Predicting the protein target of cucurbitacin E through a novel approach of protein folding shape code

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Authors: Liqing Ke, Yuchen Yang, Xiangbo Meng, Guoyuan Zhu, Jiaan Yang, Ying Wang, Xinluan Wang, Peng Zhang, Xun Gong, Wenxiang Cheng
Cited by: 0
References: 36

Abstract

Background: Traditional Chinese medicine (TCM) constitutes a complex system involving numerous chemical components that work synergistically to achieve therapeutic efficacy. However, the unclear molecular targets of TCM compounds remain a significant challenge for modern research in this field. Cucurbitacin E (CuE) is a tetracyclic triterpenoid compound derived from TCM plants. Previous studies have demonstrated its ability to inhibit cancer cell proliferation and mitigate inflammatory responses. Nevertheless, the specific target of this compound remains undetermined. Methods: In this study, we used a novel bioinformatics tool, the protein folding shape code (PFSC), to comprehensively investigate the protein target of CuE. The PFSC is designed to characterize protein 3-dimensional (3D) folding shapes. Subsequently, we utilized Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for the functional annotation and pathway analysis of the predicted targets. Moreover, the validity of these predictions was experimentally confirmed using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: A search of the PDBeChem database identified 4 compounds with >80% similarity to CuE. On the basis of this analysis, CuE was predicted to potentially interact with the protein structures 2VDY and 3TC5. Subsequent screening yielded more than 10 high-scoring protein targets, which were implicated in processes such as inflammation, angiogenesis, and immune regulation. From this pool, 8 specific targets (PIN1, PPARG, FGFR1, BRAF, JAK1, JAK3, STAT3, and IL-6) were selected for experimental validation. The results from cellular-level assays indicated that CuE could regulate the gene expression levels of these selected targets. Conclusions: The findings of this study demonstrate that PFSC is a highly effective methodology for analyzing protein 3D structures and is well-suited for identifying the multiple targets of TCM compounds.

Fields of Study & Subjects

Computational Drug Discovery Methods Protein Interaction Studies and Fluorescence Analysis Natural Compounds in Disease Treatment

Publication details

Journal / source
Guidelines and Standards of Chinese Medicine
Volume
4
Issue
1
Pages
41–48
Publication date
3 February 2026
Language
EN