Bayesian structure reconstruction from single molecule X-ray scattering data

Röntgenlicht-Freie-Elektronen-Laser (XFEL) schaffen neue Möglichkeiten für die molekulare Strukturbestimmung in Einzelmolekülexperimenten. In dieser Arbeit stelle ich zwei alternative bayessche Verfahren vor, das Orientational Bayes und das Structural Bayes Verfahren, die das Extrahieren der Strukturinformationen aus dünn besetzten und verrauschten Streuungsbildern ermöglichen. Im ersten Verfahren wird ein "Seed"-Modell verwendet, um die zugrunde liegende molekulare Orientierung für jedes aufgezeichnete Streuungsbild separat zu bestimmen. Eine verbesserte molekulare Transformation der bestrahlten Moleküle wird durch Ausrichten und Mitteln dieser Bilder im dreidimensionalen reziproken Raum erhalten. Im Structural Bayes Verfahren wird ein Realraum-Strukturmodell optimiert, sodass es am besten zum gesamten Streuungsbildersatz passt. Auf diese Weise wird ermöglicht, zwischen verschiedenen Strukturmodellen zu unterscheiden. Ich habe die Auflösung bei der Abbildung einzelner Moleküle mit unterschiedlichen Massen für verschiedene XFEL Strahlintensitäten abgeschätzt. Die Ergebnisse zeigen, dass die erreichbare strukturelle Auflösung mit der Molekülmasse wie M^{-1/ 6} steigt. Laut dieser Skalierung ist hierbei, im Gegensatz zur traditionellen Röntgenkristallographie, die hochaufgelöste Strukturbestimmung kleiner Einzelmoleküle, im Vergleich zu großen Molekülen, schwieriger. Als Machbarkeitsnachweis des Orientational Bayes Verfahrens wurde beispielhaft die Elektronendichte eines Glutathion-Moleküls aus 20.000 synthetischen Streuungsbildern, mit durchschnittlich 82 aufgezeichneten elastisch gestreuten Photonen und bis zu 50% zusätzlichem Hintergrundrauschen pro Bild, berechnet. Um die Anwendbarkeit des Structural Bayes Verfahrens in einer de novo Strukturbestimmung zu testen, wurde zudem die Struktur des Glutathion-Moleküls in einer Monte Carlo-Verfeinerungs-Simulation gelöst, für die zufällige Aminosäure-Konformationen als Ausgangsmaterial verwendet wurden. Um zusätzlich zu prüfen, ob mehrere Längenskalen umfassende Strukturänderungen in einem komplexen Molekül unter Verwendung des Structural Bayes Verfahrens rückverfolgbar sind, wurden Konformationsänderungen von drei Immunglobulin-Domänen eines Titin-Moleküls sowie der tRNA-Translokationsvorgang im Ribosom untersucht. Die Ergebnisse zeigen, dass es möglich ist sowohl zwischen unterschiedlichen molekularen Konformationen zu unterscheiden als auch kleinere strukturelle Änderungen, die mit der tRNA-Translokation assoziiert sind, zu erkennen. Insgesamt betrachtet deuten die Ergebnisse dieser Arbeit darauf hin, dass sich mithilfe der beiden hier vorgestellten bayesschen Verfahren die Struktur einzelner Moleküle mit atomarer Auflösung von dünn besetzten und verrauschten Röntgenstreuungsbildern aus XFEL-Einzelmolekülexperimenten für ein breites Spektrum von Molekülmassen bestimmen lässt

Optimized data exploration applied to the simulation of a chemical process

In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it can be very difficult to determine feasible parameter regions, especially without previous knowledge. We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way. Moreover, we include an additional optimization target in the algorithm to guide the exploration towards regions of interest and to improve the classification therein. In our method we make use of well-established concepts from the field of machine learning like kernel support vector machines and kernel ridge regression. From a comparison with a Kriging-based exploration approach based on recently published results we can show the advantages of our algorithm in a binary feasibility classification scenario with a discrete feasibility constraint violation. In this context, we also propose an improvement of the Kriging-based exploration approach. We apply our novel method to a fully realistic, industrially relevant chemical process simulation to demonstrate its practical usability and find a comparably good approximation of the data space topology from relatively few data points.Comment: 45 pages, 6 figure

Computer vision-based automated peak picking applied to protein NMR spectra

Motivation: A detailed analysis of multidimensional NMR spectra of macromolecules requires the identification of individual resonances (peaks). This task can be tedious and time-consuming and often requires support by experienced users. Automated peak picking algorithms were introduced more than 25 years ago, but there are still major deficiencies/flaws that often prevent complete and error free peak picking of biological macromolecule spectra. The major challenges of automated peak picking algorithms is both the distinction of artifacts from real peaks particularly from those with irregular shapes and also picking peaks in spectral regions with overlapping resonances which are very hard to resolve by existing computer algorithms. In both of these cases a visual inspection approach could be more effective than a ‘blind' algorithm. Results: We present a novel approach using computer vision (CV) methodology which could be better adapted to the problem of peak recognition. After suitable ‘training' we successfully applied the CV algorithm to spectra of medium-sized soluble proteins up to molecular weights of 26 kDa and to a 130 kDa complex of a tetrameric membrane protein in detergent micelles. Our CV approach outperforms commonly used programs. With suitable training datasets the application of the presented method can be extended to automated peak picking in multidimensional spectra of nucleic acids or carbohydrates and adapted to solid-state NMR spectra. Availability and implementation: CV-Peak Picker is available upon request from the authors. Contact: [email protected]; [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Subexponential-Time Algorithms for Finding Large Induced Sparse Subgraphs

Let C and D be hereditary graph classes. Consider the following problem: given a graph G in D, find a largest, in terms of the number of vertices, induced subgraph of G that belongs to C. We prove that it can be solved in 2^{o(n)} time, where n is the number of vertices of G, if the following conditions are satisfied: - the graphs in C are sparse, i.e., they have linearly many edges in terms of the number of vertices; - the graphs in D admit balanced separators of size governed by their density, e.g., O(Delta) or O(sqrt{m}), where Delta and m denote the maximum degree and the number of edges, respectively; and - the considered problem admits a single-exponential fixed-parameter algorithm when parameterized by the treewidth of the input graph. This leads, for example, to the following corollaries for specific classes C and D: - a largest induced forest in a P_t-free graph can be found in 2^{O~(n^{2/3})} time, for every fixed t; and - a largest induced planar graph in a string graph can be found in 2^{O~(n^{3/4})} time

Responsive core-shell DNA particles trigger lipid-membrane disruption and bacteria entrapment.

Biology has evolved a variety of agents capable of permeabilizing and disrupting lipid membranes, from amyloid aggregates, to antimicrobial peptides, to venom compounds. While often associated with disease or toxicity, these agents are also central to many biosensing and therapeutic technologies. Here, we introduce a class of synthetic, DNA-based particles capable of disrupting lipid membranes. The particles have finely programmable size, and self-assemble from all-DNA and cholesterol-DNA nanostructures, the latter forming a membrane-adhesive core and the former a protective hydrophilic corona. We show that the corona can be selectively displaced with a molecular cue, exposing the 'sticky' core. Unprotected particles adhere to synthetic lipid vesicles, which in turn enhances membrane permeability and leads to vesicle collapse. Furthermore, particle-particle coalescence leads to the formation of gel-like DNA aggregates that envelop surviving vesicles. This response is reminiscent of pathogen immobilisation through immune cells secretion of DNA networks, as we demonstrate by trapping E. coli bacteria

Deleted in Liver Cancer 2 (DLC2) protein expression in hepatocellular carcinoma

Deleted in Liver Cancer (DLC) proteins belong to the family of RhoGAPs and are believed to operate as negative regulators of the Rho family of small GTPases. So far, the role of the first identified member from the DLC family, DLC1, was established as a tumor suppressor in hepatocellular carcinoma. The function of its close family relative, DLC2 is unequivocal. In the present study we attempted to determine whether the loss of DLC2 is a common feature of hepatocellular carcinoma tissue. We examined two types of hepatocellular carcinoma- typical and fibrolamellar one. Our analysis revealed that DLC2 protein is not diminished in cancer tissue when compared to non-cancerous liver specimens. What is more, we observed DLC2 to be more abundantly expressed in cancer tissue, particularly in tumors with the inflammation background. In addition, we found that DLC2 gene status was diploid in virtually all tumor samples examined. Our results indicate that DLC2 is not diminished in hepatocellular carcinoma cells. It appears that members of the DLC family, although structurally highly related, may function differently in cancer cells

Spatial-Mode Discrimination in Guided and Antiguided Arrays of Long-Wavelength VCSELs

Three means of optical confinement imposed on InAlGaAs/InP 1.3 mu m VCSEL arrays are investigated with self-consistent numerical model of laser operation. Laterally patterned tunnel junction (TJ), in-build guiding realized with air-gap patterning, and antiguiding schemes are investigated and optimized to achieve single-mode operation. The analysis shows that mode discrimination in laterally patterned TJ is very responsive to the injected current, the air-gap patterning reduces influence of the working conditions and supports multimode operation, and finally, antiguiding schemes provide single-mode operation for prescribed geometrical design