An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2 - Zhang, Haiping; Li, Junxin; Saravanan, Konda Mani; Wu, Hao; Wang, Zhichao; Wu, Du; Wei, Yanjie; Lu, Zhen; Chen, Youhai H.; Wan, Xiaochun; Pan, Yi (Frontiers in Pharmacology, 2021)
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 to 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 do 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 biniding 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 UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in drug virtual screening and provides highly potential inhibitors against TIPE2 for drug development.