Proteomics最新文献

Drug protein-target identification in past decades required screening compound libraries against known proteins to determine drugs binding to specific protein. Protein targets used in drug-target screening were selected predominantly used laborious genetic manipulation assays. In 2013, a team led by Pär Nordlund from Karolinska Institutet (Stockholm, Sweden) developed Cellular Thermal Shift Assay (CETSA), a method which, for the first time, enabled the possibility of drug protein-target identification in the complex cellular proteome. High throughput, quantitative mass spectrometry (MS) proteomics appeared as a compatible analytical method of choice to complement CETSA, aka Thermal Protein Profiling assay (TPP). Since the seminal CETSA-MS/ TPP-MS publications, different protein-target deconvolution strategies emerged including Proteome Integral Solubility Alteration (PISA). The work of Emery-Corbin et al. (Proteomics 2024, 2300644), titled Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA), introduces Data-Independent Acquisition (DIA) as a quantification method, opening new avenues in drug target-deconvolution field. Application of DIA for target deconvolution offers attractive alternative to widely used data dependent methodology.

Salinity stress induces ionic and osmotic imbalances in rice plants that in turn negatively affect the photosynthesis rate, resulting in growth retardation and yield penalty. Efforts have, therefore, been carried out to understand the mechanism of salt tolerance, however, the complexity of biological processes at proteome levels remains a major challenge. Here, we performed a comparative proteome and phosphoproteome profiling of microsome enriched fractions of salt-tolerant (cv. IR73; indica) and salt-susceptible (cv. Dongjin/DJ; japonica) rice varieties. This approach led to the identification of 5856 proteins, of which 473 and 484 proteins showed differential modulation between DJ and IR73 sample sets, respectively. The phosphoproteome analysis led to the identification of a total of 10,873 phosphopeptides of which 2929 and 3049 phosphopeptides showed significant differences in DJ and IR73 sample sets, respectively. The integration of proteome and phosphoproteome data showed activation of ABA and Ca²⁺ signaling components exclusively in the salt-tolerant variety IR73 in response to salinity stress. Taken together, our results highlight the changes at proteome and phosphoproteome levels and provide a mechanistic understanding of salinity stress tolerance in rice.

Seed development is complex, influenced by genetic and environmental factors. Understanding proteome profiles at different seed developmental stages is key to improving seed composition and quality. We used label-free quantitative proteomics to analyze round and wrinkled pea seeds at five growth stages: 4, 7, 12, 15, and days after anthesis (DAA), and at maturity. Wrinkled peas had lower starch content (30%) compared to round peas (47%-55%). Proteomic analysis identified 3659 protein groups, with 21%-24% shared across growth stages. More proteins were identified during early seed development than at maturity. Statistical analysis found 735 significantly different proteins between wrinkled and round seeds, regardless of the growth stage. The detected proteins were categorized into 31 functional classes, including metabolic enzymes, proteins involved in protein biosynthesis and homeostasis, carbohydrate metabolism, and cell division. Cell division-related proteins were more abundant in early stages, while storage proteins were more abundant later in seed development. Wrinkled seeds had lower levels of the starch-branching enzyme (SBEI), which is essential for amylopectin biosynthesis. Seed storage proteins like legumin and albumin (PA2) were more abundant in round peas, whereas vicilin was more prevalent in wrinkled peas. This study enhances our understanding of seed development in round and wrinkled peas. The study highlighted the seed growth patterns and protein profiles in round and wrinkled peas during seed development. It showed how protein accumulation changed, particularly focusing on proteins implicated in cell division, seed reserve metabolism, as well as storage proteins and protease inhibitors. These findings underscore the crucial role of these proteins in seed development. By linking the proteins identified to Cameor-based pea reference genome, our research can open avenues for deeper investigations into individual proteins, facilitate their practical application in crop improvement, and advance our knowledge of seed development.

With an aging population, the increased interest in the monitoring of skeletal diseases such as osteoporosis led to significant progress in the discovery and measurement of bone turnover biomarkers since the 2000s. Multiple markers derived from type I collagen, such as CTX, NTX, PINP, and ICTP, have been developed. Extensive efforts have been devoted to characterizing these molecules; however, their complex crosslinked structures have posed significant analytical challenges, and to date, these biomarkers remain poorly characterized. Previous attempts at characterization involved gel-based separation methods and MALDI-TOF analysis on collagen peptides directly extracted from bone. However, using bone powder, which is rich in collagen, does not represent the true structure of the peptides in the biofluids as it was cleaved. In this study, our goal was to characterize plasma and serum CTX for subsequent LC-MS/MS method development. We extracted and characterized type I collagen peptides directly from human plasma and serum using a proteomics workflow that integrates preparative LC, affinity chromatography, and HR-MS. Subsequently, we successfully identified numerous CTX species, providing valuable insights into the characterization of these crucial biomarkers.

The study uses Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH)-MS in conjunction with secretome proteomics to identify key proteins that Pseudomonas aeruginosa secretes against methicillin-resistant Staphylococcus aureus (MRSA). Variations in the inhibition zones indicated differences in strain resistance. Multivariate statistical methods were applied to filter the proteomic results, revealing five potential protein biomarkers, including Peptidase M23. Gene ontology (GO) analysis and sequence alignment supported their antibacterial activity. Thus, SWATH-MS provides a comprehensive understanding of the secretome of P. aeruginosa in its action against MRSA, guiding future antibacterial research.

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.