Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening
Protein arginine methyltransferases (PRMTs) play crucial roles in gene regulation, signal transduction, mRNA splicing, DNA repair, cell differentiation, and embryonic development. Due to its significant impact, PRMTs is a target for the prevention and treatment of various diseases. Among the PRMT fa...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Chemistry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2025.1548812/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593904763928576 |
|---|---|
| author | Jungan Zhang Yixin Ren Yixin Ren Yun Teng Han Wu Jingsu Xue Lulu Chen Xiaoyue Song Yan Li Ying Zhou Zongran Pang Zongran Pang Hao Wang Hao Wang Hao Wang |
| author_facet | Jungan Zhang Yixin Ren Yixin Ren Yun Teng Han Wu Jingsu Xue Lulu Chen Xiaoyue Song Yan Li Ying Zhou Zongran Pang Zongran Pang Hao Wang Hao Wang Hao Wang |
| author_sort | Jungan Zhang |
| collection | DOAJ |
| description | Protein arginine methyltransferases (PRMTs) play crucial roles in gene regulation, signal transduction, mRNA splicing, DNA repair, cell differentiation, and embryonic development. Due to its significant impact, PRMTs is a target for the prevention and treatment of various diseases. Among the PRMT family, PRMT1 is the most abundant and ubiquitously expressed in the human body. Although extensive research has been conducted on PRMT1, the reported inhibitors have not successfully passed clinical trials. In this study, deep learning was employed to analyze the characteristics of existing PRMTs inhibitors and to construct a classification model for PRMT1 inhibitors. Through a classification model and molecular docking, a series of potential PRMT1 inhibitors were identified. The representative compound (compound 156) demonstrates stable binding to the PRMT1 protein by molecular hybridization, molecular dynamics simulations, and binding free energy analyses. The study discovered novel scaffolds for potential PRMT1 inhibitors. |
| format | Article |
| id | doaj-art-05e11eb85e9e4945a8167a33279f68f7 |
| institution | Kabale University |
| issn | 2296-2646 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj-art-05e11eb85e9e4945a8167a33279f68f72025-01-20T07:20:31ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462025-01-011310.3389/fchem.2025.15488121548812Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screeningJungan Zhang0Yixin Ren1Yixin Ren2Yun Teng3Han Wu4Jingsu Xue5Lulu Chen6Xiaoyue Song7Yan Li8Ying Zhou9Zongran Pang10Zongran Pang11Hao Wang12Hao Wang13Hao Wang14School of Pharmacy, Minzu University of China, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaInstitute of National Security, Minzu University of China, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaInstitute of National Security, Minzu University of China, Beijing, ChinaInstitute of National Security, Minzu University of China, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaKey Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, ChinaSchool of Pharmacy, Minzu University of China, Beijing, ChinaInstitute of National Security, Minzu University of China, Beijing, ChinaKey Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, ChinaProtein arginine methyltransferases (PRMTs) play crucial roles in gene regulation, signal transduction, mRNA splicing, DNA repair, cell differentiation, and embryonic development. Due to its significant impact, PRMTs is a target for the prevention and treatment of various diseases. Among the PRMT family, PRMT1 is the most abundant and ubiquitously expressed in the human body. Although extensive research has been conducted on PRMT1, the reported inhibitors have not successfully passed clinical trials. In this study, deep learning was employed to analyze the characteristics of existing PRMTs inhibitors and to construct a classification model for PRMT1 inhibitors. Through a classification model and molecular docking, a series of potential PRMT1 inhibitors were identified. The representative compound (compound 156) demonstrates stable binding to the PRMT1 protein by molecular hybridization, molecular dynamics simulations, and binding free energy analyses. The study discovered novel scaffolds for potential PRMT1 inhibitors.https://www.frontiersin.org/articles/10.3389/fchem.2025.1548812/fullPRMT1machine learningmolecular dockingmolecular dynamics simulationmolecular hybridization |
| spellingShingle | Jungan Zhang Yixin Ren Yixin Ren Yun Teng Han Wu Jingsu Xue Lulu Chen Xiaoyue Song Yan Li Ying Zhou Zongran Pang Zongran Pang Hao Wang Hao Wang Hao Wang Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening Frontiers in Chemistry PRMT1 machine learning molecular docking molecular dynamics simulation molecular hybridization |
| title | Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening |
| title_full | Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening |
| title_fullStr | Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening |
| title_full_unstemmed | Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening |
| title_short | Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening |
| title_sort | discovery of novel prmt1 inhibitors a combined approach using ai classification model and traditional virtual screening |
| topic | PRMT1 machine learning molecular docking molecular dynamics simulation molecular hybridization |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2025.1548812/full |
| work_keys_str_mv | AT junganzhang discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT yixinren discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT yixinren discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT yunteng discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT hanwu discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT jingsuxue discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT luluchen discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT xiaoyuesong discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT yanli discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT yingzhou discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT zongranpang discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT zongranpang discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT haowang discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT haowang discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening AT haowang discoveryofnovelprmt1inhibitorsacombinedapproachusingaiclassificationmodelandtraditionalvirtualscreening |