Detection of unrealistic molecular environments in protein structures based on expected electron densities

Understanding the relationship between protein structure and biological function is a central theme in structural biology. Advances are severely hampered by errors in experimentally determined protein structures. Detection and correction of such errors is therefore of utmost importance. Electron densities in molecular structures obey certain rules which depend on the molecular environment. Here we present and discuss a new approach that relates electron densities computed from a structural model to densities expected from prior observations on identical or closely related molecular environments. Strong deviations of computed from expected densities reveal unrealistic molecular structures. Most importantly, structure analysis and error detection are independent of experimental data and hence may be applied to any structural model. The comparison to state-of-the-art methods reveals that our approach is able to identify errors that formerly remained undetected. The new technique, called RefDens, is accessible as a public web service at http://refdens.services.came.sbg.ac.at

SHIFTX2: significantly improved protein chemical shift prediction

A new computer program, called SHIFTX2, is described which is capable of rapidly and accurately calculating diamagnetic 1H, 13C and 15N chemical shifts from protein coordinate data. Compared to its predecessor (SHIFTX) and to other existing protein chemical shift prediction programs, SHIFTX2 is substantially more accurate (up to 26% better by correlation coefficient with an RMS error that is up to 3.3× smaller) than the next best performing program. It also provides significantly more coverage (up to 10% more), is significantly faster (up to 8.5×) and capable of calculating a wider variety of backbone and side chain chemical shifts (up to 6×) than many other shift predictors. In particular, SHIFTX2 is able to attain correlation coefficients between experimentally observed and predicted backbone chemical shifts of 0.9800 (15N), 0.9959 (13Cα), 0.9992 (13Cβ), 0.9676 (13C′), 0.9714 (1HN), 0.9744 (1Hα) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231 ppm, respectively. The correlation between SHIFTX2’s predicted and observed side chain chemical shifts is 0.9787 (13C) and 0.9482 (1H) with RMS errors of 0.9754 and 0.1723 ppm, respectively. SHIFTX2 is able to achieve such a high level of accuracy by using a large, high quality database of training proteins (>190), by utilizing advanced machine learning techniques, by incorporating many more features (χ2 and χ3 angles, solvent accessibility, H-bond geometry, pH, temperature), and by combining sequence-based with structure-based chemical shift prediction techniques. With this substantial improvement in accuracy we believe that SHIFTX2 will open the door to many long-anticipated applications of chemical shift prediction to protein structure determination, refinement and validation. SHIFTX2 is available both as a standalone program and as a web server (http://www.shiftx2.ca)

SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database

We present SimShiftDB, a new program to extract conformational data from protein chemical shifts using structural alignments. The alignments are obtained in searches of a large database containing 13,000 structures and corresponding back-calculated chemical shifts. SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space. We compare SimShiftDB to HHSearch, a state-of-the-art sequence-based search tool, and to TALOS, the current standard tool for the task. We show that for a significant fraction of the predicted similarities, SimShiftDB outperforms the other two methods. Particularly, the high coverage afforded by the larger database often allows predictions to be made for residues not involved in canonical secondary structure, where TALOS predictions are both less frequent and more error prone. Thus SimShiftDB can be seen as a complement to currently available methods

Viral load is strongly associated with length of stay in adults hospitalised with viral acute respiratory illness

Background: respiratory viruses are detectable in a large proportion of adults hospitalised with acute respiratory illness. For influenza and other viruses there is evidence that viral load and persistence are associated with certain clinical outcomes but it is not known if there is an association between viral load and hospital length of stay. Methods: 306 adults hospitalised with viral acute respiratory illness were studied. Associations between viral load and length of stay were examined. Multiple linear regression analysis was performed to control for age, comorbidity, influenza vaccine status, duration of illness prior to hospitalisation, bacterial co-infection, clinical group and virus subtype.Results: high viral load was associated with a longer duration of hospitalisation for all patients (p &lt;0.0001). This remained significant across all virus types and clinical groups and when adjusted for age, comorbidity, duration of illness prior to hospitalisation, bacterial co-infection and other factors. Conclusions: high viral loads are associated with prolonged hospital length of stay in adults with viral acute respiratory illness. This further supports existing evidence demonstrating that viral acute respiratory illness is a viral load driven process and suggests that viral load could be used in clinical practise to predict prolonged hospitalisation and prioritise antivirals. International Standard Randomised Controlled Trial Number (ISRCTN): 21521552<br/

Conjugation of Organoruthenium(II) 3-(1H-Benzimidazol-2-yl)pyrazolo[3,4-b]pyridines and Indolo[3,2-d]benzazepines to Recombinant Human Serum Albumin: a Strategy To Enhance Cytotoxicity in Cancer Cells

Five organoruthenium complexes [RuCl(η6-arene)(L)]Cl with a modified arene ligand, namely, 4-formylphenoxyacetyl-η6-benzylamide, and L = 3-(1H-benzimidazol-2-yl)-1H-pyrazolo[3,4-b]pyridines or indolo[3,2-d]benzazepines were synthesized and conjugated to recombinant human serum albumin in order to improve their drug targeting and delivery to cancer cells, and a marked increase in cytotoxicity was observed

Organometallic indolo[3,2-c]quinolines versus indolo[3,2-d]benzazepines: synthesis, structural and spectroscopic characterization, and biological efficacy

The synthesis of ruthenium(II) and osmium(II) arene complexes with the closely related indolo[3,2-c]quinolines N-(11H-indolo[3,2-c]quinolin-6-yl)-ethane-1,2-diamine (L1) and N′-(11H-indolo[3,2-c]quinolin-6-yl)-N,N-dimethylethane-1,2-diamine (L2) and indolo[3,2-d]benzazepines N-(7,12-dihydroindolo-[3,2-d][1]benzazepin-6-yl)-ethane-1,2-diamine (L3) and N′-(7,12-dihydroindolo-[3,2-d][1]benzazepin-6-yl)-N,N-dimethylethane-1,2-diamine (L4) of the general formulas [(η6-p-cymene)MII(L1)Cl]Cl, where M is Ru (4) and Os (6), [(η6-p-cymene)MII(L2)Cl]Cl, where M is Ru (5) and Os (7), [(η6-p-cymene)MII(L3)Cl]Cl, where M is Ru (8) and Os (10), and [(η6-p-cymene)MII(L4)Cl]Cl, where M is Ru (9) and Os (11), is reported. The compounds have been comprehensively characterized by elemental analysis, electrospray ionization mass spectrometry, spectroscopy (IR, UV–vis, and NMR), and X-ray crystallography (L1·HCl, 4·H2O, 5, and 9·2.5H2O). Structure–activity relationships with regard to cytotoxicity and cell cycle effects in human cancer cells as well as cyclin-dependent kinase (cdk) inhibition and DNA intercalation in cell-free settings have been established. The metal-free indolo[3,2-c]quinolines inhibit cancer cell growth in vitro, with IC50 values in the high nanomolar range, whereas those of the related indolo[3,2-d]benzazepines are in the low micromolar range. In cell-free experiments, these classes of compounds inhibit the activity of cdk2/cyclin E, but the much higher cytotoxicity and stronger cell cycle effects of indoloquinolines L1 and 7 are not paralleled by a substantially higher kinase inhibition compared with indolobenzazepines L4 and 11, arguing for additional targets and molecular effects, such as intercalation into DNA

Expression of eEF1A2 is associated with clear cell histology in ovarian carcinomas: overexpression of the gene is not dependent on modifications at the EEF1A2 locus

The tissue-specific translation elongation factor eEF1A2 is a potential oncogene that is overexpressed in human ovarian cancer. eEF1A2 is highly similar (98%) to the near-ubiquitously expressed eEF1A1 (formerly known as EF1-α) making analysis with commercial antibodies difficult. We wanted to establish the expression pattern of eEF1A2 in ovarian cancer of defined histological subtypes at both the RNA and protein level, and to establish the mechanism for the overexpression of eEF1A2 in tumours. We show that while overexpression of eEF1A2 is seen at both the RNA and protein level in up to 75% of clear cell carcinomas, it occurs at a lower frequency in other histological subtypes. The copy number at the EEF1A2 locus does not correlate with expression level of the gene, no functional mutations were found, and the gene is unmethylated in both normal and tumour DNA, showing that overexpression is not dependent on genetic or epigenetic modifications at the EEF1A2 locus. We suggest that the cause of overexpression of eEF1A2 may be the inappropriate expression of a trans-acting factor. The oncogenicity of eEF1A2 may be related either to its role in protein synthesis or to potential non-canonical functions