Cerebrospinal fluid (CSF) can be considered the most promising biosample for the discovery and analysis of biomarkers in neuroscience, an area of great medical need. CSF is a body fluid that surrounds the brain and provides a rich pool of biochemical markers, both proteomic and metabolomic, that reflect the state of neurological processes. Such biomarkers can either serve as diagnostic or prognostic biomarkers to improve the characterization of patients and preclinical disease models, or can be used to demonstrate drug-related exposure and efficacy. Here, we describe the proteomic toolbox for studying CSF from a drug-discovery perspective, and the trends and challenges that lie ahead.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1586/epr.12.6 | DOI Listing |
Methods Mol Biol
January 2025
Institute for Biomedicine, Eurac Research, Bolzano, Italy.
Metabolomics data analysis includes, next to the preprocessing, several additional repetitive tasks that can however be heavily dataset dependent or experiment setup specific due to the vast heterogeneity in instrumentation, protocols, or also compounds/samples that are being measured. To address this, various toolboxes and software packages in Python or R have been and are being developed providing researchers and analysts with bioinformatic/chemoinformatic tools to create their own workflows tailored toward their specific needs. This chapter presents tools and example workflows for common tasks focusing on the functionality provided by R packages developed as part of the RforMassSpectrometry initiative.
View Article and Find Full Text PDFJ Cell Biol
February 2025
Institute of Molecular Biology, Mainz, Germany.
Functional genomics with libraries of knockout alleles is limited to non-essential genes and convoluted by the potential accumulation of suppressor mutations in knockout backgrounds, which can lead to erroneous functional annotations. To address these limitations, we constructed genome-wide libraries of conditional alleles based on the auxin-inducible degron (AID) system for inducible degradation of AID-tagged proteins in the budding yeast Saccharomyces cerevisiae. First, we determined that N-terminal tagging is at least twice as likely to inadvertently impair protein function across the proteome.
View Article and Find Full Text PDFAcc Chem Res
January 2025
Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Katsura, Nishikyo-ku, Kyoto 615-8510, Japan.
ConspectusSelective chemical modification of endogenous proteins in living systems with synthetic small molecular probes is a central challenge in chemical biology. Such modification has a variety of applications important for biological and pharmaceutical research, including protein visualization, protein functionalization, proteome-wide profiling of enzyme activity, and irreversible inhibition of protein activity. Traditional chemistry for selective protein modification in cells largely relies on the high nucleophilicity of cysteine residues to ensure target-selectivity and site-specificity of modification.
View Article and Find Full Text PDFProtein-protein interaction (PPI) networks are a fundamental resource for modeling cellular and molecular function, and a large and sophisticated toolbox has been developed to leverage their structure and topological organization to predict the functional roles of under-studied genes, proteins, and pathways. However, the overwhelming majority of experimentally-determined interactions from which such networks are constructed come from a small number of well-studied model organisms. Indeed, most species lack even a single experimentally-determined interaction in these databases, much less a network to enable the analysis of cellular function, and methods for computational PPI prediction are too noisy to apply directly.
View Article and Find Full Text PDFGenomics Proteomics Bioinformatics
December 2024
State Key Laboratory of Green Pesticide, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!