Biopolym. Cell. 2014; 30(4):321-325.
Bioinformatics
Design of potentially active ligands for SH2 domains by molecular modeling methods
1Hurmach V. V., 1Balinskyi O. M., 2Platonov M. O., 1Boyko O. M., 1Borysko P. O., 1Prylutskyy Yu. I.
  1. Taras Shevchenko National University of Kyiv
    64, Volodymyrska Str., Kyiv, Ukraine, 01033
  2. Institute of Molecular Biology and Genetics, NAS of Ukraine
    150, Akademika Zabolotnoho Str., Kyiv, Ukraine, 03680

Abstract

Search for new chemical structures possessing specific biological activity is a complex problem that needs the use of the latest achievements of molecular modeling technologies. It is well known that SH2 domains play a major role in ontogenesis as intermediaries of specific protein-protein interactions. Aim. Developing an algorithm to investigate the properties of SH2 domain binding, search for new potential active compounds for the whole SH2 domains class. Methods. In this paper, we utilize a complex of computer modeling methods to create a generic set of potentially active compounds targeting universally at the whole class of SH2 domains. A cluster analysis of all available three-dimensional structures of SH2 domains was performed and general pharmacophore models were formulated. The models were used for virtual screening of collection of drug-like compounds provided by Enamine Ltd. Results. The design technique for library of potentially active compounds for SH2 domains class was proposed. Conclusions. The original algorithm of SH2 domains research with molecular docking method was developed. Using our algorithm, the active compounds for SH2 domains were found.
Keywords: molecular modeling, SH2 domains, pharmacophore models, virtual screening

References

[1] Alm E, Arkin AP. Biological networks. Curr Opin Struct Biol. 2003;13(2):193-202.
[2] Silva CM. Role of STATs as downstream signal transducers in Src family kinase-mediated tumorigenesis. Oncogene. 2004;23(48):8017-23.
[3] Koch CA, Anderson D, Moran MF, Ellis C, Pawson T. SH2 and SH3 domains: elements that control interactions of cytoplasmic signaling proteins. Science. 1991;252(5006):668-74.
[4] Haiyun L, Shamima Banu Bte Sm Rashid, Hao Li, Wee KL, YihCherng L. Knowledge-guided docking of flexible ligands to SH2 domain proteins. IEEE International Conference on Bioinformatics and Bioengineering (Philadelphia, Pennsylvania, USA, May 31–June 3 2010). Philadelphia, 2010:185–190.
[5] Dominguez C, Boelens R, Bonvin AM. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc. 2003;125(7):1731-7.
[6] Pawson T, Gish GD, Nash P. SH2 domains, interaction modules and cellular wiring. Trends Cell Biol. 2001;11(12):504-11.
[7] Liu BA, Jablonowski K, Raina M, Arcé M, Pawson T, Nash PD. The human and mouse complement of SH2 domain proteins-establishing the boundaries of phosphotyrosine signaling. Mol Cell. 2006;22(6):851-68.
[8] Balinskyi OM, Sudakov OO, Platonov MO. Small-molecule ligand search for protein interaction domains involving shape- based molecular modeling methods. Bulletin of Taras Shevchenko National University of Kyiv. Series Physics & Mathematics. 2012; 3:287–91.
[9] Chuprina A, Lukin O, Demoiseaux R, Buzko A, Shivanyuk A. Drug- and lead-likeness, target class, and molecular diversity analysis of 7.9 million commercially available organic compounds provided by 29 suppliers. J Chem Inf Model. 2010;50(4):470–9.
[10] Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS. A critical assessment of docking programs and scoring functions. J Med Chem. 2006;49(20):5912–31.
[11] McMartin C, Bohacek RS. QXP: powerful, rapid computer algorithms for structure-based drug design. J Comput Aided Mol Des. 1997;11(4):333-44.
[12] Sudakov OO, Balinskyi OM, Platonov MO, Kovalskyy DB. Geo metric filters for protein–ligand complexes based on phenomenological molecular models. Biopolym Cell. 2013; 29(5):418–23.