One of the fundamental weaknesses of X-ray crystallography, when used for the solution of macromolecular structures, is that the constructed models are based on the subjective interpretation of the electron density by the crystallographer.
This can lead to poor or simply incorrect models, as discussed by Stanfield et al. in their recent paper “Comment on Three X-ray Crystal Structure Papers” (link below). Here, they assert that the basis of several papers by Dr. Salunke and his coworkers, a series of antibody-peptide complexes, are fundamentally flawed. It is argued that the experimental electron density does not support the presence of the peptide models: there is no significant positive OMIT density for the peptides when they are removed from the model, and the quality of the constructed models is poor, with unreasonably large B-factors.
Link to paper: http://www.jimmunol.org/content/196/2/521.1.
Firstly, a quick recap on crystallographic maps and how they are used. Two map types are principally used in macromolecular crystallography: composite maps and difference maps.
The composite map is used to approximate the electron density of the crystal. It consists of the modelled density subtracted from the observed electron density (multiplied by 2) and contains correction factors to minimise phase bias (m, D). The weighting of 2 of the observed map is used to compensate for the poor phases, which cause un-modelled features to appear only weakly in the density. It is universally represented as a blue mesh:
The difference map is the modelled density subtracted from the observed density, and is used to identify un-modelled areas of the electron density. It contains the same correction factors to compensate for phase bias. It is universally represented as a green mesh for positive values, and a red mesh for negative values. The green and red meshes are always contoured at the same absolute values, e.g. ±1 or ±1.4.
The problem of identifying features to model in the electron density is the point where the subjectivity of the crystallographer is most influential. For ligands, this means identifying blobs that are “significant”, and that match the shape of the molecule to be modelled.
When a crystallographer is actively searching for the presence of a binding molecule, in this case a peptide, it is easy to misinterpret density as the molecule you are searching for. You have to be disciplined and highly critical before accidentally contouring to levels that are too low, and modelling into density that does not really match the model. This is the case in the series of structures that are criticised by Stanfield et al.
Specific concerns with the structures of Dr Salenke et al.
1: Contouring difference maps at only positive values (and colouring them blue?)
The first questionable thing that Dr Salunke et al do is to present a difference map contoured at only positive values as evidence for the bound peptide. This oddity is compounded by colouring the resulting map as blue, which is unusual for a difference map.
2: Contouring difference maps to low values
Salunke et al claim that the image above shows adequate evidence for the binding of the peptide in a difference map contoured at 1.7𝛔.
When contouring difference maps to such low levels, weak features will indeed be detectable, if they are present, but in solvent channels, where the crystal is an ensemble of disordered states, there is no way to interpret the density as an atomic model. Hence, a difference map at 1.7𝛔 will show blobs across all of the solvent channels in the crystal.
This fact, in itself, does not prove that the model is wrong, but makes it highly likely that the model is a result of observation bias. This observation bias occurs because the authors were looking for evidence of the binding peptide, and so inspected the density at the binding site. This has lead to the over-interpretation of noisy and meaningless density as the peptide.
The reason that the 3𝛔 limit is used to identify crystallographic features in difference maps is that this identifies only strong un-modelled features that are unlikely to be noise, or a disordered feature.
More worryingly, the model does not actually fit the density very well.
3: Poor Model Quality
Lastly, the quality of the modelled peptides is very poor. The B-factors of the ligands are much higher than the B-factors of the surrounding protein side-chains. This is symptomatic of the modelled feature not being present in the data, and the refinement program tries to “erase” the presence of the model by inflating the B-factors. This, again, does not prove that the model is wrong, but highlights the poor quality of the model.
Lastly, the ramachandran outliers in the peptides are extreme, with values in the 0th percentile of empirical values. This means that the conformer of the peptide is highly strained, and therefore highly unlikely.
Combining all of the evidence above, as presented in the article written by Stanfield et al, there is little doubt that the models presented by Salunke et al are incorrect. Individual failings in the models in one area could be explained, but such a range of errors across such a range of quality metrics cannot.