Zentrum für Biomolekulare Magnetische Resonanz (BMRZ)
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Structural biology and life sciences in general, and NMR in particular, have always been associated with advanced computing. The current challenges in the post-genomic era call for virtual research platforms that provide the worldwide research community with both user-friendly tools, platforms for data analysis and exchange, and an underlying e-Infrastructure. WeNMR, a three-year European Commission co-funded project started in November 2010, groups different research teams into a worldwide virtual research community. It builds on the established eNMR e-Infrastructure and its steadily growing virtual organisation, which is currently the second largest VO in the area of life sciences. WeNMR provides an e-Infrastructure platform and Science Gateway for structural biology. It involves researchers from around the world and will build bridges to other areas of structural biology.
The mfl-riboswitch regulates expression of ribonucleotide reductase subunit in Mesoplasma florum by binding to 2´-deoxyguanosine and thereby promoting transcription termination. We characterized the structure of the ligand-bound aptamer domain by NMR spectroscopy and compared the mfl-aptamer to the aptamer domain of the closely related purine-sensing riboswitches. We show that the mfl-aptamer accommodates the extra 2´-deoxyribose unit of the ligand by forming a more relaxed binding pocket than these found in the purine-sensing riboswitches. Tertiary structures of the xpt-aptamer bound to guanine and of the mfl-aptamer bound to 2´-deoxyguanosine exhibit very similar features, although the sequence of the mfl-aptamer contains several alterations compared to the purine-aptamer consensus sequence. These alterations include the truncation of a hairpin loop which is crucial for complex formation in all purine-sensing riboswitches characterized to date. We further defined structural features and ligand binding requirements of the free mfl-aptamer and found that the presence of Mg2+ is not essential for complex formation, but facilitates ligand binding by promoting pre-organization of key structural motifs in the free aptamer.
Background: The automation of objectively selecting amino acid residue ranges for structure superpositions is important for meaningful and consistent protein structure analyses. So far there is no widely-used standard for choosing these residue ranges for experimentally determined protein structures, where the manual selection of residue ranges or the use of suboptimal criteria remain commonplace. Results: We present an automated and objective method for finding amino acid residue ranges for the superposition and analysis of protein structures, in particular for structure bundles resulting from NMR structure calculations. The method is implemented in an algorithm, CYRANGE, that yields, without protein-specific parameter adjustment, appropriate residue ranges in most commonly occurring situations, including low-precision structure bundles, multi-domain proteins, symmetric multimers, and protein complexes. Residue ranges are chosen to comprise as many residues of a protein domain that increasing their number would lead to a steep rise in the RMSD value. Residue ranges are determined by first clustering residues into domains based on the distance variance matrix, and then refining for each domain the initial choice of residues by excluding residues one by one until the relative decrease of the RMSD value becomes insignificant. A penalty for the opening of gaps favours contiguous residue ranges in order to obtain a result that is as simple as possible, but not simpler. Results are given for a set of 37 proteins and compared with those of commonly used protein structure validation packages. We also provide residue ranges for 6351 NMR structures in the Protein Data Bank. Conclusions: The CYRANGE method is capable of automatically determining residue ranges for the superposition of protein structure bundles for a large variety of protein structures. The method correctly identifies ordered regions. Global structure superpositions based on the CYRANGE residue ranges allow a clear presentation of the structure, and unnecessary small gaps within the selected ranges are absent. In the majority of cases, the residue ranges from CYRANGE contain fewer gaps and cover considerably larger parts of the sequence than those from other methods without significantly increasing the RMSD values. CYRANGE thus provides an objective and automatic method for standardizing the choice of residue ranges for the superposition of protein structures. Additional files Additional file 1: Dependence of Q on the order parameter rank. The quantity Qi is plotted against the order parameter rank i for 9 different protein structure bundles. Additional file 2: Dependence of P on the clustering stage. The quantity Pi is plotted against the clustering stage i for 9 different protein structure bundles. Additional file 3: Dependence of CYRANGE results on the minimal cluster size parameter my. The sequence coverage (red) and RMSD (blue) of the residue ranges determined by CYRANGE were plotted as a function of my for 9 different protein structure bundles. The dotted vertical line indicates the default value, my = 8. Where CYRANGE found two domains, the RMSD values of the individual domains are shown in light and dark blue. Additional file 4: Dependence of CYRANGE results on the domain boundary extension parameter m. See Additional File 3 for details. Additional file 5: Dependence of CYRANGE results on the minimal gap width g. See Additional File 3 for details. Additional file 6: Dependence of CYRANGE results on the relative RMSD decrease parameter delta. See Additional File 3 for details. Additional file 7: Dependence of CYRANGE results on the absolute RMSD decrease parameter delta abs. See Additional File 3 for details. Additional file 8: Dependence of CYRANGE results on the gap penalty parameter gamma. See Additional File 3 for details. Additional file 9: Correlation between the sequence coverage from CYRANGE, FindCore and PSVS, and the GDT total score, GDT_TS. Each data point represents a protein shown in Figures 3 and 4. The coverage is the percentage of amino acid residues included in the residue ranges found by the different methods. The GDT_TS value is defined by GDT_TS = (P1 + P2 + P4 + P8)/4, where Pd is the fraction of residues that can be superimposed under a distance cutoff of d Å. Additional file 10: Correlation between the RMSD value for the residue ranges from CYRANGE, FindCore and PSVS, and the GDT total score, GDT_TS. Each data point represents one protein domain. See Additional File 9 for details.
G-quadruplex topologies of telomeric repeat sequences from vertebrates were investigated in the presence of molecular crowding (MC) mimetics, namely polyethylene glycol 200 (PEG), Ficoll 70 as well as Xenopus laevis egg extract by CD and NMR spectroscopy and native PAGE. Here, we show that the conformational behavior of the telomeric repeats in X. laevis egg extract or in Ficoll is notably different from that observed in the presence of PEG. While the behavior of the telomeric repeat in X. laevis egg extract or in Ficoll resembles results obtained under dilute conditions, PEG promotes the formation of high-order parallel topologies. Our data suggest that PEG should not be used as a MC mimetic.