ACS Publications最新文献

{"title":"Sex-Specific Markers of Neuroinflammation and Neurodegeneration in the Spinal Cord Proteome of the SOD1^G93A Mouse Model of Amyotrophic Lateral Sclerosis.","authors":"Liam M Koehn, Joel R Steele, Ralf B Schittenhelm, Joseph A Nicolazzo","doi":"10.1021/acs.jproteome.4c00990","DOIUrl":"10.1021/acs.jproteome.4c00990","url":null,"abstract":"<p><p>Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has no cure. The underlying mechanistic details of sex differences in the ALS spinal cord, the site of disease onset, are not understood to an extent that could guide novel drug development. To address this, the spinal cords of 120-day-old wild-type (WT) and SOD1^G93A (familial mouse model of ALS with mutant superoxide dismutase 1) mice were subjected to untargeted, quantitative proteomics using tandem mass tag acquisition on high-resolution mass spectrometric instrumentation. Compared to WT, both male and female SOD1^G93A spinal cords exhibited an upregulation of neuroinflammatory cascades of both peripheral and central origins, as well as a downregulation of proteins reflective of death and dysfunction of cells within the spinal cord. However, female and male SOD1^G93A mouse spinal cords exhibited sex-specific differences in proteins compared to respective WT that related to immune response, as well as cellular structure, function, and homeostasis. The proteomic datasets presented provide entire cohort and sex-specific spinal cord drug targets and disease biomarkers in the SOD1^G93A mouse model of ALS that may guide future drug development and sex selection in preclinical study designs utilizing the SOD1^G93A model.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1956-1970"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672928","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":"ProSIMSIt: The Best of Both Worlds in Data-Driven Rescoring and Identification Transfer.","authors":"Firas Hamood, Wassim Gabriel, Pia Pfeiffer, Bernhard Kuster, Mathias Wilhelm, Matthew The","doi":"10.1021/acs.jproteome.4c00967","DOIUrl":"10.1021/acs.jproteome.4c00967","url":null,"abstract":"<p><p>Multibatch isobaric labeling experiments are frequently applied for clinical and pharmaceutical studies of large sample cohorts. To tackle the critical issue of missing values in such studies, we introduce the ProSIMSIt pipeline. It combines the advantages of tandem mass spectrum clustering via SIMSI-Transfer and data-driven rescoring via Prosit and Oktoberfest. We demonstrate that these two tools are complementary and mutually beneficial. On large-scale cancer cohort data, ProSIMSIt increased the number of peptide spectrum matches (PSMs) by 40% on both global and phosphoproteome data sets. Furthermore, on data from proteome-wide drug-response profiling of post-translational modifications (decryptM), our pipeline substantially increased drug-PTM relations and revealed previously unseen downstream effects of drug target inhibition. ProSIMSIt is available as an open-source Python package with a simple command line interface that allows easy application to MaxQuant result files.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"2173-2180"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676641","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":"<i>Candida albicans</i>: A Comprehensive View of the Proteome.","authors":"Leticia Gomez-Artiguez, Samuel de la Cámara-Fuentes, Zhi Sun, María Luisa Hernáez, Ana Borrajo, Aída Pitarch, Gloria Molero, Lucía Monteoliva, Robert L Moritz, Eric W Deutsch, Concha Gil","doi":"10.1021/acs.jproteome.4c01020","DOIUrl":"10.1021/acs.jproteome.4c01020","url":null,"abstract":"<p><p>We describe a new release of the <i>Candida albicans</i> PeptideAtlas proteomics spectral resource (build 2024-03), providing a sequence coverage of 79.5% at the canonical protein level, matched mass spectrometry spectra, and experimental evidence identifying 3382 and 536 phosphorylated serine and threonine sites with false localization rates of 1% and 5.3%, respectively. We provide a tutorial on how to use the PeptideAtlas and associated tools to access this information. The <i>C. albicans</i> PeptideAtlas summary web page provides \"Build overview\", \"PTM coverage\", \"Experiment contribution\", and \"Data set contribution\" information. The protein and peptide information can also be accessed via the <i>Candida</i> Genome Database via hyperlinks on each protein page. This allows users to peruse identified peptides, protein coverage, post-translational modifications (PTMs), and experiments that identify each protein. Given the value of understanding the PTM landscape in the sequence of each protein, a more detailed explanation of how to interpret and analyze PTM results is provided in the PeptideAtlas of this important pathogen. <i>Candida albicans</i> PeptideAtlas web page: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=578.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1636-1648"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622880","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":"HRaDeX: R Package and Web Server for Computing High-Resolution Deuterium Uptake Rates for HDX-MS Data.","authors":"Weronika Puchała, Michał Kistowski, Liliya Zhukova, Michał Burdukiewicz, Michał Dadlez","doi":"10.1021/acs.jproteome.4c00700","DOIUrl":"10.1021/acs.jproteome.4c00700","url":null,"abstract":"<p><p>Hydrogen-deuterium exchange monitored by mass spectrometry (HDX-MS) is a well-established and powerful technique used to study protein dynamics and stability by capturing local and global unfolding events in protein structures. However, in this technique, obtaining region-specific information requires proteolytic digestion that breaks the protein into peptide fragments, causing the HDX data to reflect averages over these fragments rather than individual amino acids. We propose a new computational method that provides deuterium uptake kinetic parameters with high resolution, considering deuterium uptake trajectories of superimposed peptides. Our algorithm, HRaDeX, is available as a web server and an R package capable of processing data from single-state and comparative HDX-MS studies. Utilizing eight benchmark data sets, we demonstrate that HRaDeX reaches an average root-mean-square error of 7.15% in the reconstitution of experimental normalized deuterium uptake curves.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1688-1700"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655511","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":"ART-SM: Boosting Fragment-Based Backmapping by Machine Learning.","authors":"Christian Pfaendner, Viktoria Korn, Pritom Gogoi, Benjamin Unger, Kristyna Pluhackova","doi":"10.1021/acs.jctc.5c00189","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00189","url":null,"abstract":"<p><p>In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy of atomistic simulations, the resolution is altered over time. While coarse-graining is straightforward once the mapping between atomistic and coarse-grained resolution is defined, reintroducing the atomistic details is still a nontrivial process called backmapping. Here, we present ART-SM, a fragment-based backmapping framework that learns from atomistic simulation data to seamlessly switch from coarse-grained to atomistic resolution. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment via machine learning to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments by optimizing specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small and linear molecules from Martini coarse-grained beads and that the resulting high-resolution structures are representative of the input coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we demonstrate that ART-SM can be applied to larger linear molecules as well. To illustrate the efficiency of the local and autoregressive approach of ART-SM, we simulated a large realistic system containing the surfactants TAPB and SDS in solution using the Martini3 force field. The self-assembled micelles of various shapes were backmapped with ART-SM after training on only short atomistic simulations of a single water-solvated SDS or TAPB molecule. Together, these results indicate the potential for the method to be extended to more complex molecules such as lipids, proteins, macromolecules, and materials in the future.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the Potential of Single-Cell Omics","authors":"Nikolai Slavov*,&nbsp;","doi":"10.1021/acs.jproteome.5c0019710.1021/acs.jproteome.5c00197","DOIUrl":"https://doi.org/10.1021/acs.jproteome.5c00197https://doi.org/10.1021/acs.jproteome.5c00197","url":null,"abstract":"","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":"24 4","pages":"1481 1481"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767233","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":"Comprehensive Protein Inference Analysis with PyProteinInference Elucidates Biological Understanding of Tandem Mass Spectrometry Data.","authors":"Trent B Hinkle, Corey E Bakalarski","doi":"10.1021/acs.jproteome.4c00734","DOIUrl":"10.1021/acs.jproteome.4c00734","url":null,"abstract":"<p><p>Selection and application of protein inference algorithms can have a significant impact on the data output from tandem mass spectrometry (MS/MS) experiments. However, this critical step is often taken for granted, with many studies simply utilizing the inference method embedded within the end-to-end software pipeline employed for analysis without consideration of the particular algorithm's suitability for the experiment at hand or its effects on the resulting data. Although many individual inference algorithms have been demonstrated, few unified tools are available that allow the researcher to quickly apply a variety of different inference algorithms to meet the needs of their analysis, are agnostic of other tools in the analysis pipeline, and are easy to use for the bench biologist. PyProteinInference provides a comprehensive suite of tools that enable researchers to apply different inference algorithms and compute protein-level set-based false discovery rates (FDR) from MS/MS data through a unified interface. Here, we describe the software and its application to a traditional protein inference benchmarking data set and to a K562 whole-cell lysate to demonstrate its utility in facilitating conclusions about underlying biological mechanisms in proteomic data.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"2135-2140"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522096","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":"Discovery of Key Candidate Protein Biomarkers in Early-Stage Nonsmall Cell Lung Carcinoma through Quantitative Proteomics.","authors":"Baby Rorielyn T Dimayacyac-Esleta, Ferdinand D Mira, Lorenzo M Zarate, Ben Joshua O Porras, Dave Laurence A Juntilla, Lara Beatrice L Suñga, Venus B Pondevida, Sullian S Naval, Treah May S Sayo, Herdee Gloriane C Luna, Eloise I Prieto","doi":"10.1021/acs.jproteome.4c00764","DOIUrl":"10.1021/acs.jproteome.4c00764","url":null,"abstract":"<p><p>Difficulties in early-stage diagnosis are among the factors contributing to the high mortality of nonsmall cell lung carcinoma (NSCLC) patients. Unfortunately, diagnostic biomarkers are currently lacking, limiting options in the clinic. To discover proteins that have potential for biomarker applications, we performed an in-depth quantitative proteomic analysis on a cohort of Filipino early-stage NSCLC lung adenocarcinoma (LUAD) patients. Differentially expressed proteins (DEPs) were obtained by using tandem mass tag (TMT) labeling and mass spectrometry (MS)-based quantitative proteomics. A total of 6240 quantified proteins were identified with 3155 significantly upregulated and 1248 significantly downregulated. Integration of the proteomic result with curated transcriptome data allowed the identification of 33 proteins with biomarker potential. This study also provided insights into relevant pathways in NSCLC LUAD, such as protein translation and metabolic pathways. Interestingly, all of the enzymes in the hexosamine biosynthetic pathway (HBP) are found to be upregulated, suggesting its important role in NSCLC LUAD. It is worthwhile to look at the potential of targeting the metabolic vulnerability of NSCLC LUAD as a new strategy in drug development. All MS data were deposited into ProteomeXchange with the identifier PXD050598.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1701-1714"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522108","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":"Large-Scale Quantitative Cross-Linking and Mass Spectrometry Provide New Insight into Protein Conformational Plasticity within Organelles, Cells, and Tissues.","authors":"Andrew Keller, Anna Bakhtina, James E Bruce","doi":"10.1021/acs.jproteome.4c01030","DOIUrl":"10.1021/acs.jproteome.4c01030","url":null,"abstract":"<p><p>Many proteins can exist in multiple conformational states <i>in vivo</i> to achieve distinct functional roles. These states include alternative conformations, variable post-translational modifications (PTMs), and associations with interacting protein, nucleotide, and ligand partners. Quantitative chemical cross-linking of live cells, organelles, or tissues together with mass spectrometry provides the relative abundance of cross-link levels formed in two or more compared samples, which depends both on the relative levels of existent protein conformational states in the compared samples and on the relative likelihood of the cross-link originating from each. Because cross-link conformational state preferences can vary widely, one expects intraprotein cross-link levels from proteins with high conformational plasticity to display divergent quantitation among samples with differing conformational ensembles. Here we use the large volume of quantitative cross-linking data available on the public XLinkDB database to cluster intraprotein cross-links according to their quantitation in many diverse compared samples to provide the first widescale glimpse of cross-links grouped according to the protein conformational state(s) from which they predominantly originate. We further demonstrate how cluster cross-links can be aligned with any protein structure to assess the likelihood that they were derived from it.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"2017-2025"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690444","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":"Targeted Lipidomics and Transcriptomics Unveil Aberrant Lipid Metabolic Remodeling in Visceral and Subcutaneous Adipose Tissue under ER Stress.","authors":"Ping He, Li Zhang, Peng Ma, Tianshu Xu, Zijing Wang, Li Li, Guanhua Du, Guifen Qiang, Cuiqing Liu","doi":"10.1021/acs.jproteome.4c00952","DOIUrl":"10.1021/acs.jproteome.4c00952","url":null,"abstract":"<p><p>Endoplasmic reticulum (ER) stress is known to impair the function of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), disrupting lipid metabolism. Despite the crucial role lipid plays in regulating adipose tissue function, the specific lipidomic alterations in VAT and SAT under ER stress remain unclear. In this study, ER stress was induced in VAT and SAT, and targeted lipidomic and transcriptomic approaches were used to analyze lipid metabolism and gene expression profiles. The results revealed that VAT exhibited a stronger ER stress response, characterized by a significant increase in binding immunoglobulin protein (BiP) expression and notable lipidomic disruptions, especially in glycerides and sterols. These disruptions were marked by a decrease in protective polyunsaturated fatty acyl species and the accumulation of lipotoxic molecules. In contrast, SAT displayed less severe lipidomic alterations. Transcriptomic analysis indicated that VAT was more susceptible to immune activation, inflammation, and metabolic dysfunction, while SAT primarily showed alterations in protein folding processes. These findings underscore the tissue-specific mechanisms of ER stress adaptation in VAT and SAT. In conclusion, VAT appears to be a critical target for addressing metabolic dysfunction in obesity and related disorders, with potential therapeutic implications for managing ER stress-induced metabolic diseases.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1971-1982"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603063","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}