Genomic DNA in bacteria exists in a condensed state, which exhibits different biochemical and biophysical properties from a dilute solution. DNA was concentrated on streptavidin-covered single-walled carbon nanotubes (StrepSWNTs) through biotin-streptavidin interactions. We reasoned that confining DNA within a defined space through mechanical constraints, rather than by manipulating buffer conditions, would more closely resemble physiological conditions. By ensuring a high streptavidin loading on SWNTs of about 1 streptavidin tetramer per 4 nm of SWNT, we were able to achieve dense DNA binding. DNA is bound to StrepSWNTs at a tunable density and up to as high as 0.5 mg mL(-1) in solution and 29 mg mL(-1) on a 2D surface. This platform allows us to observe the aggregation behavior of DNA at high concentrations and the counteracting effects of HU protein (a histone-like protein from Escherichia coli strain U93) on the DNA aggregates. This provides an in vitro model for studying DNA-DNA and DNA-protein interactions at a high DNA concentration.

Density modification often suffers from an overestimation of phase quality, as seen by escalated figures of merit. A new cross-validation-based method to address this estimation bias by applying a bias-correction parameter 'beta' to maximum-likelihood phase-combination functions is proposed. In tests on over 100 single-wavelength anomalous diffraction data sets, the method is shown to produce much more reliable figures of merit and improved electron-density maps. Furthermore, significantly better results are obtained in automated model building iterated with phased refinement using the more accurate phase probability parameters from density modification.

For its first release in 2004, CRANK was shown to effectively detect and phase anomalous scatterers from single-wavelength anomalous diffraction data. Since then, CRANK has been significantly improved and many more structures can be built automatically with single-or multiple-wavelength anomalous diffraction or single isomorphous replacement with anomalous scattering data. Here, the new algorithms that have been developed that have led to these substantial improvements are discussed and CRANK's performance on over 100 real data sets is shown. The latest version of CRANK is freely available for download at http://www.bfsc.leidenuniv.nl/software/crank/ and from CCP4 (http://www.ccp4.ac.uk/).

Growing X-ray grade crystals of a specific protein is a process of trial and error. Usually, hundreds or even thousands of conditions are screened in order to identify useful crystallization conditions. Heterogeneous nucleants have been shown to increase the success rate of crystallization trials, and (human) hair has previously been identified as a promising nucleant. Here, we describe and evaluate a method for preparing crystallization plates that are locally coated with fragments of human hair, allowing automated, high-throughput crystallization trials in a fashion that is entirely compatible with standard hanging or sitting drop crystallization techniques. We assessed the effect of these nucleants on the crystallization of 11 different proteins in more than 4000 crystallization trials. We found additional crystallization conditions for 10 out of 11 proteins when using the standard JCSG+ screen (96 different conditions). In total, 34 additional crystallization conditions could be identified (13.1% of the total number of successful crystallizations). The increase in crystallization conditions ranged between 33.3% (two additional conditions were identified for myoglobin on top of four homogeneous crystallizations) to 1.2% (we identified a single additional condition for insulin, which crystallized in 85 out of 96 conditions); the median increase in crystallization hits was 14%. On the basis of these numbers, we conclude that the inclusion of human hair fragments in high throughput crystallization screens may be beneficial. The method is inexpensive, straightforward with standard equipment and uses materials available in any crystallization lab. Furthermore, initial experiments with the crystallization of membrane proteins on hair show the technique may also be beneficial for growing membrane proteins.

Density modification is a standard technique in macromolecular crystallography that can significantly improve an initial electron-density map. To obtain optimal results, the initial and density-modified map are combined. Current methods assume that these two maps are independent and propagate the initial map information and its accuracy indirectly through previously determined coefficients. A multivariate equation has been derived that no longer assumes independence between the initial and density-modified map, considers the observed diffraction data directly and refines the errors that can occur in a single-wavelength anomalous diffraction experiment. The equation has been implemented and tested on over 100 real data sets. The results are dramatic: the method provides significantly improved maps over the current state of the art and leads to many more structures being built automatically.

In our postgenomic era, understanding of protein-protein interactions by characterizing the structure of the corresponding protein complex is becoming increasingly important. An important problem is that many protein complexes are only stable for a few minutes. Dissociation will occur when using the typical, time-consuming purification methods such as tandem affinity purification and multiple chromatographic separations. Therefore, there is an urgent need for a quick and efficient protein-complex purification method for 3D structure characterization. The graphene oxide (GO)center dot streptavidin complex is prepared via a GO center dot biotin center dot streptavidin strategy and used for affinity purification The complex shows a strong biotin recognition capability and an excellent loading capacity. Capturing biotinylated DNA, fluorophores and Au nanoparticles on the GO center dot streptavidin complexes demonstrates the usefulness of the GO center dot streptavidin complex as a docking matrix for affinity purification. GO shows a high transparency towards electron beams, making it specifically well suited for direct imaging by electron microscopy. The captured protein complex can be separated via a filtration process or even via on-grid purification and used directly for single-particle analysis via cryo-electron microscopy. Therefore, the purification, sample preparation, and characterization are rolled into one single step.

A likelihood function based on the multivariate probability distribution of all observed structure-factor amplitudes from a single isomorphous replacement with anomalous scattering experiment has been derived and implemented for use in substructure refinement and phasing as well as macromolecular model refinement. Efficient calculation of a multidimensional integration required for function evaluation has been achieved by approximations based on the function's properties. The use of the function in both phasing and protein model building with iterative refinement was essential for successful automated model building in the test cases presented.

Previously, the direct use of prior phase information from a single-wavelength anomalous diffraction (SAD) experiment with a multivariate likelihood function applied to automated model building with iterative refinement has been proposed [Skubak et al. (2004), Acta Cryst. D60, 2196-2201]. In this approach, the anomalous information from the experimental data is used in refinement to derive phase information in a maximum-likelihood formalism and provided a more theoretically valid way of incorporating prior phase information compared with current approaches. In the present work, the SAD multivariate likelihood function that directly uses prior phase information is tested against currently used functions on many different SAD data sets which exhibit a wide range of resolution limits and anomalous signal. The results clearly show the importance of the more theoretically valid utilization of prior phase information: the SAD function extends the resolution and phase-quality limits needed for successful automated model building with iterative refinement. Indeed, the multivariate likelihood function reduces the overfitting in the refinement procedure and performs consistently better than the current refinement targets in terms of the quality of the models obtained and the number of residues built.

The incorporation of prior phase information into a maximum-likelihood formalism has been shown to strengthen model refinement. However, the currently available likelihood refinement target using prior phase information has shortcomings; the 'phased' refinement target considers experimental phase information indirectly and statically in the form of Hendrickson - Lattman coefficients. Furthermore, the current refinement target implicitly assumes that the prior phase information is independent of the calculated model structure factor. This paper describes the derivation of a multivariate likelihood function that overcomes these shortcomings and directly incorporates experimental phase information from a single-wavelength anomalous diffraction ( SAD) experiment. This function, which simultaneously refines heavy-atom and model parameters, has been implemented in the refinement program REFMAC5. The SAD function used in conjunction with the automated model-building procedures of ARP/wARP leads to a successful solution when current likelihood functions fail in a test case shown.

Recently, there has been a resurgence in phasing using the single- wavelength anomalous diffraction ( SAD) experiment; data from a single wavelength in combination with techniques such as density modification have been used to solve macromolecular structures, even with a very small anomalous signal. Here, a formulation for SAD phasing and refinement employing multivariate statistical techniques is presented. The equation developed accounts explicitly for the correlations among the observed and calculated Friedel mates in a SAD experiment. The correlated SAD equation has been implemented and test cases performed on real diffraction data have revealed better results compared with currently used programs in terms of correlation with the final map and obtaining more reliable phase probability statistics.