The IUCr Newsletter is the web magazine of the International Union of Crystallography. Since 1993, the IUCr Newsletter has been published quarterly and distributed to over 13,000 crystallographers worldwide. In August 2018, it was transformed into a dynamic digital platform with a fresh new look. Its aim remains to bring together the crystallographic community.
|
William L. Duax Patricia Potter Jane Griffin The IUCr Newsletter is distributed electronically to 13,000 crystallographers and other interested individuals in 102 countries. Articles from the Newsletter are also available on this web site by following the links on the left-hand side. Feature articles, meeting announcements and reports, information on research or other items of potential interest to crystallographers may be submitted to the editor at any time. Items will be selected for publication on the basis of suitability, content, style, timeliness and appeal. Please send contributions to: newsletter@iucr.org Matters pertaining to advertisements should also be sent to the above address. Email address changes or corrections and requests to be added to the mailing list should be accomplished through the Subscribe and Unsubscribe hyperlinks above. Reuse permissions • The original article in which the material appeared is cited. • Copyright permission is indicated by the wording "Reproduced with permission of the International Union of Crystallography Newsletter". In electronic form, this acknowledgement must be visible at the same time as the reused materials, and must be hyperlinked to the IUCr Newsletter (http://www.iucr.org/news/newsletter). Please be sure to include the following information: • The title, author(s) and page extent of the article you wish to republish, or, in the case where you do not wish to reproduce the whole article, details of the section(s) to be reproduced. • The volume/issue number and year of publication in which the article appeared, and the page numbers of the article. • Details of the publication in which you wish to reprint the Newsletter material. • If the material is being edited or amended in any way, a copy of the final material as it will appear in your publication. • Contact details for yourself, including a postal address. |
![[Cover v25n2]](https://www.iucr.org/__data/assets/image/0018/135180/cover25n2.jpg) Click here for PDF Use menu on left to access previous digital issues |
Annual Reports to the IUCr Executive Committee
The IUCr Newsletter (ISSN 1067-0696; coden IUC-NEB) is published quarterly (4x) by the International Union of Crystallography (IUCr Executive Secretary e-mail execsec@iucr.org). Members receive the IUCr Newsletter by virtue of country membership in the IUCr. Periodical postage rates paid at Buffalo, NY and additional mailing offices. POSTMASTER: Please send changes of address to IUCr Newsletter Editorial Office, c/o Hauptmann-Woodward Medical Research Institute, 700 Ellicott St. Buffalo, NY 14203 USA.
The Critical Assessment of Techniques for Protein Structure Prediction (CASP) challenge is a serious and very well done effort these last decades carried out by global organisers: Dr John Moult (chair), University of Maryland, USA; Dr Krzysztof Fidelis, UC Davis, USA; Dr Andriy Kryshtafovych, UC Davis, USA; Dr Torsten Schwede, University of Basel and SIB Swiss Institute of Bioinformatics, Switzerland; and Dr Maya Topf, Birkbeck, University of London, UK and CSSB (HPI and UKE) Hamburg, Germany. The CASP14 Press release of 30th November 2020 https://predictioncenter.org/casp14/doc/CASP14_press_release.html stated that:
'Today (Monday), researchers at the 14th Community Wide Experiment on the CASP14 will announce that an artificial intelligence (AI) solution to the challenge has been found.' And 'During the latest round of the challenge, DeepMind's AlphaFold program has determined the shape of around two-thirds of the proteins with accuracy comparable to laboratory experiments*. AlphaFold's accuracy with most of the other proteins was also high, though not quite at that level."
The asterisk above is all-important to exploring precisely what has been achieved in this undoubted breakthrough:
'Notes to editors: *AlphaFold produced models for about two-thirds of the CASP14 target proteins (JRH: 84 targets; full details are here: https://predictioncenter.org/casp14/index.cgi) with global distance test scores above 90 out of 100. Above the 90-score threshold, remaining differences between the models and the experimental structures are small and of the size expected for experimental artefacts and errors and alternative low energy local conformations. Note that these CASP targets are single proteins or domains, not protein complexes, which are a next frontier. The global distance test is a measure of how closely the shape of the protein model matches the shape from lab experiments [1, 2]."
This is clearly, i.e. all the expert judges of the CASP series of challenges agree, a breakthrough in the field of prediction of protein folds. To understand this better, we need to clarify what is a fold, and what is a structure? The polypeptide chain fold is described by simply linking the alpha carbon in each peptide in the protein's amino acid sequence, one after the other. This leads to the well known ribbon representation of a protein. Then, where the individual atoms are in the structure is a critical aspect, and as W. L. Bragg most famously said: with crystallography, we can see atoms.
The experimental probes of X-rays, electrons and neutrons in crystallography, and electrons in microscopy, as well as NMR, then strive for precise protein models derived as the best fit to their measurements. The term accuracy, which means as close as we can get to the truth, requires more than one method and, in this context, is termed integrated structural biology. Our experimental methods have weaknesses though. There is radiation damage to the protein structure, if not usually to the fold, by X-rays or electrons according to the dose absorbed by the crystal sample. Then there are changes to the details of the structure and its structural dynamics according to whether cryo-temperatures are used. In recent years experimentalists have explored more and more of these changes, termed 'artefacts', and strive for damage-free structures at physiologically relevant temperatures. Cryo-structures can be substantiated by accompanying the structure with a functional assay and/or undertaking one of a group of structures at room temperature, including comparing structures in solution with those in the crystal such as by solution X-ray or neutron scattering. The characterisation of what precision of a protein structure has been achieved is a big topic, but in 1999 Durward Cruickshank formulated the Diffraction Precision Index [3] to assist with this assessment. The CASP14 press release's sentence, "Above the 90-score threshold, remaining differences between the models and the experimental structures are small and of the size expected for experimental artefacts and errors, and alternative low energy local conformations", has I think to be assessed carefully, case by case, and the precision of placement atom by atom. I suspect that a new terminology has arrived: allowing for prediction artefacts to add to our own list of experimental artefacts.
Referred to as Levinthal's paradox, in 1969 Cyrus Levinthal [4] noted that, because of the vast number of degrees of freedom in an unfolded polypeptide chain, the molecule has an astronomical number of possible conformations. Based upon the observation that proteins fold much faster than this, Levinthal then proposed that a random conformational search does not occur, and the protein must, therefore, fold through a series of metastable intermediate states.
A famous article in this field is by Christian Anfinsen [5], which stated in conclusion that 'empirical considerations of the large amount of data now available on correlations between sequence and three-dimensional structure, together with an increasing sophistication in the theoretical treatment of the energetics of polypeptide chain folding are beginning to make more realistic the idea of the a priori prediction of protein conformation. It is certain that major advances in the understanding of cellular organization, and of the causes and control of abnormalities in such organization, will occur when we can predict, in advance, the three-dimensional phenotypic consequences of a genetic message.'
Indeed the Nobel Prize in Chemistry in 1972 (https://www.nobelprize.org/prizes/chemistry/1972/press-release/) was awarded 'one half to Christian B. Anfinsen for his work on ribonuclease, especially concerning the connection between the amino acid sequence and the biologically active conformation, the other half jointly to Stanford Moore and William H. Stein for their contribution to the understanding of the connection between chemical structure and catalytic activity of the active centre of the ribonuclease molecule.'
![[Long protein]](https://www.iucr.org/__data/assets/image/0003/150762/image1Smaller.jpg)
Here David Phillips unveils the lysozyme's molecular model (on the table in front of him) to the public during a historic lecture at the Royal Institution in London in 1965. Hanging from the ceiling was a 32-foot-long string of the enzyme's 129 amino acids. And on the counter next to Phillips was the same string, this time folded into a globular shape a few feet across. The folding, Phillips explained, locked the amino acids together in a way that gave the enzyme its catalytic activity. Reproduced with the permission of the Royal Institution of Great Britain (RIGB). This photo and caption were located via Google at the C&EN website, who we also thank.
The great protein crystallographer David Phillips, who with his team determined the first enzyme crystal structure [6], and later was Sir David Phillips and then Lord Phillips of Ellesmere, wrote already in 1966 in Scientific American [7] of the folding of lysozyme: 'It seems a reasonable assumption that, as the synthesis proceeds, the amino end of the chain becomes separated by an increasing distance from the point of attachment to the ribosome, and that the folding of the protein chain to its native conformation begins at this end even before the synthesis is complete. According to our present ideas, parts of the polypeptide chain, particularly those near the terminal amino end, may fold into stable conformations that can still be recognized in the finished molecule and that act as 'internal templates', or centres, around which the rest of the chain is folded.'
At this point in the history, one should note that [8] '(while) Anfinsen (1973) showed that all information needed for a polypeptide to fold into basic secondary structure elements such as α-helices and β-strands and their subsequent collapse into a compact structure is entirely contained within the amino acid sequence. (and) Although small single-domain proteins can fold spontaneously and in a reversible way, most multi-domain proteins need the help of chaperones to adopt the ultimate native and active conformation.... These proteins thus violate Anfinsen's rule.'
From the DeepMind blog https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery the public at large can learn that DeepMind's methods 'relied on deep neural networks that are trained to predict properties of the protein from its genetic sequence. The properties our networks predict are: (a) the distances between pairs of amino acids and (b) the angles between chemical bonds that connect those amino acids. The first development is an advance on commonly used techniques that estimate whether pairs of amino acids are near each other.'
They had already entered the CASP13 competition in 2018, which they described in [10]. In that, they make clear [9] that:
'Source code for the distogram, reference distogram, and torsion prediction neural networks, together with the neural network weights and input data for the CASP13 targets, are available for research and non-commercial use at https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13. We make use of several open-source libraries to conduct our experiments, particularly HHblits36, PSI-BLAST37, and the machine-learning framework TensorFlow (https://github.com/tensorflow/tensorflow) along with the TensorFlow library Sonnet (https://github.com/deepmind/sonnet), which provides implementations of individual model components50. We also used Rosetta9 under license.
Data availability: Our training, validation, and test data splits (CATH domain codes) are also available from https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13.
The following versions of public datasets were used in this study: PDB 2018-03-15; CATH 2018-03-16; Uniclust30 2017-10; and PSI-BLAST nr dataset (as of 15 December 2017).'
The CASP14 work involved rewriting their software such that there were accuracy improvements in AlphaFold in CASP14 over that used by them in CASP13 which 'lead to more accurate interpretations of function; better interface prediction for protein-protein interactions; better binding pocket prediction and improved molecular replacement in crystallography.'
Most importantly for us experimentalists, we read: 'All the neural network models are trained on structures extracted from the PDB. We extract non-redundant domains by utilizing the CATH7 35% sequence similarity cluster representatives (CATH version: 2018-03-16). This gives 31 247 domains, which are split into train and test sets (29 427 and 1820 proteins, respectively), keeping all domains from the same homologous superfamily (H-level in the CATH classification) in the same partition.'
A major strength of DeepMind is the breadth of its other areas of research involving AI. Further details of these are available at their website and include:
Traffic prediction with advanced graph neural networks
Using AI to predict retinal disease progression
Using WaveNet technology to reunite speech-impaired users with their original voices
Advanced machine learning helps Play Store users discover personalised apps
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
Using machine learning to accelerate ecological research
Using AI to give doctors a 48-hour head start on life-threatening illness
DeepMind AI reduces Google data centre cooling bill by 40%
In their Nature article [9] Acknowledgements, the authors state, 'We thank ...the CASP13 organisers and the experimentalists whose structures enabled the assessment.' So, that is good, but in the blog of DeepMind's view of experimental methods ( https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery) I find wide of the mark their use of the words 'trial and error':
'Over the past five decades, researchers have been able to determine shapes of proteins in labs using experimental techniques like cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography, but each method depends on a lot of trial and error, which can take years of work, and cost tens or hundreds of thousands of dollars per protein structure. This is why biologists are turning to AI methods as an alternative to this long and laborious process for difficult proteins. The ability to predict a protein's shape computationally from its genetic code alone - rather than determining it through costly experimentation - could help accelerate research.'
This also brings us back to being clear about fold versus structure and the all-important precision of atom placements, whether by experiment [3] or by AI prediction, as yet not parameterised.
There is also the question that while the reproducibility of the DeepMind Nature paper is feasible via the authors placing the workflows at github, the competing interests statement in their Nature paper states: 'A.W.S., J.K., T.G., J.J., L.S., R.E., H.P., C.Q., K.S., A.Ž. and A.B. have filed provisional patent applications relating to machine learning for predicting protein structures.'
And re. reproducibility, (https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13), 'This code can't be used to predict the structure of an arbitrary protein sequence. It can be used to predict structure only on the CASP13 dataset (links below). The feature generation code is tightly coupled to our internal infrastructure as well as external tools. Hence we are unable to open-source it. We give guide as to the features used for those accustomed to computing them below.....'
This, to me, seems like a culture clash of the fully open PDB versus the private enterprise and profit of DeepMind.
As I have explored in this article, it is the detailed protein structure that determines function, not the protein fold per se, but DeepMind are clearly entering the area of positioning atoms in detail, although not really with a clear level of precision at the atom-by-atom detailed level. Secondly, multi-domain proteins are currently outside of this achievement. Thirdly, where a structure is known, I note that structural dynamics studies start, i.e. in effect, while there may be one fold, there isn't one structure in function terms! In our experimental arena, kinetic crystallography is set to thrive with an expansion of X-ray lasers, the ESRF EBS, Diamond II etc. building on many earlier developments [11]. Diffuse scattering is all there for the measuring and interpreting, i.e. structural dynamics again.
Onto more specific topics: a third of all proteins are metalloproteins. Predicting where a metal might bind and which metal element it is, is not solved. And not just metal ions but all sorts of other ligands too will still have to be done experimentally, although see DeepMind's Nature paper Extended Data Fig. 8 and caption statement on ligands, and thereby drug design/discovery:
'Ligand pocket visualizations for T1011. T1011 (PDB 6M9T) is the EP3 receptor bound to misoprostol-FA55. a, The native structure showing the ligand in a pocket. b, c, Submission 5 (78.0 GDT TS) by AlphaFold (b), made without knowledge of the ligand, shows a pocket more similar to the true pocket than that of the best other submission (322, model 3, 68.7 GDT TS) (c). Both submissions are aligned to the native protein using the same subset of residues from the helices close to the ligand pocket and visualized with the interior pocket together with the native ligand position.'
The impact on the AI field as a whole in its efforts with predicting protein structure from amino acid sequence has already been major. Since the DeepMind publication [9] it has been cited 248 times since January 2020. A wide-ranging very recent review of the whole field, with 252 references, is in press in the journal Patterns [12].
I am keen to get started with DeepMind, who are owned by Google, which I imagine will proceed as follows:
JRH: Google, I am starting research on protein 427 in human chromosome 14. What is DeepMind's prediction of the 3D structure?
Google: I will submit the job to our computer cluster...(2 minutes later)...The nearest 3D structure is PDB entry 8XYZ but has a GDT score of only 55%. So, DeepMind offers a fold with an 89% GDT score. NB. it is likely a single-domain protein, so DeepMind is very confident in this GDT score estimate. This 3D structure prediction is in your mailbox.
JRH: Thankyou Google.
Google: You're welcome. Good luck with your research.
[NB For DeepMind to provide a GDT estimate without an experimental structure available is not yet solved!]
I am going to leave the last words to DeepMind out of respect for their achievement:
'Some rare diseases involve mutations in a single gene, resulting in a malformed protein which can have profound effects on the health of an entire organism. A tool like AlphaFold might help rare disease researchers predict the shape of a protein of interest rapidly and economically.'
![[DeepMind team]](https://www.iucr.org/__data/assets/image/0004/150763/image2.png)
The DeepMind AlphaFold team, in Zoom gallery view mode, at the moment that they got the email from Dr John Moult, Chair of CASP14, read out to them by their Team Leader John Jumper. Image credit: DeepMind.
Also, here is their figure animation, kindly provided by the DeepMind Press Office.
![[GDT gif]](https://www.iucr.org/__data/assets/image/0005/150764/image3.gif)
Two examples of protein targets in the free modelling category. AlphaFold predicts highly accurate structures measured against experimental result. image credit: DeepMind.
The more complete 8-minute video from DeepMind on their work is to be found here, which I found also highly interesting: https://www.youtube.com/watch?v=gg7WjuFs8F4&feature=youtu.be
I am very grateful to Mike Glazer for helpful discussions during the preparation of this article, as well as to Sarah Froggatt and Brian McMahon of IUCr Chester for expert technical help. I thank the DeepMind Press Office and the Royal Institution of Great Britain for prompt responses to my requests for figure, gif and movie materials.
[1] Zemla, A., Venclovas, Č, Moult, J. & Fidelis, K. (2001). Processing and evaluation of predictions in CASP4. Proteins, 45(Suppl 5), 13-21.
[2] Zemla, A. (2001). LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res. 31, 3370-3374.
[3] Cruickshank, D. W. J. (1999). Remarks about protein structure precision. Acta Cryst. D55, 583-601.
[4] Levinthal, C. (1968). Are there pathways for protein folding? Journal de Chimie Physique et de Physico-Chimie Biologique, 65, 44-45.
[5] Anfinsen, C. B. (1973). Principles that govern the folding of protein chains. Science, 181, 223-230.
[6] Blake, C. C. F. et al. (1965). The structure of hen egg white lysozyme: a three dimensional Fourier synthesis at 2Å resolution. Nature, 196, 1173-1176.
[7] Phillips, D. C. (1966). The three-dimensional structure of an enzyme molecule. Scientific American, 215(5), 78-93.
[8] Pauwels, K., Van Molle, I., Tommassen, J. & Van Gelder, P. (2007). Chaperoning Anfinsen: the steric foldases. Molecular Microbiology, 64, 917-922.
[9] Senior, A. W. et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577, 706-710. https://doi.org/10.1038/s41586-019-1923-7
[10] Senior, A. W. et al. (2019). Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins, 87, 1141-1148. https://doi.org/10.1002/prot.25834
[11] Cruickshank, D. W. J., Helliwell, J. R. & Johnson, L. N. (1992). Editors. Time-Resolved Macromolecular Crystallography. Proceedings of a Royal Society Discussion Meeting.
[12] Gao, W., Mahajan, S. P., Sulam, J. & Gray, J. J. (2020). Deep Learning in Protein Structural Modeling and Design. (2020). Patterns. In the press. https://doi.org/10.1016/j.patter.2020.100142
John Meurig Thomas, who was a world-renowned solid-state scientist, sadly died on 13 November 2020 at the age of 87.
Thomas was born the son of a coal miner in the Gwendraeth Valley in South Wales. He graduated with BSc and PhD degrees in Chemistry from the University College of Swansea. From 1958, he held academic positions at the University of Wales, initially as Lecturer at Bangor and then (from 1969) as Professor at Aberystwyth. In 1978, he was appointed as Professor and Head of the Department of Physical Chemistry at the University of Cambridge, before taking up the prestigious role of Director of the Royal Institution (RI) in 1986. Subsequently, he was Master of Peterhouse while continuing his research programme at the RI, and later took up honorary appointments in the Department of Materials Science at the University of Cambridge and the School of Chemistry at Cardiff University.
Thomas made major contributions to many key areas of chemical and materials sciences: his early work focused on the developing field of the physical chemistry of solids and included key contributions to mineralogy but his most significant scientific legacy will be in catalytic science, where he pioneered new techniques and systems, focusing on the development of fundamental knowledge that has allowed the optimisation and development of new catalytic technologies. His science in the 1990s and the earlier part of this century focused on microporous catalytic solids – zeolites and microporous aluminophosphates – where he and his team developed new materials with novel structural and catalytic properties. Much of his work in this field was accomplished in the Davy Faraday Laboratory of the RI, where Thomas continued the long history of world-leading scientific innovation.
Thomas published prolifically with over 1100 papers reviews and books during his long career. He also received recognition worldwide for his achievement, including the Royal Medal of the Royal Society, the Willard Gibbs Award of the American Chemical Society and the Bragg Prize Lectureship of the British Crystallographic Association.
Thomas’s contributions to science were, however, much broader than his wide-ranging research achievements: he mentored and encouraged scientists, especially younger colleagues, at key stages in their career; he showed great prescience in appreciating the significance of new developments, for example, solid-state NMR and computation in materials chemistry; and he was a passionate and eloquent advocate of the wonder and elegance of scientific knowledge and never happier than when lecturing to audiences of all ages, giving several discourse and schools lectures at the RI, perhaps most notably the Christmas lectures in 1987, which he gave jointly with David Phillips. He was also fascinated by the history of science and wrote lucidly and with great insight about some of the great figures of science. As Director of the RI in the 1980s, he was able to combine his passions for research, advocacy and the history of science.
![[JMT lecturing]](https://www.iucr.org/__data/assets/image/0003/150744/jmt_lecturing_1_0.png)
Photo by Nathan Pitt.
Thomas was also a patriotic Welshman who loved his native land, language and literature and indeed is celebrated in Wales not only as a scientist but as a poet. His work will be a lasting contribution to materials, structural and catalytic science and he will be missed, remembered and honoured worldwide.
For a list of papers by Thomas appearing in IUCr journals click here.
The International Summer School on Novel Methods of Atomic and Electronic Structure Studies at High Pressures was held in Bayreuth, Germany, from 31 August to 4 September 2020 (https://www.dubrovinskaia.uni-bayreuth.de/en/High-Pressure-Summer-School-2020/index.html). It was approved by the IUCr (through the IUCr Commission on High Pressure, CHP) and by the German Society for Crystallography (DGK). We expected to host our students from all over the world at the University of Bayreuth. However, the COVID-19 pandemic prevented the participants from gathering in the heart of Bavaria, but they enthusiastically supported the idea put forward by the organizers to attend via Zoom. The School was a great success, and we truly believe that it will become an annual event.
![[Fig1]](https://www.iucr.org/__data/assets/image/0018/150732/Fig1.jpg)
Bayreuth Team of lecturers and students (from the left to the right and from the last row to the front row): Timofey Fedotenko, Saiana Khandarkhaeva, Dominique Laniel, Thomas Meier, Kirill Vlasov, Sergey Ovsyannikov, Alena Krupp, Andrey Aslandukov, Alena Aslandukova, Natalia Dubrovinskaia, and Leonid Dubrovinsky (Achim Schaller is not in the photo).
The idea of the School emerged from a wish to share the experience collected within the Dubrovinsky-Dubrovinskaia group (https://www.dubrovinskaia.uni-bayreuth.de), whose research during the last decade has been mainly focused on the development of high-pressure single-crystal X-ray diffraction (sc-XRD) and nuclear magnetic resonance (NMR) in a diamond anvil cell (DAC). Our efforts enabled sc-XRD and NMR experiments at simultaneous high pressures of over 180 GPa and temperatures of several thousand degrees. This has led to remarkable findings in solid-state physics, chemistry and materials science at extreme conditions.
The School, whose organization was pushed by Dominique Laniel and Thomas Meier, aimed to provide an introduction to novel methods of atomic and electronic structure studies at high pressures in a DAC to early-career stage scientists (Masters and PhD students, post-docs), as well as scientists working in the field of high-pressure research. The focus was set on the new developments pioneered in high-pressure sc-XRD and NMR.
An overview of the necessary fundamental theoretical and methodological principles, including DAC preparation and strategy of experiments, was covered through lectures accompanied by multiple demonstrations, hands-on sessions and step-by-step tutorials. At the end of the School, participants were expected to be able to perform basic high-pressure sc-XRD and NMR experiments, as well as data processing and interpretation.
![[Fig2]](https://www.iucr.org/__data/assets/image/0019/150733/Fig2.jpg)
The academic background of the participants.
The School was attended online by 25 participants from 7 countries: Germany (8), India (6), France (3), China (3), Italy (2), UK (2), and Slovenia (1). Among them were 11 PhD students, seven post-docs, 2 Masters students, and 5 staff scientists. They were taught by 12 lecturers and instructors, ten of whom are either present or former members of our Bayreuth group.
The curriculum of the School comprised 30 teaching hours (6 hours per day) distributed as follows: The sc-XRD part of 23 hours included crystallography lectures (10 hours), covering topics ranging from the basics of crystallography and X-ray diffraction to advanced themes on data handling and software, lectures on the technical aspects of working with DACs, including a historical insight (3 hours), and lectures on synchrotron setups (2 hours); 2 hours were dedicated to application cases aimed at giving an insight into a wide range of scientific problems in physics, chemistry and materials sciences, which could be addressed using the methods taught at the School. The sc-XRD hands-on sessions included 6 hours of exercises (6 different hands-on sessions were led by four instructors for 4 groups of students in parallel). The NMR part of 7 hours included NMR lectures (4 hours) and hands-on sessions (3 hours) for three groups at different times.
![[Fig3]](https://www.iucr.org/__data/assets/image/0020/150734/Fig3.jpg)
Left: 28.08.20 - The Bayreuth team is ready to start! Right: 31.08.2020 - The lecture has just finished!
Undoubtedly, a School via Zoom differs a lot from an on-site workshop. As pointed out by many lecturers, the absence of immediate audience feedback, especially during the 'teaching' lectures, created an essential difficulty. Interestingly, the majority of lecturers attended the lectures of the other lecturers as well. For those who joined us online from the east coast of the USA, the School started in the middle of the night (at 2:30 a.m.) but, as one of the participants wrote, they 'enjoyed the School very much despite the time-zone differences with Germany.'
Taking into account the large variety of backgrounds and prior knowledge of the participants, the programme of the School was very diverse. The lectures spanned topics from the basics of symmetry and NMR to very complex material for advanced crystallographers and interesting cases encountered in real experiments. The majority of the participants evaluated this broadness very positively: on the one hand, everyone could follow the material corresponding to their own knowledge, and hence could improve it. On the other hand, one could see the material beyond one's own level and consider it as the next goal for studying.
We do believe that our experience would be useful for other colleagues who intend to run online schools and workshops in the future. We have summarized a few technical recommendations which could make online events even more efficient:
- It would be very helpful if the participants could download all materials and software at least one week before the course starts with no rush or stress;
- During the week prior to the course, we would suggest organizing one or two online sessions for testing the software: it would help to eliminate possible problems in advance and save valuable time at the beginning of the course;
- It should be specified in advance that the use of a large monitor or two separate monitors during hands-on sessions is an important technical prerequisite in order to realize the practical exercises efficiently. As several programs have to run in parallel and one has to quickly switch back and forth between the shared screen of the instructor and one's own running programs, just a single small monitor is insufficient.
- Time should be more generously allocated for the hands-on sessions; a common suggestion for our sc-XRD part was to increase it by a factor of 1.5, i.e. to plan 9 hours of hands-on instead of 6. This means that almost 40% of the teaching time should be allocated for hands-on.
- One should take care of the breaks between the lectures. According to the participants, a reasonable time (up to 15 min) for breaks would allow one to think about the lecture that had just finished and to get prepared for the following one.
![[Fig4]](https://www.iucr.org/__data/assets/image/0003/150735/Fig4.jpg)
Our teachers:
ND - Natalia Dubrovinskaia (Bayreuth), DL - Dominique Laniel, TM - Thomas Meier, KG - Konstantin Glazyrin, SK - Saiana Khandarkhaeva, AS - Achim Schaller, VP - Vitali Prakapenka, TF - Timofey Fedotenko, SC - Stella Chariton, SO - Sergey Ovsyannikov, EB - Elena Bykova, MB - Maxim Bykov, KF - Karen Friese, LSD - Leonid Dubrovinsky.
Student's feedback: 'All speakers did their talks in a 'not boring way', for that I would like to say special thanks to them.'
<![[Fig5]](https://www.iucr.org/__data/assets/image/0004/150736/Fig5.jpg)
The School is over. Thanks to all and goodbye!
Like Sleeping Beauty in the traditional story, after one hundred years of sleep, a number of enigmatic crystal structures are now coming to life. In the November 2020 issue of IUCrJ, the group of Mauro Gemmi in Pisa report the structure of KAlSiO4 (Mugnaioli et al., 2020). This is at least the third complicated crystal structure that has been studied by X-ray crystallography for a century without being solved, waiting for electron crystallography to reach its present level of sophistication. Recently, Ute Kolb’s group in Mainz solved the mineral vaterite (Mugnaioli et al., 2012) and Ken Inge et al. in Xiaodong Zou’s group in Stockholm solved the structure of the much used medicine bismuth subgallate (Wang et al., 2017). The reason for this surge in solving hard problems in crystallography is the rapid development of instrumentation, software and theory in electron crystallography.
X-ray crystallography is a (set of) scientific methods for determining atomic structure. Its results are so profound and spectacular, from the first ever correct description of a chemical structure [NaCl by Bragg (1913)], that one feels obliged to apologize when saying there are cases where other techniques are better. Yet, it is clear from the name of the method, X-ray ‘crystallography’, that it should be applied to crystals. Although very many things around us are crystalline, from NaCl to almost any mineral and including many organic and biological compounds if pure and concentrated, not everything is crystalline. This holds not only for gases and liquids, but also for very many solid objects, from some minerals to biological tissues. Notably, (to the general public) the most spectacular and well known result of X-ray crystallography, the double helix structure of DNA, is strictly speaking not a crystal but a rather well ordered fibre.
Having thus duly given our apologies, we may consider where we may apply variations of the tremendously powerful technique of X-ray crystallography, in order to depict atomic structures in cases where X-ray crystallography fails or at least is limited. The first consideration may be how to overcome the need for (large) crystals. Even a grain of sand, invisible to the human eye, is almost 10 micrometres in each direction and contains some 1012–1015 atoms. Many minerals are made up of crystals much smaller than this. They may form twins of random or preferred relative orientations or even sets of related but nonidentical structures. Luckily there is an alternative to X-rays for such cases; the use of electrons rather than X-rays.
Electrons react orders of magnitude more strongly with matter than do X-rays, and this opens the door to studying smaller crystals and even non-crystalline objects. Unfortunately, this extremely strong interaction has a major drawback: if the sample is more than a few nanometres thick, the electrons may scatter more than once on their way through the sample. This multiple scattering soon leads to uninterpretable results. For many decades, it was believed that this would make electron crystallography unsuitable for solving atomic structures in a fashion similar to X-ray crystallography. Only a very small number of groups pursued structure determination by electron diffraction and electron microscopy. However, in the last decade, their endurance has proven to be fruitful. A spectacular development in instrumentation, with super-fast and accurate electronic cameras and computer-controlled electron microscopes, has made it possible to collect complete 3D electron diffraction data on a crystal a million times smaller than needed for X-ray crystallography, in but a few seconds. It has turned out that the most severe problem for electron crystallography was not the multiple scattering, but the lack of complete 3D data.
The group led by Mauro Gemmi in Pisa is one of those who can take full advantage of the spectacularly improved situation. In collaboration with excellent mineralogists, they are now in a position where many enigmatic mineral structures can be accurately determined by electron crystallography.
Twinning and superstructures, with not only two but numerous intergrowing crystals, are notoriously hard to solve. In a very informative electron micrograph [see Fig. 2 of Mugnaioli et al. (2012)], a typical part of the mineral is shown. Although the sample is less than 100 × 200 nm, it is obvious that there are several small crystals intergrowing. Even without any knowledge of crystallography, it should be clear that it must be very difficult to solve the atomic structure of such a compound. And, for those who know the pros and cons of single-crystal and powder X-ray crystallography, it should be evident why this mineral has resisted being solved, in spite of numerous attempts, some even before W. H. Bragg and W. L. Bragg invented X-ray crystallography in 1912. The crystal size is smaller than what is needed even for X-ray powder diffraction.
Now, Gemmi and his group and several experienced mineralogists have at last solved the atomic structure of this mineral. It was possible by electron crystallography. For one thing, the very much stronger interaction of electrons than of X-rays with matter, makes it possible to use extremely small samples for the crystallographic analysis. It is clear from the above-mentioned figure that although the mineral is heavily twinned, small areas of some tens of unit cells (i.e. a few tens of nanometres) may in fact be single crystals.
Electron crystallography has the unique advantage over X-ray and neutron crystallography that both real and reciprocal space can be directly observed. Electron microscopy data, unlike any type of diffraction data, contain the crystallographic structure factor phases.
In addition to the contribution to the development of electron crystallography, the mineral kaliophilite (KAlSiO4), studied here, is of general interest for several reasons. It is one of a dozen minerals found with almost the same stoichiometric composition, but with varying degrees of superstructure. The present is by far the most complicated of them all, with a unit cell 27 times larger than the smallest in the series. These compounds are built up by chains and rings of AlO4 and SiO4 tetrahedra. The rings are mainly 6-rings, but they can vary in how the tetrahedra point upwards (U) or downwards (D). A most intriguing pattern in kaliophilite, making up just one unit cell, is shown in Fig. 1. The present mineral is the first with three different such schemes. Is this a portal into a world of infinite variations of these (and other!) minerals? It will certainly prompt X-ray crystallographers to keep an eye on what goes on in electron crystallography. In a wide number of fields, also outside mineralogy and pharmaceuticals, the many structures being quickly and accurately solved by electron crystallography should encourage the use of not only single-crystal and powder X-ray and neutron diffraction, but also electron diffraction.
Bragg, W. L. (1913). Proc. R. Soc. London A, 88, 428–438.
Mugnaioli, E., Andrusenko, I., Schüler, T., Loges, N., Dinnebier, R. E., Panthöfer, M., Tremel, W. & Kolb, U. (2012). Angew. Chem. Int. Ed. 51, 7041–7045.
Wang, Y., Takki, S., Cheung, O., Xu, H., Wan, W., Öhrström, L. & Inge, A. K. (2017). Chem. Commun. 53, 7018–7021.
This article was originally published in IUCrJ (2020). 7, 951–952.
As I write this, the news of the COVID-19 pandemic is finally starting to lighten, with the development of new vaccines that seem to be highly effective. So far, two in Russia have been approved, but without the usual Phase 3 trials needed for international use. Two others (Pfizer and Moderna) seem to have something like a 90–95% efficacy. There are many more in various stages of development and testing. The remarkable speed with which this has happened is a real triumph of science, in which crystallography has played a significant role. Here at Oxford University, working with AstraZeneca, a third vaccine is showing promise. So, with a bit of luck, within the next few months, we shall be able to resume some normal life. It has been a long haul, hasn’t it? But we are not finished yet. I do seem to remember that I used to have an office back in the lab; I look forward to some time soon being able to get back to the lab and meet face to face (rather than Zoom) with my colleagues over morning coffee. And as I write, the UK has approved the Pfizer vaccine!
Recently, the Gregori-Aminoff Prize for 2021 was awarded to Henry Chapman (University of Hamburg, Germany), Janos Hajdu (Uppsala University, Sweden) and John Spence (Arizona State University, Tempe, AZ, USA). I am pleased that John Spence agreed to write a special article for us in this issue of the IUCr Newsletter about serial crystallography at X-ray free-electron lasers and synchrotrons. The enormous speed with which vast amounts of data can be collected using modern techniques like this is just breathtaking, especially for someone like me, who grew up taking X-ray crystallographic data using the old-fashioned film cameras. Today, X-ray data can be collected in seconds, where it used to take several weeks. Crystallographers of today have it much easier!
When I first began research in crystallography, we were all told that crystals were defined in terms of periodic arrangements of molecules and atoms, maybe with a little disorder thrown in to make them more real. However, nature has a habit, now and then, of throwing a curveball, with the historic discovery of quasicrystals by Dan Shechtman in 1984. Years before, crystallographers like Alan Mackay at Birkbeck College London already had the foresight to question the doctrine of strict periodicity. In this issue, István and Balazs Hargittai tell us about Alan Mackay and the latest Nobel Laureate, Roger Penrose. Intriguingly, the story involves a legal dispute over the Penrose pattern and … wait for it ... toilet paper! Read about it here.
The Seventh Crystal Structure Prediction (CSP) Blind Test has been announced. The ability to predict a crystal structure, especially one containing organic molecules, is of great importance to the pharmaceutical industries in their bid to design new drugs. Most new pharmaceutical compounds are patented using crystallographic data, and one of the areas of interest is polymorphism, where a molecule can form crystals with different structures. Each polymorph may have different properties, such as solubility differences that affect the drug uptake, and can be patented separately. The Cambridge Crystallographic Data Centre invites applications to participate.
Clearly, structure prediction is gaining in importance. By coincidence, just the other day, another type of structure prediction has hit the news. This is the announcement that has excited the media headlines from the recent Critical Assessment of Protein Structure Prediction (CASP) experiments. These have shown that it is possible, with apparently staggering success, to start with an amino acid sequence and predict the way it folds up into a protein molecule. For crystallographers, this raises a question. Do we still need to do diffraction measurements in structural biology? John Helliwell discusses this important topic in this issue of the Newsletter.
One of the most disagreeable aspects of getting older is that you hear ever more often of the passing of old friends. So, it was with regret that I heard about the recent death, after a long illness, of Sir John Meurig Thomas. We both go back a long way, having first encountered each other when he sent in a manuscript for publication, and I was asked by the journal editor of the time to be a reviewer. It turned out, by chance, that I happened to be the only other person in the UK who had inside knowledge about the topic. I won’t bore you with the details, but suffice it to say that we got into a friendly discussion on the subject of his paper, which then led to a long-lasting friendship. John was a superb scientist, working both in the fields of crystallography and materials science. He was also an excellent speaker and science presenter. In 1987, when he was Director of the famous Royal Institution in London, he gave that year’s Christmas Lectures on Crystals and Lasers. I recommend that you watch these yourself here. I was lucky enough to be in the audience during his first lecture.
But it doesn’t stop there. I have just heard that another old friend, Patience Thomson, has also passed on this week. Patience was the younger daughter of W. L. Bragg and granddaughter of W. H. Bragg. I got to know her when we were planning the Two Braggs Exhibition at the University of Warwick in 2013 (and see https://www.diamond.ac.uk/Home/News/LatestNews/02-09-14.html). Some years earlier, I was invited to give a lecture on the Braggs, and I was able to meet up with Lady Margaret Heath, Patience’s elder sister. She very kindly (and I thought rashly) let me take home the Bragg family photograph album (now in the archives of the Royal Institution). Margaret then introduced me to Patience. She, and other members of the Bragg family, joined in the planning, giving access to their homes and material, much of which has never been published. Patience was such a lovely person and was well known to the crystallography community. I used to enjoy visiting her at her home south of Oxford and seeing all the great paintings and drawings made by her father and grandfather, and chatting about all the famous people she had met. That resulted in a joint book on the autobiographies of Sir Lawrence and Lady Bragg. Patience was married to David, son of G. P. Thomson and grandson of J. J. Thomson (that’s four Nobel Prize winners!) – quite a record.
![[Patience and David Thomson]](https://www.iucr.org/__data/assets/image/0015/150720/exhib-62rev.jpg)
Patience and David Thomson at the Two Braggs Exhibition in 2013.
Have you seen the stunning new website for the IUCr? If you have not done so already, do have a look. I should also like to mention that the early back issues of the Newsletter are now online and available here.
In the meantime, I hope you will stay safe and wish you well for the New Year.
Carbonic anhydrase (CA) is one of the most studied proteins in molecular life sciences. It has been especially widely used as a biophysical model for protein stability and binding studies (Krishnamurthy et al., 2008). CA is also one of the fastest known enzymes that catalyze the reversible hydration of carbon dioxide to bicarbonate anion and acid protons. Humans contain 15 highly homologous CA isoforms. Three of them do not perform the catalytic function of CA owing to the absence of Zn(II) in the active center as at least one of the three His residues holding the metal cation has changed to another amino acid. The remaining 12 isoforms have variable expression in tissues: some of them are localized intracellularly in the cytosol or mitochondria, whereas others are located outside of and attached to the cell membrane via a lipid anchor or a transmembrane helix. Several of these isoforms have been implicated and are over-expressed in numerous cancers. Thus research in the CA field is quite active and has a significant impact on all sectors of protein science (Dodgson et al., 1991; Chegwidden et al., 2000; Frost & McKenna, 2014; Supuran & Simone, 2015; Matulis, 2019).
Since the discovery of sulfanilamide and acetazolamide in the 1940s, it has been established that primary sulfonamides are highly specific binders and inhibitors of CAs. Numerous sulfonamides bearing thiazide, furosemide and other moieties have become clinical drugs used to treat hypertension and edema, as well as being used as diuretics. Dorzolamide and brinzolamide have been used as antiglaucoma agents. However, there is also a discussion on the applicability of some recently designed CA inhibitors (Jonsson & Liljas, 2020).
Since the first X-ray crystal structure of a CA (Liljas et al., 1972), the atomic structures of ten human CA isoforms have been determined. The missing structures are isoforms expressed in mitochondria, CA VA and CA VB. However, isoform CA II is by far the most abundant in the Protein Data Bank (Linkuvienė et al., 2018). Most efforts to crystallize CA isoforms have been in the field of drug design for understanding how low-molecular-weight compounds recognize and distinguish CA isoforms. A good drug is supposed to selectively inhibit only one CA isoform, the target of a disease.
Despite these numerous studies, a truly detailed atomic level understanding of CA enzymatic activity, interaction of the enzyme with its substrate CO2 and especially the behavior of water molecules are not fully understood. The situation is further complicated by the substrate being gaseous, making the methodology more complex.
Writing in the November 2020 issue of IUCrJ, Kim et al. (2020) advance our understanding of the catalytic mechanism of CA II by producing a mutation and comparing it with the native protein. The mutation (V143I) has previously been introduced (Fierke et al., 1991; West et al., 2012). The change of valine to isoleucine is one of the smallest possible perturbations of a protein structure (where one hydrophobic amino acid is changed to another hydrophobic amino acid that differs by a single CH2 group) that can be introduced into a protein with the hope that such a change would cause almost no deviation in the catalytic properties of the enzyme.
The mutation actually changed the catalytic properties of the enzyme quite substantially. The kcat value was not significantly affected, but the affinity of the substrate CO2 for CA II decreased tenfold, from 10 mM Km for CA II to 100 mM Km in the mutant. The study by Kim et al. (2020) demonstrates the structural arrangement of the CO2 in the active site and shows its diminished mobility in the mutant as a result of the decrease of available space in the substrate binding pocket.
As in a previous study where pressurized CO2 was applied (Kim et al., 2016), this study determined the crystal structures not only at ambient pressure in air, but also at 7, 13 and 15 atm pressure of CO2 gas (the 7 atm binding site is illustrated in Fig. 1). The concentration of CO2 in the experiment increased from approximately 300 ppm in ambient air to more than 10 atm in the pressurized setting. Thus, the increase was approximately 30-thousand-fold, enabling visualization of the CO2 molecule bound in the active site of the protein. The mutation caused a shift of its position and partial conversion to an HCO3– bicarbonate anion.
Furthermore, the study demonstrated that the proton transfer pathway around His64 has been largely unaffected, but there were subtle changes in the structure of the entrance conduit (EC) water molecules. It would be very interesting to see the positions of each proton in the conduit, but this would require neutron crystallography (Fisher et al., 2012). Still, this remarkable study shows the effect of a mutation on the structure of the catalytic mechanism of CA II in the greatest detail so far and will be used in the design of compounds for pharmaceutical purposes.
Fierke, C. A., Calderone, T. L. & Krebs, J. F. (1991). Biochemistry, 30, 11054–11063.
Jonsson, B.-H. & Liljas, A. (2020). Biophys. J. 119, 1275–1280.
This article was originally published in IUCrJ (2020). 7, 953–954.
Over a decade ago, the first proof-of-principle serial femtosecond crystallography (SFX) experiment started the era of nano- and micro-crystallography at X-ray free-electron lasers (XFELs) (Chapman et al., 2011). This opened up unmatched opportunities for structural studies of radiation-sensitive metalloproteins (Kern et al., 2015; Suga et al., 2020) and small, weakly diffracting protein crystals, including membrane proteins (Johansson et al., 2017).
Moreover, the femtosecond XFEL pulse duration allows light to be shed on the structure–function relationship of photo-activated proteins by enabling sub-picosecond time-resolution in optical-pump X-ray-probe SFX studies (Orville, 2020). In this case, small crystal sizes are a necessity due to the limited penetration depth of the pump laser in protein crystals. By combining radiation-damage-free data collection from tiny crystals at room temperature with the sub-picosecond time-resolution in pump–probe experiments, SFX is a very powerful tool in the hands of structural biologists (Spence, 2017).
However, despite continuous efforts to improve state-of-the art SFX, it still suffers from major technical challenges that limit the throughput and accessibility of this relatively new technique, especially for non-expert user groups. These challenges occur in all aspects of the experiment, starting with radically different sample requirements (Beale et al., 2019), non-standardized methods and complex sample delivery equipment (Grünbein & Nass Kovacs, 2019), and finishing with demanding and heavily supervised data collection and analysis that requires significant computational resources.
In particular, analysis of the SFX data is complex. A fundamental limitation is the femtosecond pulse duration precluding rotation of the crystal during exposure, which results in measuring diffraction patterns from still crystals in random orientations that contain only partially integrated reflections. Moreover, it suffers from many sources of errors due to the stochastic nature of XFEL pulses and morphological differences between measured crystals. The XFEL pulse instability affects shot-to-shot intensity, width and centre of the wavelength spread, and the exact shape of the energy spectrum. On the sample side, the distribution of crystal sizes and unit-cell dimensions and the degree of non-isomorphism and mosaicity contribute to the uncertainty in SFX data analysis. All this makes assembly of a complete set of full and accurate structure factor amplitudes for molecular replacement strategies challenging, and even more so for de novo structure determination (Nass et al., 2016).
To date, the most popular SFX data processing programs use the so-called `Monte Carlo' method, which is based on averaging partial intensity measurements of equivalent reflections from many different crystals. This method can decrease the contribution of random sources of errors described above to the structure factor amplitudes, but it is inefficient in terms of sample and beam time usage, as it requires tens of thousands of indexed diffraction patterns to converge (Kirian et al., 2010). Extension of this `Monte Carlo' method with post-refinement and partiality corrections recently provided tangible improvements to the determination of accurate anomalous structure factor differences required for de novo phasing by needing significantly fewer diffraction images than before (Nass et al., 2020). However, these methods still rely on integration of the Bragg spot intensities located under globally defined, fixed-width areas predicted by incomplete diffraction models without taking into account the shape and size of the spots (Fig. 1).
In this issue of IUCrJ, Mendez and co-workers (Mendez et al., 2020) present a new data analysis approach, diffBragg, that proposes to increase the accuracy of structure factor amplitudes attainable in SFX experiments. In contrast to the Monte Carlo approach defined in Kirian et al., diffBragg employs an elaborated physical model and maximum-likelihood estimation to describe the intensity of all observed pixels in Bragg spots and in their vicinity across all images. As opposed to other SFX data processing programs, the model presented by the authors takes into account most of the important factors that determine the intricate shapes, sizes and intensity profiles of Bragg spots to improve accuracy of final structure factor amplitudes. The parameters optimized by the pixel modelling include crystal orientation, unit-cell parameters, intensity scale factor, mosaic parameters, incident photon spectra and a starting list of structure factor amplitudes provided by an initial round of conventional data processing.
This work paves the way for next-generation SFX data analysis by enabling us to decouple contributions of these various experimental sources of error from the measured Bragg spot intensities, which otherwise obscure structure factor amplitude determination. The authors achieve an order of magnitude reduction in the number of required diffraction images yielding the same accuracy of the anomalous structure factor amplitudes, compared with the conventional Monte Carlo approach. Although these improvements were demonstrated with simulated diffraction data of ytterbium-derivative lysozyme crystals, the method was shown to be robust against many experimental sources of error (e.g. background scattering, measurement noise, typical detector panel displacement). This indicates that diffBragg could have a remarkable impact on future SFX experiments by addressing one of the main bottlenecks of SFX – the need for high amounts of data, translating into large sample quantities. Furthermore, the high accuracy achieved with pixel-level refinement can provide a clear view on extremely sensitive details such as two differently oxidized metal atoms (Sauter et al., 2020). The ability to observe such level of detail expands opportunities for new and exciting experiments to consider for the upcoming years of SFX science.
Grünbein, M. L. & Nass Kovacs, G. (2019). Acta Cryst. D75, 178–191.
Johansson, L. C., Stauch, B., Ishchenko, A. & Cherezov, V. (2017). Trends Biochem. Sci. 42, 749–762.
Kern, J., Yachandra, V. K. & Yano, J. (2015). Curr. Opin. Struct. Biol. 34, 87–98.
Nass, K. (2019). Acta Cryst. D75, 211–218.
Orville, A. M. (2020). Curr. Opin. Struct. Biol. 65, 193–208.
Sauter, N. K., Kern, J., Yano, J. & Holton, J. M. (2020). Acta Cryst. D76, 176–192.
Spence, J. C. H. (2017). IUCrJ, 4, 322–339.
This article was originally published in IUCrJ. (2020). 7, 949–950.
On behalf of the National Institute of Materials Physics at Bucharest-Măgurele, Romania, and as a last collaborator of Dr Nicolae C. Popa, a Romanian physicist with many contributions to IUCr journals (and, lastly, to Volume H of International Tables for Crystallography) as both author and reviewer, I am charged with announcing his passing away on 15 September 2020, after a very long physical illness and a very short time after his wife’s decease (on 23 August 2020).
Dr Nicolae C. Popa was born in Deduleşti (Argeş), Romania, on 17 December 1945. He graduated from the Faculty of Physics within the University of Bucharest in 1968, and in that very year was employed as a researcher at the Institute of Atomic Physics at Bucharest-Măgurele, defending his doctoral dissertation entitled Extinction processes in diffraction of neutrons and X-rays in quasi-ideal crystals on 3 February 1989, under supervision of Dr Dorel Bally.
He was an employee of several Romanian research centers during his career, including the Institute of Atomic Physics at Bucharest-Măgurele (1968–1977), Institute of Nuclear Power Reactors at Piteşti (1977–1981), Institute of Nuclear Physics and Engineering, Bucharest-Măgurele (1989–1990), and National Institute of Materials Physics, Bucharest-Măgurele, formerly the Institute of Physics and Technology of Materials (1987–1989, 1990–2000, 2001–2002, 2007–2016), and fellow of three foreign centers for longer or shorter periods: University of Rio de Janeiro (December 1973–December 1974) as IAEA fellow, Joint Institute for Nuclear Research, Dubna, USSR (November 1981–April 1987) as researcher, National Institute of Standards and Technology, Boulder, CO, USA (June 2000–December 2001) as guest researcher, and again Joint Institute for Nuclear Research, Dubna, Russia (October 2002–May 2007), this time as Neutron Physics Laboratory Deputy Director.
During his career, Dr Nicolae C. Popa published both theoretical and experimental studies in X-ray and neutron diffraction, 18 original and highly visible papers [see e.g. Popa (1998)] being published in IUCr journals since 1976 (Popa, 1976). As a recognition of his contributions to the applied crystallography field, he was invited to join the author contributors for the new volume of the prestigious International Tables for Crystallography on the topic of powder diffraction, in which he wrote on determination of elastic stress and strain in polycrystalline samples via powder diffraction (Popa, 2019).
With sincere regrets for this loss, Dr George-Adrian Lungu.
Popa, N. C. (1976). Acta Cryst. A32, 635–641.
Popa, N. C. (1998). J. Appl. Cryst. 31, 176–180.
This article was originally published in J. Appl. Cryst. (2020). 53, 1628.
The IUCr Newsletter (ISSN 1067-0696; coden IUC-NEB) is published electronically by the International Union of Crystallography. Feature articles, meeting announcements and reports, information on research or other items of potential interest to crystallographers may be submitted to the Editors at any time. Items will be selected for publication on the basis of suitability, content, style, timeliness and appeal.
Copyright © - All Rights Reserved - International Union of Crystallography
