A Software for Prediction of Adhesins and Adhesin-like proteins using Neural networks

Motivation: The adhesion of microbial pathogens to host cells is mediated by adhesins. Experimental methods used for characterizing adhesins are time consuming and demand large resources. The availability of specialized software can rapidly aid experimenters in simplifying this problem. We have employed 105 compositional properties and ANN to develop SPAAN, which predicts the probability of a protein being an adhesin (Pad). Results: SPAAN had optimal sensitivity of 89% and specificity of 100% on a defined test set and could identify 97.4% of known adhesins at high Pad value from a wide range of bacteria. Further, SPAAN guided in improved annotation of several proteins as adhesins. Novel adhesins were identified in 17 pathogenic organisms causing diseases in humans and plants. In the Severe Acute Respiratory Syndrome (SARS) associated human corona virus, the spike glycoprotein and nsp’s (nsp1, nsp5, nsp6 and nsp7) were identified with adhesin-like characteristics. These results offer new leads for rapid experimental testing. Availability: SPAAN is freely available through ftp from either 203.195.151.45 or 203.90.127.75. Retrieve SPAAN.tar.gz Contact: ramu@igib.res.in; ramucbt@yahoo.com at Penylvania State U niersity on Feruary 1, 2013 httpioinform atics.oxjournals.org/ D ow nladed from Introduction Microbial pathogens encode adhesins that mediate their adherence to host cell surface receptors, membranes, or extracellular matrix for successful colonization. Investigations in this primary event of host-pathogen interaction have revealed a wide array of adhesins in a variety of pathogenic microbes (Finlay and Falkow, 1997). New approaches to vaccine development focus on targeting adhesins to abrogate the colonization process (Wizemann et al. 1999). However, the specific roles of particular adhesins in several pathogens remain to be elucidated. One of the best-understood mechanisms of bacterial adherence is attachment mediated by pili or fimbriae. The well-studied adhesins in this category are FimH and PapG adhesins of Escherichia coli (Hahn et al., 2002) and the type IV pili adhesins in Pseudomonas aeruginosa, Neisseria, Moraxella, enteropathogenic Escherichia coli and Vibrio cholerae (Strom and Lory, 1993). Several adhesins from other commonly known bacterial pathogens include MrkD protein of Kleibsella pneumoniae (Gerlach et al. 1989), Hia of H. influenzae (Barenkamp and St Geme1996), and many others (see http://www.igib.res.in/data/seepath/spaan_data.html for details). Several vaccine formulations either currently approved or being evaluated, use adhesins as immunizing agents. Examples include filamentous hemagglutinin and pertactin proteins against B. pertussis (Halperin et al. 2003), FimH against pathogenic E. coli (Langermann et al. 2000), PsaA against pneumococcal disease (Rapola et al. 2003), outer membrane vesicle preparations including BabA adhesin against H. pylori infections (Prinz et al. 2003) and a synthetic peptide anti-adhesin vaccine against Pseudomonas aeruginosa infections (Cachia and Hodges 2003). Experimental identification of adhesins is an arduous task. Computational methods such as homology search can aid but this procedure suffers from limitations when the homologues are not characterized. Sequence analysis based on compositional properties provides relief from this problem. Amino acid composition is a fundamental attribute of a protein and it has significant correlation to its location, function, folding type, shape and in vivo stability (Nakashima and Nishikawa 1994; Nandi, T. et al. 2003). Recently, compositional properties have been applied to problems as diverse as prediction of functional roles (Hobohm and Sander 1995), protein secondary structures at Penylvania State U niersity on Feruary 1, 2013 httpioinform atics.oxjournals.org/ D ow nladed from (Rost and Sander 1993), secretory proteins and apicoplast targeted proteins in Plasmodium falciparum (Schneider 1999; Zuegge, et al. 2001). We report a non-homology method using 105 compositional properties combined with artificial neural networks (ANN) to identify adhesins and adhesin-like proteins in species belonging to wide phylogenetic spectrum. Systems and Methods