1
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Claushuis B, Cordfunke RA, de Ru AH, Otte A, van Leeuwen HC, Klychnikov OI, van Veelen PA, Corver J, Drijfhout JW, Hensbergen PJ. In-Depth Specificity Profiling of Endopeptidases Using Dedicated Mix-and-Split Synthetic Peptide Libraries and Mass Spectrometry. Anal Chem 2023; 95:11621-11631. [PMID: 37495545 PMCID: PMC10413326 DOI: 10.1021/acs.analchem.3c01215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/10/2023] [Indexed: 07/28/2023]
Abstract
Proteases comprise the class of enzymes that catalyzes the hydrolysis of peptide bonds, thereby playing a pivotal role in many aspects of life. The amino acids surrounding the scissile bond determine the susceptibility toward protease-mediated hydrolysis. A detailed understanding of the cleavage specificity of a protease can lead to the identification of its endogenous substrates, while it is also essential for the design of inhibitors. Although many methods for protease activity and specificity profiling exist, none of these combine the advantages of combinatorial synthetic libraries, i.e., high diversity, equimolar concentration, custom design regarding peptide length, and randomization, with the sensitivity and detection power of mass spectrometry. Here, we developed such a method and applied it to study a group of bacterial metalloproteases that have the unique specificity to cleave between two prolines, i.e., Pro-Pro endopeptidases (PPEPs). We not only confirmed the prime-side specificity of PPEP-1 and PPEP-2, but also revealed some new unexpected peptide substrates. Moreover, we have characterized a new PPEP (PPEP-3) that has a prime-side specificity that is very different from that of the other two PPEPs. Importantly, the approach that we present in this study is generic and can be extended to investigate the specificity of other proteases.
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Affiliation(s)
- Bart Claushuis
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333 ZA, The Netherlands
| | - Robert A. Cordfunke
- Department
of Immunology, Leiden University Medical
Center, Leiden, 2333 ZA, The Netherlands
| | - Arnoud H. de Ru
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333 ZA, The Netherlands
| | - Annemarie Otte
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333 ZA, The Netherlands
| | - Hans C. van Leeuwen
- Department
of CBRN Protection, Netherlands Organization
for Applied Scientific Research TNO, Rijswijk, 2280 AA, The Netherlands
| | - Oleg I. Klychnikov
- Department
of Biochemistry, Moscow State University, Moscow 119991, Russian Federation
| | - Peter A. van Veelen
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333 ZA, The Netherlands
| | - Jeroen Corver
- Department
of Medical Microbiology, Leiden University
Medical Center, Leiden, 2333 ZA, The Netherlands
| | - Jan W. Drijfhout
- Department
of Immunology, Leiden University Medical
Center, Leiden, 2333 ZA, The Netherlands
| | - Paul J. Hensbergen
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333 ZA, The Netherlands
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2
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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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3
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Gussakovsky D, Anderson G, Spicer V, Krokhin OV. Peptide separation selectivity in proteomics LC-MS experiments: Comparison of formic and mixed formic/heptafluorobutyric acids ion-pairing modifiers. J Sep Sci 2020; 43:3830-3839. [PMID: 32818315 DOI: 10.1002/jssc.202000578] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Separation selectivity and detection sensitivity of reversed-phase high-performance liquid chromatography with tandem mass spectrometry analyses were compared for formic (0.1%) and formic/heptafluorobutyric (0.1%/0.005%) acid based eluents using a proteomic data set of ∼12 000 paired peptides. The addition of a small amount of hydrophobic heptafluorobutyric acid ion-pairing modifier increased peptide retention by up to 10% acetonitrile depending on peptide charge, size, and hydrophobicity. Retention increase was greatest for peptides that were short, highly charged, and hydrophilic. There was an ∼3.75-fold reduction in MS signal observed across the whole population of peptides following the addition of heptafluorobutyric acid. This resulted in ∼36% and ∼21% reduction of detected proteins and unique peptides for the whole cell lysate digests, respectively. We also confirmed that the separation selectivity of the formic/heptafluorobutyric acid system was very similar to the commonly used conditions of 0.1% trifluoroacetic acid, and developed a new version of the Sequence-Specific Retention calculator model for the formic/heptafluorobutyric acid system showing the same ∼0.98 R2 -value accuracy as the Sequence-Specific Retention calculator formic acid model. In silico simulation of peptide distribution in separation space showed that the addition of 0.005% heptafluorobutyric acid to the 0.1% formic acid system increased potential proteome coverage by ∼11% of detectable species (tryptic peptides ≥ four amino acids).
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Affiliation(s)
- Daniel Gussakovsky
- Department of Chemistry, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Geoff Anderson
- Department of Chemistry, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Vic Spicer
- Manitoba Centre for Proteomics and Systems Biology, Winnipeg, Manitoba, Canada
| | - Oleg V Krokhin
- Manitoba Centre for Proteomics and Systems Biology, Winnipeg, Manitoba, Canada.,Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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4
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Mohammed Y, Palmblad M. Visualization and application of amino acid retention coefficients obtained from modeling of peptide retention. J Sep Sci 2018; 41:3644-3653. [PMID: 30047222 PMCID: PMC6175132 DOI: 10.1002/jssc.201800488] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 07/17/2018] [Accepted: 07/18/2018] [Indexed: 11/08/2022]
Abstract
We introduce a method for data inspection in liquid separations of peptides using amino acid retention coefficients and their relative change across experiments. Our method allows for the direct comparison between actual experimental conditions, regardless of sample content and without the use of internal standards. The modeling uses linear regression of peptide retention time as a function of amino acid composition. We demonstrate the pH dependency of the model in a control experiment where the pH of the mobile phase was changed in controlled way. We introduce a score to identify the false discovery rate on peptide spectrum match level that corresponds to the set of most robust models, i.e. to maximize the shared agreement between experiments. We demonstrate the method utility in reversed-phase liquid chromatography using 24 datasets with minimal peptide overlap. We apply our method on datasets obtained from a public repository representing various separation designs, including one-dimensional reversed-phase liquid chromatography followed by tandem mass spectrometry, and two-dimensional online strong cation exchange coupled to reversed-phase liquid chromatography followed by tandem mass spectrometry, and highlight new insights. Our method provides a simple yet powerful way to inspect data quality, in particular for multidimensional separations, improving comparability of data at no additional experimental cost.
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Affiliation(s)
- Yassene Mohammed
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, Netherlands.,University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, Canada
| | - Magnus Palmblad
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, Netherlands
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