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The article was co-published by China Pharmaceutical University and ICE Bioscience. 

Abstract

14−3−3 proteins play a crucial role in the regulation of protein− protein interactions, impacting various cellular processes and disease mechanisms. Recent advancements have led to the development of stabilizers that enhance the binding of 14−3−3 proteins to clients, presenting promising therapeutic potentials. This perspective provides an updated overview of the latest developments in the field of 14−3−3 stabilizers, with a focus on their design, synthesis, and biological evaluation. We discuss the structural basis for the interaction between 14−3−3 proteins and their ligands, highlighting key modifications that enhance binding affinity and selectivity. Additionally, we explore the therapeutic applications of 14−3−3 stabilizers across major therapeutic areas such as cancer, metabolic disorders, and neurodegenerative diseases. By summarizing recent research findings and technological advancements, this perspective aims to shed light on the current state of 14−3−3 stabilizer developments and outline future directions for optimizing these compounds as effective therapeutic agents.

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The article was co-published by Beijing University of Chemical Technology and ICE Bioscience.

Abstract

Alzheimer’s disease is the predominant form of dementia, and disulfidptosis is the latest reported mode of cell death that impacts various disease processes. This study used bioinformatics to analyze genes associated with disulfidptosis in Alzheimer’s disease comprehensively. Based on the public datasets, the differentially expressed genes associated with disulfidptosis were identified, and immune cell infiltration was investigated through correlation analysis. Subsequently, hub genes were determined by a randomforest model. A prediction model was constructed using logistic regression. In addition, the drug-target affinity was predicted by a graph neural network model, and the results were validated by molecular docking. Five hub genes (PPEF1, NEUROD6, VIP, NUPR1, and GEM) were identified. The gene set showed significant enrichment for AD-related pathways. The logistic regression model demonstrated an AUC of 0.952, with AUC values of 0.916 and 0.864 in validated datasets. The immune infiltration analysis revealed significant heterogeneity between the Alzheimer’s disease and control groups. High-affinity drugs for hub genes were identified. Through our study, a disease prediction model was constructed using potential biomarkers, and drugs targeting the genes were predicted. These results contribute to further understanding of the molecular mechanisms underlying Alzheimer’s disease.

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Our kinase activity assays were used in the study of novel FGFR1 inhibitors for triple-negative breast cancer (TNBC).

Abstract

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schr¨odinger’s Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p- FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound’s specific binding interactions with FGFR1.

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Our recent collaborative work was published in "Archiv der Pharmazie," demonstrating our excellence in kinase activity assays.

Abstract

Oncogenic overexpression or activation of C‐terminal Src kinase (CSK) has been shown to play an important role in triple‐negative breast cancer (TNBC) progression, including tumor initiation, growth, metastasis, drug resistance. This revelation has pivoted the focus toward CSK as a potential target for novel treatments. However, until now, there are few inhibitors designed to target the CSK protein. Responding to this, our research has implemented a comprehensive virtual screening protocol. By integrating energybased screening methods with AI‐driven scoring functions, such as Attentive FP, and employing rigorous rescoring methods like Glide docking and molecular mechanics generalized Born surface area (MM/GBSA), we have systematically sought out inhibitors of CSK. This approach led to the discovery of a compound with a potent CSK inhibitory activity, reflected by an IC50 value of 1.6 nM under a homogeneous time‐resolved fluorescence (HTRF) bioassay. Subsequently, molecule 2 exhibits strong growth inhibition of MD anderson ‐ metastatic breast (MDA‐MB) ‐231, Hs578T, and SUM159 cells, showing a level of growth inhibition comparable to that observed with dasatinib. Treatment with molecule 2 also induced significant G1 phase accumulation and cell apoptosis. Furthermore, we have explored the explicit binding interactions of the compound with CSK using molecular dynamics simulations, providing valuable insights into its mechanism of action.