3. Protein contact network analysis, along with classical MD
simulations or enhanced sampling methods, can burst the
identification of allosteric pockets and design of allosteric
modulators.
References
1. Nussinov R, Tsai C-J (2013) Allostery in disease and in drug discovery. Cell 153:293–305.
https://doi.org/10.1016/j.cell.2013.03.034
2. Olsen RW (2018) GABAA receptor: positive
and negative allosteric modulators. Neuropharmacology 136:10–22. https://doi.org/10.
1016/j.neuropharm.2018.01.036
3. Perszyk R, Katzman BM, Kusumoto H, Kell
SA, Epplin MP, Tahirovic YA, Moore RL,
Menaldino D, Burger P, Liotta DC, Traynelis
SF (2018) An NMDAR positive and negative
allosteric modulator series share a binding site
and are interconverted by methyl groups. elife
7:e34711.
https://doi.org/10.7554/eLife.
34711
4. Vallee M, Vitiello S, Bellocchio L, HebertChatelain E, Monlezun S, Martin-Garcia E,
Kasanetz F, Baillie GL, Panin F, Cathala A,
Roullot-Lacarriere V, Fabre S, Hurst DP,
Lynch DL, Shore DM, Deroche-Gamonet V,
Spampinato U, Revest J-M, Maldonado R,
Reggio PH, Ross RA, Marsicano G, Piazza
PV (2014) Pregnenolone can protect the
brain from cannabis intoxication. Science
343:94–98.
https://doi.org/10.1126/sci
ence.1243985
5. Onuchic JN, Luthey-Schulten Z, Wolynes PG
(1997) Theory of protein folding: the energy
landscape perspective. Annu Rev Phys Chem
48:545–600.
https://doi.org/10.1146/
annurev.physchem.48.1.545
6. Bergonzo C, Henriksen NM, Roe DR, Swails
JM, Roitberg AE, Cheatham TE 3rd (2014)
Multidimensional replica exchange molecular
dynamics yields a converged ensemble of an
RNA tetranucleotide. J Chem Theory Comput
10:492–499.
https://doi.org/10.1021/
ct400862k
7. Marsili S, Signorini GF, Chelli R, Marchi M,
Procacci P (2010) ORAC: a molecular dynamics simulation program to explore free energy
surfaces in biomolecular systems at the atomistic level. J Comput Chem 31:1106–1116.
https://doi.org/10.1002/jcc.21388
8. Platania CBM, Salomone S, Leggio GM,
Drago F, Bucolo C (2012) Homology modeling of dopamine D2 and D3 receptors: molecular dynamics refinement and docking
evaluation. PLoS One 7:e44316. https://doi.
org/10.1371/journal.pone.0044316
9. Platania CBM, Di Paola L, Leggio GM,
Romano GL, Drago F, Salomone S, Bucolo C
(2015) Molecular features of interaction
between VEGFA and anti-angiogenic drugs
used in retinal diseases: a computational
approach. Front Pharmacol 6:248. https://
doi.org/10.3389/fphar.2015.00248
10. Corrada D, Colombo G (2013) Energetic and
dynamic aspects of the affinity maturation process: characterizing improved variants from the
bevacizumab antibody with molecular simulations. J Chem Inf Model 53:2937–2950.
https://doi.org/10.1021/ci400416e
11. Platania CBM, Giurdanella G, Di Paola L, Leggio GM, Drago F, Salomone S, Bucolo C
(2017) P2X7 receptor antagonism: implications in diabetic retinopathy. Biochem Pharmacol 138:130–139. https://doi.org/10.1016/
j.bcp.2017.05.001
12. De Ruvo M, Giuliani A, Paci P, Santoni D, Di
Paola L (2012) Shedding light on proteinligand binding by graph theory: the topological
nature
of
allostery.
Biophys
Chem
165–166:21–29. https://doi.org/10.1016/j.
bpc.2012.03.001
13. Di Paola L, Giuliani A (2015) Protein contact
network topology: a natural language for allostery. Curr Opin Struct Biol 31:43–48.
https://doi.org/10.1016/j.sbi.2015.03.001
14. Di Paola L, Platania CBM, Oliva G, Setola R,
Pascucci F, Giuliani A (2015) Characterization
of protein-protein interfaces through a protein
contact network approach. Front Bioeng Biotechnol 3:170. https://doi.org/10.3389/
fbioe.2015.00170
15. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci
U S A 103:8577–8582. https://doi.org/10.
1073/pnas.0601602103
16. Hu G, Di Paola L, Liang Z, Giuliani A (2017)
Comparative study of elastic network model
and protein contact network for protein complexes: the hemoglobin case. Biomed Res Int
2017:2483264.
https://doi.org/10.1155/
2017/2483264
17. Doruker P, Atilgan AR, Bahar I (2000)
Dynamics of proteins predicted by molecular
Molecular Dynamics and Drug Discovery
253
simulations or enhanced sampling methods, can burst the
identification of allosteric pockets and design of allosteric
modulators.
References
1. Nussinov R, Tsai C-J (2013) Allostery in disease and in drug discovery. Cell 153:293–305.
https://doi.org/10.1016/j.cell.2013.03.034
2. Olsen RW (2018) GABAA receptor: positive
and negative allosteric modulators. Neuropharmacology 136:10–22. https://doi.org/10.
1016/j.neuropharm.2018.01.036
3. Perszyk R, Katzman BM, Kusumoto H, Kell
SA, Epplin MP, Tahirovic YA, Moore RL,
Menaldino D, Burger P, Liotta DC, Traynelis
SF (2018) An NMDAR positive and negative
allosteric modulator series share a binding site
and are interconverted by methyl groups. elife
7:e34711.
https://doi.org/10.7554/eLife.
34711
4. Vallee M, Vitiello S, Bellocchio L, HebertChatelain E, Monlezun S, Martin-Garcia E,
Kasanetz F, Baillie GL, Panin F, Cathala A,
Roullot-Lacarriere V, Fabre S, Hurst DP,
Lynch DL, Shore DM, Deroche-Gamonet V,
Spampinato U, Revest J-M, Maldonado R,
Reggio PH, Ross RA, Marsicano G, Piazza
PV (2014) Pregnenolone can protect the
brain from cannabis intoxication. Science
343:94–98.
https://doi.org/10.1126/sci
ence.1243985
5. Onuchic JN, Luthey-Schulten Z, Wolynes PG
(1997) Theory of protein folding: the energy
landscape perspective. Annu Rev Phys Chem
48:545–600.
https://doi.org/10.1146/
annurev.physchem.48.1.545
6. Bergonzo C, Henriksen NM, Roe DR, Swails
JM, Roitberg AE, Cheatham TE 3rd (2014)
Multidimensional replica exchange molecular
dynamics yields a converged ensemble of an
RNA tetranucleotide. J Chem Theory Comput
10:492–499.
https://doi.org/10.1021/
ct400862k
7. Marsili S, Signorini GF, Chelli R, Marchi M,
Procacci P (2010) ORAC: a molecular dynamics simulation program to explore free energy
surfaces in biomolecular systems at the atomistic level. J Comput Chem 31:1106–1116.
https://doi.org/10.1002/jcc.21388
8. Platania CBM, Salomone S, Leggio GM,
Drago F, Bucolo C (2012) Homology modeling of dopamine D2 and D3 receptors: molecular dynamics refinement and docking
evaluation. PLoS One 7:e44316. https://doi.
org/10.1371/journal.pone.0044316
9. Platania CBM, Di Paola L, Leggio GM,
Romano GL, Drago F, Salomone S, Bucolo C
(2015) Molecular features of interaction
between VEGFA and anti-angiogenic drugs
used in retinal diseases: a computational
approach. Front Pharmacol 6:248. https://
doi.org/10.3389/fphar.2015.00248
10. Corrada D, Colombo G (2013) Energetic and
dynamic aspects of the affinity maturation process: characterizing improved variants from the
bevacizumab antibody with molecular simulations. J Chem Inf Model 53:2937–2950.
https://doi.org/10.1021/ci400416e
11. Platania CBM, Giurdanella G, Di Paola L, Leggio GM, Drago F, Salomone S, Bucolo C
(2017) P2X7 receptor antagonism: implications in diabetic retinopathy. Biochem Pharmacol 138:130–139. https://doi.org/10.1016/
j.bcp.2017.05.001
12. De Ruvo M, Giuliani A, Paci P, Santoni D, Di
Paola L (2012) Shedding light on proteinligand binding by graph theory: the topological
nature
of
allostery.
Biophys
Chem
165–166:21–29. https://doi.org/10.1016/j.
bpc.2012.03.001
13. Di Paola L, Giuliani A (2015) Protein contact
network topology: a natural language for allostery. Curr Opin Struct Biol 31:43–48.
https://doi.org/10.1016/j.sbi.2015.03.001
14. Di Paola L, Platania CBM, Oliva G, Setola R,
Pascucci F, Giuliani A (2015) Characterization
of protein-protein interfaces through a protein
contact network approach. Front Bioeng Biotechnol 3:170. https://doi.org/10.3389/
fbioe.2015.00170
15. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci
U S A 103:8577–8582. https://doi.org/10.
1073/pnas.0601602103
16. Hu G, Di Paola L, Liang Z, Giuliani A (2017)
Comparative study of elastic network model
and protein contact network for protein complexes: the hemoglobin case. Biomed Res Int
2017:2483264.
https://doi.org/10.1155/
2017/2483264
17. Doruker P, Atilgan AR, Bahar I (2000)
Dynamics of proteins predicted by molecular
Molecular Dynamics and Drug Discovery
253