Cells utilize ubiquitin as a posttranslational protein modifier to convey various signals such as proteasomal degradation. The dysfunction of ubiquitylation or following proteasomal degradation can give rise to the accumulation and aggregation of improperly ubiquitylated proteins, which is known to be a general causation of many neurodegenerative diseases. Thus, the characterization of substrate peptide sequences of E3 ligases is crucial in biological and pharmaceutical sciences. In this study, we developed a novel high-throughput screening system for substrate peptide sequences of E3 ligases using a cDNA display method, which enables covalent conjugation between peptide sequences and their corresponding cDNA sequences. First, we focused on the MDM2 E3 ligase and its known peptide substrate as a model to establish the screening method, and confirmed that cDNA display method was compatible with in vitro ubiquitylation. Then, we demonstrated identification of MDM2 substrate sequences from random libraries to identify a novel motif (VKFTGGQLA). Bioinformatics analysis of the hit sequences was performed to gain insight about endogenous substrate proteins.
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http://dx.doi.org/10.1002/cbic.202400617 | DOI Listing |
Appl Environ Microbiol
January 2025
Office of Applied Science, Center for Veterinary Medicine, U.S. Food and Drug Administration, Laurel, Maryland, USA.
As a diverse and complex food matrix, the animal food microbiota and repertoire of antimicrobial resistance (AMR) genes remain to be better understood. In this study, 16S rRNA gene amplicon sequencing and shotgun metagenomics were applied to three types of animal food samples (cattle feed, dry dog food, and poultry feed). ZymoBIOMICS mock microbial community was used for workflow optimization including DNA extraction kits and bead-beating conditions.
View Article and Find Full Text PDFParasite
January 2025
National Engineering Research Center of Marine Facilities Aquaculture, Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, Zhejiang 316022, PR China.
The aim of this study was to clarify the taxonomic identification of a hemoflagellate and assess the effect of trypanosome infection on Larimichthys crocea. Giemsa staining showed the presence of three morphotypes of trypomastigotes. The trypanosomes had the following morphological characteristics: a slender body with a long flagellum at the front; body size 12.
View Article and Find Full Text PDFProtein Sci
February 2025
Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Zaragoza, Spain.
PADI4 is one of the human isoforms of a family of enzymes involved in the conversion of arginine to citrulline. MDM2 is an E3 ubiquitin ligase that is critical for degradation of the tumor suppressor gene p53. We have previously shown that there is an interaction between MDM2 and PADI4 in cellulo, and that such interaction occurs through the N-terminal region of MDM2, N-MDM2, and in particular through residues Thr26, Val28, Phe91, and Lys98.
View Article and Find Full Text PDFProtein Sci
February 2025
Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, St. Paul, Minnesota, USA.
N-Methylation of the peptide backbone confers pharmacologically beneficial characteristics to peptides that include greater membrane permeability and resistance to proteolytic degradation. The borosin family of ribosomally synthesized and post-translationally modified peptides offer a post-translational route to install amide backbone α-N-methylations. Previous work has elucidated the substrate scope and engineering potential of two examples of type I borosins, which feature autocatalytic precursors that encode N-methyltransferases that methylate their own C-termini in trans.
View Article and Find Full Text PDFJ Proteome Res
January 2025
Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany.
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue.
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