On vital aid: the why, what and how of validation

The need for validation of macromolecular crystal structures is discussed. A general approach to validation is presented, together with examples of its implementation in the special case of macromolecular crystallography

Undergraduates improve upon published crystal structure in class assignment

Recently, 57 undergraduate students at the University of Michigan were assigned the task of solving a crystal structure, given only the electron density map of a 1.3 Å crystal structure from the electron density server, and the position of the N‐terminal amino acid. To test their knowledge of amino acid chemistry, the students were not given the protein sequence. With minimal direction from the instructor on how the students should complete the assignment, the students fared remarkably well in this task, with over half the class able to reconstruct the original sequence with over 77% sequence identity, and with structures whose median ranked in the 91st percentile of all structures of comparable resolution in terms of structure quality. Fourteen percent of the students' structures produced Molprobity steric clash validation scores even better than that of the original structure, suggesting that multiple students achieved an improvement in the overall structure quality compared to the published structure. Students were able to delineate limiting case chemical environments, such as charged interactions or complete solvent exposure, but were less able to distinguish finer details of hydrogen bonding or hydrophobicity. Our results prompt several questions: why were students able to perform so well in their structural validation scores? How were some students able to outperform the 88% sequence identity mark that would constitute a perfect score, given the level of degenerate density or surface residues with poor density? And how can the methodology used by the best students inform the practices of professional X‐ray crystallographers? © 2014 by The International Union of Biochemistry and Molecular Biology, 42(5):398–404, 2014.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108627/1/bmb20811.pd

Case-controlled structure validation.

Although many factors influence the quality of a macromolecular crystal structure, validation criteria are usually only calibrated using one of these factors, the resolution. For many purposes this is sufficient, but there are times when one wishes to compare one set of structures with another and the comparison may be invalidated by systematic differences between the sets in factors other than resolution. This problem can be circumvented by borrowing from medicine the idea of the case-matched control: each structure of interest is matched with a control structure that has similar values for all relevant factors considered in this study. In addition to resolution, these include the size of the structure (as measured by the volume of the asymmetric unit) and the year of deposition. This approach has been applied to address two questions: whether structures from structural genomics efforts reach the same level of quality as structures from traditional sources and whether the impact factor of the journal in which a structure is published correlates with structure quality. In both cases, once factors influencing quality have been controlled in the comparison, there is little evidence for a systematic difference in quality

Cryo-EM map interpretation and protein model-building using iterative map segmentation.

A procedure for building protein chains into maps produced by single-particle electron cryo-microscopy (cryo-EM) is described. The procedure is similar to the way an experienced structural biologist might analyze a map, focusing first on secondary structure elements such as helices and sheets, then varying the contour level to identify connections between these elements. Since the high density in a map typically follows the main-chain of the protein, the main-chain connection between secondary structure elements can often be identified as the unbranched path between them with the highest minimum value along the path. This chain-tracing procedure is then combined with finding side-chain positions based on the presence of density extending away from the main path of the chain, allowing generation of a Cα model. The Cα model is converted to an all-atom model and is refined against the map. We show that this procedure is as effective as other existing methods for interpretation of cryo-EM maps and that it is considerably faster and produces models with fewer chain breaks than our previous methods that were based on approaches developed for crystallographic maps

Three-dimensional Structure Databases of Biological Macromolecules

Databases of three-dimensional structures of proteins (and their associated molecules) provide: (a)Curated repositories of coordinates of experimentally determined structures, including extensive metadata; for instance information about provenance, details about data collection and interpretation, and validation of results.(b)Information-retrieval tools to allow searching to identify entries of interest and provide access to them.(c)Links among databases, especially to databases of amino-acid and genetic sequences, and of protein function; and links to software for analysis of amino-acid sequence and protein structure, and for structure prediction.(d)Collections of predicted three-dimensional structures of proteins. These will become more and more important after the breakthrough in structure prediction achieved by AlphaFold2. The single global archive of experimentally determined biomacromolecular structures is the Protein Data Bank (PDB). It is managed by wwPDB, a consortium of five partner institutions: the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), the Protein Data Bank Japan (PDBj), the BioMagResBank (BMRB), and the Electron Microscopy Data Bank (EMDB). In addition to jointly managing the PDB repository, the individual wwPDB partners offer many tools for analysis of protein and nucleic acid structures and their complexes, including providing computer-graphic representations. Their collective and individual websites serve as hubs of the community of structural biologists, offering newsletters, reports from Task Forces, training courses, and “helpdesks,” as well as links to external software. Many specialized projects are based on the information contained in the PDB. Especially important are SCOP, CATH, and ECOD, which present classifications of protein domains

Automatic rebuilding and optimization of crystallographic structures in the Protein Data Bank

Motivation: Macromolecular crystal structures in the Protein Data Bank (PDB) are a key source of structural insight into biological processes. These structures, some >30 years old, were constructed with methods of their era. With PDB_REDO, we aim to automatically optimize these structures to better fit their corresponding experimental data, passing the benefits of new methods in crystallography on to a wide base of non-crystallographer structure users

To B or not to B: a question of resolution?

A simple rule of thumb based on resolution is not adequate to identify the best treatment of atomic displacements in macromolecular structural models. The choice to use isotropic B factors, anisotropic B factors, TLS models or some combination of the three should be validated through statistical analysis of the model refinement