Proteomics最新文献

{"title":"Editorial Board: Proteomics 9'24","authors":"","doi":"10.1002/pmic.202470062","DOIUrl":"https://doi.org/10.1002/pmic.202470062","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140641795","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}

{"title":"Contents: Proteomics 9'24","authors":"","doi":"10.1002/pmic.202470063","DOIUrl":"https://doi.org/10.1002/pmic.202470063","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140641796","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}

{"title":"Model fusion for predicting unconventional proteins secreted by exosomes using deep learning","authors":"Yonglin Zhang,&nbsp;Lezheng Yu,&nbsp;Ming Yang,&nbsp;Bin Han,&nbsp;Jiesi Luo,&nbsp;Runyu Jing","doi":"10.1002/pmic.202300184","DOIUrl":"10.1002/pmic.202300184","url":null,"abstract":"<p>Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 17","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630167","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}

{"title":"The tumour-derived extracellular vesicle proteome varies by endometrial cancer histology and is confounded by an obesogenic environment","authors":"Anastasiia Artuyants,&nbsp;George Guo,&nbsp;Marcella Flinterman,&nbsp;Martin Middleditch,&nbsp;Bincy Jacob,&nbsp;Kate Lee,&nbsp;Laura Vella,&nbsp;Huaqi Su,&nbsp;Michelle Wilson,&nbsp;Lois Eva,&nbsp;Andrew N. Shelling,&nbsp;Cherie Blenkiron","doi":"10.1002/pmic.202300055","DOIUrl":"10.1002/pmic.202300055","url":null,"abstract":"<p>Endometrial cancer, the most common gynaecological cancer worldwide, is closely linked to obesity and metabolic diseases, particularly in younger women. New circulating biomarkers have the potential to improve diagnosis and treatment selections, which could significantly improve outcomes. Our approach focuses on extracellular vesicle (EV) biomarker discovery by directly profiling the proteome of EVs enriched from frozen biobanked endometrial tumours. We analysed nine tissue samples to compare three clinical subgroups—low BMI (Body Mass Index) Endometrioid, high BMI Endometrioid, and Serous (any BMI)—identifying proteins related to histological subtype, BMI, and shared secreted proteins. Using collagenase digestion and size exclusion chromatography, we successfully enriched generous quantities of EVs (range 204.8–1291.0 µg protein: 1.38 × 10¹¹–1.10 × 10¹² particles), characterised by their size (∼150 nm), expression of EV markers (CD63/81), and proposed endometrial cancer markers (L1CAM, ANXA2). Mass spectrometry-based proteomic profiling identified 2075 proteins present in at least one of the 18 samples. Compared to cell lysates, EVs were successfully depleted for mitochondrial and blood proteins and enriched for common EV markers and large secreted proteins. Further analysis highlighted significant differences in EV protein profiles between the high BMI subgroup and others, underlining the impact of comorbidities on the EV secretome. Interestingly, proteins differentially abundant in tissue subgroups were largely not also differential in matched EVs. This research identified secreted proteins known to be involved in endometrial cancer pathophysiology and proposed novel diagnostic biomarkers (EIF6, MUC16, PROM1, SLC26A2).</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 11","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202300055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677902","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}

{"title":"Review of predicting protein stability changes upon variations","authors":"Yiling Qiu,&nbsp;Tao Huang,&nbsp;Yu-Dong Cai","doi":"10.1002/pmic.202300371","DOIUrl":"10.1002/pmic.202300371","url":null,"abstract":"<p>Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 12-13","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140631114","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}

{"title":"Proteomic profiling of antibiotic-resistant Escherichia coli GW-AmxH19 isolated from hospital wastewater treated with physical plasma","authors":"Veronika Hahn,&nbsp;Daniela Zühlke,&nbsp;Hauke Winter,&nbsp;Annchristin Landskron,&nbsp;Jörg Bernhardt,&nbsp;Susanne Sievers,&nbsp;Michael Schmidt,&nbsp;Thomas von Woedtke,&nbsp;Katharina Riedel,&nbsp;Juergen F. Kolb","doi":"10.1002/pmic.202300494","DOIUrl":"10.1002/pmic.202300494","url":null,"abstract":"<p>Microorganisms which are resistant to antibiotics are a global threat to the health of humans and animals. Wastewater treatment plants are known hotspots for the dissemination of antibiotic resistances. Therefore, novel methods for the inactivation of pathogens, and in particular antibiotic-resistant microorganisms (ARM), are of increasing interest. An especially promising method could be a water treatment by physical plasma which provides charged particles, electric fields, UV-radiation, and reactive species. The latter are foremost responsible for the antimicrobial properties of plasma. Thus, with plasma it might be possible to reduce the amount of ARM and to establish this technology as additional treatment stage for wastewater remediation. However, the impact of plasma on microorganisms beyond a mere inactivation was analyzed in more detail by a proteomic approach. Therefore, <i>Escherichia coli</i> GW-AmxH19, isolated from hospital wastewater in Germany, was used. The bacterial solution was treated by a plasma discharge ignited between each of four pins and the liquid surface. The growth of <i>E. coli</i> and the pH-value decreased during plasma treatment in comparison with the untreated control. Proteome and antibiotic resistance profile were analyzed. Concentrations of nitrite and nitrate were determined as long-lived indicative products of a transient chemistry associated with reactive nitrogen species (RNS). Conversely, hydrogen peroxide served as indicator for reactive oxygen species (ROS). Proteome analyses revealed an oxidative stress response as a result of plasma-generated RNS and ROS as well as a pH-balancing reaction as key responses to plasma treatment. Both, the generation of reactive species and a decreased pH-value is characteristic for plasma-treated solutions. The plasma-mediated changes of the proteome are discussed also in comparison with the Gram-positive bacterium <i>Bacillus subtilis</i>. Furthermore, no effect of the plasma treatment, on the antibiotic resistance of <i>E. coli</i>, was determined under the chosen conditions. The knowledge about the physiological changes of ARM in response to plasma is of fundamental interest to understand the molecular basis for the inactivation. This will be important for the further development and implementation of plasma in wastewater remediation.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 19","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140679176","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}

{"title":"Intensity and retention time prediction improves the rescoring of protein-nucleic acid cross-links","authors":"Arslan Siraj,&nbsp;Robbin Bouwmeester,&nbsp;Arthur Declercq,&nbsp;Luisa Welp,&nbsp;Aleksandar Chernev,&nbsp;Alexander Wulf,&nbsp;Henning Urlaub,&nbsp;Lennart Martens,&nbsp;Sven Degroeve,&nbsp;Oliver Kohlbacher,&nbsp;Timo Sachsenberg","doi":"10.1002/pmic.202300144","DOIUrl":"https://doi.org/10.1002/pmic.202300144","url":null,"abstract":"<p>In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202300144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606317","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}

{"title":"Editorial Board: Proteomics 8'24","authors":"","doi":"10.1002/pmic.202470052","DOIUrl":"https://doi.org/10.1002/pmic.202470052","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606369","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}

{"title":"Contents: Proteomics 8'24","authors":"","doi":"10.1002/pmic.202470053","DOIUrl":"https://doi.org/10.1002/pmic.202470053","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606318","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}

{"title":"From data to discovery: The essential role of computational tools in proteomics","authors":"Wout Bittremieux","doi":"10.1002/pmic.202300081","DOIUrl":"https://doi.org/10.1002/pmic.202300081","url":null,"abstract":"<p>In the ever-evolving landscape of scientific inquiry, the saying “software is eating the world,” popularized in Silicon Valley over a decade ago, rings truer than ever before. This aphorism, initially indicative of the transformative power of software in reshaping industries and everyday life, has found a significant echo in the realm of science. Akin to a master chef who artfully combines a variety of raw ingredients to concoct a delightful meal, in proteomics, bioinformatics serves as the critical skill set that distills complex, raw data into digestible, insightful knowledge. This editorial aims to showcase the breadth of innovation and inquiry encapsulated in this special issue of <i>Proteomics</i>, dedicated to computational mass spectrometry and proteomics, and underline the indispensable role of advanced computational tools in deciphering the molecular intricacies of life itself.</p><p>Proteomics research, a cornerstone of ‘omics studies, provides a panoramic view into the molecular and cellular mechanisms underpinning life. Through the analysis of proteins, their structures, functions, and interactions with various molecules, proteomics endeavors to unravel the complex molecular tapestry of biological systems. The manuscripts featured in this special issue illuminate the wide scope of scientific knowledge that can be gleaned from proteomics experiments, made possible only through the employment of sophisticated computational tools and bioinformatics analyses.</p><p>Echoing recent advancements in artificial intelligence, several papers in this issue delve into the application of machine learning tools for enhancing the analysis of mass spectrometry-based proteomics data. For instance, Adams et al. offer a comprehensive review on utilizing predicted peptide properties like spectral similarity, retention time, and ion mobility features to refine immunopeptidomics data analysis [<span>1</span>]. In a similar vein, Siraj et al. discuss the enhancement of protein–nucleic acid cross-links detection through the prediction of fragment ion intensities and retention time [<span>2</span>]. Peptide property prediction, a task that has become increasingly commonplace in recent years, enables accurate and sensitive rescoring of spectrum assignments in bottom-up proteomics data. The contributions in this special issue demonstrate that this strategy is particularly potent in realms that exhibit non-standard and highly complex spectral data, such as immunopeptidomics and protein–RNA crosslinking mass spectrometry.</p><p>Further, Joyce and Searle's review on computational approaches for phosphoproteomics identification and localization presents the future potential of using predicted peptide properties for interpreting phosphopeptide positional isomers and disambiguating chimeric spectra containing multiple isomeric peptides that differ only in the phosphorylation location [<span>3</span>]. Additionally, Picciani et al. introduce the Oktoberfest tool, le","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202300081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606367","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}