Martini Basics. Hands on: how to prepare a Martini
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2 Martini Basics Hands on: how to prepare a Martini gin and vermouth are combined at a ratio of 2:1, stirred in a mixing glass with ice cubes, then strained into a chilled cocktail glass and garnished with an olive A Dry Martini is made with dry gin and white vermouth Martini Rosso uses red vermouth (caramel flavor) Vodka Martini uses vodka instead of gin A Perfect Martini uses equal amounts of sweet and dry vermouth Zen Martini: Martini with no gin at all, and no vermouth either Martini should be made by filling a glass with gin, then waving it in the general direction of Italy
3 Overview of this lecture Martini Basics! The power of coarse-graining! Looks of the Martini model! Validation of the Martini model! Limitations of the Martini model! Applications of the Martini model
4 The power of coarse-graining Vast range of time and length scales of biomolecular processes Computational Microscope!
5 The power of coarse-graining Different problems require different levels of resolution Martini Coarse-grain fast chemical specificity All-atom accurate slow Supra CG very fast not specific
6 Looks of the Martini model Four-to-one mapping principle Biomolecular Forcefield for Coarse-Grained Simulations "One martini is all right. Two are too many, and three are not enough." - James Thurber
7 Looks of the Martini model What s in a name? The Martini force field is developed in Groningen and named after Saint Martin, patron saint of Groningen (any association with cocktails is entirely coincidental)
8 Looks of the Martini model Systematic parameterization combining top-down and bottum-up approaches Top-down Bottum-up
9 Looks of the Martini model The building block principle amino acid! alkane! lipid! Non-bonded interactions of building blocks! parameterized based on! reproducing experimental thermodynamic data!
10 Looks of the Martini model The building block principle Building block types! C1 C2 C3 C4 C5 Apolar Qa Q0 N0 Nd/a Nda P1 P2 P3 P4 P5 Intermediate Polar Na C1 Na C1 C1 TUTORIAL! Lipids self-assemble! into a bilayer,! reproducing! known membrane! properties! Q0 Qd/a Qda C1 Charged C1 Bonded interactions parameterized to match! conformations C1 of all-atom simulations! (or structural databases)!
11 Looks of the Martini model Non-bonded interactions: LJ & Coulomb " Non-bonded interactions described by standard LJ and Coulombic energy functions + - electrostatic Coulomb dispersion overlap } Lennard- Jones... however: short-ranged by use of shifted functions (cut-off 1.2 nm, 2-3 neighbors)
12 Looks of the Martini model Hocus-pocus with LJ balls " LJ interactions depend on hydrophilicity of CG bead nine levels with 2.0 <! < 5.6 kj/mol ; " = 0.47 nm tenth level with! = 2.0 ; " = 0.62 nm " Four main classes of CG beads, 18 classes in total: VIII O IX charged (Q) polar (P) intermediate (N) apolar (C) da d a da d a
13 Looks of the Martini model Top-down: reproducing experimental partitioning data " LJ interactions are parameterized based on reproducing experimental partitioning free energies (and densities) Polar (P) Intermediate (N) Apolar (C) water octanol
14 Looks of the Martini model EXP CG EXP CG The Martini bible: mapping CG bead types to chemical building blocks
15 Looks of the Martini model Lord of the rings Rings: Four-to-one mapping inadequate Two/three-to-one mapping Reduction of interaction size and strength! = 0.7 *! standard " = 0.75 * " standard Densities & partitioning free energies for benzene and cyclohexane reproduced Condensing behavior of cholesterol in membranes similar to all-atom results
16 Looks of the Martini model Simple harmonic forms for bonded interactions Bonds Angles Dihedrals
17 Looks of the Martini model Bottom-up approach for bonded interactions " Bonded interactions are parameterized by mapping to all-atom simulations angle probability AA CG angle
18 Looks of the Martini model Simulation parameters important " Simulation parameters are considered to be part of the force-field! Cut-off 1.2 nm (using shifted potentials) Relative dielectric constant = 15 for implicit screening Effectively distant-dependent!
19 Looks of the Martini model Martini tolerates large time steps " Time steps of fs can be used (accurate sampling is less critical) CG AA Reality
20 Looks of the Martini model Welcome to the Martini zoo! proteins water lipids sugars nucleotides Key features: Chemical specificity Fast (10 3 speed-up) Compatibility (building block approach) Versatility Parameterization: polymers nanoparticles TOP DOWN Thermodynamic data BOTTUM UP Atomistic simulations SJ Marrink & DP Tieleman, Chem. Soc. Rev. (2013)
21 Examples of validation Lipid conformations match all-atom results Averaged configurational space sampled by a lipid in a bilayer AA CG
22 Examples of validation Reproducing experimental lipid phase behavior dic 16 -PC dic 18:2 -PC Chol L o L d
23 Examples of validation Matching all-atom partitioning free energies kj mol -1 LEU coarse-grain atomistic
24 Examples of validation Matching all-atom dimerization free energies Charged Polar Apolar Mixed Aromatic Backbone
25 Examples of validation Reproducing known structure of protein-protein complexes repressor of primer (ROP) protein
26 Examples of validation Reproducing counterion condensation of dsdna Charmm Martini
27 Limitations of the Martini model Good to know the limits " Limited resolution " Directional h-bonds are missing " Missing entropy, compensated by reduced enthalpy " Electrostatic screening of water is only implicit " Limited fluid range of LJ interactions " Friction from atomistic degrees of freedom is missing " Interdependency of parameters "I like to have a martini, two at the very most. After three I'm under the table, After four, I'm under my host." - Dorothy Parker
28 Limitations of the Martini model Limited resolution " Martini is fuzzy, semi-quantitative nature DLPC Martini DMPC Martini 12 carbon tail 4-bead 14 carbon 3-bead tail Bilayer Thickness [nm] Melting Temperature [K] ? DPPC Experimental 16 carbon DMPC tail
29 Limitations of the Martini model Limited resolution " Martini is fuzzy, semi-quantitative nature Solution: Realize fuzzy character, and use to your advantage Martini person: Martini person: Excuse me, I meant My DPPC bilayer Convinced referee: Annoying referee: to say DMPC melts at 295K Happy to accept That is 20K too low! your publication
30 Limitations of the Martini model Limited fluid range of LJ interactions " Martini solvents are too structured # Water freezes too easily # Solvation free energies not reproduced
31 Limitations of the Martini model Limited fluid range of LJ interactions " Martini solvents are too structured Solution: (future) Use a softer form of the non-bonded potential (now) Use anti-freeze particles to prevent freezing Anti-freeze particles (BP4) can be added at 5-10 mol% to prevent freezing To disturb the lattice packing of the uniformly sized solvent particles, the LJ parameter! for BP4-P4 interactions is scaled up to 0.57 nm instead of 0.47 nm. To avoid phase separating of antifreeze and solvent particles, the strength of the BP4-P4 interaction is raised one level
32 Limitations of the Martini model Directionality of H-bonds is missing " Secondary structure of protein and DNA is fixed Martini uses elastic networks to keep biomacromolecules in the desired configuration (more details, and possible solutions in following lectures)
33 Limitations of the Martini model Friction from atomistic degrees of freedom is missing " Time scale should be interpreted with care CG Kinetics need to be mapped on real time: A mapping factor of 2-8 describes friction dominated processes (e.g. lipid diffusion, water permeation) Kinetics of more complex processes depend on energy barriers Reality
34 Limitations of the Martini model Friction from atomistic degrees of freedom is missing " Time scale should be interpreted with care Solution (but not recommended): Apply friction to EOM
35 Limitations of the Martini model Missing entropy, compensated by reduced enthalpy atomistic DOFs } CG DOFs all-atom coarse-grained all-atom, mapped
36 Limitations of the Martini model Missing entropy, compensated by reduced enthalpy " Temperature dependence off " Driving forces wrong Solution: Recalibrate parameters for specific temperatures (not very pragmatic), or interpret driving forces with care (certainly useful!)
37 Limitations of the Martini model Electrostatic screening of water is only implicit " Change in environment not felt by charged beads ALL ATOM COARSE GRAIN OIL " r =1 + - " r = WATER " r = " r =15 + -
38 Limitations of the Martini model Electrostatic screening of water is only implicit " Change in environment not felt by charged beads Solution: Use polarizable water model when charged interactions are important
39 Limitations of the Martini model Electrostatic screening of water is only implicit Explicit screening by polarizable water model: Standard Martini water Polarizable Martini water
40 Limitations of the Martini model Electrostatic screening of water is only implicit Parameters & properties of polarizable water model:! Dielectric constant of real water reproduced Yesylevskyy, Schäfer, Sengupta, Marrink, PLoS Comp. Biol., 2010
41 Limitations of the Martini model Electrostatic screening of water is only implicit Example of improved behavior with polarizable water: electroporation of an octane slab (similar to atomistic simulations by Tieleman)
42 Limitations of the Martini model Interdependency of parameters " Local improvement may lead to global disaster Problem: Martini water freezes near solid support Local improvement: Reduce water-water interactions to 76% Global disaster: Lipid bilayer now behaves as solvated by ethanol
43 Limitations of the Martini model Electrostatic screening of water is only implicit " Local improvement may lead to global disaster Solution: Make force field optimizations with care!
44 Applications of the Martini model Main areas of research where Martini has been successfully applied " Simulation of basic (bio)molecular processes " Unraveling the workings of biomacromolecules " Computational design of advanced (bio-inspired) materials " Simulation of collective processes " Simulation of the dynamical organization of complex systems Long time scales High throughput Huge systems Large complexity
45 Examples of application Simulation of basic (bio)molecular processes L d! L o! Extraction of cholesterol from Ld domains by cyclodextrin Lopez et al, Sci Rep, 2013
46 Examples of application Simulation of collective processes inv hexagonal rhombohedral lamellar single cubic single cubic stalk / pore phase Effect of fusion peptides on lipid phase diagram Fuhrmans et al, JACS 2012
47 Examples of application Unraveling biomolecular function 1 Tb-MscL protein! 256 DOPC lipids! 2 µs simulation! bilayer under tension! Martini forcefield! Gating of a mechanosensitive membrane protein (MscL) Yefimov et al., Biophys. J., 2008
48 Examples of application Computational design of advanced materials Screening the aggregation behavior of all possible tri-peptides Frederix et al, Nat. Chem., 2015
49 Examples of application Simulation of dynamic organization of complex systems Supercomplex formation in mitochondrial membranes Arnarez et al, JACS, 2014 lipids! cardiolipin! PC, PE, PG! respiratory chain! complexes! cytochrome c oxidase, cytochrome bc1!!
50 PhD positions available!!! Multiscale method development! Design of self-assembling nanotubes! Modeling complex cell membranes! A man must defend his home, his wife, his children, and his martini." - Jackie Gleason!
51 Enjoy Sampling Martinis!!!
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