Expanding our knowledge of the protein universe: Modelling of protein structures

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing goal in structural biology. Over the last two decades, a paradigm shift has occurred: starting from a large "knowledge gap" between the huge number of protein sequences compared to a small number of experimentally known structures, today, some form of structural information - either experimental or computational - is available for the majority of amino acids encoded by common model organism genomes. Methods for structure modeling and prediction have made substantial progress of the last decades, and template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. However, computational modeling and prediction techniques often fall short in accuracy compared to high-resolution experimental structures, and it is often difficult to convey the expected accuracy and structural variability of a specific model. Retrospectively assessing the quality of blind structure prediction in comparison to experimental reference structures allows benchmarking the state-of-the-art in structure prediction and identifying areas which need further development. The Critical Assessment of Structure Prediction (CASP) experiment has for the last 20 years assessed the progress in the field of protein structure modeling based on predictions for ca. 100 blind prediction targets per experiment which are carefully evaluated by human experts. The "Continuous Model EvaluatiOn" (CAMEO) project aims to provide a fully automated blind assessment for prediction servers based on weekly pre-released sequences of the Protein Data Bank PDB. CAMEO has been made possible by the development of novel scoring methods such as lDDT, which are robust against domain movements to allow for automated continuous structure comparison without human intervention.

In-situ grazing incidence scattering in information and communication technology

Novel functional materials rely on a profound understanding of nanoscale structure and processing. Employed as multilayered thin films and coatings, they find applications in smart sensors, organic electronics, organic photovoltaics and barrier layers. To create such organic and inorganic multilayers a multitude of deposition methods is used. These include processing from liquid phase, such as ink-jet printing, spray-coating, spin-coating, as well as vacuum deposition. Grazing incidence X-ray scattering (GIXS) is a very powerful tool to investigate processes in-situ and in-operando and in real-time [1,2,3]. GIXS allows for combining with micro- and nanofocused X-ray beams as well as complementary investigation tools at the same time. Therefore one obtains full structural and morphological understanding of functional materials and to correlate this knowledge with the desired functionality. I will present selected examples for application of GIXS in information and communication technology. This comprises vacuum deposition with millisecond time resolution and on functional materials. Combining GIXS and imaging ellipsometry, we are able to follow in-situ the installation of functional thin films by droplet casing. Furthermore, this powerful combination allows for examining the stability of nanostructure and optical constants in nanostructured polymeric films for high temperature applications. I complement my talk with the example of GIXS and spray deposition as one rapid deposition method to obtain functional coatings.

From Atoms to Layers: Gold Cluster Growth Kinetics during Sputter Deposition

The adjustment of the size-dependent catalytic and optoelectronic properties of gold cluster assemblies is a very significant topic in modern applied nanotechnology [1]. For an efficient and controlled production of active nanostructured cluster surfaces, sputter deposition plays an important role [2]. In order to characterize the self-organization during nanocluster film formation, it is mandatory to understand how growth kinetics influences the cluster film morphology during sputter deposition. The first real-time investigations of gold nanocluster growth kinetics into a gold layer are enabled by combining sputter deposition and surface sensitive X-ray scattering (GISAXS). High frame-rate 2D X-ray detectors in combination with the high photon flux of micro beam spot size, available at the PETRA III beamline P03, enables a non-invasive in situ and real-time investigation of gold growth during sputter deposition. With an acquisition throughput of 67 frames per second, we were able to identify 4 different stages of growth including their thresholds with sub-monolayer resolution and concomitant phase transitions. Each stage can be characterized by a predominant surface process and its intrinsic kinetic: nucleation, diffusion, adsorption and grain growth. Moreover we introduced a flexible geometrical model to extract morphological real space parameters, such as cluster size, correlation distance, layer porosity and surface coverage, directly from the reciprocal space scattering data. The model allowed simulating, visualizing and interpreting gold cluster growth kinetics in terms of nanoscopic processes. Furthermore, we were able to deduce wetting angle on the nanoscale and onset of long-range connectivity during the deposition process [3]. This approach is a prerequisite for future investigations of the influence of different process parameters on thin metal film morphology, which is essential for optimization of manufacturing parameters, saving energy and resources.