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.

Bcell Epitope Prediction Method based on Deep Ensemble Architecture and Sequences Predicting Bcell epitope is aiming the design of molecules that can mimic the structure and function of a genuine epitope and replace it in antibody diagnostics and therapeutics, as well as the design of the potentially safer vaccine. It is an essential step for immune therapy. Conformational Bcell epitope prediction based on antigen sequence is still a challenge for researchers. In this work, we construct a deep ensemble architecture for Bcell epitope prediction based on antigen sequences. We adopted one hot vector coding and physicochemical properties schemes for encoding protein sequence fragments, and constructed seven independent convolutional neural network, then the weighted average method was used to integrate the seven networks. The proposed method is evaluated on the testing datasets of BepiPred 2.0. The experimental results show that the method achieves an AUC of 0.771, a sensitivity of 0.711, and a MCC of 0.222. In addition, we also evaluate the performance of our method on 13 independent testing cases and the results are superior to the existing methods. The comparisons show that our method is capable of predicting conformational Bcell epitopes based on a deep ensemble framework and antigen sequences in an acceptable level of AUC. The codes along with instructions to reproduce this work are available from https:github.comyangycodingDeepBcellPre.