This paper is the second part in a study concerning with various botanical characters of mugwort (Artemisia vulgaris L.). This piece of work aimed to study the anatomical structure of various plant organs and composition of the volatile oil. Data obtained could be summarized as follows:

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Identification of conformational B-cell epitopes is considered the crucial step in designing effective peptide vaccines against pathogens. Computer based methods play an important role in this process as the actual experimental determination of epitopes is very expensive in terms of cost and time. In this paper, we have carried out a comparative study and discussions for different methods based on the two major computational approaches for predicting conformational B-cell epitopes: sequence- based and structure- based approaches. As a result of this study, we developed a novel computational method “CBCPRED” to predict conformational B-cell epitope residues from the target antigen structure by combining support vector machine model with protein structural features and the propensity scores of amino acid physico – chemical properties. Using fivefold cross validation and leave-one-out cross validation techniques on the 75 antigen structures of the Discotope dataset, CBCPRED achieves an area under receiver operator characteristics curve (AUC) of 0.818 and 0.859, respectively. We benchmark “CBCPRED” on a more recent benchmark (Ponomarenko et al. 2007) dataset after removing antigens sequence redundancy where no two antigen sequences have more than 40% sequence identity, achieving AUC of 0.747. CBCPRED is available at http://www.fci.cu.edu.eg:8080/CBCPRED/predict.html.

Identification of antigen-antibody interacting sites is an important task for vaccine design, and hence reliable computer based prediction methods are highly desirable. The prediction performances of the current existing methods to predict the conformational B-cell epitope residues are still not satisfying and remain far from ideal. This is a new approach in the area of vaccine development to predict the antigenic surface patches that hold the majority number of epitope resi-dues in the surface of the antigen protein structure. The proposed method is a support vector machine based model to pre-dict the epitope patches in the antigen structures by combining the accessible surface area and B-factor structural features. The Predictions are made for the known structures of benchmark dataset after removing antigens sequence redundancy where no two antigen sequences have more than 40% sequence identity. The predictions are successful for 70% of the an-tigen structure chains of the benchmark dataset. We compared the prediction performance of our model with a protein – protein interaction prediction server “Sharp2” using the same antigen structures of the benchmark dataset and observed that our model outperforms on Sharp2 by more than 40% accuracy. This paper demonstrates that the identification of the anti-genic determinant sites in the protein surface using the antigen structural information outperforms the traditional pro-tein-protein interaction algorithms to predict the interacting sites in the antigen protein surface. It provides a new approach for the scientists to only use the predicted antigenic epitope surface patch from the target antigen structure in vaccine de-velopment rather than using the predicted epitope residues. A web server “PatchTope” has been developed for predicting antigenic epitope surface patches on an antigen protein structure surface and is available at http://www.fci.cu.edu.eg:8080/PatchTope/.