Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

Error estimates in atom coordinates and B factors in macromolecular crystallography

The overall diffraction precision index (DPI) of a biological macromolecule crystal structure was first described by Cruickshank in 1999. This topical review proceeds from this point and describes the subsequent elaboration of the index to individual atom coordinates. Additional developments were introduced by the availability of a webserver, which provides a transformed PDB entry with individual atom coordinate errors derived from applying the DPI method using the parameters provided by the authors and then subsequently added to the PDB file. This webserver has been extensively used and harnessed in describing non-covalent distance error estimates as well as assessing the significance, or otherwise, of atom movements in a variety of studies. The standard uncertainties on a biological macromolecule's atomic displacement parameters (the 'B factors') has been an entirely different challenge but is obviously important since the crystallographic community has developed the habit of quoting B factors to a false precision in papers. This can convey a false certainty in the dynamics of a structure. A method involving parallelisation of workflows for diffraction image data processing does however offer estimates of the precision of B factors.

75 years of IUCr Journals: 1948 to 2023, an Editor-in-Chief's perspective

On the 75th anniversary of the International Union of Crystallography (IUCr) and its journals, a brief history is presented, highlighting selected publications, based on citations and on other criteria of note for each journal. Emerging from the pandemic, prospects for the future are considered, especially in the context of the ongoing transformation to open-access research publication affecting all scientific research journals.

Machine learning for scattering data: strategies, perspectives and applications to surface scattering

Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.

Rapid response to emerging biomedical challenges and threats

As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.

Synchrotron Radiation as a Tool for Macromolecular X-Ray Crystallography: a XXI Century Perspective

Intense X-rays available at powerful synchrotron beamlines provide macromolecular crystallographers with an incomparable tool for investigating biological phenomena on an atomic scale. The resulting insights into the mechanism's underlying biological processes have played an essential role and shaped biomedical sciences during the last 30 years, considered the "golden age" of structural biology. In this review, we analyze selected aspects of the impact of synchrotron radiation on structural biology. Synchrotron beamlines have been used to determine over 70% of all macromolecular structures deposited into the Protein Data Bank (PDB). These structures were deposited by over 13,000 different research groups. Interestingly, despite the impressive advances in synchrotron technologies, the median resolution of macromolecular structures determined using synchrotrons has remained constant throughout the last 30 years, at about 2 Å. Similarly, the median times from the data collection to the deposition and release have not changed significantly. We describe challenges to reproducibility related to recording all relevant data and metadata during the synchrotron experiments, including diffraction images. Finally, we discuss some of the recent opinions suggesting a diminishing importance of X-ray crystallography due to impressive advances in Cryo-EM and theoretical modeling. We believe that synchrotrons of the future will increasingly evolve towards a life science center model, where X-ray crystallography, Cryo-EM, and other experimental and computational resources and knowledge are encompassed within a versatile research facility. The recent response of crystallographers to the COVID-19 pandemic suggests that X-ray crystallography conducted at synchrotron beamlines will continue to play an essential role in structural biology and drug discovery for years to come.

Ligand-centered assessment of SARS-CoV-2 drug target models in the Protein Data Bank

A bright spot in the SARS‐CoV‐2 (CoV‐2) coronavirus pandemic has been the immediate mobilization of the biomedical community, working to develop treatments and vaccines for COVID‐19. Rational drug design against emerging threats depends on well‐established methodology, mainly utilizing X‐ray crystallography, to provide accurate structure models of the macromolecular drug targets and of their complexes with candidates for drug development. In the current crisis, the structural biological community has responded by presenting structure models of CoV‐2 proteins and depositing them in the Protein Data Bank (PDB), usually without time embargo and before publication. Since the structures from the first‐line research are produced in an accelerated mode, there is an elevated chance of mistakes and errors, with the ultimate risk of hindering, rather than speeding up, drug development. In the present work, we have used model‐validation metrics and examined the electron density maps for the deposited models of CoV‐2 proteins and a sample of related proteins available in the PDB as of April 1, 2020. We present these results with the aim of helping the biomedical community establish a better‐validated pool of data. The proteins are divided into groups according to their structure and function. In most cases, no major corrections were necessary. However, in several cases significant revisions in the functionally sensitive area of protein–inhibitor complexes or for bound ions justified correction, re‐refinement, and eventually reversioning in the PDB. The re‐refined coordinate files and a tool for facilitating model comparisons are available at https://covid-19.bioreproducibility.org.

The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years

It has been increasingly recognized that preservation and public accessibility of primary experimental data are cornerstones necessary for the reproducibility of empirical sciences. In the field of molecular crystallography, many journals now recommend that authors of manuscripts presenting a new crystal structure should deposit their primary experimental data (X-ray diffraction images) to one of the dedicated resources created in recent years. Here, we describe our experiences developing the Integrated Resource for Reproducibility in Molecular Crystallography (IRRMC) and describe several examples of a crucial role that diffraction data can play in improving previously determined protein structures. In its first four years, several hundred crystallographers have deposited data from over 5200 diffraction experiments performed at over 60 different synchrotron beamlines or home sources all over the world. In addition to improving the resource and curating submitted data, we have been building a pipeline for extraction or, in some cases, reconstruction of the metadata necessary for seamless automated processing. Preliminary analysis indicates that about 95% of the archived data can be automatically reprocessed. A high rate of reprocessing success shows the feasibility of using the automated metadata extraction and automated processing as a validation step for the deposition of raw diffraction images. The IRRMC is guided by the Findable, Accessible, Interoperable, and Reusable data management principles.