Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation

被引:0
|
作者
Yasir, Muhammad [1 ]
Park, Jinyoung [1 ]
Han, Eun-Taek [2 ]
Han, Jin-Hee [2 ]
Park, Won Sun [3 ]
Hassan, Mubashir [4 ]
Kloczkowski, Andrzej [4 ]
Chun, Wanjoo [1 ]
机构
[1] Kangwon Natl Univ, Sch Med, Dept Pharmacol, Chunchon, South Korea
[2] Kangwon Natl Univ, Sch Med, Dept Med Environm Biol & Trop Med, Chunchon, South Korea
[3] Kangwon Natl Univ, Sch Med, Dept Physiol, Chunchon, South Korea
[4] Nationwide Childrens Hosp, Steve & Cindy Rasmussen Inst Genom Med, Columbus, OH USA
来源
PLOS ONE | 2024年 / 19卷 / 12期
关键词
NECROSIS-FACTOR-ALPHA; T-CELL LYMPHOMA; CONVERTING-ENZYME; BREAST-CANCER; TNF-ALPHA; IDENTIFICATION; AGENTS;
D O I
10.1371/journal.pone.0315245
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The increasing utilization of deep learning models in drug repositioning has proven to be highly efficient and effective. In this study, we employed an integrated deep-learning model followed by traditional drug screening approach to screen a library of FDA-approved drugs, aiming to identify novel inhibitors targeting the TNF-alpha converting enzyme (TACE). TACE, also known as ADAM17, plays a crucial role in the inflammatory response by converting pro-TNF-alpha to its active soluble form and cleaving other inflammatory mediators, making it a promising target for therapeutic intervention in diseases such as rheumatoid arthritis. Reference datasets containing active and decoy compounds specific to TACE were obtained from the DUD-E database. Using RDKit, a cheminformatics toolkit, we extracted molecular features from these compounds. We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. Our trained model was subsequently used to predict the TACE inhibitory potential of FDA-approved drugs. From these predictions, Vorinostat was identified as a potential TACE inhibitor. Moreover, molecular docking and molecular dynamics simulation were conducted to validate these findings, using BMS-561392 as a reference TACE inhibitor. Vorinostat, originally an FDA-approved drug for cancer treatment, exhibited strong binding interactions with key TACE residues, suggesting its repurposing potential. Biological evaluation with RAW 264.7 cell confirmed the computational results, demonstrating that Vorinostat exhibited comparable inhibitory activity against TACE. In conclusion, our study highlights the capability of deep learning models to enhance virtual screening efforts in drug discovery, efficiently identifying potential candidates for specific targets such as TACE. Vorinostat, as a newly identified TACE inhibitor, holds promise for further exploration and investigation in the treatment of inflammatory diseases like rheumatoid arthritis.
引用
收藏
页数:26
相关论文
共 50 条
  • [41] Discovery of novel targets and mechanisms of MEK inhibitor Selumetinib for LGG treatment based on molecular docking and molecular dynamics simulation
    Zhang, Dongdong
    Zhang, Tieying
    Zhu, Jianbo
    Li, Jin
    JOURNAL OF MOLECULAR MODELING, 2022, 28 (06)
  • [42] An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE
    Khedraoui, Meriem
    Abchir, Oussama
    Nour, Hassan
    Yamari, Imane
    Errougui, Abdelkbir
    Samadi, Abdelouahid
    Chtita, Samir
    PHARMACEUTICALS, 2024, 17 (07)
  • [43] Discovery of novel targets and mechanisms of MEK inhibitor Selumetinib for LGG treatment based on molecular docking and molecular dynamics simulation
    Dongdong Zhang
    Tieying Zhang
    Jianbo Zhu
    Jin Li
    Journal of Molecular Modeling, 2022, 28
  • [44] Molecular Docking and Molecular Dynamics Simulation of New Potential JAK3 Inhibitors
    Zhong, Qidi
    Qin, Jiarui
    Zhao, Kaihui
    Guo, Lihong
    Li, Dongmei
    CURRENT COMPUTER-AIDED DRUG DESIGN, 2024, 20 (06) : 764 - 772
  • [45] Screening of Potential Breast Cancer Inhibitors through Molecular Docking and Molecular Dynamics Simulation
    Pandi, Sangavi
    Kulanthaivel, Langeswaran
    Subbaraj, Gowtham Kumar
    Rajaram, Sangeetha
    Subramanian, Senthilkumar
    BIOMED RESEARCH INTERNATIONAL, 2022, 2022
  • [46] Novel 5-Substituted Oxindole Derivatives as Bruton's Tyrosine Kinase Inhibitors: Design, Synthesis, Docking, Molecular Dynamics Simulation, and Biological Evaluation
    Velavalapalli, Vani Madhuri
    Maddipati, Venkatanarayana Chowdary
    Gurska, Sona
    Annadurai, Narendran
    Liskova, Barbora
    Katari, Naresh Kumar
    Dzubak, Petr
    Hajduch, Marian
    Das, Viswanath
    Gundla, Rambabu
    ACS OMEGA, 2024, 9 (07): : 8067 - 8081
  • [47] Design, synthesis and biological activity evaluation of novel inhibitors targeting TNKS2, based on docking, virtual screening and molecular dynamics simulation studies
    Nie, Hui
    Li, Tang
    Huang, Jinwen
    Wu, Fanhong
    MOLECULAR PHYSICS, 2024,
  • [48] Discovery of potential RSK1 inhibitors for cancer therapy using virtual screening, molecular docking, molecular dynamics simulation, and MM/GBSA calculations
    Kalin, Sevil
    Onder, Ferah Comert
    JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS, 2025, 43 (03): : 1424 - 1444
  • [49] In silico Discovery of Novel FXa Inhibitors by Pharmacophore Modeling and Molecular Docking
    Pu Y.
    Liu H.
    Zhou Y.
    Peng J.
    Li Y.
    Li P.
    Li Y.
    Liu X.
    Zhang L.
    Natural Products and Bioprospecting, 2017, 7 (3) : 249 - 256
  • [50] Discovery of microtubule stabilizers with novel scaffold structures based on virtual screening, biological evaluation, and molecular dynamics simulation
    Mao, Jun
    Luo, Qing-Qing
    Zhang, Hong-Rui
    Zheng, Xiu-He
    Shen, Chen
    Qi, Hua-Zhao
    Hu, Mei-Ling
    Zhang, Hui
    CHEMICO-BIOLOGICAL INTERACTIONS, 2022, 352