Diagnosing human anisakiasis: recombinant Ani s 1 and Ani s 7 allergens versus the UniCAP 100 fluorescence enzyme immunoassay.

Commercially available serological methods for serodiagnosis of human anisakiasis either are poorly specific or do not include some of the most relevant Anisakis allergens. The use of selected recombinant allergens may improve serodiagnosis. To compare the diagnostic and clinical values of enzyme-linked immunosorbent assay (ELISA) methods based on Ani s 1 and Ani s 7 recombinant allergens and of the UniCAP 100 fluorescence enzyme immunoassay (CAP FEIA) system, we tested sera from 495 allergic and 25 non-food-related allergic patients. The decay in specific IgE antibodies in serum was also investigated in 15 positive patients over a period of 6 to 38 months. Considering sera that tested positive by either Ani s 1 or Ani s 7 ELISA, the CAP FEIA classified 25% of sera as falsely positive, mainly in the group of patients with the lowest levels of anti-Anisakis IgE antibodies, and 1.28% of positive sera as falsely negative. Considering allergens individually, the overall sensitivities of Ani s 7 ELISA and Ani s 1 ELISA were 94% and 61%, respectively. The results also showed that anti-Anisakis IgE antibodies can be detected in serum for longer with Ani s 1 ELISA than with Ani s 7 ELISA and CAP FEIA (P < 0.01). Our findings suggest that ELISA methods with Ani s 7 and Ani s 1 allergens as targets of IgE antibodies are currently the best option for serodiagnosis of human anisakiasis, combining specificity and sensitivity. The different persistence of anti-Ani s 1 and anti-Ani s 7 antibodies in serum may help clinicians to distinguish between recent and old Anisakis infections.

3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites.

The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a new and fast Markov chain model (MCM) to predict the enzyme classification (EC) number. We used both linear discriminant analysis (LDA) and/or artificial neural networks (ANN) in order to compare linear vs. non-linear classifiers. The LDA model found is very simple (three variables) and at the same time is able to predict the first EC number with an overall accuracy of 79% for a data set of 4755 proteins (859 enzymes and 3896 non-enzymes) divided into both training and external validation series. In addition, the best non-linear ANN model is notably more complex but has an overall accuracy of 98.85%. It is important to emphasize that this method may help us to predict not only new enzyme proteins but also to select peptide candidates found on the peptide mass fingerprints (PMFs) of new proteins that may improve enzyme activity. In order to illustrate the use of the model in this regard, we first report the 2D electrophoresis (2DE) and MADLI-TOF mass spectra characterization of the PMF of a new possible malate dehydrogenase sequence from Leishmania infantum. Next, we used the models to predict the contribution to a specific enzyme action of 30 peptides found in the PMF of the new protein. We implemented the present model in a server at portal Bio-AIMS (http://miaja.tic.udc.es/Bio-AIMS/EnzClassPred.php). This free on-line tool is based on PHP/HTML/Python and MARCH-INSIDE routines. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

Generalized lattice graphs for 2D-visualization of biological information.

Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: protein sequence; mass spectra (MS) of protein peptide mass fingerprints (PMF); molecular dynamic trajectory (MDTs) from structural studies; mRNA microarray data; single nucleotide polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein polymorphisms and protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of Graph theory in theoretical biology and other areas of biomedical sciences.