TANTIGEN 2.0: an online database and analysis platform for tumor T cell antigens We previously developed TANTIGEN, a comprehensive webbased database cataloging more than 1,000 T cell epitopes and HLA ligands from 292 tumor antigens. TANTIGEN 2.0 is significantly expanded the number and coverage of immune response targets (T cell epitopes and HLA ligands) of previously cataloged tumor antigens. We expanded the number of cataloged tumor antigens to more than 4,000 and have added their reported targets of immune responses. We also included neoantigens, a new class of tumor antigens generated through mutations that results in a new amino acid sequence in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes. Gene expression information was extracted from tumor antigen genemRNAprotein expression information in major human cancers provided by Human Pathology Atlas. TANTIGEN 2.0 provides a rich data resource for tumorassociated epitope and neoepitope discovery studies. It is freely available at http:projects.methilab.orgtadb.

A computational framework for deep learningbased epitope prediction by using structure and sequence information Precise prediction of the antigens that bind to major histocompatibility complex (MHC) molecules is one of the most important steps for many immune responserelated studies including identification of neoantigen capable of recognizing cancer cells for the implementation of personalized immunotherapy as well as vaccinetherapeutic protein development. To overcome the limitations of the limited prediction performance of the existing models, we developed a new convolutional neural network (CNN)based epitope prediction model by incorporating new features including protein sequence vectors (Prot2Vec), chemical and structural characteristics of an epitope and MHC molecule. Our new model is optimized to achieve better prediction performance than the conventional methods when validated with a benchmark dataset from the Immune Epitope Database (IEDB). Along with the new prediction model, we also developed a webbased framework, called Kepre, to provide interfaces to our novel CNNbased epitope prediction functionalities. Kepre provides a userfriendly and automated interface for epitope prediction. Upon the researcheru0027s submission of a sequence of interest, Kepre predicts the binding probability between the sequence and each type of the MHC molecules. Kepre runs the prediction analysis on highthroughput computing infrastructure and provides the prediction results in a userinteractable GUI and also in a pdf formatted report file.