Plasmodium vivax Tryptophan-Rich Antigen PvTRAg33.5 Contains Alpha Helical Structure and Multidomain Architecture

Tryptophan-rich proteins from several malarial parasites have been identified where they play an important role in host-parasite interaction. Structural characterization of these proteins is needed to develop them as therapeutic targets. Here, we describe a novel Plasmodium vivax tryptophan-rich protein named PvTRAg33.5. It is expressed by blood stage(s) of the parasite and its gene contains two exons. The exon 1 encodes for a 23 amino acids long putative signal peptide which is likely to be cleaved off whereas the exon 2 encodes for the mature protein of 252 amino acids. The mature protein contains B-cell epitopes which were recognized by the human immune system during P.vivax infection. The PvTRAg33.5 contains 24 (9.5%) tryptophan residues and six motifs whose patterns were similar among tryptophan-rich proteins. The modeled structure of the PvTRAg33.5 consists of a multidomain architecture which is stabilized by the presence of large number of tryptophan residues. The recombinant PvTRAg33.5 showed predominantly α helical structure and alpha helix to beta sheet transition at pH below 4.5. Protein acquires an irreversible non-native state at temperature more than 50°C at neutral pH. Its secondary and tertiary structures remain stable in the presence of 35% alcohol but these structures are destabilized at higher alcohol concentrations due to the disturbance of hydrophobic interactions between tryptophanyl residues. These structural changes in the protein might occur during its translocation to interact with other proteins at its final destination for biological function such as erythrocyte invasion

Knowledge discovery in biological databases : a neural network approach

Knowledge discovery, in databases, also known as data mining, is aimed to find significant information from a set of data. The knowledge to be mined from the dataset may refer to patterns, association rules, classification and clustering rules, and so forth. In this dissertation, we present a neural network approach to finding knowledge in biological databases. Specifically, we propose new methods to process biological sequences in two case studies: the classification of protein sequences and the prediction of E. Coli promoters in DNA sequences. Our proposed methods, based oil neural network architectures combine techniques ranging from Bayesian inference, coding theory, feature selection, dimensionality reduction, to dynamic programming and machine learning algorithms. Empirical studies show that the proposed methods outperform previously published methods and have excellent performance on the latest dataset. We have implemented the proposed algorithms into an infrastructure, called Genome Mining, developed for biosequence classification and recognition