Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design

•The paper proposes a new approach using a Genetic Algorithm for Predicting the Epitope Structure (GAPES).•The proposed tertiary structure prediction of epitopes in GAPES is based on Ab-initio Empirical Conformational Energy Program for Peptides (ECEPP) force field model.•The prediction accuracy and...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2018-01, Vol.153, p.161-170
Main Authors: Moghram, Basem Ameen, Nabil, Emad, Badr, Amr
Format: Article
Language:English
Subjects:
Algorithms
Area Under Curve
ECEPP force field
Epitope tertiary structure prediction
Epitopes - chemistry
GAPES
Genetic algorithm
Humans
Major histocompatibility complex (MHC) class-II
ROC Curve
Support Vector Machine
T-Lymphocytes - immunology
Vaccine design
Vaccines - chemistry
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Summary:•The paper proposes a new approach using a Genetic Algorithm for Predicting the Epitope Structure (GAPES).•The proposed tertiary structure prediction of epitopes in GAPES is based on Ab-initio Empirical Conformational Energy Program for Peptides (ECEPP) force field model.•The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as the performance measures.•GAPES achieved the highest prediction accuracy and AUC compared to the other benchmarking methods in the Immune Epitope Data Base (IEDB) from El-Manzalawybenchmark dataset and HLA-DRB1*0101 allele of the Wang benchmark dataset.•Results showed that GAPES is a promising technique that will help the researchers and scientists in the intelligent design of new epitope-based vaccines and in the protein structure prediction. T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. 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. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve simila
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2017.10.011