FEM: feature-enhanced map.

A method is presented that modifies a 2mFobs - DFmodel σA-weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretability and decreased model bias compared with the starting 2mFobs - DFmodel σA-weighted map.

Techniques, tools and best practices for ligand electron-density analysis and results from their application to deposited crystal structures.

As a result of substantial instrumental automation and the continuing improvement of software, crystallographic studies of biomolecules are conducted by non-experts in increasing numbers. While improved validation almost ensures that major mistakes in the protein part of structure models are exceedingly rare, in ligand-protein complex structures, which in general are most interesting to the scientist, ambiguous ligand electron density is often difficult to interpret and the modelled ligands are generally more difficult to properly validate. Here, (i) the primary technical reasons and potential human factors leading to problems in ligand structure models are presented; (ii) the most common categories of building errors or overinterpretation are classified; (iii) a few instructive and specific examples are discussed in detail, including an electron-density-based analysis of ligand structures that do not contain any ligands; (iv) means of avoiding such mistakes are suggested and the implications for database validity are discussed and (v) a user-friendly software tool that allows non-expert users to conveniently inspect ligand density is provided.

A mixed phosphine sulfide/selenide structure as an instructional example for how to evaluate the quality of a model.

This paper compares variations on a structure model derived from an X-ray diffraction data set from a solid solution of chalcogenide derivatives of cis-1,2-bis-(di-phenyl-phosphan-yl)ethyl-ene, namely, 1,2-(ethene-1,2-di-yl)bis-(di-phenyl-phoshpine sulfide/selenide), C26H22P2S1.13Se0.87. A sequence of processes are presented to ascertain the composition of the crystal, along with strategies for which aspects of the model to inspect to ensure a chemically and crystallographically realistic structure. Criteria include mis-matches between F obs 2 and F calc 2, plots of |F obs| vs |F calc|, residual electron density, checkCIF alerts, pitfalls of the OMIT command used to suppress ill-fitting data, comparative size of displacement ellipsoids, and critical inspection of inter-atomic distances. Since the structure is quite small, solves easily, and presents a number of readily expressible refinement concepts, we feel that it would make a straightforward and concise instructional piece for students learning how to determine if their model provides the best fit for the data and show students how to critically assess their structures.

Twilight reloaded: the peptide experience.

The de facto commoditization of biomolecular crystallography as a result of almost disruptive instrumentation automation and continuing improvement of software allows any sensibly trained structural biologist to conduct crystallographic studies of biomolecules with reasonably valid outcomes: that is, models based on properly interpreted electron density. Robust validation has led to major mistakes in the protein part of structure models becoming rare, but some depositions of protein-peptide complex structure models, which generally carry significant interest to the scientific community, still contain erroneous models of the bound peptide ligand. Here, the protein small-molecule ligand validation tool Twilight is updated to include peptide ligands. (i) The primary technical reasons and potential human factors leading to problems in ligand structure models are presented; (ii) a new method used to score peptide-ligand models is presented; (iii) a few instructive and specific examples, including an electron-density-based analysis of peptide-ligand structures that do not contain any ligands, are discussed in detail; (iv) means to avoid such mistakes and the implications for database integrity are discussed and (v) some suggestions as to how journal editors could help to expunge errors from the Protein Data Bank are provided.

Investigational new drugs for allergic rhinitis.

INTRODUCTION: Allergic rhinitis (AR) is a multifactorial disease characterized by paroxysmal symptoms of sneezing, rhinorrhea, postnasal drip and nasal congestion. For over a century, subcutaneous allergen immunotherapy (SCIT) has been recognized as the most effective therapy to date that may modify the underlying disease course and provide long-term benefits for individuals refractory to pharmacotherapy. However, over the past 25 years, there has been substantial growth in developing alternative therapies to traditional SCIT. Areas covered: This article will review the most current literature focusing on advancements of AR therapies. Novel AR therapies that are currently under investigation include: the addition of omalizumab, an anti-immunoglobulin E (IgE) monoclonal antibody (mAb), to SCIT; altering the method of delivery of allergen immunotherapy (AIT) including sublingual (SLIT), epicutaneous (EIT), intralymphatic (ILIT), intranasal (INIT) and oral mucosal immunotherapy (OMIT); use of capsaicin spray; novel H3 and H4 antihistamines; activation of the innate immune system through Toll-like receptor agonists; and the use of chemically altered allergens, allergoids, recombinant allergens and relevant T-cell epitope peptides to improve the efficacy and safety of AIT. Expert opinion: These promising novel therapies may offer more effective and/or safer treatment options for AR patients, and in some instances, induce immunologic tolerance.

Polder maps: improving OMIT maps by excluding bulk solvent.

The crystallographic maps that are routinely used during the structure-solution workflow are almost always model-biased because model information is used for their calculation. As these maps are also used to validate the atomic models that result from model building and refinement, this constitutes an immediate problem: anything added to the model will manifest itself in the map and thus hinder the validation. OMIT maps are a common tool to verify the presence of atoms in the model. The simplest way to compute an OMIT map is to exclude the atoms in question from the structure, update the corresponding structure factors and compute a residual map. It is then expected that if these atoms are present in the crystal structure, the electron density for the omitted atoms will be seen as positive features in this map. This, however, is complicated by the flat bulk-solvent model which is almost universally used in modern crystallographic refinement programs. This model postulates constant electron density at any voxel of the unit-cell volume that is not occupied by the atomic model. Consequently, if the density arising from the omitted atoms is weak then the bulk-solvent model may obscure it further. A possible solution to this problem is to prevent bulk solvent from entering the selected OMIT regions, which may improve the interpretative power of residual maps. This approach is called a polder (OMIT) map. Polder OMIT maps can be particularly useful for displaying weak densities of ligands, solvent molecules, side chains, alternative conformations and residues both in terminal regions and in loops. The tools described in this manuscript have been implemented and are available in PHENIX.

Volumic omit maps in ab initio dual-space phasing.

Alternating-projection-type dual-space algorithms have a clear construction, but are susceptible to stagnation and, thus, inefficient for solving the phase problem ab initio. To improve this behaviour new omit maps are introduced, which are real-space perturbations applied periodically during the iteration process. The omit maps are called volumic, because they delete some predetermined subvolume of the unit cell without searching for atomic regions or analysing the electron density in any other way. The basic algorithms of positivity, histogram matching and low-density elimination are tested by their solution statistics. It is concluded that, while all these algorithms based on weak constraints are practically useless in their pure forms, appropriate volumic omit maps can transform them to practically useful methods. In addition, the efficiency of the already useful reflector-type charge-flipping algorithm can be further improved. It is important that these results are obtained by using non-sharpened structure factors and without any weighting scheme or reciprocal-space perturbation. The mathematical background of volumic omit maps and their expected applications are also discussed.