Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics.

BACKGROUND: A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow. RESULTS: We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen. CONCLUSIONS: Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.

LL37 and hBD-3 elevate the ß-1,3-exoglucanase activity of Candida albicans Xog1p, resulting in reduced fungal adhesion to plastic.

The opportunistic fungus Candida albicans causes oral thrush and vaginal candidiasis, as well as candidaemia in immunocompromised patients including those undergoing cancer chemotherapy, organ transplant and those with AIDS. We previously found that the AMPs (antimicrobial peptides) LL37 and hBD-3 (human ß-defensin-3) inhibited C. albicans viability and its adhesion to plastic. For the present study, the mechanism by which LL37 and hBD-3 reduced C. albicans adhesion was investigated. After AMP treatment, C. albicans adhesion to plastic was reduced by up to ~60% and was dose-dependent. Our previous study indicated that LL37 might interact with the cell-wall ß-1,3-exoglucanase Xog1p, which is involved in cell-wall ß-glucan metabolism, and consequently the binding of LL37 or hBD-3 to Xog1p might cause the decrease in adhesion. For the present study, Xog1p(41-438)-6H, an N-terminally truncated, active, recombinant construct of Xog1p and Xog1p fragments were produced and used in pull-down assays and ELISA in vitro, which demonstrated that all constructs interacted with both AMPs. Enzymatic analyses showed that LL37 and hBD-3 enhanced the ß-1,3-exoglucanase activity of Xog1p(41-438)-6H approximately 2-fold. Therefore elevated Xog1p activity might compromise cell-wall integrity and decrease C. albicans adhesion. To test this hypothesis, C. albicans was treated with 1.3 µM Xog1p(41-438)-6H and C. albicans adhesion to plastic decreased 47.7%. Taken together, the evidence suggests that Xog1p is one of the LL37/hBD-3 targets, and elevated ß-1,3-exoglucanase activity reduces C. albicans adhesion to plastic.

Inhibition of the interactions between eosinophil cationic protein and airway epithelial cells by traditional Chinese herbs.

BACKGROUND: The eosinophil cationic protein (ECP) is cytotoxic to bacteria, viruses, parasites and mammalian cells. Cells are damaged via processes of pore formation, permeability alteration and membrane leaking. Some clinical studies indicate that ECP gathers in the bronchial tract of asthma sufferers, damages bronchial and airway epithelial cells, and leads to in breathing tract inflammation; therefore, prevention of the cytotoxicity caused by ECP may serve as an approach to treat airway inflammation. To achieve the purpose, reduction of the ECP-cell interactions is rational. In this work, the Chinese herbal combinative network was generated to predict and identify the functional herbs from the pools of prescriptions. It was useful to select the node herbs and to demonstrate the relative binding ability between ECP and Beas-2B cells with or withour herb treatments. RESULTS: Eighty three Chinese herbs and prescriptions were tested and five effective herbs and six prescription candidates were selected. On the basis of effective single-herbal drugs and prescriptions, a combinative network was generated. We found that a single herb, Gan-cao, served as a node connecting five prescriptions. In addition, Sheng-di-huang, Dang-guei and Mu-tong also appeared in five, four and three kinds of prescriptions, respectively. The extracts of these three herbs indeed effectively inhibited the interactions between ECP and Beas-2B cells. According to the Chinese herbal combinative network, eight of the effective herbal extracts showed inhibitory effects for ECP internalizing into Beas-2B cells. The major components of Gang-cao and Sheng-di-huang, glycyrrhizic acid and verbascose, respectively, reduced the binding affinity between ECP and cells effectively. CONCLUSIONS: Since these Chinese herbs reduced the binding affinity between ECP and cells and inhibited subsequent ECP entrance into cells, they were potential for mitigating the airway inflammation symptoms. Additionally, we mentioned a new concept to study the Chinese herbs using combinative network in the field of systems biology. The functional single herbs could be identified from the set of prescriptions.