Journal of Proteome Research最新文献

{"title":"To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.","authors":"Naim Abdul-Khalek, Mario Picciani, Omar Shouman, Reinhard Wimmer, Michael Toft Overgaard, Mathias Wilhelm, Simon Gregersen Echers","doi":"10.1021/acs.jproteome.4c00973","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00973","url":null,"abstract":"<p><p>Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is an encoder-decoder with an attention mechanism, classifying peptides as flyers or non-flyers, and providing both binary and categorical probabilities for four distinct classes defined in this study. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias toward synthesizability, improving predictive capacity and outperforming state-of-the-art predictors in benchmark comparisons across different human and cross-species datasets. The study further investigates the influence of protein abundance and rescoring, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated into the DLOmix framework and is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer.","authors":"Deanna L Plubell, Eric Huang, Sandra E Spencer, Kathleen L Poston, Thomas J Montine, Michael J MacCoss","doi":"10.1021/acs.jproteome.5c00016","DOIUrl":"https://doi.org/10.1021/acs.jproteome.5c00016","url":null,"abstract":"<p><p>Mass spectrometry based targeted proteomics methods provide a sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semistochastic sampling, or empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of the peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimer's disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits a quantitative precision similar to that of published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional upfront data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same data set.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do-It-Yourself De Novo Antibody Sequencing Workflow that Achieves Complete Accuracy of the Variable Regions.","authors":"Meng-Ting He, Ning Li, Jian-Hua Wang, Zhi-Zhong Wei, Jie Feng, Wen-Ting Li, Jian-Hua Sui, Niu Huang, Meng-Qiu Dong","doi":"10.1021/acs.jproteome.5c00210","DOIUrl":"https://doi.org/10.1021/acs.jproteome.5c00210","url":null,"abstract":"<p><p>Antibodies are widely used as research tools or therapeutic agents. Knowing the sequences of the variable regions of an antibody─both the heavy chain and the light chain─is a prerequisite for the production of recombinant antibodies. Mass spectrometry-based de novo sequencing is a frequently used, and sometimes the only approach to gaining this information. Here, we describe a workflow that enables accurate sequence determination of monoclonal antibodies based on mass spectrometry data and freely available software tools. This workflow, which we developed using a homemade anti-FLAG monoclonal antibody as a reference sample, achieved 100% accuracy of the variable regions with clear distinction between leucine (L) and isoleucine (I). Using this workflow, we successfully decoded a monoclonal anti-HA antibody, for which we had no prior knowledge of its sequence. Based on the de novo sequencing result, we generated a recombinant anti-HA antibody, and demonstrated that it has the same specificity, sensitivity, and affinity as the commercial antibody.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Melanoma Proteomics Unveiled: Harmonizing Diverse Data Sets for Biomarker Discovery and Clinical Insights via MEL-PLOT.","authors":"Áron Bartha, Boglárka Weltz, Lazaro Hiram Betancourt, Jeovanis Gil, Natália Pinto de Almeida, Giampaolo Bianchini, Beáta Szeitz, Leticia Szadai, Indira Pla, Lajos V Kemény, Ágnes Judit Jánosi, Runyu Hong, Ahmad Rajeh, Fábio Nogueira, Viktória Doma, Nicole Woldmar, Jéssica Guedes, Zsuzsanna Újfaludi, Yonghyo Kim, Tibor Szarvas, Zoltan Pahi, Tibor Pankotai, A Marcell Szasz, Aniel Sanchez, Bo Baldetorp, József Tímár, István Balázs Németh, Sarolta Kárpáti, Roger Appelqvist, Gilberto Barbosa Domont, Krzysztof Pawlowski, Elisabet Wieslander, Johan Malm, David Fenyo, Peter Horvatovich, György Marko-Varga, Balázs Győrffy","doi":"10.1021/acs.jproteome.4c00749","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00749","url":null,"abstract":"<p><p>Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, and prognostic biomarkers at the protein level. Quantitative mass-spectrometry-based proteomic data was obtained from five independent cohorts, including 489 tissue samples from 394 patients with accompanying clinical metadata. We established an interactive R-based web platform that enables the comparison of protein levels across diverse cohorts, and supports correlation analysis between proteins and clinical metadata including survival outcomes. By comparing differential protein levels between metastatic, primary tumor, and nonmalignant samples in two of the cohorts, we identified 274 proteins showing significant differences among the sample types. Further analysis of these 274 proteins in lymph node metastatic samples from a third cohort revealed that 45 proteins exhibited a significant effect on patient survival. The three most significant proteins were HP (HR = 4.67, p = 2.8e-06), LGALS7 (HR = 3.83, p = 2.9e-05), and UBQLN1 (HR = 3.2, p = 4.8e-05). The user-friendly interactive web platform, accessible at https://www.tnmplot.com/melanoma, provides an interactive interface for the analysis of proteomic and clinical data. The MEL-PLOT platform, through its interactive capabilities, streamlines the creation of a comprehensive knowledge base, empowering hypothesis formulation and diligent monitoring of the most recent advancements in the domains of biomedical research and drug development.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal Plasma Proteins Associated with Birth Weight: A Longitudinal, Large Scale Proteomic Study.","authors":"Ina Jungersen Andresen, Ane Cecilie Westerberg, Marie Cecilie Paasche Roland, Manuela Zucknick, Trond Melbye Michelsen","doi":"10.1021/acs.jproteome.4c00940","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00940","url":null,"abstract":"<p><p>Small infants for gestational age (SGA) and large infants for gestational age (LGA) have increased risk of complications during delivery and later in life. Prediction of the fetal weight is currently limited to biometric parameters obtained by ultrasound scans that can be imprecise. Biomarkers of fetal growth would be crucial for tailoring clinical management and optimizing outcomes for the mother and child. Seventy pregnant women participated in the current study, including 58, 7, and 5 giving birth to adequate for gestational age (AGA), SGA, and LGA infants, respectively. Maternal venous blood was drawn at gestational weeks 12-19, 21-27, and 28-34 and quantified for nearly 5000 proteins on the SomaLogic platform. We used machine learning algorithms with leave-one-out cross-validation to construct multiprotein models for prediction of birth weight groups. Random forest models using only 20 predefined proteins (selected by moderated <i>t</i> tests) were able to predict LGA with good discrimination (AUC > 0.8) at all three visits, while prediction of SGA was less successful. Protein differential abundance analysis revealed 148 proteins with higher abundance in LGA compared to AGA pregnancies, while only four proteins were differentially abundant between the SGA and AGA. The principal findings indicate that the maternal plasma proteome may hold potential biomarkers of LGA.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to Train a Postprocessor for Tandem Mass Spectrometry Proteomics Database Search While Maintaining Control of the False Discovery Rate.","authors":"Jack Freestone, Lukas Käll, William Stafford Noble, Uri Keich","doi":"10.1021/acs.jproteome.4c00742","DOIUrl":"10.1021/acs.jproteome.4c00742","url":null,"abstract":"<p><p>Decoy-based methods are a popular choice for the statistical validation of peptide detection in tandem mass spectrometry and proteomics data. Such methods can achieve a substantial boost in statistical power when coupled with postprocessors such as Percolator that use auxiliary features to learn a better-discriminating scoring function. However, we recently showed that Percolator can struggle to control the false discovery rate (FDR) when reporting the list of discovered peptides. To address this problem, we introduce Percolator-RESET, which is an adaptation of our recently developed RESET meta-procedure to the peptide detection problem. Specifically, Percolator-RESET fuses Percolator's iterative SVM training procedure with RESET's general framework to provide valid false discovery rate control. Percolator-RESET operates in both a standard single-decoy mode and a two-decoy mode, with the latter requiring the generation of two decoys per target. We demonstrate that Percolator-RESET controls the FDR in both modes, both theoretically and empirically, while typically reporting only a marginally smaller number of discoveries than Percolator in the single-decoy mode. The two-decoy mode is marginally more powerful than both Percolator and the single-decoy mode and exhibits less variability than the latter.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"2266-2279"},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NA_mCNN: Classification of Sodium Transporters in Membrane Proteins by Integrating Multi-Window Deep Learning and ProtTrans for Their Therapeutic Potential.","authors":"Muhammad Shahid Malik, Van The Le, Yu-Yen Ou","doi":"10.1021/acs.jproteome.4c00884","DOIUrl":"10.1021/acs.jproteome.4c00884","url":null,"abstract":"<p><p>Sodium transporters maintain cellular homeostasis by transporting ions, minerals, and nutrients across the membrane, and Na+/K+ ATPases facilitate the cotransport of solutes in neurons, muscle cells, and epithelial cells. Sodium transporters are important for many physiological processes, and their dysfunction leads to diseases such as hypertension, diabetes, neurological disorders, and cancer. The NA_mCNN computational method highlights the functional diversity and significance of sodium transporters in membrane proteins using protein language model embeddings (PLMs) and multiple-window scanning deep learning models. This work investigates PLMs that include Tape, ProtTrans, ESM-1b-1280, and ESM-2-128 to achieve more accuracy in sodium transporter classification. Five-fold cross-validation and independent testing demonstrate ProtTrans embedding robustness. In cross-validation, ProtTrans achieved an AUC of 0.9939, a sensitivity of 0.9829, and a specificity of 0.9889, demonstrating its ability to distinguish positive and negative samples. In independent testing, ProtTrans maintained a sensitivity of 0.9765, a specificity of 0.9991, and an AUC of 0.9975, which indicates its high level of discrimination. This study advances the understanding of sodium transporter diversity and function, as well as their role in human pathophysiology. Our goal is to use deep learning techniques and protein language models for identifying sodium transporters to accelerate identification and develop new therapeutic interventions.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"2324-2335"},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomic Insights into the Adaptation of <i>Acidithiobacillus ferridurans</i> to Municipal Solid Waste Incineration Residues for Enhanced Bioleaching Efficiency.","authors":"Jiri Kucera, Klemens Kremser, Pavel Bouchal, David Potesil, Tomas Vaculovic, Dalibor Vsiansky, Georg M Guebitz, Martin Mandl","doi":"10.1021/acs.jproteome.4c00527","DOIUrl":"10.1021/acs.jproteome.4c00527","url":null,"abstract":"<p><p><i>Acidithiobacillus</i> spp. have traditionally been utilized to extract metals from mineral ores through bioleaching. This process has recently expanded to include artificial ores, such as those derived from municipal solid waste incineration (MSWI) residues. Previous studies have indicated that microbial adaptation enhances bioleaching efficiency, prompting this study to identify proteins involved in the adaptation of <i>A. ferridurans</i> to MSWI residues. We employed data-independent acquisition-parallel accumulation serial fragmentation to determine the proteomic response of <i>A. ferridurans</i> DSM 583 to three distinct materials: bottom ash (BA), kettle ash (KA), and filter ash (FA), which represent typical MSWI residues. Our findings indicate that, irrespective of the residue type, a suite of membrane transporters, porins, efflux pumps, and specific electron and cation transfer proteins was notably upregulated. The upregulation of certain proteins involved in anaerobic pathways suggested the development of a spontaneous microaerobic environment, which minimally impacted the bioleaching efficiency. Additionally, the adaptation was most efficient at half the target FA concentration, marked by a significant increase in the detoxification and efflux systems required by microorganisms to tolerate high heavy metal concentrations. Given that metal recovery peaked at lower FA concentrations for most metals of interest, further adaptation at the level of protein expression may not be warranted for improved bioleaching outcomes.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"2243-2255"},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Alkaline-pH Reversed-Phase Nanoelectrospray-Tandem Mass Spectrometry Complements Traditional Phosphoproteomic Analysis via Influencing Charge State Distribution of Phosphopeptides.","authors":"Yuqiu Wang, Jing Gao, Wenfan Xie, Minchu Tang, Xin Chen, Lv Chen, Hongxu Chen, Zhicheng Yang, Qiang Gao, Yansheng Liu, Hu Zhou","doi":"10.1021/acs.jproteome.4c01091","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c01091","url":null,"abstract":"<p><p>Phosphorylation (<i>O</i>-linked and <i>N</i>-linked) plays an important role in biological functions and cell signaling. Here, we employed a one-dimensional online alkaline-pH reversed-phase nanoelectrospray-tandem mass spectrometry (alkaline-pH-MS/MS) for the investigation of global phosphorylation. In this method, phosphopeptides were separated on a nanoflow C18 column with an alkaline-pH gradient and directly introduced to the mass spectrometer through nanoelectrospray ionization. Although the phosphosites and phosphopeptides identified by alkaline-pH-MS/MS were slightly lower than those of traditional online low-pH reversed-phase tandem MS (low-pH-MS/MS), these two methods were highly complementary to each other. This alkaline-pH-MS/MS may affect the actual polarity and CSD of phosphopeptides, consequently improving the identification of multiply phosphorylated peptides. Moreover, alkaline-pH-MS/MS was compatible with other peptide fractionation and phosphopeptide enrichment techniques, such as offline high-pH or low-pH reversed-phase liquid chromatography fractionation. The complementarity of alkaline-pH-MS/MS and low-pH-MS/MS was further demonstrated by the tandem mass tag (TMT)-based quantitative phosphoproteomic analysis of five pairs of hepatocellular carcinoma (HCC) tumors and normal adjacent tissues (NATs). Furthermore, unique information on significantly changed phosphosites was observed by alkaline-pH-MS/MS. This study provided an alternative and complementary tool for global analysis of both <i>O</i>- and <i>N</i>-phosphoproteome, which may be beneficial for the discovery of phosphoproteins with significant biological functions.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":"24 5","pages":"2443-2453"},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Static Myoblast Loading on Protein Secretion Linked to Tenocyte Migration.","authors":"Junhong Li, Xin Zhou, Jialin Chen, Shaochun Zhu, Andre Mateus, Pernilla Eliasson, Paul J Kingham, Ludvig J Backman","doi":"10.1021/acs.jproteome.5c00068","DOIUrl":"10.1021/acs.jproteome.5c00068","url":null,"abstract":"<p><p>Exercise has been shown to promote wound healing, including tendon repair. Myokines released from the exercised muscles are believed to play a significant role in this process. In our previous study, we used an in vitro coculture and loading model to demonstrate that 2% static loading of myoblasts increased the migration and proliferation of cocultured tenocytes─two crucial aspects of wound healing. IGF-1, released from myoblasts in response to 2% static loading, was identified as a contributor to the increased proliferation. However, the factors responsible for the enhanced migration remained unknown. In the current study, we subjected myoblasts in single culture conditions to 2, 5, and 10% static loading and performed proteomic analysis of the cell supernatants. Gene Ontology (GO) analysis revealed that 2% static loading induced the secretion of NBL1, C5, and EFEMP1, which is associated with cell migration and motility. Further investigation by adding exogenous recombinant proteins to human tenocytes showed that NBL1 increased tenocyte migration but not proliferation. This effect was not observed with treatments using C5 and EFEMP1.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"2529-2541"},"PeriodicalIF":3.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}