Feature of DeepTMInter¶
DeepTMInter specifically targets α-helical transmembrane proteins, a class notoriously underrepresented in structural databases despite their biological significance. It predicts interaction sites solely from sequence-derived features, removing the need for 3D structural input, which is often unavailable.
DeepTMInter demonstrates biologically meaningful predictions in real membrane protein complexes, showing potential for drug target analysis and functional annotation.
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 DeepTMInter 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. 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