Conference Agenda

Correlated disorder is any type of deviation from the average crystal structure that is correlated over the range of a few unit cells only. As correlated disorder lies at the origin of the physical properties of a compound, many open questions in materials science are related to it. Unfortunately, the diffuse scattering analysis from single-crystal X-ray and neutron diffraction needs large crystals which are often not available. In the case of submicron sized crystals, pair distribution function analysis on powder samples could be applied. However, as an alternative we suggest to turn to single-crystal electron diffraction. While the quantitative analysis of diffuse X-ray and neutron scattering has already been done for different types of correlated disorder, we will present for the first time the quantitative analysis of diffuse electron scattering using an evolutionary algorithm in DISCUS [1].

In the electron diffraction patterns of Li_1.2 Ni_0.13Mn_0.54Co_0.13O₂ diffuse streaks are present, which are caused by stacking faults (i.e. variations in the stacking of subsequent Li-, O- and transition metal -layers). An evolutionary refinement algorithm in DISCUS was used to determine the stacking fault probability as well as the twin ratio in Li_1.2Ni_0.13Mn_0.54Co_0.13O₂ by a refinement of the intensity profile of the diffuse streaks_. The refinement algorithm was first tested on simulated data, after which it was applied to experimental electron diffraction data obtained by three-dimensional electron diffraction (3D ED).

Funding information

The research leading to these results has received funding from the Research Foundation Flanders (FWO Vlaanderen) (grant No. G035619N)

[1] Proffen, T., & Neder, R. B. (1997). J. Appl. Crystallogr. 30, 171-175.

The maps of electrostatic potential from cryo-electron microscopy and micro-electron diffraction are now being obtained at atomic resolution. This extends the possibility of investigating the electrostatic potential beyond determining the non-hydrogen atom positions, taking into account also the negative regions of the maps. However, accurate tools to calculate this potential for macromolecules, without reaching to the expensive quantum calculations, are lacking. Simple point charges or spherical models do not provide enough accuracy. Here, we apply the multipolar electron scattering factors and investigate the theoretically-obtained potential maps.

The multipolar electron scattering factors are derived from the aspherical atom types from Multipolar Atom Types from Theory and Statistical clustering (MATTS) databank (successor of UBDB2018 [1]). MATTS has been created since electron densities of atom types are transferable between different molecules in similar chemical environment. These atom types can be used to recreate the electron density distribution of macromolecules via structure factors [2] and to calculate the accurate electrostatic potential maps for small molecules [3]. MATTS reproduces the molecular electrostatic potential of molecules within their entire volume better than the simple point charge models used in molecular mechanics or neutral spherical models used in electron crystallography. In this study, we calculate electrostatic potential maps for several chosen macromolecules using aspherical atom databank and compare them with experimental maps from cryo-electron microscopy and micro-electron diffraction at high resolution. Calculations at different resolutions reveal at which spatial frequencies different elements become discernible. We also consider the influence of atomic displacement parameters on the theoretical maps as their physical meaning in cryo-electron microscopy is not as well established as in X-ray crystallography.

This study could potentially pave the way for distinguishing between different ions/water molecules in the active sites of macromolecules in high resolution structures, which is of interest for drug design purposes. It could also facilitate the interpretation of the less-resolved regions of the maps and also advise in simple yet questionable issue of resolution definition in cryo-electron microscopy.

The authors acknowledge NCN UMO-2017/27/B/ST4/02721 grant.

References:

[1] Kumar et al. (2019). Acta Cryst. A75, 398-408

[2] Chodkiewicz et al. (2018). J. Appl. Cryst. 51, 193-199

[3] Gruza et al. (2019), Acta Cryst. A76, 92-109

Understanding and governing the complex behavior in magnetic materials at the nanoscale is the key and the challenge not only for fundamental research but also to exploit them in applications ranging from catalysis,[1] to data storage,[2] sorption,[3-4] biomedicine,[5-6] and environmental remediation.[7] In this context, spinel ferrites (M²⁺Fe²O⁴, where M²⁺ = Fe, Co, Mn, etc.) represent ideal magnetic materials for tuning the magnetic properties through chemical manipulations, due to their strong dependence on the cation distribution, spin-canting, interface, size, shape, and interactions. Furthermore, when coupled with other phases (heterostructures), they can display rich and novel physical properties different from the original counterparts (exchange coupling, exchange bias, giant magneto-resistance), allowing them to multiply their potential use.[8] For example, the possibility to tune magnetic anisotropy and saturation magnetization by coupling magnetically hard and soft materials have found usage recently in applications based on magnetic heat induction, such as catalysis or magnetic fluid hyperthermia (MFH).[9] Therefore, it is crucial to engineer core-shell nanoparticles with homogeneous coating and low size dispersity for uniform magnetic response and to maximize the coupling between the hard and soft phases (i.e. the interface).[10] Even though some studies have reported interesting results in the field of magnetic heat induction, a systematic study on an appropriate number of samples for a better comprehension of the phenomena to optimize the performance is needed.

In this contribution, the capability of coupled hard-soft bi-ferrimagnetic nanoparticles to improve the heating ability is exploited to understand the influence of the different features on the performances. This systematic study is then based on the correlation between the heating abilities of three magnetically hard cobalt ferrite cores, covered with magnetically soft spinel iron oxide and manganese ferrite having different thickness, with their composition, structure, morphology and magnetic properties. Direct proof of the core-shell structure formation was provided by nanoscale chemical mapping, with identical results obtained through STEM-EELS, STEM-EDX, and STEM-EDX tomography. ⁵⁷Fe Mössbauer spectroscopy and DC/AC magnetometry proved the magnetic coupling between the hard and the soft phases, thanks also to the comparison among core-shell NPs, ad-hoc prepared mixed cobalt-manganese ferrites NPs, and cobalt ferrite NPs mechanically mixed with manganese ferrite NPs. The heating abilities of the aqueous colloidal dispersions of the three sets of core-shell samples revealed that, in all cases, core-shell nanoparticles showed better performances in comparison with the respective cores, with particular emphasis on the spinel iron oxide coated systems and the samples featuring thicker shells. This scenario entirely agrees with the hypothesis made based on magnetic parameters (saturation magnetization, Néel relaxation times, effective anisotropy) of the powdered samples, and demonstrated the importance of a sophisticated approach based on the synergy of chemical, structural, and magnetic probes down to a single-particle level.

[1] Polshettiwar, V. et al. Chem. Rev., 2011, 111, 3036–3075

[2] Wu, L. et al. Nano Lett., 2014, 14, 3395–3399

[3] Cara, C. et al. J. Mater. Chem. A, 2017, 5, 21688–21698

[4] Cara, C. et al. J. Phys. Chem. C, 2018, 122, 12231–12242

[5] Lim, E. et al. Chem. Rev., 2015. 115, 327-394

[6] Mameli, V. et al. Nanoscale, 2016, 8, 10124–10137

[7] Westerhoff, P. et al. Environ. Sci. Nano, 2016, 3, 1241–1253

[8] Gawande, M.B. et al. Chem. Soc. Rev. 2015, 44, 7540–7590

[9] Lee, J.-H. et al. Nat. Nanotechnol.,2011, 6, 418–422

[10]Sanna Angotzi, M. et al. J. Nanosci. Nanotechnol., 2019, 19, 4954–4963

The most conclusive and elucidating component of any small or macromolecular study is the proper structure determination. The two most commonly used tools for structure determination are being nuclear magnetic resonance spectroscopy (NMR) and X-ray diffraction. While both these techniques are extremely popular but have certain limitations. Recently a new technique 3D Electron Diffraction (3D ED) data collection for getting near to atomic resolution structures has taken a leap in the last few years. In this method, once the intensities are extracted, the structures are obtained from the 3D ED data using similar tools as for X-ray diffraction structure determination like SHELX, olex2, etc. In general Independent atom model (IAM) is used for solving the structures, where a precomputed model of electrostatic potential is built using scattering factors from isolated, spherically averaged atoms or ions. In reality, the atoms in a molecule are not isolated and spherical, moreover, the usage of improper electron scattering factors in refinement may lead to physically unrealistic values. To overcome this, an aspherical TAAM refinement has been applied both for X-ray and ED refinement which largely improved the physical representation and refinement statistics of the structure. We have chosen a model molecule β-glycine for this study for which 3D ED data is already available. Spherical and Aspherical TAAM refinement seemed to be possible in MoPro with the inclusion of electron scattering factors. Aspherical electron scattering TAAM model will be constructed using ELMAM2 and MATTS databank and refinement will be performed using MoPro. A comparison will be shown between reported data and spherical and aspherical TAAM refinement using MoPro and various statistics will be presented.

Microporous materials like zeolites have great academic and industrial applications in catalysis because of their varying properties which are strongly related to their crystalline structure. While single crystal X-ray diffraction and powder X-ray diffraction are the main techniques for structure solution, they are limited by the small crystal sizes usually obtained during the synthesis and by strong peak overlapping, respectively. Thus, in last decades a novel method called Electron Diffraction Tomography (EDT) or 3D Electron Diffraction (3D ED) has been developed allowing crystalline structure determination of nano-sized crystals performed in standard transmission electron microscopes.[1][2][3] This method can be assisted by Precession Electron Diffraction (PED) which minimizes dynamical effects and also provides reduction of excitation error.[4] Both techniques in combination with a continuous tilt of the crystal during acquisition of diffraction patterns can result on an almost complete reconstruction of reciprocal space. Thus, the unit cell and space group of zeolites, as well as intensities of reflections can be obtained from ED patterns, allowing the complete structure determination of the material.[5]

In this work, we have determined the complete structure of pure silica zeolite A, a material synthetized in our institute,[6] using a combination of EDT and PED.

Acknowledgement:

This research was supported by projects RTI2018-101784-B-I00 and SEV-2016-0683-18-3 and grant PRE2018-083623 from the Spanish government.

References:

[1] Kolb, U., Gorelik, T., & Mugnaioli, E., MRS Proceedings, 2009, 1184, 1184-GG01-05.

[2] Mauro Gemmi, Stavros Nicolopoulos, Ultramicroscopy, 2007, Volume 107, Issues 6–7, 483-494

[3] Gemmi, M.; Mugnaioli, E.; Gorelik, T. E.; Kolb, U.; Palatinus, L.; Boullay, P.; Hovmöller, S.; Abrahams, J. P. ACS Cent. Sci. 2019, 5, 1315−1329.

[4] R. Vincent, P.A. Midgley, Ultramicroscopy, 53 (1994), pp. 271-282

[5] L. Bieseki, R. Simancas, J. L. Jordá, P. J. Bereciartua, Á. Cantín, J. Simancas, S. B. Pergher, S. Valencia, F. Rey and A. Corma, Chem. Commun., 2018, 54, 2122–2125.

[6] Corma, A., Rey, F., Rius, J., Sabater, M.J. and Valencia, S. Nature, 2004, 431, 287-290.

Ruddlesden-Popper manganites La_xSr_2-xMnO_4-δ recently gained interest as promising electrode materials for solid oxid fuels cells. For 0.25 ≤ x ≤ 0.6, their stability under reducing atmosphere –along with the preservation of their K₂NiF₄-type I4/mmm symmetry –has been demonstrated using in situ high-temperature neutron and X-ray powder diffraction. [1] However, abnormally large anisotropic displacement parameters and complex changes in cell parameters point to the presence of disorder, which might explain the material’s increased electrical conductivity in diluted hydrogen. Submicron sized crystals are sufficient for electron diffraction (ED) to obtain two-dimensional single-crystal diffraction patterns, which can be interpreted in a more straightforward way than powder data. Therefore, single-crystal ED might pick up features which were missed during X-ray and powder diffraction. Using a dedicated environmental holder in a transmission electron microscope, we performed several series of in situ ED experiments to track structure transformations of La_0.5Sr_1.5MnO₄ upon heating in a 5% H₂ /He atmosphere. As the current state-of-the-art in situ equipment only permits tilting of the holder along one axis, conventional in-zone patterns cannot be obtained, and 3D ED is the optimal method to acquire sufficient diffraction data for structure analysis. We also performed the same experiments on Sr₂MnO₄ as a reference, since this material is known to undergo a space group transformation to a monoclinic P2₁/c supercell when reduced to Sr₂MnO_3.55 [2]. For La_0.5Sr_1.5MnO₄ a coexistence of both the tetragonal Ruddlesden-Popper phase and a perovskite phase has been noted upon heating to 750°C in reducing atmosphere, which has not been reported before. However, apart from the diluted hydrogen, the electron beam might possess some reductive power too, and the high temperatures can lead to decomposition. Therefore, we systematically examined the influence of different external factors, repeating the experiment with i.a. varying beam exposures, while heating in vacuum and reducing ex situ in 5% H₂ /He.

[1] Sandoval, M., Pirovano C., Capoen, E., Jooris, R., Porcher, F., Roussel, P., Gauthier, G. (2017). Int. J. Hydrog. Energy. 42 (34), 21930-21943.[2] Broux, T., Bahout, M., Hernandez, O., Tonus, F., Paofai, S., Hansen, T., Greaves, C. (2013). Inorg. Chem. 52 (2), 1009-1017.

Keywords: TEM, in situ, 3DED, Ruddlesden-Popper manganite, LSMO

This work was supported by BOF 38689 - New method to acquire in situ information on crystal structures changed by chemical reactions.

Three-dimensional electron diffraction (3DED) is an increasingly used complementary technique to X-ray crystallography and cryo-electron microscopy (cryo-EM) [1]. Using methods such as continuous rotation electron diffraction (cRED) [2] and microcrystal electron diffraction (MicroED) [3] with hybrid pixel detectors [4,5] allows fast and increasingly automated 3DED data collection[6]. These data can be processed with software commonly used for X-ray crystallography, such as XDS [7] and DIALS [8]. These programs do not have built-in graphical user interfaces (GUIs), but separate such interfaces do exist[9–12]. However, the interfaces are typically created for processing of individual datasets or data from synchrotron beamlines. As electron diffraction data and experimental geometries are less standardised, they are more likely than X-ray data to require user correction during processing. This is a challenge in automated processing, where relevant parameters can be hard or impossible to change in current interfaces.

We have developed a new GUI for processing 3DED data: scipion-ed. It is optimised for 3DED and intended to be easy for beginners to learn without preventing experienced users of the underlying programs from using specialised or newly implemented functions and parameters. Scipion-ed is an extension of the Scipion framework[13] consisting of python modules and originally designed for processing cryo-EM micrographs by combining multiple underlying programs.

The core module of the Scipion framework is scipion-pyworkflow. It defines the common GUI for all modules, a database for tracking metadata, job execution and the basic workflow engine. The workflow connects multiple steps in a processing pipeline together, and allows the user to split the processing in order to try out different methods or parameters following a certain step without overwriting previous processing. Such workflows can be exported as JSON files and deposited along with the data, imported and reused as the basis for processing of different data, or sent to collaborators.

Layered on top of the core modules are the base plugins scipion-em and scipion-ed. These define basic protocols, metadata structures and other domain-specific information for electron microscopy and electron diffraction respectively. A third layer of plugins is then added with protocols specific for individual software packages. Currently implemented into scipion-ed is scipion-ed-dials. This includes protocols for all typical processing steps: importing diffraction patterns, spot finding, indexing, model refinement, integration and scaling.

The input for each protocol is provided through a window with fields or lists of options for commonly used parameters and default values already filled in. It is thus simple to change individual values. For experienced users it is also possible to add command line parameters directly, enabling the use of additional options that might not yet have been implemented into scipion-ed. In addition to the protocols, scipion offers viewers that quickly show important results of a protocol without having to read the full output. The results summary for scipion-ed-dials protocols also includes buttons for opening the corresponding data in DIALS’ image viewer and reciprocal lattice viewer.

It is possible to add plugins for other software, such as XDS or the shelx suite, in the future. Work is also ongoing to implement streaming processing, where a prepared set of protocols are run automatically as soon as the data are available.

[1] M. Gemmi, E. Mugnaioli, T. E. Gorelik, U. Kolb, L. Palatinus, P. Boullay, S. Hovmöller, J. P. Abrahams, ACS Central Science 2019, DOI 10.1021/acscentsci.9b00394.

[2] Y. Wang, T. Yang, H. Xu, X. Zou, W. Wan, J Appl Cryst 2018, 51, 1094–1101.

[3] B. L. Nannenga, T. Gonen, Current Opinion in Structural Biology 2016, 40, 128–135.

[4] E. van Genderen, M. T. B. Clabbers, P. P. Das, A. Stewart, I. Nederlof, K. C. Barentsen, Q. Portillo, N. S. Pannu, S. Nicolopoulos, T. Gruene, J. P. Abrahams, Acta Cryst A, 2016, 72, 236–242.

[5] X. Llopart, R. Ballabriga, M. Campbell, L. Tlustos, W. Wong, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2007, 581, 485–494.

[6] B. Wang, X. Zou, S. Smeets, IUCrJ 2019, 6, 854–867.

[7] W. Kabsch, Acta Cryst D 2010, 66, 125–132.

[8] G. Winter, D. G. Waterman, J. M. Parkhurst, A. S. Brewster, R. J. Gildea, M. Gerstel, L. Fuentes-Montero, M. Vollmar, T. Michels-Clark, I. D. Young, N. K. Sauter, G. Evans, Acta Cryst D 2018, 74, 85–97.

[9] W. Kabsch, “XDS Package,” can be found under http://xds.mpimf-heidelberg.mpg.de/, 2016.

[10] L. Fuentes-Montero, J. Parkhurst, M. Gerstel, R. Gildea, G. Winter, M. Vollmar, D. Waterman, G. Evans, IUCr, “Introducing DUI, a graphical interface for DIALS,” 2016.

[11] L. Potterton, J. Agirre, C. Ballard, K. Cowtan, E. Dodson, P. R. Evans, H. T. Jenkins, R. Keegan, E. Krissinel, K. Stevenson, A. Lebedev, S. J. McNicholas, R. A. Nicholls, M. Noble, N. S. Pannu, C. Roth, G. Sheldrick, P. Skubak, J. Turkenburg, V. Uski, F. von Delft, D. Waterman, K. Wilson, M. Winn, M. Wojdyr, Acta Cryst D 2018, 74, 68–84.

[12] K. Yamashita, K. Hirata, M. Yamamoto, Acta Cryst D 2018, 74, 441–449.

[13] J. M. de la Rosa-Trevín, A. Quintana, L. del Cano, A. Zaldívar, I. Foche, J. Gutiérrez, J. Gómez-Blanco, J. Burguet-Castell, J. Cuenca-Alba, V. Abrishami, J. Vargas, J. Otón, G. Sharov, J. L. Vilas, J. Navas, P. Conesa, M. Kazemi, R. Marabini, C. O. S. Sorzano, J. M. Carazo, Journal of Structural Biology 2016, 195, 93–99

It is known that B-factors correlate with measurement temperature, crystals quality, disorder, etc.. They also correlate with resolution. In case of X-ray diffraction (XRD) higher values of B-factors are connected with lower resolution [1]. In case of electron diffraction (3D-ED) there are additional factors: dynamic scattering and radiation damage, so the trend in B-factors values is not so obvious as for XRD. But is there a systematic difference between sizes of B-factors if the only variable is a model of static density? Would it be the same for different crystal structures (small organic molecules, polypeptides, proteins)? How the difference would depends on resolution? Would it be the same in case of X-ray and electron diffraction?

It was also shown, that different scattering models – e.g. Independent Atom Model (IAM; spherical, not describing bonds, lone pairs, charge transfer etc.) or Transferable Aspherical Atom Model (TAAM; describing asphericity, but in fixed manner, not refined) can be used for structure refinements with diffraction data [2]–[4]. Here we present comparison of B-factors from IAM and TAAM refinements of different types of crystal structures: carbamazepine (small molecule) from XRD and 3D-ED [5], [6], peptide pseudoxylallemycin A from XRD [7] and peptide OsPYL/RCAR5 from 3D-ED [8], lysozyme from XRD and 3D-ED [9], [10]. We observe systematic difference in B-factors from these two refinements, however results still need discussion and we hoped for that during the poster sesion.

Support of this work by the National Centre of Science (Poland) through grant OPUS No.UMO-2017/27/B/ST4/02721 is gratefully acknowledged.

Bibliography

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[2] W. F. Sanjuan-Szklarz, A. A. Hoser, M. Gutmann, A. Ø. Madsen, and K. Woźniak, IUCrJ, 3, pp. 61–70, (2016),

[3] K. K. Jha, B. Gruza, P. Kumar, M. L. Chodkiewicz, and P. M. Dominiak, Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater., 76, pp. 296–306, (2020),

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[6] C. G. Jones et al., ACS Cent. Sci., 4 (11), pp. 1587–1592, (2018),

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Abstract: Can the location of the guest molecules inside the pores of porous materials be determined using three-dimensional electron diffraction (3DED) technique? In most cases, the guest molecules can not be located from the 3DED data. There are few cases where the positions of guest molecules have been successfully localized by combining 3DED with powder X-ray diffraction (PXRD). Simulated annealing (global optimization) and Rietveld refinement are always applied during the refinement procedure. By applying the advanced 3DED method, namely continuous rotation electron diffraction (cRED), it is possible to refine against 3DED data to visualize the conformation of the molecular backbone of guest molecules in a difference Fourier map. However, the individual atomic positions of the guest molecules have not been identified accurately yet. Here, we demonstrate for the first time that by applying cRED, each non-Hydrogen atom from the guest molecules can be separately localized from the difference Fourier map. We choose two open framework germanates, SU-8 and SU-68, as examples. Low electron dose was combined with ultrafast cRED data collection to minimize electron beam damage of the sample. As a result, atoms from the guest molecules appear as distinct, well-separated peaks in the difference Fourier maps. We also demonstrate that the atomic structure of both the framework and the guest molecules obtained by cRED is as reliable and accurate as that obtained by single-crystal X-ray diffraction (SCXRD).

Three-dimensional electron diffraction (3DED) techniques for structure determination has gained traction over the past few years (Gemmi et al., 2019). Rapid development such as increasing acquisition speed and automated data collection leads to large amounts of data that needs to be processed. At the same time, gained interest and implementation of 3DED as a standard practice has increased the demand for straightforward processing tools that can be used by scientist at the novice level for the specific data processing methods. To face these challenges, an extension of Scipion (de la Rosa-Trevín et al., 2016) for processing of 3DED data using DIALS has been developed under the name Scipion-ed (Bengtsson et al., 2021). In this work, the usefulness of Scipion-ed for processing a large number of 3DED datasets has been demonstrated. A total of 52 datasets were collected on as-grown tetragonal lysozyme (P 4₃ 2₁ 2) crystals through the continuous rotation electron diffraction method (cRED), also known as microcrystal electron diffraction (MicroED). Parallel workflows were generated in Scipion-ed for each dataset. The quality of each dataset was examined after scaling. Since the average completeness amongst all the datasets were 24%, multiple datasets had to be merged to increase the completeness for the structure solution and refinement. Three different strategies were applied to find the appropriate datasets to merge. Strategy 1 included scaling and merging of datasets with the most favourable overall merging statistics with regard to three formulated criteria (CC(1/2) > 0.8*, I/SigI > 2 and R_meas < 0.60). 14 of the processed datasets fulfilled all criteria and were scaled and merged accordingly. Strategy 2 focused on maximizing the completeness of the final merged reflection file, without consideration of the reflection statistics of the higher resolution data. On top of the 14 previously selected datasets, additional datasets were added consecutively. Datasets that did not contribute to increased completeness were removed. Strategy 3 included merging all datasets regardless of the contribution of each individual dataset to the completeness. The different merging strategies were evaluated with respect to the ability to resolve non-modelled features in the electrostatic potential map. This was done by refining the data against a modified model lacking the Trp28 residue. A previously solved X-ray diffraction model of the tetragonal lysozyme structure (PDB: 193L, Vaney et al., 1996) was used as search model. After the refinement, the Trp28 residue added and real-space refined against the un-modelled electrostatic potential region representing the location of the residue. The final model (including Trp28) was validated against the electrostatic potential map that was refined against the modified model (without Trp28). Strategy 1 resulted in the highest correlation coefficient (CC) for the Trp28 residue (CC_trp=0.974), along with the lowest R-value (R-work/R-free=0.210/0.307) for the final structure model. Strategy 1 had the lowest completeness (74.5%) but the highest overall CC(1/2) (0.993*) and with a CC(1/2) > 0.330 down to 2.7 Å, compared to strategy 2 and 3 where the corresponding limits were 3.0 and 4.3 Å. At the same time, strategy 2 and 3 resulted in a higher overall completeness (85.8% and 87.2% respectively). Strategy 2 gave a slightly better CC of the Trp28 residue (CC_trp=0.942) compared to merging all the datasets (Stratergy 3, CC_trp=0.933). However, Strategy 3 resulted in a lower R-value (R-work/R-free=0.224/0.303) compared to Strategy 2 (R-work/R-free=0.232/0.340) indicating that the overall fit of the model was better. All the strategies resulted in very similar final structure models with regards to modelling of the Trp28 residue, indicating that the difference in the CC_trp is due to the differences in the electrostatic potential map. The results suggests that it is favourable to be more selective when merging datasets with considerations to the reflection statistics at higher resolution, despite limiting the completeness. The effect of different merging strategies should be investigated further to find the appropriate balance between completeness and resolution cut off but is beyond the scope of this study. We have demonstrated the usefulness of the Scipion-ed interface when investigating different strategies in parallel, as well as processing and merging large amounts of datasets, which is the standard procedure for collecting MicroED data of highly beam sensitive materials.

Bibliography

Gemmi, M., Mugnaioli, E., Gorelik, T. E., Kolb, U., Palatinus, L., Boullay, P., Hovmöller, S. & Abrahams, J. P. (2019). ACS Central Science.

de la Rosa-Trevín, J. M., Quintana, A., del Cano, L., Zaldívar, A., Foche, I., Gutiérrez, J., Gómez-Blanco, J., Burguet-Castell, J., Cuenca-Alba, J., Abrishami, V., Vargas, J., Otón, J., Sharov, G., Vilas, J. L., Navas, J., Conesa, P., Kazemi, M., Marabini, R., Sorzano, C. O. S. & Carazo, J. M. (2016). Journal of Structural Biology. 195, 93–99.

Bengtsson, V.E.G., de la Rosa-Trevín, J. M., Hofer, G. Pacoste, L. Xu, H. Zou, X. (2021) Manuscript in preparation.

Toll-like receptors (TLRs) play a fundamental role in initiating immune response by recognition of pathogen-associated molecular patterns (PAMPs) invertebrates, which results in the production of pro-inflammatory cytokines. 10 TLRs have been identified in the human TLR family. In humans, TLR2 can form heterodimers with TLR1 and TLR6 when binding different types of ligands. The cytoplasmic Toll/interleukin-1 receptor (TIR) domain can be found in all TLRs and is responsible for transmitting extracellular signals to intracellular cytoplasmic TIR domain-containing adaptor proteins through TIR: TIR domain interactions, thus initiating downstream signaling. Two TIR-domain containing adaptor proteins, Myeloid differentiation primary response 88 (MyD88) and MyD88 adaptor-like (MAL) mediate downstream signaling in the TLR2-TLR1/6 signaling pathway. It has been previously demonstrated that higher-order assembly formation occurs in the TLR4 signaling pathway. The mechanism, which is known as signaling by cooperative assembly formation (SCAF), may occur in all TLR signal transduction. To date, the transduction mechanisms of TLR2-TLR1/6 signaling are still unclear. My project is to determine the structural basis of higher-order assemblies formed by TIR domains with a focus on assemblies in the TLR2-TLR1/6 signaling.

Crystal structure solution and refinement using three-dimensional electron diffraction (3DED) is on the cusp of becoming a widely-used technique and it seems certain that it will be used for many applications in the near future. Nevertheless, in comparison with X-ray diffraction there is still poor agreement between calculated and observed intensities for 3DED data. Since it is very well-known that the kinematical (single-scattering) theory fails to describe most electron scattering, these discrepancies have been usually explained using the catch-all excuse of 'dynamical (multiple scattering) effects'.

However, when dynamical electron diffraction effects are measured carefully, and compared with standard electronscattering theory, agreement between experiment and simulation is actually very good. The dynamical scattering effects contain phase information that is extremely sensitive to the details of the inner potential of the material and can be used to determine atomic coordinates to a precision of ~0.1pm. Examples are given using using 'digital' large-angle convergent beam electron diffraction (D-LACBED).

In order to progress in the accuracy of 3DED work we therefore need to pay attention to the experimental parameters which are specific to the method. These parameters combine with dynamical effects to change diffracted intensities and include incident beam convergence, the effect of intermediate lenses between the crystal and detector, crystal orientation, shape, defectivity, mosaicity, inelastic scattering and beam damage. Improvements in 3DED will depend on the ability to measurethese parameters and refine them using measured intensities.

Characterization of electrostatic interactions in selected complexes of IFIT5 proteins with RNA is the aim of my current project. IFITs (Interferon-induced proteins with tetratricopeptide repeats) are effectors of innate immune system, which are expressed in cells infected by viruses. By binding foreign RNA they prevent synthesis of viral proteins in human host cell. IFIT1, IFIT2 and IFIT5 bind different forms of RNA (with triphosphate group or cap at 5’ end of RNA). For my study I focused on IFIT5 proteins which are supposed to bind preferentially ppp-RNA.

Usually the most significant contribution to interaction energy has electrostatic energy. Electrostatic energy can be calculated for large complexes, thus it is a perfect tool for estimating interaction energy in biomacromolecules. One of the more advanced methods to calculate this energy is University at Buffalo Pseudoatom DataBank (UBDB) together with Exact Potential Multipole Method (EPMM). UBDB enables reconstruction of charge density for macromolecules in quantitative manner. By UBDB+EPMM approach, which takes also charge penetration effects into account, it is possible to compute electrostatic energies with similar accuracy as with quantum chemistry methods.

Calculations of energy are based on the structures of IFIT5 proteins deposited in Protein Data Bank (PDB). I wanted to verify the hypothesis of the lack of influence of RNA sequence on interaction energy in IFIT-RNA complexes investigating three IFIT5-pppRNA complexes with different short polynucleotide chains. I examined thoroughly electrostatic interactions and also penetration effect.

Describing the nature of IFIT proteins interaction can help to expand our knowledge about mechanism of selective binding RNA and how human immune system recognizes and destroys viruses.

Project was financed from the grant PRELUDIUM11 of National Science Centre, Poland nr 2016/21/N/ST4/03722.

Following the in situ development of glycine polymorphs from an aqueous solution via 3D electron diffraction, revealing three polymorphs crystallizing at differing timepoints according to stability. Beta glycine forms after 3 minutes, followed by alpha glycine after only one minute more. Gamma glycine forms after prolonged standing. The same methodology was applied to carbamazepine which, alongside the expected dihydrate form, shows four forms after 30 seconds of crystallization. When the time is reduced to 20 seconds, dark, liquid-like, droplets appear to agglomerate together and form the dihydrate. This suggests the dihydrate forms via the non-classical nucleation similar to liquid-liquid phase separation. High pressure X-ray crystallography is a well-established technique for studying polymorphism and was used to probe the nature of the dimer interactions within isostructural organic ‘Blatter’ radicals. One of which showed a pseudo 2^nd order phase transition. This transition is driven by flexibility on the phenyl ring which rotates and allows the benzotriazine moiety to flatten and become more planar. Implementation of 3DED/MicroED technique on TEM in Edinburgh for routine structure solution, previously only used for imaging has also been successfully accomplished.

Zeolites are often synthesized as small polycrystalline powders that make their structure determination by single crystal X-ray diffraction challenging. 3D electron diffraction (3D ED) methods, especially continuous rotation electron diffraction (cRED), overcome the size limitation and can reveal structures of sub-micrometer sized crystals with similar resolution as single crystal X-ray diffraction. In this work, we have utilized cRED to reveal unprecedented disordered chains that link together the non cages [4¹5⁸] in nonasil (NON)to form its complete 3D framework. The refinement against the cRED data in the reported Fmmm space group revealed residual peaks in the electrostatic potential maps that clearly indicate two configurations of the zig-zag chains that link the non cages together. These atoms reside on a mirror plane perpendicular to the c-axis. Another mirror plane that is perpendicular to the b-axis prevents them from relaxing into either configuration, and replacing it with a two-fold rotation axis allows full relaxation. Hence, the structure is best described by superposition of two Fm2m models, or two 50% occupant chain configurations in Fmmm. Herein, with cRED and computational aid, we uncover the origin of the disorder and demonstrate that the same disorder is prevalent in all non-cage containing zeolite structures CIT-13 (*CTH), ERS-18 (EEI), EMM-25, EU-1 (EUO), ITQ-27 (IWV), and NU-87 (NES), except for ITQ-32 (IHW) and IM-12 (UTL).