Konrad Brunner

Nonradiative relaxation times in diagonal transition Si/SiGe quantum cascade structures

Here, we explore experimentally and theoretically the possibility to prolong the upper hole state nonradiative lifetime of Si/SiGe quantum cascade (QC) structures by using a spatially indirect diagonal transition between two SiGe quantum well ground states. With the recent observation of well resolved midinfrared electroluminescence from heavy hole intersubband transitions in Si/SiGe valence-band QC structures, a Si-based QC laser seems no longer to be out of reach. A long carrier lifetime and maybe population inversion, however, appear to be impossible for structure designs with a vertical intersubband transition studied so far. This is due to the nonresonant behavior of deformation potential scattering dominant in unipolar SiGe. We report on calculations of the band structure using a six-band k⋅p model and of hole deformation potential scattering that predict significantly increased nonradiative lifetimes for large barrier thickness, reaching about 20 ps for 35 A Si barrier layer width. Electroluminesen...

Mapping of protein structural ensembles by chemical shifts

Applying the chemical shift prediction programs SHIFTX and SHIFTS to a data base of protein structures with known chemical shifts we show that the averaged chemical shifts predicted from the structural ensembles explain better the experimental data than the lowest energy structures. This is in agreement with the fact that proteins in solution occur in multiple conformational states in fast exchange on the chemical shift time scale. However, in contrast to the real conditions in solution at ambient temperatures, the standard NMR structural calculation methods as well chemical shift prediction methods are optimized to predict the lowest energy ground state structure that is only weakly populated at physiological temperatures. An analysis of the data shows that a chemical shift prediction can be used as measure to define the minimum size of the structural bundle required for a faithful description of the structural ensemble.

NMR in the SPINE Structural Proteomics project

This paper describes the developments, role and contributions of the NMR spectroscopy groups in the Structural Proteomics In Europe (SPINE) consortium. Focusing on the development of high-throughput (HTP) pipelines for NMR structure determinations of proteins, all aspects from sample preparation, data acquisition, data processing, data analysis to structure determination have been improved with respect to sensitivity, automation, speed, robustness and validation. Specific highlights are protonless (13)C-direct detection methods and inferential structure determinations (ISD). In addition to technological improvements, these methods have been applied to deliver over 60 NMR structures of proteins, among which are five that failed to crystallize. The inclusion of NMR spectroscopy in structural proteomics pipelines improves the success rate for protein structure determinations.

A general method for the unbiased improvement of solution NMR structures by the use of related X-ray data, the AUREMOL-ISIC algorithm.

BackgroundRapid and accurate three-dimensional structure determination of biological macromolecules is mandatory to keep up with the vast progress made in the identification of primary sequence information. During the last few years the amount of data deposited in the protein data bank has substantially increased providing additional information for novel structure determination projects. The key question is how to combine the available database information with the experimental data of the current project ensuring that only relevant information is used and a correct structural bias is produced. For this purpose a novel fully automated algorithm based on Bayesian reasoning has been developed. It allows the combination of structural information from different sources in a consistent way to obtain high quality structures with a limited set of experimental data. The new ISIC (I ntelligent S tructural I nformation C ombination) algorithm is part of the larger AUREMOL software package.ResultsOur new approach was successfully tested on the improvement of the solution NMR structures of the Ras-binding domain of Byr2 from Schizosaccharomyces pombe, the Ras-binding domain of RalGDS from human calculated from a limited set of NMR data, and the immunoglobulin binding domain from protein G from Streptococcus by their corresponding X-ray structures. In all test cases clearly improved structures were obtained. The largest danger in using data from other sources is a possible bias towards the added structure. In the worst case instead of a refined target structure the structure from the additional source is essentially reproduced. We could clearly show that the ISIC algorithm treats these difficulties properly.ConclusionIn summary, we present a novel fully automated method to combine strongly coupled knowledge from different sources. The combination with validation tools such as the calculation of NMR R-factors strengthens the impact of the method considerably since the improvement of the structures can be assessed quantitatively. The ISIC method can be applied to a large number of similar problems where the quality of the obtained three-dimensional structures is limited by the available experimental data like the improvement of large NMR structures calculated from sparse experimental data or the refinement of low resolution X-ray structures. Also structures may be refined using other available structural information such as homology models.

Protein structure calculation with data imputation: the use of substitute restraints

The amount of experimental restraints e.g., NOEs is often too small for calculating high quality three-dimensional structures by restrained molecular dynamics. Considering this as a typical missing value problem we propose here a model based data imputation technique that should lead to an improved estimation of the correct structure. The novel automated method implemented in AUREMOL makes a more efficient use of the experimental information to obtain NMR structures with higher accuracy. It creates a large set of substitute restraints that are used either alone or together with the experimental restraints. The new approach was successfully tested on three examples: firstly, the Ras-binding domain of Byr2 from Schizosaccharomyces pombe, the mutant HPr (H15A) from Staphylococcus aureus, and a X-ray structure of human ubiquitin. In all three examples, the quality of the resulting final bundles was improved considerably by the use of additional substitute restraints, as assessed quantitatively by the calculation of RMSD values to the “true” structure and NMR R-factors directly calculated from the original NOESY spectra or the published diffraction data.

Chemical shift optimization in multidimensional NMR spectra by AUREMOL-SHIFTOPT.

A problem often encountered in multidimensional NMR-spectroscopy is that an existing chemical shift list of a protein has to be used to assign an experimental spectrum but does not fit sufficiently well for a safe assignment. A similar problem occurs when temperature or pressure series of n-dimensional spectra are to be evaluated automatically. We have developed two different algorithms, AUREMOL-SHIFTOPT1 and AUREMOL-SHIFTOPT2 that fulfill this task. In the present contribution their performance is analyzed employing a set of simulated and experimental two-dimensional and three-dimensional spectra obtained from three different proteins. A new z-score based on atom and amino acid specific chemical shift distributions is introduced to weight the chemical shift contributions in different dimensions properly.