Introduction to DeepHelicon¶
DeepHelicon uses a deep learning framework that integrates coevolutionary signals, transmembrane topology, and a two-stage residual architecture to accurately predict inter-helical residue contacts in membrane proteins, with a design adaptable to other structurally constrained systems.
Sun’s series work in drug discovery¶
Jianfeng Sun is spearheading a research plan dedicated to the AI-based discovery of small molecule therapeutics targeting non-coding RNAs and proteins, working in collaboration with both experimentalists and computational scientists across the globe. He has released 5 studies as in Table 1.
Table 1:Sun’s work in drug discovery. ➵ stands for the current work.
| Field | Molecule | Tool name | Function | Technology | Publication |
|---|---|---|---|---|---|
| Systems Biology | noncoding RNA | DeepsmirUD | drug discovery | Artificial intelligence | Sun et al., 2023. International Journal of Molecular Sciences |
| DeepdlncUD | drug discovery | Artificial intelligence | Sun et al., 2023. Computers in Biology and Medicine | ||
| protein | Drutai | drug discovery | Artificial intelligence | Sun et al., 2023. European Journal of Medicinal Chemistry | |
| Structural Biology | protein | ➵DeepHelicon | structural prediction | Artificial intelligence | Sun and Frishman, 2020. Journal of Structural Biology |
| DeepTMInter | protein-protein interaction prediction | Artificial intelligence | Sun and Frishman, 2021. Computational and Structural Biotechnology Journal |
Up-to-date¶
We updated the DeepHelicon program (accessed via 0.0.1) to make it compatible with up-to-date dependencies. It vastly reduces operations from back ends of users.
Runtime¶
Once intermediate files by the external tools get prepared, it runs per protein in a very fast speed.
- Sun, J., Ru, J., Ramos-Mucci, L., Qi, F., Chen, Z., Chen, S., Cribbs, A. P., Deng, L., & Wang, X. (2023). DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning. International Journal of Molecular Sciences, 24(3). 10.3390/ijms24031878
- Sun, J., Si, S., Ru, J., & Wang, X. (2023). DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning. Computers in Biology and Medicine, 163, 107226. https://doi.org/10.1016/j.compbiomed.2023.107226
- Sun, J., Xu, M., Ru, J., James-Bott, A., Xiong, D., Wang, X., & Cribbs, A. P. (2023). Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications. European Journal of Medicinal Chemistry, 115500. https://doi.org/10.1016/j.ejmech.2023.115500
- Sun, J., & Frishman, D. (2020). DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks. Journal of Structural Biology, 212(1), 107574. https://doi.org/10.1016/j.jsb.2020.107574
- Sun, J., & Frishman, D. (2021). Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning. Computational and Structural Biotechnology Journal, 19, 1512–1530. https://doi.org/10.1016/j.csbj.2021.03.005