1 Introduction
Proteins play a vital role in biological systems and have numerous functions such as
catalysts, transporters, regulators of signal transduction. They are linear
heteropolymers folded into three-dimensional structures. The amino acid residues
interact through various covalent and non-covalent bonds in a specific manner to
obtain a particular three-dimensional structure, which determines their functions.
Knowledge of the relationship between protein structure and its function is
important in drug design, molecular medicine, and biotechnology.
Different computational methods have been used for investigations of protein
structures and their functions, finding functionally important residues, prediction
protein–protein interactions, discovering new biological active compounds. In the
most approaches, the protein structures have been viewed as linear sequences of
amino acid residues packed into 3D globules. In the last decade, an alternative view
of proteins structures has emerged that describe the protein spatial structure as
network of amino acids residues interaction.
Network analysis has successfully used in different fields, such as social networks [1], Internet networks [2], road networks [3]. In biology, this method is
widely used for analysis of networks of gene regulation, protein–protein interaction,
metabolites flow, prediction of drug side effects, etc., [4–9]. Applying network
methodology for polypharmacology was reviewed in [10].
A network method is based on the graph theory and includes a set of entities
(nodes) and of the relationships (edges) occurring among them. These nodes and
edges can have various attributes. Depending on the object of the study, nodes can
represent genes, proteins, small compounds, and edges connecting these nodes
represent the physical interactions, genetic regulatory, or other properties linking
the nodes. Edges can have additional information, such as weights, directions.
According to the structure of protein, every amino acid residue in it is considered
to be a “node” or “vertex,” and the interaction of residues represents “edge”
(Fig. 1). The existence of an edge between two nodes depends only on their spatial
position in protein globule and has no relation to position in their primary sequence.
The interaction can be represented as distance between C a or any other atoms of
amino acid residues, non-covalent interaction (electrostatic, hydrophobic, H-bonds)
of the particular amino acids [11]. Additionally, in residue interaction network
(RIN), the energy of interaction between residues can be used for weighting the
edges [12, 13]. Proteins can be also modeled as subnetworks of amino acid residues
having similar physiochemical properties. RIN method reduces spatial protein
architectures to simple maps including nodes (residues) and edges (inter-residue
interactions). Analysis of these graphs yields a characterization of the protein’s
topology and network characteristics.
There are several names of the resultant intraprotein amino acid residue interaction networks. They are called residue interaction graphs [14], protein structure
graphs [15, 16], protein residue networks [17], protein contact networks [18],
protein energy networks [13], amino acid networks [19], protein structure networks
56
D. Shcherbinin and A. Veselovsky
Proteins play a vital role in biological systems and have numerous functions such as
catalysts, transporters, regulators of signal transduction. They are linear
heteropolymers folded into three-dimensional structures. The amino acid residues
interact through various covalent and non-covalent bonds in a specific manner to
obtain a particular three-dimensional structure, which determines their functions.
Knowledge of the relationship between protein structure and its function is
important in drug design, molecular medicine, and biotechnology.
Different computational methods have been used for investigations of protein
structures and their functions, finding functionally important residues, prediction
protein–protein interactions, discovering new biological active compounds. In the
most approaches, the protein structures have been viewed as linear sequences of
amino acid residues packed into 3D globules. In the last decade, an alternative view
of proteins structures has emerged that describe the protein spatial structure as
network of amino acids residues interaction.
Network analysis has successfully used in different fields, such as social networks [1], Internet networks [2], road networks [3]. In biology, this method is
widely used for analysis of networks of gene regulation, protein–protein interaction,
metabolites flow, prediction of drug side effects, etc., [4–9]. Applying network
methodology for polypharmacology was reviewed in [10].
A network method is based on the graph theory and includes a set of entities
(nodes) and of the relationships (edges) occurring among them. These nodes and
edges can have various attributes. Depending on the object of the study, nodes can
represent genes, proteins, small compounds, and edges connecting these nodes
represent the physical interactions, genetic regulatory, or other properties linking
the nodes. Edges can have additional information, such as weights, directions.
According to the structure of protein, every amino acid residue in it is considered
to be a “node” or “vertex,” and the interaction of residues represents “edge”
(Fig. 1). The existence of an edge between two nodes depends only on their spatial
position in protein globule and has no relation to position in their primary sequence.
The interaction can be represented as distance between C a or any other atoms of
amino acid residues, non-covalent interaction (electrostatic, hydrophobic, H-bonds)
of the particular amino acids [11]. Additionally, in residue interaction network
(RIN), the energy of interaction between residues can be used for weighting the
edges [12, 13]. Proteins can be also modeled as subnetworks of amino acid residues
having similar physiochemical properties. RIN method reduces spatial protein
architectures to simple maps including nodes (residues) and edges (inter-residue
interactions). Analysis of these graphs yields a characterization of the protein’s
topology and network characteristics.
There are several names of the resultant intraprotein amino acid residue interaction networks. They are called residue interaction graphs [14], protein structure
graphs [15, 16], protein residue networks [17], protein contact networks [18],
protein energy networks [13], amino acid networks [19], protein structure networks
56
D. Shcherbinin and A. Veselovsky