Structure of catalase determined by MicroED
Abstract
MicroED is a recently developed method that uses electron diffraction for structure determination from very small three-dimensional crystals of biological material. Previously we used a series of still diffraction patterns to determine the structure of lysozyme at 2.9 Å resolution with MicroED (Shi et al., 2013). Here we present the structure of bovine liver catalase determined from a single crystal at 3.2 Å resolution by MicroED. The data were collected by continuous rotation of the sample under constant exposure and were processed and refined using standard programs for X-ray crystallography. The ability of MicroED to determine the structure of bovine liver catalase, a protein that has long resisted atomic analysis by traditional electron crystallography, demonstrates the potential of this method for structure determination.
https://doi.org/10.7554/eLife.03600.001Introduction
MicroED is an emerging method, which uses electron diffraction to obtain structural information from extremely small three-dimensional (3D) crystals of biological material. In the original MicroED proof of concept paper (Shi et al., 2013), electron diffraction data were collected from stationary lysozyme microcrystals and the structure was determined to 2.9 Å resolution from a series of still diffraction patterns. The method was substantially improved by employing ‘continuous rotation’ where data were recorded as the crystals were continuously rotated, resulting in higher quality data and allowing simple integration with existing processing programs used for X-ray crystallography (Nannenga et al., 2014). This led to the structure of lysozyme being determined to 2.5 Å resolution with improved statistics and data quality relative to the original lysozyme MicroED study.
In this work, we used thin bovine liver catalase 3D microcrystals for structure determination by MicroED. Catalase is a more difficult target than lysozyme, because it has a much larger unit cell, lower symmetry, and four molecules in the asymmetric unit. Moreover, each catalase monomer contains one heme group as well as a bound NADP molecule (Fita and Rossmann, 1985). Catalase is one of the earliest samples studied by EM, but despite extensive efforts spanning decades, the 3D structure of catalase has not been solved using electron diffraction. This is because the crystals have variable thicknesses (6–10 protein layers have been reported [Dorset and Parsons, 1975b]), and therefore these crystals were not suitable for 3D structure determination by traditional electron crystallography procedures (Longley, 1967; Matricardi et al., 1972; Unwin, 1975; Unwin and Henderson, 1975; Dorset and Parsons, 1975a). Here we report the 3.2 Å structure of catalase determined by MicroED. This is an important next step for the MicroED method as the analysis was rapid, taking a total of 3 weeks from crystal growth to final structural refinement, and used data from only a single catalase microcrystal.
Results and discussion
Sample preparation and data collection
Catalase was chosen for this study as it readily forms thin 3D microcrystals, which can be analyzed by transmission electron microscopy (TEM) (Sumner and Dounce, 1937; Dorset and Parsons, 1975a; Baker et al., 2010). Well-ordered catalase microcrystals were grown by solubilizing crystalline catalase from an aqueous suspension followed by overnight dialysis in 0.05 M Sodium phosphate, pH 6.3. Following crystal formation, crystals were deposited on holey carbon grids and the sample was blotted and vitrified in liquid ethane prior to TEM analysis. The grids were screened in over-focused diffraction mode for the presence of thin 3D crystals (Figure 1, inset). The average crystal dimensions were found to be on the order of 8 µm by 4 µm in length and width and approximately 150 nm thick, in good agreement with previous crystallization results (Dorset and Parsons, 1975b). Suitable crystals were assessed by collecting a single still diffraction pattern at a total dose of 0.05 e−/Å2, where well-ordered crystals showed sharp reflections extending to approximately 3.0 Å for untilted crystals (Figure 1). When high-quality diffraction was observed for an untilted crystal, the crystal was then tilted to 60° to check the diffraction quality at higher tilt angles as crystal flatness and embedding could affect the diffraction quality at higher tilt (Gonen, 2013). Typically, well-embedded and relatively flat crystals yielded data to ∼2.8 Å resolution untilted but only ∼3.2 Å at high tilt. Data sets were then collected as a sequence of 6 s exposures per frame. An example data set is shown in Video 1 and was recorded as the stage was continuously rotated as described previously (Nannenga et al., 2014).
Data analysis, processing, and structure refinement
We sought to analyze the levels of dynamic scattering in our catalase data in order to validate whether kinematical scattering could be assumed. We quantified the dynamic scattering of the catalase crystals using the ratio of the strongest diffracted intensity to the unscattered incident beam (Unwin and Henderson, 1975), as well as the ratio of the sum of all diffracted intensities on an image to the incident beam intensity as described by Dorset and Parsons (1975a). For the kinematical theory to apply, these ratios must be low. A representative crystal, which was approximately 200 nm thick, showed a ratio of 8.4 × 10−3 for the maximum intensity and 0.18 for the sum of all intensities, which are close to the previously reported values (Unwin and Henderson, 1975; Dorset and Parsons, 1975a), indicating that kinematical assumption is valid for these diffraction data.
Data sets from five microcrystals were each integrated with MOSFLM (Leslie and Powell, 2007) followed by merging and scaling with POINTLESS (Evans, 2011) and AIMLESS (Evans and Murshudov, 2013). All crystals yielded comparable resolution but varied in data completeness (Table 1). We combined all five data sets in an effort to increase completeness, multiplicity and to improve the quality of the data and in parallel we processed the data from crystal 4 separately for comparison. Crystal 4 was chosen for processing separately because it had a good compromise between resolution and completeness. The data sets were processed to a range of resolutions (Table 2) and phases were determined by molecular replacement (MR) as implemented in MOLREP (Vagin and Teplyakov, 1997) with PDB ID: 3NWL (Foroughi et al., 2011) as a search model. Following MR, refinement using PHENIX and REFMAC (Murshudov et al., 1997; Adams et al., 2010) with electron scattering factors was performed. Merging data from multiple crystals did not have a significant effect on data completeness, because the catalase crystals all orient on the grid with the c-axis parallel to the electron beam (Table 2). When comparing the statistics presented in Table 2, it was clear that merging multiple crystals had a negative impact on the final refinement statistics, most likely due to non-isomorphism between crystals, and therefore the multiple crystal data sets were disregarded. Crystals 1, 2, 3, and 5 were relatively isomorphous and their diffraction data merged well, but the completeness of this data set was low (∼62%), and we chose not to use it.
The single crystal 4 dataset was used for the remainder of the study. Close analysis indicated that the information content in the 3–3.2 Å resolution shell was too low for inclusion, which was not surprising as the crystals did not diffract well beyond 3.2 Å at high tilt angles. We therefore truncated the resolution to 3.2 Å yielding a final model with acceptable refinement statistics (Rwork/Rfree = 26.2%/30.8%) and geometry (Table 3, Figure 2A, Video 2). The final 2mFobs-DFcalc density map shows well-defined density surrounding the final refined model (overall map CC = 92.3%), both around the backbone and the side-chains (Figure 2B, Video 2), without significant peaks in the mFobs-DFcalc difference density map (Figure 2C). Additionally, the solvent channels between the tetramers in the crystal lattice show very little density (Figure 2D), further evidence of the quality of the model and data. The final structure of catalase at 3.2 Å resolution determined by MicroED agrees well with previously solved X-ray structures, with an RMSD of 0.358 Å and 0.440 Å between the MicroED structure and PDB ID: 3NWL (Foroughi et al., 2011) and PDB ID: 4BLC (Ko et al., 1999), respectively.
Model validation
The orientation of our crystals prevented the full sampling of reciprocal space leading to systematic incompleteness (missing wedge or missing cone). While the incompleteness of data is significant, the resulting maps are still expected to be good enough for proper interpretation (Glaeser et al., 1989). To test the quality of the data and the resulting maps, and to identify any significant model bias or negative effects of the data incompleteness, the data were put through several validation tests. First, the robustness of the MR solution was tested by repeating the MR with a single monomeric chain of PDB ID: 3NWL (Foroughi et al., 2011) instead of the complete tetramer that was used originally. Even with a single chain, a strong solution was found with all four molecules successfully placed to recreate the complete tetramer (Figure 2—figure supplement 1; top MOLREP contrast score = 35.8, where a score of >3.0 is considered a strong solution. An identical test with synchrotron X-ray data yielded a top score of 23.7). We also phased the data with a poly-alanine model derived from PDB ID: 3NWL, and the resulting maps show clear density beyond the model where the correct side-chains could be rebuilt (Figure 2—figure supplement 1B,C).
In order to test the quality of the resulting maps following MR, autobuilding with Buccaneer (Cowtan, 2006) was performed and yielded 2120 residues in 160 fragments (Figure 2—figure supplement 1D). Out of the built residues, 1280 traced the correct backbone and 467 side-chains were correctly assigned. Manual curation in Coot (Emsley et al., 2010), exploiting the fourfold non-crystallographic symmetry, resulted in a nearly complete model indicating the maps initially produced from our data were of good quality.
The next validation test performed involved removing sections of the model and analyzing the effect this had on the resulting refined density maps. This was done in order to examine the strength of the data and to find any potential model bias introduced by the MR search model. For this test, two validation models were used, in which the same sections of all four monomers of the final tetrameric structure were removed. The first model had residues 181 to 185 removed (∆181–185) and the second model lacked the four heme groups (∆heme) that are normally found in catalase. Following refinement and simulated annealing, the resulting difference maps from the ∆181–185 model (Figure 3A) and ∆heme model (Figure 3C) both showed significant positive difference density corresponding to the deleted regions of the model. Additionally, automated ligand identification was performed on the ∆heme maps using phenix.ligand_identification (Terwilliger et al., 2006, 2007), and the program was able to correctly place two of the four heme groups present in the structure. The results of these tests indicate that the data do not suffer from bias introduced by the MR search model.
Next, we sought to determine whether data from MicroED was of sufficient accuracy to locate small molecule ligands and corresponding protein conformational changes in the ligand-binding pocket. Bovine catalase binds four NADP cofactors, one per monomer through several side-chain interactions including F197 (Kirkman and Gaetani, 1984; Fita and Rossmann, 1985). Recently, the structure of bovine catalase was solved lacking the NADP cofactor (PDB ID: 3RGP) (Purwar et al., 2011), and in the NADP-free structure F197 underwent a conformational change as it no longer interacted with NADP. The crystals used for our MicroED analysis do contain NADP. Therefore, we used PDB ID: 3RGP (NADP-free structure) as a molecular replacement model against the MicroED data to determine whether NADP could be visualized in our catalase crystals using difference maps. When analyzing the difference maps, positive density was observed in the location expected for NADP although it appeared fragmented even at lower contour levels (Figure 3E,G). For visual comparison, structure factors from PDB ID: 3NWL, which was solved by X-ray crystallography, were truncated to 3.2 Å, and difference maps were calculated for ∆181–185, ∆heme and NADP-free model (Figure 3B,D,F,H). Maps from both MicroED and synchrotron X-ray diffraction appear fragmented around the NADP even at lower contour levels (Figure 3G,H). The difference maps for both the MicroED and X-ray synchrotron data suggest that F197 should change its orientation to assume its correct position for NADP binding.
These results demonstrate the MicroED data is of sufficient quality to detect subtle differences among structures at atomic resolution. At the current level of methodology with samples that suffer from missing data like catalase, MicroED produces lower quality maps than synchrotron X-ray diffraction. However, the catalase crystals used for MicroED were approximately 1000 times smaller in volume than those used at the synchrotron (Foroughi et al., 2011), and the resulting maps are still of high enough quality to determine the structure.
Concluding Remarks
We present here the second protein structure determined by the emerging MicroED method. Bovine liver catalase resisted structural determination by traditional electron crystallography for decades, but the structure was readily determined by MicroED in 3 weeks from crystal formation to final structure determination using a single crystal. This is the second example where a single crystal was sufficient for structure determination by MicroED (Nannenga et al., 2014). Moreover, the continuous rotation method yields data similar in quality to X-ray diffraction allowing simple processing with existing X-ray data reduction software and further accelerating structure analysis by MicroED (Nannenga et al., 2014). The resulting maps allow us to distinguish between subtly different protein conformations and to identify of small-molecule ligands such as NADP. This study shows that MicroED can be used as an alternative to X-ray crystallography using extremely small crystals for both mechanistic studies as well as structure-based drug design studies where small ligands are assayed.
Materials and methods
Catalase crystallization and sample preparation
Request a detailed protocolCatalase was recrystallized from a commercial aqueous suspension of catalase (C100; Sigma–Aldrich, St. Louis, MO) by first centrifuging the crystalline suspension and dissolving the pellet in 1.7 M NaCl. The solubilized catalase was then centrifuged and the supernatant was dialyzed against 50 mM sodium phosphate pH 6.3 overnight at 4°C. Crystals were removed from dialysis, stored in an Eppendorf tube, and incubated an additional 24 hr at 4°C. Catalase crystals were stored at 4°C and were washed with water prior to sample preparation. To prepare samples for the TEM, crystals were resuspended and the undiluted catalase crystal suspension was applied, blotted and vitrified in liquid ethane as described previously (Shi et al., 2013).
Collection of electron diffraction data
Request a detailed protocolAll electron diffraction was performed on a FEI Tecnai F20 TEM operated at 200 kV with a selected area aperture (6 μm in diameter at the specimen) and data were collected with 4k × 4k TVIPS F416 CMOS cameras (15.6 μm pixel size). Diffraction data were collected with a frame rate of 1 frame per 6 s as the sample was continuously rotated from high to low tilt angle at ∼0.09° s−1 (0.54°/frame) as described previously (Nannenga et al., 2014). A data set of approximately 61° was collected from a single crystal. Crystal thickness was estimated by measuring the intensity of the crystal (I) relative to the intensity of a hole in the carbon film (I0) from an image and using Beer's law:
where ε is the molar absorptivity, c is the molar concentration, and t is the crystal thickness. As an approximation, the value of εc for catalase was assumed to be the same as those for calculated for lysozyme. The lysozyme coefficients were determined using images of lysozyme microcrystals with a known thickness as described previously (Nannenga et al., 2014).
Data processing and structure refinement
Request a detailed protocolRaw TEM diffraction data were converted and processed using MOSFLM v7.1.0 (Leslie and Powell, 2007) and it's graphical interface iMOSFLM v1.0.7 (Battye et al., 2011), POINTLESS (Evans, 2006), and AIMLESS (Evans and Murshudov, 2013) as described in previous work (Nannenga et al., 2014). MOLREP (Vagin and Teplyakov, 1997) was used to perform molecular replacement using catalase PDB ID: 3NWL (Foroughi et al., 2011) as a search model (MOLREP contrast score = 40.8), and the molecular replacement solution was refined in PHENIX (Adams et al., 2010) and REFMAC (Murshudov et al., 1997) using a 5% free data set. Maps in Figure 3E,F,G and H were calculated using BUSTER-TNT (Blanc et al., 2004). Maps and models were displayed using the UCSF Chimera package (Pettersen et al., 2004).
Data availability
-
The crystal structure of the P212121 form of bovine liver catalase previously characterized by electron microscopyPublicly available at RCSB Protein Data Bank.
-
The structure of orthorhombic crystals of beef liver catalasePublicly available at RCSB Protein Data Bank.
-
Structural and kinetic analysis of the beef liver catalase complexed with nitric oxidePublicly available at RCSB Protein Data Bank.
References
-
PHENIX: a comprehensive Python-based system for macromolecular structure solutionActa Crystallographica Section D, Biological Crystallography 66:213–221.https://doi.org/10.1107/S0907444909052925
-
The resolution dependence of optimal exposures in liquid nitrogen temperature electron cryomicroscopy of catalase crystalsJournal of Structural Biology 169:431–437.https://doi.org/10.1016/J.Jsb.2009.11.014
-
iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLMActa Crystallographica Section D, Biological Crystallography 67:271–281.https://doi.org/10.1107/S0907444910048675
-
Refinement of severely incomplete structures with maximum likelihood in BUSTER-TNTActa Crystallographica Section D, Biological Crystallography 60:2210–2221.https://doi.org/10.1107/S0907444904016427
-
MolProbity: all-atom structure validation for macromolecular crystallographyActa Crystallographica Section D, Biological Crystallography 66:12–21.https://doi.org/10.1107/S0907444909042073
-
The Buccaneer software for automated model building. 1. Tracing protein chainsActa Crystallographica Section D, Biological Crystallography 62:1002–1011.https://doi.org/10.1107/S0907444906022116
-
Electron-diffraction from single, fully-hydrated, ox liver catalase microcrystalsActa Crystallographica Section D, Biological Crystallography 31:210–215.https://doi.org/10.1107/S0567739475000423
-
Thickness measurements of wet protein crystals in electron-microscopeJournal of Applied Crystallography 8:12–14.https://doi.org/10.1107/S0021889875009430
-
Features and development of CootActa Crystallographica Section D, Biological Crystallography 66:486–501.https://doi.org/10.1107/S0907444910007493
-
Scaling and assessment of data qualityActa Crystallographica Section D, Biological Crystallography 62:72–82.https://doi.org/10.1107/S0907444905036693
-
An introduction to data reduction: space-group determination, scaling and intensity statisticsActa Crystallographica Section D, Biological Crystallography 67:282–292.https://doi.org/10.1107/S090744491003982x
-
How good are my data and what is the resolution?Acta Crystallographica Section D, Biological Crystallography 69:1204–1214.https://doi.org/10.1107/S0907444913000061
-
The NADPH binding site on beef liver catalaseProceedings of the National Academy of Sciences of USA 82:1604–1608.https://doi.org/10.1073/pnas.82.6.1604
-
The collection of high-resolution electron diffraction dataMethods in Molecular Biology 955:153–169.https://doi.org/10.1007/978-1-62703-176-9_9
-
Catalase: a tetrameric enzyme with four tightly bound molecules of NADPHProceedings of the National Academy of Sciences of USA 81:4343–4347.https://doi.org/10.1073/pnas.81.14.4343
-
Structure of orthorhombic crystals of beef liver catalaseActa Crystallographica Section D, Biological Crystallography 55:1383–1394.https://doi.org/10.1107/S0907444999007052
-
Processing diffraction data with MOSFLMNATO Science Series II: Mathematics, Physics and Chemistry 245:41–51.https://doi.org/10.1007/978-1-4020-6316-9_4
-
Crystal structure of bovine liver catalase - a combined study by x-ray diffraction and electron microscopyJournal of Molecular Biology 30:323–327.https://doi.org/10.1016/S0022-2836(67)80042-1
-
Refinement of macromolecular structures by the maximum-likelihood methodActa Crystallographica Section D, Biological Crystallography 53:240–255.https://doi.org/10.1107/S0907444996012255
-
UCSF Chimera–a visualization system for exploratory research and analysisJournal of Computational Chemistry 25:1605–1612.https://doi.org/10.1002/jcc.20084
-
Ligand identification using electron-density map correlationsActa Crystallographica Section D, Biological Crystallography 63:101–107.https://doi.org/10.1107/S0907444906046233
-
Automated ligand fitting by core-fragment fitting and extension into densityActa Crystallographica Section D, Biological Crystallography 62:915–922.https://doi.org/10.1107/S0907444906017161
-
Beef liver catalase structure: interpretation of electron micrographsJournal of Molecular Biology 98:235–242.https://doi.org/10.1016/S0022-2836(75)80111-2
-
Molecular structure determination by electron microscopy of unstained crystalline specimensJournal of Molecular Biology 94:425–440.https://doi.org/10.1016/0022-2836(75)90212-0
-
MOLREP: an automated program for molecular replacementJournal of Applied Crystallography 30:1022–1025.https://doi.org/10.1107/S0021889897006766
Article and author information
Author details
Funding
Howard Hughes Medical Institute
- Brent L Nannenga
- Dan Shi
- Johan Hattne
- Francis E Reyes
- Tamir Gonen
The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
The authors wish to thank Garib Murshudov (MRC LMB) for providing a version of REFMAC with support for electron scattering factors and Andrew Leslie (MRC LMB) for data processing support and advice. We also would like to thank Steven Sawtelle (HHMI Janelia Research Campus) for technical support and Joanita Jakana (Baylor) for the protocol for catalase crystallization. Work in the Gonen lab is supported by the Howard Hughes Medical Institute.
Copyright
© 2014, Nannenga et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 5,018
- views
-
- 607
- downloads
-
- 108
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Biochemistry and Chemical Biology
The conformational ensemble and function of intrinsically disordered proteins (IDPs) are sensitive to their solution environment. The inherent malleability of disordered proteins, combined with the exposure of their residues, accounts for this sensitivity. One context in which IDPs play important roles that are concomitant with massive changes to the intracellular environment is during desiccation (extreme drying). The ability of organisms to survive desiccation has long been linked to the accumulation of high levels of cosolutes such as trehalose or sucrose as well as the enrichment of IDPs, such as late embryogenesis abundant (LEA) proteins or cytoplasmic abundant heat-soluble (CAHS) proteins. Despite knowing that IDPs play important roles and are co-enriched alongside endogenous, species-specific cosolutes during desiccation, little is known mechanistically about how IDP-cosolute interactions influence desiccation tolerance. Here, we test the notion that the protective function of desiccation-related IDPs is enhanced through conformational changes induced by endogenous cosolutes. We find that desiccation-related IDPs derived from four different organisms spanning two LEA protein families and the CAHS protein family synergize best with endogenous cosolutes during drying to promote desiccation protection. Yet the structural parameters of protective IDPs do not correlate with synergy for either CAHS or LEA proteins. We further demonstrate that for CAHS, but not LEA proteins, synergy is related to self-assembly and the formation of a gel. Our results suggest that functional synergy between IDPs and endogenous cosolutes is a convergent desiccation protection strategy seen among different IDP families and organisms, yet the mechanisms underlying this synergy differ between IDP families.
-
- Biochemistry and Chemical Biology
- Stem Cells and Regenerative Medicine
Human induced pluripotent stem cells (hiPSCs) have great potential to be used as alternatives to embryonic stem cells (hESCs) in regenerative medicine and disease modelling. In this study, we characterise the proteomes of multiple hiPSC and hESC lines derived from independent donors and find that while they express a near-identical set of proteins, they show consistent quantitative differences in the abundance of a subset of proteins. hiPSCs have increased total protein content, while maintaining a comparable cell cycle profile to hESCs, with increased abundance of cytoplasmic and mitochondrial proteins required to sustain high growth rates, including nutrient transporters and metabolic proteins. Prominent changes detected in proteins involved in mitochondrial metabolism correlated with enhanced mitochondrial potential, shown using high-resolution respirometry. hiPSCs also produced higher levels of secreted proteins, including growth factors and proteins involved in the inhibition of the immune system. The data indicate that reprogramming of fibroblasts to hiPSCs produces important differences in cytoplasmic and mitochondrial proteins compared to hESCs, with consequences affecting growth and metabolism. This study improves our understanding of the molecular differences between hiPSCs and hESCs, with implications for potential risks and benefits for their use in future disease modelling and therapeutic applications.