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Protein-protein interactions (PPIs) play an important role in numerous biological processes such as cell-cycle regulation and multiple diseases. The family of 14-3-3 proteins is an attractive target as they serve as binding partner to various proteins and are therefore capable of regulating their biological activities. Discovering small-molecule modulators, in particular stabilizers, of such complexes via traditional screening approaches is a challenging task. Herein, we pioneered the first application of dynamic combinatorial chemistry (DCC) to a PPI target, to find modulators of 14-3-3 proteins. Evaluation of the amplified hits from the DCC experiments for their binding affinity via surface plasmon resonance (SPR), revealed that the low-micromolar ( 15-16 μM) acylhydrazones are 14-3-3/synaptopodin PPI stabilizers. Thus, DCC appears to be ideally suited for the discovery of not only modulators but even the more elusive stabilizers of notoriously challenging PPIs.
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http://dx.doi.org/10.1021/acsmedchemlett.9b00541 | DOI Listing |
Biomed Environ Sci
August 2025
Gastrointestinal Disease Centre, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China.
Objective: To explore the correlation between chromosome 8 open reading frame 76 (C8orf76) and cyclin-dependent kinase 4 (CDK4) and the potential predictive effect of C8orf76 and CDK4 on the prognosis of colorectal cancer (CRC).
Methods: We constructed a protein-protein interaction network of C8orf76-related genes and analyzed the prognostic signatures of C8orf76 and CDK4. Clinicopathological features of C8orf76 and CDK4 were visualized using a nomogram.
Front Immunol
September 2025
Department of Thoracic Surgery, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, China.
Background: Lung cancer remains the leading cause of cancer-related mortality globally, primarily due to late-stage diagnosis, molecular heterogeneity, and therapy resistance. Key biomarkers such as EGFR, ALK, KRAS, and PD-1 have revolutionized precision oncology; however, comprehensive structural and clinical validation of these targets is crucial to enhance therapeutic efficacy.
Methods: Protein sequences for EGFR, ALK, KRAS, and PD-1 were retrieved from UniProt and modeled using SWISS-MODEL to generate high-confidence 3D structures.
Biochem Biophys Rep
December 2025
Division of Breast Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
Purpose: This study aimed to conduct functional proteomics across breast cancer subtypes with bioinformatics analyses.
Methods: Candidate proteins were identified using nanoscale liquid chromatography with tandem mass spectrometry (NanoLC-MS/MS) from core needle biopsy samples of early stage (0-III) breast cancers, followed by external validation with public domain gene-expression datasets (TCGA TARGET GTEx and TCGA BRCA).
Results: Seventeen proteins demonstrated significantly differential expression and protein-protein interaction (PPI) found the strong networks including COL2A1, COL11A1, COL6A1, COL6A2, THBS1 and LUM.
Biochem Biophys Rep
June 2025
Department of Public Health, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Background: Synaptic dysfunction and synapse loss occur in Alzheimer's disease (AD). The current study aimed to identify synaptic-related genes with diagnostic potential for AD.
Methods: Differentially expressed genes (DEGs) were overlapped with phenotype-associated module selected through weighted gene co-expression network analysis (WGCNA), and synaptic-related genes.
Front Med (Lausanne)
August 2025
State Key Laboratory of Respiratory Diseases, Guangzhou Medical University, Guangzhou, China.
Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease. However, the biological role of mitochondrial metabolism (MM) in COPD remains poorly understood. This study aimed to explore the underlying mechanisms of MM in COPD using bioinformatics methods.
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