Testing of a new docking scoring function on the example of inhibitors of protein tyrosine phosphatase 1B


  • V. Yu. Tanchuk Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, Ukraine
  • V. O. Tanin Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, Ukraine




protein tyrosine phosphatase 1B, PTP1B, inhibition, molecular docking


A new scoring function H1 recently developed for molecular docking has been tested on the complexes of protein tyrosine phosphatase 1B (PTP1B) from the PDB data bank and using docking of a set of inhibitors from the NIH database. The function is based on the scoring functions of AutoDock and AutoDock Vina and is implemented in the modified version of the AutoDock. The function performed well both in the case of the complexes from the PDB databank and in a real docking process. Calculation of pKi for the complexes from the PDB databank was very accurate. The molecular docking has been done with a modified version of AutoDock that uses spatial constraints and a new search engine. Energies of complexes were minimized, and pKi values of the resulting complexes were estimated by the new scoring function. As shown previously, conformations of PTP1B in complexes with ligands can be divided into five clusters. All five typical conformations of PTP1B binding pocket were used for docking. Better docking results were obtained on the clusters with open WPD loop though some compounds could not be docked well to such conformations of the enzyme. The function has shown a good “scoring power” (i. e. the ability to predict pKi values) and “screening power” (the ability to enrich top 10 or 20% of predictions by real active compounds) thus proving to be suitable for the virtual screening of potential PTP1B inhibitors. The performance of the new scoring function H1 was much better than that of the original scoring function of AutoDock tested earlier.


Tonks N. Nat. Rev. Mol. Cell. Biol., 2006, Vol. 7, No.11, pp.833-846.

Vintonyak V., Antonchick A., Rauh D., Waldmann H. Curr. Opin. Chem. Biol., 2009, Vol. 13, No.3, pp.13272-13283.

Tabernero L., Aricescu A., Jones E., Szedlacsek S. FEBS J., 2008, Vol. 275, No.5, pp.867-882.

Kasibhatla B., Wos J., Peters K. Curr. Opin. Invest. Drugs, 2007, Vol. 8, No.10, pp.805-813.

Koren S., Fantus I. Best Pract. Res. Clin. Endocrinol. Metab., 2007, Vol. 21, No.4, pp.621-640.

Tanchuk V. Yu., Tanin V. O., Vovk A. I. ЖОФХ, 2013, Т. 11, вип. 2, No.42, pp.51-56.

Hu X., Vujanac M., Stebbins C. J. Mol. Graph. Model., 2004, Vol. 23, No.2, pp.175-187.

Hu X. Bioorg. Med. Chem. Lett., 2006, Vol. 16, No.24, pp.6321-6327.

Bernstein F., Koetzle T., Williams G., Meyer E., Brice E., Rodgers M., Kennard O., Shimanouchi T., Tasumi M. J. Mol. Biol., 1977, Vol. 112, No.2, pp.535-542.

Tanchuk V., Tanin V., Vovk A. Chem. Biol. Drug Des., 2012, Vol. 80, No.1, pp.121-128.

Tanin V., Tanchuk V. Proсeedings of the National Aviation University, 2014, No.3, pp.87-92.

Tanchuk V., Tanin V., Vovk A., Poda G. Current Drug Discov. Technol., 2015, Vol. 12, No.3, pp.170-178.

Morris G., Huey R., Lindstrom W. et al. J. Comput. Chem., 2009, Vol. 30, No.16, pp.2785-2791.

Trott O., Olson A. J. J. Comput. Chem., 2010, Vol. 31, No.2, pp.455-461.

Wang R., Fang X., Lu Y., Wang S. J. Med. Chem., 2004, Vol. 47, No.12, pp.2977-2980.

Wang L., Wu Y., Deng Y., Kim B., Pierce L., Krilov G. et al. J. Am. Chem. Soc., 2015, Vol. 137, No.7, pp.2695-703.

Liu T., Lin Y., Wen X., Jorrisen R., Gilson M. Nucleic Acids Res., 2007, Vol. 35, Database issue, pp.D198-D201.

Tanchuk V., Tanin V., Vovk A. III Intern. Conf. “High Performance Computing” HPC-UA 2013, Ukraine, Kyiv, 2013, pp.399-401, http://hpc-ua.org/hpcua-13/files/proceedings/76.pdf

Li Y., Li J., Han L., Liu J., Zhao Z., Wang R. J. Chem. Inf. Model., 2014, Vol. 54, No.6, pp.1717-1736.