[1]
Galperin, M.Y. The molecular biology database collection: 2007 update. Nucleic Acids Res., 2007, 35(Database issue), D3-D4. [http://dx.doi.org/ 10.1093/nar/gkl1008]. [PMID: 17148484].
[2]
Suter, B.; Kittanakom, S.; Stagljar, I. Two-hybrid technologies in proteomics research. Curr. Opin. Biotechnol., 2008, 19(4), 316-323. [http://dx.doi.org/ 10.1016/j.copbio.2008.06.005]. [PMID: 18619540].
[3]
Alonso-López, D.; Gutiérrez, M.A.; Lopes, K.P.; Prieto, C.; Santamaría, R.; De Las Rivas, J. APID interactomes: Providing proteome-based interactomes with controlled quality for multiple species and derived networks. Nucleic Acids Res., 2016, 44(W1), W529-35. [http://dx.doi.org/ 10.1093/nar/gkw363]. [PMID: 27131791].
[4]
Sahni, N.; Yi, S.; Zhong, Q.; Jailkhani, N.; Charloteaux, B.; Cusick, M.E.; Vidal, M. Edgotype: A fundamental link between genotype and phenotype. Curr. Opin. Genet. Dev., 2013, 23(6), 649-657. [http://dx.doi.org/ 10.1016/j.gde.2013.11.002]. [PMID: 24287335].
[5]
Goñi, J.; Esteban, F.J.; de Mendizábal, N.V.; Sepulcre, J.; Ardanza-Trevijano, S.; Agirrezabal, I.; Villoslada, P. A computational analysis of protein-protein interaction networks in neurodegenerative diseases. BMC Syst. Biol., 2008, 2, 52. [http://dx.doi.org/ 10.1186/1752-0509-2-52]. [PMID: 18570646].
[6]
Murakami, Y.; Tripathi, L.P.; Prathipati, P.; Mizuguchi, K. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery. Curr. Opin. Struct. Biol., 2017, 44, 134-142. [http://dx.doi.org/ 10.1016/j.sbi.2017.02.005]. [PMID: 28364585].
[7]
Droit, A.; Poirier, G.G.; Hunter, J.M. Experimental and bioinformatic approaches for interrogating protein-protein interactions to determine protein function. J. Mol. Endocrinol., 2005, 34(2), 263-280. [http://dx.doi.org/ 10.1677/jme.1.01693]. [PMID: 15821096].
[8]
Huthmacher, C.; Gille, C.; Holzhutter, H.G. Computational analysis of protein-protein interactions in metabolic networks of Escherichia coli and yeast. Genome Inform., 2007, 18, 162-172. [http://dx.doi.org/ 10.1142/9781860949920_0016].
[9]
Bakail, M.; Ochsenbein, F. Targeting protein–protein interactions, A wide open field for drug design. C. R. Chim., 2016, 19, 19-27. [http://dx.doi.org/ 10.1016/j.crci.2015.12.004].
[10]
Bashor, C.J.; Horwitz, A.A.; Peisajovich, S.G.; Lim, W.A. Rewiring cells: Synthetic biology as a tool to interrogate the organizational principles of living systems. Annu. Rev. Biophys., 2010, 39, 515-537. [http://dx.doi.org/ 10.1146/annurev.biophys.050708. 133652]. [PMID: 20192780].
[11]
Mohamed, R.; Degac, J.; Helms, V. Composition of overlapping protein-protein and protein-ligand interfaces. PLoS One, 2015, 10(10), e0140965. [http://dx.doi.org/ 10.1371/journal.pone. 0140965]. [PMID: 26517868].
[12]
Zhu, H.; Domingues, F.S.; Sommer, I.; Lengauer, T. NOXclass: Prediction of protein-protein interaction types. BMC Bioinformatics, 2006, 7, 27. [http://dx.doi.org/ 10.1186/1471-2105-7-27]. [PMID: 16423290].
[14]
Acuner Ozbabacan, S.E.; Engin, H.B.; Gursoy, A.; Keskin, O. Transient protein-protein interactions. Protein Eng. Des. Sel., 2011, 24(9), 635-648. [http://dx.doi.org/ 10.1093/protein/gzr025]. [PMID: 21676899].
[15]
Ansari, S.; Helms, V. Statistical analysis of predominantly transient protein-protein interfaces. Proteins, 2005, 61(2), 344-355. [http://dx.doi.org/ 10.1002/prot.20593]. [PMID: 16104020].
[16]
Lo Conte, L.; Chothia, C.; Janin, J. The atomic structure of protein-protein recognition sites. J. Mol. Biol., 1999, 285(5), 2177-2198. [http://dx.doi.org/ 10.1006/jmbi.1998.2439]. [PMID: 9925793].
[17]
Nooren, I.M.; Thornton, J.M. Diversity of protein-protein interactions. EMBO J., 2003, 22(14), 3486-3492. [http://dx.doi.org/ 10.1093/emboj/cdg359]. [PMID: 12853464].
[18]
Nooren, I.M.; Thornton, J.M. Structural characterisation and functional significance of transient protein-protein interactions. J. Mol. Biol., 2003, 325(5), 991-1018. [http://dx.doi.org/ 10.1016/S0022-2836(02)01281-0]. [PMID: 12527304].
[19]
Mintseris, J.; Weng, Z. Structure, function, and evolution of transient and obligate protein-protein interactions. Proc. Natl. Acad. Sci. USA, 2005, 102(31), 10930-10935. [http://dx.doi.org/ 10.1073/pnas.0502667102]. [PMID: 16043700].
[20]
Mintseris, J.; Weng, Z. Atomic contact vectors in protein-protein recognition. Proteins, 2003, 53(3), 629-639. [http://dx.doi.org/ 10.1002/prot.10432]. [PMID: 14579354].
[21]
Chakrabarti, P.; Janin, J. Dissecting protein-protein recognition sites. Proteins, 2002, 47(3), 334-343. [http://dx.doi.org/ 10.1002/prot.10085]. [PMID: 11948787].
[22]
Keskin, O.; Tuncbag, N.; Gursoy, A. Predicting protein-protein interactions from the molecular to the proteome level. Chem. Rev., 2016, 116(8), 4884-4909. [http://dx.doi.org/10.1021/acs.chemrev.5b00683]. [PMID: 27074302].
[23]
Matalon, O.; Horovitz, A.; Levy, E.D. Different subunits belonging to the same protein complex often exhibit discordant expression levels and evolutionary properties. Curr. Opin. Struct. Biol., 2014, 26, 113-120. [http://dx.doi.org/ 10.1016/j.sbi.2014.06.001]. [PMID: 24997301].
[24]
Dey, S.; Pal, A.; Chakrabarti, P.; Janin, J. The subunit interfaces of weakly associated homodimeric proteins. J. Mol. Biol., 2010, 398(1), 146-160. [http://dx.doi.org/ 10.1016/j.jmb.2010.02.020]. [PMID: 20156457].
[25]
Jones, S.; Thornton, J.M. Analysis of protein-protein interaction sites using surface patches. J. Mol. Biol., 1997, 272(1), 121-132. [http://dx.doi.org/ 10.1006/jmbi.1997.1234]. [PMID: 9299342].
[26]
Kim, P.M.; Lu, L.J.; Xia, Y.; Gerstein, M.B. Relating three-dimensional structures to protein networks provides evolutionary insights. Science, 2006, 314(5807), 1938-1941. [http://dx.doi.org/ 10.1126/science.1136174]. [PMID: 17185604].
[27]
Patil, A.; Kinoshita, K.; Nakamura, H. Hub promiscuity in protein-protein interaction networks. Int. J. Mol. Sci., 2010, 11(4), 1930-1943. [http://dx.doi.org/ 10.3390/ijms11041930]. [PMID: 20480050].
[28]
Guerois, R.; Serrano, L. The SH3-fold family: experimental evidence and prediction of variations in the folding pathways. J. Mol. Biol., 2000, 304(5), 967-982. [http://dx.doi.org/ 10.1006/jmbi. 2000.4234]. [PMID: 11124040].
[29]
Nickson, A.A.; Stoll, K.E.; Clarke, J. Folding of a LysM domain: entropy-enthalpy compensation in the transition state of an ideal two-state folder. J. Mol. Biol., 2008, 380(3), 557-569. [http://dx.doi.org/ 10.1016/j.jmb.2008.05.020]. [PMID: 18538343].
[30]
Krishna, S.S.; Aravind, L. The bridge-region of the Ku superfamily is an atypical zinc ribbon domain. J. Struct. Biol., 2010, 172(3), 294-299. [http://dx.doi.org/ 10.1016/j.jsb.2010.05.011]. [PMID: 20580930].
[31]
Ponstingl, H.; Henrick, K.; Thornton, J.M. Discriminating between homodimeric and monomeric proteins in the crystalline state. Proteins, 2000, 41(1), 47-57. [http://dx.doi.org/ 10.1002/1097-0134(20001001)41:1<47:AID-PROT80>3.0.CO;2-8]. [PMID: 10944393].
[32]
Bahadur, R.P.; Chakrabarti, P.; Rodier, F.; Janin, J. A dissection of specific and non-specific protein-protein interfaces. J. Mol. Biol., 2004, 336(4), 943-955. [http://dx.doi.org/ 10.1016/j.jmb.2003. 12.073]. [PMID: 15095871].
[33]
Janin, J.; Chothia, C. The structure of protein-protein recognition sites. J. Biol. Chem., 1990, 265(27), 16027-16030. [PMID: 2204619].
[34]
Nyfeler, B.; Michnick, S.W.; Hauri, H.P. Capturing protein interactions in the secretory pathway of living cells. Proc. Natl. Acad. Sci. USA, 2005, 102(18), 6350-6355. [http://dx.doi.org/ 10.1073/pnas. 0501976102]. [PMID: 15849265].
[35]
Alberts, B.; Bray, D.; Hopkin, K.; Johnson, A.; Lewis, J.; Raff, M.; Roberts, K.; Walter, P. Essential Cell Biology, 3rd ed; Garland Science: New York, 2009.
[36]
Clore, G.M.; Venditti, V. Structure, dynamics and biophysics of the cytoplasmic protein-protein complexes of the bacterial phosphoenolpyruvate: sugar phosphotransferase system. Trends Biochem. Sci., 2013, 38(10), 515-530. [http://dx.doi.org/ 10.1016/j.tibs.2013. 08.003]. [PMID: 24055245].
[37]
Pammolli, F.; Magazzini, L.; Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nat. Rev. Drug Discov., 2011, 10(6), 428-438. [http://dx.doi.org/ 10.1038/nrd3405]. [PMID: 21629293].
[38]
Swinney, D.C. Phenotypic vs. target-based drug discovery for first-in-class medicines. Clin. Pharmacol. Ther., 2013, 93(4), 299-301. [http://dx.doi.org/ 10.1038/clpt.2012.236]. [PMID: 23511784].
[39]
Bermudez, M.; Rakers, C.; Wolber, G. Structural characteristics of the allosteric binding site represent a key to subtype selective modulators of muscarinic acetylcholine receptors. Mol. Inform., 2015, 34(8), 526-530. [http://dx.doi.org/ 10.1002/minf.201500025]. [PMID: 27490498].
[40]
Owens, J. Determining druggability. Nat. Rev. Drug Discov., 2007, 6, 187. [http://dx.doi.org/ 10.1038/nrd2275].
[41]
Katsila, T.; Spyroulias, G.A.; Patrinos, G.P.; Matsoukas, M.T. Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J., 2016, 14, 177-184. [http://dx.doi.org/ 10.1016/j.csbj.2016.04.004]. [PMID: 27293534].
[42]
Bender, A.; Glen, R.C. Molecular similarity: A key technique in molecular informatics. Org. Biomol. Chem., 2004, 2(22), 3204-3218. [http://dx.doi.org/ 10.1039/b409813g]. [PMID: 15534697].
[43]
Bender, A.; Young, D.W.; Jenkins, J.L.; Serrano, M.; Mikhailov, D.; Clemons, P.A.; Davies, J.W. Chemogenomic data analysis: Prediction of small-molecule targets and the advent of biological fingerprint. Comb. Chem. High Throughput Screen., 2007, 10(8), 719-731. [http://dx.doi.org/ 10.2174/138620707782507313]. [PMID: 18045083].
[44]
Jenkins, J.; Bender, A.W.; Davies, J. In silico target fishing: predicting biological targets from chemical structure. Drug Discov. Today. Technol., 2006, 3, 413-421. [http://dx.doi.org/ 10.1016/j. ddtec.2006.12.008].
[45]
Brown, R.D.; Martin, Y.C. The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J. Chem. Inf. Comput. Sci., 1997, 37, 1-9. [http://dx.doi.org/ 10.1021/ ci960373c].
[46]
Martin, Y.C.; Kofron, J.L.; Traphagen, L.M. Do structurally similar molecules have similar biological activity? J. Med. Chem., 2002, 45(19), 4350-4358. [http://dx.doi.org/ 10.1021/jm020155c]. [PMID: 12213076].
[47]
Mitchell, J.B. The relationship between the sequence identities of alpha helical proteins in the PDB and the molecular similarities of their ligands. J. Chem. Inf. Comput. Sci., 2001, 41(6), 1617-1622. [http://dx.doi.org/ 10.1021/ci010364q]. [PMID: 11749588].
[48]
Patterson, D.E.; Cramer, R.D.; Ferguson, A.M.; Clark, R.D.; Weinberger, L.E. Neighborhood behavior: A useful concept for validation of “molecular diversity” descriptors. J. Med. Chem., 1996, 39(16), 3049-3059. [http://dx.doi.org/ 10.1021/jm960290n]. [PMID: 8759626].
[49]
Schuffenhauer, A.; Floersheim, P.; Acklin, P.; Jacoby, E. Similarity metrics for ligands reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci., 2003, 43(2), 391-405. [http://dx.doi.org/ 10.1021/ci025569t]. [PMID: 12653501].
[50]
Rognan, D. Structure-based approaches to target fishing and ligand profiling. Mol. Inform., 2010, 29(3), 176-187. [http://dx.doi.org/ 10.1002/minf.200900081]. [PMID: 27462761].
[51]
Keiser, M.J.; Setola, V.; Irwin, J.J.; Laggner, C.; Abbas, A.I.; Hufeisen, S.J.; Jensen, N.H.; Kuijer, M.B.; Matos, R.C.; Tran, T.B.; Whaley, R.; Glennon, R.A.; Hert, J.; Thomas, K.L.; Edwards, D.D.; Shoichet, B.K.; Roth, B.L. Predicting new molecular targets for known drugs. Nature, 2009, 462(7270), 175-181. [http://dx.doi.org/ 10.1038/nature08506]. [PMID: 19881490].
[52]
Lounkine, E.; Keiser, M.J.; Whitebread, S.; Mikhailov, D.; Hamon, J.; Jenkins, J.L.; Lavan, P.; Weber, E.; Doak, A.K.; Côté, S.; Shoichet, B.K.; Urban, L. Large-scale prediction and testing of drug activity on side-effect targets. Nature, 2012, 486(7403), 361-367. [http://dx.doi.org/ 10.1038/nature11159]. [PMID: 22722194].
[53]
Ripphausen, P.; Nisius, B.; Peltason, L.; Bajorath, J. Quo vadis, virtual screening? A comprehensive survey of prospective applications. J. Med. Chem., 2010, 53(24), 8461-8467. [http://dx.doi.org/ 10.1021/jm101020z]. [PMID: 20929257].
[54]
Scior, T.; Bender, A.; Tresadern, G.; Medina-Franco, J.L.; Martínez-Mayorga, K.; Langer, T.; Cuanalo-Contreras, K.; Agrafiotis, D.K. Recognizing pitfalls in virtual screening: A critical review. J. Chem. Inf. Model., 2012, 52(4), 867-881. [http://dx.doi. org/10.1021/ci200528d]. [PMID: 22435959].
[55]
Varnek, A.; Baskin, I. Machine learning methods for property prediction in chemoinformatics: Quo Vadis? J. Chem. Inf. Model., 2012, 52(6), 1413-1437. [http://dx.doi.org/ 10.1021/ci200409x]. [PMID: 22582859].
[56]
Vyas, V.K.; Ukawala, R.D.; Ghate, M.; Chintha, C. Homology modeling a fast tool for drug discovery: Current perspectives. Indian J. Pharm. Sci., 2012, 74(1), 1-17. [http://dx.doi.org/ 10.4103/ 0250-474X.102537]. [PMID: 23204616].
[57]
Bernini, A.; Spiga, O.; Venditti, V.; Prischi, F.; Bracci, L.; Huang, J.; Tanner, J.A.; Niccolai, N. Tertiary structure prediction of SARS coronavirus helicase. Biochem. Biophys. Res. Commun., 2006, 343(4), 1101-1104. [http://dx.doi.org/ 10.1016/j.bbrc.2006.03.069]. [PMID: 16579970].
[58]
Fusi, F.; Durante, M.; Spiga, O.; Trezza, A.; Frosini, M.; Floriddia, E.; Teodori, E.; Dei, S.; Saponara, S. In vitro and in silico analysis of the vascular effects of asymmetrical N,N-bis(alkanol)amine aryl esters, novel multidrug resistance-reverting agents. Naunyn Schmiedebergs Arch. Pharmacol., 2016, 389(9), 1033-1043. [http://dx.doi.org/ 10.1007/s00210-016-1266-y]. [PMID: 27351883].
[59]
Fusi, F.; Spiga, O.; Trezza, A.; Sgaragli, G.; Saponara, S. The surge of flavonoids as novel, fine regulators of cardiovascular Cav channels. Eur. J. Pharmacol., 2017, 796, 158-174. [http://dx.doi.org/ 10.1016/j.ejphar.2016.12.033]. [PMID: 28012974].
[60]
Fusi, F.; Trezza, A.; Spiga, O.; Sgaragli, G.; Bova, S. Cav1.2 channel current block by the PKA inhibitor H-89 in rat tail artery myocytes via a PKA-independent mechanism: Electrophysiological, functional, and molecular docking studies. Biochem. Pharmacol., 2017, 140, 53-63. [http://dx.doi.org/ 10.1016/j.bcp.2017.05.020]. [PMID: 28583845].
[61]
Galvagni, F.; Nardi, F.; Spiga, O.; Trezza, A.; Tarticchio, G.; Pellicani, R.; Andreuzzi, E.; Caldi, E.; Toti, P.; Tosi, G.M.; Santucci, A.; Iozzo, R.V.; Mongiat, M.; Orlandini, M. Dissecting the CD93-Multimerin 2 interaction involved in cell adhesion and migration of the activated endothelium. Matrix Biol., 2017, 64, 112-127. [http://dx.doi.org/ 10.1016/j.matbio.2017.08.003]. [PMID: 28912033].
[62]
Khanh, P.; Spiga, O.; Trezza, A.; Ho Kim, Y.; Cuong, N. Coumarins isolated from murraya paniculata in vietnam and their inhibitory effects against enzyme soluble Epoxide Hydrolase (sEH). Planta Med. Int. Open, 2017, 3, e68-e71. [http://dx.doi.org/ 10.1055/s-0042-120325].
[63]
Khanh, P.N.; Huong, T.T.; Spiga, O.; Trezza, A.; Son, N.T.; Cuong, T.D.; Ha, V.T.; Cuong, N.M. In silico screening of anthraquinones from Prismatomeris memecyloides as novel phosphodiesterase type-5 inhibitors (PDE-5Is). Rev. Int. Androl., 2018, 16(4), 147-158. [PMID: 30286869].
[64]
Pessina, F.; Gamberucci, A.; Chen, J.; Liu, B.; Vangheluwe, P.; Gorelli, B.; Lorenzini, S.; Spiga, O.; Trezza, A.; Sgaragli, G.; Saponara, S. Negative chronotropism, positive inotropism and lusitropism of 3,5-di-t-butyl-4-hydroxyanisole (DTBHA) on rat heart preparations occur through reduction of RyR2 Ca2+ leak. Biochem. Pharmacol., 2018, 155, 434-443. [http://dx.doi.org/ 10.1016/ j.bcp.2018.07.026]. [PMID: 30036502].
[65]
Trezza, A.; Cicaloni, V.; Porciatti, P.; Langella, A.; Fusi, F.; Saponara, S.; Spiga, O. From in silico to in vitro: A trip to reveal flavonoid binding on the Rattus norvegicus Kir6.1 ATP-sensitive inward rectifier potassium channel. PeerJ, 2018, 6, e4680. [http://dx.doi.org/ 10.7717/peerj.4680]. [PMID: 29736333].
[66]
Bernini, A.; Spiga, O.; Ciutti, A.; Chiellini, S.; Bracci, L.; Yan, X.; Zheng, B.; Huang, J.; He, M.L.; Song, H.D.; Hao, P.; Zhao, G.; Niccolai, N. Prediction of quaternary assembly of SARS coronavirus peplomer. Biochem. Biophys. Res. Commun., 2004, 325(4), 1210-1214. [http://dx.doi.org/ 10.1016/j.bbrc.2004.10.156]. [PMID: 15555555].
[67]
Huang, Y.A.; You, Z.H.; Chen, X.; Chan, K.; Luo, X. Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinformatics, 2016, 17(1), 184. [http://dx.doi.org/ 10.1186/s12859-016-1035-4]. [PMID: 27112932].
[68]
Garg, A.; Raghava, G.P. ESLpred2: Improved method for predicting subcellular localization of eukaryotic proteins. BMC Bioinformatics, 2008, 9, 503. [http://dx.doi.org/ 10.1186/1471-2105-9-503]. [PMID: 19038062].
[69]
Sun, T.; Zhou, B.; Lai, L.; Pei, J. Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinformatics, 2017, 18(1), 277. [http://dx.doi.org/ 10.1186/s12859-017-1700-2]. [PMID: 28545462].
[70]
Berman, H.; Henrick, K.; Nakamura, H.; Markley, J.L. The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Res., 2007, 35(Database issue), D301-D303. [http://dx.doi.org/ 10.1093/nar/gkl971]. [PMID: 17142228].
[71]
Hosur, R.; Xu, J.; Bienkowska, J.; Berger, B. iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions. J. Mol. Biol., 2011, 405(5), 1295-1310. [http://dx.doi.org/ 10.1016/j.jmb.2010.11.025]. [PMID: 21130772].
[72]
Lu, L.; Lu, H.; Skolnick, J. MULTIPROSPECTOR: An algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins, 2002, 49(3), 350-364. [http://dx.doi.org/ 10.1002/prot.10222]. [PMID: 12360525].
[73]
Xenarios, I.; Salwínski, L.; Duan, X.J.; Higney, P.; Kim, S.M.; Eisenberg, D. DIP, the Database of Interacting Proteins: A research tool for studying cellular networks of protein interactions. Nucleic Acids Res., 2002, 30(1), 303-305. [http://dx.doi.org/ 10.1093/nar/30.1.303]. [PMID: 11752321].
[74]
Memišević, V.; Wallqvist, A.; Reifman, J. Reconstituting protein interaction networks using parameter-dependent domain-domain interactions. BMC Bioinformatics, 2013, 14, 154. [http://dx.doi.org/ 10.1186/1471-2105-14-154]. [PMID: 23651452].
[75]
Wojcik, J.; Schächter, V. Protein-protein interaction map inference using interacting domain profile pairs. Bioinformatics, 2001, 17(Suppl. 1), S296-S305. [http://dx.doi.org/ 10.1093/bioinfor-matics/17.suppl_1.S296]. [PMID: 11473021].
[76]
Enright, A.J.; Iliopoulos, I.; Kyrpides, N.C.; Ouzounis, C.A. Protein interaction maps for complete genomes based on gene fusion events. Nature, 1999, 402(6757), 86-90. [http://dx.doi.org/ 10.1038/47056]. [PMID: 10573422].
[77]
Overbeek, R.; Fonstein, M.; D’Souza, M.; Pusch, G.D.; Maltsev, N. The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA, 1999, 96(6), 2896-2901. [http://dx.doi.org/ 10.1073/pnas.96.6.2896]. [PMID: 10077608].
[78]
Guo, W.; Wisniewski, J.A.; Ji, H. Hot spot-based design of small-molecule inhibitors for protein-protein interactions. Bioorg. Med. Chem. Lett., 2014, 24(11), 2546-2554. [http://dx.doi.org/ 10.1016/j.bmcl.2014.03.095]. [PMID: 24751445].
[79]
Hirst, J.D.; Glowacki, D.R.; Baaden, M. Molecular simulations and visualization: Introduction and overview. Faraday Discuss., 2014, 169, 9-22. [http://dx.doi.org/ 10.1039/C4FD90024C]. [PMID: 25285906].
[80]
Venditti, V.; Egner, T.K.; Clore, G.M. Hybrid approaches to structural characterization of conformational ensembles of complex macromolecular systems combining nmr residual dipolar couplings and solution x-ray scattering. Chem. Rev., 2016, 116(11), 6305-6322. [http://dx.doi.org/ 10.1021/acs.chemrev.5b00592]. [PMID: 26739383].
[81]
Ángyán, A.F.; Gáspári, Z. Ensemble-based interpretations of NMR structural data to describe protein internal dynamics. Molecules, 2013, 18(9), 10548-10567. [http://dx.doi.org/ 10.3390/molecules 180910548]. [PMID: 23999727].
[82]
Epa, V.; Winkler, D.; Tran, L. Computational approaches In: Adverse
Effects of Engineered Nanomaterials, 1st Ed.; Elsevier. 2012. Vol. 5, pp. 85-96 [http://dx.doi.org/ 10.1016/B978-0-12-386940-1.00005-2].
[83]
Dror, R.O.; Dirks, R.M.; Grossman, J.P.; Xu, H.; Shaw, D.E. Biomolecular simulation: A computational microscope for molecular biology. Annu. Rev. Biophys., 2012, 41, 429-452. [http://dx.doi.org/ 10.1146/annurev-biophys-042910-155245]. [PMID: 22577825].
[84]
Eyrisch, S.; Helms, V. Transient pockets on protein surfaces involved in protein-protein interaction. J. Med. Chem., 2007, 50(15), 3457-3464. [http://dx.doi.org/ 10.1021/jm070095g]. [PMID: 17602601].
[85]
Joerger, A.C.; Bauer, M.R.; Wilcken, R.; Baud, M.G.J.; Harbrecht, H.; Exner, T.E.; Boeckler, F.M.; Spencer, J.; Fersht, A.R. Exploiting transient protein states for the design of small-molecule stabilizers of mutant p53. Structure, 2015, 23(12), 2246-2255. [http://dx.doi.org/ 10.1016/j.str.2015.10.016]. [PMID: 26636255].
[86]
Luscombe, N.M.; Laskowski, R.A.; Thornton, J.M. Amino acid-base interactions: A three-dimensional analysis of protein-DNA interactions at an atomic level. Nucleic Acids Res., 2001, 29(13), 2860-2874. [http://dx.doi.org/ 10.1093/nar/29.13.2860]. [PMID: 11433033].
[87]
Janin, J. Wet and dry interfaces: The role of solvent in protein-protein and protein-DNA recognition. Structure, 1999, 7(12), R277-R279. [http://dx.doi.org/ 10.1016/S0969-2126(00)88333-1]. [PMID: 10647173].
[88]
Huggins, D.J.; Marsh, M.; Payne, M.C. Thermodynamic properties of water molecules at a protein-protein interaction surface. J. Chem. Theory Comput., 2011, 7(11), 3514-3522. [http://dx.doi.org/ 10.1021/ct200465z]. [PMID: 24554921].
[89]
Shoemaker, B.A.; Panchenko, A.R. Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLOS Comput. Biol., 2007, 3(4), e43. [http://dx.doi.org/ 10.1371/journal.pcbi.0030043]. [PMID: 17465672].
[90]
Wells, J.A.; McClendon, C.L. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature, 2007, 450(7172), 1001-1009. [http://dx.doi.org/ 10.1038/nature06526]. [PMID: 18075579].
[91]
Massova, I.; Kollman, P.A. Computational alanine scanning to probe protein−protein interactions: A novel approach to evaluate binding free energies. J. Am. Chem. Soc., 1999, 121, 8133-8143. [http://dx.doi.org/ 10.1021/ja990935j].
[92]
Ahmad, M.; Gu, W.; Geyer, T.; Helms, V. Adhesive water networks facilitate binding of protein interfaces. Nat. Commun., 2011, 2, 261. [http://dx.doi.org/ 10.1038/ncomms1258]. [PMID: 21448160].
[93]
De Simone, A.; Dodson, G.G.; Verma, C.S.; Zagari, A.; Fraternali, F. Prion and water: Tight and dynamical hydration sites have a key role in structural stability. Proc. Natl. Acad. Sci. USA, 2005, 102(21), 7535-7540. [http://dx.doi.org/ 10.1073/pnas.0501748102]. [PMID: 15894615].
[94]
Lounnas, V.; Pettitt, B.M.; Phillips, G.N., Jr A global model of the protein-solvent interface. Biophys. J., 1994, 66(3 Pt 1), 601-614. [http://dx.doi.org/ 10.1016/S0006-3495(94)80835-5]. [PMID: 8011893].
[95]
Qiu, W.; Wang, L.; Lu, W.; Boechler, A.; Sanders, D.A.; Zhong, D. Dissection of complex protein dynamics in human thioredoxin. Proc. Natl. Acad. Sci. USA, 2007, 104(13), 5366-5371. [http://dx.doi.org/ 10.1073/pnas.0608498104]. [PMID: 17369362].
[96]
Venditti, V.; Bernini, A.; De Simone, A.; Spiga, O.; Prischi, F.; Niccolai, N. MD and NMR studies of alpha-bungarotoxin surface accessibility. Biochem. Biophys. Res. Commun., 2007, 356(1), 114-117. [http://dx.doi.org/ 10.1016/j.bbrc.2007.02.094]. [PMID: 17336923].
[97]
Bernini, A.; Venditti, V.; Spiga, O.; Niccolai, N. Probing protein surface accessibility with solvent and paramagnetic molecules. Prog. Nucl. Magn. Reson. Spectrosc., 2009, 54, 278-289. [http://dx.doi.org/ 10.1016/j.pnmrs.2008.10.003].
[98]
Niccolai, N.; Ciutti, A.; Spiga, O.; Scarselli, M.; Bernini, A.; Bracci, L.; Di Maro, D.; Dalvit, C.; Molinari, H.; Esposito, G.; Temussi, P.A. NMR studies of protein surface accessibility. J. Biol. Chem., 2001, 276(45), 42455-42461. [http://dx.doi.org/10.1074/ jbc.M107387200]. [PMID: 11546818].
[99]
Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera--A visualization system for exploratory research and analysis. J. Comput. Chem., 2004, 25(13), 1605-1612. [http://dx.doi.org/ 10.1002/jcc. 20084]. [PMID: 15264254].
[100]
Sommer, J.; Jonah, C.; Fukuda, R.; Bersohn, R. Production and subsequent second-order decomposition of protein disulfide anions lengthy collisions between proteins. J. Mol. Biol., 1982, 159(4), 721-744. [http://dx.doi.org/ 10.1016/0022-2836(82)90110-3]. [PMID: 6815333].
[101]
Brune, D.; Kim, S. Hydrodynamic steering effects in protein association. Proc. Natl. Acad. Sci. USA, 1994, 91(8), 2930-2934. [http://dx.doi.org/ 10.1073/pnas.91.8.2930]. [PMID: 8159682].
[102]
Przytycka, T.M.; Singh, M.; Slonim, D.K. Toward the dynamic interactome: It’s about time. Brief. Bioinform., 2010, 11(1), 15-29. [http://dx.doi.org/ 10.1093/bib/bbp057]. [PMID: 20061351].
[103]
Carbonell, P.; Nussinov, R.; del Sol, A. Energetic determinants of protein binding specificity: insights into protein interaction networks. Proteomics, 2009, 9(7), 1744-1753. [http://dx.doi.org/ 10.1002/pmic.200800425]. [PMID: 19253304].
[104]
Cheng, Y.; Holst, M.J.; McCammon, J.A. Finite element analysis of drug electrostatic diffusion: Inhibition rate studies in N1 neuraminidase. Pac. Symp. Biocomput., 2009, 281-292. [PMID: 19209708].
[105]
Elcock, A.H.; Gabdoulline, R.R.; Wade, R.C.; McCammon, J.A. Computer simulation of protein-protein association kinetics: Acetylcholinesterase-fasciculin. J. Mol. Biol., 1999, 291(1), 149-162. [http://dx.doi.org/ 10.1006/jmbi.1999.2919]. [PMID: 10438612].
[106]
Sept, D.; Elcock, A.H.; McCammon, J.A. Computer simulations of actin polymerization can explain the barbed-pointed end asymmetry. J. Mol. Biol., 1999, 294(5), 1181-1189. [http://dx.doi.org/ 10.1006/jmbi.1999.3332]. [PMID: 10600376].
[107]
Wlodek, S.T.; Shen, T.; McCammon, J.A. Electrostatic steering of substrate to acetylcholinesterase: analysis of field fluctuations. Biopolymers, 2000, 53(3), 265-271. [http://dx.doi.org/ 10.1002/(SICI) 1097-0282(200003)53:3<265:AID-BIP6>3.0.CO;2-N]. [PMID: 10679631].
[108]
Gunasekaran, K.; Pentony, M.; Shen, M.; Garrett, L.; Forte, C.; Woodward, A.; Ng, S.B.; Born, T.; Retter, M.; Manchulenko, K.; Sweet, H.; Foltz, I.N.; Wittekind, M.; Yan, W. Enhancing antibody Fc heterodimer formation through electrostatic steering effects: applications to bispecific molecules and monovalent IgG. J. Biol. Chem., 2010, 285(25), 19637-19646. [http://dx.doi.org/ 10.1074/jbc.M110.117382]. [PMID: 20400508].
[109]
Hemsath, L.; Dvorsky, R.; Fiegen, D.; Carlier, M.F.; Ahmadian, M.R. An electrostatic steering mechanism of Cdc42 recognition by Wiskott-Aldrich syndrome proteins. Mol. Cell, 2005, 20(2), 313-324. [http://dx.doi.org/ 10.1016/j.molcel.2005.08.036]. [PMID: 16246732].
[110]
Meltzer, R.H.; Thompson, E.; Soman, K.V.; Song, X.Z.; Ebalunode, J.O.; Wensel, T.G.; Briggs, J.M.; Pedersen, S.E. Electrostatic steering at acetylcholine binding sites. Biophys. J., 2006, 91(4), 1302-1314. [http://dx.doi.org/ 10.1529/biophysj.106.081463]. [PMID: 16751247].
[111]
Persson, B.A.; Jönsson, B.; Lund, M. Enhanced protein steering: Cooperative electrostatic and van der Waals forces in antigen-antibody complexes. J. Phys. Chem. B, 2009, 113(30), 10459-10464. [http://dx.doi.org/ 10.1021/jp904541g]. [PMID: 19583233].
[112]
Honig, B.; Nicholls, A. Classical electrostatics in biology and chemistry. Science, 1995, 268(5214), 1144-1149. [http://dx.doi.org/ 10.1126/science.7761829]. [PMID: 7761829].
[113]
Kukić, P.; Nielsen, J.E. Electrostatics in proteins and protein-ligand complexes. Future Med. Chem., 2010, 2(4), 647-666. [http://dx.doi.org/ 10.4155/fmc.10.6]. [PMID: 21426012].
[114]
McCammon, J.A. Darwinian biophysics: Electrostatics and evolution in the kinetics of molecular binding. Proc. Natl. Acad. Sci. USA, 2009, 106(19), 7683-7684. [http://dx.doi.org/ 10.1073/pnas. 0902767106]. [PMID: 19416830].
[115]
Wong, G.C.; Pollack, L. Electrostatics of strongly charged biological polymers: ion-mediated interactions and self-organization in nucleic acids and proteins. Annu. Rev. Phys. Chem., 2010, 61, 171-189. [http://dx.doi.org/ 10.1146/annurev.physchem.58.032806. 104436]. [PMID: 20055668].
[116]
Kästner, J.; Thiel, W. Bridging the gap between thermodynamic integration and umbrella sampling provides a novel analysis method: “Umbrella integration. J. Chem. Phys., 2005, 123(14), 144104. [http://dx.doi.org/ 10.1063/1.2052648]. [PMID: 16238371].
[117]
Watson, H. Biological membranes. Essays Biochem., 2015, 59, 43-69. [http://dx.doi.org/ 10.1042/bse0590043]. [PMID: 26504250].
[118]
Pagadala, N.S.; Syed, K.; Tuszynski, J. Software for molecular docking: A review. Biophys. Rev., 2017, 9(2), 91-102. [http://dx.doi.org/ 10.1007/s12551-016-0247-1]. [PMID: 28510083].
[119]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2013, 66(1), 334-395. [http://dx.doi.org/ 10.1124/pr.112.007336]. [PMID: 24381236].
[120]
Vakser, I.A. Protein-protein docking: From interaction to interactome. Biophys. J., 2014, 107(8), 1785-1793. [http://dx.doi.org/ 10.1016/j.bpj.2014.08.033]. [PMID: 25418159].
[122]
Pavelka, A.; Chovancova, E.; Damborsky, J. HotSpot Wizard: A
web server for identification of hot spots in protein engineering. Nucleic Acids Res, 2009, 37(Web Server issue) W376-W383 [PMID: 19465397].
[123]
Krüger, D.M.; Garzón, J.I.; Montes, P.C.; Gohlke, H. Predicting protein-protein interactions with DrugScorePPI: Fully-flexible docking, scoring, and in silicoalanine-scanning. J. Chem., 2011, 3, 36. [http://dx.doi.org/ 10.1186/1758-2946-3-S1-P36].
[124]
Geppert, T.; Hoy, B.; Wessler, S.; Schneider, G. Context-based identification of protein-protein interfaces and “hot-spot” residues. Chem. Biol., 2011, 18(3), 344-353. [http://dx.doi.org/ 10.1016/j. chembiol.2011.01.005]. [PMID: 21439479].
[125]
Shingate, P.; Manoharan, M.; Sukhwal, A.; Sowdhamini, R. ECMIS: Computational approach for the identification of hotspots at protein-protein interfaces. BMC Bioinformatics, 2014, 15, 303. [http://dx.doi.org/ 10.1186/1471-2105-15-303]. [PMID: 25228146].
[126]
Banday, Z.; Ashraf, G. Protein-protein interactions as potential
targets of drug designing. In: Advances in Biochemistry & Application
in Medicine, 3rd Ed., Open Access ebooks 919, North Market
Street, Suite 425 Wilmington, 2018, pp. 1-15.
[127]
Fischer, G.; Rossmann, M.; Hyvönen, M. Alternative modulation of protein-protein interactions by small molecules. Curr. Opin. Biotechnol., 2015, 35, 78-85. [http://dx.doi.org/ 10.1016/j.copbio.2015.04.006]. [PMID: 25935873].
[128]
Laraia, L.; McKenzie, G.; Spring, D.R.; Venkitaraman, A.R.; Huggins, D.J. Spring, David R.; Venkitaraman, Ashok R.; Huggins, David J., Overcoming chemical, biological, and computational challenges in the development of inhibitors targeting protein-protein interactions. Chem. Biol., 2015, 22(6), 689-703. [http://dx.doi.org/ 10.1016/j.chembiol.2015.04.019]. [PMID: 26091166].
[129]
Jin, L.; Wang, W.; Fang, G. Targeting protein-protein interaction by small molecules. Annu. Rev. Pharmacol. Toxicol., 2014, 54, 435-456. [http://dx.doi.org/ 10.1146/annurev-pharmtox-011613-140028]. [PMID: 24160698].
[130]
Rosell, M.; Fernández-Recio, J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opin. Drug Discov., 2018, 13(4), 327-338. [http://dx.doi.org/ 10.1080/17460441. 2018.1430763]. [PMID: 29376444].
[131]
Modell, A.E.; Blosser, S.L.; Arora, P.S. Systematic targeting of protein-protein interactions. Trends Pharmacol. Sci., 2016, 37(8), 702-713. [http://dx.doi.org/ 10.1016/j.tips.2016.05.008]. [PMID: 27267699].
[132]
Milroy, L-G.; Grossmann, T.N.; Hennig, S.; Brunsveld, L.; Ottmann, C. Modulators of protein-protein interactions. Chem. Rev., 2014, 114(9), 4695-4748. [http://dx.doi.org/ 10.1021/cr400698c]. [PMID: 24735440].
[133]
Spiga, O.; Bernini, A.; Scarselli, M.; Ciutti, A.; Bracci, L.; Lozzi, L.; Lelli, B.; Di Maro, D.; Calamandrei, D.; Niccolai, N. Peptide-protein interactions studied by surface plasmon and nuclear magnetic resonances. FEBS Lett., 2002, 511(1-3), 33-35. [http://dx.doi.org/ 10.1016/S0014-5793(01)03274-4]. [PMID: 11821044].
[134]
Pelay-Gimeno, M.; Glas, A.; Koch, O.; Grossmann, T.N. Structure-based design of inhibitors of protein-protein interactions: mimicking peptide binding epitopes. Angew. Chem. Int. Ed. Engl., 2015, 54(31), 8896-8927. [http://dx.doi.org/ 10.1002/anie.201412070]. [PMID: 26119925].
[135]
Planel, S.; Salomon, A.; Jalinot, P.; Feige, J.J.; Cherradi, N. A novel concept in antiangiogenic and antitumoral therapy: multitarget destabilization of short-lived mRNAs by the zinc finger protein ZFP36L1. Oncogene, 2010, 29(45), 5989-6003. [http://dx.doi.org/ 10.1038/onc.2010.341]. [PMID: 20802528].
[136]
Smith, B.A.; Daniels, D.S.; Coplin, A.E.; Jordan, G.E.; McGregor, L.M.; Schepartz, A. Minimally cationic cell-permeable miniature proteins via α-helical arginine display. J. Am. Chem. Soc., 2008, 130(10), 2948-2949. [http://dx.doi.org/ 10.1021/ja800074v]. [PMID: 18271592].
[137]
London, N.; Movshovitz-Attias, D.; Schueler-Furman, O. The structural basis of peptide-protein binding strategies. Structure, 2010, 18(2), 188-199. [http://dx.doi.org/ 10.1016/j.str.2009.11.012]. [PMID: 20159464].
[138]
Trabuco, L.G.; Lise, S.; Petsalaki, E.; Russell, R.B. PepSite: Prediction
of peptide-binding sites from protein surfaces. Nucleic Acids
Res, 2012, 40(Web Server issue) W423-W427 [PMID: 22600738].
[139]
Tünnemann, G.; Martin, R.M.; Haupt, S.; Patsch, C.; Edenhofer, F.; Cardoso, M.C. Cargo-dependent mode of uptake and bioavailability of TAT-containing proteins and peptides in living cells. FASEB J., 2006, 20(11), 1775-1784. [http://dx.doi.org/ 10.1096/fj.05-5523com]. [PMID: 16940149].
[140]
Gellman, S.H. Foldamers: a manifesto. Acc. Chem. Res., 1998, 31, 173-180. [http://dx.doi.org/ 10.1021/ar960298r].
[141]
Checco, J.W.; Kreitler, D.F.; Thomas, N.C.; Belair, D.G.; Rettko, N.J.; Murphy, W.L.; Forest, K.T.; Gellman, S.H. Targeting diverse protein-protein interaction interfaces with α/β-peptides derived from the Z-domain scaffold. Proc. Natl. Acad. Sci. USA, 2015, 112(15), 4552-4557. [http://dx.doi.org/ 10.1073/pnas.1420380112]. [PMID: 25825775].
[142]
Checco, J.W.; Lee, E.F.; Evangelista, M.; Sleebs, N.J.; Rogers, K.; Pettikiriarachchi, A.; Kershaw, N.J.; Eddinger, G.A.; Belair, D.G.; Wilson, J.L.; Eller, C.H.; Raines, R.T.; Murphy, W.L.; Smith, B.J.; Gellman, S.H.; Fairlie, W.D. α/β-peptide foldamers targeting intracellular protein–protein interactions with activity in living cells. J. Am. Chem. Soc., 2015, 137(35), 11365-11375. [http://dx.doi.org/ 10.1021/jacs.5b05896]. [PMID: 26317395].
[143]
Johnson, L.M.; Gellman, S.H. α-Helix mimicry with α/β-peptides. Methods Enzymol., 2013, 523, 407-429. [http://dx.doi.org/ 10.1016/B978-0-12-394292-0.00019-9]. [PMID: 23422441].
[144]
Werner, H.M.; Horne, W.S. Folding and function in α/β-peptides: targets and therapeutic applications. Curr. Opin. Chem. Biol., 2015, 28, 75-82. [http://dx.doi.org/ 10.1016/j.cbpa.2015.06.013]. [PMID: 26136051].
[145]
Orner, B.P.; Ernst, J.T.; Hamilton, A.D. Toward proteomimetics: terphenyl derivatives as structural and functional mimics of extended regions of an α-helix. J. Am. Chem. Soc., 2001, 123(22), 5382-5383. [http://dx.doi.org/ 10.1021/ja0025548]. [PMID: 11457415].
[146]
T., E. J.; Jorge, B.; Soon, P. H.; Hang, Y.; D.H.A. Design and application of an α-helix-mimetic scaffold based on an oligoamide-foldamer strategy: Antagonism of the Bak BH3/Bcl-xL complex. Angew. Chem. Int. Ed., 2003, 42, 535-539. [http://dx.doi.org/ 10.1002/anie.200390154].
[147]
Haase, H.S.; Peterson-Kaufman, K.J.; Lan Levengood, S.K.; Checco, J.W.; Murphy, W.L.; Gellman, S.H. Extending foldamer design beyond α-helix mimicry: α/β-peptide inhibitors of vascular endothelial growth factor signaling. J. Am. Chem. Soc., 2012, 134(18), 7652-7655. [http://dx.doi.org/ 10.1021/ja302469a]. [PMID: 22548447].
[148]
Azzarito, V.; Prabhakaran, P.; Bartlett, A.I.; Murphy, N.S.; Hardie, M.J.; Kilner, C.A.; Edwards, T.A.; Warriner, S.L.; Wilson, A.J. 2-O-alkylated para-benzamide α-helix mimetics: The role of scaffold curvature. Org. Biomol. Chem., 2012, 10(32), 6469-6472. [http://dx.doi.org/ 10.1039/c2ob26262b]. [PMID: 22785578].
[149]
Renfrew, P.D.; Craven, T.W.; Butterfoss, G.L.; Kirshenbaum, K.; Bonneau, R. A rotamer library to enable modeling and design of peptoid foldamers. J. Am. Chem. Soc., 2014, 136(24), 8772-8782. [http://dx.doi.org/ 10.1021/ja503776z]. [PMID: 24823488].
[150]
Lao, B.B.; Drew, K.; Guarracino, D.A.; Brewer, T.F.; Heindel, D.W.; Bonneau, R.; Arora, P.S. Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions. J. Am. Chem. Soc., 2014, 136(22), 7877-7888. [http://dx.doi.org/ 10.1021/ja502310r]. [PMID: 24972345].
[151]
Chène, P. Drugs targeting protein-protein interactions. ChemMedChem, 2006, 1(4), 400-411. [http://dx.doi.org/ 10.1002/cmdc. 200600004]. [PMID: 16892375].
[152]
Grembecka, J.; Belcher, A.M.; Hartley, T.; Cierpicki, T. Molecular basis of the mixed lineage leukemia-menin interaction: Implications for targeting mixed lineage leukemias. J. Biol. Chem., 2010, 285(52), 40690-40698. [http://dx.doi.org/ 10.1074/jbc.M110. 172783]. [PMID: 20961854].
[153]
Zhou, H.; Liu, L.; Huang, J.; Bernard, D.; Karatas, H.; Navarro, A.; Lei, M.; Wang, S. Structure-based design of high-affinity macrocyclic peptidomimetics to block the menin-mixed lineage leukemia 1 (MLL1) protein-protein interaction. J. Med. Chem., 2013, 56(3), 1113-1123. [http://dx.doi.org/ 10.1021/jm3015298]. [PMID: 23244744].
[154]
Cierpicki, T.; Grembecka, J. Challenges and opportunities in targeting the menin-MLL interaction. Future Med. Chem., 2014, 6(4), 447-462. [http://dx.doi.org/ 10.4155/fmc.13.214]. [PMID: 24635524].
[155]
Perez, E.A. Microtubule inhibitors: Differentiating tubulin-inhibiting agents based on mechanisms of action, clinical activity, and resistance. Mol. Cancer Ther., 2009, 8(8), 2086-2095. [http://dx.doi.org/ 10.1158/1535-7163.MCT-09-0366]. [PMID: 19671735].
[156]
Jordan, M.A. Mechanism of action of antitumor drugs that interact with microtubules and tubulin. Curr. Med. Chem. Anticancer Agents, 2002, 2(1), 1-17. [http://dx.doi.org/ 10.2174/156801102 3354290]. [PMID: 12678749].
[157]
Gigant, B.; Wang, C.; Ravelli, R.B.G.; Roussi, F.; Steinmetz, M.O.; Curmi, P.A.; Sobel, A.; Knossow, M. Structural basis for the regulation of tubulin by vinblastine. Nature, 2005, 435(7041), 519-522. [http://dx.doi.org/ 10.1038/nature03566]. [PMID: 15917812].
[158]
Ravelli, R.B.G.; Gigant, B.; Curmi, P.A.; Jourdain, I.; Lachkar, S.; Sobel, A.; Knossow, M. Insight into tubulin regulation from a complex with colchicine and a stathmin-like domain. Nature, 2004, 428(6979), 198-202. [http://dx.doi.org/ 10.1038/nature02393]. [PMID: 15014504].
[159]
Lu, Y.; Chen, J.; Xiao, M.; Li, W.; Miller, D.D. An overview of tubulin inhibitors that interact with the colchicine binding site. Pharm. Res., 2012, 29(11), 2943-2971. [http://dx.doi.org/ 10.1007/s11095-012-0828-z]. [PMID: 22814904].
[160]
Andrei, S.A.; Sijbesma, E.; Hann, M.; Davis, J.; O’Mahony, G.; Perry, M.W.D.; Karawajczyk, A.; Eickhoff, J.; Brunsveld, L.; Doveston, R.G.; Milroy, L.G.; Ottmann, C. Stabilization of protein-protein interactions in drug discovery. Expert Opin. Drug Discov., 2017, 12(9), 925-940. [http://dx.doi.org/ 10.1080/17460441. 2017.1346608]. [PMID: 28695752].
[161]
Thiel, P.; Kaiser, M.; Ottmann, C. Small-molecule stabilization of protein-protein interactions: an underestimated concept in drug discovery? Angew. Chem. Int. Ed. Engl., 2012, 51(9), 2012-2018. [http://dx.doi.org/ 10.1002/anie.201107616]. [PMID: 22308055].
[162]
Aymami, J.; Barril, X.; Rodríguez-Pascau, L.; Martinell, M. Pharmacological chaperones for enzyme enhancement therapy in genetic diseases. Pharm. Pat. Anal., 2013, 2(1), 109-124. [http://dx.doi.org/ 10.4155/ppa.12.74]. [PMID: 24236974].
[163]
Leeson, P.D.; Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat. Rev. Drug Discov., 2007, 6(11), 881-890. [http://dx.doi.org/ 10.1038/nrd2445]. [PMID: 17971784].
[164]
Ringe, D.; Petsko, G.A. What are pharmacological chaperones and why are they interesting? J. Biol., 2009, 8(9), 80. [http://dx.doi.org/ 10.1186/jbiol186]. [PMID: 19833004].
[165]
Bier, D.; Thiel, P.; Briels, J.; Ottmann, C. Stabilization of protein-protein interactions in chemical biology and drug discovery. Prog. Biophys. Mol. Biol., 2015, 119(1), 10-19. [http://dx.doi.org/ 10.1016/j.pbiomolbio.2015.05.002]. [PMID: 26093250].
[166]
Makley, L.N.; Gestwicki, J.E. Expanding the number of ‘druggable’ targets: Non-enzymes and protein-protein interactions. Chem. Biol. Drug Des., 2013, 81(1), 22-32. [http://dx.doi.org/ 10.1111/cbdd.12066]. [PMID: 23253128].
[167]
Bulawa, C.E.; Connelly, S.; Devit, M.; Wang, L.; Weigel, C.; Fleming, J.A.; Packman, J.; Powers, E.T.; Wiseman, R.L.; Foss, T.R.; Wilson, I.A.; Kelly, J.W.; Labaudinière, R. Tafamidis, a potent and selective transthyretin kinetic stabilizer that inhibits the amyloid cascade. Proc. Natl. Acad. Sci. USA, 2012, 109(24), 9629-9634. [http://dx.doi.org/ 10.1073/pnas.1121005109]. [PMID: 22645360].
[168]
Ranganath, L.R.; Milan, A.M.; Hughes, A.T.; Dutton, J.J.; Fitzgerald, R.; Briggs, M.C.; Bygott, H.; Psarelli, E.E.; Cox, T.F.; Gallagher, J.A.; Jarvis, J.C.; van Kan, C.; Hall, A.K.; Laan, D.; Olsson, B.; Szamosi, J.; Rudebeck, M.; Kullenberg, T.; Cronlund, A.; Svensson, L.; Junestrand, C.; Ayoob, H.; Timmis, O.G.; Sireau, N.; Le Quan Sang, K.H.; Genovese, F.; Braconi, D.; Santucci, A.; Nemethova, M.; Zatkova, A.; McCaffrey, J.; Christensen, P.; Ross, G.; Imrich, R.; Rovensky, J. Suitability Of Nitisinone In Alkaptonuria 1 (SONIA 1): An international, multicentre, randomised, open-label, no-treatment controlled, parallel-group, dose-response study to investigate the effect of once daily nitisinone on 24-h urinary homogentisic acid excretion in patients with alkaptonuria after 4 weeks of treatment. Ann. Rheum. Dis., 2016, 75(2), 362-367. [http://dx.doi.org/ 10.1136/annrheumdis-2014-206033]. [PMID: 25475116].
[169]
Bernini, A.; Henrici De Angelis, L.; Morandi, E.; Spiga, O.; Santucci, A.; Assfalg, M.; Molinari, H.; Pillozzi, S.; Arcangeli, A.; Niccolai, N. Searching for protein binding sites from Molecular Dynamics simulations and paramagnetic fragment-based NMR studies. Biochim. Biophys. Acta, 2014, 1844(3), 561-566. [http://dx.doi.org/ 10.1016/j.bbapap.2013.12.012]. [PMID: 24373878].
[170]
Hussein, H.A.; Borrel, A.; Geneix, C.; Petitjean, M.; Regad, L.; Camproux, A.C. PockDrug-Server: A new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res., 2015, 43(W1), W436-42. [http://dx.doi.org/ 10.1093/nar/ gkv462]. [PMID: 25956651].
[171]
Borrel, A.; Regad, L.; Xhaard, H.; Petitjean, M.; Camproux, A.C. PockDrug: A model for predicting pocket druggability that overcomes pocket estimation uncertainties. J. Chem. Inf. Model., 2015, 55(4), 882-895. [http://dx.doi.org/ 10.1021/ci5006004]. [PMID: 25835082].
[172]
Bernini, A.; Galderisi, S.; Spiga, O.; Bernardini, G.; Niccolai, N.; Manetti, F.; Santucci, A. Toward a generalized computational workflow for exploiting transient pockets as new targets for small molecule stabilizers: Application to the homogentisate 1,2-dioxygenase mutants at the base of rare disease Alkaptonuria. Comput. Biol. Chem., 2017, 70, 133-141. [http://dx.doi.org/ 10.1016/j.compbiolchem.2017.08.008]. [PMID: 28869836].
[173]
Schrodinger, LLC The AxPyMOL Molecular Graphics Plugin for
Microsoft PowerPoint, Version 1.8 2015.
[174]
Nilsson, J.; Jonasson, P.; Samuelsson, E.; Ståhl, S.; Uhlén, M. Integrated production of human insulin and its C-peptide. J. Biotechnol., 1996, 48(3), 241-250. [http://dx.doi.org/ 10.1016/0168-1656(96)01514-3]. [PMID: 8862001].
[175]
Winter, J.; Lilie, H.; Rudolph, R. Renaturation of human proinsulin--A study on refolding and conversion to insulin. Anal. Biochem., 2002, 310(2), 148-155. [http://dx.doi.org/ 10.1016/S0003-2697(02)00287-7]. [PMID: 12423632].
[176]
Chen, J-Q.; Zhang, H-T.; Hu, M-H.; Tang, J-G. Production of human insulin in an E. coli system with Met-Lys-human proinsulin as the expressed precursor. Appl. Biochem. Biotechnol., 1995, 55(1), 5-15. [http://dx.doi.org/ 10.1007/BF02788744]. [PMID: 7486987].
[177]
Min, C-K.; Son, Y-J.; Kim, C-K.; Park, S-J.; Lee, J-W. Increased expression, folding and enzyme reaction rate of recombinant human insulin by selecting appropriate leader peptide. J. Biotechnol., 2011, 151(4), 350-356. [http://dx.doi.org/ 10.1016/j.jbiotec.2010. 12.023]. [PMID: 21219941].
[178]
Jung, S.H.; Kim, C-K.; Lee, G.; Yoon, J.; Lee, M. Structural analysis of recombinant human preproinsulins by structure prediction, molecular dynamics, and protein-protein docking. Genomics Inform., 2017, 15(4), 142-146. [http://dx.doi.org/ 10.5808/GI.2017. 15.4.142]. [PMID: 29307140].
[179]
Gurova, K. New hopes from old drugs: revisiting DNA-binding small molecules as anticancer agents. Future Oncol., 2009, 5(10), 1685-1704. [http://dx.doi.org/ 10.2217/fon.09.127]. [PMID: 20001804].
[180]
Neznanov, N.; Gorbachev, A.V.; Neznanova, L.; Komarov, A.P.; Gurova, K.V.; Gasparian, A.V.; Banerjee, A.K.; Almasan, A.; Fairchild, R.L.; Gudkov, A.V. Anti-malaria drug blocks proteotoxic stress response: anti-cancer implications. Cell Cycle, 2009, 8(23), 3960-3970. [http://dx.doi.org/ 10.4161/cc.8.23.10179]. [PMID: 19901558].
[181]
Guo, C.; Gasparian, A.V.; Zhuang, Z.; Bosykh, D.A.; Komar, A.A.; Gudkov, A.V.; Gurova, K.V. 9-Aminoacridine-based anticancer drugs target the PI3K/AKT/mTOR, NF-kappaB and p53 pathways. Oncogene, 2009, 28(8), 1151-1161. [http://dx.doi.org/ 10.1038/onc.2008.460]. [PMID: 19137016].
[182]
Gurova, K.V.; Hill, J.E.; Guo, C.; Prokvolit, A.; Burdelya, L.G.; Samoylova, E.; Khodyakova, A.V.; Ganapathi, R.; Ganapathi, M.; Tararova, N.D.; Bosykh, D.; Lvovskiy, D.; Webb, T.R.; Stark, G.R.; Gudkov, A.V. Small molecules that reactivate p53 in renal cell carcinoma reveal a NF-kappaB-dependent mechanism of p53 suppression in tumors. Proc. Natl. Acad. Sci. USA, 2005, 102(48), 17448-17453. [http://dx.doi.org/ 10.1073/pnas.0508888102]. [PMID: 16287968].
[183]
Preet, R.; Mohapatra, P.; Mohanty, S.; Sahu, S.K.; Choudhuri, T.; Wyatt, M.D.; Kundu, C.N. Quinacrine has anticancer activity in breast cancer cells through inhibition of topoisomerase activity. Int. J. Cancer, 2012, 130(7), 1660-1670. [http://dx.doi.org/ 10.1002/ijc.26158]. [PMID: 21544805].
[184]
Mohapatra, P.; Preet, R.; Das, D.; Satapathy, S.R.; Choudhuri, T.; Wyatt, M.D.; Kundu, C.N. Quinacrine-mediated autophagy and apoptosis in colon cancer cells is through a p53- and p21-dependent mechanism. Oncol. Res., 2012, 20(2-3), 81-91. [http://dx.doi.org/ 10.3727/096504012X13473664562628]. [PMID: 23193914].
[185]
Preet, R.; Mohapatra, P.; Das, D.; Satapathy, S.R.; Choudhuri, T.; Wyatt, M.D.; Kundu, C.N. Lycopene synergistically enhances quinacrine action to inhibit Wnt-TCF signaling in breast cancer cells through APC. Carcinogenesis, 2013, 34(2), 277-286. [http://dx.doi.org/ 10.1093/carcin/bgs351]. [PMID: 23129580].
[186]
Wang, W.; Gallant, J-N.; Katz, S.I.; Dolloff, N.G.; Smith, C.D.; Abdulghani, J.; Allen, J.E.; Dicker, D.T.; Hong, B.; Navaraj, A.; El-Deiry, W.S. Quinacrine sensitizes hepatocellular carcinoma cells to TRAIL and chemotherapeutic agents. Cancer Biol. Ther., 2011, 12(3), 229-238. [http://dx.doi.org/ 10.4161/cbt.12.3.17033]. [PMID: 21725212].
[187]
Das, S.; Tripathi, N.; Preet, R.; Siddharth, S.; Nayak, A.; Bharatam, P.V.; Kundu, C.N. Quinacrine induces apoptosis in cancer cells by forming a functional bridge between TRAIL-DR5 complex and modulating the mitochondrial intrinsic cascade. Oncotarget, 2017, 8(1), 248-267. [http://dx.doi.org/ 10.18632/oncotarget.11335]. [PMID: 27542249].
[188]
Yeger-Lotem, E.; Sharan, R. Human protein interaction networks across tissues and diseases. Front. Genet., 2015, 6, 257. [http://dx.doi.org/ 10.3389/fgene.2015.00257]. [PMID: 26347769].
[189]
Welch, C.J.; Faul, M.M.; Tummala, S.; Papageorgiou, C.D.; Hicks, F.; Hawkins, J.M.; Thomson, N.; Cote, A.; Bordawekar, S.; Wittenberger, S.J.; Laffan, D.; Purdie, M.; Boulas, P.; Irdam, E.; Horspool, K.; Yang, B-S.; Tom, J.; Fernandez, P.; Ferretti, A.; May, S.; Seibert, K.; Wells, K.; McKeown, R. (ETC): fostering precompetitive collaborations on new enabling technologies for pharmaceutical research and development. Org. Process Res. Dev., 2017, 21, 414-419. [http://dx.doi.org/ 10.1021/acs.oprd.6b00427].
[190]
Pavlopoulos, G.A.; Secrier, M.; Moschopoulos, C.N.; Soldatos, T.G.; Kossida, S.; Aerts, J.; Schneider, R.; Bagos, P.G. Using graph theory to analyze biological networks. BioData Min., 2011, 4, 10. [http://dx.doi.org/ 10.1186/1756-0381-4-10]. [PMID: 21527005].
[191]
Sikandar, A.; Anwar, W.; Bajwa, U.I.; Wang, X.; Sikandar, M.; Yao, L.; Jiang, Z.L.; Chunkai, Z. Decision tree based approaches for detecting protein complex in Protein Protein Interaction network (PPI) via link and sequence analysis. IEEE Access, 2018, 6, 22108-22120. [http://dx.doi.org/ 10.1109/ACCESS.2018.2807811].
[192]
Seo, M-H.; Kim, P.M. The present and the future of motif-mediated protein-protein interactions. Curr. Opin. Struct. Biol., 2018, 50, 162-170. [http://dx.doi.org/ 10.1016/j.sbi.2018.04.005]. [PMID: 29730529].
[193]
Wang, W.; Yang, Y.; Yin, J.; Gong, X. Different protein-protein interface patterns predicted by different machine learning methods. Sci. Rep., 2017, 7(1), 16023. [http://dx.doi.org/ 10.1038/s41598-017-16397-z]. [PMID: 29167570].
