Identify virtual ligand hits using consensus scoring approach for drug target S. Aureus

  • Authors

    • P Bharath Siva Varma
    • Adimulam Yesubabu
    • K Subrahmanyam
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10265
  • Consensus scoring, Search algorithm, Scoring function, AutoDock, X-score.

  • Abstract

    The associations amongst protein and ligand can be assessed for scoring functions based on the binding modes of ligand obtained from search algorithms. Several scoring functions have been proposed and every method has their own strengths and weaknesses, hence a multiple scoring analysis referred as consensus scoring increases the overall signal noise ratio. Therefore, top ten ligands obtained and were subjected to re-scoring using mcule, AutoDock and X-score functions. A computational consensus scoring analysis was taken up for a dataset of top ten PubChem compounds which are identified to exhibit better inhibitory properties against phosphotransacetylase, a putative drug target for S. aureus.

  • References

    1. [1] Jensen, L. J., Gupta, R., Staerfeldt, H. H., &Brunak, S. (2003). Prediction of human protein functions according to Gene Ontology categories. Bioinformatics, 19(5), 635-642.

      [2] Marianne A. Grant. INTEGRATING COMPUTATIONAL PROTEIN FUNCTION PREDICTION INTO DRUG DISCOVERY INITIATIVES. Drug Dev Res. 2011 Feb; 72(1): 4–16.

      [3] Ballester, P. J., & Mitchell, J. B. O. (2010). A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics (Oxford, England), 26(9), 1169–1175.

      [4] Kitchen DB, Decornez H, Furr JR, Bajorath J (2004). "Docking and scoring in virtual screening for drug discovery: methods and applications". Nature reviews. Drug discovery 3 (11): 935–49.

      [5] Luty BA, Wasserman ZR, Stouten PFW, Hodges CN, Zacharias M, McCammon JA. A molecular mechanics/grid

      [6] http://www.health.state.mn.us/divs/idepc/diseases/staph/basics.html

      [7] Konrad Plata, Adriana E. Rosato and GrzegorzWęgrzyn. Staphylococcus aureus as an infectious agent: overview of biochemistry and molecular genetics of its pathogenicity. Vol. 56 No. 4/2009, 597–612.

      [8] P. Bharath Siva Varma, YesubabuAdimulam and K. Subrahmanyam, “In Silico virtual screening of PubChem compounds against phosphotransacetylase, a putative drug target for Staphylococcus aureusâ€, International Journal ofComputational Biology and Drug Design, Vol. 10, No. 1, 2017.

      [9] Morris, G.M., Goodsell, D.S., Halliday, S., Huey, R., Hart, W.E., Belew, R.K. and Olson, A.J., J. Comput. Chem., 19 (1998) 1639.

      [10] Wang, R.; Lai, L.; Wang, S.Further Development and Validation of Empirical Scoring Functions for Structure-Based Binding Affinity Prediction. J. Comput.-Aided Mol. Des. 2002, 16, 11-26.

  • Downloads

  • How to Cite

    Bharath Siva Varma, P., Yesubabu, A., & Subrahmanyam, K. (2018). Identify virtual ligand hits using consensus scoring approach for drug target S. Aureus. International Journal of Engineering & Technology, 7(2.7), 84-87. https://doi.org/10.14419/ijet.v7i2.7.10265

    Received date: 2018-03-18

    Accepted date: 2018-03-18

    Published date: 2018-03-18