more tolerable prediction was obtained with local closeness [52]. It seems that the
ligand binding sites in enzymes are correlated with centrality due to their typical
location in cavities of the enzymes, whereas in oligomer proteins, the protein–
protein interfaces are more flat [53], which reduces the centrality of their residues.
Coevolution residues networks, which include information about coevolved
residues, were also used for predicting functionally important residues [54, 55].
RIN analysis was applied for prediction similarity of ligand binding sites in different proteins [56, 57].
The node-weighted RIN, called node-weighted amino acid contact energy network (NACEN) was developed for prediction hotspots, catalytic residues, and
allosteric residues. Nodes were weighted based on structural, sequence, physicochemical and dynamic properties of the residues. SVM was used for design model
to identify functionally important residues. The results revealed that parameters
from node-weighted RIN have advantages over ones from unweighted network
[58].
Poirrette et al. [56] designed RIN of the influenza sialidase binding site of
Zanamivir and used it to predict proteins having the similar binding sites. Such an
approach may be used for repurposing drugs or prediction of side effects.
3.2 Protein–Protein Interactions
Protein–protein interactions (PPIs) are crucial for many biological processes and
functions; inhibition of PPIs with small molecules is a perspective way in drug
design [53]. RIN method was used for analysis of protein–protein interfaces, prediction of hotspots, and selection of protein poses in the protein–protein docking.
Several investigations were done using RIN for analysis of protein–protein
interfaces. They showed that hydrophobic and charged residues are predominant in
the dimer interface and that arginine, histidine, glutamic acid, phenylalanine, and
tyrosine are located in clusters at the interface [59, 60]. In those clusters, highly
connected residues correlate with experimentally identified hotspots in the protein
complexes [15, 16, 61, 62].
Correct prediction of protein–protein complexes using individual proteins by
docking method is a big challenge, since the docking gives many false-positive
solutions [63, 64]. Protein–protein complex formation may be viewed as combining
of two RINs, where additional edges have appeared between nodes from different
subunits. The interaction of residues occurs in accordance with their properties.
Since native protein–protein complexes are far from random, the correct and
incorrect poses have different topologies.
Chang et al. [65] designed hydrophobic and hydrophilic RINs of a protein–
protein complex. Three terms based on these networks (degree, clustering coefficient, and characteristic path length) were calculated and used in network-based
scoring function HPNet. Combining it with energy terms of RosettaDock [66]
60
D. Shcherbinin and A. Veselovsky
ligand binding sites in enzymes are correlated with centrality due to their typical
location in cavities of the enzymes, whereas in oligomer proteins, the protein–
protein interfaces are more flat [53], which reduces the centrality of their residues.
Coevolution residues networks, which include information about coevolved
residues, were also used for predicting functionally important residues [54, 55].
RIN analysis was applied for prediction similarity of ligand binding sites in different proteins [56, 57].
The node-weighted RIN, called node-weighted amino acid contact energy network (NACEN) was developed for prediction hotspots, catalytic residues, and
allosteric residues. Nodes were weighted based on structural, sequence, physicochemical and dynamic properties of the residues. SVM was used for design model
to identify functionally important residues. The results revealed that parameters
from node-weighted RIN have advantages over ones from unweighted network
[58].
Poirrette et al. [56] designed RIN of the influenza sialidase binding site of
Zanamivir and used it to predict proteins having the similar binding sites. Such an
approach may be used for repurposing drugs or prediction of side effects.
3.2 Protein–Protein Interactions
Protein–protein interactions (PPIs) are crucial for many biological processes and
functions; inhibition of PPIs with small molecules is a perspective way in drug
design [53]. RIN method was used for analysis of protein–protein interfaces, prediction of hotspots, and selection of protein poses in the protein–protein docking.
Several investigations were done using RIN for analysis of protein–protein
interfaces. They showed that hydrophobic and charged residues are predominant in
the dimer interface and that arginine, histidine, glutamic acid, phenylalanine, and
tyrosine are located in clusters at the interface [59, 60]. In those clusters, highly
connected residues correlate with experimentally identified hotspots in the protein
complexes [15, 16, 61, 62].
Correct prediction of protein–protein complexes using individual proteins by
docking method is a big challenge, since the docking gives many false-positive
solutions [63, 64]. Protein–protein complex formation may be viewed as combining
of two RINs, where additional edges have appeared between nodes from different
subunits. The interaction of residues occurs in accordance with their properties.
Since native protein–protein complexes are far from random, the correct and
incorrect poses have different topologies.
Chang et al. [65] designed hydrophobic and hydrophilic RINs of a protein–
protein complex. Three terms based on these networks (degree, clustering coefficient, and characteristic path length) were calculated and used in network-based
scoring function HPNet. Combining it with energy terms of RosettaDock [66]
60
D. Shcherbinin and A. Veselovsky