StructureBased Antibody Paratope Prediction with 3D Zernike Descriptors and SVM Antibodies currently represent the most valuable category of biopharmaceuticals for both diagnostic and therapeutic applications. They are a class of Yshaped proteins capable of specifically recognizing and binding to a virtually infinite number of antigens. Being able to identify the antigenbinding residues in an antibody structure is crucial for all antibody design methods and for shedding light on the complex mechanisms that govern antigen recognition and binding. This paper presents a method for antibody interface prediction from their experimentallysolved structures based on 3D Zernike Descriptors. Rototranslationally invariant descriptors are computed from circular patches of the antibody surface enriched with a chosen subset of physicochemical properties from the AAindex1 amino acid index set, and are used as samples for a binary classification problem. An SVM classifier is used to distinguish interface surface patches from noninterface ones. The proposed method outperforms other antigenbinding interface prediction software, namely Paratome, Antibody iPatch and Parapred, on a novel dataset of experimentallysolved antibodyxe2x80x93antigen complex structures.

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.