At the moment, single-crystal diffraction remains the most popular and widespread method for solving spatial structures of varying complexity for coordination chemistry and biology. Using a synchrotron radiation source for conducting this type of experiment allows one to achieve high resolution in the shortest time.

Despite the daily increase in demand for solving coordination chemistry problems and, accordingly, working with small molecules, the number of synchrotron beamlines for single crystal diffraction by small molecules is quite small, and protein crystallography stations are given priority.

The main ones are stations I19 on Diamond, 11.3.1 on ALS, BM01 on ESRF, XRD on Elettra, while the quantity of beamlines specialized for macromolecular objects are an order of magnitude larger. This is mainly due to the popularity of biological problems in the modern world, as well as the peculiarities of macromolecule crystals, because of which data collection at a laboratory source becomes almost impossible.

To make it easier for Russian users and provide an additional opportunity for foreign users to access this type of synchrotron beamline and to quickly collect high-resolution diffraction data from a wide range of samples, one of the installations of the Kurchatov synchrotron radiation source was optimized for working with crystalline samples in mass flow measurements, which allowed it to become a device that has no analogues in Russia for conducting this type of experiment [1]. Subsequently, to increase the quality of the data obtained, the diffractometer from the Belok station was transferred to the XSA beamline (Fig. 1). In addition, due to an increase in the intensity of the photon beam and the quality of the data collected, the number of experiments on the study of protein crystals has increased several times.

Raytracing of the XSA station was carried out and a noticeable increase in the photon flux on the sample was shown in comparison with the Belok station, where the hight-throutput single-crystal small-molecular crystallography experiments had previously been started [2]. The optimization of all stages of the structure solution and the demonstration of the quality of the data obtained were carried out using various classes of compounds as an example.

Serial femtosecond X-ray crystallography (SFX) involves the collection of thousands to up to millions of images in a few minutes. Being able to process these data at speeds that match the data collection rate is critical for scientists who need fast feedback on their data quality. Doing this while simultaneously creating data that fits FAIR standards (Findability, Accessibility, Interoperability, and Reusability) is challenging, and has been the focus of the High Data-Rate Macromolecular Crystallography (HDRMX) working group. We have recently published a consensus best-practice NeXus representation of a complex, multi-panel detector, the Jungfrau 16M from the SwissFEL Bernina endstation [1-2]. 256 individual panels are described and positioned in real space using a vector transformation system that is standard in NeXus, is machine readable, and completely specifies the experimental geometry.

We have implemented this approach during a subsequent data collection at the EuXFEL on the AGIPD detector, similar in geometry to the Jungfrau 16M [3]. Here we collected data at 2kHz and demonstrated the ability to process these data at these speeds with the software package DIALS on the Maxwell computing cluster at DESY, using 96 nodes, 80 cores per node. This required careful attention to how the data were laid out on disc. These methods and the NeXus framework for SFX will be presented.

[1] Bernstein, H.J., et. al. (2020). Gold Standard for macromolecular crystallography diffraction data. IUCrJ, 7(5) 784–792.

[2] Ingold, G., et.al. (2019). Experimental station Bernina at SwissFEL: condensed matter physics on femtosecond time scales investigated by X-ray diffraction and spectroscopic methods. J. Synchrotron Rad. 26, 874–886.

[3] Allahgholi, A., et. al. (2015). AGIPD, a high dynamic range fast detector for the European XFEL. JINST 10 C01023.

The work was supported in part by funding from Dectris Ltd., from the U. S. Department of Energy (BES KP1605010, KP1607011, DESC0012704), from the U. S. National Institutes of Health (NIGMS P30GM133893, R01GM117126).

We present a new, highly efficient metric for comparison of crystallographic lattices based on the Dirichlet cell (or Wigner-Seitz cell) which provides a very similar topology to that obtained with the G⁶ and S⁶ metrics, but without the combinatorial explosions sometimes seen with those metrics. As with G⁶, DC⁷ begins with Niggli reduction, but instead of comparing the G⁶ parameters, [a.a, b.b, c.c, 2 b.c, 2 a.c, 2b.c] or the S6 parameters [b.c, a.c, a.b, a.d, b.d, c.d], the squares of the 13 lengths of the Niggli cell edges, face diagonals and body diagonals considered in finding the Dirichlet cell, [||a||, ||b||, ||c||, ||b+c||, ||b-c||, ||a+c||, ||a-c||, ||a+b||, ||a-b||, ||a+b+c||, ||a+b-c||, ||a-b+c||, ||-a+b+c||] are sorted and the seven shortest taken as an identifying spectrum, corresponding to the distances between the pairs of faces forming the general Dirichlet cell. It is conjectured that the seven shortest of the thirteen lengths are sufficient to characterize the Niggli reduced cell from which they came, but at present it is best simply to retain the original cell along with the derived spectrum rather than try to recover the cell from the spectrum.

Work supported in part by supported by funding from the U.S. National Institutes of Health NIGMS (grant No. P30GM133893), and by the U. S. Department of Energy Office of Biological and Environmental Research (grant No. KP1607011), Office of Basic Energy Sciences (contract No. DE-SC0012704).

Efficient 1-dimensional seed-skewness algorithm adapted for X-ray diffraction signal detection together with signal integration procedure are presented. The method was shown to work well for both the standard single-crystal X-ray diffraction data, as well as, for more specific photocrystallographic time-resolved Laue data collected at Advanced Photon Source and European Synchrotron Radiation Facility. It enables reasonable separation of signal from the background in single 1-dimentional data vectors, it is capable of determining small changes of reflection shapes and intensities resulting from exposure of the sample to laser light, and allows for extracting relatively weak reflections from the background. The last is possible through adjusting of “trust level” and “signal level” parameters in the algorithm. Otherwise, the procedure is objective and does relay only on skewness computation and its subsequent minimization, which enable the best possible background estimation. The intensities of strong reflections are determined comparably as via the Kruskal-Wallis test method, whereas weak reflections are more sensitive to the algorithm setting parameters. In turn, both methods estimate the background level equally-well.

R.K., D.S. and P.Ł. would like to thank the SONATA grant (2016/21/D/ST4/03753) of the National
Science Centre in Poland for financial support. The time-resolved X-ray diffraction experiments
were performed at the ID09 beamline of the European Synchrotron Radiation Facility (ESRF),
Grenoble, France. The research used resources of the Advanced Photon Source, a U.S. Department
of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by
Argonne National Laboratory under contract No. DE-AC02-06CH11357. Use of BioCARS was
also supported by the National Institute of General Medical Sciences of the National Institutes of
Health (NIH) under grant No. R24GM111072 (note: the content is solely the responsibility of the
authors and does not necessarily represent the official views of NIH). Time-resolved set-up at
Sector 14 was funded in part through collaboration with Philip Anfinrud (NIH/NIDDK).

The Cambridge Structural Database (CSD)¹ is a database of over 1.1 million small molecule organic and metal-organic crystal structures. Each structure added to the database is curated to ensure important details about the structure are recorded alongside the entry. This curation process is particularly important for structures submitted directly to the CSD as a CSD Communication, with no accompanying journal article. As the CSD continues to grow and new techniques emerge it is essential that information is recorded consistently to ensure the data is findable. Consistency also allows the CSD to be utilised in data-driven approaches, such as machine learning, reducing the need for curation before it is ingested into models.

In addition to processing new data each year, the CCDC undertakes a series of improvement projects to assess the data stored in the CSD, ensure it is consistent and correct any errors. Ongoing projects also aim to capture additional information about the structure, such as if a specialist refinement technique is used. In addition, the CCDC is working towards updating the underlying format of the database, allowing new metadata about the structure to be stored. This poster will present highlights from work to continue to improve and grow the CSD.

1. Groom, C., Bruno, I., Lightfoot, M., & Ward, S. (2016). Acta Cryst. B 72, 171-179.

Reliable knowledge about structure and properties of chemical compounds is essential for pharmacology, food safety, environment preservation, design of new materials and understanding of functions of small molecules in living organisms. The number of unique substances known to humanity currently exceeds 100 million [1], and only the use of computers makes coping with the amount of available information possible.

The most accurate data about the structure of molecules are obtained from X-ray crystallographic (XRC) analyses. Currently, about 1 million crystal structures are known and the use of this information is enabled by crystallographic databases [2‑4]. These data, however, are not immediately usable by chemists. XRC determines accurate 3D coordinates of each atom in a crystal, and, in extreme cases, electron densities along chemical bonds, but it does not detect atomic charges, bond types or the presence of lone electrons in radicals. All such information needs to be inferred from the crystallographic data based on current chemical knowledge, either manually [5], or using heuristics, implemented as computer programs [6‑7]. However, the existing programs rarely consider information other than the coordinates. What is more, heuristics are usually specifically tailored for organic molecules. As a result, the derivation of chemical annotations by these programs is not always reliable, especially for metal-organic complexes.

Nevertheless, atomic coordinates in crystal structure reports are usually accompanied by additional chemical information. Systematic chemical names are often provided or derivable from publication titles or texts. Connectivity details in machine-readable formats may follow as well, albeit usually in forms not suitable for automated overlaying on the coordinate data. All this information could be employed to annotate crystallographic data with chemical details provided the mapping between different representations is known.

The largest open access crystallographic database, the Crystallography Open Database (COD, [2]), contains computer readable chemical descriptions for nearly half of its entries [5]. Currently, these descriptions are not linked to particular atoms in crystals, thus studies that require the combined crystallographic and chemical information have to infer the correspondence on their own. This task is tedious, involves repetition of work, and disregards readily available high-quality chemical descriptions.

Graph-based algorithms can be used to determine the links between the crystallographic and chemical data in the COD. Establishment of isomorphism between graphs derived from atomic coordinates and graphs derived from chemical descriptions enables the assignment of chemical attributes to individual bonds and atoms. Open-access nature of the COD allows dissemination of this information under FAIR (Findability, Accesibility, Interoperability and Reusability [8]) principles on the Web, immediately enabling numerous computational searches and research by pharmaceutical companies and academic groups. Thus, publishing and maintaining chemical annotations for crystallographic data in the COD would enhance research capabilities in pharmaceutical science, bio- and cheminformatics, materials science.

[1] CAS REGISTRY, https://www.cas.org/support/documentation/chemical-substances

[2] Gražulis et al. (2012). Nucleic Acids Research, 40. doi:10.1093/nar/gkr900

[3] wwPDB consortium (2019). Nucleic Acids Research, 47. doi:10.1093/nar/gky949

[4] Groom & Allen (2014). Angewandte Chemie International Edition, 53, 3. doi:10.1002/anie.201306438

[5] Quirós et al. (2018). Journal of Cheminformatics, 10, 1. doi:10.1186/s13321-018-0279-6

[6] O'Boyle et al. (2011). Journal of Cheminformatics, 3. doi:10.1186/1758-2946-3-33

[7] Willighagen et al. (2017). Journal of Cheminformatics, 9, 1. doi:10.1186/s13321-017-0220-4

[8] Wilkinson et al. (2016). Scientific Data, 3. doi:10.1038/sdata.2016.18

The development of new classes of high energetic materials (HEM) with high efficiency and low impact sensitivity is in the focus of numerous experimental and theoretical studies [1]. However, the high efficiency of HEM molecules is usually related to the high sensitivity towards detonation [2]. It is known that the sensitivity of HEM molecules towards detonation depends on many factors, including oxygen balance, energy content, and positive values of electrostatic potential above the central regions of the molecular surface. Analysis of positive values of molecular electrostatic potentials (MEP) showed to be an excellent tool in the assessment of impact sensitivities of high energetic molecules since positive values of MEP above the central regions of molecules are associated with high sensitivity towards detonation of HEM molecules [2]. Here we analysed the influence of hydrogen bonding on the values of the electrostatic potentials of fragments of HEM molecules extracted from crystal structures [3].

Crystal structures of three selected high energetic molecules were extracted from Cambridge Structural Database (CSD) and analysed in terms of non-covalent interactions. Three well-known HEM molecules were selected for the analysis: 1,3,5-Trinitrobenzene (TNB), 2,4,6-Trinitrophenol (TNP), and 2,4,6-Trinitrotoluene (TNT). Geometries of these molecules were used for electrostatic potentials calculations and for the design of model systems for interaction energies calculations. Electrostatic potential maps were calculated for TNB, TNP, and TNT geometries extracted from crystal structures for free molecules and molecules involved in hydrogen bonding. Values of electrostatic potentials above the central regions of molecules were analysed and compared for non-bonded HEM molecules and HEM molecules involved in hydrogen bonding.

Analysis of crystal structures showed that selected HEM molecules are involved in three types of hydrogen bonds: O-H…O-N interactions, C-H…O-H interactions, and in the case of TNP molecule O-H…O-H interactions. Analysis of positive values of the electrostatic potentials showed that hydrogen bonds have a significant influence on the values of the electrostatic potential in the central regions of HEM molecules. Calculations performed at M06/cc-PVDZ level showed that in the case when HEM molecules are involved in hydrogen bonding as hydrogen atom donors, positive values of electrostatic potentials in the centres of molecules decreased by 20 – 25%. In the case when HEM molecules were involved in hydrogen bonding as hydrogen atom acceptors, positive values of electrostatic potentials in the centres of HEM molecules increased by 10%.

Results presented in this study show that hydrogen bonds could be used as a tool for the modification of positive values of MEP above the central regions of HEM molecules and for the modification of their sensitivities towards detonation. Moderate change of positive electrostatic potential values above the central regions of HEM molecules upon formation of hydrogen bonds provide an opportunity for fine-tuning of sensitivities of HEM molecules towards detonation.

This research was supported by the Science Fund of the Republic of Serbia, PROMIS, #6066886, CD-HEM. This work was supported by the Serbian Ministry of Education, Science and Technological Development (Grant No. 451-03-9/2021-14/200026, 451-03-9/2021-14/ 200288 and 451-03-9/2021-14/200168).

[1] Liu, G., Wei & S.-H., Zhang, C., (2020). Cryst. Growth Des. 20, 7065.

[2] Politzer, P. & Murray, J. S., (2015). J Mol Model, 21, 1.

[3] Kretić, D. S., Radovanović, J. I. & Veljković, D. Ž., (2021). Phys. Chem. Chem. Phys., 23, 7472.

Crystallography Open Database (COD) [1] is the largest open-access FAIR [2] collection of small-molecule crystal structures that currently contains over 475 000 entries. In recent years, several notable improvements have been made to enhance the data curation process as well as expand the data search capabilities.

Data curation tasks of the COD heavily rely on the Crystallographic Information Framework (CIF) therefore recent CIF-related IUCr innovations stipulated the appropriate changes to the COD software. The F/LOSS cod-tools software package was updated to support the CIF2 data format [3] and the DDLm [4] dictionary language thus enabling the routine formal validation of all COD CIF files against the latest generation of CIF dictionaries [5]. The collected validation results were compiled in a publicly available CIF validation issue database that has already proven useful in data maintenance and ontology development tasks. A set of programs intended to aid in the dictionary migration from the now deprecated DDL1 language to the novel DDLm language was also created.

Effective search is another aspect of the COD database that has been greatly improved. Efficient chemical structure search in a crystallographic database requires that certain properties of the crystallised materials, such as molecular connectivity and other chemical features, be described in a machine-readable way. However, completely automated derivation of such information from CIF files is difficult and often provides suboptimal results. With this in mind, a set of high-quality manually curated SMILES that cover more than 40% of all COD entries have been made publicly available and can be used for chemical substructure search in the COD or for any other purpose on an open-access basis. The conventions that have been followed to represent various types of compounds as well as description of the semi-automatic SMILES derivation pipeline have also been extensively described [6] to improve the reusability and reproducibility of the data.

The COD data search capabilities were even further enhanced by implementing the OPTIMADE application programming interface (API) [7, 8] that aims to improve the interoperability between materials databases. It is extremely beneficial to be able to access information from multiple materials databases as they often differ in fidelity and focus across material classes and properties. However, retrieving data from multiple databases is difficult as each database has its own specific API. Moreover, as the APIs of individual databases inevitably evolve, existing clients must also evolve and are required to translate the responses from the new API to the internal representation of the client, which can require significant effort. The OPTIMADE consortium aims to alleviate most of these problems by providing a common RESTful API based on the JSON:API specification [9].

These recent changes to the COD are aimed at improving the data quality assurance process as well as ensuring that the data remain open, FAIR and readily available for a diverse range of applications in fields such as cheminformatics and materials science.

[1] Gražulis, Saulius et al. (2012). Nucleic Acids Res. 40, D420. doi: 10.1093/nar/gkr900

[2] Wilkinson, Mark D. et al. (2016). Scientific Data, 3. doi: 10.1038/sdata.2016.18

[3] Bernstein, Herbert J. et al. (2016). J. Appl.Crystallogr, 49, 277. doi: 10.1107/S1600576715021871

[4] Spadaccini, N. & Hall, S. R. (2012). J. Chem. Inf. Model. 52, 1907. doi: 10.1021/ci300075z

[5] Vaitkus, Antanas et. al. (2021). J. Appl. Crystallogr. 50, 661. doi: 10.1107/S1600576720016532

[6] Quirós, Miguel et. al. (2018). J. Cheminformatics 10. doi: 10.1186/s13321-018-0279-6

[7] Andersen, Casper W. et al. (2020). The OPTIMADE Specification. doi: 10.5281/zenodo.4195050

[8] Andersen, Casper W. et al. (2021). OPTIMADE: an API for exchanging materials data. url: https://arxiv.org/abs/2103.02068

[9] JSON:API v1.0. url: https://jsonapi.org/format/1.0/

During macromolecular crystallography (MX) data collection one or more crystals are exposed to a high flux of ionising radiation, which even at cryo-temperatures can damage the crystal(s), causing structural and chemical changes. If sufficiently severe, this damage can prevent solution of the crystal structure. However, even when structure solution remains possible, specific damage artefacts can confuse the biological conclusions drawn from a structure: hence their identification is important. Traditionally however the detection of specific radiation damage artefacts within crystal structures has proven difficult.

To address this problem, previously the Garman group developed the B_Damage metric^[1], calculated by the CCP4^[2] program RABDAM^[3]. B_Damage is a per-atom metric that highlights potential sites of specific radiation damage as atoms with high B-factor values as compared to other atoms in a similar local environment in the parent crystalline structure. Whilst this metric is useful at identifying damage artefacts in individual structures, unfortunately B_Damage values can not be compared between different structures. To address this limitation, here we present a derivative of the B_Damage metric, B_net, a per-structure metric that can be used to compare the relative damage suffered by different protein crystal structures. After validating that B_net is an appropriate metric on a dataset of structures known to contain specific damage artefacts, we use B_net to analyse the specific radiation damage present in a dataset of 94,145 protein crystal structures in the Protein Data Bank (PDB). Notably, many of the structures identified as damaged by B_net, and which on closer inspection contain obvious damage artefacts, have reasonable or excellent values for the metrics typically reported for PDB structures.

[1] Gerstel, M., Deane, C. M. & Garman, E. F. (2015). J. Synchrotron Rad. 22, 201–212.

[2] Winn, M. D. et al. (2011). Acta Cryst. D67, 235–242.

[3] Shelley, K. L., Dixon, T. P. E., Brooks-Bartlett, J. C. & Garman, E. F. (2018). J. Appl. Cryst. 51, 552–559.

The Taiwan Photon Source (TPS) 13A biological small-angle X-ray scattering (SAXS) beamline at the National Synchrotron Radiation Research Center was recently opened to users. The beamline is designed for probing biological structures and kinetics in wide length and time scales, from angstrom to micrometer and from microsecond to minutes. A 4-m IU24 undulator provides high flux X-rays in the energy range of 4.0 to 23.0 keV. MoB₄C double-multilayer and Si-(111) double-crystal monochromators (DMM/DCM) are combined on the same rotating platform for a smooth transition from high flux beams (~4x10¹⁴ photons/s) to a high-energy-resolution beam ((delta E)/E^­ ~ 1.5x10^-4). USAXS and microbeam modes are also available through a series of carefully designed optical components. An X-ray detecting system comprising two in-vacuum detectors was designed to perform synchronized small- and wide-angle X-ray scattering data collections at the endstation.

TPS 13A beamline adopts the Experimental Physics and Industrial Control System (EPICS) for integrated hardware and software controls, including all motors of the optical components and their corresponding sensing and cooling systems. Communication among local systems was achieved via defined process variables (PVs) provided by the EPICS Input/Output Controller (IOC) program. We have integrated two main clients of PVs: (1) Control-System Studio (CSS) GUI and (2) command-line system SPEC for beamline control. The data acquisition on TPS 13A is based on a synchronous operation of all detectors, ms-shutter, and intensity monitors. With the high frame rate of the detectors and a large number of pixels, a typical protein solution SEC-SAXS experiment can generate a few GB of data. The data storage, remote data access, and data treatment problems become essential if the user community continues to grow and even more critical for future high-throughput screening applications. Here we collaborate with Academia Sinica Grid-computing Center to create an online platform called Distributed Cloud Operating System (DiCOS)-BioSAXS platform (https://bioswan.twgrid.org/). It provides TPS 13A BioSAXS beamline users a friendly interface to access their experimental data, analyze data, and submit SAXS simulation jobs.