MAAP: Malarial adhesins and adhesin‐like proteins predictor (original) (raw)

SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks

Bioinformatics, 2004

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 artificial neural networks 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. Furthermore, SPAAN facilitated 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 nsps (nsp2, nsp5, nsp6 and nsp7) were identified as having adhesin-like cha...

PlasmoSEP: Predicting surface-exposed proteins on the malaria parasite using semisupervised self-training and expert-annotated data

Accurate and comprehensive identification of surface-exposed proteins (SEPs) in parasites is a key step in developing novel subunit vaccines. However, the reliability of MS-based high-throughput methods for proteome-wide mapping of SEPs continues to be limited due to high rates of false positives (i.e., proteins mistakenly identified as surface exposed) as well as false negatives (i.e., SEPs not detected due to low expression or other technical limitations). We propose a framework called PlasmoSEP for the reliable identification of SEPs using a novel semisupervised learning algorithm that combines SEPs identified by high-throughput experiments and expert annotation of high-throughput data to augment labeled data for training a predictive model. Our experiments using high-throughput data from the Plasmodium falci-parum surface-exposed proteome provide several novel high-confidence predictions of SEPs in P. falciparum and also confirm expert annotations for several others. Furthermore, Plas-moSEP predicts that 25 of 37 experimentally identified SEPs in Plasmodium yoelii salivary gland sporozoites are likely to be SEPs. Finally, PlasmoSEP predicts several novel SEPs in P. yoelii and Plasmodium vivax malaria parasites that can be validated for further vaccine studies. Our computational framework can be easily adapted to improve the interpretation of data from high-throughput studies.

Lectin-Glycan Interaction Network-Based Identification of Host Receptors of Microbial Pathogenic Adhesins

The first step in the infection of humans by microbial pathogens is their adherence to host tissue cells, which is frequently based on the binding of carbohydrate-binding proteins (lectin-like adhesins) to human cell receptors that expose gly-cans. In only a few cases have the human receptors of pathogenic adhesins been described. A novel strategy— based on the construction of a lectin-glycan interaction (LGI) network—to identify the potential human binding receptors for pathogenic adhesins with lectin activity was developed. The new approach is based on linking glycan array screening results of these ad-hesins to a human glycoprotein database via the construction of an LGI network. This strategy was used to detect human receptors for virulent Escherichia coli (FimH adhesin), and the fungal pathogens Candida albicans (Als1p and Als3p adhesins) and C. glabrata (Epa1, Epa6, and Epa7 adhesins), which cause candidiasis. This LGI network strategy allows the profiling of potential adhesin binding receptors in the host with prioritization, based on experimental binding data, of the most relevant interactions. New potential targets for the selected adhesins were predicted and experimentally confirmed. This methodology was also used to predict lectin interactions with envelope glycoproteins of human-pathogenic viruses. It was shown that this strategy was successful in revealing that the FimH adhesin has anti-HIV activity. IMPORTANCE Microbial pathogens may express a wide range of carbohydrate-specific adhesion proteins that mediate adherence to host tissues. Pathogen attachment to host cells is achieved through the binding of these lectin-like adhesins to glycans on human glycoproteins. In only a few cases have the human receptors of pathogenic adhesins been described. We developed a new strategy to predict these interacting receptors. Therefore, we developed a novel LGI network that would allow the mapping of potential adhesin binding receptors in the host with prioritization, based on the experimental binding data, of the most relevant interactions. New potential targets for the selected adhesins (bacterial uroepithelial FimH from E. coli and fungal Epa and Als adhesins from C. glabrata and C. albicans) were predicted and experimentally confirmed. This methodology was also used to predict lectin interactions with human-pathogenic viruses and to discover whether FimH adhesin has anti-HIV activity.