An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2
The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:1139ef86755d49f2b3368ed770d9f5be2021-11-30T14:09:35ZAn Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE21663-981210.3389/fphar.2021.772296https://doaj.org/article/1139ef86755d49f2b3368ed770d9f5be2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphar.2021.772296/fullhttps://doaj.org/toc/1663-9812The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly identify novel drug candidates against TIPE2 protein. With the current advances in deep learning and molecular dynamics simulation-based drug screening, large-scale exploration of inhibitory candidates for TIPE2 becomes possible. In this work, we apply deep learning-based methods to perform a preliminary screening against TIPE2 over several commercially available compound datasets. Then, we carried a fine screening by molecular dynamics simulations, followed by metadynamics simulations. Finally, four compounds were selected for experimental validation from 64 candidates obtained from the screening. With surprising accuracy, three compounds out of four can bind to TIPE2. Among them, UM-164 exhibited the strongest binding affinity of 4.97 µM and was able to interfere with the binding of TIPE2 and PIP2 according to competitive bio-layer interferometry (BLI), which indicates that UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in virtual drug screening and provides high potential inhibitors against TIPE2 for drug development.Haiping ZhangJunxin LiKonda Mani SaravananHao WuZhichao WangDu WuYanjie WeiZhen LuYouhai H. ChenXiaochun WanYi PanFrontiers Media S.A.articleTIPE2UM-164virtual screeningdeep learningmolecular dynamics simulationTherapeutics. PharmacologyRM1-950ENFrontiers in Pharmacology, Vol 12 (2021) |
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DOAJ |
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TIPE2 UM-164 virtual screening deep learning molecular dynamics simulation Therapeutics. Pharmacology RM1-950 |
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TIPE2 UM-164 virtual screening deep learning molecular dynamics simulation Therapeutics. Pharmacology RM1-950 Haiping Zhang Junxin Li Konda Mani Saravanan Hao Wu Zhichao Wang Du Wu Yanjie Wei Zhen Lu Youhai H. Chen Xiaochun Wan Yi Pan An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2 |
| description |
The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly identify novel drug candidates against TIPE2 protein. With the current advances in deep learning and molecular dynamics simulation-based drug screening, large-scale exploration of inhibitory candidates for TIPE2 becomes possible. In this work, we apply deep learning-based methods to perform a preliminary screening against TIPE2 over several commercially available compound datasets. Then, we carried a fine screening by molecular dynamics simulations, followed by metadynamics simulations. Finally, four compounds were selected for experimental validation from 64 candidates obtained from the screening. With surprising accuracy, three compounds out of four can bind to TIPE2. Among them, UM-164 exhibited the strongest binding affinity of 4.97 µM and was able to interfere with the binding of TIPE2 and PIP2 according to competitive bio-layer interferometry (BLI), which indicates that UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in virtual drug screening and provides high potential inhibitors against TIPE2 for drug development. |
| format |
article |
| author |
Haiping Zhang Junxin Li Konda Mani Saravanan Hao Wu Zhichao Wang Du Wu Yanjie Wei Zhen Lu Youhai H. Chen Xiaochun Wan Yi Pan |
| author_facet |
Haiping Zhang Junxin Li Konda Mani Saravanan Hao Wu Zhichao Wang Du Wu Yanjie Wei Zhen Lu Youhai H. Chen Xiaochun Wan Yi Pan |
| author_sort |
Haiping Zhang |
| title |
An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2 |
| title_short |
An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2 |
| title_full |
An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2 |
| title_fullStr |
An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2 |
| title_full_unstemmed |
An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2 |
| title_sort |
integrated deep learning and molecular dynamics simulation-based screening pipeline identifies inhibitors of a new cancer drug target tipe2 |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/1139ef86755d49f2b3368ed770d9f5be |
| work_keys_str_mv |
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