Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.

UNLABELLED: A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. AVAILABILITY: The InterProSurf web server is available at http://curie.utmb.edu/

BACKGROUND: Total sequence decomposition, using the web-based MASIA tool, identifies areas of conservation in aligned protein sequences. By structurally annotating these motifs, the sequence can be parsed into individual building blocks, molecular legos ("molegos"), that can eventually be related to function. Here, the approach is applied to the apurinic/apyrimidinic endonuclease (APE) DNA repair proteins, essential enzymes that have been highly conserved throughout evolution. The APEs, DNase-1 and inositol 5'-polyphosphate phosphatases (IPP) form a superfamily that catalyze metal ion based phosphorolysis, but recognize different substrates. RESULTS: MASIA decomposition of APE yielded 12 sequence motifs, 10 of which are also structurally conserved within the family and are designated as molegos. The 12 motifs include all the residues known to be essential for DNA cleavage by APE. Five of these molegos are sequentially and structurally conserved in DNase-1 and the IPP family. Correcting the sequence alignment to match the residues at the ends of two of the molegos that are absolutely conserved in each of the three families greatly improved the local structural alignment of APEs, DNase-1 and synaptojanin. Comparing substrate/product binding of molegos common to DNase-1 showed that those distinctive for APEs are not directly involved in cleavage, but establish protein-DNA interactions 3' to the abasic site. These additional bonds enhance both specific binding to damaged DNA and the processivity of APE1. CONCLUSION: A modular approach can improve structurally predictive alignments of homologous proteins with low sequence identity and reveal residues peripheral to the traditional "active site" that control the specificity of enzymatic activity.