Twilight reloaded: the peptide experience

Controlled dehydration, structural flexibility and gadolinium MRI contrast compound binding in the human plasma glycoprotein afamin

Afamin, which is a human blood plasma glycoprotein, a putative multifunctional transporter of hydrophobic molecules and a marker for metabolic syndrome, poses multiple challenges for crystallographic structure determination, both practically and in analysis of the models. Several hundred crystals were analysed, and an unusual variability in cell volume and difficulty in solving the structure despite an ∼34% sequence identity with nonglycosylated human serum albumin indicated that the molecule exhibits variable and context-sensitive packing, despite the simplified glycosylation in insect cell-expressed recombinant afamin. Controlled dehydration of the crystals was able to stabilize the orthorhombic crystal form, reducing the number of molecules in the asymmetric unit from the monoclinic form and changing the conformational state of the protein. An iterative strategy using fully automatic experiments available on MASSIF-1 was used to quickly determine the optimal protocol to achieve the phase transition, which should be readily applicable to many types of sample. The study also highlights the drawback of using a single crystallographic structure model for computational modelling purposes given that the conformational state of the binding sites and the electron density in the binding site, which is likely to result from PEGs, greatly varies between models. This also holds for the analysis of nonspecific low-affinity ligands, where often a variety of fragments with similar uncertainty can be modelled, inviting interpretative bias. As a promiscuous transporter, afamin also seems to bind gadoteridol, a magnetic resonance imaging contrast compound, in at least two sites. One pair of gadoteridol molecules is located near the human albumin Sudlow site, and a second gadoteridol molecule is located at an intermolecular site in proximity to domain IA. The data from the co-crystals support modern metrics of data quality in the context of the information that can be gleaned from data sets that would be abandoned on classical measures.

Against Method: Table 1-Cui Bono?

The almost universally required "Table 1," summarizing data-collection and data-processing statistics, has in its present form outlived its usefulness in almost all publications of biomolecular crystal structure reports. Information contained in "Table 1" is insufficient to evaluate or repeat the experiment; is redundant with information extractable from deposited diffraction data; and includes data items whose meaning is under increased scrutiny in the crystallographic community. Direct and consistent extraction and analysis of data quality metrics from preferably unmerged intensity data with graphical presentation of reciprocal space features, including impact on map and model features, should replace "Table 1."

An Investigation of Atomic Structures Derived from X-ray Crystallography and Cryo-Electron Microscopy Using Distal Blocks of Side-Chains

Cryo-electron microscopy (cryo-EM) is a structure determination method for large molecular complexes. As more and more atomic structures are determined using this technique, it is becoming possible to perform statistical characterization of side-chain conformations. Two data sets were involved to characterize block lengths for each of the 18 types of amino acids. One set contains 9131 structures resolved using X-ray crystallography from density maps with better than or equal to 1.5 Å resolutions, and the other contains 237 protein structures derived from cryo-EM density maps with 2–4 Å resolutions. The results show that the normalized probability density function of block lengths is similar between the X-ray data set and the cryo-EM data set for most of the residue types, but differences were observed for ARG, GLU, ILE, LYS, PHE, TRP, and TYR for which conformations with certain shorter block lengths are more likely to be observed in the cryo-EM set with 2–4 Å resolutions.

Detect, correct, retract: How to manage incorrect structural models

The massive technical and computational progress of biomolecular crystallography has generated some adverse side effects. Most crystal structure models, produced by crystallographers or well-trained structural biologists, constitute useful sources of information, but occasional extreme outliers remind us that the process of structure determination is not fail-safe. The occurrence of severe errors or gross misinterpretations raises fundamental questions: Why do such aberrations emerge in the first place? How did they evade the sophisticated validation procedures which often produce clear and dire warnings, and why were severe errors not noticed by the depositors themselves, their supervisors, referees and editors? Once detected, what can be done to either correct, improve or eliminate such models? How do incorrect models affect the underlying claims or biomedical hypotheses they were intended, but failed, to support? What is the long-range effect of the propagation of such errors? And finally, what mechanisms can be envisioned to restore the validity of the scientific record and, if necessary, retract publications that are clearly invalidated by the lack of experimental evidence? We suggest that cognitive bias and flawed epistemology are likely at the root of the problem. By using examples from the published literature and from public repositories such as the Protein Data Bank, we provide case summaries to guide correction or improvement of structural models. When strong claims are unsustainable because of a deficient crystallographic model, removal of such a model and even retraction of the affected publication are necessary to restore the integrity of the scientific record.

Molstack-Interactive visualization tool for presentation, interpretation, and validation of macromolecules and electron density maps

Our understanding of the world of biomolecular structures is based upon the interpretation of macromolecular models, of which ∼90% are an interpretation of electron density maps. This structural information guides scientific progress and exploration in many biomedical disciplines. The Protein Data Bank's web portals have made these structures available for mass scientific consumption and greatly broaden the scope of information presented in scientific publications. The portals provide numerous quality metrics; however, the portion of the structure that is most vital for interpretation of the function may have the most difficult to interpret electron density and this ambiguity is not reflected by any single metric. The possible consequences of basing research on suboptimal models make it imperative to inspect the agreement of a model with its experimental evidence. Molstack, a web-based interactive publishing platform for structural data, allows users to present density maps and structural models by displaying a collection of maps and models, including different interpretation of one's own data, re-refinements, and corrections of existing structures. Molstack organizes the sharing and dissemination of these structural models along with their experimental evidence as an interactive session. Molstack was designed with three groups of users in mind; researchers can present the evidence of their interpretation, reviewers and readers can independently judge the experimental evidence of the authors' conclusions, and other researchers can present or even publish their new hypotheses in the context of prior results. The server is available at http://molstack.bioreproducibility.org.