Proteomics最新文献_第4页

Editorial Board: Proteomics 3'25 编辑委员会：蛋白质组学3'25

IF 3.4 4区生物学

Proteomics Pub Date : 2025-02-04 DOI: 10.1002/pmic.202570012

引用次数: 0

IF 3.4 4区生物学

Proteomics Pub Date : 2025-02-04 DOI: 10.1002/pmic.202570013

引用次数: 0

Comparative Analysis of Data-Driven Rescoring Platforms for Improved Peptide Identification in HeLa Digest Samples 数据驱动评分平台在HeLa消化样品中改进多肽鉴定的比较分析。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-02-02 DOI: 10.1002/pmic.202400225

Jesus D. Castaño, Francis Beaudry

{"title":"Comparative Analysis of Data-Driven Rescoring Platforms for Improved Peptide Identification in HeLa Digest Samples","authors":"Jesus D. Castaño, Francis Beaudry","doi":"10.1002/pmic.202400225","DOIUrl":"10.1002/pmic.202400225","url":null,"abstract":"Mass spectrometry is a critical tool to understand complex changes in biological processes. Despite significant advances in search engine technology, many spectra remain unassigned. This research evaluates the performance of three rescoring platforms, Oktoberfest, MS2Rescore, and inSPIRE, using MaxQuant output. The results indicated a substantial increase in identifications at the peptide level (40%–53%) and PSM level (64%–67%). However, some peptides were lost due to limitations in processing posttranslational modifications (PTMs)—with up to 75% of lost peptides exhibiting PTMs. Each platform displayed distinct strengths and weaknesses. For instance, inSPIRE performed best in terms of peptide identifications and unique peptides, while MS2Rescore performed better for PSMs at higher FDR values. Differences in platform performance stemmed from different sources: original search engine feature selection, type of ion series predicted, retention time predictor, and PTMs compatibility. Overall, inSPIRE showed a superior ability to harness original search engine results. Taken all together, rescoring platforms clearly outperformed original search results; however, they demanded additional computation time (up to 77%) and manual adjustments. The findings here underline the necessity of integrating rescoring platforms into current proteomics pipelines but also address some challenges in their implementation and optimization. Future integrated platforms may help enhance adoption.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202400225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078112","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}

引用次数: 0

Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing. 超越数据库：解决从头测序的挑战和潜力。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-31 DOI: 10.1002/pmic.202400321

Tim Van Den Bossche, Denis Beslic, Sam van Puyenbroeck, Tomi Suomi, Tanja Holstein, Lennart Martens, Laura L Elo, Thilo Muth

{"title":"Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing.","authors":"Tim Van Den Bossche, Denis Beslic, Sam van Puyenbroeck, Tomi Suomi, Tanja Holstein, Lennart Martens, Laura L Elo, Thilo Muth","doi":"10.1002/pmic.202400321","DOIUrl":"https://doi.org/10.1002/pmic.202400321","url":null,"abstract":"Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes. De novo sequencing presents a promising alternative, which derives peptide sequences directly from mass spectra without requiring a database. Over time, this approach has evolved from manual annotation to advanced graph-based, tag-based, and deep learning-based methods, significantly improving the accuracy of peptide identification. This Viewpoint explores the evolution, advantages, limitations, and future opportunities of de novo sequencing in metaproteomics. We highlight recent technological advancements that have improved its potential for detecting unsequenced species and for providing deeper functional insights into microbial communities.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400321"},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143062337","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}

引用次数: 0

Computational Drug Repositioning in Cardiorenal Disease: Opportunities, Challenges, and Approaches. 心肾疾病的计算药物重新定位：机遇、挑战和方法。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-31 DOI: 10.1002/pmic.202400109

Paul Perco, Matthias Ley, Kinga Kęska-Izworska, Dorota Wojenska, Enrico Bono, Samuel M Walter, Lucas Fillinger, Klaus Kratochwill

引用次数: 0

Editorial Board: Proteomics 1–2'25 编辑委员会：蛋白质组学1-2 '25

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-15 DOI: 10.1002/pmic.202570002

引用次数: 0

Contents: Proteomics 1–2'25 内容：蛋白质组学1-2 '25

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-15 DOI: 10.1002/pmic.202570003

引用次数: 0

The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease. 组学驱动的机器学习路径对慢性肾脏疾病具有成本效益的精准医疗。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-10 DOI: 10.1002/pmic.202400108

Marta B Lopes, Roberta Coletti, Flore Duranton, Griet Glorieux, Mayra Alejandra Jaimes Campos, Julie Klein, Matthias Ley, Paul Perco, Alexia Sampri, Aviad Tur-Sinai

{"title":"The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.","authors":"Marta B Lopes, Roberta Coletti, Flore Duranton, Griet Glorieux, Mayra Alejandra Jaimes Campos, Julie Klein, Matthias Ley, Paul Perco, Alexia Sampri, Aviad Tur-Sinai","doi":"10.1002/pmic.202400108","DOIUrl":"https://doi.org/10.1002/pmic.202400108","url":null,"abstract":"Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi-omics datasets, AI can provide real-time, patient-specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI-driven discoveries to ensure their efficacy, relevance, and cost-effectiveness in patient care.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400108"},"PeriodicalIF":3.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942016","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}

引用次数: 0

The Proteomic Landscape of the Coronary Accessible Heart Cell Surfaceome 冠状动脉可达性心脏细胞表面体的蛋白质组学研究。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-10 DOI: 10.1002/pmic.202400320

Iasmin Inocencio, Alin Rai, Daniel Donner, David W. Greening

{"title":"The Proteomic Landscape of the Coronary Accessible Heart Cell Surfaceome","authors":"Iasmin Inocencio, Alin Rai, Daniel Donner, David W. Greening","doi":"10.1002/pmic.202400320","DOIUrl":"10.1002/pmic.202400320","url":null,"abstract":"Cell surface proteins (surfaceome) represent key signalling and interaction molecules for therapeutic targeting, biomarker profiling and cellular phenotyping in physiological and pathological states. Here, we employed coronary artery perfusion with membrane-impermeant biotin to label and capture the surface-accessible proteome in the neo-native (intact) heart. Using quantitative proteomics, we identified 701 heart cell surfaceome accessible by the coronary artery, including receptors, cell surface enzymes, adhesion and junctional molecules. This surfaceome comprises to 216 cardiac cell-specific surface proteins, including 29 proteins reported in cardiomyocytes (CXADR, CACNA1C), 12 in cardiac fibroblasts (ITGA8, COL3A1) and 63 in multiple cardiac cell types (ICAM1, SLC3A2, CDH2). Further, this surfaceome comprises to 53 proteins enriched in heart tissue compared to other tissues in humans and implicated in cardiac cell signalling networks involving cardiomyopathy (CDH2, DTNA, PTKP2, SNTA1, CAM, K2D/B), cardiac muscle contraction and development (ENG, SNTA1, SGCG, MYPN), calcium ion binding (SGCA, MASP1, THBS4, FBLN2, GSN) and cell metabolism (SDHA, NUDFS1, GYS1, ACO2, IDH2). This method offers a powerful tool for dissecting the molecular landscape of the coronary artery accessible heart cell surfaceome, its role in maintaining cardiac and vascular function, and potential molecular leads for studying cardiac cell interactions and systemic delivery to the neo-native heart.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202400320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942018","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}

引用次数: 0

Proteomic Insight Into Alzheimer's Disease Pathogenesis Pathways. 蛋白质组学洞察阿尔茨海默病的发病途径。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-10 DOI: 10.1002/pmic.202400298

Taekyung Ryu, Kyungdo Kim, Nicholas Asiimwe, Chan Hyun Na

引用次数: 0