1. Introduction
Molecular dialog among proteins, nucleic acids and small molecules is the essence of Life at an atomic resolution and, hence, understanding the basis of such dialog represents a step forward for genetic medicine [1]. Nowadays, genotype-to-phenotype associations in human diseases can be efficiently explored by accessing to mutation databases, such as HGMD [2], SwissVar [3], COSMIC [4], HuVarBase [5], HUMSAVAR [6] and ClinVar [7], where information on pathogenic mutations are collected. The fact that missense mutations constitute the most common sequence alteration in Mendelian disorders [8] offers a good starting point to understand the mechanisms of disease appearance due to amino acid variations in mutated proteins. Pathogenicity can arise from missense mutations whenever mutated proteins alter their structural stability and, consequently, their function. The large array of examples of this kind has driven the implementation of several algorithms to predict functional damages due to amino acid replacements [9-13]. An additional way to explain the pathological effect of some missense mutations takes into account the protein interactome dimension [14]. In the interactome, missense mutated proteins can be considered as the network nodes, being responsible for altered biochemical or biophysical properties that represent the network edges. Thus, specific modifications of the interaction pattern due to protein mutations define an edgotype, which has been proposed as a way of monitoring the effects that link genotypes to phenotypes [15,16]. The abundance of structures that are currently available in the Protein Data Bank (PDB) [17] allows a detailed view of protein interactome, particularly in light of tools such as PISA (Protein Structure, Interface and Assembly) provided by EBI (the European Bioinformatics Institute) [18]. PISA, indeed, is a database of pre-calculated results for the whole PDB archive for retrieving information on structural and chemical properties of macromolecular surfaces and interfaces. In the present report, we have performed a structural bioinformatic analysis of human mutation databases by using PISA database to obtain information on mechanisms of pathogenicity of missense mutations at atomic resolution. By comparing benign and pathological missense mutations we tried to improve our understanding of specific roles of single amino acids in biological processes.