We are developing a rapid, time-resolved method using laser-activated cross-linking to capture protein-peptide interactions as a means to interrogate the interaction of serum proteins as delivery systems for peptides and other molecules. A model system was established to investigate the interactions between bovine serum albumin (BSA) and 2 peptides, the tridecapeptide budding-yeast mating pheromone (α-factor) and the decapeptide human gonadotropin-releasing hormone (GnRH). Cross-linking of α-factor, using a biotinylated, photoactivatable p-benzoyl-L-phenylalanine (Bpa)-modified analog, was energy-dependent and achieved within seconds of laser irradiation. Protein blotting with an avidin probe was used to detect biotinylated species in the BSA-peptide complex. The cross-linked complex was trypsinized and then interrogated with nano-LC-MS/MS to identify the peptide cross-links. Cross-linking was greatly facilitated by Bpa in the peptide, but some cross-linking occurred at higher laser powers and high concentrations of a non-Bpa-modified α-factor. This was supported by experiments using GnRH, a peptide with sequence homology to α-factor, which was likewise found to be cross-linked to BSA by laser irradiation. Analysis of peptides in the mass spectra showed that the binding site for both α-factor and GnRH was in the BSA pocket defined previously as the site for fatty acid binding. This model system validates the use of laser-activation to facilitate cross-linking of Bpa-containing molecules to proteins. The rapid cross-linking procedure and high performance of MS/MS to identify cross-links provides a method to interrogate protein-peptide interactions in a living cell in a time-resolved manner.
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http://dx.doi.org/10.1002/jmr.2680 | DOI Listing |
Expert Rev Proteomics
January 2025
Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
Introduction: Molecular recognition features (MoRFs) are regions in protein sequences that undergo induced folding upon binding partner molecules. MoRFs are common in nature and can be predicted from sequences based on their distinctive sequence signatures.
Areas Covered: We overview twenty years of progress in the sequence-based prediction of MoRFs which resulted in the development of 25 predictors of MoRFs that interact with proteins, peptides and lipids.
Int J Biol Macromol
January 2025
Department of Biotechnology, School of Bioengineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603 203, Tamil Nadu, India. Electronic address:
In this study, five seagrass species Halodule uninervis, Thalassia hemprichii, Enhalus acoroides, Cymodocea serrulata, and Syringodium isoetifolium collected from the Mandapam coastal region of Rameswaram (Palk Bay region), Tamil Nadu, India, were selected to identify the antioxidant-rich proteins/peptides. The primary objective was to identify the proteins/peptides present in these seagrass filtrates extracted by using four different pH-based buffer extracts and to assess their antioxidant activity. Among the various buffer extracts, 0.
View Article and Find Full Text PDFFront Mol Biosci
December 2024
Department of Chemistry, Western Washington University, Bellingham, WA, United States.
Cellular signaling networks are modulated by multiple protein-protein interaction domains that coordinate extracellular inputs and processes to regulate cellular processes. Several of these domains recognize short linear motifs, or SLiMs, which are often highly conserved and are closely regulated. One such domain, the Src homology 3 (SH3) domain, typically recognizes proline-rich SLiMs and is one of the most abundant SLiM-binding domains in the human proteome.
View Article and Find Full Text PDFCancers (Basel)
November 2024
Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 22254, Saudi Arabia.
Background/objectives: Human epidermal growth factor receptor 2 (HER2) is overexpressed in several malignancies, such as breast, gastric, ovarian, and lung cancers, where it promotes aggressive tumor proliferation and unfavorable prognosis. Targeting HER2 has thus emerged as a crucial therapeutic strategy, particularly for HER2-positive malignancies. The present study focusses on the design and optimization of peptide inhibitors targeting HER2, utilizing machine learning to identify and enhance peptide candidates with elevated binding affinities.
View Article and Find Full Text PDFPNAS Nexus
December 2024
Department of Physics, University of Missouri, Columbia, MO 65211, USA.
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