Estimating the probability for a protein to have a new fold: A statistical computational model

Structural genomics aims to solve a large number of protein structures that represent the protein space. Currently an exhaustive solution for all structures seems prohibitively expensive, so the challenge is to define a relatively small set of proteins with new, currently unknown folds. This paper presents a method that assigns each protein with a probability of having an unsolved fold. The method makes extensive use of protomap, a sequence-based classification, and scop, a structure-based classification. According to protomap, the protein space encodes the relationship among proteins as a graph whose vertices correspond to 13,354 clusters of proteins. A representative fold for a cluster with at least one solved protein is determined after superposition of all scop (release 1.37) folds onto protomap clusters. Distances within the protomap graph are computed from each representative fold to the neighboring folds. The distribution of these distances is used to create a statistical model for distances among those folds that are already known and those that have yet to be discovered. The distribution of distances for solved/unsolved proteins is significantly different. This difference makes it possible to use Bayes' rule to derive a statistical estimate that any protein has a yet undetermined fold. Proteins that score the highest probability to represent a new fold constitute the target list for structural determination. Our predicted probabilities for unsolved proteins correlate very well with the proportion of new folds among recently solved structures (new scop 1.39 records) that are disjoint from our original training set.

Efficacy of atenolol and captopril in reducing risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 39

Objective: To determine whether tight control of blood pressure with either a β blocker or an angiotensin converting enzyme inhibitor has a specific advantage or disadvantage in preventing the macrovascular and microvascular complications of type 2 diabetes.

PR-39, a proline-rich antibacterial peptide that inhibits phagocyte NADPH oxidase activity by binding to Src homology 3 domains of p47 phox.

Reactive oxygen intermediates generated by the phagocyte NADPH oxidase are critically important components of host defense. However, these highly toxic oxidants can cause significant tissue injury during inflammation; thus, it is essential that their generation and inactivation are tightly regulated. We show here that an endogenous proline-arginine (PR)-rich antibacterial peptide, PR-39, inhibits NADPH oxidase activity by blocking assembly of this enzyme through interactions with Src homology 3 domains of a cytosolic component. This neutrophil-derived peptide inhibited oxygen-dependent microbicidal activity of neutrophils in whole cells and in a cell-free assay of NADPH oxidase. Both oxidase inhibitory and direct antimicrobial activities were defined within the amino-terminal 26 residues of PR-39. Oxidase inhibition was attributed to binding of PR-39 to the p47phox cytosolic oxidase component. Its effects involve both a polybasic amino-terminal segment and a proline-rich core region of PR-39 that binds to the p47phox Src homology 3 domains and, thereby, inhibits interaction with the small subunit of cytochrome b558, p22phox. These findings suggest that PR-39, which has been shown to be involved in tissue repair processes, is a multifunctional peptide that can regulate NADPH oxidase production of superoxide anion O2-. thus limiting excessive tissue damage during inflammation.