Protein crystal structures are potentially over-interpreted since they are routinely refined without any restraint on the upper limit of atomic B-factors. Consequently, some of their atoms, undetected in the electron density maps, are allowed to reach extremely large B-factors, even above 100 square Angstroms, and their final positions are purely speculative and not based on any experimental evidence. A strategy to define B-factors upper limits is described here, based on the analysis of protein crystal structures deposited in the Protein Data Bank prior 2008, when the tendency to allow B-factor to arbitrary inflate was limited. This B-factor upper limit (B_max) is determined by extrapolating the relationship between crystal structure average B-factor and percentage of crystal volume occupied by solvent (pcVol) to pcVol =100%, when, ab absurdo, the crystal contains only liquid solvent, the structure of which is, by definition, undetectable in electron density maps. It is thus possible to highlight structures with average B-factors larger than B_max, which should be considered with caution by the users of the information deposited in the Protein Data Bank, in order to avoid scientifically deleterious over-interpretations.

On the occasion of their fiftieth birthday, it is opportune to review the first half century of Ramachandran plots. In the present review, some of the most relevant aspects of this fifty-year history are summarized, from the original ideas of Gopalasamudram Narayana Ramachandran to subsequent revisions and to applications in structural biology. This will not be a guided walk through five decades of Ramachandran plots, but a commented summary of the lines along which the original ideas evolved and continue to develop, and of their applications.

Nearly all the macromolecular three-dimensional structures deposited in Protein Data Bank were determined by either crystallographic (X-ray) or Nuclear Magnetic Resonance (NMR) spectroscopic methods. This paper reports a systematic comparison of the crystallographic and NMR results deposited in the files of the Protein Data Bank, in order to find out to which extent these information can be aggregated in bioinformatics. A non-redundant data set containing 109 NMR – X-ray structure pairs of nearly identical proteins was derived from the Protein Data Bank. A series of comparisons were performed by focusing the attention towards both global features and local details. It was observed that: (1) the RMDS values between NMR and crystal structures range from about 1.5 Å to about 2.5 Å; (2) the correlation between conformational deviations and residue type reveals that hydrophobic amino acids are more similar in crystal and NMR structures than hydrophilic amino acids; (3) the correlation between solvent accessibility of the residues and their conformational variability in solid state and in solution is relatively modest (correlation coefficient = 0.462); (4) beta strands on average match better between NMR and crystal structures than helices and loops; (5) conformational differences between loops are independent of crystal packing interactions in the solid state; (6) very seldom, side chains buried in the protein interior are observed to adopt different orientations in the solid state and in solution.

Despite the chloride anion is involved in fundamental biological processes, its interactions with proteins are little known. In particular, we lack a systematic survey of its coordination spheres.

Chalcogen bonds are the specific interactions involving group 16 elements as electrophilic sites. The role of chalcogen atoms as sticky sites in biomolecules is underappreciated, and the few available studies have mostly focused on S. Here, we carried out a statistical analysis over 3562 protein structures in the Protein Data Bank (PDB) containing 18 266 selenomethionines and found that Se···O chalcogen bonds are commonplace. These findings may help the future design of functional peptides and contribute to understanding the role of Se in nature.

Despite its well-documented limitations, the root-mean-square-distance (rmsd) between pairs of equivalent atoms is routinely used to monitor the degree of similarity between two optimally superposed protein three-dimensional structures. A robust method for assessing the statistical significance of the difference between two rmsd values is presented here. It is based on the comparison of two protein structures through the correlation coefficient between equivalent inter-atomic distances and the subsequent application of the Fisher transformation that allows one to estimate the probability of identity between two correlation coefficient values. The relationship between the rmsd and Fisher correlation coefficient allows then to estimate the statistical significance of the difference between two rmsd values. Such a procedure is exemplified with the analysis of the possible classifications of the immunoglobulin-like domains of filamin and is compared to related estimations of structural similarity. The possibility to estimate the probability of the difference between two rmsd values can be used to optimize the protein structural classifications and comparisons, independent of the procedure used to derive the rmsds.