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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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Kalathiya U, Padariya M, Faktor J, Coyaud E, Alfaro JA, Fahraeus R, Hupp TR, Goodlett DR. Interfaces with Structure Dynamics of the Workhorses from Cells Revealed through Cross-Linking Mass Spectrometry (CLMS). Biomolecules 2021; 11:382. [PMID: 33806612 PMCID: PMC8001575 DOI: 10.3390/biom11030382] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 02/26/2021] [Accepted: 03/01/2021] [Indexed: 12/28/2022] Open
Abstract
The fundamentals of how protein-protein/RNA/DNA interactions influence the structures and functions of the workhorses from the cells have been well documented in the 20th century. A diverse set of methods exist to determine such interactions between different components, particularly, the mass spectrometry (MS) methods, with its advanced instrumentation, has become a significant approach to analyze a diverse range of biomolecules, as well as bring insights to their biomolecular processes. This review highlights the principal role of chemistry in MS-based structural proteomics approaches, with a particular focus on the chemical cross-linking of protein-protein/DNA/RNA complexes. In addition, we discuss different methods to prepare the cross-linked samples for MS analysis and tools to identify cross-linked peptides. Cross-linking mass spectrometry (CLMS) holds promise to identify interaction sites in larger and more complex biological systems. The typical CLMS workflow allows for the measurement of the proximity in three-dimensional space of amino acids, identifying proteins in direct contact with DNA or RNA, and it provides information on the folds of proteins as well as their topology in the complexes. Principal CLMS applications, its notable successes, as well as common pipelines that bridge proteomics, molecular biology, structural systems biology, and interactomics are outlined.
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Affiliation(s)
- Umesh Kalathiya
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Monikaben Padariya
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Jakub Faktor
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Etienne Coyaud
- Protéomique Réponse Inflammatoire Spectrométrie de Mass—PRISM, Inserm U1192, University Lille, CHU Lille, F-59000 Lille, France;
| | - Javier A. Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, UK
| | - Robin Fahraeus
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Ted R. Hupp
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, UK
| | - David R. Goodlett
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
- Department of Biochemistry & Microbiology, University of Victoria, Victoria, BC V8Z 7X8, Canada
- Genome BC Proteome Centre, University of Victoria, Victoria, BC V8Z 5N3, Canada
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Xu F, Wang L, Ju X, Zhang J, Yin S, Shi J, He R, Yuan Q. Transepithelial Transport of YWDHNNPQIR and Its Metabolic Fate with Cytoprotection against Oxidative Stress in Human Intestinal Caco-2 Cells. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2017; 65:2056-2065. [PMID: 28218523 DOI: 10.1021/acs.jafc.6b04731] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Studies on antioxidant peptides extracted from foodstuff sources have included not only experiments to elucidate their chemical characteristics but also to investigate their bioavailability and intracellular mechanisms. This study was designed to clarify the absorption and antioxidative activity of YWDHNNPQIR (named RAP), which is derived from rapeseed protein using a Caco-2 cell transwell model. Results showed that 0.8% RAP (C0 = 0.2 mM, t = 90 min) could maintain the original structure across the Caco-2 cell monolayers via the intracellular transcytosis pathway, and the apparent drug absorption rate (Papp) was (6.6 ± 1.24) × 10-7 cm/s. Three main fragments (WDHNNPQIR, DHNNPQIR, and YWDHNNPQ) and five modified peptides derived from RAP were found in both the apical and basolateral side of the Caco-2 cell transwell model. Among these new metabolites, WDHNNPQIR had the greatest antioxidative activity in Caco-2 cells apart from the DPPH assay. With a RAP concentration of 200 μM, there were significant differences in four antioxidative indicators (T-AOC, GSH-Px, SOD, and MDA) compared to the oxidative stress control (P < 0.05). In addition, RAP may also influence apoptosis of the Caco-2 cells, which was caused by AAPH-induced oxidative damage.
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Affiliation(s)
- Feiran Xu
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
| | - Lifeng Wang
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
| | - Xingrong Ju
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
| | - Jing Zhang
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
| | - Shi Yin
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
| | - Jiayi Shi
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
| | - Rong He
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
| | - Qiang Yuan
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics , Nanjing 210023, P.R. China
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Schliekelman P, Liu S. Quantifying the effect of competition for detection between coeluting peptides on detection probabilities in mass-spectrometry-based proteomics. J Proteome Res 2013; 13:348-61. [PMID: 24313442 DOI: 10.1021/pr400034z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
There are many factors that contribute to the variation in detection probabilities of proteins in LC-MS/MS experiments, and currently little is known about their relative importance. In this study, we analyze the effect of competition for detection between coeluting peptides on peptide detection probability. Using a novel method for estimating peptide detection probabilities, we show that these probabilities can vary by an order of magnitude between peptides that elute from the liquid chromatograph at the same time as many other peptides and those that elute with fewer other peptides. To explore these results, we use a mathematical model to show that competition for detection between peptides is expected to be a major source of missed detections in complex mixtures because there will be many MS/MS scanning intervals that contain more coeluting peptides than can be subjected to MS/MS analysis. Our data and simulation results show that the number of coeluting peptides is a primary determinant of whether a peptide will be detected. In our data, this had a several-fold larger effect on peptide detection probability than did peptide abundance. Furthermore, the distribution of elution times for the most frequently detected peptides was strongly shifted toward values where there were few coeluting peptides, indicating that the number of coeluting peptides is a major determinant of whether a peptide is proteotypic.
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Affiliation(s)
- Paul Schliekelman
- Department of Statistics, University of Georgia , 204 Statistics Building, Athens, Georgia 30602, United States
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Data Acquisition Strategy for Mass Spectrometers Applied to Bottom-up-Based Protein Identification. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2013. [DOI: 10.1016/s1872-2040(13)60742-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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