Biocalcification by Bacillus pasteurii urease: a novel application.

Biocalcification, also known as microbiologically induced calcite precipitation (MICP), is a phenomenon involving the activity of the enzyme urease. A large number of soil microorganisms exhibit urease-producing ability. A novel application of MICP to improve properties of bricks by a soil bacteria Bacillus pasteurii NCIM 2477 was studied. Most of the deterioration of brick structures takes place because of the presence of moisture. Deposition of calcite on the surface and in voids of bricks reduces the water absorption substantially. A favorable effect of microbes to improve the durability of bricks by reducing water absorption was demonstrated as a novel concept in this paper.

pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties.

BACKGROUND: Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively. RESULTS: In this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the dataset provided by Park and Kanehisa and we obtained prediction accuracies for the different classes ranging from 87.7%-97.0% with an overall accuracy of 93.1%. CONCLUSION: This study presents a physicochemical property based protein localization prediction algorithm. Unlike other algorithms, contextual information is preserved by dividing the protein sequences into clusters. The prediction accuracy shows an improvement over other algorithms based on various types of amino acid composition (single, pair and gapped pair). We have also implemented a web server to predict protein localization across the six classes (available at http://pslip.bii.a-star.edu.sg/).