| Summary: | 碩士 === 國立交通大學 === 生醫工程研究所 === 100 === Identification of B-cell epitopes plays an important role in vaccine development. Current prediction algorithms mostly rely on amino acid propensity scales and their variants, the results of which depend on a single antigenic phenotype. That viral sequences undergo continuous genetic changes, promoting the emergence of drug resistant strains, renders current prediction methods impractical. In this study, a novel set of features are proposed based on the protein free energy associated with point-mutated structures. To the best of our knowledge, this is the first attempt in this area to predict continuous B-cell epitopes based on protein free energy. I evaluated the novel features on k-nearest neighbor, support vector machine, and artificial neural network models, and achieved prediction accuracy of 74.3%, 66.1%, and 80.0% respectively. In comparison to current predictors, namely ABCPred, BCPred, and AAP, the energy-based models demonstrated better performance.
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