identification of amino acid propensities that are strong determinants of linear b-cell epitope using neural networks强有力的决定因素的识别氨基酸倾向使用神经网络线性b细胞表位.pdfVIP

Identification of Amino Acid Propensities That Are Strong Determinants of Linear B-cell Epitope Using Neural Networks 1 2 3 1 Chun-Hung Su , Nikhil R. Pal , Ken-Li Lin , I-Fang Chung * 1 Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan, Republic of China, 2 Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta, India, 3 Computer Center, Chung Hua University, Hsinchu,Taiwan, Republic of China Abstract Background: Identification of amino acid propensities that are strong determinants of linear B-cell epitope is very important to enrich our knowledge about epitopes. This can also help to obtain better epitope prediction. Typical linear B-cell epitope prediction methods combine various propensities in different ways to improve prediction accuracies. However, fewer but better features may yield better prediction. Moreover, for a propensity, when the sequence length is k, there will be k values, which should be treated as a single unit for feature selection and hence usual feature selection method will not work. Here we use a novel Group Feature Selecting Multilayered Perceptron, GFSMLP, which treats a group of related information as a single entity and selects useful propensities related to linear B-cell epitopes, and uses them to predict epitopes. Methodology/ Principal Findings: We use eight widely known propensities and four data sets. We use GFSMLP to rank propensities by the frequency with which they are selected. We find that Chou’s beta-turn and Ponnuswamy’s polarity are better features for prediction of linear B-cell epitope. We examine the individual and combined discriminating power of the selected propensities and analyze the correlation between paired propensities. Our results show that the selected