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Yu T, Hu T, Na K, Zhang L, Lu S, Guo X. Glutamine-derived peptides: Current progress and future directions. Compr Rev Food Sci Food Saf 2024; 23:e13386. [PMID: 38847753 DOI: 10.1111/1541-4337.13386] [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: 01/21/2024] [Revised: 04/25/2024] [Accepted: 05/18/2024] [Indexed: 06/13/2024]
Abstract
Glutamine, the most abundant amino acid in the body, plays a critical role in preserving immune function, nitrogen balance, intestinal integrity, and resistance to infection. However, its limited solubility and instability present challenges for its use a functional nutrient. Consequently, there is a preference for utilizing glutamine-derived peptides as an alternative to achieve enhanced functionality. This article aims to review the applications of glutamine monomers in clinical, sports, and enteral nutrition. It compares the functional effectiveness of monomers and glutamine-derived peptides and provides a comprehensive assessment of glutamine-derived peptides in terms of their classification, preparation, mechanism of absorption, and biological activity. Furthermore, this study explores the potential integration of artificial intelligence (AI)-based peptidomics and synthetic biology in the de novo design and large-scale production of these peptides. The findings reveal that glutamine-derived peptides possess significant structure-related bioactivities, with the smaller molecular weight fraction serving as the primary active ingredient. These peptides possess the ability to promote intestinal homeostasis, exert hypotensive and hypoglycemic effects, and display antioxidant properties. However, our understanding of the structure-function relationships of glutamine-derived peptides remains largely exploratory at current stage. The combination of AI based peptidomics and synthetic biology presents an opportunity to explore the untapped resources of glutamine-derived peptides as functional food ingredients. Additionally, the utilization and bioavailability of these peptides can be enhanced through the use of delivery systems in vivo. This review serves as a valuable reference for future investigations of and developments in the discovery, functional validation, and biomanufacturing of glutamine-derived peptides in food science.
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Affiliation(s)
- Tianfei Yu
- College of Life Science, South-Central Minzu University, Wuhan City, China
| | - Tianshuo Hu
- College of Life Science, South-Central Minzu University, Wuhan City, China
| | - Kai Na
- College of Life Science, South-Central Minzu University, Wuhan City, China
| | - Li Zhang
- College of Life Science, South-Central Minzu University, Wuhan City, China
| | - Shuang Lu
- College of Life Science, South-Central Minzu University, Wuhan City, China
| | - Xiaohua Guo
- College of Life Science, South-Central Minzu University, Wuhan City, China
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2
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Lin L, Li C, Zhang T, Xia C, Bai Q, Jin L, Shen Y. An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion. Anal Chim Acta 2024; 1298:342419. [PMID: 38462343 DOI: 10.1016/j.aca.2024.342419] [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: 08/20/2023] [Revised: 12/23/2023] [Accepted: 02/26/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. RESULTS A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. SIGNIFICANCE The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.
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Affiliation(s)
- Like Lin
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Cong Li
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China.
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Chaoshuang Xia
- Center for Biomedical Mass Spectrometry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, United States
| | - Qiuhong Bai
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Lihua Jin
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Yehua Shen
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China.
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3
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Stastna M. Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches. FEBS J 2024. [PMID: 38440918 DOI: 10.1111/febs.17108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/22/2024] [Accepted: 02/20/2024] [Indexed: 03/06/2024]
Abstract
Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.
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Affiliation(s)
- Miroslava Stastna
- Institute of Analytical Chemistry of the Czech Academy of Sciences, Brno, Czech Republic
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4
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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5
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Retout M, Amer L, Yim W, Creyer MN, Lam B, Trujillo DF, Potempa J, O'Donoghue AJ, Chen C, Jokerst JV. A Protease-Responsive Polymer/Peptide Conjugate and Reversible Assembly of Silver Clusters for the Detection of Porphyromonas gingivalis Enzymatic Activity. ACS NANO 2023; 17:17308-17319. [PMID: 37602819 PMCID: PMC10561899 DOI: 10.1021/acsnano.3c05268] [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] [Indexed: 08/22/2023]
Abstract
We report the reversible aggregation of silver nanoparticle (AgNP) assemblies using the combination of a cationic arginine-based peptide and sulfur-capped polyethylene glycol (PEG). The formation and dissociation of the aggregates were studied by optical methods and electron microscopy. The dissociation of silver clusters depends on the peptide sequence and PEG size. A molecular weight of 1 kDa for PEG was optimal for the dissociation. The most important feature of this dissociation method is that it can operate in complex biofluids such as plasma, saliva, bile, urine, cell media, or even seawater without a significant decrease in performance. Moreover, the peptide-particle assemblies are highly stable and do not degrade (or express of loss of signal upon dissociation) when dried and resolubilized, frozen and thawed, or left in daylight for a month. Importantly, the dissociation capacity of PEG can be reduced via the conjugation of a peptide-cleavable substrate. The dissociation capacity is restored in the presence of an enzyme. Based on these findings, we designed a PEG-peptide hybrid molecule specific to the Porphyromonas gingivalis protease RgpB. Our motivation was that this bacterium is a key pathogen in periodontitis, and RgpB activity has been correlated with chronic diseases including Alzheimer's disease. The RgpB limit of detection was 100 pM RgpB in vitro. This system was used to measure RgpB in gingival crevicular fluid (GCF) samples with a detection rate of 40% with 0% false negatives versus PCR for P. gingivalis (n = 37). The combination of PEG-peptide and nanoparticles dissociation method allows the development of convenient protease sensing that can operate independently of the media composition.
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Affiliation(s)
- Maurice Retout
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Lubna Amer
- Materials Science and Engineering Program, University of California, San Diego, La Jolla, California 92093, United States
| | - Wonjun Yim
- Materials Science and Engineering Program, University of California, San Diego, La Jolla, California 92093, United States
| | - Matthew N Creyer
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Benjamin Lam
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Diego F Trujillo
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
| | - Jan Potempa
- Department of Microbiology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow 30-387, Poland
- Department of Oral Immunology and Infectious Diseases, School of Dentistry, University of Louisville, Louisville, Kentucky 40202, United States
| | - Anthony J O'Donoghue
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
| | - Casey Chen
- Herman Ostrow School of Dentistry, University of Southern California, 925 West 34th Street, Los Angeles, California 90089, United States
| | - Jesse V Jokerst
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093, United States
- Materials Science and Engineering Program, University of California, San Diego, La Jolla, California 92093, United States
- Department of Radiology, University of California, San Diego, La Jolla, California 92093, United States
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6
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Maasch JRMA, Torres MDT, Melo MCR, de la Fuente-Nunez C. Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. Cell Host Microbe 2023; 31:1260-1274.e6. [PMID: 37516110 DOI: 10.1016/j.chom.2023.07.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/12/2023] [Accepted: 07/06/2023] [Indexed: 07/31/2023]
Abstract
Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.
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Affiliation(s)
- Jacqueline R M A Maasch
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
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7
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Son J, Na S, Paek E. DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning. Anal Chem 2023; 95:11193-11200. [PMID: 37459568 PMCID: PMC10401496 DOI: 10.1021/acs.analchem.3c00460] [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: 01/31/2023] [Accepted: 07/05/2023] [Indexed: 08/02/2023]
Abstract
Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino acid sequences or physicochemical properties, despite the fact that peptides detected by MS are dependent on many factors, including protein sample preparation, digestion, separation, ionization, and precursor selection during MS experiments. DbyDeep (Detectability by Deep learning) is an innovative end-to-end LSTM network model for peptide detectability prediction that incorporates sequence contexts of peptides and their cleavage sites (by protease). Utilizing the cleavage site contexts could improve the performance of prediction, and DbyDeep outperformed existing methods in predicting peptides recognizable from multiple MS/MS data sets with diverse species and MS instruments. We argue for the necessity of a learning model that encompasses several contexts associated with peptide detection, as opposed to depending just on peptide sequences. There is a Python implementation of DbyDeep at https://github.com/BISCodeRepo/DbyDeep.
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Affiliation(s)
- Juho Son
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Seungjin Na
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
- Institute
for Artificial Intelligence Research, Hanyang
University, Seoul 04763, Republic
of Korea
| | - Eunok Paek
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
- Institute
for Artificial Intelligence Research, Hanyang
University, Seoul 04763, Republic
of Korea
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8
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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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9
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Sun B, Liu Z, Liu J, Zhao S, Wang L, Wang F. The utility of proteases in proteomics, from sequence profiling to structure and function analysis. Proteomics 2023; 23:e2200132. [PMID: 36382392 DOI: 10.1002/pmic.202200132] [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: 08/11/2022] [Revised: 11/08/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022]
Abstract
In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.
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Affiliation(s)
- Binwen Sun
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Zheyi Liu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
| | - Jin Liu
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Shan Zhao
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
| | - Liming Wang
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Fangjun Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049, China
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10
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Retout M, Jin Z, Tsujimoto J, Mantri Y, Borum R, Creyer MN, Yim W, He T, Chang YC, Jokerst JV. Di-Arginine Additives for Dissociation of Gold Nanoparticle Aggregates: A Matrix-Insensitive Approach with Applications in Protease Detection. ACS APPLIED MATERIALS & INTERFACES 2022; 14:52553-52565. [PMID: 36346346 PMCID: PMC10464667 DOI: 10.1021/acsami.2c17531] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We report the reversible aggregation of gold nanoparticles (AuNPs) assemblies via a di-arginine peptide additive and thiolated PEGs (HS-PEGs). The AuNPs were first aggregated by attractive forces between the citrate-capped surface and the arginine side chains. We found that the HS-PEG thiol group has a higher affinity for the AuNP surface, thus leading to redispersion and colloidal stability. In turn, there was a robust and obvious color change due to on/off plasmonic coupling. The assemblies' dissociation was directly related to the HS-PEG structural properties such as their size or charge. As an example, HS-PEGs with a molecular weight below 1 kDa could dissociate 100% of the assemblies and restore the exact optical properties of the initial AuNP suspension (prior to the assembly). Surprisingly, the dissociation capacity of HS-PEGs was not affected by the composition of the operating medium and could be performed in complex matrices such as plasma, saliva, bile, urine, cell lysates, or even seawater. The high affinity of thiols for the gold surface encompasses by far the one of endogenous molecules and is thus favored. Moreover, starting with AuNPs already aggregated ensured the absence of a background signal as the dissociation of the assemblies was far from spontaneous. Remarkably, it was possible to dry the AuNP assemblies and solubilize them back with HS-PEGs, improving the colorimetric signal generation. We used this system for protease sensing in biological fluids. Trypsin was chosen as the model enzyme, and highly positively charged peptides were conjugated to HS-PEG molecules as cleavage substrates. The increase of positive charge of the HS-PEG-peptide conjugate quenched the dissociation capacity of the HS-PEG molecules, which could only be restored by the proteolytic cleavage. Picomolar limit of detection was obtained as well as the detection in saliva or urine.
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Affiliation(s)
- Maurice Retout
- Department of NanoEngineering, University of California, San Diego, La Jolla, California92093, United States
| | - Zhicheng Jin
- Department of NanoEngineering, University of California, San Diego, La Jolla, California92093, United States
| | - Jason Tsujimoto
- Department of Bioengineering, University of California, San Diego, La Jolla, California92093, United States
| | - Yash Mantri
- Department of Bioengineering, University of California, San Diego, La Jolla, California92093, United States
| | - Raina Borum
- Department of NanoEngineering, University of California, San Diego, La Jolla, California92093, United States
| | - Matthew N Creyer
- Department of NanoEngineering, University of California, San Diego, La Jolla, California92093, United States
| | - Wonjun Yim
- Materials Science and Engineering Program, University of California, San Diego, La Jolla, California92093, United States
| | - Tengyu He
- Materials Science and Engineering Program, University of California, San Diego, La Jolla, California92093, United States
| | - Yu-Ci Chang
- Materials Science and Engineering Program, University of California, San Diego, La Jolla, California92093, United States
| | - Jesse V Jokerst
- Department of NanoEngineering, University of California, San Diego, La Jolla, California92093, United States
- Materials Science and Engineering Program, University of California, San Diego, La Jolla, California92093, United States
- Department of Radiology, University of California, San Diego, La Jolla, California92093, United States
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11
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Creyer MN, Jin Z, Retout M, Yim W, Zhou J, Jokerst JV. Gold-Silver Core-Shell Nanoparticle Crosslinking Mediated by Protease Activity for Colorimetric Enzyme Detection. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2022; 38:14200-14207. [PMID: 36351199 DOI: 10.1021/acs.langmuir.2c02219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Plasmonic nanoparticles produce a localized surface plasmon resonance (LSPR) under optical excitation. The LSPR of nanoparticles can shift in response to changes in the local dielectric environment and produce a color change. This color change can be observed by the naked eye due to the exceptionally large extinction coefficients (108-1011 M-1 cm-1) of plasmonic nanoparticles. Herein, we investigate the optical shifts (i.e., color change) of three unique gold-silver core-shell nanoparticle structures in response to changes in their dielectric environment upon nanoparticle aggregation. Aggregation is induced by a cysteine-containing peptide that has a sulfhydryl near its N and C termini, which crosslinks nanoparticles. Furthermore, we demonstrate that adding proline spacers between the cysteines impacts the degree of aggregation and, ultimately, the color response. Using this information, we construct a colorimetric enzyme assay, where the signal produced from nanoparticle aggregation is modulated by proteolysis. The degree of aggregation and the resulting optical shift can be correlated with enzyme concentration with high linearity (R2 = 0.998). Overall, this study explores the optical properties of gold-silver core-shell nanoparticles in a dispersed vs aggregated state and leverages that information to develop an enzyme sensor with a spectral LOD of 0.47 ± 0.09 nM.
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12
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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.
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13
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Ekvall M, Truong P, Gabriel W, Wilhelm M, Käll L. Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities. J Proteome Res 2022; 21:1359-1364. [PMID: 35413196 PMCID: PMC9087333 DOI: 10.1021/acs.jproteome.1c00870] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Machine learning
has been an integral part of interpreting data
from mass spectrometry (MS)-based proteomics for a long time. Relatively
recently, a machine-learning structure appeared successful in other
areas of bioinformatics, Transformers. Furthermore, the implementation
of Transformers within bioinformatics has become relatively convenient
due to transfer learning, i.e., adapting a network trained for other
tasks to new functionality. Transfer learning makes these relatively
large networks more accessible as it generally requires less data,
and the training time improves substantially. We implemented a Transformer
based on the pretrained model TAPE to predict MS2 intensities. TAPE
is a general model trained to predict missing residues from protein
sequences. Despite being trained for a different task, we could modify
its behavior by adding a prediction head at the end of the TAPE model
and fine-tune it using the spectrum intensity from the training set
to the well-known predictor Prosit. We demonstrate that the predictor,
which we call Prosit Transformer, outperforms the recurrent neural-network-based
predictor Prosit, increasing the median angular similarity on its
hold-out set from 0.908 to 0.929. We believe that Transformers will
significantly increase prediction accuracy for other types of predictions
within MS-based proteomics.
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Affiliation(s)
- Markus Ekvall
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology─KTH, Box 1031, SE-17121 Solna, Sweden
| | - Patrick Truong
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology─KTH, Box 1031, SE-17121 Solna, Sweden
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Lukas Käll
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology─KTH, Box 1031, SE-17121 Solna, Sweden
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14
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Kozlowski LP. Proteome-pI 2.0: proteome isoelectric point database update. Nucleic Acids Res 2022; 50:D1535-D1540. [PMID: 34718696 PMCID: PMC8728302 DOI: 10.1093/nar/gkab944] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 09/28/2021] [Accepted: 10/04/2021] [Indexed: 11/18/2022] Open
Abstract
Proteome-pI 2.0 is an update of an online database containing predicted isoelectric points and pKa dissociation constants of proteins and peptides. The isoelectric point-the pH at which a particular molecule carries no net electrical charge-is an important parameter for many analytical biochemistry and proteomics techniques. Additionally, it can be obtained directly from the pKa values of individual charged residues of the protein. The Proteome-pI 2.0 database includes data for over 61 million protein sequences from 20 115 proteomes (three to four times more than the previous release). The isoelectric point for proteins is predicted by 21 methods, whereas pKa values are inferred by one method. To facilitate bottom-up proteomics analysis, individual proteomes were digested in silico with the five most commonly used proteases (trypsin, chymotrypsin, trypsin + LysC, LysN, ArgC), and the peptides' isoelectric point and molecular weights were calculated. The database enables the retrieval of virtual 2D-PAGE plots and customized fractions of a proteome based on the isoelectric point and molecular weight. In addition, isoelectric points for proteins in NCBI non-redundant (nr), UniProt, SwissProt, and Protein Data Bank are available in both CSV and FASTA formats. The database can be accessed at http://isoelectricpointdb2.org.
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Affiliation(s)
- Lukasz Pawel Kozlowski
- Institute of Informatics, Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Mazovian Voivodeship 02-097, Poland
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15
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Hruska M, Holub D. Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2021; 27:217-234. [PMID: 34989269 DOI: 10.1177/14690667211066725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact--increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.
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Affiliation(s)
- Miroslav Hruska
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, 98735Palacky University, Olomouc, Czech Republic
- Department of Computer Science, Faculty of Science, 98735Palacky University, Olomouc, Czech Republic
| | - Dusan Holub
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, 98735Palacky University, Olomouc, Czech Republic
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16
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Creyer MN, Jin Z, Moore C, Yim W, Zhou J, Jokerst JV. Modulation of Gold Nanorod Growth via the Proteolysis of Dithiol Peptides for Enzymatic Biomarker Detection. ACS APPLIED MATERIALS & INTERFACES 2021; 13:45236-45243. [PMID: 34520186 PMCID: PMC8549377 DOI: 10.1021/acsami.1c11620] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Gold nanorods possess optical properties that are tunable and highly sensitive to variations in their aspect ratio (length/width). Therefore, the development of a sensing platform where the gold nanorod morphology (i.e., aspect ratio) is modulated in response to an analyte holds promise in achieving ultralow detection limits. Here, we use a dithiol peptide as an enzyme substrate during nanorod growth. The sensing mechanism is enabled by the substrate design, where the dithiol peptide contains an enzyme cleavage site in-between cysteine amino acids. When cleaved, the peptide dramatically impacts gold nanorod growth and the resulting optical properties. We demonstrate that the optical response can be correlated with enzyme concentration and achieve a 45 pM limit of detection. Furthermore, we extend this sensing platform to colorimetrically detect tumor-associated inhibitors in a biologically relevant medium. Overall, these results present a subnanomolar method to detect proteases that are critical biomarkers found in cancers, infectious diseases, and inflammatory disorders.
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Affiliation(s)
- Matthew N Creyer
- Department of Nanoengineering, University of California, La Jolla, San Diego, California 92093, United States
| | - Zhicheng Jin
- Department of Nanoengineering, University of California, La Jolla, San Diego, California 92093, United States
| | - Colman Moore
- Department of Nanoengineering, University of California, La Jolla, San Diego, California 92093, United States
| | - Wonjun Yim
- Materials Science and Engineering Program, University of California, La Jolla, San Diego, California 92093, United States
| | - Jiajing Zhou
- Department of Nanoengineering, University of California, La Jolla, San Diego, California 92093, United States
| | - Jesse V Jokerst
- Department of Nanoengineering, University of California, La Jolla, San Diego, California 92093, United States
- Materials Science and Engineering Program, University of California, La Jolla, San Diego, California 92093, United States
- Department of Radiology, University of California, La Jolla, San Diego, California 92093, United States
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17
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Mann M, Kumar C, Zeng WF, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst 2021; 12:759-770. [PMID: 34411543 DOI: 10.1016/j.cels.2021.06.006] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/07/2021] [Accepted: 06/28/2021] [Indexed: 12/14/2022]
Abstract
There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.
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Affiliation(s)
- Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| | - Chanchal Kumar
- Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
| | - Wen-Feng Zeng
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
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18
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Sun B, Smialowski P, Straub T, Imhof A. Investigation and Highly Accurate Prediction of Missed Tryptic Cleavages by Deep Learning. J Proteome Res 2021; 20:3749-3757. [PMID: 34137619 DOI: 10.1021/acs.jproteome.1c00346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Trypsin is one of the most important and widely used proteolytic enzymes in mass spectrometry (MS)-based proteomic research. It exclusively cleaves peptide bonds at the C-terminus of lysine and arginine. However, the cleavage is also affected by several factors, including specific surrounding amino acids, resulting in frequent incomplete proteolysis and subsequent issues in peptide identification and quantification. The accurate annotations on missed cleavages are crucial to database searching in MS analysis. Here, we present deep-learning predicting missed cleavages (dpMC), a novel algorithm for the prediction of missed trypsin cleavage sites. This algorithm provides a very high accuracy for predicting missed cleavages with area under the curves (AUCs) of cross-validation and holdout testing above 0.99, along with the mean F1 score and the Matthews correlation coefficient (MCC) of 0.9677 and 0.9349, respectively. We tested our algorithm on data sets from different species and different experimental conditions, and its performance outperforms other currently available prediction methods. In addition, the method also provides a better insight into the detailed rules of trypsin cleavages coupled with propensity and motif analysis. Moreover, our method can be integrated into database searching in the MS analysis to identify and quantify mass spectra effectively and efficiently.
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Affiliation(s)
- Bo Sun
- Biomedical Center, Protein Analysis Unit, Faculty of Medicine, Ludwig-Maximilians-Universität München, Großhaderner Strasse 9, 82152 Planegg-Martinsried, Germany
| | - Pawel Smialowski
- Institute of Stem Cell Research, Helmholtz Center Munich, German Research Center for Environmental Health, 85764 Munich, Germany.,Biomedical Center, Computational Biology Unit, Faculty of Medicine, Ludwig-Maximilians-Universität München, Großhaderner Strasse 9, 82152 Planegg-Martinsried, Germany
| | - Tobias Straub
- Biomedical Center, Computational Biology Unit, Faculty of Medicine, Ludwig-Maximilians-Universität München, Großhaderner Strasse 9, 82152 Planegg-Martinsried, Germany
| | - Axel Imhof
- Biomedical Center, Protein Analysis Unit, Faculty of Medicine, Ludwig-Maximilians-Universität München, Großhaderner Strasse 9, 82152 Planegg-Martinsried, Germany
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