network topologies and interactions with ligands for several targets may indicate the
promiscuity of drug candidates and possibly their side effects.
The development of inhibitors of protein–protein interactions is a perspective
way in drug design, and RIN showed their applicability for this purpose. The
analysis of networks may help to select correct poses in protein–protein docking
that is important for the selection of inhibitor binding sites; incorporation of the
terms from RINs may improve docking scoring functions.
Allosteric inhibitors are another mainstream in drug design in last decade. It is
proposed that such inhibitors may regulate cellular processes more accurately.
Allosteric regulation is the common property of protein, which may increase the
number of druggable targets. RINs are convenient for finding allosteric sites,
investigation of mechanism of intraprotein signal transmission. Prediction of the
effect of amino acid mutations on protein structure and dynamics is crucial for the
development drugs against diseases with a high probability of occurrence drug
resistance, in particular antibacterial, antiviral, and anticancer drugs.
Nowadays, the application of RIN methods for drug discovery is at their early
stage, but they already help to understand intimate properties of proteins and
provide a new view for drug discovery.
References
1. Otte E, Rousseau R (2002) Social network analysis: a powerful strategy, also for the
information sciences. J Inform Sci 28:441–453
2. Meusel R, Vigna S, Lehmberg O, Bizer C (2015) The graph structure in the web – analyzed
on different aggregation levels. J Web Sci 1:33–47
3. Bottinelli A, Louf R, Gherardi M (2017) Balancing building and maintenance costs in
growing transport networks. Phys Rev E 96:032316
4. Murakami Y, Tripathi LP, Prathipati P, Mizuguchi K (2017) Network analysis and in silico
prediction of protein-protein interactions with applications in drug discovery. Curr Opin
Struct Biol 44:134–142
5. Zhao B, Wang J, Wu FX (2017) Computational methods to predict protein functions from
protein-protein interaction networks. Curr Protein Pept Sci 18:1120–1131
6. Miryala SK, Anbarasu A, Ramaiah S (2018) Discerning molecular interactions: a
comprehensive review on biomolecular interaction databases and network analysis tools.
Gene 642:84–94
7. Laddach A, Ng JC, Chung SS, Fraternali F (2018) Genetic variants and protein-protein
interactions: a multidimensional network-centric view. Curr Opin Struct Biol 50:82–90
8. Yao V, Wong AK, Troyanskaya OG (2018) Enabling precision medicine through integrative
network models. J Mol Biol 430(18 Pt A):2913–2923
9. Xie L, Li J, Xie L, Bourne PE (2009) Drug discovery using chemical systems biology:
identification of the protein-ligand binding network to explain the side effects of CETP
inhibitors. PLoS Comput Biol 5:e1000387
10. Li P, Fu Y, Wang Y (2015) Network based approach to drug discovery: a mini review.
Mini-Rev Med Chem 15:687–695
11. Aftabuddin M, Kundu S (2007) Hydrophobic, hydrophilic, and charged amino acid networks
within protein. Biophys J 93:225–231
Analysis of Protein Structures Using Residue Interaction …
65
promiscuity of drug candidates and possibly their side effects.
The development of inhibitors of protein–protein interactions is a perspective
way in drug design, and RIN showed their applicability for this purpose. The
analysis of networks may help to select correct poses in protein–protein docking
that is important for the selection of inhibitor binding sites; incorporation of the
terms from RINs may improve docking scoring functions.
Allosteric inhibitors are another mainstream in drug design in last decade. It is
proposed that such inhibitors may regulate cellular processes more accurately.
Allosteric regulation is the common property of protein, which may increase the
number of druggable targets. RINs are convenient for finding allosteric sites,
investigation of mechanism of intraprotein signal transmission. Prediction of the
effect of amino acid mutations on protein structure and dynamics is crucial for the
development drugs against diseases with a high probability of occurrence drug
resistance, in particular antibacterial, antiviral, and anticancer drugs.
Nowadays, the application of RIN methods for drug discovery is at their early
stage, but they already help to understand intimate properties of proteins and
provide a new view for drug discovery.
References
1. Otte E, Rousseau R (2002) Social network analysis: a powerful strategy, also for the
information sciences. J Inform Sci 28:441–453
2. Meusel R, Vigna S, Lehmberg O, Bizer C (2015) The graph structure in the web – analyzed
on different aggregation levels. J Web Sci 1:33–47
3. Bottinelli A, Louf R, Gherardi M (2017) Balancing building and maintenance costs in
growing transport networks. Phys Rev E 96:032316
4. Murakami Y, Tripathi LP, Prathipati P, Mizuguchi K (2017) Network analysis and in silico
prediction of protein-protein interactions with applications in drug discovery. Curr Opin
Struct Biol 44:134–142
5. Zhao B, Wang J, Wu FX (2017) Computational methods to predict protein functions from
protein-protein interaction networks. Curr Protein Pept Sci 18:1120–1131
6. Miryala SK, Anbarasu A, Ramaiah S (2018) Discerning molecular interactions: a
comprehensive review on biomolecular interaction databases and network analysis tools.
Gene 642:84–94
7. Laddach A, Ng JC, Chung SS, Fraternali F (2018) Genetic variants and protein-protein
interactions: a multidimensional network-centric view. Curr Opin Struct Biol 50:82–90
8. Yao V, Wong AK, Troyanskaya OG (2018) Enabling precision medicine through integrative
network models. J Mol Biol 430(18 Pt A):2913–2923
9. Xie L, Li J, Xie L, Bourne PE (2009) Drug discovery using chemical systems biology:
identification of the protein-ligand binding network to explain the side effects of CETP
inhibitors. PLoS Comput Biol 5:e1000387
10. Li P, Fu Y, Wang Y (2015) Network based approach to drug discovery: a mini review.
Mini-Rev Med Chem 15:687–695
11. Aftabuddin M, Kundu S (2007) Hydrophobic, hydrophilic, and charged amino acid networks
within protein. Biophys J 93:225–231
Analysis of Protein Structures Using Residue Interaction …
65