TY  - BOOK
AU  - Basem Ameen Ahmed Mahyoub
AU  - Amr Anwar Badr , 
AU  - Emad Nabil Hassan , 
TI  - Epitope prediction for vaccine design / 
PY  - 2016///
CY  - Cairo : 
PB  - Basem Ameen Ahmed Mahyoub , 
KW  - Epitope tertiary structure prediction
KW  - Genetic Algorithm
KW  - Major histocompatibility complex (MHC) class-II
N1  - Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science; Issued also as CD
N2  - 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
UR  - http://172.23.153.220/th.pdf
ER  -