basic Medical Science, Anhui Medical University, Hefei, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing, China
- *Correspondence: Yangjun Zhang, ; Weijie Qin,
| | - Weijie Qin
- School of Basic Medical Science, Anhui Medical University, Hefei, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing, China
- *Correspondence: Yangjun Zhang, ; Weijie Qin,
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23
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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%.
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24
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Weiß L, Gaelings L, Reiner T, Mergner J, Kuster B, Fehér A, Hensel G, Gahrtz M, Kumlehn J, Engelhardt S, Hückelhoven R. Posttranslational modification of the RHO of plants protein RACB by phosphorylation and cross-kingdom conserved ubiquitination. PLoS One 2022; 17:e0258924. [PMID: 35333858 PMCID: PMC8956194 DOI: 10.1371/journal.pone.0258924] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/10/2021] [Indexed: 11/19/2022] Open
Abstract
Small RHO-type G-proteins act as signaling hubs and master regulators of polarity in eukaryotic cells. Their activity is tightly controlled, as defective RHO signaling leads to aberrant growth and developmental defects. Two major processes regulate G-protein activity: canonical shuttling between different nucleotide bound states and posttranslational modification (PTM), of which the latter can support or suppress RHO signaling, depending on the individual PTM. In plants, regulation of Rho of plants (ROPs) signaling activity has been shown to act through nucleotide exchange and GTP hydrolysis, as well as through lipid modification, but there is little data available on phosphorylation or ubiquitination of ROPs. Hence, we applied proteomic analyses to identify PTMs of the barley ROP RACB. We observed in vitro phosphorylation by barley ROP binding kinase 1 and in vivo ubiquitination of RACB. Comparative analyses of the newly identified RACB phosphosites and human RHO protein phosphosites revealed conservation of modified amino acid residues, but no overlap of actual phosphorylation patterns. However, the identified RACB ubiquitination site is conserved in all ROPs from Hordeum vulgare, Arabidopsis thaliana and Oryza sativa and in mammalian Rac1 and Rac3. Point mutation of this ubiquitination site leads to stabilization of RACB. Hence, this highly conserved lysine residue may regulate protein stability across different kingdoms.
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Affiliation(s)
- Lukas Weiß
- Chair of Phytopathology, Technical University of Munich (TUM), Freising, Germany
| | - Lana Gaelings
- Chair of Phytopathology, Technical University of Munich (TUM), Freising, Germany
| | - Tina Reiner
- Chair of Phytopathology, Technical University of Munich (TUM), Freising, Germany
| | - Julia Mergner
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Germany
- Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), TUM, Freising, Germany
| | - Attila Fehér
- Chair of Plant Biology, University of Szeged, and Institute of Plant Biology, Biological Research Centre, Szeged, Hungary
| | - Götz Hensel
- Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Manfred Gahrtz
- Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Jochen Kumlehn
- Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Stefan Engelhardt
- Chair of Phytopathology, Technical University of Munich (TUM), Freising, Germany
| | - Ralph Hückelhoven
- Chair of Phytopathology, Technical University of Munich (TUM), Freising, Germany
- * E-mail:
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25
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Riffle M, Hoopmann MR, Jaschob D, Zhong G, Moritz RL, MacCoss MJ, Davis TN, Isoherranen N, Zelter A. Discovery and Visualization of Uncharacterized Drug-Protein Adducts Using Mass Spectrometry. Anal Chem 2022; 94:3501-3509. [PMID: 35184559 PMCID: PMC8892443 DOI: 10.1021/acs.analchem.1c04101] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
![]()
Drugs are often metabolized
to reactive intermediates that form
protein adducts. Adducts can inhibit protein activity, elicit immune
responses, and cause life-threatening adverse drug reactions. The
masses of reactive metabolites are frequently unknown, rendering traditional
mass spectrometry-based proteomics approaches incapable of adduct
identification. Here, we present Magnum, an open-mass search algorithm
optimized for adduct identification, and Limelight, a web-based data
processing package for analysis and visualization of data from all
existing algorithms. Limelight incorporates tools for sample comparisons
and xenobiotic-adduct discovery. We validate our tools with three
drug/protein combinations and apply our label-free workflow to identify
novel xenobiotic-protein adducts in CYP3A4. Our new methods and software
enable accurate identification of xenobiotic-protein adducts with
no prior knowledge of adduct masses or protein targets. Magnum outperforms
existing label-free tools in xenobiotic-protein adduct discovery,
while Limelight fulfills a major need in the rapidly developing field
of open-mass searching, which until now lacked comprehensive data
visualization tools.
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Affiliation(s)
- Michael Riffle
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | | | - Daniel Jaschob
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Guo Zhong
- Department of Pharmaceutics, University of Washington, Seattle, Washington 98195, United States
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Trisha N Davis
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nina Isoherranen
- Department of Pharmaceutics, University of Washington, Seattle, Washington 98195, United States
| | - Alex Zelter
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
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26
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Mizero B, Yeung D, Spicer V, Krokhin OV. Peptide retention time prediction for peptides with post-translational modifications: N-terminal (α-amine) and lysine (ε-amine) acetylation. J Chromatogr A 2021; 1657:462584. [PMID: 34619563 DOI: 10.1016/j.chroma.2021.462584] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 10/20/2022]
Abstract
Development of a peptide retention prediction model in reversed-phase chromatography is reported for acetylated peptides - both N-terminal (α-) and side chain of Lys (ε-amine) residues. Large-scale proteomic 2D LC-MS analyses of acetylated/non-acetylated tryptic digest of whole human cell lysate have been used to assemble representative retention data sets of 25,000+ modified/non-modified pairs. This allowed elucidating chromatographic behaviour of modified peptides in three different separation modes: high pH reversed-phase, HILIC separation on amide phase (first dimension of 2D) and reversed-phase separation with formic acid as ion-pairing modifier in the second dimension. On average, N-terminal acetylation increases peptide RP retention at acidic pH by 5 Hydrophobicity Index units (% acetonitrile). Acetylation of first lysine adds another 4.1%. The magnitude of the retention shift varies greatly depending on the number of modified amines, peptide length, and N-terminal peptide sequence. Large retention shifts have been observed for peptides with hydrophobic N-termini and specifically peptides carrying sequences characteristic for amphipathic helical structures - all in complete agreement with major sequence-specific features of RP retention mechanism. The utility of the modified Sequence Specific Retention Calculator model has been verified for the in-vivo N-terminally acetylated peptides detected by 2D LC-MS/MS analysis of a yeast tryptic digest. The effect of N-terminal acetylation was also evaluated for six different HILIC columns, strong cation- and strong anion exchange separations using previously acquired 2D LC-MS/MS data.
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Affiliation(s)
- Benilde Mizero
- Department of Chemistry, University of Manitoba, 360 Parker Building, 144 Dysart Road, Winnipeg, R3T 2N2, Canada
| | - Darien Yeung
- Department of Biochemistry and Medical Genetics, University of Manitoba, 336 BMSB, 745 Bannatyne Avenue, Winnipeg, R3E 0J9, Canada
| | - Vic Spicer
- Manitoba Centre for Proteomics and Systems Biology, 799 JBRC, 715 McDermot Avenue, Winnipeg, R3E 3P4, Canada
| | - Oleg V Krokhin
- Department of Chemistry, University of Manitoba, 360 Parker Building, 144 Dysart Road, Winnipeg, R3T 2N2, Canada; Department of Biochemistry and Medical Genetics, University of Manitoba, 336 BMSB, 745 Bannatyne Avenue, Winnipeg, R3E 0J9, Canada; Manitoba Centre for Proteomics and Systems Biology, 799 JBRC, 715 McDermot Avenue, Winnipeg, R3E 3P4, Canada; Department of Internal Medicine, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg, R3E 3P4, Canada.
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27
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Bouwmeester R, Gabriels R, Hulstaert N, Martens L, Degroeve S. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nat Methods 2021; 18:1363-1369. [PMID: 34711972 DOI: 10.1038/s41592-021-01301-5] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 09/13/2021] [Indexed: 11/09/2022]
Abstract
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.
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Affiliation(s)
- Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Niels Hulstaert
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium. .,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
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28
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Dai J, Yu F, Zhou C, Yu W. Understanding the Limit of Open Search in the Identification of Peptides With Post-translational Modifications - A Simulation-Based Study. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2884-2890. [PMID: 32356758 DOI: 10.1109/tcbb.2020.2991207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Peptide identification from tandem mass spectrometry data is a fundamental task in computational proteomics. Traditional algorithms perform well when facing unmodified peptides. However, when peptides have post-translational modifications (PTMs), these methods cannot provide satisfactory results. Recently, open search methods have been proposed to identify peptides with PTMs. While the performance of these new methods is promising, the identification results vary greatly with respect to the quality of tandem mass spectra and the number of PTMs in peptides. This motivates us to systematically study the relationship between the performance of open search methods and the quality parameters of tandem mass spectrometry data as well as the number of PTMs in peptides. In this paper, we have proposed an analytical model derived from simulated data to describe the relationship between the probability of obtaining correct results and the spectrum quality as well as the number of PTMs. The proposed model is verified using 1,464,146 real experimental spectra. The consistent trend observed in both simulated data and real data reveals the necessary conditions to effectively apply open search methods. Source code of our study is available at http://bioinformatics.ust.hk/PST.html.
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29
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Jakobsson ME. Enzymology and significance of protein histidine methylation. J Biol Chem 2021; 297:101130. [PMID: 34461099 PMCID: PMC8446795 DOI: 10.1016/j.jbc.2021.101130] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/23/2021] [Accepted: 08/26/2021] [Indexed: 12/21/2022] Open
Abstract
Cells synthesize proteins using 20 standard amino acids and expand their biochemical repertoire through intricate enzyme-mediated post-translational modifications (PTMs). PTMs can either be static and represent protein editing events or be dynamically regulated as a part of a cellular response to specific stimuli. Protein histidine methylation (Hme) was an elusive PTM for over 5 decades and has only recently attracted considerable attention through discoveries concerning its enzymology, extent, and function. Here, we review the status of the Hme field and discuss the implications of Hme in physiological and cellular processes. We also review the experimental toolbox for analysis of Hme and discuss the strengths and weaknesses of different experimental approaches. The findings discussed in this review demonstrate that Hme is widespread across cells and tissues and functionally regulates key cellular processes such as cytoskeletal dynamics and protein translation. Collectively, the findings discussed here showcase Hme as a regulator of key cellular functions and highlight the regulation of this modification as an emerging field of biological research.
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30
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Levitsky LI, Bubis JA, Gorshkov MV, Tarasova IA. AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results. J Proteomics 2021; 248:104350. [PMID: 34389500 DOI: 10.1016/j.jprot.2021.104350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/21/2021] [Accepted: 08/04/2021] [Indexed: 11/18/2022]
Abstract
Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source command-line tool available at https://github.com/SimpleNumber/aa_stat. SIGNIFICANCE: Mass spectrometry-based PTM characterization has a long history, yet most of the methods rely on a priori knowledge of modifications of interest and do not provide a whole proteome modification landscape in a blind manner. The open database search is an efficient attempt to address this challenge by identifying peptides with mass shifts corresponding to possible modifications. Then, interpreting these mass shifts is required. Therefore, development of bioinformatics software for post-processing of the open search results, which is capable of detection and accurate annotation of new or unexpected modifications, from characterization of sample preparation efficiency and quality control to discovery of rare post-translational modifications, is of high importance.
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Affiliation(s)
- Lev I Levitsky
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Julia A Bubis
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Irina A Tarasova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia.
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31
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Zhang N, Shi S, Yuan X, Ni W, Wang X, Yoo B, Jia TZ, Li W, Zhang S. A General LC-MS-Based Method for Direct and De Novo Sequencing of RNA Mixtures Containing both Canonical and Modified Nucleotides. Methods Mol Biol 2021; 2298:261-277. [PMID: 34085251 DOI: 10.1007/978-1-0716-1374-0_17] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry (MS)-based sequencing has advantages in direct sequencing of RNA, compared to cDNA-based RNA sequencing methods, as it is completely independent of enzymes and base complementarity errors in sample preparation. In addition, it allows for sequencing of different RNA modifications in a single study, rather than just one specific modification type per study. However, many technical challenges remain in de novo MS sequencing of RNA, making it difficult to MS sequence mixed RNAs or to differentiate isomeric modifications such as pseudouridine (Ψ) from uridine (U). Our recent study incorporates a two-dimensional hydrophobic end labeling strategy into MS-based sequencing (2D-HELS MS Seq) to systematically address the current challenges in MS sequencing of RNA, making it possible to directly and de novo sequence purified single RNA and mixed RNA containing both canonical and modified nucleotides. Here, we describe the method to sequence representative single-RNA and mixed-RNA oligonucleotides, each with a different sequence and/or containing modified nucleotides such as Ψ and 5-methylcytosine (m5C), using 2D-HELS MS Seq.
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Affiliation(s)
- Ning Zhang
- Department of Biological and Chemical Sciences, New York Institute of Technology, New York, NY, USA.,Department of Chemical Engineering, Columbia University, New York, NY, USA
| | - Shundi Shi
- Department of Chemical Engineering, Columbia University, New York, NY, USA
| | - Xiaohong Yuan
- Department of Biological and Chemical Sciences, New York Institute of Technology, New York, NY, USA
| | - Wenhao Ni
- Department of Biological and Chemical Sciences, New York Institute of Technology, New York, NY, USA
| | - Xuanting Wang
- Department of Chemical Engineering, Columbia University, New York, NY, USA
| | - Barney Yoo
- Department of Chemistry, Hunter College, City University of New York, New York, NY, USA
| | - Tony Z Jia
- Earth-Life Science Institute, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan.,Blue Marble Space Institute of Science, Seattle, WA, USA
| | - Wenjia Li
- Department of Computer Science, New York Institute of Technology, New York, NY, USA
| | - Shenglong Zhang
- Department of Biological and Chemical Sciences, New York Institute of Technology, New York, NY, USA.
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32
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Schmidt T, Samaras P, Dorfer V, Panse C, Kockmann T, Bichmann L, van Puyvelde B, Perez-Riverol Y, Deutsch EW, Kuster B, Wilhelm M. Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison. J Proteome Res 2021; 20:3388-3394. [PMID: 33970638 DOI: 10.1021/acs.jproteome.1c00096] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Here, we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can be either provided manually by the user (table format) or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high-quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other web sites (https://github.com/kusterlab/universal_spectrum_explorer).
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Affiliation(s)
- Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany
| | - Patroklos Samaras
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, 4232 Hagenberg im Mühlkreis, Austria
| | - Christian Panse
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology in Zurich/University of Zurich, 8092 Zürich, Switzerland.,SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Tobias Kockmann
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology in Zurich/University of Zurich, 8092 Zürich, Switzerland
| | - Leon Bichmann
- Applied Bioinformatics, Department of Computer Science, University Tübingen, 72074 Tübingen, Germany
| | - Bart van Puyvelde
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, B9000, Ghent, Denmark
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States of America
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany.,Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich (TUM), Freising 85354, Germany
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany.,Computational Mass Spectrometry, Technical University of Munich, Freising 85354, Germany
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33
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Small Mass but Strong Information: Diagnostic Ions Provide Crucial Clues to Correctly Identify Histone Lysine Modifications. Proteomes 2021; 9:proteomes9020018. [PMID: 33922761 PMCID: PMC8167651 DOI: 10.3390/proteomes9020018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/13/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023] Open
Abstract
(1) Background: The proteomic analysis of histones constitutes a delicate task due to the combination of two factors: slight variations in the amino acid sequences of variants and the multiplicity of post-translational modifications (PTMs), particularly those occurring on lysine residues. (2) Methods: To dissect the relationship between both aspects, we carefully evaluated PTM identification on lysine 27 from histone H3 (H3K27) and the artefactual chemical modifications that may lead to erroneous PTM determination. H3K27 is a particularly interesting example because it can bear a range of PTMs and it sits nearby residues 29 and 31 that vary between H3 sequence variants. We discuss how the retention times, neutral losses and immonium/diagnostic ions observed in the MS/MS spectra of peptides bearing modified lysines detectable in the low-mass region might help validate the identification of modified sequences. (3) Results: Diagnostic ions carry key information, thereby avoiding potential mis-identifications due to either isobaric PTM combinations or isobaric amino acid-PTM combinations. This also includes cases where chemical formylation or acetylation of peptide N-termini artefactually occurs during sample processing or simply in the timeframe of LC-MS/MS analysis. Finally, in the very subtle case of positional isomers possibly corresponding to a given mass of lysine modification, the immonium and diagnostic ions may allow the identification of the in vivo structure.
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34
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Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics 2021; 20:100076. [PMID: 33823297 PMCID: PMC8214147 DOI: 10.1016/j.mcpro.2021.100076] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 11/17/2022] Open
Abstract
Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting. First proteogenomics with PSM rescoring using machine learning–predicted spectra Demonstrated on both ribosome profiling and nanopore RNA-Seq–derived databases Rescoring leads to elevated stringency and increased identification rates Rescoring compensates for the search space size issues in proteogenomics
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Affiliation(s)
- Steven Verbruggen
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; OHMX.bio, Ghent, Belgium
| | - Siegfried Gessulat
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Ralf Gabriels
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | | | | | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Sven Degroeve
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Lennart Martens
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Wim Van Criekinge
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Gerben Menschaert
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; OHMX.bio, Ghent, Belgium.
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35
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Cifani P, Li Z, Luo D, Grivainis M, Intlekofer AM, Fenyö D, Kentsis A. Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics. J Proteome Res 2021; 20:1835-1848. [PMID: 33749263 PMCID: PMC8341206 DOI: 10.1021/acs.jproteome.0c00638] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Recent studies have revealed diverse amino acid, post-translational, and noncanonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of substoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEI's robust parametrization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).
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Affiliation(s)
- Paolo Cifani
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10021, United States
| | - Zhi Li
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, New York 10016, United States
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, New York 10016, United States
| | - Danmeng Luo
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10021, United States
| | - Mark Grivainis
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, New York 10016, United States
| | - Andrew M Intlekofer
- Human Oncology & Pathogenesis Program and Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 10021, United States
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, New York 10016, United States
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, New York 10016, United States
| | - Alex Kentsis
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10021, United States
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, and Departments of Pediatrics, Pharmacology, and Physiology & Biophysics, Weill Medical College of Cornell University, New York, New York 10021, United States
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36
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Moyer TB, Parsley NC, Sadecki PW, Schug WJ, Hicks LM. Leveraging orthogonal mass spectrometry based strategies for comprehensive sequencing and characterization of ribosomal antimicrobial peptide natural products. Nat Prod Rep 2021; 38:489-509. [PMID: 32929442 PMCID: PMC7956910 DOI: 10.1039/d0np00046a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Covering: Up to July 2020Ribosomal antimicrobial peptide (AMP) natural products, also known as ribosomally synthesized and post-translationally modified peptides (RiPPs) or host defense peptides, demonstrate potent bioactivities and impressive complexity that complicate molecular and biological characterization. Tandem mass spectrometry (MS) has rapidly accelerated bioactive peptide sequencing efforts, yet standard workflows insufficiently address intrinsic AMP diversity. Herein, orthogonal approaches to accelerate comprehensive and accurate molecular characterization without the need for prior isolation are reviewed. Chemical derivatization, proteolysis (enzymatic and chemical cleavage), multistage MS fragmentation, and separation (liquid chromatography and ion mobility) strategies can provide complementary amino acid composition and post-translational modification data to constrain sequence solutions. Examination of two complex case studies, gomesin and styelin D, highlights the practical implementation of the proposed approaches. Finally, we emphasize the importance of heterogeneous AMP peptidoforms that confer varying biological function, an area that warrants significant further development.
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Affiliation(s)
- Tessa B Moyer
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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37
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Muroski JM, Fu JY, Nguyen HH, Loo RRO, Loo JA. Leveraging Immonium Ions for Targeting Acyl-Lysine Modifications in Proteomic Datasets. Proteomics 2021; 21:e2000111. [PMID: 32896103 PMCID: PMC8742405 DOI: 10.1002/pmic.202000111] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/03/2020] [Indexed: 11/08/2022]
Abstract
Acyl modifications vary greatly in terms of elemental composition and site of protein modification. Developing methods to identify acyl modifications more confidently can help to assess the scope of these modifications in large proteomic datasets. The utility of acyl-lysine immonium ions is analyzed for identifying the modifications in proteomic datasets. It is demonstrated that the cyclized immonium ion is a strong indicator of acyl-lysine presence when its rank or relative abundance compared to other ions within a spectrum is considered. Utilizing a stepped collision energy method in a shotgun experiment highlights the immonium ion. By implementing an analysis that accounted for features within each MS2 spectrum, the method clearly identifies peptides with short chain acyl-lysine modifications from complex lysates. Immonium ions can also be used to validate novel acyl modifications; in this study, the first examples of 3-hydroxylpimelyl-lysine modifications are reported and they are validated using immonium ions. Overall these results solidify the use of the immonium ion as a marker for acyl-lysine modifications in complex proteomic datasets.
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Affiliation(s)
- John M. Muroski
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | - Janine Y. Fu
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | - Hong Hanh Nguyen
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | - Rachel R. Ogorzalek Loo
- David Geffen School of Medicine, Department of Biological Chemistry, University of California, Los Angeles, CA, USA
- UCLA-DOE Institute, University of California, Los Angeles, CA, USA
- UCLA Molecular Biology Institute, University of California, Los Angeles, CA, USA
| | - Joseph A. Loo
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
- David Geffen School of Medicine, Department of Biological Chemistry, University of California, Los Angeles, CA, USA
- UCLA-DOE Institute, University of California, Los Angeles, CA, USA
- UCLA Molecular Biology Institute, University of California, Los Angeles, CA, USA
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38
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Villacrés C, Spicer V, Krokhin OV. Confident Identification of Citrullination and Carbamylation Assisted by Peptide Retention Time Prediction. J Proteome Res 2021; 20:1571-1581. [PMID: 33523662 DOI: 10.1021/acs.jproteome.0c00775] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The chromatographic behavior of peptides carrying citrulline and homocitrulline residues in proteomic two-dimensional (2D) liquid chromatography-mass spectrometry (LC-MS) experiments has been investigated. The primary goal of this study was to determine the chromatographic conditions that allow differentiating between arginine citrullination and deamidation of asparagine based on retention data, improving the confidence of MS-based identifications. Carbamylation was used as a reference point due to a high degree of similarity between modification products and anticipated changes in chromatographic behavior. We applied 2D LC-MS/MS (a high-pH-low-pH reversed phase (RP), hydrophilic interaction liquid chromatography (HILIC)-low-pH RP, and strong cation exchange (SCX)-low-pH RP) to acquire retention data for modified-nonmodified peptide pairs in the four separation modes. Modifications of a standard protein mixture were induced enzymatically (PAD-2) or chemically (urea) for citrullination and carbamylation, respectively. Deamidation occurs spontaneously. Similar retention shifts were observed for all three modifications in a high-pH RP (decrease) and a low-pH RP (increase), thus limiting the applicability of this 2D LC combination. HILIC on bare silica and strong cation exchange separations have been probed to amplify the effect of charge loss upon citrullination, with SCX demonstrating the most differentiating power: the elimination of basic residues upon citrullination/carbamylation results in an ∼58 mM KCl retention decrease, while retention of deamidated products decreases slightly.
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Affiliation(s)
- Carina Villacrés
- Manitoba Centre for Proteomics and Systems Biology, Department of Internal Medicine, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
| | - Victor Spicer
- Manitoba Centre for Proteomics and Systems Biology, Department of Internal Medicine, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
| | - Oleg V Krokhin
- Manitoba Centre for Proteomics and Systems Biology, Department of Internal Medicine, University of Manitoba, Winnipeg, MB R3E 3P4, Canada.,Internal Medicine, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
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39
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Pino LK, Rose J, O'Broin A, Shah S, Schilling B. Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications. Biochem Soc Trans 2020; 48:1953-1966. [PMID: 33079175 PMCID: PMC7609030 DOI: 10.1042/bst20191091] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/14/2022]
Abstract
Research into the basic biology of human health and disease, as well as translational human research and clinical applications, all benefit from the growing accessibility and versatility of mass spectrometry (MS)-based proteomics. Although once limited in throughput and sensitivity, proteomic studies have quickly grown in scope and scale over the last decade due to significant advances in instrumentation, computational approaches, and bio-sample preparation. Here, we review these latest developments in MS and highlight how these techniques are used to study the mechanisms, diagnosis, and treatment of human diseases. We first describe recent groundbreaking technological advancements for MS-based proteomics, including novel data acquisition techniques and protein quantification approaches. Next, we describe innovations that enable the unprecedented depth of coverage in protein signaling and spatiotemporal protein distributions, including studies of post-translational modifications, protein turnover, and single-cell proteomics. Finally, we explore new workflows to investigate protein complexes and structures, and we present new approaches for protein-protein interaction studies and intact protein or top-down MS. While these approaches are only recently incipient, we anticipate that their use in biomedical MS proteomics research will offer actionable discoveries for the improvement of human health.
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Affiliation(s)
- Lindsay K. Pino
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, U.S.A
| | - Jacob Rose
- Buck Institute for Research on Aging, Novato, CA, U.S.A
| | - Amy O'Broin
- Buck Institute for Research on Aging, Novato, CA, U.S.A
| | - Samah Shah
- Buck Institute for Research on Aging, Novato, CA, U.S.A
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40
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Huh S, Hwang D, Kim MS. Statistical Modeling for Enhancing the Discovery Power of Citrullination from Tandem Mass Spectrometry Data. Anal Chem 2020; 92:12975-12986. [PMID: 32876429 DOI: 10.1021/acs.analchem.0c01687] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Citrullination is a post-translational modification implicated in various human diseases including rheumatoid arthritis, Alzheimer's disease, multiple sclerosis, and cancers. Due to a relatively low concentration of citrullinated proteins in the total proteome, confident identification of citrullinated proteome is challenging in mass spectrometry (MS)-based proteomic analysis. From these MS-based analyses, MS features that characterize citrullination, such as immonium ions (IMs) and neutral losses (NLs), called diagnostic ions, have been reported. However, there has been a lack of systematic approaches to comprehensively search for diagnostic ions and no statistical methods for the identification of citrullinated proteome based on these diagnostic ions. Here, we present a systematic approach to identify diagnostic IMs, internal ions (INTs), and NLs for citrullination from tandem mass (MS/MS) spectra. Diagnostic INTs mainly consisted of internal fragment ions for di- and tripeptides that contained two and three amino acids with at least one citrullinated arginine, respectively. A statistical logistic regression model was built for a confident assessment of citrullinated peptides that database searches identified (true positives) and prediction of citrullinated peptides that database searches failed to identify (false negatives) using the diagnostic IMs, INTs, and NLs. Applications of our model to complex global proteome data sets demonstrated the increased accuracy in the identification of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes.
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Affiliation(s)
- Sunghyun Huh
- Department of New Biology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea
| | - Daehee Hwang
- School of Biological Sciences, Seoul National University, Seoul 88026, Republic of Korea
| | - Min-Sik Kim
- Department of New Biology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea
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41
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Abstract
INTRODUCTION The N-terminus of a protein can encode several protein features, including its half-live and its localization. As the proteomics field remains dominated by bottom-up approaches and as N-terminal peptides only account for a fraction of all analyzable peptides, there is a need for their enrichment prior to analysis. COFRADIC, TAILS, and the subtiligase method were among the first N-terminomics methods developed, and several variants and novel methods were introduced that often reduce processing time and/or the amount of material required. AREAS COVERED We present an overview of how the field of N-terminomics developed, including a discussion of the founding methods, several updates made to these and introduce newer methods such as TMPP-labeling, biotin-based methods besides some necessary improvements in data analysis. EXPERT OPINION N-terminomic methods remain being used and improved methods are published however, more efficient use of contemporary mass spectrometers, promising data-independent approaches, and mass spectrometry-free single peptide or protein sequences may threat the N-terminomics field.
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Affiliation(s)
- Annelies Bogaert
- VIB Center for Medical Biotechnology , Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University , Ghent, Belgium
| | - Kris Gevaert
- VIB Center for Medical Biotechnology , Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University , Ghent, Belgium
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42
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Helena Duarte Sagawa C, Zaini PA, de A. B. Assis R, Saxe H, Salemi M, Jacobson A, Wilmarth PA, Phinney BS, M. Dandekar A. Deep Learning Neural Network Prediction Method Improves Proteome Profiling of Vascular Sap of Grapevines during Pierce's Disease Development. BIOLOGY 2020; 9:biology9090261. [PMID: 32882865 PMCID: PMC7565608 DOI: 10.3390/biology9090261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/24/2020] [Accepted: 08/28/2020] [Indexed: 12/31/2022]
Abstract
Plant secretome studies highlight the importance of vascular plant defense proteins against pathogens. Studies on Pierce’s disease of grapevines caused by the xylem-limited bacterium Xylella fastidiosa (Xf) have detected proteins and pathways associated with its pathobiology. Despite the biological importance of the secreted proteins in the extracellular space to plant survival and development, proteome studies are scarce due to methodological challenges. Prosit, a deep learning neural network prediction method is a powerful tool for improving proteome profiling by data-independent acquisition (DIA). We explored the potential of Prosit’s in silico spectral library predictions to improve DIA proteomic analysis of vascular leaf sap from grapevines with Pierce’s disease. The combination of DIA and Prosit-predicted libraries increased the total number of identified grapevine proteins from 145 to 360 and Xf proteins from 18 to 90 compared to gas-phase fractionation (GPF) libraries. The new proteins increased the range of molecular weights, assisted in the identification of more exclusive peptides per protein, and increased identification of low-abundance proteins. These improvements allowed identification of new functional pathways associated with cellular responses to oxidative stress, to be investigated further.
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Affiliation(s)
- Cíntia Helena Duarte Sagawa
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, CA 95616, USA; (C.H.D.S.); (P.A.Z.); (R.d.A.B.A.); (H.S.); (A.J.)
| | - Paulo A. Zaini
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, CA 95616, USA; (C.H.D.S.); (P.A.Z.); (R.d.A.B.A.); (H.S.); (A.J.)
| | - Renata de A. B. Assis
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, CA 95616, USA; (C.H.D.S.); (P.A.Z.); (R.d.A.B.A.); (H.S.); (A.J.)
- Departamento de Ciências Biológicas, Instituto de Ciências Exatas e Biológicas, Núcleo de Pesquisas em Ciências Biológicas, Universidade Federal de Ouro Preto, 122-Bauxita, Ouro Preto-MG 35400-000, Brazil
| | - Houston Saxe
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, CA 95616, USA; (C.H.D.S.); (P.A.Z.); (R.d.A.B.A.); (H.S.); (A.J.)
| | - Michelle Salemi
- Proteomics Core Facility, University of California, Davis, 1 Shields Ave, CA 95616, USA; (M.S.); (B.S.P.)
| | - Aaron Jacobson
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, CA 95616, USA; (C.H.D.S.); (P.A.Z.); (R.d.A.B.A.); (H.S.); (A.J.)
| | - Phillip A. Wilmarth
- Proteomics Shared Resource, Oregon Health and Science University, Medical Research Building, 3252 SW Research Drive, Portland, OR 97239, USA;
| | - Brett S. Phinney
- Proteomics Core Facility, University of California, Davis, 1 Shields Ave, CA 95616, USA; (M.S.); (B.S.P.)
| | - Abhaya M. Dandekar
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, CA 95616, USA; (C.H.D.S.); (P.A.Z.); (R.d.A.B.A.); (H.S.); (A.J.)
- Correspondence:
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43
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Bouwmeester R, Gabriels R, Van Den Bossche T, Martens L, Degroeve S. The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows. Proteomics 2020; 20:e1900351. [PMID: 32267083 DOI: 10.1002/pmic.201900351] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 03/21/2020] [Indexed: 12/30/2022]
Abstract
A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. Therefore, highly promising recent machine learning developments in proteomics are pointed out in this viewpoint, alongside some of the remaining challenges.
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Affiliation(s)
- Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
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44
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Hartel NG, Liu CZ, Graham NA. Improved Discrimination of Asymmetric and Symmetric Arginine Dimethylation by Optimization of the Normalized Collision Energy in Liquid Chromatography–Mass Spectrometry Proteomics. J Proteome Res 2020; 19:3123-3129. [DOI: 10.1021/acs.jproteome.0c00116] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Nicolas G. Hartel
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089, United States
| | - Christopher Z. Liu
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089, United States
| | - Nicholas A. Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089, United States
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California 90089, United States
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45
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Samaras P, Schmidt T, Frejno M, Gessulat S, Reinecke M, Jarzab A, Zecha J, Mergner J, Giansanti P, Ehrlich HC, Aiche S, Rank J, Kienegger H, Krcmar H, Kuster B, Wilhelm M. ProteomicsDB: a multi-omics and multi-organism resource for life science research. Nucleic Acids Res 2020; 48:D1153-D1163. [PMID: 31665479 PMCID: PMC7145565 DOI: 10.1093/nar/gkz974] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 10/11/2019] [Accepted: 10/15/2019] [Indexed: 11/22/2022] Open
Abstract
ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.
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Affiliation(s)
- Patroklos Samaras
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
| | - Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
| | - Martin Frejno
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
| | - Siegfried Gessulat
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany.,Innovation Center Network, SAP SE, Potsdam, Germany
| | - Maria Reinecke
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anna Jarzab
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
| | - Jana Zecha
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
| | - Julia Mergner
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
| | - Piero Giansanti
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
| | | | | | - Johannes Rank
- Chair for Information Systems, Technical University of Munich (TUM), Garching, Germany.,SAP University Competence Center, Technical University of Munich (TUM), Garching, Germany
| | - Harald Kienegger
- Chair for Information Systems, Technical University of Munich (TUM), Garching, Germany.,SAP University Competence Center, Technical University of Munich (TUM), Garching, Germany
| | - Helmut Krcmar
- Chair for Information Systems, Technical University of Munich (TUM), Garching, Germany.,SAP University Competence Center, Technical University of Munich (TUM), Garching, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany.,Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich (TUM), Freising, Bavaria, Germany
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany
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46
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Gingras AC, Carr SA, Burlingame AL. Virtual Issue: Technological Innovations. Mol Cell Proteomics 2020; 19:572-573. [PMID: 32184224 DOI: 10.1074/mcp.e120.002042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Indexed: 11/06/2022] Open
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47
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Steckel A, Uray K, Kalló G, Csosz É, Schlosser G. Investigation of Neutral Losses and the Citrulline Effect for Modified H4 N-Terminal Pentapeptides. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:565-573. [PMID: 31967473 PMCID: PMC7309534 DOI: 10.1021/jasms.9b00036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 01/18/2020] [Accepted: 01/21/2020] [Indexed: 05/15/2023]
Abstract
Tandem mass spectrometry is an indispensable tool in proteomics used for protein sequencing and quantitation. On the basis of the sequential fragments usually generated from peptide ions via collision-induced dissociation, electron-transfer dissociation, or a combination of the two, probabilistic database search engines could be used for the identification of the peptides. The correct localization of posttranslational modifications (PTMs) poses a more challenging problem than the general identification of proteins. Histones are involved in the regulation of DNA transcription via the wealth of PTMs on their N-terminal tail. In this study, we analyzed the histone H4 peptide SGRGK incorporating four different posttranslational modifications: citrullination, acetylation, phosphorylation, and arginine methylation at various positions. The pentapeptides model the enzymatic cleavage of the N-terminal tail of human histone H4 protein by LysC protease. Fragmentation of the peptides was investigated using higher-energy collisional dissociation (HCD), electron-transfer dissociation (ETD), and electron-transfer higher-energy collisional dissociation (EThcD) on an ultrahigh resolution and mass accuracy instrument. We found that while all three techniques have their unique characteristics, advantages, and pitfalls, EThcD generated the most fragment ion-rich spectra. Despite potential ambiguities regarding exact fragment identities, full sequence coverage and PTM mapping may also be achievable. We also found novel neutral losses from the charge-reduced precursors characteristic to citrullination in ETD and EThcD which may be used in proteomic applications. N-Terminal acetylation and arginine methylation could also be confirmed by their characteristic neutral losses from the charge-reduced precursors.
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Affiliation(s)
- Arnold Steckel
- Hevesy György PhD School of Chemistry,
ELTE Eötvös Loránd University, Budapest,
Pázmány Péter sétány 1/A, 1117,
Hungary
- MTA-ELTE Research Group of Peptide Chemistry,
ELTE Eötvös Loránd University, Budapest,
Pázmány Péter sétány 1/A, 1117,
Hungary
| | - Katalin Uray
- MTA-ELTE Research Group of Peptide Chemistry,
ELTE Eötvös Loránd University, Budapest,
Pázmány Péter sétány 1/A, 1117,
Hungary
| | - Gergo Kalló
- Proteomics Core Facility, Department of Biochemistry and
Molecular Biology, Faculty of Medicine, University of Debrecen,
Debrecen, Nagyerdei krt. 98, 4032, Hungary
| | - Éva Csosz
- Proteomics Core Facility, Department of Biochemistry and
Molecular Biology, Faculty of Medicine, University of Debrecen,
Debrecen, Nagyerdei krt. 98, 4032, Hungary
| | - Gitta Schlosser
- MTA-ELTE Research Group of Peptide Chemistry,
ELTE Eötvös Loránd University, Budapest,
Pázmány Péter sétány 1/A, 1117,
Hungary
- Department of Analytical Chemistry, ELTE
Eötvös Loránd University, Budapest,
Pázmány Péter sétány 1/A, 1117,
Hungary
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Bollenbach A, Gambaryan S, Mindukshev I, Pich A, Tsikas D. GC-MS and LC-MS/MS pilot studies on the guanidine (N G)-dimethylation in native, asymmetrically and symmetrically N G-dimethylated arginine-vasopressin peptides and proteins in human red blood cells. J Chromatogr B Analyt Technol Biomed Life Sci 2020; 1141:122024. [PMID: 32062367 DOI: 10.1016/j.jchromb.2020.122024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 02/05/2020] [Accepted: 02/06/2020] [Indexed: 12/16/2022]
Abstract
Protein-arginine methyltransferases catalyze the methylation of the guanidine (NG) group of proteinic L-arginine (Arg) to produce monomethyl and dimethylarginine proteins. Their proteolysis releases the free amino acids monomethylarginine (MMA), symmetric dimethylarginine (SDMA) and asymmetric dimethylarginine (ADMA), respectively. MMA, SDMA and ADMA are inhibitors of the nitric oxide synthase (NOS) activity. High circulating and low urinary concentrations of ADMA and SDMA are considered risk factors in the cardiovascular and renal systems, mainly due to their inhibitory action on NOS activity. Identity, biological activity and concentration of NG-methylated proteins are largely unknown. The present study addressed these issues by using GC-MS and LC-MS/MS approaches. GC-MS was used to quantify free ADMA released by classical HCl-catalyzed hydrolysis of three synthetic Arg-vasopressin (V) peptides and of unknown endogenous NG-dimethylated proteins. The cyclic (c) disulfide forms of Arg-vasopressin analogs, i.e., Arg-vasopressin (cV-Arg-Gly-NH2), asymmetrically NG-dimethylated vasopressin (cV-ADMA-Gly-NH2) and symmetrically NG-dimethylated vasopressin (cV-SDMA-Gly-NH2) were used as model peptides in quantitative GC-MS analyses of ADMA, SDMA and other expected amino acids from the hydrolyzed Arg-vasopressin analogs. cV-ADMA-Gly-NH2 and cV-SDMA-Gly-NH2 were discriminated from cV-Arg-Gly-NH2 by LC-MS and LC-MS/MS, yet they were indistinguishable from each other. The same applies to the respective open (o) reduced and di-S-acetamide forms of oV-ADMA-Gly-NH2, oV-SDMA-Gly-NH2 and oV-Arg-Gly-NH2. Our LC-MS and LC-MS/MS studies suggest that the Arg-vasopressin analogs form [(M-H)]+ and [(M-H)+H]+ in the positive ESI mode and undergo in part conversion of their terminal Gly-NH2 (NH2, 16 Da) group to Gly-OH (OH, 17 Da). The product ion mass spectra of the di-S-acetamide forms are complex and contain several intense mass fragments differing by 1 Da. cV-ADMA-Gly-NH2 and cV-SDMA-Gly-NH2 induced platelet aggregation in platelet-rich human plasma with moderately different initial velocity and maximal aggregation rates compared to cV-Arg-Gly-NH2. Previous studies showed that human red blood cells are rich in large (>50 kDa) ADMA-containing proteins of unknown identity. Our LC-MS/MS proteomic study identified several membrane and cytosolic erythrocytic NG-dimethylated proteins, including spectrin-α (280 kDa), spectrin-β (247 kDa) and protein 4.1 (80 kDa). Being responsible for the stability of the erythrocyte membrane, the newly identified main targets for NG-dimethylation in human erythrocytes should be given a closer look in erythrocytic diseases like hereditary spherocytosis.
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Affiliation(s)
- Alexander Bollenbach
- Institute of Toxicology and Core Unit Proteomics, Hannover Medical School, 30623 Hannover, Germany
| | - Stepan Gambaryan
- Sechenov Institute of Evolutionary Physiology and Biochemistry Russian Academy of Sciences, St. Petersburg 194223, Russia
| | - Igor Mindukshev
- Sechenov Institute of Evolutionary Physiology and Biochemistry Russian Academy of Sciences, St. Petersburg 194223, Russia
| | - Andreas Pich
- Institute of Toxicology and Core Unit Proteomics, Hannover Medical School, 30623 Hannover, Germany
| | - Dimitrios Tsikas
- Institute of Toxicology and Core Unit Proteomics, Hannover Medical School, 30623 Hannover, Germany.
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The Cannabis Proteome Draft Map Project. Int J Mol Sci 2020; 21:ijms21030965. [PMID: 32024021 PMCID: PMC7037972 DOI: 10.3390/ijms21030965] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 02/06/2023] Open
Abstract
Recently we have seen a relaxation of the historic restrictions on the use and subsequent research on the Cannabis plants, generally classified as Cannabis sativa and Cannabis indica. What research has been performed to date has centered on chemical analysis of plant flower products, namely cannabinoids and various terpenes that directly contribute to phenotypic characteristics of the female flowers. In addition, we have seen many groups recently completing genetic profiles of various plants of commercial value. To date, no comprehensive attempt has been made to profile the proteomes of these plants. We report herein our progress on constructing a comprehensive draft map of the Cannabis proteome. To date we have identified over 17,000 potential protein sequences. Unfortunately, no annotated genome of Cannabis plants currently exists. We present a method by which “next generation” DNA sequencing output and shotgun proteomics data can be combined to produce annotated FASTA files, bypassing the need for annotated genetic information altogether in traditional proteomics workflows. The resulting material represents the first comprehensive annotated protein FASTA for any Cannabis plant. Using this annotated database as reference we can refine our protein identifications, resulting in the confident identification of 13,000 proteins with putative function. Furthermore, we demonstrate that post-translational modifications play an important role in the proteomes of Cannabis flower, particularly lysine acetylation and protein glycosylation. To facilitate the evolution of analytical investigations into these plant materials, we have created a portal to host resources developed from our proteomic and metabolomic analysis of Cannabis plant material as well as our results integrating these resources.
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Ayciriex S, Carrière R, Bardet C, Blanc JCYL, Salvador A, Fortin T, Lemoine J. Streamlined Development of Targeted Mass Spectrometry‐Based Method Combining Scout‐MRM and a Web‐Based Tool Indexed with Scout Peptides. Proteomics 2020; 20:e1900254. [DOI: 10.1002/pmic.201900254] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 12/10/2019] [Indexed: 11/12/2022]
Affiliation(s)
- Sophie Ayciriex
- Univ Lyon, CNRSUniversité Claude Bernard Lyon 1ENS de LyonInstitut des Sciences AnalytiquesUMR 5280 5 rue de la Doua F‐69100 Villeurbanne France
| | - Romain Carrière
- Univ Lyon, CNRSUniversité Claude Bernard Lyon 1ENS de LyonInstitut des Sciences AnalytiquesUMR 5280 5 rue de la Doua F‐69100 Villeurbanne France
| | - Chloé Bardet
- Anaquant 5 rue de la Doua F‐69100 Villeurbanne France
| | | | - Arnaud Salvador
- Univ Lyon, CNRSUniversité Claude Bernard Lyon 1ENS de LyonInstitut des Sciences AnalytiquesUMR 5280 5 rue de la Doua F‐69100 Villeurbanne France
| | - Tanguy Fortin
- Anaquant 5 rue de la Doua F‐69100 Villeurbanne France
| | - Jérôme Lemoine
- Univ Lyon, CNRSUniversité Claude Bernard Lyon 1ENS de LyonInstitut des Sciences AnalytiquesUMR 5280 5 rue de la Doua F‐69100 Villeurbanne France
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