Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%)

Immunological interactions of virus peptides at the antigen presenting MHC I proteins

Regarding the recent outbreak of Zika virus (ZIKV) infection, there is an urgent need to develop a preventive or a therapeutic ZIKV vaccine. In this thesis, computational analysis was performed to predict suitable peptide candidates for vaccine design. Computational approaches such as docking and molecular dynamics simulations (MD simulations) were employed to evaluate the binding energy and stability of candidate T-cell epitope peptides of ZIKV proteins at the antigen-presenting MHC class I molecules. For the docking step and the following MD simulations, MHC I alleles HLA-A*0101, HLA-A*0201, HLA-B*2705 and HLA-C*0801 were used as receptor structures and eight different peptides from ZIKV proteins (E, NS3, NS5) were docked to the MHC I molecules. All predicted peptide-HLA complexes and experimental reference peptide-HLA complexes were submitted to a 10-ns MD simulation in explicit water for further refinement and to examine and compare their stability. Hydrogen bonding network, Root-Mean-Square Deviation (RMSD) for both the MHC I peptide-binding domain and the peptides, atomic fluctuation and solvent accessibility of the bound peptides, interaction energies and the dimensions of the peptide binding groove were analyzed to evaluate the stability and strength of the peptide-HLA complexes. The computational analysis provided two T-cell epitopes from the ZIKV proteins (GLDFSDLYY, FSDLYYLTM) with a high affinity to the studied MHC I alleles. These could be introduced as putative candidates for vaccine development

Epitope Prediction Algorithms for Peptide-based Vaccine Design

Peptide-based vaccines, in which small peptides derived from target proteins (epitopes) are used to provoke an immune reaction, have attracted considerable attention recently as a potential means both of treating infectious diseases and promoting the destruction of cancerous cells by a patient's own immune system. With the availability of large sequence databases and computers fast enough for rapid processing of large numbers of peptides, computer aided designofpeptide-based vaccines has emerged as a promising approach to screening among billions of possible immune-active peptides to nd those likely to provoke an immune response to a particular cell type. In this paper, we describe the development of three novel classes of methods for the prediction of class I epitopes. Each one of the three classes of methods gives aspeci c set of insights into the epitope prediction problem. We present a quadratic programming approach that can be trained on quantitative as well as qualitative data. The second method uses linear programming to counteract the fact that our training data contains mostly positive examples. The third class of methods uses sequence pro les obtained by clustering known epitopes to score candidate peptides. By integrating these methods, using a simple voting heuristic, we achieve improved accuracy over the state of the art.