Proteomics最新文献

N-Linked glycosylation is crucial for various biological processes such as protein folding, immune response, and cellular transport. Traditional experimental methods for determining N-linked glycosylation sites entail substantial time and labor investment, which has led to the development of computational approaches as a more efficient alternative. However, due to the limited availability of 3D structural data, existing prediction methods often struggle to fully utilize structural information and fall short in integrating sequence and structural information effectively. Motivated by the progress of protein pretrained language models (pLMs) and the breakthrough in protein structure prediction, we introduced a high-accuracy model called CoNglyPred. Having compared various pLMs, we opt for the large-scale pLM ESM-2 to extract sequence embeddings, thus mitigating certain limitations associated with manual feature extraction. Meanwhile, our approach employs a graph transformer network to process the 3D protein structures predicted by AlphaFold2. The final graph output and ESM-2 embedding are intricately integrated through a co-attention mechanism. Among a series of comprehensive experiments on the independent test dataset, CoNglyPred outperforms state-of-the-art models and demonstrates exceptional performance in case study. In addition, we are the first to report the uncertainty of N-linked glycosylation predictors using expected calibration error and expected uncertainty calibration error.

Ectodomain shedding, which is the proteolytic release of transmembrane proteins from the cell surface, is crucial for cell-to-cell communication and other biological processes. The metalloproteinase ADAM17 mediates ectodomain shedding of over 50 transmembrane proteins ranging from cytokines and growth factors, such as TNF and EGFR ligands, to signalling receptors and adhesion molecules. Yet, the ADAM17 sheddome is only partly defined and biological functions of the protease have not been fully characterized. Some ADAM17 substrates (e.g., HB-EGF) are known to bind to heparan sulphate proteoglycans (HSPG), and we hypothesised that such substrates would be under-represented in traditional secretome analyses, due to their binding to cell surface or pericellular HSPGs. Thus, to identify novel HSPG-binding ADAM17 substrates, we developed a proteomic workflow that involves addition of heparin to solubilize HSPG-binding proteins from the cell layer, thereby allowing their mass spectrometry detection by heparin-treated secretome (HEP-SEC) analysis. Applying this methodology to murine embryonic fibroblasts stimulated with an ADAM17 activator enabled us to identify 47 transmembrane proteins that were shed in response to ADAM17 activation. This included known HSPG-binding ADAM17 substrates (i.e., HB-EGF, CX3CL1) and 14 novel HSPG-binding putative ADAM17 substrates. Two of these, MHC-I and IL1RL1, were validated as ADAM17 substrates by immunoblotting.

Alcohol consumption and high-fat (HF) diets often coincide in Western society, resulting in synergistic negative effects on liver function. Although studies have analyzed the global protein expression in the context of alcoholic liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD), none has offered specific insights on liver dysregulation at the membrane proteome level. Membrane-specific profiling of metabolic and compensatory phenomena is usually overshadowed in conventional proteomic workflows. In this study, we use the Peptidisc method to isolate and compare the membrane protein (MP) content of the liver with its unique biological functions. From mice fed with an HF diet and ethanol in drinking water, we annotate over 1500 liver proteins with half predicted to have at least one transmembrane segment. Among them, we identify 106 integral MPs that are dysregulated compared to the untreated sample. Gene Ontology analysis reveals several dysregulated membrane-associated processes like lipid metabolism, cell adhesion, xenobiotic processing, and mitochondrial membrane formation. Pathways related to cholesterol and bile acid transport are also mutually affected, suggesting an adaptive mechanism to counter the upcoming steatosis of the liver model. Taken together, our Peptidisc-based profiling of the diet-dysregulated liver provides specific insights and hypotheses into the role of the transmembrane proteome in disease development, and flags desirable MPs for therapeutic and diagnostic targeting.

Prediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates, proposed and existing methods on an independent dataset containing 80 AFPs and 73 non-AFPs obtained from Uniport, which have been already reviewed by experts. Initially, we constructed machine learning models for AFP prediction using selected composition-based protein features and achieved a peak AUROC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUROC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins. Furthermore, we explored hybrid models integrating our machine learning approaches with BLAST-based similarity and motif-based methods. However, the performance of these hybrid models either matched or was inferior to that of our best machine-learning model. Our best model based on evolutionary information outperforms all existing methods on independent/validation dataset. To facilitate users, a user-friendly web server with a standalone package named "AFPropred" was developed (https://webs.iiitd.edu.in/raghava/afpropred).

The fibrotic stroma characterizing pancreatic ductal adenocarcinoma (PDAC) derives from a progressive tissue rigidification, which induces epithelial mesenchymal transition and metastatic dissemination. The aim of this study was to investigate the influence of matrix stiffness on PDAC progression by analyzing the proteome of PDAC-derived extracellular vesicles (EVs). PDAC cell lines (mPDAC and KPC) were grown on synthetic supports with a stiffness close to non-tumor (NT) or tumor tissue (T), and the protein expression levels in cell-derived EVs were analyzed by a quantitative MS^E label-free mass spectrometry approach. Our analysis figured out 15 differentially expressed proteins (DEPs) in mPDAC-EVs and 20 DEPs in KPC-EVs in response to matrix rigidification. Up-regulated proteins participate to the processes of metabolism, matrix remodeling, and immune response, altogether hallmarks of PDAC progression. A multimodal network analysis revealed that the majority of DEPs are strongly related to pancreatic cancer. Interestingly, among DEPs, 11 related genes (ACTB/ANXA7/C3/IGSF8/LAMC1/LGALS3/PCD6IP/SFN/TPM3/VARS/YWHAZ) for mPDAC-EVs and 9 (ACTB/ALDH2/GAPDH/HNRNPA2B/ITGA2/NEXN/PKM/RPN1/S100A6) for KPC-EVs were significantly overexpressed in tumor tissues according to gene expression profiling interaction analysis (GEPIA). Concerning the potential clinical relevance of these data, the cluster of ACTB, ITGA2, GAPDH and PKM genes displayed an adverse effect (p < 0.05) on the overall survival of PDAC patients.

Extracellular vesicles (EVs), such as exosomes, play a critical role in cell-to-cell communication and regulating cellular processes in recipient cells. Non-tuberculous mycobacteria (NTM), such as Mycobacterium abscessus, are a group of environmental bacteria that can cause severe lung infections in populations with pre-existing lung conditions, such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). There is limited knowledge of the engagement of EVs in the host-pathogen interactions in the context of NTM infections. In this study, we found that M. abscessus infection increased the release of a subpopulation of exosomes (CD9, CD63, and/or CD81 positive) by mouse macrophages in cell culture. Proteomic analysis of these vesicles demonstrated that M. abscessus infection affects the enrichment of host proteins in exosomes released by macrophages. When compared to exosomes from uninfected macrophages, exosomes released by M. abscessus-infected macrophages significantly improved M. abscessus growth and downregulated the intracellular level of glutamine in recipient macrophages in cell culture. Increasing glutamine concentration in the medium rescued intracellular glutamine levels and M. abscessus killing in recipient macrophages that were treated with exosomes from M. abscessus-infected macrophages. Taken together, our results indicate that exosomes may serve as extracellular glutamine eliminators that interfere with glutamine-dependent M. abscessus killing in recipient macrophages.