420 research outputs found
Weighting of experimental evidence in macromolecular structure determination
No full textThe determination of macromolecular structures requires weighting of experimental evidence relative to prior physical information. Although it can critically affect the quality of the calculated structures, experimental data are routinely weighted on an empirical basis. At present, cross-validation is the most rigorous method to determine the best weight. We describe a general method to adaptively weight experimental data in the course of structure calculation. It is further shown that the necessity to define weights for the data can be completely alleviated. We demonstrate the method on a structure calculation from NMR data and find that the resulting structures are optimal in terms of accuracy and structural quality. Our method is devoid of the bias imposed by an empirical choice of the weight and has some advantages over estimating the weight by cross-validation
ISD: A Software Package for Bayesian NMR Structure Calculation
No full textSUMMARY: The conventional approach to calculating biomolecular structures from nuclear magnetic resonance (NMR) data is often viewed as subjective due to its dependence on rules of thumb for deriving geometric constraints and suitable values for theory parameters from noisy experimental data. As a result, it can be difficult to judge the precision of an NMR structure in an objective manner. The Inferential Structure Determination (ISD) framework, which has been introduced recently, addresses this problem by using Bayesian inference to derive a probability distribution that represents both the unknown structure and its uncertainty. It also determines additional unknowns, such as theory parameters, that normally need be chosen empirically. Here we give an overview of the ISD software package, which implements this methodology. AVAILABILITY: The program is available at http://www.bioc.cam.ac.uk/is
H-1 and N-15 NMR resonance assignments and secondary structure of titin type I domains
No full textTitin/connectin is a giant muscle protein with a highly modular architecture consisting of multiple repeats of two sequence motifs, named type I and type II. Type I modules have been suggested to be intracellular members of the fibronectin type III (Fn3) domain family. Along the titin sequence they are exclusively present in the region of the molecule located in the sarcomere A-band. This region has been shown to interact with myosin and C-protein. One of the most noticeable features of type I modules is that they are particularly rich in semiconserved prolines, since these residues account for about 8% of their sequence. We have determined the secondary structure of a representative type I domain (A71) by 15N and 1H NMR. We show that the type I domains of titin have the Fn3 fold as proposed, consisting of a three- and a four-stranded beta-sheet. When the two sheets are placed on top of each other to form the beta-sandwich characteristic of the Fn3 fold, 8 out of 10 prolines are found on the same side of the molecule and form an exposed hydrophobic patch. This suggests that the semiconserved prolines might be relevant for the function of type I modules, providing a surface for binding to other A-band proteins. The secondary structure of A71 was structurally aligned to other extracellular Fn3 modules of known 3D structure. The alignment shows that titin type I modules have closest similarity to the first Fn3 domain of Drosophila neuroglian
H-1 and N-15 NMR resonance assignments and secondary structure of titin type I domains
No full textTitin/connectin is a giant muscle protein with a highly modular architecture consisting of multiple repeats of two sequence motifs, named type I and type II. Type I modules have been suggested to be intracellular members of the fibronectin type III (Fn3) domain family. Along the titin sequence they are exclusively present in the region of the molecule located in the sarcomere A-band. This region has been shown to interact with myosin and C-protein. One of the most noticeable features of type I modules is that they are particularly rich in semiconserved prolines, since these residues account for about 8% of their sequence. We have determined the secondary structure of a representative type I domain (A71) by 15N and 1H NMR. We show that the type I domains of titin have the Fn3 fold as proposed, consisting of a three- and a four-stranded beta-sheet. When the two sheets are placed on top of each other to form the beta-sandwich characteristic of the Fn3 fold, 8 out of 10 prolines are found on the same side of the molecule and form an exposed hydrophobic patch. This suggests that the semiconserved prolines might be relevant for the function of type I modules, providing a surface for binding to other A-band proteins. The secondary structure of A71 was structurally aligned to other extracellular Fn3 modules of known 3D structure. The alignment shows that titin type I modules have closest similarity to the first Fn3 domain of Drosophila neuroglian
The three-dimensional structure of a type I module from titin: a prototype of intracellular fibronectin type III domains
No full textAbstractBackground: Titin is a huge protein (∼3 MDa) that is present in the contractile unit (sarcomere) of striated muscle and has a key role in muscle assembly and elasticity. Titin is mainly composed of two types of module (type I and II). Type I modules are found exclusively in the region of titin localised in the A band, where they are arranged in a super-repeat pattern that correlates with the ultrastructure of the thick filament. No structure of a titin type I module has been reported so far.Results: We have determined the structure of a representative type I module, A71, using nuclear magnetic resonance (NMR) spectroscopy. The structure has the predicted fibronectin type III fold. Titin-specific conserved residues are either located at the putative module–module interfaces or along one side of the protein surface. Several proline residues that contribute to two stretches in a polyproline II helix conformation are solvent-exposed and line up as a continuous ribbon extending over more than two-thirds of the module surface. Homology models of the type I module N-terminal to A71 (A70) and the double module A70–A71 were used to discuss possible intermodule interactions and their role in module–module orientation.Conclusions: As residues at the module–module interfaces are highly conserved, we speculate that similar interactions govern all of the interfaces between type I modules in titin. This conservation would lead to a regular multiple array of similar surface structures. Such an arrangement would allow arrays of contiguous type I modules to expose multiple proline stretches in a highly regular way and these may act as binding sites for other thick filament proteins
Combining computational modeling with sparse and low-resolution data
No full textStructural biology is moving into a new era by shifting its focus from static structures of single proteins and protein domains to large and often fragile multi-component complexes. Over the past decade, structural genomics initiatives aimed to fill the voids in fold space and to provide a census of all protein structures. Completion of such an atlas of protein structures is still ongoing, but not sufficient for a mechanistic understanding of how living cells function. One of the great challenges is to bridge the gap between atomic resolution detail and the more fuzzy description of the molecular complexes that govern cellular processes or host–pathogen interactions. We want to move from cartoon-like representations of multi-component complexes to atomic resolution structures. To characterize the structures of the increasingly large and often flexible complexes, high resolution structure determination (as was possible for example for the ribosome) will likely stay the exception. Rather, data from many different methods providing information on the shape (X-ray crystallography, electron microscopy, SAXS, AFM, etc.) or on contacts between components (mass spectrometry, co-purification, or spectroscopic methods) need to be integrated with prior structural knowledge to build a consistent model of the complex. A particular difficulty is that the ratio between the number of conformational degrees of freedom and the number of measurements becomes unfavorable as we work with large complexes: data become increasingly sparse.
Structural characterization of large molecular assemblies often involves a loss in resolution as well as in number and quality of data. We are good at solving structures of single proteins, but classical high-resolution structure determination by X-ray crystallography and NMR spectroscopy is often facing its limits as we move to higher molecular mass and increased flexibility. Therefore, structural studies on large complexes rely on new experimental techniques that complement the classical high resolution methods. But also computational approaches are becoming more important when it comes to integrating and analyzing structural information of often heterogeneous nature. Cryoelectron microscopy may serve as an example of how experimental methods can benefit from computation. Low-resolution data from cryo-EM show their true power when combined with modeling and bioinformatics methods such rigid docking and secondary structure hunting.
Even in high resolution structure determination, molecular modeling is always necessary to calculate structures from data, to complement the missing information and to evaluate and score the obtained structures. With sparse data, all these three aspects become increasingly difficult, and the quality of the modeling approach becomes more important. With data alone, algorithms may not converge any more; scoring against data becomes meaningless; and the potential energy function becomes central not only as a help in making algorithms converge but also to score and evaluate the structures. In addition to the sparsity of the data, hybrid approaches bring the additional difficulty that the different sources of data may have rather different quality, and may be in the extreme case incompatible with each other. In addition to scoring the structures, modeling should also score in some way the data going into the calculation.
This special issue brings together some of the numerous efforts to solve the problems that come from sparsity of data and from integrating data from different sources in hybrid approaches. The methods range from predominantly force-field based to mostly data based. Systems of very different sizes, ranging from single domains to multi-component complexes, are treated. We hope that you will enjoy reading the issue and find it a useful and inspiring resource
Psychological approaches to pain in Germany
No full textPain perception is a complex experience that entails somatic and psychological factors. This is especially true for chronic pain where increasing chronicity leads to a growing significance of psychological factors such as learning and memory processes or cognitive evaluation at the expense of nociceptive processes. Hardly any other area of health-related research and health care has such an interdisciplinary organization of research, treatment, and education. Psychological pain research and psychological treatment of pain have become specializations in their own right. For the future of this research area, a differential analysis of the contribution of psychological factors to chronicity is important. For a mechanism-oriented treatment, the development of new treatment approaches and the analysis of specific subgroups for a better differential indication of treatments is needed
The three-dimensional structure of a type I module from titin: A prototype of intracellular fibronectin type III domains
No full textBACKGROUND:
Titin is a huge protein ( approximately 3 MDa) that is present in the contractile unit (sarcomere) of striated muscle and has a key role in muscle assembly and elasticity. Titin is mainly composed of two types of module (type I and II). Type I modules are found exclusively in the region of titin localised in the A band, where they are arranged in a super-repeat pattern that correlates with the ultrastructure of the thick filament. No structure of a titin type I module has been reported so far.
RESULTS:
We have determined the structure of a representative type I module, A71, using nuclear magnetic resonance (NMR) spectroscopy. The structure has the predicted fibronectin type III fold. Titin-specific conserved residues are either located at the putative module-module interfaces or along one side of the protein surface. Several proline residues that contribute to two stretches in a polyproline II helix conformation are solvent-exposed and line up as a continuous ribbon extending over more than two-thirds of the module surface. Homology models of the type I module N-terminal to A71 (A70) and the double module A70-A71 were used to discuss possible intermodule interactions and their role in module-module orientation.
CONCLUSIONS:
As residues at the module-module interfaces are highly conserved, we speculate that similar interactions govern all of the interfaces between type I modules in titin. This conservation would lead to a regular multiple array of similar surface structures. Such an arrangement would allow arrays of contiguous type I modules to expose multiple proline stretches in a highly regular way and these may act as binding sites for other thick filament proteins
STRUCTURE OF THE DSRNA BINDING DOMAIN OF ESCHERICHIA-COLI RNASE-III
No full textThe double-stranded RNA binding domain (dsRBD) is a approximately 70 residue motif found in a variety of modular proteins exhibiting diverse functions, yet always in association with dsRNA. We report here the structure of the dsRBD from RNase III, an enzyme present in most, perhaps all, living cells. It is involved in processing transcripts, such as rRNA precursors, by cleavage at short hairpin sequences. The RNase III protein consists of two modules, a approximately 150 residue N-terminal catalytic domain and a approximately 70 residue C-terminal recognition module, homologous with other dsRBDs. The structure of the dsRBD expressed in Escherichia coli has been investigated by homonuclear NMR techniques and solved with the aid of a novel calculation strategy. It was found to have an alpha-beta-beta-beta-alpha topology in which a three-stranded anti-parallel beta-sheet packs on one side against the two helices. Examination of 44 aligned dsRBD sequences reveals several conserved, positively charged residues. These residues map to the N-terminus of the second helix and a nearby loop, leading to a model for the possible contacts between the domain and dsRNA
Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data.
No full textChromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells with Hi-C, a genome-wide 3C variant, demonstrating a high cell-to-cell variability of genome organization. In principle, restraint-based modeling should allow us to infer the 3D structure of chromosomes from single-cell contact data, but suffers from the sparsity and low resolution of chromosomal contacts. To address these challenges, we adapt the Bayesian Inferential Structure Determination (ISD) framework, originally developed for NMR structure determination of proteins, to infer statistical ensembles of chromosome structures from single-cell data. Using ISD, we are able to compute structural error bars and estimate model parameters, thereby eliminating potential bias imposed by ad hoc parameter choices. We apply and compare different models for representing the chromatin fiber and for incorporating singe-cell contact information. Finally, we extend our approach to the analysis of diploid chromosome data
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