Proteomics最新文献

In this study, we present a high-resolution dataset and bioinformatic analysis of the proteome of Bacillus subtilis 168 trp+ (BSB1) during germination and spore outgrowth. Samples were collected at 14 different time points (ranging from 0 to 130 min) in three biological replicates after spore inoculation into germination medium. A total of 2191 proteins were identified and categorized based on their expression kinetics. We observed four distinct clusters that were analyzed for functional categories and KEGG pathways annotations. The examination of newly synthesized proteins between successive time points revealed significant changes, particularly within the first 50 min. The dataset provides an information base that can be used for modeling purposes and inspire the design of new experiments.

Mass spectrometry (MS)-based top-down proteomics (TDP) analysis of histone proteoforms provides critical information about combinatorial post-translational modifications (PTMs), which is vital for pursuing a better understanding of epigenetic regulation of gene expression. It requires high-resolution separations of histone proteoforms before MS and tandem MS (MS/MS) analysis. In this work, for the first time, we combined SDS-PAGE-based protein fractionation (passively eluting proteins from polyacrylamide gels as intact species for mass spectrometry, PEPPI-MS) with capillary zone electrophoresis (CZE)-MS/MS for high-resolution characterization of histone proteoforms. We systematically studied the histone proteoform extraction from SDS-PAGE gel and follow-up cleanup as well as CZE-MS/MS, to determine an optimal procedure. The optimal procedure showed reproducible and high-resolution separation and characterization of histone proteoforms. SDS-PAGE separated histone proteins (H1, H2, H3, and H4) based on their molecular weight and CZE provided additional separations of proteoforms of each histone protein based on their electrophoretic mobility, which was affected by PTMs, for example, acetylation and phosphorylation. Using the technique, we identified over 200 histone proteoforms from a commercial calf thymus histone sample with good reproducibility. The orthogonal and high-resolution separations of SDS-PAGE and CZE made our technique attractive for the delineation of histone proteoforms extracted from complex biological systems.

Liquid chromatography–mass spectrometry (LC-MS) intact mass analysis and LC-MS/MS peptide mapping are decisional assays for developing biological drugs and other commercial protein products. Certain PTM types, such as truncation and oxidation, increase the difficulty of precise proteoform characterization owing to inherent limitations in peptide and intact protein analyses. Top-down MS (TDMS) can resolve this ambiguity via fragmentation of specific proteoforms. We leveraged the strengths of flow-programmed (fp) denaturing online buffer exchange (dOBE) chromatography, including robust automation, relatively high ESI sensitivity, and long MS/MS window time, to support a TDMS platform for industrial protein characterization. We tested data-dependent (DDA) and targeted strategies using 14 different MS/MS scan types featuring combinations of collisional- and electron-based fragmentation as well as proton transfer charge reduction. This large, focused dataset was processed using a new software platform, named TDAcquireX, that improves proteoform characterization through TDMS data aggregation. A DDA-based workflow provided objective identification of αLac truncation proteoforms with a two-termini clipping search. A targeted TDMS workflow facilitated the characterization of αLac oxidation positional isomers. This strategy relied on using sliding window-based fragment ion deconvolution to generate composite proteoform spectral match (cPrSM) results amenable to fragment noise filtering, which is a fundamental enhancement relevant to TDMS applications generally.

Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.

The mzIdentML data format, originally developed by the Proteomics Standards Initiative in 2011, is the open XML data standard for peptide and protein identification results coming from mass spectrometry. We present mzIdentML version 1.3.0, which introduces new functionality and support for additional use cases. First of all, a new mechanism for encoding identifications based on multiple spectra has been introduced. Furthermore, the main mzIdentML specification document can now be supplemented by extension documents which provide further guidance for encoding specific use cases for different proteomics subfields. One extension document has been added, covering additional use cases for the encoding of crosslinked peptide identifications. The ability to add extension documents facilitates keeping the mzIdentML standard up to date with advances in the proteomics field, without having to change the main specification document. The crosslinking extension document provides further explanation of the crosslinking use cases already supported in mzIdentML version 1.2.0, and provides support for encoding additional scenarios that are critical to reflect developments in the crosslinking field and facilitate its integration in structural biology. These are: (i) support for cleavable crosslinkers, (ii) support for internally linked peptides, (iii) support for noncovalently associated peptides, and (iv) improved support for encoding scores and the corresponding thresholds.

Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.

Extracellular vesicles (EVs) are anucleate particles enclosed by a lipid bilayer that are released from cells via exocytosis or direct budding from the plasma membrane. They contain an array of important molecular cargo such as proteins, nucleic acids, and lipids, and can transfer these cargoes to recipient cells as a means of intercellular communication. One of the overarching paradigms in the field of EV research is that EV cargo should reflect the biological state of the cell of origin. The true relationship or extent of this correlation is confounded by many factors, including the numerous ways one can isolate or enrich EVs, overlap in the biophysical properties of different classes of EVs, and analytical limitations. This presents a challenge to research aimed at detecting low-abundant EV-encapsulated nucleic acids or proteins in biofluids for biomarker research and underpins technical obstacles in the confident assessment of the proteomic landscape of EVs that may be affected by sample-type specific or disease-associated proteoforms. Improving our understanding of EV biogenesis, cargo loading, and developments in top-down proteomics may guide us towards advanced approaches for selective EV and molecular cargo enrichment, which could aid EV diagnostics and therapeutics research.