Plasmodium spp. membrane glutathione S-transferases: detoxification units and drug targets.

Membrane glutathione S-transferases from the class of membrane-associated proteins in eicosanoid and glutathione metabolism (MAPEG) form a superfamily of detoxification enzymes that catalyze the conjugation of reduced glutathione (GSH) to a broad spectrum of xenobiotics and hydrophobic electrophiles. Evolutionarily unrelated to the cytosolic glutathione S-transferases, they are found across bacterial and eukaryotic domains, for example in mammals, plants, fungi and bacteria in which significant levels of glutathione are maintained. Species of genus Plasmodium, the unicellular protozoa that are commonly known as malaria parasites, do actively support glutathione homeostasis and maintain its metabolism throughout their complex parasitic life cycle. In humans and in other mammals, the asexual intraerythrocytic stage of malaria, when the parasite feeds on hemoglobin, grows and eventually asexually replicates inside infected red blood cells (RBCs), is directly associated with host disease symptoms and during this critical stage GSH protects the host RBC and the parasite against oxidative stress from parasite-induced hemoglobin catabolism. In line with these observations, several GSH-dependent Plasmodium enzymes have been characterized including glutathione reductases, thioredoxins, glyoxalases, glutaredoxins and glutathione S-transferases (GSTs); furthermore, GSH itself have been found to associate spontaneously and to degrade free heme and its hydroxide, hematin, which are the main cytotoxic byproducts of hemoglobin catabolism. However, despite the apparent importance of glutathione metabolism for the parasite, no membrane associated glutathione S-transferases of genus Plasmodium have been previously described. We recently reported the first examples of MAPEG members among Plasmodium spp.

A large-scale evaluation of computational protein function prediction.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Identity and function of a large gene network underlying mutagenic repair of DNA breaks.

Mechanisms of DNA repair and mutagenesis are defined on the basis of relatively few proteins acting on DNA, yet the identities and functions of all proteins required are unknown. Here, we identify the network that underlies mutagenic repair of DNA breaks in stressed Escherichia coli and define functions for much of it. Using a comprehensive screen, we identified a network of ≥93 genes that function in mutation. Most operate upstream of activation of three required stress responses (RpoS, RpoE, and SOS, key network hubs), apparently sensing stress. The results reveal how a network integrates mutagenic repair into the biology of the cell, show specific pathways of environmental sensing, demonstrate the centrality of stress responses, and imply that these responses are attractive as potential drug targets for blocking the evolution of pathogens.

The MASH pipeline for protein function prediction and an algorithm for the geometric refinement of 3D motifs.

The development of new and effective drugs is strongly affected by the need to identify drug targets and to reduce side effects. Resolving these issues depends partially on a thorough understanding of the biological function of proteins. Unfortunately, the experimental determination of protein function is expensive and time consuming. To support and accelerate the determination of protein functions, algorithms for function prediction are designed to gather evidence indicating functional similarity with well studied proteins. One such approach is the MASH pipeline, described in the first half of this paper. MASH identifies matches of geometric and chemical similarity between motifs, representing known functional sites, and substructures of functionally uncharacterized proteins (targets). Observations from several research groups concur that statistically significant matches can indicate functionally related active sites. One major subproblem is the design of effective motifs, which have many matches to functionally related targets (sensitive motifs), and few matches to functionally unrelated targets (specific motifs). Current techniques select and combine structural, physical, and evolutionary properties to generate motifs that mirror functional characteristics in active sites. This approach ignores incidental similarities that may occur with functionally unrelated proteins. To address this problem, we have developed Geometric Sieving (GS), a parallel distributed algorithm that efficiently refines motifs, designed by existing methods, into optimized motifs with maximal geometric and chemical dissimilarity from all known protein structures. In exhaustive comparison of all possible motifs based on the active sites of 10 well-studied proteins, we observed that optimized motifs were among the most sensitive and specific.