Motivation: Owing to the size and complexity of large multi-component biological assemblies, the most tractable approach to determining their atomic structure is often to fit high-resolution radiographic or nuclear magnetic resonance structures of isolated components into lower resolution electron density maps of the larger assembly obtained using cryo-electron microscopy (cryo-EM). This hybrid approach to structure determination requires that an atomic resolution structure of each component, or a suitable homolog, is available. If neither is available, then the amount of structural information regarding that component is limited by the resolution of the cryo-EM map. However, even if a suitable homolog cannot be identified using sequence analysis, a search for structural homologs should still be performed because structural homology often persists throughout evolution even when sequence homology is undetectable, As macromolecules can often be described as a collection of independently folded domains, one way of searching for structural homologs would be to systematically fit representative domain structures from a protein domain database into the medium/low resolution cryo-EM map and return the best fits. Taken together, the best fitting non-overlapping structures would constitute a ‘mosaic’ backbone model of the assembly that could aid map interpretation and illuminate biological function.
Result: Using the computational principles of the Scale-Invariant Feature Transform (SIFT), we have developed FOLD-EM—a computational tool that can identify folded macromolecular domains in medium to low resolution (4–15 Å) electron density maps and return a model of the constituent polypeptides in a fully automated fashion. As a by-product, FOLD-EM can also do flexible multi-domain fitting that may provide insight into conformational changes that occur in macromolecular assemblies.
Availability and implementation: FOLD-EM is available at: http://cs.stanford.edu/~mitul/foldEM/, as a free open source software to the structural biology scientific community.
firstname.lastname@example.org or email@example.com
Supplementary data are available at Bioinformatics online.
Electron cryo-microscopy (cryo-EM) experiments yield low-resolution (3–30Å) 3D-density maps of macromolecules. These density maps are segmented to identify structurally distinct proteins, protein domains, and sub-units. Such partitioning aids the inference of protein motions and guides fitting of high-resolution atomistic structures. Cryo-EM density map segmentation has traditionally required tedious and subjective manual partitioning or semi-supervised computational methods, while validation of resulting segmentations has remained an open problem in this field. Our network-based bias-free segmentation method for cryo-EM density map segmentation, Nhs (Network-based hierarchical segmentation), provides the user with a multi-scale partitioning, reflecting local and global clustering, while requiring no user input. This approach models each map as a graph, where map voxels constitute nodes and edges connect neighboring voxels. Nhs initiates Markov diffusion (or random walk) on the weighted graph. As Markov probabilities homogenize through diffusion, an intrinsic segmentation emerges. We validate the segmentations with ground-truth maps based on atomistic models. When implemented on density maps in the 2010 Cryo-EM Modeling Challenge, Nhs efficiently and objectively partitions macromolecules into structurally and functionally relevant sub-regions at multiple scales.
An automated ligand-fitting procedure is applied to (F
o − F
c)exp(iϕc) difference density for 200 commonly found ligands from macromolecular structures in the Protein Data Bank to identify ligands from density maps.
A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F
o − F
c)exp(iϕc) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.
model building; model completion; shape analysis
Motivation: Single-particle cryo electron microscopy (cryoEM) typically produces density maps of macromolecular assemblies at intermediate to low resolution (∼5–30 Å). By fitting high-resolution structures of assembly components into these maps, pseudo-atomic models can be obtained. Optimizing the quality-of-fit of all components simultaneously is challenging due to the large search space that makes the exhaustive search over all possible component configurations computationally unfeasible.
Results: We developed an efficient mathematical programming algorithm that simultaneously fits all component structures into an assembly density map. The fitting is formulated as a point set matching problem involving several point sets that represent component and assembly densities at a reduced complexity level. In contrast to other point matching algorithms, our algorithm is able to match multiple point sets simultaneously and not only based on their geometrical equivalence, but also based on the similarity of the density in the immediate point neighborhood. In addition, we present an efficient refinement method based on the Iterative Closest Point registration algorithm. The integer quadratic programming method generates an assembly configuration in a few seconds. This efficiency allows the generation of an ensemble of candidate solutions that can be assessed by an independent scoring function. We benchmarked the method using simulated density maps of 11 protein assemblies at 20 Å, and an experimental cryoEM map at 23.5 Å resolution. Our method was able to generate assembly structures with root-mean-square errors <6.5 Å, which have been further reduced to <1.8 Å by the local refinement procedure.
Availability: The program is available upon request as a Matlab code package.
Contact: firstname.lastname@example.org and email@example.com
Supplementary information: Supplementary data are available at Bioinformatics Online.
Recent advancements of experimental techniques for determining protein tertiary structures raise significant challenges for protein bioinformatics. With the number of known structures of unknown function expanding at a rapid pace, an urgent task is to provide reliable clues to their biological function on a large scale. Conventional approaches for structure comparison are not suitable for a real-time database search due to their slow speed. Moreover, a new challenge has arisen from recent techniques such as electron microscopy (EM), which provide low-resolution structure data. Previously, we have introduced a method for protein surface shape representation using the 3D Zernike descriptors (3DZDs). The 3DZD enables fast structure database searches, taking advantage of its rotation invariance and compact representation. The search results of protein surface represented with the 3DZD has showngood agreement with the existing structure classifications, but some discrepancies were also observed.
The three new surface representations of backbone atoms, originally devised all-atom-surface representation, and the combination of all-atom surface with the backbone representation are examined. All representations are encoded with the 3DZD. Also, we have investigated the applicability of the 3DZD for searching protein EM density maps of varying resolutions. The surface representations are evaluated on structure retrieval using two existing classifications, SCOP and the CE-based classification.
Overall, the 3DZDs representing backbone atoms show better retrieval performance than the original all-atom surface representation. The performance further improved when the two representations are combined. Moreover, we observed that the 3DZD is also powerful in comparing low-resolution structures obtained by electron microscopy.
The increasing power and popularity of cryo-electron (cryo-EM) microscopy in structural biology brought about the development of so-called hybrid methods, which permit the interpretation of cryo-EM density maps beyond their nominal resolution in terms of atomic models. The Cryo-EM Modeling Challenge 2010 is the first community effort to bring together developers of hybrid methods as well as cryo-EM experimentalists. Participating in the challenge, the molecular dynamics flexible fitting (MDFF) method was applied to a number of cryo-EM density maps. The results are described here with special emphasis on the use of symmetry-based restraints to improve the quality of atomic models derived from density maps of symmetric complexes; on a comparison of the stereochemical quality of atomic models resulting from different hybrid methods; and on application of MDFF to electron crystallography data.
Single-particle cryo electron microscopy (cryoEM) is a technique for determining three-dimensional (3D) structures from projection images of molecular complexes preserved in their “native,” noncrystalline state. Recently, atomic or near-atomic resolution structures of several viruses and protein assemblies have been determined by single-particle cryoEM, allowing ab initio atomic model building by following the amino acid side chains or nucleic acid bases identifiable in their cryoEM density maps. In particular, these cryoEM structures have revealed extended arms contributing to molecular interactions that are otherwise not resolved by the conventional structural method of X-ray crystallography at similar resolutions. High-resolution cryoEM requires careful consideration of a number of factors, including proper sample preparation to ensure structural homogeneity, optimal configuration of electron imaging conditions to record high-resolution cryoEM images, accurate determination of image parameters to correct image distortions, efficient refinement and computation to reconstruct a 3D density map, and finally appropriate choice of modeling tools to construct atomic models for functional interpretation. This progress illustrates the power of cryoEM and ushers it into the arsenal of structural biology, alongside conventional techniques of X-ray crystallography and NMR, as a major tool (and sometimes the preferred one) for the studies of molecular interactions in supramolecular assemblies or machines.
Chilo iridescent virus (CIV) is a large (~1850 Å diameter) insect virus with an icosahedral, T=147 capsid, a dsDNA genome, and an internal lipid membrane. The structure of CIV was determined to 13 Å resolution by means of cryo-electron microscopy (cryoEM) and three-dimensional image reconstruction. A homology model of P50, the CIV major capsid protein (MCP), was built based on its amino acid sequence and the structure of the homologous Paramecium bursaria chlorella virus 1 (PBCV-1) Vp54 MCP. This model was fitted into the cryoEM density for each of the 25 trimeric CIV capsomers per icosahedral asymmetric unit. A difference map, in which the fitted CIV MCP capsomers were subtracted from the CIV cryoEM reconstruction, showed that there are at least three different types of minor capsid proteins associated with the capsomers outside the lipid membrane. “Finger” proteins are situated at many, but not all, of the spaces between three adjacent capsomers within each trisymmetron, and “zip” proteins are situated between sets of three adjacent capsomers at the boundary between neighboring trisymmetrons and pentasymmetrons. Based on the results of segmentation and density correlations, there are at least eight finger proteins, and three dimeric and two monomeric zip proteins in one asymmetric unit of the CIV capsid. These minor proteins appear to stabilize the virus by acting as intercapsomer cross-links. One transmembrane “anchor” protein per icosahedral asymmetric unit, which extends from beneath one of the capsomers in the pentasymmetron to the internal leaflet of the lipid membrane, may provide additional stabilization for the capsid. These results are consistent with the observations for other large, icosahedral dsDNA viruses that also utilize minor capsid proteins for stabilization and determining their assembly.
large DNA virus; cryo-electron microscopy; 3D image reconstruction; enveloped virus; minor capsid proteins
Cryo-Electron Microscopy can visualize large macromolecular assemblies at resolutions often below 10 Å and recently as good as 3.8–4.5 Å. These density maps provide important insights into the biological functioning of molecular machineries such as viruses or the ribosome, in particular if atomic-resolution crystal structures or models of individual components of the assembly can be placed into the density map. The present work introduces a novel algorithm termed BCL::EM-Fit that accurately fits atomic-detail structural models into medium resolution density maps. In an initial step, a “geometric hashing” algorithm provides a short list of likely placements. In a follow up Monte Carlo/Metropolis refinement step, the initial placements are optimized by their cross correlation coefficient. The resolution of density maps for a reliable fit was determined to be 10 Å or better using tests with simulated density maps. The algorithm was applied to fitting of capsid proteins into an experimental cryoEM density map of human adenovirus at a resolution of 6.8 and 9.0 Å, and fitting of the GroEL protein at 5.4 Å. In the process, the handedness of the cryoEM density map was unambiguously identified. The BCL::EM-Fit algorithm offers an alternative to the established Fourier/Real space fitting programs. BCL::EM-Fit is free for academic use and available from a webserver or as downloadable binary file at http://www.meilerlab.org.
Cryo-electron microscopy; cryoEM; geometric hashing; real space; Monte Carlo Metropolis; fitting; docking
The ribosome is a highly dynamic machine responsible for protein synthesis within the cell. Cryo-electron microscopy (cryo-EM) and X-ray crystallography structures of ribosomal particles, alone and in complex with diverse ligands (protein factors, RNAs and small molecules), have revealed the dynamic nature of the ribosome and provided much needed insight into translation and its regulation. In the past years, there has been exponential growth in the deposition of cryo-EM maps into the Electron Microscopy Data Bank (EMDB) as well as atomic structures into the Protein Data Bank (PDB). Unfortunately, the deposited ribosomal particles usually have distinct orientations with respect to one another, which complicate the comparison of the available structures. To simplify this, we have developed a Database of Aligned Ribosomal Complexes, the DARC site (http://darcsite.genzentrum.lmu.de/darc/), which houses the available cryo-EM maps and atomic coordinates of ribosomal particles from the EMDB and PDB aligned within a common coordinate system. An easy-to-use, searchable interface allows users to access and download >130 cryo-EM maps and >300 atomic models in the format of brix and pdb files, respectively. The aligned coordinate system substantially simplifies direct visualization of conformational changes in the ribosome, such as subunit rotation and head-swiveling, as well as direct comparison of bound ligands, such as antibiotics or translation factors.
Today, electron cryomicroscopy (cryo-EM) can routinely achieve subnanometer resolutions of complex macromolecular assemblies. From a density map, one can extract key structural and functional information using a variety of computational analysis tools. At subnanometer resolution, these tools make it possible to isolate individual subunits, identify secondary structures, and accurately fit atomic models. With several cryo-EM studies achieving resolutions beyond 5 Å, computational modeling and feature recognition tools have been employed to construct backbone and atomic models of the protein components directly from a density map. In this chapter, we describe several common classes of computational tools that can be used to analyze and model subnanometer resolution reconstructions from cryo-EM. A general protocol for analyzing subnanometer resolution density maps is presented along with a full description of steps used in analyzing the 4.3 Å resolution structure of Mm-cpn.
The current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 Å resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.
We have developed an approach to predict the 3-dimensional structure for the helical skeletons that can be detected from the low resolution protein density map. This approach does not require the construction of the entire chain and distinguishes the structures based on the conformation of the helices. A test with 35 low resolution density maps shows that the highest ranked structure with the correct topology can be found within the top 1% of the list ranked by the effective energy formed by the helices.
The results in this paper suggest that it is possible to eliminate the great majority of the bad conformations of the helices even without the construction of the entire chain of the protein. For many proteins, the effective contact energy formed by the secondary structures alone can distinguish a small set of likely structures from the pool.
Hybrid computational methods for combining structural data from different sources and resolutions are becoming an essential part of structural biology, especially as the field moves toward the study of large macromolecular assemblies. We have developed the molecular dynamics flexible fitting (MDFF) method for combining high-resolution atomic structures with cryo-electron microscopy (cryo-EM) maps, that results in atomic models representing the conformational state captured by cryo-EM. The method has been applied successfully to the ribosome, a ribonucleoprotein complex responsible for protein synthesis. MDFF involves a molecular dynamics simulation in which a guiding potential, based on the cryo-EM map, is added to the standard force field. Forces proportional to the gradient of the density map guide an atomic structure, available from X-ray crystallography, into high-density regions of a cryo-EM map. In this paper we describe the necessary steps to set up, run, and analyze MDFF simulations and the software packages that implement the corresponding functionalities.
MDFF; flexible fitting; cryo-EM; X-ray crystallography; NAMD; VMD; docking
The structures of canine parvovirus (CPV) and feline parvovirus (FPV) complexed with antibody fragments from eight different neutralizing monoclonal antibodies were determined by cryo-electron microscopy (cryoEM) reconstruction to resolutions varying from 8.5 to 18 Å. The crystal structure of one of the Fab molecules and the sequence of the variable domain for each of the Fab molecules have been determined. The structures of Fab fragments not determined crystallographically were predicted by homology modeling according to the amino acid sequence. Fitting of the Fab and virus structures into the cryoEM densities identified the footprints of each antibody on the viral surface. As anticipated from earlier analyses, the Fab binding sites are directed to two epitopes, A and B. The A site is on an exposed part of the surface near an icosahedral threefold axis, whereas the B site is about equidistant from the surrounding five-, three-, and twofold axes. One antibody directed to the A site binds CPV but not FPV. Two of the antibodies directed to the B site neutralize the virus as Fab fragments. The differences in antibody properties have been linked to the amino acids within the antibody footprints, the position of the binding site relative to the icosahedral symmetry elements, and the orientation of the Fab structure relative to the surface of the virus. Most of the exposed surface area was antigenic, although each of the antibodies had a common area of overlap that coincided with the positions of the previously mapped escape mutations.
Motivation: Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this information is non-trivial due to the low resolution and low signal-to-noise ratio in tomograms. Current pattern recognition methods identify complexes by matching known structures to the cryo electron tomogram. However, so far only a small fraction of all protein complexes have been structurally resolved. It is, therefore, of great importance to develop template-free methods for the discovery of previously unknown protein complexes in cryo electron tomograms.
Results: Here, we have developed an inference method for the template-free discovery of frequently occurring protein complexes in cryo electron tomograms. We provide a first proof-of-principle of the approach and assess its applicability using realistically simulated tomograms, allowing for the inclusion of noise and distortions due to missing wedge and electron optical factors. Our method is a step toward the template-free discovery of the shapes, abundance and spatial distributions of previously unknown macromolecular complexes in whole cell tomograms.
Supplementary information: Supplementary data are available at Bioinformatics online.
Rotary ATPases are molecular rotary motors involved in biological energy conversion. They either synthesize or hydrolyze the universal biological energy carrier adenosine triphosphate. Recent work has elucidated the general architecture and subunit compositions of all three sub-types of rotary ATPases. Composite models of the intact F-, V- and A-type ATPases have been constructed by fitting high-resolution X-ray structures of individual subunits or sub-complexes into low-resolution electron densities of the intact enzymes derived from electron cryo-microscopy. Electron cryo-tomography has provided new insights into the supra-molecular arrangement of eukaryotic ATP synthases within mitochondria and mass-spectrometry has started to identify specifically bound lipids presumed to be essential for function. Taken together these molecular snapshots show that nano-scale rotary engines have much in common with basic design principles of man made machines from the function of individual “machine elements” to the requirement of the right “fuel” and “oil” for different types of motors.
biological motors; rotary motors; energy conversion; ATP synthase; vacuolar ATPase; A-type ATPase; structural biology; X-ray crystallography; electron microscopy
The structures of large macromolecular complexes in different functional states can be determined by cryo-electron microscopy, which yields electron density maps of low to intermediate resolutions. The maps can be combined with high-resolution atomic structures of components of the complex, to produce a model for the complex that is more accurate than the formal resolution of the map. To this end, methods have been developed to dock atomic models into density maps rigidly or flexibly, and to refine a docked model so as to optimize the fit of the atomic model into the map. We have developed a new refinement method called YUP.SCX. The electron density map is converted into a component of the potential energy function to which terms for stereochemical restraints and volume exclusion are added. The potential energy function is then minimized (using simulated annealing) to yield a stereochemically-restrained atomic structure that fits into the electron density map optimally. We used this procedure to construct an atomic model of the 70S ribosome in the pre-accommodation state. Although some atoms are displaced by as much as 33 Å, they divide themselves into nearly rigid fragments along natural boundaries with smooth transitions between the fragments.
Electron microscopy; simulated annealing; structural refinement
An important problem in high-throughput protein crystallography is constructing a protein model from an electron-density map. DiMaio et al. (2006) describe an automated approach to this otherwise time-consuming process. One important step involves searching the density map for many small protein fragments, or templates. The previous approach uses Fourier convolution to quickly compare some rotation of the template to the entire density map. We propose to instead use the spherical-harmonic decomposition of the template and of some region in the density map. In this new framework, we are able to eliminate areas of the map from the search process if they are unlikely to match to any templates. We design several “first-pass filters” for this elimination task, including one filter which uses a set of rotation-invariant descriptors (derived from the spherical-harmonic decomposition) of a sphere of density to train an accurate classifier. We show our new template-matching method improves accuracy and reduces running time, compared to our previous approach. Protein models constructed using this matching also show significant accuracy improvement. We extend our method to produce a structural-homology detection algorithm that, due to its use of electron-density maps, is more sensitive than sequence-only methods.
spherical harmonics; protein-structure determination; electron-density map interpretation
In recent work with large high symmetry viruses, single particle electron cryomicroscopy (cryoEM) has reached the milestone of determining near atomic resolution structures by allowing direct fitting of atomic models into experimental density maps. However, achieving this goal with smaller particles of lower symmetry remains extraordinarily challenging. Using a newly developed single electron counting detector, we confirm that electron beam induced motion significantly degrades resolution and, importantly, show how the combination of rapid readout and nearly noiseless electron counting allow image blurring to be corrected to subpixel accuracy. Thus, intrinsic image information can be restored to high resolution (Thon rings visible to ~3 Å). Using this approach we determined a 3.3 Å resolution structure of a ~700 kDa protein with D7 symmetry showing clear side chain density. Our method greatly enhances image quality and data acquisition efficiency - key bottlenecks in applying near atomic resolution cryoEM to a broad range of protein samples.
A novel computational method for fitting high-resolution structures of multiple proteins into a cryoelectron microscopy map is presented. The method named EMLZerD generates a pool of candidate multiple protein docking conformations of component proteins, which are later compared with a provided electron microscopy (EM) density map to select the ones that fit well into the EM map. The comparison of docking conformations and the EM map is performed using the 3D Zernike descriptor (3DZD), a mathematical series expansion of three-dimensional functions. The 3DZD provides a unified representation of the surface shape of multimeric protein complex models and EM maps, which allows a convenient, fast quantitative comparison of the three dimensional structural data. Out of 19 multimeric complexes tested, near native complex structures with a root mean square deviation of less than 2.5 Å were obtained for 14 cases while medium range resolution structures with correct topology were computed for the additional 5 cases.
electron microscopy fitting; multimeric protein docking; cryoelecton microscopy; macromolecular structure; 3D Zernike descriptor; molecular surface; structure prediction; structural biology
Cryo-elecron microscopy (Cryo-EM) can provide important structural information of large macromolecular assemblies in different conformational states. Recent years have seen an increase in structures deposited in the Protein Data Bank (PDB) by fitting a high-resolution structure into its low-resolution cryo-EM map. A commonly used protocol for accommodating the conformational changes between the X-ray structure and the cryo-EM map is rigid body fitting of individual domains. With the emergence of different flexible fitting approaches, there is a need to compare and revise these different protocols for the fitting. We have applied three diverse automated flexible fitting approaches on a protein dataset for which rigid domain fitting (RDF) models have been deposited in the PDB. In general, a consensus is observed in the conformations, which indicates a convergence from these theoretically different approaches to the most probable solution corresponding to the cryo-EM map. However, the result shows that the convergence might not be observed for proteins with complex conformational changes or with missing densities in cryo-EM map. In contrast, RDF structures deposited in the PDB can represent conformations that not only differ from the consensus obtained by flexible fitting but also from X-ray crystallography. Thus, this study emphasizes that a “consensus” achieved by the use of several automated flexible fitting approaches can provide a higher level of confidence in the modeled configurations. Following this protocol not only increases the confidence level of fitting, but also highlights protein regions with uncertain fitting. Hence, this protocol can lead to better interpretation of cryo-EM data.
Flexible fitting; Rigid fitting; X-ray structure; Electron microscopy; Protein data bank
Cryo-electron microscopy (cryo-EM) is a powerful technique for 3D structure determination of protein complexes by averaging information from individual molecular images. The resolutions that can be achieved with single-particle cryo-EM are frequently limited by inaccuracies in assigning molecular orientations based solely on 2D projection images. Tomographic data collection schemes, however, provide powerful constraints that can be used to more accurately determine molecular orientations necessary for 3D reconstruction. Here, we propose “constrained single-particle tomography” as a general strategy for 3D structure determination in cryo-EM. A key component of our approach is the effective use of images recorded in tilt series to extract high-resolution information and correct for the contrast transfer function. By incorporating geometric constraints into the refinement to improve orientational accuracy of images, we reduce model bias and overrefinement artifacts and demonstrate that protein structures can be determined at resolutions of ~8 Å starting from low-dose tomographic tilt series.
An automated ligand-fitting procedure has been developed and tested on 9327 ligands and (F
o − F
c)exp(iϕc) difference density from macromolecular structures in the Protein Data Bank.
A procedure for fitting of ligands to electron-density maps by first fitting a core fragment of the ligand to density and then extending the remainder of the ligand into density is presented. The approach was tested by fitting 9327 ligands over a wide range of resolutions (most are in the range 0.8–4.8 Å) from the Protein Data Bank (PDB) into (F
o − F
c)exp(iϕc) difference density calculated using entries from the PDB without these ligands. The procedure was able to place 58% of these 9327 ligands within 2 Å (r.m.s.d.) of the coordinates of the atoms in the original PDB entry for that ligand. The success of the fitting procedure was relatively insensitive to the size of the ligand in the range 10–100 non-H atoms and was only moderately sensitive to resolution, with the percentage of ligands placed near the coordinates of the original PDB entry for fits in the range 58–73% over all resolution ranges tested.
model building; model completion; shape analysis
Understanding the relationship between protein structure and biological function is a central theme in structural biology. Advances are severely hampered by errors in experimentally determined protein structures. Detection and correction of such errors is therefore of utmost importance. Electron densities in molecular structures obey certain rules which depend on the molecular environment. Here we present and discuss a new approach that relates electron densities computed from a structural model to densities expected from prior observations on identical or closely related molecular environments. Strong deviations of computed from expected densities reveal unrealistic molecular structures. Most importantly, structure analysis and error detection are independent of experimental data and hence may be applied to any structural model. The comparison to state-of-the-art methods reveals that our approach is able to identify errors that formerly remained undetected. The new technique, called RefDens, is accessible as a public web service at http://refdens.services.came.sbg.ac.at.
Electronic Supplementary Material
The online version of this article (doi: 10.1007/s10858-010-9408-x) contains supplementary material, which is available to authorized users.
Protein structure; Error detection; Electron density
Apolipoprotein E (apoE), one of the major protein components of lipoproteins in the peripheral and central nervous systems, regulates cholesterol metabolism through its interaction with members of the low density lipoprotein receptor family. One key to understanding apoE function is determining the structure of lipid-bound forms of apoE. Negative-staining (NS) electron microscopy (EM) is an easy and rapid approach for studying the structure and morphology of lipid-bound forms of apoE. However, an artifact of using the conventional NS protocol is that the apoE•phospholipid particles form rouleaux. In this study, we used cryo-electron microscopy (cryo-EM) to examine apoE4•palmitoyl-oleoylphosphatidylcholine (POPC) particles in a frozen-hydrated native state. By comparing the particle sizes and shapes produced by different NS protocols to those produced by cryo-EM, we propose an optimized protocol to examine apoE4•POPC particles. Statistical analysis demonstrated that the particle sizes differ by less than 5% between the optimized protocol and the cryo-EM method, with similar shapes. The high contrast and fine detail of particle images produced using this optimized protocol lend themselves to the structural study of lipid-bound forms of apoE.
apolipoprotein E; cryo-EM; electron microscopy; phospholipid