Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Micelle/water partition: A combination of molecular dynamics simulations and COSMOmic D. Yordanova, I. Smirnova, S. Jakobtorweihen Institute of Thermal Separation Processes, Hamburg University of Technology Introduction Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Micelles – aggregates of amphiphilic molecules (surfactants) – Can solubilize hydrophobic compounds – Used in separation processes Hydrophilic head Hydrophobic tail Hydrophobic solute Aqueous solution ciMicelle Pi Water ci Decisive parameter regarding the selectivity and efficiency of micellar systems COSMOmic as potential screening tool Graphic: Amalgam Modelmaking Ltd 2 COSMOmic for micelles Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ COSMOmic – extension of COSMO-RS for anisotropic systems (membranes, micelles) – Micelle structures – radially divided into shells – The last layer should contain only water – Micelles – assumed as spheres – Layer thickness – constant – Different volume in each layer 3 Self-assembly MD simulation Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Micelle structures – Input for COSMOmic Self-assembly – a microscopic process – Molecular dynamics (MD) simulations 216 surfactant monomers 120515 water molecules 50 ns A random configuration Max micelle size 50 100 ns Max micelle size 180 4 Aims of the work Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Self-assembly MD simulations Suitable micelle structures for COSMOmic Prediction of partition coefficients of neutral solutes in micelle/water systems Influence of micelle structure (size and shape) on the prediction quality The same strategy for mixed micelle systems 5 Triton X-series Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Triton X-114, Triton X-100 – – – – Nonionic surfactants Triton X-114 – mild extraction conditions Used in surfactant based extraction processes Membrane protein purifications n = 7-8 (Triton X-114) n = 9-10 (Triton X-100) Comparison with theoretical methods is of interest Influence of additives (ionic surfactants, salts) on the self-assembly process, micelle structure, partition behaviour of solutes Glembin et al., Sep. Purif. Technol. 2014, 127-134. Safonova et al., Chem. Eng. Res. Des. 2014, 2840-2850. 6 Methods – MD simulations Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Force field – the fundamental model behind every MD simulation – Interaction model – Describes the potential energy of a system of particles (molecules and atoms) Vtotal = Vintramolecular + Vintermolecular Vintermolecular = Vvan der Waals + Velectrostatic Pastor and MacKerell, J. Phys. Chem. Lett. 2011, 1526-1532. Klauda et al., J. Phys. Chem. B 2010, 7830-7843. Vanommeslaeghe et al., J. Comput. Chem. 2009, 671-690. 7 Parametrization of Triton X-molecules Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ CHARMM36 additive force field – CHARMM General Force Field (CGenFF) for „drug-like“ molecules – Limited data base – Triton X-molecules – not available Parametrization of the Triton X-molecules Hydrophobic part: – – – – Hydrophilic part: 6 charges 1 bond 4 angles 8 dihedrals – 1 dihedral Yordanova et al., J. Chem. Theory Comput. 2015, submitted. 8 Input structure from self-assembly simulation Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ Input for COSMOmic: micelle structure from a self-assembly simulation – Snapshot (single micelle) – Average atomic distributions [ Jakobtorweihen et al., J. Comput. Chem. 2013, 1332-1340.] More physically reasonable approach Reduces the effect of outliers – Most probable micelle sizes, obtained in the MD simulations – The atomic distributions were averaged over at least 200 micelles 9 Input structure from self-assembly simulation Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ Storm et al. – gaps in the water layer around some micelles, deriving from another micelle in close proximity worse prediction quality – An approach to adjust the atomic distributions to the correct density – Surfactant atoms not part of the micelle, are replaced by an equivalent amount of water Overcomes the effect of outliers, resulting from a deficient water layer around the micelle Storm et al., J. Phys. Chem. B 2014, 3593-3604. 10 Input structures: pre-assembled micelles Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ Pre-assembled micelles – A single micelle with predetermined micelle size – Built with Packmol – Simulated in a water box for 40 ns ■ Advantages: – Smaller systems – A single micelle in the box no gaps in the water shell – Micelle size can be freely choosen Packmol: Martinez et al., J. Comput. Chem. 2009, 2157-2164. 11 Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Prediction of partition coefficients in the systems Triton X-114/water and Triton X-100/water 12 Prediction of partition coefficients between Triton X-micelles and water Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Triton X-114/ water Aggregation number = 32 Triton X-100/ water Aggregation number = 33 10 neutral solutes RMSE = 0.40 14 neutral solutes RMSE = 0.32 RMSE 1 n log n i 1 P MW ,COSMOmic i log P MW ,exp 2 i 13 Prediction of partition coefficients between Triton X-micelles and water Triton X-114/ water Aggregation number = 32 10 neutral solutes RMSE = 0.40 Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Triton X-100/ water Aggregation number = 33 14 neutral solutes RMSE = 0.32 14 Prediction of partition coefficients Influence of size and shape ■ COSMOmic – assumes the micelle structure as sphere 0 Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik - the micelle is perfect sphere Micelle geometry (size and shape) has an effect on the prediction quality Structures which show the most stable results are favored 15 Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Prediction of partition coefficients in mixed micellar systems 16 Prediction of partition coefficients in the system Triton X-114/SDS Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ Most technically applicable micelle systems – mixed micelles ■ Influence of the anionic surfactant Sodium Dodecyl Sulfate (SDS) ■ Self-assembled micelles – Triton X-114 and SDS – equimolar (100 ˣ Triton X-114, 100 ˣ SDS, 83234 ˣ Water, 100 ˣ Na ■ Pre-assembled micelles – Constant Triton X-114/SDS ratio – Aggregation numbers – 40, 50, 100 17 Prediction of partition coefficients in the system Triton X-114/SDS Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ Self-assembled micelles No significant differences in the obtained aggregation numbers, eccentricities, RMSEs ■ Pre-assembled micelles Worse prediction quality when using larger micelles (100) 18 Prediction of partition coefficients in the system Triton X-114/SDS Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ Self-assembled micelle: ■ Pre-assembled micelle: – Aggregation number = 44 – RMSE = 0.30 – Aggregation number = 40 – RMSE = 0.32 9 neutral solutes RMSEs in mixed micelles – in the same range as in the pure Triton X/water systems 19 Conclusions and outlook Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Micelle structures from MD simulations for both pure and mixed micelle systems – Both self-assembled and pre-assembled micelles can be used as input for COSMOmic – Small spherical micelles are recommended Prediction of partition coefficients – An overall good agreement with experimental results for neutral solutes in both Triton X/water systems and in mixed micelle systems Future work – Influence of other additives (cationic surfactants, salts) – Prediction of partition coefficients of charged solutes – Simulation of other nonionic surfactants 20 Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Acknowledgements: Thank you for your attention! Cloud Point Extraction T > Cloud Point Temperature Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik water-rich phase surfactant-rich phase 22 Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik 23 ParamChem Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik Automation of the CGenFF Input: mol2 file (structure) Assignment of parameters by analogy Output: CHARMM toppar stream file “Penalty” – the highest penalty score of the associated http://www.paramchem.org parameters Vanommeslaeghe and MacKerell, J. Chem. Inf. Model. 2012, 3144-3154. 24 Parametrization workflow Structure Preparation (PSF/PDB files) Geometry optimization (QM) Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik – According to the CGenFF parametrization procedure MP2/6-31G(d) level of theory CHARGES Water Interaction Energy (QM) Charge optimization BONDS & ANGLES HF/6-31G(d) interactions with a TIP3P water molecule Hessian calculation (QM) Bond & Angle optimization DIHEDRALS Torsion Scans (QM) Dihedral optimization MP2/6-31G(d) Potential Energy Scans (PES) – Force field Toolkit Plugin (ffTK) – Gaussian 03 Mayne et al., J. Comput. Chem. 2013, 2757-2770. 25 Optimized dihedrals Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik – 8 dihedrals optimized in the hydrophobic part (1,1,3,3-tetramethyl-benzene) Energy [kcal/mol] – 1 dihedral optimized in ethylene glycol monomethyl ether Energy [kcal/mol] Conformation RMSE = 0.125 Conformation RMSE = 0.044 Very good agreement with QM potential energy scans 26 Self-assembly simulations Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ Critical micelle concentration (CMC) – 0.17 mM (Triton X-114) – 0.22 mM (Triton X-100) ■ Much higher concentrations in the simulations Surfactant Surfactant molecules Water molecules Total number of atoms Csurf [mol/L] T [K] Simulation time [ns] TX114 216 53360 180168 0.22 313 100 TX114 216 53360 180168 0.22 283 200 TX114 216 120515 381633 0.1 283 200 TX100 216 53407 183333 0.22 283 200 ■ Very large systems ■ Limitations due to the system size Linke, Methods Enzymol. 2009, 603-617. Cuypers et al., Chemosphere 2002, 1235-1245. 27 COSMOmic Technische Universität Hamburg-Harburg Institut für Thermische Verfahrenstechnik ■ COSMO-RS – Conductor like Screening Model for Real Solvents – based on quantum chemistry and statistical thermodynamics ■ COSMOmic – extension of COSMO-RS for anisotropic systems (membranes, micelles) – Aggregates are radially divided in layers – Probability of a solute to be suited in each layer can be calculated Chemical potential: MX r, d MX ,res r, d MX ,comb r, d d r Partition function: X M r , d Z Z ri exp kT i i i X M n n X m m Probability: MX (ri ) Z X M ri Z MX Free energy profile Partition coefficient r – layer; d – direction of solute Klamt et al., J. Phys. Chem. B 2008, 12148-12157. 28
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