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Updated: Feb 27, 2026
Fluorescence-Based Detection of FEN1 Nuclease Activity and Screening of Small-Molecule Inhibitors
Published on: June 27, 2025
Identification of human flap endonuclease 1 (FEN1) inhibitors using a machine learning based consensus virtual
Amit Laxmikant Deshmukh1, Sharat Chandra2, Deependra Kumar Singh1
1Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, B.S. 10/1, Janakipuram Extension, Sitapur Road, Lucknow, 226031, India. d.banerjee@cdri.res.in.
Abstract:
Human Flap endonuclease1 (FEN1) is an enzyme that is indispensable for DNA replication and repair processes and inhibition of its Flap cleavage activity results in increased cellular sensitivity to DNA damaging agents (cisplatin, temozolomide, MMS, etc.), with the potential to improve cancer prognosis. Reports of the high expression levels of FEN1 in several cancer cells support the idea that FEN1 inhibitors may target cancer cells with minimum side effects to normal cells. In this study, we used large publicly available, high-throughput screening data of small molecule compounds targeted against FEN1. Two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), were utilized to generate four classification models from huge PubChem bioassay data containing probable FEN1 inhibitors and non-inhibitors. We also investigated the influence of randomly selected Zinc-database compounds as negative data on the outcome of classification modelling. The results show that the SVM model with inactive compounds was superior to RF with Matthews's correlation coefficient (MCC) of 0.67 for the test set. A Maybridge database containing approximately 53 000 compounds was screened and top ranking 5 compounds were selected for enzyme and cell-based in vitro screening. The compound JFD00950 was identified as a novel FEN1 inhibitor with in vitro inhibition of flap cleavage activity as well as cytotoxic activity against a colon cancer cell line, DLD-1.
Insights
Human Flap endonuclease 1 (FEN1) is crucial for DNA repair. Inhibiting FEN1 enhances cancer treatment sensitivity. This study identified a novel FEN1 inhibitor, JFD00950, showing promise against colon cancer cells.
Area of Science:
- Biochemistry
- Molecular Biology
- Computational Chemistry
Background:
- Human Flap endonuclease 1 (FEN1) is vital for DNA replication and repair.
- FEN1 inhibition increases cancer cell sensitivity to DNA damaging agents, suggesting therapeutic potential.
- Elevated FEN1 expression in cancers indicates its utility as a drug target.
Purpose of the Study:
- To identify novel small molecule inhibitors of FEN1 using high-throughput screening data.
- To develop and compare machine learning models for predicting FEN1 inhibitors.
- To validate potential inhibitors through in vitro enzyme and cell-based assays.
Main Methods:
- Utilized large-scale public high-throughput screening data for FEN1 inhibitors.
- Developed Support Vector Machine (SVM) and Random Forest (RF) classification models.
- Screened Maybridge database and performed in vitro enzyme and cytotoxicity assays.
Main Results:
- The SVM model demonstrated superior performance (MCC of 0.67) compared to RF.
- Screening identified five top-ranking compounds from the Maybridge database.
- Compound JFD00950 exhibited novel FEN1 inhibition and cytotoxic activity against DLD-1 colon cancer cells.
Conclusions:
- Machine learning models can effectively predict FEN1 inhibitors from large datasets.
- FEN1 inhibitors hold promise for targeted cancer therapy.
- JFD00950 represents a potential lead compound for colon cancer treatment.
