DeepMHCI: An anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction

Bioinformatics. 2023 Sep 5:btad551. doi: 10.1093/bioinformatics/btad551. Online ahead of print.ABSTRACTMOTIVATION: Computationally predicting MHC-I peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels.RESULTS: The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as five-fold cross-validation, validation with the independent testing set, external HPV vaccine identification and external CD8+ epitope identification. Experimental results with visualization of bi...
Source: Cancer Control - Category: Cancer & Oncology Authors: Source Type: research