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

Abstract

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.

A study on the structure, mechanism, and biochemistry of kanamycin B dioxygenase (KanJ)—an enzyme with a broad range of substrates

“…The initial https://doi.org/10.2210/pdb6S0R/pdb model was used as a model for a molecular replacement (MR) to determine the https://doi.org/10.2210/pdb6S0T/pdb structure, and the refined https://doi.org/10.2210/pdb6S0T/pdb model was used as a MR model to determine the remaining structures. The structures were refined as described in a recent review [35] and validated using molprobity [36] and wwpdb validation service [37].…”

Validation of the Crystallography Open Database using the Crystallographic Information Framework

“…Robust automated syntax checks, validation against formal dictionaries (Adams et al, 2011) and especially the application of formal ontologies (Hester, 2016) are among the key actions that have the potential to greatly improve data reusability and interoperability. Reusability can be even further enhanced by employing data analysis pipelines that enable one to automatically reproduce or even improve the collected results as showcased in recent work in the field of macromolecular crystallography (Joosten et al, 2014;Grabowski et al, 2019). The discipline of crystallography in general is known for placing great importance on ensuring that data are both reproducible and FAIR, with the development of CIF being recognized as one of the most important achievements towards this goal (Helliwell, 2019).…”

Rapid response to emerging biomedical challenges and threats

“…Some repositories check the consistency between the diffraction data and structural models submitted to the PDB. For example, in the IRRMC resource at https:// proteindiffraction.org, all diffraction data are automatically re-processed to verify that the correct data are associated with each structure (Grabowski et al, , 2019. It is important to note that roughly 5% of the original data deposited in the IRRMC were initially inconsistent with the corresponding PDB deposition, which shows that data management in crystallographic laboratories is still in need of improvement (Zimmerman et al, 2014;Cooper et al, 2021).…”

“…The importance of archiving primary diffraction data has been underscored by IUCr Journals in a joint editorial (Helliwell et al, 2019). To archive their diffraction data, crystallographers now have specialized repositories at their disposal, with SBGRID (Meyer et al, 2016) and IRRMC (Grabowski et al, 2019) being the most important. In addition, several general-purpose data repositories such as Zenodo (https://zenodo.org/) and Figshare (Singh, 2011) can be used; however, the general-purpose repositories are usually unstructured and accept data 'as is'.…”