アブストラクト
| Date | 2:00pm Sep 18, 2019 |
| Speaker | Jiangning Song Associate Professor and Group Leader, Monash Biomedicine Discovery Institute (BDI), Monash University, Australia |
| Title | DeepCleave: a deep learning-based approach and tool for more accurate prediction of protease-specific cleavage sites |
| Abstract | Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acids of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of protease functions and their physiological roles, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy from sequence information. In this talk, I will describe DeepCleave, which is the first deep learning-based predictor for protease-specific substrates and cleavage sites. It uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of the DeepCleave models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical benchmarking tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. In addition, I will briefly introduce several other bioinformatics tools we develop that might be of interest. |