MHCherryPan. a novel model to predict the binding affinity of panspecific class I HLApeptide The human leukocyte antigen (HLA) system or complex plays an essential role in regulating the immune system in humans. Accurate prediction of peptide binding with HLA can efficiently help to identify those neoantigens which potentially make a big difference in immune drug development. HLA is one of the most polymorphic genetic systems in humans and thousands of HLA allelic versions exist 1. Due to the high polymorphism of the HLA complex, it remains difficulties to accurately predict the binding affinity. In this paper, we present a new algorithm to combine a convolutional neural network and long shortterm memory to solve this problem. Compared with other current popular models, our model achieved stateof theart results.

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.