الفهرس 
Epitope prediction for vaccine design
TABLE OF CONTENTSOnly 14 pages are availabe for public view
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Abstract
T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic proteins are a set of amino acids that binds with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells so as to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope{u2019}s three-dimensional molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes{u2019} structure is a significant step towards epitope-based vaccine design and understanding of the immune system.In this thesis, we propose a new technique called Epitope Structure Prediction using Genetic Algorithm and Support Vector Machine (ESPGASVM) to predict the structure of MHC class II epitops based on their sequence. We developed a simple Elitist-based genetic algorithm for predicting the epitope{u2019}s tertiary structure based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. As well as, we proposed a secondary structure prediction technique based on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment to find the similarity metrics between the predicted epitopes{u2019} structures. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance