Proteomics最新文献_第6页

Contents: Proteomics 20'24 内容：蛋白质组学 20'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-10-10 DOI: 10.1002/pmic.202470163

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Astronaut proteomics: Japan leads the way for transformative studies in space 宇航员蛋白质组学：日本引领太空变革性研究。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-10-10 DOI: 10.1002/pmic.202300645

Alexia Tasoula, Nathaniel Szewczyk

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Editorial Board: Proteomics 20'24 编辑委员会：蛋白质组学 20'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-10-10 DOI: 10.1002/pmic.202470162

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CoNglyPred: Accurate Prediction of N-Linked Glycosylation Sites Using ESM-2 and Structural Features With Graph Network and Co-Attention. CoNglyPred：利用 ESM-2 和结构特征以及图形网络和共注意力准确预测 N-连接糖基化位点。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-10-03 DOI: 10.1002/pmic.202400210

Hongmei Wang, Long Zhao, Ziyuan Yu, Ximin Zeng, Shaoping Shi

{"title":"CoNglyPred: Accurate Prediction of N-Linked Glycosylation Sites Using ESM-2 and Structural Features With Graph Network and Co-Attention.","authors":"Hongmei Wang, Long Zhao, Ziyuan Yu, Ximin Zeng, Shaoping Shi","doi":"10.1002/pmic.202400210","DOIUrl":"https://doi.org/10.1002/pmic.202400210","url":null,"abstract":"<p><p>N-Linked glycosylation is crucial for various biological processes such as protein folding, immune response, and cellular transport. Traditional experimental methods for determining N-linked glycosylation sites entail substantial time and labor investment, which has led to the development of computational approaches as a more efficient alternative. However, due to the limited availability of 3D structural data, existing prediction methods often struggle to fully utilize structural information and fall short in integrating sequence and structural information effectively. Motivated by the progress of protein pretrained language models (pLMs) and the breakthrough in protein structure prediction, we introduced a high-accuracy model called CoNglyPred. Having compared various pLMs, we opt for the large-scale pLM ESM-2 to extract sequence embeddings, thus mitigating certain limitations associated with manual feature extraction. Meanwhile, our approach employs a graph transformer network to process the 3D protein structures predicted by AlphaFold2. The final graph output and ESM-2 embedding are intricately integrated through a co-attention mechanism. Among a series of comprehensive experiments on the independent test dataset, CoNglyPred outperforms state-of-the-art models and demonstrates exceptional performance in case study. In addition, we are the first to report the uncertainty of N-linked glycosylation predictors using expected calibration error and expected uncertainty calibration error.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400210"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142363612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Editorial Board: Proteomics 19'24 编辑委员会：蛋白质组学 19'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-10-02 DOI: 10.1002/pmic.202470152

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Standard abbreviations 标准缩写。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-10-02 DOI: 10.1002/pmic.202470154

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Contents: Proteomics 19'24 内容：蛋白质组学 19'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-10-02 DOI: 10.1002/pmic.202470153

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Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques 利用机器学习技术在单细胞水平鉴定胎儿肠道发育过程中的关键基因

IF 3.4 4区生物学

Proteomics Pub Date : 2024-09-26 DOI: 10.1002/pmic.202400104

QingLan Ma, Mei Meng, XianChao Zhou, Wei Guo, KaiYan Feng, Tao Huang, Yu-Dong Cai

{"title":"Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques","authors":"QingLan Ma, Mei Meng, XianChao Zhou, Wei Guo, KaiYan Feng, Tao Huang, Yu-Dong Cai","doi":"10.1002/pmic.202400104","DOIUrl":"10.1002/pmic.202400104","url":null,"abstract":"<div>\u0000 \u0000 <p>The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells. Our study aimed to enhance our understanding of fetal gut development by employing advanced machine-learning techniques on single-cell sequencing data. This dataset consisted of 62,849 samples, each characterized by 33,694 distinct gene features. Four feature ranking algorithms were utilized to sort features according to their significance, resulting in four feature lists. Then, these lists were fed into an incremental feature selection method to extract essential genes, classification rules, and build efficient classifiers. Several important genes were recognized by multiple feature ranking algorithms, such as FGG, MDK, RBP1, RBP2, IGFBP7, and SPON2. These features were key in differentiating specific developing intestinal cells, including epithelial, immune, mesenchymal, and vasculature cells of the colon, duo jejunum, and ileum cells. The classification rules showed special gene expression patterns on some intestinal cell types and the efficient classifiers can be useful tools for identifying intestinal cells.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 23-24","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142338036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Development of a Proteomic Workflow for the Identification of Heparan Sulphate Proteoglycan-Binding Substrates of ADAM17 开发用于鉴定 ADAM17 的硫酸肝素蛋白多糖结合底物的蛋白质组工作流程。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-09-24 DOI: 10.1002/pmic.202400076

Matteo Calligaris, Donatella Pia Spanò, Maria Chiara Puccio, Stephan A. Müller, Simone Bonelli, Margot Lo Pinto, Giovanni Zito, Carl P. Blobel, Stefan F. Lichtenthaler, Linda Troeberg, Simone Dario Scilabra

{"title":"Development of a Proteomic Workflow for the Identification of Heparan Sulphate Proteoglycan-Binding Substrates of ADAM17","authors":"Matteo Calligaris, Donatella Pia Spanò, Maria Chiara Puccio, Stephan A. Müller, Simone Bonelli, Margot Lo Pinto, Giovanni Zito, Carl P. Blobel, Stefan F. Lichtenthaler, Linda Troeberg, Simone Dario Scilabra","doi":"10.1002/pmic.202400076","DOIUrl":"10.1002/pmic.202400076","url":null,"abstract":"<p>Ectodomain shedding, which is the proteolytic release of transmembrane proteins from the cell surface, is crucial for cell-to-cell communication and other biological processes. The metalloproteinase ADAM17 mediates ectodomain shedding of over 50 transmembrane proteins ranging from cytokines and growth factors, such as TNF and EGFR ligands, to signalling receptors and adhesion molecules. Yet, the ADAM17 sheddome is only partly defined and biological functions of the protease have not been fully characterized. Some ADAM17 substrates (e.g., HB-EGF) are known to bind to heparan sulphate proteoglycans (HSPG), and we hypothesised that such substrates would be under-represented in traditional secretome analyses, due to their binding to cell surface or pericellular HSPGs. Thus, to identify novel HSPG-binding ADAM17 substrates, we developed a proteomic workflow that involves addition of heparin to solubilize HSPG-binding proteins from the cell layer, thereby allowing their mass spectrometry detection by heparin-treated secretome (HEP-SEC) analysis. Applying this methodology to murine embryonic fibroblasts stimulated with an ADAM17 activator enabled us to identify 47 transmembrane proteins that were shed in response to ADAM17 activation. This included known HSPG-binding ADAM17 substrates (i.e., HB-EGF, CX3CL1) and 14 novel HSPG-binding putative ADAM17 substrates. Two of these, MHC-I and IL1RL1, were validated as ADAM17 substrates by immunoblotting.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 23-24","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202400076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142338035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Sensitive Profiling of Mouse Liver Membrane Proteome Dysregulation Following a High-Fat and Alcohol Diet Treatment 高脂和酒精饮食治疗后小鼠肝脏膜蛋白质组失调的灵敏分析。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-09-23 DOI: 10.1002/pmic.202300599

Frank Antony, Zora Brough, Mona Orangi, Mohammed Al-Seragi, Hiroyuki Aoki, Mohan Babu, Franck Duong van Hoa

{"title":"Sensitive Profiling of Mouse Liver Membrane Proteome Dysregulation Following a High-Fat and Alcohol Diet Treatment","authors":"Frank Antony, Zora Brough, Mona Orangi, Mohammed Al-Seragi, Hiroyuki Aoki, Mohan Babu, Franck Duong van Hoa","doi":"10.1002/pmic.202300599","DOIUrl":"10.1002/pmic.202300599","url":null,"abstract":"<p>Alcohol consumption and high-fat (HF) diets often coincide in Western society, resulting in synergistic negative effects on liver function. Although studies have analyzed the global protein expression in the context of alcoholic liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD), none has offered specific insights on liver dysregulation at the membrane proteome level. Membrane-specific profiling of metabolic and compensatory phenomena is usually overshadowed in conventional proteomic workflows. In this study, we use the Peptidisc method to isolate and compare the membrane protein (MP) content of the liver with its unique biological functions. From mice fed with an HF diet and ethanol in drinking water, we annotate over 1500 liver proteins with half predicted to have at least one transmembrane segment. Among them, we identify 106 integral MPs that are dysregulated compared to the untreated sample. Gene Ontology analysis reveals several dysregulated membrane-associated processes like lipid metabolism, cell adhesion, xenobiotic processing, and mitochondrial membrane formation. Pathways related to cholesterol and bile acid transport are also mutually affected, suggesting an adaptive mechanism to counter the upcoming steatosis of the liver model. Taken together, our Peptidisc-based profiling of the diet-dysregulated liver provides specific insights and hypotheses into the role of the transmembrane proteome in disease development, and flags desirable MPs for therapeutic and diagnostic targeting.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 23-24","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202300599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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