Journal of Proteome Research最新文献

Building custom data analysis platforms has traditionally required extensive software engineering expertise, limiting access for many researchers. Here, I demonstrate that modern large language models (LLMs) and autonomous coding agents can dramatically lower this barrier through a process called “vibe coding”, an iterative, conversational style of software creation where users describe goals in natural language and AI agents generate, test, and refine executable code in real time. Importantly, the goal here is not to introduce a new analysis platform. Instead, the example application illustrates that, in minutes, LLMs can now perform work that would normally require at least days of manual programming effort, lowering the cost and time investment by orders of magnitude. As a proof of concept, I used vibe coding to create a fully functional proteomics data analysis platform capable of performing standard tasks, including data normalization, differential expression testing, and volcano plot visualization. The entire application, including user interface, backend logic, and data upload pipeline, was developed in less than 10 min using only four natural language prompts, without writing any additional code by hand, at a model usage cost of under $2, not including hosting or personnel time. Previous works in this area have typically required substantial investment of personnel time from highly trained programmers, often amounting to tens of thousands of dollars in total research effort. I detail the step-by-step generation process and evaluate the resulting code’s functionality. This demonstration highlights how vibe coding enables domain experts to rapidly prototype sophisticated analytical tools, transforming the pace and accessibility of computational biology software development.

Single-pot, solid-phase-enhanced sample preparation (SP3) is a rapid, automatable, and cost-effective protein cleanup technique facilitated by the aggregation and digestion of proteins on paramagnetic beads. In this study, we document how plasma protein-to-bead ratio influences the performance in terms of the number of identified peptides and proteins for two chemically modified paramagnetic SP3 beads, i.e., Carboxylate-Speedbeads and MagReSyn-Hydroxyl beads. The optimized protein-to-bead ratio enabled the identification of 17% more plasma protein groups than the nonoptimized condition. Furthermore, we evidenced differences in the quantification results of peptides upon aggregation on hydroxyl- and carboxylate-modified beads. By assessing the physicochemical properties of these peptides, significant differences were revealed in their pI values, charge, polarity, and aspartic and glutamic acid composition. Our results highlight that the choice of beads and protein-to-bead ratio are two important parameters that require optimization depending on the physicochemical properties of the targeted proteins.

Gallbladder cancer (GBC) is an aggressive malignancy often associated with gallstones (GBCGS), a condition distinct from gallstone disease (GSD). Both GBC and GBCGS are rare, with unclear pathogenesis and no established biomarker-based diagnostics. This pilot study aimed to identify distinct metabolic signatures in GBC and GBCGS for early diagnosis and stratification of high-risk GSD patients. Comparative untargeted serum metabolomic profiling was performed across three groups: GBC (n₁ = 9), GBCGS (n₂ = 11), and GSD (n₃ = 10). A total of 35,385 mass features with MS/MS characteristics were detected and annotated into 736 biochemicals. Differential metabolome analyses relative to GSD identified 180 altered metabolites in GBC and 225 in GBCGS, with 138 shared by both. Correlation network and biomarker analyses subsequently identified 12 GBC-specific, 20 GBCGS-specific, and 30 shared metabolite signatures with high diagnostic efficiency, predominantly upregulated. Key metabolites identified included cholic acid, glycocholic acid, glycochenodeoxycholic acid, kynurenine, and glutamine, implicated in promoting metastasis and epithelial-to-mesenchymal transitions. Thus, serum metabolome reprogramming in GBC and GBCGS revealed a shared deregulation of metabolic pathways involving bile acids, amino acids, and their intermediates alongside distinct condition-specific biomarkers. These findings provide novel insights into the pathogenesis of GBC and GBCGS, advancing future diagnostic, prognostic, and therapeutic interventions.

The HUPO Human Proteome Project (HPP) aims to complete the human protein parts list by detecting evidence of expression and of function for all proteins in the human proteome, and make proteomics an integral part of multiomics studies of health and disease. Here we describe the state of the 2025 HPP reference proteome of 19,435 proteins, based on GENCODE v48, UniProtKB 2025_03, Human Protein Atlas 24, MassIVE-KB 2023, and PeptideAtlas 2025-01. We evaluate the progress in the past year, with 93.6% of the proteome detected, and examine the proteins that have not yet been detected to determine where further progress can be made. We also evaluate the progress in determining at least one function for every protein in the HPP target list, finding an increase of 288 proteins in the highest category (FE1) to 5562. Finally, we provide highlights from 12 Biology/Disease-based HPP initiatives, HPP resource pillars, and π-HuB.

Host cell proteins (HCPs), particularly high-risk species, are critical process-related impurities that can affect the quality, safety, and efficacy of biopharmaceuticals. We developed iRT-assisted targeted mass spectrometry (iRTarget-MS), a robust platform for profiling 31 high-risk HCPs in Chinese Hamster Ovary (CHO) cells across five risk categories: drug aggregation, drug degradation, polysorbate degradation, immunogenic response, and direct biological activity. Optimized for multiplexed, reproducible, and sensitive quantification, iRTarget-MS incorporated iRT values for retention time calibration, enabling the confident identification of low-abundance HCPs. Compared to conventional shotgun proteomics, iRTarget-MS demonstrated significant sensitivity improvement at the subppm level. For example, PLBL2 quantification by iRTarget-MS demonstrated comparable yet more sensitive results compared with the protein-specific enzyme-linked immunosorbent assay (ELISA). Case studies have highlighted the broad applications of iRTarget-MS, including supporting high-throughput process optimization, verification of complete HCP removal while mapping its clearance pathways, and antibody coverage analysis for high-risk HCP subsets. In summary, iRTarget-MS serves as a transformative tool that complements ELISA and shotgun proteomics for high-risk HCP analysis, enhancing the process understanding and accelerating process development. With its ease of operation, streamlined data analysis, and accessible instrumentation, iRTarget-MS opens up opportunities for adopting liquid chromatography–mass spectrometry (LC-MS)-based HCP analysis as a routine monitoring strategy for large sample sets in the biopharmaceutical industry.

The objective was to analyze seasonal changes in the seminal plasma proteome of crab-eating fox (Cerdocyon thous). Semen was collected in Brazil from March 2021 to March 2022 from five healthy adult males housed individually. Collections were performed without chemical or physical restraint by digital manipulation of the penis, and seminal plasma proteomics were assessed by mass spectrometry (ESI Q-Tof MS/MS) on 43 ejaculates from the reproductive season and four from the nonreproductive season. A total of 408 proteins were identified: 219 exclusives to the reproductive season (June–September), 90 to the nonreproductive season (October–May), and 99 shared between both. Protein abundance differed significantly between seasons. Proteins related to enzymatic and oxidoreductase functions predominated in the nonreproductive season, whereas those linked to sperm metabolism and reproductive processes were more abundant in the reproductive season. Among these, olfactory receptor, strawberry notch homologue, and zinc finger protein were considered potential reproductive season biomarkers, with AUC > 0.80 in the receiver operating characteristic analysis. This is the first study describing the seminal plasma proteome and its seasonal variation in the crab-eating fox, identifying biomarkers with potential applications in conservation and reproductive management of this and other endangered canids.

Concanavalin-A (ConA)-induced acute liver injury (ALI) is a widely used model for immune-mediated liver damage, but its molecular mechanisms remain poorly understood. We applied a multiomics approach that integrates transcriptomics, metabolomics, and proteomics to characterize the pathogenic features of ConA-induced ALI. Our analysis revealed significant downregulation of Cyp7a1 and Cyp8b1, two key enzymes in bile acid biosynthesis, as potential hallmark features of this model. Mechanically, suppression of these genes was correlated with altered bile acid metabolism, increased proinflammatory cytokine production (e.g., TNF-α, IL-6, and IL-1β), and elevated markers of hepatocyte apoptosis. Furthermore, multiomics network analysis highlighted interactions among bile acid dysregulation, oxidative stress, and immune activation, suggesting a synergistic role in ConA-induced liver injury. These findings improve our understanding of immune-mediated ALI and suggest the downregulation of Cyp7a1/Cyp8b1 as a diagnostic marker or therapeutic target for acute hepatotoxicity.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality, and current biomarkers such as alpha-fetoprotein (AFP) lack diagnostic accuracy. Here, we report the first comprehensive profiling of the plasma endogenous phosphopeptidome in HCC, cirrhosis, and healthy controls using a digestion-free LC–MS/MS workflow. From 60 plasma samples, 1,365 phosphopeptides corresponding to 549 proteins were identified and quantified. Among these, the statherin-derived peptide DSSEEKFLR demonstrated outstanding discrimination between HCC and cirrhosis (AUC = 0.968), outperforming AFP (AUC = 0.648). Additional peptides, including PPGAPHTEEEGAE (NST1), YEYDELPAKDD (C4A), SLPGESEEMMEEVD (ITIH4), and VSLGSPSGEVSHPRKT (AHSG), also showed high accuracy (AUC > 0.80). Functional enrichment revealed perturbations in acute-phase response, coagulation, lipid metabolism, and LXR/RXR signaling. Collectively, this work defines a novel plasma phosphopeptide signature that reflects disease-specific proteolytic and phosphorylation dynamics, providing a foundation for developing biomarkers for early detection and clinical monitoring of HCC.

Casanovo is a state-of-the-art deep learning model for de novo peptide sequencing from mass spectrometry and proteomics data. Here, we report on a series of enhancements to Casanovo, aimed at improving the interpretability of the scores assigned to predicted peptides, generalizing the software for use in database searches, speeding up training and prediction runtimes, and providing workflows and visualization tools to facilitate adoption of Casanovo and interpretation of its results. Our goal is to make Casanovo accurate and easy to use for applications such as metaproteomics, antibody sequencing, immunopeptidomics, and the discovery of novel peptide sequences in standard proteomics analyses. Casanovo is available as open source at https://github.com/Noble-Lab/casanovo.