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

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

Bioinformatics Methods for NMR Chemical Shift Data

Nuclear magnetic resonance spectroscopy (NMR) is one of the most important methods for measuring the three-dimensional structure of biomolecules. Despite major progress in the NMR methodology, the solution of a protein structure is still a tedious and time-consuming task. The goal of this thesis is to develop bioinformatics methods which may strongly accelerate the NMR process. This work concentrates on a special type of measurements, the so-called chemical shifts. Chemical shifts are routinely measured at the beginning of a structure resolution process. As all data from the laboratory, chemical shifts may be error-prone, which might complicate or even circumvent the use of this data. Therefore, as the first result of the thesis, we present CheckShift, a method which automatically corrects a frequent error in NMR chemical shift data. However, the main goal of this thesis is the extraction of structural information hidden in chemical shifts. SimShift was developed as a first step in this direction. SimShift is the first approach to identify structural similarities between proteins based on chemical shifts. Compared to methods based on the amino acid sequence alone, SimShift shows its strength in detecting distant structural relationships. As a natural further development of the pairwise comparison of proteins, the SimShift algorithm is adapted for database searching. Given a protein, the improved algorithm, named SimShiftDB, searches a database of solved proteins for structurally homologue entries. The search is based only on the amino acid sequence and the associated chemical shifts. The detected similarities are additionally ranked based on calculations of statistical significance. Finally, the Chemical Shift Pipeline, the main result of this work, is presented. By combining automatic chemical shift error correction (CheckShift) and the database search algorithm (SimShiftDB), it is possible to achieve very high quality in 70% to 80% of the similarities identified. Thereby, only about 10% of the predictions are in error.Die nukleare Magnetresonanz-Spektroskopie (NMR) ist eine der wichtigsten Methoden, um die drei-dimensionale Struktur von Biomolekülen zu bestimmen. Trotz großer Fortschritte in der Methodik der NMR ist die Auflösung einer Proteinstruktur immer noch eine komplizierte und zeitraubende Aufgabe. Das Ziel dieser Doktorarbeit ist es, Bioinformatik-Methoden zu entwickeln, die den Prozess der Strukturaufklärung durch NMR erheblich beschleunigen können. Zu diesem Zweck konzentriert sich diese Arbeit auf bestimmte Messdaten aus der NMR, die so genannten chemischen Verschiebungen. Chemische Verschiebungen werden standardmäßig zu Beginn einer Strukturauflösung bestimmt. Wie alle Labordaten können chemische Verschiebungen Fehler enthalten, die die Analyse erschweren, wenn nicht sogar unmöglich machen. Als erstes Resultat dieser Arbeit wird darum CheckShift präsentiert, eine Methode, die es ermöglich einen weit verbreiteten Fehler in chemischen Verschiebungsdaten automatisch zu korrigieren. Das Hauptziel dieser Doktorarbeit ist es jedoch, strukturelle Informationen aus chemischen Verschiebungen zu extrahieren. Als erster Schritt in diese Richtung wurde SimShift entwickelt. SimShift ermöglicht es zum ersten Mal, strukturelle Ähnlichkeiten zwischen Proteinen basierend auf chemischen Verschiebungen zu identifizieren. Der Vergleich zu Methoden, die nur auf der Aminosäurensequenz basieren, zeigt die Überlegenheit des verschiebungsbasierten Ansatzes. Als eine natürliche Erweiterung des paarweisen Vergleichs von Proteinen wird darauffolgend SimShiftDB vorgestellt. Gegeben ein Protein, durchsucht SimShiftDB eine Datenbank bekannter Proteinstrukturen nach strukturell homologen Einträgen. Die Suche basiert hierbei nur auf der Aminosäuresequenz und den chemischen Verschiebungen des Proteins. Die detektierten Ähnlichkeiten werden zusätzlich nach statistischer Signifikanz bewertet. Mit der Chemical Shift Pipeline wird schließlich das Hauptresultat der Dissertation vorgestellt. Durch die Kombination der automatischen Fehlerkorrektur (CheckShift) mit dem Datenbank-Suchalgorithmus (SimShiftDB), wird in 70% bis 80% der vorhergesagten strukturellen Ähnlichkeiten eine sehr hohe Qualität erreicht. Der Anteil der fehlerhaften Vorhersagen beträgt nur etwa 10%

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)

A spatial analysis of the determinants of Inter-regional migration: evidence from Ghana

Sub-Saharan Africa has experienced a rapid population increase and growing urbanization rates in recent years and is bound to have the world's largest urban population. If no steps are taken against it, the fast rise in the urban population will result in severe consequences for urban localities in the developing countries located in this region. Along with the natural population increase, internal migration is one prime reason for a fast-rising urbanization process. Since this type of migration is very common in developing countries, this following paper conducts a spatial analysis of inter-regional migration with special reference to Ghana. Specifically, it analyzes the Ghana's migration patterns in Ghana by visualizing the regional differences in net migration and the major migration flows from one region to another. Data for this analysis were collected from a population census and a household survey. A cross-sectional regression analysis was conducted to examine which factors explain inter-regional migration flows in the country. The regression model employed in the analysis is based on the gravity model of migration, which explains how the size of and the distance between two places affects the movement between them, and added the rate of urbanization as well as the average annual income per capita of both regions. The regression results reveal that the distance between two administrative regions in Ghana and the birth region's urbanization rate refrain people from migrating to other regions. In contrast, the urbanization rate and the average income of the destination region are positively associated with the inflow of migrants. Nevertheless, due to the data's limitations, the nexus between migration flows and regional disparities cannot be fully investigated. Therefore, this paper calls for more research to be done in this field

Detection of 1p19q Deletion by Real-Time Comparative Quantitative PCR

1p/19q (1p and/or 19q) deletions are prognostic factors in oligodendroglial tumors (OT) and predict better survival after both chemotherapy and radiotherapy. While studying 1p/19q status as a potential variable within multivariate prognosis models for OT, we have frequently encountered unknown 1p/19q status within our glioma sample database due to lack of paired blood samples for loss of heterozygosity (LOH) assay and/or failure to perform fluorescence in situ hybridization (FISH). We realized that a 1p and 19q deletion assay that could be reliably performed solely on tumor DNA samples would allow us to fill in these molecular biology data “holes”. We built recombinant DNA with fragments of the selected “marker” genes in 1p (E2F2, NOTCH2), and 19q (PLAUR) and “reference” genes (ERC2, SPOCK1, and SPAG16 ) and used it as quantification standard in real-time PCR to gain absolute ratios of marker/reference gene copy numbers in tumor DNA samples, thus called comparative quantitative PCR (CQ-PCR). Using CQ-PCR, we identified 1p and/ or 19q deletions in majority of pure low-grade oligodenroglioma (OG) tumors (17/21, 81%), a large portion of anaplastic oligodendroglioma (AO) tumors (6/15, 47%), but rarely found in mixed oligoastrcytomas (OA) tumors (1/8, 13%). These data are consistent with results of LOH and FISH assays generally reported for these tumor types. In addition, 15 out 18 samples showed concordant results between FISH and CQ-PCR. We conclude that CQ-PCR is a potential means to gain 1p/19q deletion information, which prognostic and predictive values of CQ-PCR-derived 1p/19q status will be determined in a future study

Rapid and simple comparison of messenger RNA levels using real-time PCR

Real-time polymerase chain reaction (PCR) constitutes a significant improvement over traditional end-point PCR, as it allows the quantification of starting amounts of nucleic acid templates, in real-time. However, quantification requires validation through numerous internal controls and standard curves. We describe in this paper a simple protocol which uses real-time PCR to compare mRNA levels of a gene of interest between different experimental conditions. Comparative real-time PCR can be a relatively low-cost method and does not require sequence-specific fluorescent reporters. Moreover, several genes from a set of experiments can be assessed in a single run. Thus, in addition to providing a comparative profile for the expression of a gene of interest, this method can also provide information regarding the relative abundance of different mRNA species

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies

BACKGROUND: Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA. RESULTS: Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R(2 )= 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index). CONCLUSION: In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies

Backbone and side chain 1H, 15N and 13C assignments for a thiol-disulphide oxidoreductase from the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125

Enzymes produced by psychrophilic organisms have successfully overcome the low temperature challenge and evolved to maintain high catalytic rates in their permanently cold environments. As an initial step in our attempt to elucidate the cold-adaptation strategies used by these enzymes we report here the 1H, 15N and 13C assignments for the reduced form of a thiol-disulphide oxidoreductase from the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125.The NMR spectrometers are part of The National NMR Network (REDE/1517/RMN/2005), supported by ‘‘Programa Operacional Ciência e Inovação (POCTI) 2010’’ and Fundação para a Ciência e a Tecnologia (FCT). This work was funded by FCT, POCTI and FEDER; Projects POCI/BIA-PRO/57263/2004 and PTDC/BIO/70806/2006. TC is holder of a long term EMBO fellowship. MM is thankful to the Fundação para a Ciência e Tecnologia for its support through Programa Ciência 2007.info:eu-repo/semantics/publishedVersio