Stabilizing non-covalent interactions of ligand aromatic moieties and proline in ligand–protein systems
- 214 Downloads
Proline, due to its conformational specificity, is known to show some unique properties and has significant functions in the tertiary structure of proteins. It was suggested that proline could have an important influence on some vital interactions in protein as well, by engaging in non-covalent stabilization interactions with some aromatic moieties. In this work, the interactions that occur between proline and some aromatic moieties in ligands were investigated by means of the density functional theory using an exchange–correlation functional capable of taking into account dispersion interactions. The obtained results showed that the stabilization energy between a properly placed proline and an aromatic moiety could be as large as 25 kJ/mol and hence be a significant factor in placing a ligand in binding site of a protein. This indicates that the error in determining the most favorable structure of ligand–protein complexes obtained by usual molecular docking experiments sometimes could be the result of neglecting this type of interactions.
KeywordsAb initio calculations Non-covalent interactions Proteins Molecular modeling Density functional theory
This research was supported by (1) the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 172055) and (2) NATO’s Public Diplomacy Division in the framework of “Science for Peace” project SfP983638. The authors wish to thank Prof. Dušan Sladić for his help in preparing this manuscript.
- 8.http://www.rcsb.org/pdb/explore.do?structureId=1l2y. Accessed 9 Oct 2013
- 12.http://www.rcsb.org/pdb/explore.do?structureId=2XPU. Accessed 9 Oct 2013
- 21.Gilson MK, Honig B (2004) Proteins Struct Funct. Bioinf 4:7Google Scholar
- 22.Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery Jr JA, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam NJ, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ, Wallingford CT (2009) Gaussian 09, Revision A.02. Gaussian Inc., PittsburghGoogle Scholar
- 23.Jaguar, version 7.9 (2012) Schrödinger. LLC, New YorkGoogle Scholar
- 24.Maestro, version 9.3 (2012) Schrödinger. LLC, New York, NYGoogle Scholar
- 25.http://www.chemcraftprog.com. Accessed 9 Oct 2013
- 27.Epik, version 2.3 (2012) Schrödinger. LLC, New YorkGoogle Scholar
- 28.MacroModel, version 9.9 (2012) Schrödinger. LLC, New YorkGoogle Scholar