Predicting Efflux of Antibiotics by AcrAB-TolC Pumps - PowerPoint PPT Presentation

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Predicting Efflux of Antibiotics by AcrAB-TolC Pumps

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  1. Predicting Efflux of Antibiotics by AcrAB-TolC Pumps Kelcey Anderson Bioinformatics and Bioengineering Summer Institute Virginia Commonwealth University August 10, 2010

  2. Efflux Pumps • Found in Gram-negative bacteria, including E. coli • AcrAB-TolC • Composed of three parts spanning both the inner and outer cell membranes • Responsible for extruding a variety of mainly lipophilic substances Symmons, et al. 2009. PNAS 106:7173-78

  3. AcrB • AcrB has three protomers • Protomers exist in either the access, binding, or extrusion state • Substrates enter the access state near the inner membrane Nature,2006, 443, 173-179

  4. Why Study Efflux Pumps? • Responsible for extrusion of antibiotics • Multidrug resistant strains overexpress efflux pump proteins • Development of new antibiotics Minocycline Penicillin G Acriflavine

  5. Drug Action in the Cell Cannon, et al. 2009. Clin. Microbiol. Rev. 22,2:291-321

  6. Research Questions • What factors would determine whether an antibiotic is likely to be extruded by the cell? • Can we predict the antibiotics that are more likely to be extruded by the efflux pump?

  7. Experimental Data • Knock out efflux pump genes • Measure MIC values for different substances • Ratio of normal vs. knocked out MIC values • Is there a correlation between the ratio of efflux and the properties of the compounds? Literature Sources: Mazzariol et al. 2000 Nishino et al. 2003 Sulavik et al. 2001 Lee et al. 2000 Nikaido et al. 1998

  8. Calculation of Molecular Parameters • Molecular Width • Molecular dynamics • LogP • Measure of hydrophobicity • Can be determined experimentally or computationally

  9. Docking • Place molecules into different areas of the pump apparatus in many positions • Charged vs. neutral molecules • Four significant areas contribute to model for efflux • Neutral molecules in the binding state • Charged molecules in the extrusion state • Charged molecules in the intermonomer region (“AcrB hole”) • Charged molecules in “Zone 3” of TolC

  10. Dock Compounds into Pump

  11. Score Docked Positions using HINT • Hydropathic INTeractions (HINT) scores the strength of the interaction between the protein and substrate • The position with the highest HINT score was used to correlate with efflux

  12. Partial Least Squares (PLS) • Statistical technique for finding a model relating efflux and molecular properties • LogP • Molecular width • HINT score at four different locations

  13. First Model for Efflux • Efflux = - 1.31 – (1.7x10-4)*HINTnB – (5.3x10-4)*HINTcE + (6.9x10-4)*HINTAcrB(hole) – (1.0x10-3)*HINTZ3 + 1.10*LogP + 0.43*MolWidth

  14. Linear vs. Quadratic Regression

  15. Second Model for Efflux • Efflux = - 2.09 – (4.9x10-5)*HINTnB – (6.1x10-4)*HINTcE + (2.3x10-4)*HINTAcrB(hole) – (6.8x10-4)*HINTZ3 + 0.66*LogP + 0.44*MolWidth + 0.22*LogP2 + (2.1x10-7)*HINTAcrB(hole)2 + (1.04x10-7)*HINTZ32

  16. Further Validation • Randomly selected training sets of 33 molecules • Randomly selected test sets of 11 molecules • Predict efflux of test set from training set • Calculate R2 of predicted versus actual efflux

  17. Training Set/Test Set Validation

  18. Correct Predictions of the Models

  19. Comprehensive Model for Efflux • Neutral molecules are captured by AcrB • Access state changes to extrusion state • Substrate exposed to water • Charged substrate moves out of pump

  20. Summary • Calculated LogP and molecular width • Docked molecules into four areas of pump • Calculated and selected the best HINT score • Analyzed the correlation between efflux and parameters using PLS

  21. Conclusions • Model for efflux is able to predict “high” and “low” efflux • The model for efflux depends on the entire pump system

  22. Acknowledgements • Aurijit Sarkar • Dr. Kellogg • BBSI