ProSAS: a database for analyzing alternative splicing in the context of protein structures

Alternative splicing is known to be one of the major sources for functional diversity in higher eukaryotes. Several splicing isoforms have been characterized in the literature that play important roles in cellular processes like apoptosis or signal transduction pathways. Splicing events can often be detected on the mRNA level by large-scale cDNA or EST experiments and such data is collected and annotated in several databases. Nevertheless, the effects of splicing on the structure of a protein are largely unknown. The ProSAS (Protein Structure and Alternative Splicing) database fills this gap and provides a unified resource for analyzing effects of alternative splicing events in the context of protein structures. ProSAS comprehensively annotates and models protein structures for several Ensembl genomes as well as SwissProt entries harbouring splicing events. Alternative isoforms annotated in Ensembl or SwissProt can be analyzed on the protein structure and protein function level using an intuitive user interface that provides several features and tools for a structure-based analysis of alternative splicing events. The ProSAS database is freely accessible at http://www.bio.ifi.lmu.de/ProSAS

Scaling clearance in paediatric pharmacokinetics: All models are wrong, which are useful?

AIM: When different models for weight and age are used in paediatric pharmacokinetic studies it is difficult to compare parameters between studies or perform model-based meta-analyses. This study aimed to compare published models with the proposed standard model (allometric weight(0.75) and sigmoidal maturation function). METHODS: A systematic literature search was undertaken to identify published clearance (CL) reports for gentamicin and midazolam and all published models for scaling clearance in children. Each model was fitted to the CL values for gentamicin and midazolam, and the results compared with the standard model (allometric weight exponent of 0.75, along with a sigmoidal maturation function estimating the time in weeks of postmenstrual age to reach half the mature value and a shape parameter). For comparison, we also looked at allometric size models with no age effect, the influence of estimating the allometric exponent in the standard model and, for gentamicin, using a fixed allometric exponent of 0.632 as per a study on glomerular filtration rate maturation. Akaike information criteria (AIC) and visual predictive checks were used for evaluation. RESULTS: No model gave an improved AIC in all age groups, but one model for gentamicin and three models for midazolam gave slightly improved global AIC fits albeit using more parameters: AIC drop (number of parameters), -4.1 (5), -9.2 (4), -10.8 (5) and -10.1 (5), respectively. The 95% confidence interval of estimated CL for all top performing models overlapped. CONCLUSION: No evidence to reject the standard model was found; given the benefits of standardised parameterisation, its use should therefore be recommended

Enabling comparative modeling of closely related genomes: Example genus Brucella

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this short report, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as well as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.We thank Jean Jacques Letesson, Maite Iriarte, Stephan Kohler and David O'Callaghan for their input on improving specific annotations. This project has been funded by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN272200900040C, awarded to BW Sobral, and from the United States National Science Foundation under Grant MCB-1153357, awarded to CS Henry. J.P.F. acknowledges funding from [FRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) Ph.D. scholarship

The response of the tandem pore potassium channel TASK-3 (K2P9.1) to voltage : gating at the cytoplasmic mouth

Although the tandem pore potassium channel TASK-3 is thought to open and shut at its selectivity filter in response to changes of extracellular pH, it is currently unknown whether the channel also shows gating at its inner, cytoplasmic mouth through movements of membrane helices M2 and M4.We used two electrode voltage clamp and single channel recording to show that TASK-3 responds to voltage in a way that reveals such gating. In wild-type channels, Popen was very low at negative voltages, but increased with depolarisation. The effect of voltage was relatively weak and the gating charge small, ∼0.17.Mutants A237T (in M4) and N133A (in M2) increased Popen at a given voltage, increasing mean open time and the number of openings per burst. In addition, the relationship between Popen andvoltagewas shifted to lesspositive voltages. Mutation of putative hinge glycines (G117A, G231A), residues that are conserved throughout the tandem pore channel family, reduced Popen at a given voltage, shifting the relationship with voltage to a more positive potential range. None of these mutants substantially affected the response of the channel to extracellular acidification. We have used the results from single channel recording to develop a simple kinetic model to show how gating occurs through two classes of conformation change, with two routes out of the open state, as expected if gating occurs both at the selectivity filter and at its cytoplasmic mouth