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Kehl N, Gessner A, Maas R, Fromm MF, Taudte RV. A supervised machine-learning approach for the efficient development of a multi method (LC-MS) for a large number of drugs and subsets thereof: focus on oral antitumor agents. Clin Chem Lab Med 2024; 62:293-302. [PMID: 37606251 DOI: 10.1515/cclm-2023-0468] [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: 05/09/2023] [Accepted: 07/31/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVES Accumulating evidence argues for a more widespread use of therapeutic drug monitoring (TDM) to support individualized medicine, especially for therapies where toxicity and efficacy are critical issues, such as in oncology. However, development of TDM assays struggles to keep pace with the rapid introduction of new drugs. Therefore, novel approaches for faster assay development are needed that also allow effortless inclusion of newly approved drugs as well as customization to smaller subsets if scientific or clinical situations require. METHODS We applied and evaluated two machine-learning approaches i.e., a regression-based approach and an artificial neural network (ANN) to retention time (RT) prediction for efficient development of a liquid chromatography mass spectrometry (LC-MS) method quantifying 73 oral antitumor drugs (OADs) and five active metabolites. Individual steps included training, evaluation, comparison, and application of the superior approach to RT prediction, followed by stipulation of the optimal gradient. RESULTS Both approaches showed excellent results for RT prediction (mean difference ± standard deviation: 2.08 % ± 9.44 % ANN; 1.78 % ± 1.93 % regression-based approach). Using the regression-based approach, the optimum gradient (4.91 % MeOH/min) was predicted with a total run time of 17.92 min. The associated method was fully validated following FDA and EMA guidelines. Exemplary modification and application of the regression-based approach to a subset of 14 uro-oncological agents resulted in a considerably shortened run time of 9.29 min. CONCLUSIONS Using a regression-based approach, a multi drug LC-MS assay for RT prediction was efficiently developed, which can be easily expanded to newly approved OADs and customized to smaller subsets if required.
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Affiliation(s)
- Niklas Kehl
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arne Gessner
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Renke Maas
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- FAU NeW - Research Center for New Bioactive Compounds, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Martin F Fromm
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- FAU NeW - Research Center for New Bioactive Compounds, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - R Verena Taudte
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Core Facility for Metabolomics, Department of Medicine, Philipps-Universität Marburg, 35043 Marburg, Germany
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Abdul-Khalek N, Wimmer R, Overgaard MT, Gregersen Echers S. Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach. Comput Struct Biotechnol J 2023; 21:3715-3727. [PMID: 37560124 PMCID: PMC10407266 DOI: 10.1016/j.csbj.2023.07.027] [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: 03/06/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/11/2023] Open
Abstract
Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.
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Affiliation(s)
- Naim Abdul-Khalek
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Reinhard Wimmer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
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Neely BA, Dorfer V, Martens L, Bludau I, Bouwmeester R, Degroeve S, Deutsch EW, Gessulat S, Käll L, Palczynski P, Payne SH, Rehfeldt TG, Schmidt T, Schwämmle V, Uszkoreit J, Vizcaíno JA, Wilhelm M, Palmblad M. Toward an Integrated Machine Learning Model of a Proteomics Experiment. J Proteome Res 2023; 22:681-696. [PMID: 36744821 PMCID: PMC9990124 DOI: 10.1021/acs.jproteome.2c00711] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.
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Affiliation(s)
- Benjamin A Neely
- National Institute of Standards and Technology, Charleston, South Carolina 29412, United States
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | | | - Lukas Käll
- Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Solna, Sweden
| | - Pawel Palczynski
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, Utah 84602, United States
| | - Tobias Greisager Rehfeldt
- Institute for Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | | | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Julian Uszkoreit
- Medical Proteome Analysis, Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801 Bochum, Germany.,Medizinisches Proteom-Center, Medical Faculty, Ruhr University Bochum, 44801 Bochum, Germany
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), 85354 Freising, Germany
| | - Magnus Palmblad
- Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, The Netherlands
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Leeming MG, Ang CS, Nie S, Varshney S, Williamson NA. Simulation of mass spectrometry-based proteomics data with Synthedia. BIOINFORMATICS ADVANCES 2022; 3:vbac096. [PMID: 36698761 PMCID: PMC9825309 DOI: 10.1093/bioadv/vbac096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/08/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
Motivation A large number of experimental and bioinformatic parameters must be set to identify and quantify peptides in mass spectrometry experiments and each of these will impact the results. An ability to simulate raw data with known contents would allow researchers to rapidly explore the effects of varying experimental parameters and systematically investigate downstream processing software. A range of data simulators are available for established data-dependent acquisition methodologies, but these do not extend to the rapidly developing field of data-independent acquisition (DIA) strategies. Results Here, we present Synthedia-a software package to simulate DIA liquid chromatography-mass spectrometry for bottom-up proteomics experiments. Synthedia can generate datasets with known peptide precursor ions and fragments and allows for the customization of a wide variety of chromatographic and mass spectrometry parameters. Availability and implementation Synthedia is freely available via the internet and can be used through a graphical website (https://synthedia.org/) or locally via the command line (https://github.com/mgleeming/synthedia/). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | - Ching-Seng Ang
- Bio21 Molecular Science & Biotechnology Institute, Melbourne Mass Spectrometry and Proteomics Facility, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Shuai Nie
- Bio21 Molecular Science & Biotechnology Institute, Melbourne Mass Spectrometry and Proteomics Facility, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Swati Varshney
- Bio21 Molecular Science & Biotechnology Institute, Melbourne Mass Spectrometry and Proteomics Facility, The University of Melbourne, Melbourne, VIC 3052, Australia
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Rodriguez J, Gomez-Cano L, Grotewold E, de Leon N. Normalizing and Correcting Variable and Complex LC-MS Metabolomic Data with the R Package pseudoDrift. Metabolites 2022; 12:435. [PMID: 35629939 PMCID: PMC9144304 DOI: 10.3390/metabo12050435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 01/27/2023] Open
Abstract
In biological research domains, liquid chromatography-mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC-MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC-MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants.
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Affiliation(s)
- Jonas Rodriguez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Lina Gomez-Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA; (L.G.-C.); (E.G.)
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA; (L.G.-C.); (E.G.)
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA;
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