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.