The Cambridge Structural Database (CSD) currently contains over 400 000 transition‐metal‐containing entries, however many entries still lack curated oxidation‐state assignments. Surveying and editing the remaining entries would be far too resource‐ and time‐intensive to be carried out manually. Here, a highly reliable automated workflow for oxidation‐state assignment in transition‐metal coordination complexes via CSD Python API (application programming interface) scripts is presented. The strengths and limitations of the bond‐valence sum (BVS) method are discussed and the use of complementary methods for improved assignment confidence is explored. In total, four complementary techniques have been implemented in this study. The resulting workflow overcomes the limitations of the BVS approach, widening the applicability of an automated procedure to more CSD entries. Assignments are successful for 99% of the cases where a high consensus between different methodologies is observed. Out of a total number of 54 999 unique metal atoms in a test dataset, the procedure yielded the correct oxidation state in 47 072 (86%) of cases.
The abruptness of spin crossover (SCO) is related to intermolecular energy changes occurring over the course of an SCO transition. Crossover is abrupt when SCO-induced strain is accommodated synergistically in a few key interactions.
The interpretation of crystal structures in terms of intermolecular interaction energies enables phase stability and polymorphism to be rationalized in terms of quantitative thermodynamic models, while also providing insight into the origin of physical and chemical properties including solubility, compressibility and host–guest formation. The Pixel method is a semi-empirical procedure for the calculation of intermolecular interactions and lattice energies based only on crystal structure information. Molecules are represented as blocks of undistorted ab initio molecular electron and nuclear densities subdivided into small volume elements called pixels. Electrostatic, polarization, dispersion and Pauli repulsion terms are calculated between pairs of pixels and nuclei in different molecules, with the accumulated sum equating to the intermolecular interaction energy, which is broken down into physically meaningful component terms. The MrPIXEL procedure enables Pixel calculations to be carried out with minimal user intervention from the graphical interface of Mercury, which is part of the software distributed with the Cambridge Structural Database (CSD). Following initial setup of a crystallographic model, one module assigns atom types and writes necessary input files. A second module then submits the required electron-density calculation either locally or to a remote server, downloads the results, and submits the Pixel calculation itself. Full lattice energy calculations can be performed for structures with up to two molecules in the crystallographic asymmetric unit. For more complex cases, only molecule–molecule energies are calculated. The program makes use of the CSD Python API, which is also distributed with the CSD.
During the COVID-19 pandemic, structural biologists have rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 200 structures from SARS-CoV previously solved, 367 structures covering 16 of the viral proteins have been released in the span of only 6 months.These structural models serve as basis for research worldwide to understand how the virus hijacks human cells, for structure-based drug design and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and are even more common among these structures, which were solved under immense pressure.From the beginning of the pandemic, the Coronavirus Structural Taskforce has categorized, evaluated and reviewed all of these experimental protein structures in order to help downstream users and original authors. Our website also offers improved models for many key structures, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge, and others. Here, we describe our work for the first time, give an overview of common problems, and describe a few of these structures that have since acquired better versions in the worldwide Protein Data Bank, either from new data or as depositor re-versions using our suggested changes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.