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Microarray screening technology has transformed the life sciences arena over the last decade. The platform is widely used in the area of mapping interaction networks, to molecular fingerprinting and small molecular inhibitor discovery. The technique has significantly impacted both basic and applied research. The microarray platform can likewise enable high-throughput screening and discovery of protein-protein interaction (PPI) inhibitors. Herein we demonstrate the application of microarray-guided PPI inhibitor discovery, using human BRCA1 as an example. Mutations in BRCA1 have been implicated in ~50 % of hereditary breast cancers. By targeting the (BRCT) domain, we showed compound 15a and its prodrug 15b inhibited BRCA1 activities in tumor cells. Unlike previously reported peptide-based PPI inhibitors of BRCA1, the compounds identified could be directly administered to tumor cells, thus making them useful in targeting BRCA1/PARP-related pathways involved in DNA damage and repair response, for cancer therapy.
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http://dx.doi.org/10.1007/978-1-4939-6584-7_10 | DOI Listing |
Bioinformatics
September 2025
Centre National de Recherche en Génomique Humaine, Institut François Jacob CEA Université Paris-Saclay.
Motivation: Graph Neural Network (GNN) models have emerged in many fields and notably for biological networks constituted by genes or proteins and their interactions. The majority of enrichment study methods apply over-representation analysis and gene/protein set scores according to the existing overlap between pathways. Such methods neglect knowledges coming from the interactions between the gene/protein sets.
View Article and Find Full Text PDFJ Phys Chem B
September 2025
Hefei National Research Center for Physical Sciences at the Microscale and Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Multivalent protein-protein interactions play essential roles in mediating liquid-liquid phase separation (LLPS) that drives biomolecular condensate formation. Here, we systematically investigate how the spatial distribution and relative size of protein binding domains (PBDs) would influence LLPS in a mixture of spherical proteins and RNA single strands by using a patchy-particle polymer model, wherein each protein contains a fixed number of PBDs on the surface distributed closely or sparsely. Intriguingly, we find that LLPS behavior exhibits a nontrivial dependence on the cooperative interplay between PBD distribution and protein size: while sparsely distributed PBDs are more favorable to LLPS for small proteins, closely packed PBDs facilitate LLPS for larger counterparts.
View Article and Find Full Text PDFBrief Bioinform
August 2025
State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
The systematic identification of human-virus protein-protein interactions (PPIs) is a critical step toward elucidating the underlying mechanisms of viral infection, directly informing the development of targeted interventions against existing and emerging viral threats. In this work, we presented DeepGNHV, an end-to-end framework that integrated a pretrained protein language model with structural features derived from AlphaFold2 and leveraged graph attention networks to predict human-virus PPIs. In comparison to other state-of-the-art approaches, DeepGNHV exhibited superior predictive performance, especially when applied to viral proteins absent from the training process, indicating its strong generalization capability for detecting newly emerging virus-related PPIs.
View Article and Find Full Text PDFJ Burn Care Res
September 2025
Department of Burn Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Background: Burn injuries trigger complex immune responses and gene expression changes, impacting wound healing and systemic inflammation. Understanding these changes is crucial for identifying biomarkers and therapeutic targets.
Methods: We analyzed two GEO datasets (wound tissue (GSE8056) and blood (GSE37069)) to identify differentially expressed genes (DEGs) in burn injury samples versus controls.
Clin Exp Immunol
September 2025
Orthopedic Center, Sunshine Union Hospital, High-tech Zone, Weifang City, Shandong Province, China.
Introduction: We attempted to perform a comprehensive bioinformatics analyses on osteoarthritis (OA) based on the NKT-related genes and explore the clinical related critical genes.
Methods: Differentially expressed genes (DEGs) and NKT-related genes from WGCNA were obtained using the dataset GSE114007, followed by intersection analysis to obtain NKT-related DEGs. Lasso regression, support vector machine and random forest were performed to screen feature genes, followed by verification with ROC curve, and nomogram model.