1. 125:583 Biointerfacial Characterization Molecular Models 2 November 6, 2006

2. Focal Areas Surrogate Molecular Modeling to Accelerate Polymer Design and Optimization Virtual Combinatorial Chemistry: Compressing Large Polymer Libraries into Representative Subsets Quantitative Structure-Performance Relationship (QSPR) Models: Predicting Cell-Material Interactions from the Polymer’s Chemical Structure Atomistic Molecular Modeling to Explore Polymer Properties and Polymer-Protein Interactions Molecular Simulations of Water Transport Through Polymers Scoring Functions for Study of Polymer-Protein Interactions

3.  F = MA exp(-  E/kT) domain quantum chemistry molecular dynamics Monte Carlo mesoscale continuum Scales of Time & Dimension in Molecular Simulations Length Scale Time Scale 10 -10 M10 -8 M10 -6 M10 -4 M 10 -12 S 10 -8 S 10 -6 S

4. Motivation: Why atomistic MD simulations? Mechanisms  MD simulations provide a molecular level picture of structure and dynamic property/structure relationships  “What If” experiments  MD simulations provide a bridge between modelers and experimentalists, leading to synergies and new insights into materials properties Property Prediction MD simulations allow prediction of properties for  Existing materials whose properties are difficult to measure or poorly understood  Novel materials which have not been synthesized

5. Example: PEG in Water PEG is a water soluble polymer used in wide variety of biomedical applications poly(ethylene glycol) Water POLYMER CHAINS

6.  static properties (structure, energy, pressure, molecule packing)  dynamic and transport properties (diffusion coefficient, atom mobility, thermal conductivity, shear viscosity, infrared absorption coefficient ) Extracting Properties from Simulations polymer-water solutions radial (pair) distribution function

7. Skin interaction model: Layers from the top: (1) Vacuum; (2) Polymer; (3) skin model. Polymer, Small Molecule water

8. Research Study Water uptake and flux in polymers Experimental data from Michniak Laboratory PolymerBatch No Equilibration Water Content % Flux (µl/cm 2 /day) n=2 p(DTM suberate)AR1_033004_54612.19±0.03 p(DTO succinate)AR1_040504_73.211.05±2.41 p(DTB succinate)AR1_102604_107.025.57±0.20 p(DTE glutarate)AR1_012604_319.619.33±10.25 p(DtiP succinate)AR1_042004_16.54 p(DTE methyl adipate)AR1_012604_29.52 p(DTBn succinate)AR1_040504_16.33 p(DTD sebacate)AR1_040504_12.44 p(DTO diglycolate)AR1_012604_46.64 p(HTH suberate)AR1_030204_82.92 p(DTB methyl adipate)AR1_012604_62.45 p(DTH succinate)AR1_042004_33.13 p(DTiB adipate)AR1_012604_126.59 p(DTH glutarate)AR2_062504_12.49 p(DTsB adipate)AR1_012604_13.46

9. Water uptake and flux in polymers Preliminary QSPR model based on the water uptake for 13 polyarylates and suggested values for two outliers RowTargetModel 1Model 2Model 3 16.5900005.3091426.1750776.079881 23.4600002.4663133.6494623.733147 36.6400005.0227814.2624244.424498 42.4900003.2861433.2266873.462757 52.4500005.1919855.2479615.214285 69.5200008.2017937.7478967.669293 72.4400002.8435592.5304482.374064 87.0200007.5354086.5026826.331982 96.3300005.9861816.2256226.323708 103.1300004.9134964.3549714.261541 116.5400006.7534077.7852957.896947 123.2000002.9223262.7122752.684760 132.9200002.5080702.4002412.368610 1419.6000004.9134964.2985324.179128 1546.0000005.1324934.2999444.325232

10. Results Water uptake and flux in polymers Water uptake data analysis Data are segregated into three isolated islands which yields a poor distribution for QSPR modeling. Suggestion: add more experimental values to void space

11. Scoring Function Based Approach to Predict Protein-Surface Interactions D. Sun, K. Fears, R.A. Latour, W.J. Welsh

12. Lysozyme adsorption on surfaces with polymeric functional groups OH-functional groups CH 3 -functional groups COOH-functional groups Sun Y., Welsh W.J., and Latour R.A., Prediction of the orientations of adsorbed protein using an empirical energy function with implicit solvation, Langmuir, 21: 5616-5626 (2005). Scoring Function Based Approach to Predict Protein-Surface Interactions

13. Simulation of Protein Adsorption Using A Scoring Function Approach Docking & Scoring methods are used by biopharma industry to evaluate ligand-receptor binding affinity for drug design. Research Question: Can similar methods be developed for use with surrogate modeling to accurately predict protein adsorption to polymer surfaces? Critical issues: –Efficient sampling of multiple low-energy orientations –Calculation of overall adsorption energy –Implementation with surrogate modeling for polyarylate library of polymers

14. Initial Studies Model systems –Surface: Alkanethiol self assembled monolayers (SAMs) on gold Well characterized – easily simulated Easily synthesized for experimental use –Proteins: Small bioactive proteins (enzymes) Provide systems that can be experimentally validated Evaluate energy function of selected scoring method (AutoDock) –Existing parameterization – designed for ligand-receptor binding –Reparameterization required for residue-surface adsorption behavior Application of new scoring function: –Develop efficient methods to map energy as function of orientation –Identify probable adsorbed orientations & overall adsorption energy

15. Protein Adsorption Simulation with AutoDock Preferred orientations and energy of adsorbed lysozyme vs. SAM surface CH3 SAM 30%CH3/OH SAM30%COO-/OH SAM 50%CH3/OH SAM (arrows indicate bioactive site) (Energy function:  G =  G vdw +  G H-bond +  G electrostatic +  G entropy +  G solvation )

16. Map of energy vs. orientation angles of protein on surface (θ and  ). For each combination of θ and , a minimum energy value was searched as a function of y and ψ. Boltzmann distribution of 90,792 orientation states (T = 310 K) generated by sampling. Average adsorption free energy: -3.62 kcal/mol. x,  y,  z,  Lysozyme on CH 3 SAM with Modified Force Field

17. Orientations of Adsorbed Lysozyme on CH 3 SAM: Sideways Orientation Preferred side-on Lys96 end-on Gly126 end-on Pro70 OrientationsAngles (phi, theta) Point of attachment Energy ( kcal/mol) Side-on(110,155)LYS96-5.297 End -on (80,240) GLY126-2.092 End -on (100,45) PRO70-2.226

18. Protein Adsorption Simulation with AutoDock Preferred orientations and energy of adsorbed lysozyme vs. SAM surface CH3 SAM 30%CH3/OH SAM30%COO-/OH SAM 50%CH3/OH SAM (arrows indicate bioactive site) (Energy function:  G =  G vdw +  G H-bond +  G electrostatic +  G entropy +  G solvation )

19. Map of energy vs. orientation angles of protein on surface (θ and  ). For each combination of θ and , a minimum energy value was searched as a function of y and ψ. Boltzmann distribution of 90,792 orientation states (T = 310 K) generated by sampling. Average adsorption free energy: -3.62 kcal/mol. x,  y,  z,  Lysozyme on CH 3 SAM with Modified Force Field