With a structural bioinformatic approach, we have explored amino acid compositions at PISA defined interfaces between small molecules and proteins that are contained in an optimized subset of 11,351 PDB files. The use of a series of restrictions, to prevent redundancy and biases from interactions between amino acids with charged side chains and ions, yielded a final data set of 45,230 protein-small molecule interfaces. We have compared occurrences of natural amino acids in surface exposed regions and binding sites for all the proteins of our data set. From our structural bioinformatic survey, the most relevant signal arose from the unexpected Gly abundance at enzyme catalytic sites. This finding suggested that Gly must have a fundamental role in stabilizing concave protein surface moieties. Subsequently, we have tried to predict the effect of in silico Gly mutations in hen egg white lysozyme to optimize those conditions that can reshape the protein surface with the appearance of new pockets. Replacing amino acids having bulky side chains with Gly in specific protein regions seems a feasible way for designing proteins with additional surface pockets, which can alter protein surface dynamics, therefore, representing controllable switches for protein activity.

Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents a common complication in ultra-rare disorders like AKU. This is the reason why we developed a comprehensive tool, called ApreciseKUre, able to collect AKU patients deriving data, to analyse the complex network among genotypic and phenotypic information and to get new insight in such multi-systemic disease. By taking advantage of the dataset, containing the highest number of AKU patient ever considered, it is possible to apply more sophisticated computational methods (such as machine learning) to achieve a first AKU patient stratification based on phenotypic and genotypic data in a typical precision medicine perspective. Thanks to our sufficiently populated and organized dataset, it is possible, for the first time, to extensively explore the phenotype-genotype relationships unknown so far. This proof of principle study for rare diseases confirms the importance of a dedicated database, allowing data management and analysis and can be used to tailor treatments for every patient in a more effective way.

MOTIVATION: For a complex molecular system the delineation of atom-atom contacts, exposed surface and binding sites represents a fundamental step to predict its interaction with solvent, ligands and other molecules. Recently, atom depth has been also considered as an additional structural descriptor to correlate protein structure with folding and functional properties. The distance between an atom and the nearest water molecule or the closest surface dot has been proposed as a measure of the atom depth, but, in both cases, the 3D character of depth is largely lost. In the present study, a new approach is proposed to calculate atom depths in a way that the molecular shape can be taken into account. RESULTS: An algorithm has been developed to calculate intersections between the molecular volume and spheres centered on the atoms whose depth has to be quantified. Many proteins with different size and shape have been chosen to compare the results obtained from distance-based and volume-based depth calculations. From the wealth of experimental data available for hen egg white lysozyme, H/D exchange rates and TEMPOL induced paramagnetic perturbations have been analyzed both in terms of depth indexes and of atom distances to the solvent accessible surface. The algorithm here proposed yields better correlations between experimental data and atom depth, particularly for those atoms which are located near to the protein surface. AVAILABILITY: Instructions to obtain source code and the executable program are available either from http://sienabiografix.com or http://sadic.sourceforge.net CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://www.Sienabiogzefix.com/publication.