Proteomics最新文献

In the ever-evolving landscape of scientific inquiry, the saying “software is eating the world,” popularized in Silicon Valley over a decade ago, rings truer than ever before. This aphorism, initially indicative of the transformative power of software in reshaping industries and everyday life, has found a significant echo in the realm of science. Akin to a master chef who artfully combines a variety of raw ingredients to concoct a delightful meal, in proteomics, bioinformatics serves as the critical skill set that distills complex, raw data into digestible, insightful knowledge. This editorial aims to showcase the breadth of innovation and inquiry encapsulated in this special issue of Proteomics, dedicated to computational mass spectrometry and proteomics, and underline the indispensable role of advanced computational tools in deciphering the molecular intricacies of life itself.

Proteomics research, a cornerstone of ‘omics studies, provides a panoramic view into the molecular and cellular mechanisms underpinning life. Through the analysis of proteins, their structures, functions, and interactions with various molecules, proteomics endeavors to unravel the complex molecular tapestry of biological systems. The manuscripts featured in this special issue illuminate the wide scope of scientific knowledge that can be gleaned from proteomics experiments, made possible only through the employment of sophisticated computational tools and bioinformatics analyses.

Echoing recent advancements in artificial intelligence, several papers in this issue delve into the application of machine learning tools for enhancing the analysis of mass spectrometry-based proteomics data. For instance, Adams et al. offer a comprehensive review on utilizing predicted peptide properties like spectral similarity, retention time, and ion mobility features to refine immunopeptidomics data analysis [1]. In a similar vein, Siraj et al. discuss the enhancement of protein–nucleic acid cross-links detection through the prediction of fragment ion intensities and retention time [2]. Peptide property prediction, a task that has become increasingly commonplace in recent years, enables accurate and sensitive rescoring of spectrum assignments in bottom-up proteomics data. The contributions in this special issue demonstrate that this strategy is particularly potent in realms that exhibit non-standard and highly complex spectral data, such as immunopeptidomics and protein–RNA crosslinking mass spectrometry.

Further, Joyce and Searle's review on computational approaches for phosphoproteomics identification and localization presents the future potential of using predicted peptide properties for interpreting phosphopeptide positional isomers and disambiguating chimeric spectra containing multiple isomeric peptides that differ only in the phosphorylation location [3]. Additionally, Picciani et al. introduce the Oktoberfest tool, le

Human embryonic stem cells (hESCs) resemble the pluripotent epiblast cells found in the early postimplantation human embryo and represent the “primed” state of pluripotency. One factor that helps primed pluripotent cells retain pluripotency and prepare genes for differentiation is the transcription factor TCF7L1, a member of a small family of proteins known as T cell factors/Lymphoid enhancer factors (TCF/LEF) that act as downstream components of the WNT signaling pathway. Transcriptional output of the WNT pathway is regulated, in part, by the activity of TCF/LEFs in conjunction with another component of the WNT pathway, β-CATENIN. Because TCF7L1 plays an important role in regulating pluripotency, we began to characterize the protein complex associated with TCF7L1 when bound to chromatin in hESCs using rapid immunoprecipitation of endogenous proteins (RIME).  Data are available via ProteomeXchange with identifier PXD047582. These data identify known and new partners of TCF7L1 on chromatin and provide novel insights into how TCF7L1 and pluripotency itself might be regulated.

The value of accurate protein structural models closely conforming to the experimental data is indisputable. DREAMweb deploys an improved DREAM algorithm, DREAMv2, that incorporates a tighter bound in the constraint set of the underlying optimization approach. This reduces the artifacts while modeling the protein structure by solving the distance-geometry problem. DREAMv2 follows a bottom-up strategy of building smaller substructures for regions with a larger concentration of experimental bounds and consolidating them before modeling the rest of the protein structure. This improves secondary structure conformance in the final models consistent with experimental data. The proposed method efficiently models regions with sparse coverage of experimental data by reducing the possibility of artifacts compared to DREAM. To balance performance and accuracy, smaller substructures ($��\sim �200�$ atoms) are solved in this regime, allowing faster builds for the other parts under relaxed conditions. DREAMweb is accessible as an internet resource. The improvements in results are showcased through benchmarks on 10 structures. DREAMv2 can be used in tandem with any NMR-based protein structure determination workflow, including an iterative framework where the NMR assignment for the NOESY spectra is incomplete or ambiguous. DREAMweb is freely available for public use at http://pallab.cds.iisc.ac.in/DREAM/ and downloadable at https://github.com/niladriranjandas/DREAMv2.git.

Currently, nine polyglutamine (polyQ) expansion diseases are known. They include spinocerebellar ataxias (SCA1, 2, 3, 6, 7, 17), spinal and bulbar muscular atrophy (SBMA), dentatorubral-pallidoluysian atrophy (DRPLA), and Huntington's disease (HD). At the root of these neurodegenerative diseases are trinucleotide repeat mutations in coding regions of different genes, which lead to the production of proteins with elongated polyQ tracts. While the causative proteins differ in structure and molecular mass, the expanded polyQ domains drive pathogenesis in all these diseases. PolyQ tracts mediate the association of proteins leading to the formation of protein complexes involved in gene expression regulation, RNA processing, membrane trafficking, and signal transduction. In this review, we discuss commonalities and differences among the nine polyQ proteins focusing on their structure and function as well as the pathological features of the respective diseases. We present insights from AlphaFold-predicted structural models and discuss the biological roles of polyQ-containing proteins. Lastly, we explore reported protein–protein interaction networks to highlight shared protein interactions and their potential relevance in disease development.

Lipidomic data often exhibit missing data points, which can be categorized as missing completely at random (MCAR), missing at random, or missing not at random (MNAR). In order to utilize statistical methods that require complete datasets or to improve the identification of potential effects in statistical comparisons, imputation techniques can be employed. In this study, we investigate commonly used methods such as zero, half-minimum, mean, and median imputation, as well as more advanced techniques such as k-nearest neighbor and random forest imputation. We employ a combination of simulation-based approaches and application to real datasets to assess the performance and effectiveness of these methods. Shotgun lipidomics datasets exhibit high correlations and missing values, often due to low analyte abundance, characterized as MNAR. In this context, k-nearest neighbor approaches based on correlation and truncated normal distributions demonstrate best performance. Importantly, both methods can effectively impute missing values independent of the type of missingness, the determination of which is nearly impossible in practice. The imputation methods still control the type I error rate.

Mild cognitive impairment (MCI) is an early stage of memory loss that affects cognitive abilities with the aging of individuals, such as language or visual/spatial comprehension. MCI is considered a prodromal phase of more complicated neurodegenerative diseases such as Alzheimer's. Therefore, accurate diagnosis and better understanding of the disease prognosis will facilitate prevention of neurodegeneration. However, the existing diagnostic methods fail to provide precise and well-timed diagnoses, and the pathophysiology of MCI is not fully understood. Alterations of the serum N-glycoproteome expression could represent an essential contributor to the overall pathophysiology of neurodegenerative diseases and be used as a potential marker to assess MCI diagnosis using less invasive procedures. In this approach, we identified N-glycopeptides with different expressions between healthy and MCI patients from serum glycoproteins. Seven of the N-glycopeptides showed outstanding AUC values, among them the antithrombin-III Asn224 + 4-5-0-2 with an AUC value of 1.00 and a p value of 0.0004. According to proteomics and ingenuity pathway analysis (IPA), our data is in line with recent publications, and the glycoproteins carrying the identified N-sites play an important role in neurodegeneration.

The group 2 σ factor for RNA polymerase SigE plays important role in regulating central carbon metabolism in cyanobacteria. However, the regulation of SigE for these pathways at a proteome level remains unknown. Using a sigE-deficient strain (ΔsigE) of Synechocystis sp. PCC 6803 and quantitative proteomics, we found that SigE depletion induces differential protein expression for sugar catabolic pathways including glycolysis, oxidative pentose phosphate (OPP) pathway, and glycogen catabolism. Two glycogen debranching enzyme homologues Slr1857 and Slr0237 are found differentially expressed in ΔsigE. Glycogen determination indicated that Δslr0237 accumulated glycogen under photomixotrophic condition but was unable to utilize these reserves in the dark, whereas Δslr1857 accumulates and utilizes glycogen in a similar way as the WT strain does in the same condition. These results suggest that Slr0237 plays the major role as the glycogen debranching enzyme in Synechocystis.