Cell division protein FtsZ can form single-stranded filaments with a cooperative behavior by self-switching assembly. Subsequent condensation and bending of FtsZ filaments are important for the formation and constriction of the cytokinetic ring. PC190723 is an effective bactericidal cell division inhibitor that targets FtsZ in the pathogen Staphylococcus aureus and Bacillus subtilis and does not affect Escherichia coli cells, which apparently binds to a zone equivalent to the binding site of the antitumor drug taxol in tubulin (Haydon, D. J., Stokes, N. R., Ure, R., Galbraith, G., Bennett, J. M., Brown, D. R., Baker, P. J., Barynin, V. V., Rice, D. W., Sedelnikova, S. E., Heal, J. R., Sheridan, J. M., Aiwale, S. T., Chauhan, P. K., Srivastava, A., Taneja, A., Collins, I., Errington, J., and Czaplewski, L. G. (2008) Science 312, 1673-1675). We have found that the benzamide derivative PC190723 is an FtsZ polymer-stabilizing agent. PC190723 induced nucleated assembly of Bs-FtsZ into single-stranded coiled protofilaments and polymorphic condensates, including bundles, coils, and toroids, whose formation could be modulated with different solution conditions. Under conditions for reversible assembly of Bs-FtsZ, PC190723 binding reduced the GTPase activity and induced the formation of straight bundles and ribbons, which was also observed with Sa-FtsZ but not with nonsusceptible Ec-FtsZ. The fragment 2,6-difluoro-3-methoxybenzamide also induced Bs-FtsZ bundling. We propose that polymer stabilization by PC190723 suppresses in vivo FtsZ polymer dynamics and bacterial division. The biochemical action of PC190723 on FtsZ parallels that of the microtubule-stabilizing agent taxol on the eukaryotic structural homologue tubulin. Both taxol and PC190723 stabilize polymers against disassembly by preferential binding to each assembled protein. It is yet to be investigated whether both ligands target structurally related assembly switches.

Motivation: Obtaining soluble proteins in sufficient concentrations is a recurring limiting factor in various experimental studies. Solubility is an individual trait of proteins which, under a given set of experimental conditions, is determined by their amino acid sequence. Accurate theoretical prediction of solubility from sequence is instrumental for setting priorities on targets in large-scale proteomics projects. Results: We present a machine-learning approach called PROSO to assess the chance of a protein to be soluble upon heterologous expression in Escherichia coli based on its amino acid composition. The classification algorithm is organized as a two-layered structure in which the output of primary support vector machine (SVM) classifiers serves as input for a secondary Naive Bayes classifier. Experimental progress information from the TargetDB database as well as previously published datasets were used as the source of training data. In comparison with previously published methods our classification algorithm possesses improved discriminatory capacity characterized by the Matthews Correlation Coefficient (MCC) of 0.434 between predicted and known solubility states and the overall prediction accuracy of 72% (75 and 68% for positive and negative class, respectively). We also provide experimental verification of our predictions using solubility measurements for 31 mutational variants of two different proteins. Availability: A web server for protein solubility prediction is available at http://webclu.bio.wzw.tum.de:8080/proso Contact:d.frishman@wzw.tum.de Supplementary information: Supplementary data are available at Bioinformatics online

Streptococcuspneumoniaeis the main etiological agent of community-acquired pneumonia and a major cause of mortality and morbidity among children and the elderly. Genome sequencing of several pneumococcal strains revealed valuable information about the potential proteins and genetic diversity of this prevalent human pathogen. However, little is known about its transcriptional regulation and its small regulatory noncoding RNAs. In this study, we performed deep sequencing of theS. pneumoniaeTIGR4 strain RNome to identify small regulatory RNA candidates expressed in this pathogen. We discovered 1047 potential small RNAs including intragenic, 5′- and/or 3′-overlapping RNAs and 88 small RNAs encoded in intergenic regions. With this approach, we recovered many of the previously identified intergenic small RNAs and identified 68 novel candidates, most of which are conserved in both sequence and genomic context in otherS. pneumoniaestrains. We confirmed the independent expression of 17 intergenic small RNAs and predicted putative mRNA targets for six of them using bioinformatics tools. Preliminary results suggest that one of these six is a key player in the regulation of competence development. This study is the biggest catalog of small noncoding RNAs reported to date inS. pneumoniaeand provides a highly complete view of the small RNA network in this pathogen.

Partition systems are responsible for the process whereby large and essential plasmids are accurately positioned to daughter cells during bacterial division. They are typically made of three components: a centromere-like DNA zone, an adaptor protein, and an assembling protein that is either a Walker-box ATPase (type I) or an actin-like ATPase (type II). A recently described type III segregation system has a tubulin/FtsZ-like protein, called TubZ, for plasmid movement. Here, we present the 2.3 Å structure and dynamic assembly of a TubZ tubulin homolog from a bacteriophage and unravel the Clostridium botulinum phage c-st type III partition system. Using biochemical and biophysical approaches, we prove that a gene upstream from tubZ encodes the partner TubR and localize the centromeric region ( tubS ), both of which are essential for anchoring phage DNA to the motile TubZ filaments. Finally, we describe a conserved fourth component, TubY, which modulates the TubZ-R- S complex interaction.

The transcriptional response of Streptococcus pneumoniae was examined after exposure to the GyrB-inhibitor novobiocin. Topoisomer distributions of an internal plasmid confirmed DNA relaxation and recovery of the native level of supercoiling at low novobiocin concentrations. This was due to the up-regulation of DNA gyrase and the down-regulation of topoisomerases I and IV. In addition, >13% of the genome exhibited relaxation-dependent transcription. The majority of the responsive genes (>68%) fell into 15 physical clusters (14.6–85.6 kb) that underwent coordinated regulation, independently of operon organization. These genomic clusters correlated with AT content and codon composition, showing the chromosome to be organized into topology-reacting gene clusters that respond to DNA supercoiling. In particular, down-regulated clusters were flanked by 11–40 kb AT-rich zones that might have a putative structural function. This is the first case where genes responding to changes in the level of supercoiling in a coordinated manner were found organized as functional clusters. Such an organization revealed DNA supercoiling as a general feature that controls gene expression superimposed on other kinds of more specific regulatory mechanisms.

Motivation: The analysis of co-evolving residues has been exhaustively evaluated for the prediction of intramolecular amino acid contacts in soluble proteins. Although a variety of different methods for the detection of these co-evolving residues have been developed, the fraction of correctly predicted contacts remained insufficient for their reliable application in the construction of structural models. Membrane proteins, which constitute between one-fourth and one-third of all proteins in an organism, were only considered in few individual case studies. Results: We present the first general study of correlated mutations in α-helical membrane proteins. Using seven different prediction algorithms, we extracted co-evolving residues for 14 membrane proteins having a solved 3D structure. On average, distances between correlated pairs of residues lying on different transmembrane segments were found to be significantly smaller compared to a random prediction. Covariation of residues was frequently found in direct sequence neighborhood to helix–helix contacts. Based on the results obtained from individual prediction methods, we constructed a consensus prediction for every protein in the dataset that combines obtained correlations from different prediction algorithms and simultaneously removes likely false positives. Using this consensus prediction, 53% of all predicted residue pairs were found within one helix turn of an observed helix–helix contact. Based on the combination of co-evolving residues detected with the four best prediction algorithms, interacting helices could be predicted with a specificity of 83% and sensitivity of 42%. Availability:http://webclu.bio.wzw.tum.de/helixcorr/ Contact:d.frishman@wzw.tum.de Supplementary information: Supplementary data are available at Bioinformatics online.