Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.02.13.528393v1?rss=1
Authors: Zhang, X., Wu, J., Baeza, J., Gu, K., Zhou, Z.
Abstract:
The transport of antigenic peptides from cytoplasm to the endoplasmic reticulum (ER) via transporter associated with antigen processing (TAP) is a critical step during the presentation of tumor neoantigens. The application of computational approaches significantly speed up the analysis of this biological process. Here, we present a tool named DeepTAP for TAP-binding peptide prediction, which employs a sequence-based multi-layered recurrent neural network (RNN). Compared with traditional machine learning and other available prediction tools, DeepTAP achieves state-of-the-art performance on the benchmark datasets. The source code and dataset of DeepTAP are available freely via GitHub at https://github.com/zjupgx/DeepTAP.
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