Interesting papers out today:
* van Anders et al, "Entropically Patchy Particles", arXiv:1304.7545. From Sharon Glotzer's group, how you can think of particle shape complementarity in the presence of a depletant in terms of "patches". They're then able to make particles with rationally selected shapes that assemble into particular target structures.
* Zhu et al, "Self-assembled, aptamer-tethered DNA nanotrains for targeted transport of molecular drugs in cancer theranostics", PNAS Early Edition (2013). They present a system of ssDNA strands that, in isolation, just forms hairpins, but in the presence of a certain cell receptor (that will later mediate endocytosis), they self-assemble into a dsDNA "train". Drug molecules bind to this dsDNA train, and are then swallowed by the target cell (but by no other cells).
* Zheng et al, "A Hydrophobic Surface Is Essential To Inhibit the Aggregation of a Tau-Protein-Derived Hexapeptide", JACS Article ASAP (2013): Large hydrophobic patches on a surface inhibit protein aggregation, and reducing/increasing hydrophobic correlates with reduced/increased inhibition. Seems to agree with our previous general findings.
* Engel et al, "Hard-disk equation of state: First-order liquid-hexatic transition in two dimensions with three simulation methods", Phys. Rev. E 87, 042134 (2013): The write-up of a very interesting talk by Werner Krauth at last January's mini Stat Mech meeting, where they demonstrate the hexatic phase in a 2D system of disks.
Wednesday, 1 May 2013
Monday, 29 April 2013
Antifreeze proteins
Tdoay there was an interesting article in Chemistry World on antifreeze proteins called "Survival in the freezer", by James Mitchell Crow (unfortunately, it doesn't seem to be online yet, but I'm sure it will be shortly). The first of these was discovered by Arthur DeVries in 1969 (it was a glycoprotein), and the first carbohydrate-free one was discovered by his student John Duman in 1974. Both were found in fish that live in the Arctic, but antifreeze proteins are also present in other species, like Alaskan beetles. In fact, there's a great variety of antifreeze proteins, which probably evolved separately.
How do these proteins work? It seems that most have a flattish, slightly hydrophobic face with a regular repeating pattern. This surface seems to organize the waters immediately adjacent to it into ice-like structures, and so the protein binds nascent ice crystals, arresting their growth. One interesting application to antifreeze proteins in industry is to make low-fat ice cream (Unilever): by reducing the size of the ice crystals, less fat is required to make the ice cream equally creamy. Another potential application (not yet out) is to genetically modify salmon to make them produce antifreeze proteins, so that their growing season is a bit longer.
Finally, there are some other interesting compounds being made with antifreeze properties. A concrete example, from Matthew Gibson's group at Warwick, is polyvinyl alcohol (PVA), which seems to work very well although they don't really know why. The polymer doesn't seem to bind ice crystals, but they suggest it associates with the liquid-like "premelting" layer of water on the surface of ice.
Who knows? In any case, antifreeze proteins and their relationship to the surrounding water and ice might be a nice field to get into.
How do these proteins work? It seems that most have a flattish, slightly hydrophobic face with a regular repeating pattern. This surface seems to organize the waters immediately adjacent to it into ice-like structures, and so the protein binds nascent ice crystals, arresting their growth. One interesting application to antifreeze proteins in industry is to make low-fat ice cream (Unilever): by reducing the size of the ice crystals, less fat is required to make the ice cream equally creamy. Another potential application (not yet out) is to genetically modify salmon to make them produce antifreeze proteins, so that their growing season is a bit longer.
Finally, there are some other interesting compounds being made with antifreeze properties. A concrete example, from Matthew Gibson's group at Warwick, is polyvinyl alcohol (PVA), which seems to work very well although they don't really know why. The polymer doesn't seem to bind ice crystals, but they suggest it associates with the liquid-like "premelting" layer of water on the surface of ice.
Who knows? In any case, antifreeze proteins and their relationship to the surrounding water and ice might be a nice field to get into.
Sunday, 28 April 2013
Final WATSURF 2013 Day and interesting papers
The last day of WATSURF 2013 had one highlight for me, which was a comment by Volker Kempter (TU Clausthal, Germany) that ionic liquids (ILs) could serve as model systems for understanding phenomena in biology. He emphasised that:
* There's lots of segregation phenomena, pattern formation, etc.
* Ionic liquids also have interesting behaviour under confinement and are easier to study than systems in water, because you can work at ultra-high vacuum (ILs have very low vapour pressure).
* Can deposit monolayers and can do surface-sensitive characterization.
* Can study IL-water mixtures (many of the chemical groups at the interface are similar with biology).
* Theory on IL's is tractable: systems are simple enough to model at the quantum level, and can check force fields at all times, so there's fewer force field issues. There's also lots of other kinds of interactions that may play a role that may be underappreciated in biology (e.g., pi-pi interactions, metal ion coordination).
I asked him about basic outstanding issues with ILs in general, and he mentioned that the interaction of ILs with surfaces is underexplored (relevance for electrochemistry, lubrication). If I ever want to get into this field, he suggested I look into the references mentioned in the introduction of this paper.
On a similar note of other interesting fields, Thomas Loerting and Werner Kuhs suggested three different places to look at to learn more about atmospheric chemistry and ice.
Finally, a couple of interesting papers that came out over the weekend:
* Rubinovich and Polak, "The Intrinsic Role of Nanoconfinement in Chemical Equilibrium: Evidence from DNA Hybridization", Nano Letters (Just Accepted) (2013): When small numbers of reactants A and B in a small volume can react to form AB dimers, the observed relation between the number of monomers and dimers differs from the thermodynamic limit result (this is fairly obvious, but somehow they manage to give this effect a fancy name: Nanoconfinement Effect on Chemical Equilibrium, or NCECE). They work out the deviations expected from simple stat mech with low numbers of particles and compare to single-molecule experiments of hybridising ssDNA strands to confirm their theoretical predictions.
* Choi et al, "Mechanism for the endocytosis of spherical nucleic acid nanoparticle conjugates", PNAS Early Edition (2013): Chad Mirkin's group works out the mechanism for cells to swallow DNA-coated nanoparticles (it's quite active, apparently), and suggests that these particles can be used as vectors to deliver payloads (e.g. drugs) into cells.
* There's lots of segregation phenomena, pattern formation, etc.
* Ionic liquids also have interesting behaviour under confinement and are easier to study than systems in water, because you can work at ultra-high vacuum (ILs have very low vapour pressure).
* Can deposit monolayers and can do surface-sensitive characterization.
* Can study IL-water mixtures (many of the chemical groups at the interface are similar with biology).
* Theory on IL's is tractable: systems are simple enough to model at the quantum level, and can check force fields at all times, so there's fewer force field issues. There's also lots of other kinds of interactions that may play a role that may be underappreciated in biology (e.g., pi-pi interactions, metal ion coordination).
I asked him about basic outstanding issues with ILs in general, and he mentioned that the interaction of ILs with surfaces is underexplored (relevance for electrochemistry, lubrication). If I ever want to get into this field, he suggested I look into the references mentioned in the introduction of this paper.
On a similar note of other interesting fields, Thomas Loerting and Werner Kuhs suggested three different places to look at to learn more about atmospheric chemistry and ice.
Finally, a couple of interesting papers that came out over the weekend:
* Rubinovich and Polak, "The Intrinsic Role of Nanoconfinement in Chemical Equilibrium: Evidence from DNA Hybridization", Nano Letters (Just Accepted) (2013): When small numbers of reactants A and B in a small volume can react to form AB dimers, the observed relation between the number of monomers and dimers differs from the thermodynamic limit result (this is fairly obvious, but somehow they manage to give this effect a fancy name: Nanoconfinement Effect on Chemical Equilibrium, or NCECE). They work out the deviations expected from simple stat mech with low numbers of particles and compare to single-molecule experiments of hybridising ssDNA strands to confirm their theoretical predictions.
* Choi et al, "Mechanism for the endocytosis of spherical nucleic acid nanoparticle conjugates", PNAS Early Edition (2013): Chad Mirkin's group works out the mechanism for cells to swallow DNA-coated nanoparticles (it's quite active, apparently), and suggests that these particles can be used as vectors to deliver payloads (e.g. drugs) into cells.
Thursday, 25 April 2013
WATSURF 2013: Day 9 and interesting papers
Today is the second-to-last day of the conference, and was somewhat more simulation-heavy.
Mounir Tarek (U. of Nancy, France) gave a two-part talk. The first part was titled "Insight into 'bio-systems' hydration water dynamics from Molecular Dynamics simulations". The main point was to try to make the connection between hydration water in MD with the results of neutron scattering, and in this, he was very convincing.
First, he looked at ribonuclease at "full hydration" (0.35 g water / g protein). In MD, you see that you can't build even the first hydration layer with this amount of water. Instead, you have a protein powder with a bit of confined water here and there. However, A. Oleinikova (JPCB, 2005) has shown that the water *does* form a spanning network, which mediates long-range transmission of correlated motions of the protein. In any case, if you simulate this system at low T (100 K), you only see quick beta relaxation in I(Q,t) (single-particle motion), then structural arrest. As you raise the temperature, around 150-200 K, you start seeing some motion. In Tarek et al JACS (1999), they show that they can nearly reproduce the protein dynamical transition observed in Doster et al, Nature (?), 1989. Around 230 K, all sort of groups on the proteins start moving, so it's not just one kind of motion that's starting. MD of RNase hydration water at 300 K: I(Q,t) at long times is well-fit by stretched exponentials.
So what's happening in the MD simulation? Can characterize H-bond dynamics in terms of fast motions (survival times tau_HB of a single bond) and slow network rearrangement (correlation time tau_R of the indicator function of a single pair of bonds, that can break and reform many times before definitely breaking after a collective rearrangement). What they find is that it is tau_R that takes off around and below 200 K, whereas tau_HB is mostly unaffected. Another computer experiment: put harmonic restraints on oxygen atoms: get a similar effect on tau_R as in cooling. So there is a connection between slow dynamics and restraining of waters into a particular position.
Glycerol, which is used as a bioprotectant for pharmaceutical industry, slows down motion in proteins (MSD observed in neutron scattering is intermediate between dry powder and hydrated protein powder). In the MD simulations, what they see is that the protein dynamical transition is happening at the moment when the *solvent* starts moving, whatever the solvent is. Glycerol just starts moving at a higher temperature than water.
Now for something different: coherent motions of proteins. If motion is describable by generalized thermodynamics, then S(Q, omega) is a sum of Lorentzians (modes), Rayleigh peak at omega = 0 (heat) and a Brillouin doublet around omega = omega_s (speed of sound). From S(Q,omega) in simulation, can obtain dispersion curves for phonons in sample (optical and acoustic modes). In early MD simulations of just water, find different dispersion curves for transverse and longitudinal modes. Tarek and Tobias have looked at dispersion curves of water next to proteins, find good agreement with experiments.
Of course, getting dispersion relations in this way is an artifact of what you can measure in neutron scattering experiments. In MD, they can directly measure current correlation functions (<J(x,t) J(0,0)>, where J(x,t) = sum_i v_i(t) delta(x - r_i(t)) to obtain dispersion relations. They did this for a system of maltose binding protein crystal at 0.42 g / g hydration. Water dispersion (transverse and longitudinal modes) are similar to bulk. But if you just focus on waters close to the protein, then you start seeing different things: new modes start appearing at high energies (high frequencies). You can correlate these new modes in water to collective modes in the protein. This is a direct demonstration of coupling between protein and water dynamics.
Once you have access to the current correlation functions, you have access to macroscopic elastic properties (e.g., adiabatic bulk modulus, longitudinal modulus, shear modulus).
In the second part of the talk, he discussed "Electroporation of lipid membranes: insight from molecular dynamics simulations". Electroporation = put a cell in a high electric field, if it's high enough, make pores in cell membrane, either transiently or permanently. Reversible electroporation can be used to insert drugs into cell, e.g. chemotherapy delivered directly to sick cells, which reduces the total dose => reduction in side effects. Used medically today. Irreversible electroporation finds application in food processing: exploding cells from grapes, beets and oranges respectively increases extraction of wine, sugar and orange juice by a factor of 2-3. There's a big group of people interested in this stuff.
They use MD simulations to understand the basic physics of electroporation. To put E-fields from MD into context, he mentioned that you can now experimentally apply huge E-field pulses (O(1 MV/cm)) for about a nanosecond.
Inside membrane, there is a huge potential difference (about 1V, exp 0.3-1.5 V) between outside and inside of hydrophobic core. Experimentally, this rationalizes different solubilities of fat-soluble ions. In simulations, measured directly by Wang et al, PNAS, 2006. So around the membrane edge, have an E-field of about 1V/nm. If you apply an electric field across the membrane (no ions), you rearrange the water dipoles on both sides of the membrane, result in a potential difference between both sides of the membrane.
Ideally, you would look in simulations at vescicles 10-20nm in size: not possible, do it in planar membranes. Observe pores forming in membranes in simulations (after a few ns) upon applied electric field (about 2-2.5 V across membrane). The physics: dipoles of waters in both sides reorient, create a large sustained electric field inside hydrophobic core, to which waters outside are strongly attracted. Once water finds its way there, head groups on membrane rearrange to "seal" edges of pore.
They've simulated transport of siRNAs across membrane via a few ns E-field pulse: it works and it's been experimentally realized.
If you replace amphiphilic lipids by just negatively charged head group, then head groups *do not* stabilize the pores that form, so pores exist only during the length of the E-field pulse.
The following talk was by Benjamin Bouvier (IBCP, Lyon, France) talked about "Water in biomolecular recognition processes: the forgotten partner?" He began with a summary of what makes water interesting for the purposes of biology, which I thought was quite nice. Here were the main properties he suggested: a) High heat capacity => oceans act as heat sinks & sources, stabilising climate; b) Density maximum at 4 C => lakes and rivers freeze from the top, life can continue below. c) Water vapor absorbs IR, "greenhouse effect", keeps Earth warm (a bit too warm these days!); d) High surface tension = important for dynamics in biology e) Universal solvent.
What about water in the living cell? 50-400g/L of biomolecules, occupy 20-40% of cellular volume => high crowding. Lots of contacts form randomly. Can water help distinguish random contacts from productive one? Upon structural rearrangements, there are large numbers of waters either released or taken up, so they make a large contributions to association/dissociation energetics, despite low contribution of individual molecules.
From here on, most of the talk was a pedagogical introduction to MD from the point of view of biological simulations (the conference is, after all, supposed to be a school). But there were some results at the end:
His work focuses on the role of water in protein-protein and protein-DNA interactions. To motivate, he showed an example of one antifreeze protein, which recognizes one of the faces of ice Ih and attaches to it, stopping growth of crystals (Madura, J. Mol. Recognition, 2000).
DNA and water: in the minor groove, you have a fairly ordered chain of water ("the hydration spine"), H-bonded to bases or bridging phosphate groups on either strand. The next two layers are somewhat structured as a consequence, and this transmits sequence information to outside the DNA. Binding lifetimes vary depending on sequence. There are specific water cluster structures at certain sequences, e.g. at the boundary between a GC-rich and AC-rich region.
He first mentioned looking at ubiquitin. Binds other ubiquitin via lots of lysine residues on surface, building poly-ubiquitin chains. Different polyubiquitins signal different thing, e.g. a protein bound to di-ubiquitin (linked by the Lys 48 residue) is marked for degradation by the proteasome. The same hydrophobic interface in ubiquitin is used to bind other ubiquitins and the proteasome. They calculated a PMF for ubiquitin dimer as a function of minimum distance between any atom pair (much smaller bias than, say, distance between monomer CoMs). The result is unpublished (so can't talk about it here!) but it's submitted to JCTC.
Now for protein-protein recognition. The current idea is based on "hotspots": <5% of residues contribute non-negligibly to energy of binding (Bogan, Thorn, J. Mol. Biol., 1998). Can hotspots be predicted? (would be useful for drug targets). How about monitoring the behavior of water around proteins? Can areas of low solvent flow correspond to free energy hotspots (maximize the hydrophobic effect)? Such residues would be called "dehydrons" (Scheraga et al, PNAS, 2003). One possible statistic would be to calculate average inverse lifetimes of waters in contact with each residue. Hard to sample and requires expensive simulations. What about evolutionarily conserved residues? Very little statistical power. However, there is some correlation between dry residues and conserved residues.
One idea to identify "dry residues" almost statically is to quickly solvate the protein, equilibrate for a tiny amount of time, then perform a Voronoi tesselation of space according to heavy atom positions (some correction for different atom sizes). Using this, you can map the "depth" of a residue inside a protein by measuring distances from residue to bulk water region in units of Voronoi cells (the details went by too quickly for me to take notes, but it's described in Bouvier, Proteins, 2009). Apparently, there's a good correlation between this "depth" and dryness of the residues.
Now about protein-DNA recognition. Two mechanisms: direct recognition (bonds formed with interfaces) and indirect recognition (structural or mechanical differences w.r.t. B-DNA due to sequence). Looked at one particular DNA-protein interaction (Bouvier & Lavery, JACS 2009). The protein barely deforms upon binding/unbinding, but the DNA bends a lot. Also, they find that the hydration "spine" in the minor groove that the protein has to displace moves collectively.
They also simulation the pathway of a drug (daunmycin) sliding along target DNA sequence, PMF minimum correlates with experimental binding position, and barriers can be correlated to firmly attached waters on the DNA that are hard to displace by the drug.
Now for some interesting papers that came out today:
* G. Grazziano, "On the signature of the hydrophobic effect at a single molecule level", Phys. Chem. Chem. Phys., 15, 7389-7395 (2013): A putative alternative explanation for the experiments of Li and Walker on the free energy changes upon hydrophobic collapse of a homopolymer, measured by AFM. He claims that their rationalization by LCW is illusory; as far as I could tell from a cursory reading, he claims that the results can be understood entirely in terms of changes in the total solvent-excluded volume, and surface tension plays basically no role. The parabolic temperature dependence of per-residue solvation free energy changes (in the LCW-language, due to a shift in the crossover distance from above to below the monomer size) is then explained in terms of temperature dependences on the effective size of each monomer and some link to scaled-particle theory, as well as a rotational entropic loss of the monomers when going from extended to collapsed states. To my ears, this all sounds extremely fishy, but perhaps it's worth a closer look some day during a slow afternoon.
* Burns et al, "Self-Assembled DNA Nanopores that Span Lipid Bilayers", Nano Lett., Just Accepted Manuscript (2013): A 3D DNA-origami of an open tube, where the DNA in the middle is chemically modified to be hydrophobic. The barrel inserts into lipid bilayers and acts as a pore.
* Abascal et al, "Homogeneous bubble nucleation in water at negative pressure: A Voronoi polyhedra analysis", J. Chem. Phys. 138, 084508 (2013): Not from today, but Chantal Valeriani sent me this paper of hers and her co-workers after reading our work on evaporation. They look at the nucleation of vapor bubbles in TIP4P/2005 water at 280 K under negative pressure. They have a nice trick for identifying which molecules are at the cavity interface based on a Voronoi tessellation of space, so can monitor the sizes of small, transient cavities. Just above the spinodal line, using a neat mean-first passage time (MFPT) analysis due to Wedekind et al, they measure the nucleation rate and critical bubble size in a way that's only mildly sensitive to their exact definition of cavity volumes. They find that CNT describes the critical bubble size well, but underestimates the rate by 8 orders of magnitude (not too surprising, given that CNT assumes that the cavity free energy is of the form gamma * area + Delta mu * volume, which isn't true for small cavities in water). Almost by definition, they find that just below the spinodal line, bubble growth proceeds through spinodal decomposition, but they show how this growth shows up in an MFPT vs. bubble volume plot. I wonder if they would find similar results using the Willard-Chandler interface to estimate cavity sizes.
* Feng et al, "DNA Patchy Particles", Adv. Mater. (2013): An experimental realisation by the NYU group of a colloid coated with one type of DNA on most of the surface, but a different type in a single (small) patch region. It's conceptually easy (Fig 1 of their paper says more than I could by writing), but it's good to see it done in experiments.
Mounir Tarek (U. of Nancy, France) gave a two-part talk. The first part was titled "Insight into 'bio-systems' hydration water dynamics from Molecular Dynamics simulations". The main point was to try to make the connection between hydration water in MD with the results of neutron scattering, and in this, he was very convincing.
First, he looked at ribonuclease at "full hydration" (0.35 g water / g protein). In MD, you see that you can't build even the first hydration layer with this amount of water. Instead, you have a protein powder with a bit of confined water here and there. However, A. Oleinikova (JPCB, 2005) has shown that the water *does* form a spanning network, which mediates long-range transmission of correlated motions of the protein. In any case, if you simulate this system at low T (100 K), you only see quick beta relaxation in I(Q,t) (single-particle motion), then structural arrest. As you raise the temperature, around 150-200 K, you start seeing some motion. In Tarek et al JACS (1999), they show that they can nearly reproduce the protein dynamical transition observed in Doster et al, Nature (?), 1989. Around 230 K, all sort of groups on the proteins start moving, so it's not just one kind of motion that's starting. MD of RNase hydration water at 300 K: I(Q,t) at long times is well-fit by stretched exponentials.
So what's happening in the MD simulation? Can characterize H-bond dynamics in terms of fast motions (survival times tau_HB of a single bond) and slow network rearrangement (correlation time tau_R of the indicator function of a single pair of bonds, that can break and reform many times before definitely breaking after a collective rearrangement). What they find is that it is tau_R that takes off around and below 200 K, whereas tau_HB is mostly unaffected. Another computer experiment: put harmonic restraints on oxygen atoms: get a similar effect on tau_R as in cooling. So there is a connection between slow dynamics and restraining of waters into a particular position.
Glycerol, which is used as a bioprotectant for pharmaceutical industry, slows down motion in proteins (MSD observed in neutron scattering is intermediate between dry powder and hydrated protein powder). In the MD simulations, what they see is that the protein dynamical transition is happening at the moment when the *solvent* starts moving, whatever the solvent is. Glycerol just starts moving at a higher temperature than water.
Now for something different: coherent motions of proteins. If motion is describable by generalized thermodynamics, then S(Q, omega) is a sum of Lorentzians (modes), Rayleigh peak at omega = 0 (heat) and a Brillouin doublet around omega = omega_s (speed of sound). From S(Q,omega) in simulation, can obtain dispersion curves for phonons in sample (optical and acoustic modes). In early MD simulations of just water, find different dispersion curves for transverse and longitudinal modes. Tarek and Tobias have looked at dispersion curves of water next to proteins, find good agreement with experiments.
Of course, getting dispersion relations in this way is an artifact of what you can measure in neutron scattering experiments. In MD, they can directly measure current correlation functions (<J(x,t) J(0,0)>, where J(x,t) = sum_i v_i(t) delta(x - r_i(t)) to obtain dispersion relations. They did this for a system of maltose binding protein crystal at 0.42 g / g hydration. Water dispersion (transverse and longitudinal modes) are similar to bulk. But if you just focus on waters close to the protein, then you start seeing different things: new modes start appearing at high energies (high frequencies). You can correlate these new modes in water to collective modes in the protein. This is a direct demonstration of coupling between protein and water dynamics.
Once you have access to the current correlation functions, you have access to macroscopic elastic properties (e.g., adiabatic bulk modulus, longitudinal modulus, shear modulus).
In the second part of the talk, he discussed "Electroporation of lipid membranes: insight from molecular dynamics simulations". Electroporation = put a cell in a high electric field, if it's high enough, make pores in cell membrane, either transiently or permanently. Reversible electroporation can be used to insert drugs into cell, e.g. chemotherapy delivered directly to sick cells, which reduces the total dose => reduction in side effects. Used medically today. Irreversible electroporation finds application in food processing: exploding cells from grapes, beets and oranges respectively increases extraction of wine, sugar and orange juice by a factor of 2-3. There's a big group of people interested in this stuff.
They use MD simulations to understand the basic physics of electroporation. To put E-fields from MD into context, he mentioned that you can now experimentally apply huge E-field pulses (O(1 MV/cm)) for about a nanosecond.
Inside membrane, there is a huge potential difference (about 1V, exp 0.3-1.5 V) between outside and inside of hydrophobic core. Experimentally, this rationalizes different solubilities of fat-soluble ions. In simulations, measured directly by Wang et al, PNAS, 2006. So around the membrane edge, have an E-field of about 1V/nm. If you apply an electric field across the membrane (no ions), you rearrange the water dipoles on both sides of the membrane, result in a potential difference between both sides of the membrane.
Ideally, you would look in simulations at vescicles 10-20nm in size: not possible, do it in planar membranes. Observe pores forming in membranes in simulations (after a few ns) upon applied electric field (about 2-2.5 V across membrane). The physics: dipoles of waters in both sides reorient, create a large sustained electric field inside hydrophobic core, to which waters outside are strongly attracted. Once water finds its way there, head groups on membrane rearrange to "seal" edges of pore.
They've simulated transport of siRNAs across membrane via a few ns E-field pulse: it works and it's been experimentally realized.
If you replace amphiphilic lipids by just negatively charged head group, then head groups *do not* stabilize the pores that form, so pores exist only during the length of the E-field pulse.
The following talk was by Benjamin Bouvier (IBCP, Lyon, France) talked about "Water in biomolecular recognition processes: the forgotten partner?" He began with a summary of what makes water interesting for the purposes of biology, which I thought was quite nice. Here were the main properties he suggested: a) High heat capacity => oceans act as heat sinks & sources, stabilising climate; b) Density maximum at 4 C => lakes and rivers freeze from the top, life can continue below. c) Water vapor absorbs IR, "greenhouse effect", keeps Earth warm (a bit too warm these days!); d) High surface tension = important for dynamics in biology e) Universal solvent.
What about water in the living cell? 50-400g/L of biomolecules, occupy 20-40% of cellular volume => high crowding. Lots of contacts form randomly. Can water help distinguish random contacts from productive one? Upon structural rearrangements, there are large numbers of waters either released or taken up, so they make a large contributions to association/dissociation energetics, despite low contribution of individual molecules.
From here on, most of the talk was a pedagogical introduction to MD from the point of view of biological simulations (the conference is, after all, supposed to be a school). But there were some results at the end:
His work focuses on the role of water in protein-protein and protein-DNA interactions. To motivate, he showed an example of one antifreeze protein, which recognizes one of the faces of ice Ih and attaches to it, stopping growth of crystals (Madura, J. Mol. Recognition, 2000).
DNA and water: in the minor groove, you have a fairly ordered chain of water ("the hydration spine"), H-bonded to bases or bridging phosphate groups on either strand. The next two layers are somewhat structured as a consequence, and this transmits sequence information to outside the DNA. Binding lifetimes vary depending on sequence. There are specific water cluster structures at certain sequences, e.g. at the boundary between a GC-rich and AC-rich region.
He first mentioned looking at ubiquitin. Binds other ubiquitin via lots of lysine residues on surface, building poly-ubiquitin chains. Different polyubiquitins signal different thing, e.g. a protein bound to di-ubiquitin (linked by the Lys 48 residue) is marked for degradation by the proteasome. The same hydrophobic interface in ubiquitin is used to bind other ubiquitins and the proteasome. They calculated a PMF for ubiquitin dimer as a function of minimum distance between any atom pair (much smaller bias than, say, distance between monomer CoMs). The result is unpublished (so can't talk about it here!) but it's submitted to JCTC.
Now for protein-protein recognition. The current idea is based on "hotspots": <5% of residues contribute non-negligibly to energy of binding (Bogan, Thorn, J. Mol. Biol., 1998). Can hotspots be predicted? (would be useful for drug targets). How about monitoring the behavior of water around proteins? Can areas of low solvent flow correspond to free energy hotspots (maximize the hydrophobic effect)? Such residues would be called "dehydrons" (Scheraga et al, PNAS, 2003). One possible statistic would be to calculate average inverse lifetimes of waters in contact with each residue. Hard to sample and requires expensive simulations. What about evolutionarily conserved residues? Very little statistical power. However, there is some correlation between dry residues and conserved residues.
One idea to identify "dry residues" almost statically is to quickly solvate the protein, equilibrate for a tiny amount of time, then perform a Voronoi tesselation of space according to heavy atom positions (some correction for different atom sizes). Using this, you can map the "depth" of a residue inside a protein by measuring distances from residue to bulk water region in units of Voronoi cells (the details went by too quickly for me to take notes, but it's described in Bouvier, Proteins, 2009). Apparently, there's a good correlation between this "depth" and dryness of the residues.
Now about protein-DNA recognition. Two mechanisms: direct recognition (bonds formed with interfaces) and indirect recognition (structural or mechanical differences w.r.t. B-DNA due to sequence). Looked at one particular DNA-protein interaction (Bouvier & Lavery, JACS 2009). The protein barely deforms upon binding/unbinding, but the DNA bends a lot. Also, they find that the hydration "spine" in the minor groove that the protein has to displace moves collectively.
They also simulation the pathway of a drug (daunmycin) sliding along target DNA sequence, PMF minimum correlates with experimental binding position, and barriers can be correlated to firmly attached waters on the DNA that are hard to displace by the drug.
Now for some interesting papers that came out today:
* G. Grazziano, "On the signature of the hydrophobic effect at a single molecule level", Phys. Chem. Chem. Phys., 15, 7389-7395 (2013): A putative alternative explanation for the experiments of Li and Walker on the free energy changes upon hydrophobic collapse of a homopolymer, measured by AFM. He claims that their rationalization by LCW is illusory; as far as I could tell from a cursory reading, he claims that the results can be understood entirely in terms of changes in the total solvent-excluded volume, and surface tension plays basically no role. The parabolic temperature dependence of per-residue solvation free energy changes (in the LCW-language, due to a shift in the crossover distance from above to below the monomer size) is then explained in terms of temperature dependences on the effective size of each monomer and some link to scaled-particle theory, as well as a rotational entropic loss of the monomers when going from extended to collapsed states. To my ears, this all sounds extremely fishy, but perhaps it's worth a closer look some day during a slow afternoon.
* Burns et al, "Self-Assembled DNA Nanopores that Span Lipid Bilayers", Nano Lett., Just Accepted Manuscript (2013): A 3D DNA-origami of an open tube, where the DNA in the middle is chemically modified to be hydrophobic. The barrel inserts into lipid bilayers and acts as a pore.
* Abascal et al, "Homogeneous bubble nucleation in water at negative pressure: A Voronoi polyhedra analysis", J. Chem. Phys. 138, 084508 (2013): Not from today, but Chantal Valeriani sent me this paper of hers and her co-workers after reading our work on evaporation. They look at the nucleation of vapor bubbles in TIP4P/2005 water at 280 K under negative pressure. They have a nice trick for identifying which molecules are at the cavity interface based on a Voronoi tessellation of space, so can monitor the sizes of small, transient cavities. Just above the spinodal line, using a neat mean-first passage time (MFPT) analysis due to Wedekind et al, they measure the nucleation rate and critical bubble size in a way that's only mildly sensitive to their exact definition of cavity volumes. They find that CNT describes the critical bubble size well, but underestimates the rate by 8 orders of magnitude (not too surprising, given that CNT assumes that the cavity free energy is of the form gamma * area + Delta mu * volume, which isn't true for small cavities in water). Almost by definition, they find that just below the spinodal line, bubble growth proceeds through spinodal decomposition, but they show how this growth shows up in an MFPT vs. bubble volume plot. I wonder if they would find similar results using the Willard-Chandler interface to estimate cavity sizes.
* Feng et al, "DNA Patchy Particles", Adv. Mater. (2013): An experimental realisation by the NYU group of a colloid coated with one type of DNA on most of the surface, but a different type in a single (small) patch region. It's conceptually easy (Fig 1 of their paper says more than I could by writing), but it's good to see it done in experiments.
Wednesday, 24 April 2013
WATSURF 2013 Day 8 and interesting papers
Today was a lull for me in the conference, so I decided to catch up a bit on the latest literature. Before getting to that, though, we also took a trip in the afternoon to the Mer de Glace, the longest glacier in France.
Here's the group at the bus stop to go to Chamonix:
Here's what it looks like once you get to the told of Montenvers. There's a hotel at the top (who stays there?), and a gondola that takes you partway down (the rest is by stairs) to a grotto in the glacier that they carve out anew every year:
One of the most impressive things on the way down is that they have little plaques indicating the level of the glacier at various times in the past 25 years. Here's the plaque for 1990. For a sense of scale, the dots towards the bottom are people skiing on the glacier. It's more or less at the same height as the snow line on the other side.
Here's what the grotto looks like inside (close to the entrance, where there's enough light):
Now back to business! I wrote down a few neat factoids from some of the talks today. From Giusseppe Zaccai (ILL, Grenoble, France), I learned that trehalose, a disaccharide, can form a glass that protects protein structures (so they don't denature) and effectively can replace water (except that it freezes dynamics); this effect is used by some insects and plants to survive dry conditions for up to years. From Wolfgang Doster (TU München, Germany), I learned that there is nice data for a "phase diagram" of cytochrome C, which denatures under both cold and high pressure conditions (in Doster and Friedrich in Protein Folding Handbook, Part 1 (wiley VCH 2005)). From Davide Orsi (PhD student in U. of Parma, Italy), I learned that giant lipid vesicles (microns in radius R) have surfaces that fluctuate very appreciably under thermal motions, and that the fluctuations projected onto the equatorial plane for modes 5+ are well-described by that of a planar elastic sheet of side-length 2 pi R.
Now for some interesting papers that came out today:
* Wei et al, "Mapping the Thermal Behavior of DNA Origami Nanostructures", J. Am. Chem. Soc. 135, 6165−6176 (2013): They put FRET fluorophores into two adjacent staple strands of a DNA origami sheet to monitor assembly kinetics. Find very cooperative assembly/melting transition over just a few degrees C (with only a little hysteresis), but different parts of the structure seem to assemble at slightly different temperatures. If they remove some (even most!) staple strands far from the fluorophores, little change in kinetics observed, but if they remove the immediate neighbors of the fluorophores, assembly/melting transition broadens enormously (evidence for local cooperativity). For 3D DNA origamis (log-cabin style), hysteresis is much larger.
* Ruff et al, "Precision Templating with DNA of a Virus-like Particle with Peptide Nanostructures", J. Am. Chem. Soc. 135, 6211−6219 (2013): A nice synthetic assembly system where mushroom-like particles (stem is protein coiled-coil, cap is long PEG) attach at their base to dsDNA, form something that looks like a rod virus.
* Roy et al, "Silver Nanoassemblies Constructed from Boranephosphonate DNA", J. Am. Chem. Soc. 135, 6234−6241 (2013): This is cool! Apparently, you can make ssDNA where the phosphate ions have one oxygen replaced by a BH_3 group. Upon exposure to a silver salt, silver granules form around the BH_3's. So if you "boronate" an arbitrary subset of staple strands in a DNA origami, you can grow silver granules in arbitrary pattern on the origami. Maybe an interesting step towards nanoelectronics?
* Lee et al, "Integration of Gold Nanoparticles into Bilayer Structures via Adaptive Surface Chemistry", J. Am. Chem. Soc. 135, 5950−5953 (2013): Another cool idea: you coat a gold nanoparticle with both hydrophilic and hydrophobic polymers, which are mobile on the surface (at least under the conditions they used). Then, when you expose lipid vesicles to these particles, they can insert into the membrane thanks to the hydrophobic polymers mixing with the interior of the bilayer, while the hydrophilic polymers act as a kind of extension of the lipid bilayer around the nanoparticle. You can incorporate very large particles, and also many particles in one vescicle.
* Xin et al, "Regulation of an Enzyme Cascade Reaction by a DNA Machine", Small, (online, no issue/page yet) (2013): Another interesting idea with DNA: Arrange two enzymes that work in cascade (the product of A is the substrate of B) at the ends of a DNA "hinge". The hinge is held together by a piece of ssDNA, which is floppy, so the enzymes tend to be closeby => fast reaction. Now add a complementary "fuel" strand, with a toehold, to make the hinge a rigid (and longer) dsDNA. This separates the two enzymes in space, so the reactive slows down. If you add an "antifuel" strand that is complementary to the whole fuel strand, the hinge becomes floppy ssDNA again. As a result, you can switch between fast and slow catalysis by adding or removing fuel strands.
Here's the group at the bus stop to go to Chamonix:
Here's what it looks like once you get to the told of Montenvers. There's a hotel at the top (who stays there?), and a gondola that takes you partway down (the rest is by stairs) to a grotto in the glacier that they carve out anew every year:
One of the most impressive things on the way down is that they have little plaques indicating the level of the glacier at various times in the past 25 years. Here's the plaque for 1990. For a sense of scale, the dots towards the bottom are people skiing on the glacier. It's more or less at the same height as the snow line on the other side.
Here's what the grotto looks like inside (close to the entrance, where there's enough light):
Now back to business! I wrote down a few neat factoids from some of the talks today. From Giusseppe Zaccai (ILL, Grenoble, France), I learned that trehalose, a disaccharide, can form a glass that protects protein structures (so they don't denature) and effectively can replace water (except that it freezes dynamics); this effect is used by some insects and plants to survive dry conditions for up to years. From Wolfgang Doster (TU München, Germany), I learned that there is nice data for a "phase diagram" of cytochrome C, which denatures under both cold and high pressure conditions (in Doster and Friedrich in Protein Folding Handbook, Part 1 (wiley VCH 2005)). From Davide Orsi (PhD student in U. of Parma, Italy), I learned that giant lipid vesicles (microns in radius R) have surfaces that fluctuate very appreciably under thermal motions, and that the fluctuations projected onto the equatorial plane for modes 5+ are well-described by that of a planar elastic sheet of side-length 2 pi R.
Now for some interesting papers that came out today:
* Wei et al, "Mapping the Thermal Behavior of DNA Origami Nanostructures", J. Am. Chem. Soc. 135, 6165−6176 (2013): They put FRET fluorophores into two adjacent staple strands of a DNA origami sheet to monitor assembly kinetics. Find very cooperative assembly/melting transition over just a few degrees C (with only a little hysteresis), but different parts of the structure seem to assemble at slightly different temperatures. If they remove some (even most!) staple strands far from the fluorophores, little change in kinetics observed, but if they remove the immediate neighbors of the fluorophores, assembly/melting transition broadens enormously (evidence for local cooperativity). For 3D DNA origamis (log-cabin style), hysteresis is much larger.
* Ruff et al, "Precision Templating with DNA of a Virus-like Particle with Peptide Nanostructures", J. Am. Chem. Soc. 135, 6211−6219 (2013): A nice synthetic assembly system where mushroom-like particles (stem is protein coiled-coil, cap is long PEG) attach at their base to dsDNA, form something that looks like a rod virus.
* Roy et al, "Silver Nanoassemblies Constructed from Boranephosphonate DNA", J. Am. Chem. Soc. 135, 6234−6241 (2013): This is cool! Apparently, you can make ssDNA where the phosphate ions have one oxygen replaced by a BH_3 group. Upon exposure to a silver salt, silver granules form around the BH_3's. So if you "boronate" an arbitrary subset of staple strands in a DNA origami, you can grow silver granules in arbitrary pattern on the origami. Maybe an interesting step towards nanoelectronics?
* Lee et al, "Integration of Gold Nanoparticles into Bilayer Structures via Adaptive Surface Chemistry", J. Am. Chem. Soc. 135, 5950−5953 (2013): Another cool idea: you coat a gold nanoparticle with both hydrophilic and hydrophobic polymers, which are mobile on the surface (at least under the conditions they used). Then, when you expose lipid vesicles to these particles, they can insert into the membrane thanks to the hydrophobic polymers mixing with the interior of the bilayer, while the hydrophilic polymers act as a kind of extension of the lipid bilayer around the nanoparticle. You can incorporate very large particles, and also many particles in one vescicle.
* Xin et al, "Regulation of an Enzyme Cascade Reaction by a DNA Machine", Small, (online, no issue/page yet) (2013): Another interesting idea with DNA: Arrange two enzymes that work in cascade (the product of A is the substrate of B) at the ends of a DNA "hinge". The hinge is held together by a piece of ssDNA, which is floppy, so the enzymes tend to be closeby => fast reaction. Now add a complementary "fuel" strand, with a toehold, to make the hinge a rigid (and longer) dsDNA. This separates the two enzymes in space, so the reactive slows down. If you add an "antifuel" strand that is complementary to the whole fuel strand, the hinge becomes floppy ssDNA again. As a result, you can switch between fast and slow catalysis by adding or removing fuel strands.
Tuesday, 23 April 2013
WATSURF 2013: Day 7
Today, the conference has shifted gears, and we are now discussing "Water/ Surface Model/Biological Interfaces 2".
Martin Weik (IBS, Grenoble, France) gave a talk titled "Proteins need it wet, don't they?". Towards the end, there was a very interesting set of experiments on proteins that work in a solvent-free environment (more below). But let's not get ahead of ourselves yet...
In the introduction, he touched upon the many roles of water in protein structure and function: a) it mediates protein-protein and protein-DNA interactions, b) it's sometimes an integral part of a protein's structure; c) it modulates ligand binding; d) it's involved in allostery; e) it's a reagent in biochemical processes; f) it mediates electron and proton transfer. The conventional wisdom is that the first hydration layer is necessary protein function (controversional: Lopez et al (2010) Biophys J. 99, L62).
He studies hydration mainly with neutron scattering. For experiments, they deuterate the proteins in order to focus on the hydration waters (when only one hydration layer, ~70% of signal is from hydration water; vs. if you deuterate the water, ~98% of signal from protein. Deuterated proteins are *extremely* expensive to make, several 10,000s of Euros). Relevant questions: is there a difference in protein and water dynamics? Hydration water dynamics at the protein dynamical transition? Does protein core dynamics respond to hydration? Can proteins function in the absence of water? In the end, he also mentioned an interesting new technique called "temperature-controlled crystallography" to study protein intermediate states.
When studying proteins, neutron spectroscopy is probing essentially only side chain motions on the ps-to-ns timescale. You also see basically only hydrogens (not deuteriums), which are more or less uniformly distributed inside proteins (85% of them are in side chains). Elastic neutron scattering essentially reports mean-square displacements of single particles.
When you study "dry" proteins (~4 waters/protein), MSDs are very low. When hydrated and above ~230 K, MSDs start growing much more quickly: dynamical transition of protein dynamics (Frauenfelder, Nat. Struct. Biol.)
They now study hydrated protein powders (0.4 g water / g protein = 1 hydration layer). Why powder? Minimizes water contribution, no protein tumbling and no crystalline ice formation at cryo-T. However, it's difficult to characterize the protein state (e.g. unfolded?) in the powder state.
Example #1: Maltose binding protein (41 kDa, 387 aa). Here you see the "protein dynamical transition" where MSD of water takes off around 200 K; if you do the hydrogenated protein in D2O, you see a similar transition at 200 K. Doug Tobias has done corresponding simulations (the agreement is not perfect, but there is a change in dynamics around 250 K).
Example #2: Purple membrane (bateriorhodopsin + lipids + water). Here, transition in protein dynamics and water dynamics do not coincide in temperature: little coupling between protein and water dynamics. Instead, if you look at lipid dynamics, then its dynamical transition coincides with the protein one. "Soluble proteins under tighter hydration-water control than membrane proteins".
In the third example, he looked at intrinsically disordered proteins, but I couldn't make out a single clear take-home message.
Now for the interesting bit. There are some enzymes that work (even work better) in organic solvents (Kibanov (?) Nature 409, 11 Jan 2001). An interesting recent example of this idea taken to the extreme is due to Parriman et al. (2010) Nature Chemistry 2, 622: a myoglobin where some of the side chains are decorated with long polymer chains, long enough to cover the entire protein surface. The result is a myoglobin "liquid" that is biologically active, with no solvent! It seems that all you needed from the solvent was the flexibility and lubrication, at least for myoglobin activity. They seem to have done this for 5 or 6 proteins, so it doesn't seem to be specific to myoglobin. The proteins unfold and refold reversibly under temperature cycling. These experiments made it seem almost like the story of hydrophobic collapse (being mediated by the unusual properties of water as a solvent, i.e. high surface tension, proximity to liquid-gas transition, low compressibility) is almost an afterthought instead of the central story in protein folding. I asked him a question on this, and he promised he would get back to me.
In the last ten minutes, he talked about X-ray kinetic crystallography. The idea is to build a protein crystal of an inactive protein, flash cool at 100 K, somehow trigger the start of an activity (they do this by targeted radiation damage in the synchrotron), then heat to just above dynamical transition temperature (which one is unclear) for a little bit of time before flash cooling again. Hopefully, you've then trapped a number of intermediates present during the protein's activity.
As an example, they studied acetylcholinesterase, which is one of the fastest enzymes out there (~10,000 cycles per second). The puzzle (1991) was that the active site is buried well inside the protein. Speculation that the protein, after decomposing acetylcholine (a neurotransmitter), opens a sort of backdoor to quickly release the products of the reaction. "Back door" not visible in static structure.
Radiation damage in synchrotrons is well-charaterized, and especially attacks strained conformations (e.g. near active sites), cleaves disulfide bonds, decarboxylates several amino acids and reduces metal centers (very quickly). If they crystallise acetylcholinesterase with a non-hydrolysable acetylcholine analog, then use radiation damage to cleave the molecule, then you can trap the intermediate in the reaction. They think they indeed see a backdoor in a tryptophan residue that "hinges out" as soon as acetylcholine is broken down, to allow choline molecule to leave (almost like an exhaust valve).
In the afternoon, Antonio Deriu (U. of Parma, Italy) talked about "Structural and Dynamical Properties of Organised Structures of Saccharide Systems in Aqueous Solutions". The part I found most interesting about the talk was actually the introduction, since I knew very little about carbohydrates (the results were heavy neutron scattering spectra...)
Where do carbohydrates show up? a) Structure and texture of plants (e.g., cellulose) b) Structure and organisation of insect cuticles (chitin) c) Lubrication and viscoelastic properties in animals (e.g., hyalluronic acid) d) Cell surfaces are decorated with carbohydrates, which influences adhesion & recognition, etc. Of course, this is beyond the role of carbohydrates in food! (talked a bit about "nutraceutics", i.e. using food as a matrix for active ingredients of pharmaceuticals; properties of food can tailor drug properties, e.g. release kinetics)
Carbohydrates can be classified according to the number of sugar rings in them: monosaccharides (glucose, galactose, fructose), oligosaccharides (most important ones are disaccharides: maltose = glucose + glucose, lactose = glucose + galactose, sucros = glucose + fructose) and polysaccharides (aka, glycans; classified into homo- and hetero-polysaccharides. Examples of homo-polysaccharides: glycogen, starch, cellulose all made from glucose [different stereochemistry of polymerisation]. Example heteropolysaccharides: hyalluronic acid. Polysaccharides tend to be huge (MW > 200,000) white and amorphous polymers, no sweet taste, interesting viscoelastic properties.
Glycogen is "animal starch". Stored in muscles and liver, and present in cells as granules. Branched polymer, with a branch point every 8-12 glucose units. Complete hydrolysis yields glucose. Hydrolysed by enzymes, e.g., by alpha- and beta-amylase.
Starch is storage polymer of plants. Made of two polysaccharides: 10-30% Amylose and 70-90% Amylopectin. Amylose is a linear polymer of glucose that is soluble in water (MW 50,000 - 200,000). Amylopectin is highly branched, insoluble in water. MW 70,000 - 1,000,000. Each segment between branching points has ~25 units. Amylose is floppy and tends to form helices in solution. Iodine can insert in the middle of these helices and turn deep blue (standard test for starch).
Chitin is present in the cell wall of fungi & exoskeletons of crustaceans, insects & spiders. Used commercially for coatings (e.g., vegetable shine).
Agarose is a galatose polymer. Dissolved in hot water and cooled, becomes gelatinous. Lots of uses, e.g. "vegetarian gelatin".
Heteropolysaccharides tend to have charged monomer units, so behave like polyelectrolytes. An important example is hyaluronic acid (lubrication in joints?). Heteropolysaccharides form weak gels, controlled by temperature, solvent quality, ionic environment. Upon cooling, individual polymers start coiling up. Coils join up into bundles with junctions between them. This gel can hold large amounts of water.
Starch granules have an onion-like structures, with regions that are relatively crystalline alternating with amorphous structures.
The last (short) talk of the day was also quite interesting. Carlos Drummond (U. of Bordeaux, France, no web page?) talked about "Ions-Induced Nanostructuration of Hydrophobic Polymer Surfaces". The portion about ions flew over my head (the talk itself went too quickly), but he showed AFM images of pancake-like "nanodroplets" of radius ~50 nm on the surface of polystyrene film on a wafer. I've heard of these before, but why are they stable? One cartoon he suggestively sketched is that the surface is rough, and the nanobubbles are spanning concave regions of the surface. They have lots of experiments on the formation of gas droplets on surfaces. One interesting set of AFM images is a time-evolution series over 7 days, where the nanodroplets slowly disappear. So perhaps they're not thermodynamically stable, just kinetically slow to relax. When I asked him about it, he confirmed that the drops are not thermodynamically stable, they are simply very long-lived.
Finally, Alenka Luzar passed on to me the reference for the first paper that showed dewetting in the context of proteins (not Bruce Berne in Nature 2005!): Huang, Ding, Hua, Yang, Chen, J. Chem. Phys. 121 (4), 1969 (2004).
Martin Weik (IBS, Grenoble, France) gave a talk titled "Proteins need it wet, don't they?". Towards the end, there was a very interesting set of experiments on proteins that work in a solvent-free environment (more below). But let's not get ahead of ourselves yet...
In the introduction, he touched upon the many roles of water in protein structure and function: a) it mediates protein-protein and protein-DNA interactions, b) it's sometimes an integral part of a protein's structure; c) it modulates ligand binding; d) it's involved in allostery; e) it's a reagent in biochemical processes; f) it mediates electron and proton transfer. The conventional wisdom is that the first hydration layer is necessary protein function (controversional: Lopez et al (2010) Biophys J. 99, L62).
He studies hydration mainly with neutron scattering. For experiments, they deuterate the proteins in order to focus on the hydration waters (when only one hydration layer, ~70% of signal is from hydration water; vs. if you deuterate the water, ~98% of signal from protein. Deuterated proteins are *extremely* expensive to make, several 10,000s of Euros). Relevant questions: is there a difference in protein and water dynamics? Hydration water dynamics at the protein dynamical transition? Does protein core dynamics respond to hydration? Can proteins function in the absence of water? In the end, he also mentioned an interesting new technique called "temperature-controlled crystallography" to study protein intermediate states.
When studying proteins, neutron spectroscopy is probing essentially only side chain motions on the ps-to-ns timescale. You also see basically only hydrogens (not deuteriums), which are more or less uniformly distributed inside proteins (85% of them are in side chains). Elastic neutron scattering essentially reports mean-square displacements of single particles.
When you study "dry" proteins (~4 waters/protein), MSDs are very low. When hydrated and above ~230 K, MSDs start growing much more quickly: dynamical transition of protein dynamics (Frauenfelder, Nat. Struct. Biol.)
They now study hydrated protein powders (0.4 g water / g protein = 1 hydration layer). Why powder? Minimizes water contribution, no protein tumbling and no crystalline ice formation at cryo-T. However, it's difficult to characterize the protein state (e.g. unfolded?) in the powder state.
Example #1: Maltose binding protein (41 kDa, 387 aa). Here you see the "protein dynamical transition" where MSD of water takes off around 200 K; if you do the hydrogenated protein in D2O, you see a similar transition at 200 K. Doug Tobias has done corresponding simulations (the agreement is not perfect, but there is a change in dynamics around 250 K).
Example #2: Purple membrane (bateriorhodopsin + lipids + water). Here, transition in protein dynamics and water dynamics do not coincide in temperature: little coupling between protein and water dynamics. Instead, if you look at lipid dynamics, then its dynamical transition coincides with the protein one. "Soluble proteins under tighter hydration-water control than membrane proteins".
In the third example, he looked at intrinsically disordered proteins, but I couldn't make out a single clear take-home message.
Now for the interesting bit. There are some enzymes that work (even work better) in organic solvents (Kibanov (?) Nature 409, 11 Jan 2001). An interesting recent example of this idea taken to the extreme is due to Parriman et al. (2010) Nature Chemistry 2, 622: a myoglobin where some of the side chains are decorated with long polymer chains, long enough to cover the entire protein surface. The result is a myoglobin "liquid" that is biologically active, with no solvent! It seems that all you needed from the solvent was the flexibility and lubrication, at least for myoglobin activity. They seem to have done this for 5 or 6 proteins, so it doesn't seem to be specific to myoglobin. The proteins unfold and refold reversibly under temperature cycling. These experiments made it seem almost like the story of hydrophobic collapse (being mediated by the unusual properties of water as a solvent, i.e. high surface tension, proximity to liquid-gas transition, low compressibility) is almost an afterthought instead of the central story in protein folding. I asked him a question on this, and he promised he would get back to me.
In the last ten minutes, he talked about X-ray kinetic crystallography. The idea is to build a protein crystal of an inactive protein, flash cool at 100 K, somehow trigger the start of an activity (they do this by targeted radiation damage in the synchrotron), then heat to just above dynamical transition temperature (which one is unclear) for a little bit of time before flash cooling again. Hopefully, you've then trapped a number of intermediates present during the protein's activity.
As an example, they studied acetylcholinesterase, which is one of the fastest enzymes out there (~10,000 cycles per second). The puzzle (1991) was that the active site is buried well inside the protein. Speculation that the protein, after decomposing acetylcholine (a neurotransmitter), opens a sort of backdoor to quickly release the products of the reaction. "Back door" not visible in static structure.
Radiation damage in synchrotrons is well-charaterized, and especially attacks strained conformations (e.g. near active sites), cleaves disulfide bonds, decarboxylates several amino acids and reduces metal centers (very quickly). If they crystallise acetylcholinesterase with a non-hydrolysable acetylcholine analog, then use radiation damage to cleave the molecule, then you can trap the intermediate in the reaction. They think they indeed see a backdoor in a tryptophan residue that "hinges out" as soon as acetylcholine is broken down, to allow choline molecule to leave (almost like an exhaust valve).
In the afternoon, Antonio Deriu (U. of Parma, Italy) talked about "Structural and Dynamical Properties of Organised Structures of Saccharide Systems in Aqueous Solutions". The part I found most interesting about the talk was actually the introduction, since I knew very little about carbohydrates (the results were heavy neutron scattering spectra...)
Where do carbohydrates show up? a) Structure and texture of plants (e.g., cellulose) b) Structure and organisation of insect cuticles (chitin) c) Lubrication and viscoelastic properties in animals (e.g., hyalluronic acid) d) Cell surfaces are decorated with carbohydrates, which influences adhesion & recognition, etc. Of course, this is beyond the role of carbohydrates in food! (talked a bit about "nutraceutics", i.e. using food as a matrix for active ingredients of pharmaceuticals; properties of food can tailor drug properties, e.g. release kinetics)
Carbohydrates can be classified according to the number of sugar rings in them: monosaccharides (glucose, galactose, fructose), oligosaccharides (most important ones are disaccharides: maltose = glucose + glucose, lactose = glucose + galactose, sucros = glucose + fructose) and polysaccharides (aka, glycans; classified into homo- and hetero-polysaccharides. Examples of homo-polysaccharides: glycogen, starch, cellulose all made from glucose [different stereochemistry of polymerisation]. Example heteropolysaccharides: hyalluronic acid. Polysaccharides tend to be huge (MW > 200,000) white and amorphous polymers, no sweet taste, interesting viscoelastic properties.
Glycogen is "animal starch". Stored in muscles and liver, and present in cells as granules. Branched polymer, with a branch point every 8-12 glucose units. Complete hydrolysis yields glucose. Hydrolysed by enzymes, e.g., by alpha- and beta-amylase.
Starch is storage polymer of plants. Made of two polysaccharides: 10-30% Amylose and 70-90% Amylopectin. Amylose is a linear polymer of glucose that is soluble in water (MW 50,000 - 200,000). Amylopectin is highly branched, insoluble in water. MW 70,000 - 1,000,000. Each segment between branching points has ~25 units. Amylose is floppy and tends to form helices in solution. Iodine can insert in the middle of these helices and turn deep blue (standard test for starch).
Chitin is present in the cell wall of fungi & exoskeletons of crustaceans, insects & spiders. Used commercially for coatings (e.g., vegetable shine).
Agarose is a galatose polymer. Dissolved in hot water and cooled, becomes gelatinous. Lots of uses, e.g. "vegetarian gelatin".
Heteropolysaccharides tend to have charged monomer units, so behave like polyelectrolytes. An important example is hyaluronic acid (lubrication in joints?). Heteropolysaccharides form weak gels, controlled by temperature, solvent quality, ionic environment. Upon cooling, individual polymers start coiling up. Coils join up into bundles with junctions between them. This gel can hold large amounts of water.
Starch granules have an onion-like structures, with regions that are relatively crystalline alternating with amorphous structures.
The last (short) talk of the day was also quite interesting. Carlos Drummond (U. of Bordeaux, France, no web page?) talked about "Ions-Induced Nanostructuration of Hydrophobic Polymer Surfaces". The portion about ions flew over my head (the talk itself went too quickly), but he showed AFM images of pancake-like "nanodroplets" of radius ~50 nm on the surface of polystyrene film on a wafer. I've heard of these before, but why are they stable? One cartoon he suggestively sketched is that the surface is rough, and the nanobubbles are spanning concave regions of the surface. They have lots of experiments on the formation of gas droplets on surfaces. One interesting set of AFM images is a time-evolution series over 7 days, where the nanodroplets slowly disappear. So perhaps they're not thermodynamically stable, just kinetically slow to relax. When I asked him about it, he confirmed that the drops are not thermodynamically stable, they are simply very long-lived.
Finally, Alenka Luzar passed on to me the reference for the first paper that showed dewetting in the context of proteins (not Bruce Berne in Nature 2005!): Huang, Ding, Hua, Yang, Chen, J. Chem. Phys. 121 (4), 1969 (2004).
WATSURF 2013: Day 6
[Ack! Somehow the final draft of this post did not get saved (or posted) last night!]
After a weekend pause, we continue to discuss "Water/Surface Model/Biological Interfaces 1", whatever that means! I took notes on the first two spectroscopy talks of the day and a later one on dewetting.
Today, Stephen Meech (East Anglia, UK) gave a talk on "Time Domain Optical Kerr Effect (OKE) Studies of Aqueous Solvation: Ions to Proteins". This is the experiment that corresponds to measuring polarisability anisotropy correlation functions (which Branka Ladanyi talked about on Day 5). The measurement results from measuring the third-order electric susceptibility (chi^(3)) through a pump-probe experiment (the second-order susceptibility, chi^(2), which plays the starring role in sum-frequency generation, is zero in the bulk medium, so chi^(3) is the lowest-order non-linear effect in the bulk). The basic idea is to get two pump signals to polarize the sample (changing its refractive index), then use the probe signal to pass through this polarized material and measure the transmitted intensity. Meech's slides have all the details (there are many), and the slides will hopefully be on the WatSurf 2013 website within a few days. Essentially, OKE, is a time domain Raman spectroscopy. You get a plot of intensity vs. delay between pump and probe (ps). In a few fs, you get electronic transitions. Within 1/4 ps, you get librations. Then for delay times > ~ 1/4 ps, you see lots of vibrations. In the long time limit, you get diffusive reorientation. If you FT the signal, you get the low wavenumber Raman spectrum (< ~200 cm^-1).
Really, what you measure is the following response function (Branka Ladanyi is the person who has worked out how to read OKE signal from simulations):
R_ijkl^(3) ~ -beta d/dt < Pi_ij(t) Pi_kl(0) >,
where Pi is the sample's polarizability tensor. You can split Pi into molecular and intermolecular part. It's impossible to measure these separately, but you can get some information about the splitting through games with polarization of the pump and probe signal. In water, the molecular polarisability is essentially isotropic, so most of the OKE signal is due to interaction-induced translational dynamics. What do you get?
The FT of the OKE signal shows dynamics of H-bond modes: bending-like motions around 50 cm^-1, stretching-like motions around 150 cm^-1 (really very collective modes), some (weak) libration-like motions around 500 cm^-1. Back in the time domain, there's a long exponential decay tail for delay times between ~0.5ps to 3-4 ps. This is related to the relaxation of the H-bond network accompanying reorientation (his explanation made a connection to the Laage-Hynes jump model).
He did some OKE measurements in solutions of urea (hydrophilic) and TMAO (amphiphilic) to look at relaxation of water in their presence. Work at low mole fractions < 0.02 to avoid OKE signal having a large component due to the osmolytes themselves. Urea seems to change the H-bonding modes significantly, but TMAO barely changes it (even at 4M TMAO). One explanation might be that TMAO is forming micellar structures, and so most waters are really in a bulk-like environment. By looking at average relaxation time of water, and decomposing it as a linear combination of "bulk-like" relaxation and "solvation-shell-like" relaxation, weighted by molar ratio of solute to solvent (very hand-wavy), find that solvation water relaxes 3-6 times more slowly than in bulk water, more so in a hydrophilic solute (urea).
Summary of his conclusions: solutes generally retard dynamics in solvation shell; retardation greatest near hydrophilic solutes; H-bond structures (as seen in bimodal THz repsonse) is persistent, especially for hydrophobic solutes.
Now switch to hydration around peptides: NAGMA (hydrophilic everywhere, hydration shell has ~30-35 water molecules) and NALMA (hydrophobic leucine residue in the middle), hydration shell with 40-45 waters. What do you see? Water structure is disrupted when half water molecules have peptide as nearest neighbour. At high concentration (3M) of NAGMA, find very slow relaxation dynamics (but may be due to peptide itself). At low concentrations, can do the previous separation of bulk- and hydration-layer-like dynamics. Find solvation shell relaxation time of 12 ps for NAGMA, 7 ps for NALMA (were ~4 ps for urea and ~1ps for TMAO). They're 10-20 slower than bulk water. Perhaps due to multiple H-bond sites?
What about larger proteins? BSA, lysozyme, trypsin. For low concetrations of peptide (few % by wt), find relaxation times of 3-4 ps for water in solvation shells around these proteins. Ordering is consistent with higher hydrophilic surface areas leading to more retardation. There may be small populations of waters that are highly retarded, but the experiment is not sensitive to them.
Now for something completely different: Time-Resolved Fluorescence. You put a fluorophore at a particular place on a molecule. Then, three measurements are possible: a) time-dependent frequency-solvation dynamics, b) t.d. population-reaction dynamics; c) t.d. orientation-rotational dynamics. These tell you something about the environment around the fluorophore, but the caveat is that the fluorophore perturbs that environment. Solvation dynamics follows the usual story: water around unexcited fluorophore at equilibrium; fluorophore excited and acquires a dipole; solvation structure slowly adjusts to new fluorophore state; so energy difference between excited and ground state changes with time. Measure that time-dependent shift in fluorescence frequency: S(t) = nu(t) - nu(infty) / (nu(0) - nu(infty)). Reaction dynamics measures the total emission as the fluorophore is changing, R(t) = int_0^t dt' nu(t'); gives some indication of the dynamics of the fluorophore change. They have lots of optical tricks to measure fluorescence response to probe with fs accuracy and blurred by ~50 fs pulse widths. Since the pulse width is known, they make an attempt at deconvoluting it. Also, they can really measure the *spectrum* of the light at any delay time, not just its intensity.
First experiment: Auramine O (goes bright to dark based on a single bond twist) confined to a 1-10 nm radius sphere of water (inside a reverse micelle). Even in a 10 nm micelle, AuO reaction is slowed down enormously -> AuO is sitting at the micelle interface, not the bulk. As you increase confinement below 3 nm diameter, reaction slows down substantially => all the water in the micelle starts looking like "interfacial" water instead of bulk water. [These reaction dynamics experiments can be fit very well with simple 1D reaction-diffusion model.] Effect of surfactant cation: only charge density (singly-charged vs. multiply-charged cations) makes a difference, the size of the cations is not very important. What about a neutral surfactant ("Igepal 5")? Essentially the same result as for singly-charged cations.
Thomas Elsaesser (Max Born Institute, Berlin, Germany) "Ultrafast Hydration Processes of DNA and Phospholipids". Focus on 2DIR of water around DNA, then energy exchange between DNA and its hydration shell, as well as hydration of reverse micelles.
H-bonding sites in DNA, in reverse order of importance: 1) outer region of phosphates (by far strongest), 2) inner region of phosphate, 3),4),5) typical H-bonding sites in ribose oxygen and carboxyl/amide groups in bases. "Fully hydrated DNA" = about 20-25 water molecules per base pair. Summary of established results about DNA hydration: A) rigid spine of water in minor groove, more flexible hydration in major groove & phosphate groups. B) H-bond lifetimes in first longer longer than in bulk (few ps - 500 ps). C) Residence times of water molecules in first shells > 50 ps. D) Water reorientation: sterically hindered in minor groove, ps timescale in major groove. E) Highly controversial: "biological water" (outrageously rigid water, "100 ps - several ns" lifetime of some waters as measured by solvation dynamics experiments, A. Zewail PNAS 100, 8113 (2003)) -> controversial because slowing down is attributed entirely to water and not to DNA response [perhaps tenable at < 10 ps, but not for long times] and because the probe chromophore affects the water around the DNA.
To test this idea of biological water, use non-invasive probe: vibrational spectra. Typical vibration periods: OH / N-H stretch = 3000 cm^-1 = ~ 0.01 ps; H-bond mode <= 300 cm^-1 = ~0.1-0.5 ps. In a fluctuating electrical environment like that provided by surrounding waters, the energy levels between vibrational modes changes all the time, which leads to dephasing, spectral diffusion ~0.05 ps to several ps. Early MD simulations from Laage & Hynes tracked changes in, say, OH stretch frequency nu_1(t). Oscillates about the average value by about +/- 250 cm^-1, and is well explained by perturbation theory (?). The time-correlation function of nu_1(t) can be measured by 2D-FTIR. But not only do you have this spectral diffusion, you have couplings between different transitions. An excitation at nu_e leads to an emmision at nu_d, where a) if the waiting time is almost zero, nu_d =~= nu_e; b) spectral diffusion at long times leads to nu_d and nu_e being mostly uncorrelated; c) coupling between transitions shows up as "jumps" between values of nu_d and nu_e without much signal at intermediate frequencies.
He then explained how you actually measure 2D-FTIR spectra using Photon Echo Spectroscopy: so many pulses!!! Sequence of three pulses, with delay tau between first two, delay T between next two. Think of two states |i> and |f>. Pulse 1 generates a coherent superposition of 2 quantum states ('coherence' or polarization). Pulse 2 transform coherence into population. Pulse 3 generates coherence in the excited sample after population time T. Coherence radiates signal field.
What are the results? At short T and 31 C, you get a fast spectral diffusion of < 100 fs due to hugh-frequency librations and resonant energy transfer between different molecules. As you lower temperature, you get an overall slowing down of spectral diffusion, spectral diffusion at ~3300 cm^-1 is slower (i.e. longer correlations between excitation and detected frequencies without jumps due to coupled transitions). Vibrational lifetimes: OH stretch 200 fs, OH bend 170 fs, Librations < 100 fs. Energy dissipation ('hot ground state'): 0.7-1ps.
Now add DNA to the system. Use 23-bp DNA with alternative A-T-A-T-... sequences, with Na+ counterions replaced by surfactant molecules (CTMA) (if you just put DNA in 55 M water, the water spectrum completely dominates, so using a surfactant allows you to make thin films of "hydrated" DNA). DNA film is placed in a humidity cell, and which allows control of water content in film. The main change as a function of relative humidity (r.h.) is that the 3000-3500 cm^-1 region of the IR spectrum shoots up (at 0% r.h., there's still signal there owing to N-H stretches in DNA itself). More detailed features: PO_3 symmetric and asymmetric stretches (1000-1300 cm^-1) change. Asymmetric stretch shifts to higher wavenumbers at higher r.h. => water interacts strongly with phosphate group.
At this point, he showed *lots* of 2D-FTIR spectra of DNA with varying r.h. His main conclusions were: there is a very 'rigid' first solvation shell, with a slow resonant energy transfer due to reduced water concentration (10M). The water around the DNA forms an efficient heat sink for excitations in the DNA. In reverse micelles, there are very slow structural fluctuations in the first hydration shells.
Alenka Luzar (Virginia Commonwealth University, USA) talked about "Tunable Hydration at the Nanoscale". Talked a bit about dewetting and summarized the usual argument for critical distance between two plates for dewetting to happen (including pressure and solid-liquid attraction). Emphasized the kinetic bottleneck to dewet a very large gap between two hydrocarbon plates. The free energy barrier to dewet scales as (separation)^2 / cos(theta_contact) => spontaneous dewetting only occurs for molecular distances. Main point: hydrophobic interaction under kinetic control. Interesting factoid: the first simulations of dewetting in proteins weren't the melittin ones by Bruce Berne's group in 2005, but by Huang, Ding, Hua, Yang, Chen, J. Chem. Phys. 121 (4), 1969 (2004). Rationalize critical distance almost quantitatively for melittin dimers between Berne & Rossky (curved vs "flattened") melittin entirely in terms of hemispheric vs flat geometry of surfaces involved. Main point: the modified Kelvin equation works down to molecular length scales.
She then looked at how dewetting is affected by the presence of surface charges (after all, this is how nature does it in protein-protein interactions). What does an electric field do? Well, in the usual two-plate setup to analyse dewetting, it adds a bulk pressure-like energy term eps_0 |E|^2 / 2 to the wet state (electrostriction: an electric field can cause water to fill pores that it wouldn't otherwise enter), which reduces the critical distance to dewetting. This is a macroscopic argument. Microscopically, however, waters at a surface have a nonisotropic orientation distribution, so the reaction to parallel and perpendicular electric fields is different. For example, when you apply an electric field perpendicular to the normal of the plate surfaces, nothing special happens. But when you apply an electric field parallel to the plate normal, the water density becomes asymmetric: waters orient along direction of E, which costs a lot of bonds on one side (the one with higher electrical potential, which, therefore, looks hydrophobic) but not so many on the other side (which, therefore, looks relatively hydrophilic). In fact, the water exerts a *torque* on the walls: water would be happier if it didn't have to make a choice between aligning with the E-field and forming lots of H-bonds, and one way to achieve that is to rotate the *walls* so that the normal is perpendicular to the E-field. The size of this torque is about twice as large as predicted by a simple continuum theory (where the water and solute have different dielectric constants), owing to these microscopic surface effects. This suggest a way to align particles in water. Reaction times to "low" applied fields (0.02 V / A) is on the order of hundreds of ps for plates of a few nanometres in size.
Finally, some publicity for the Liquids 2014 - 9th Liquid Matter Conference:
"The 9th Liquid Matter Conference will take place at the University of Lisbon, Portugal, 21-25 July 2014. Previous conferences in this series were held in Lyon (1990), Firenze (1993), Norwich (1996), Granada (1999), Konstanz (2002), Utrecht (2005), Lund (2008), and Vienna (2011). The conference is organized jointly by the Liquids Section of the Condensed Matter Division of the European Physical Society, the University of Lisbon, and the School of Engineering of the Lisbon Polytechnic Institute (ISEL).
The conference will consist of plenary lectures, topical symposia with keynote lectures and contributed oral presentations, as well as poster sessions. The highlights of the conference will be published in a special issue of the Journal of Physics: Condensed Matter.
Topics
1 Ionic Liquids and Liquid Metals
2. Water and Solutions
3. Liquid Crystals
4. Polymers, Polyelectrolytes, Biopolymers
5. Colloids
6 Films, Foams, Surfactants, Emulsions
7. Confined Fluids, Interfacial Phenomena
8. Supercooled Liquids, Glasses, Gels
9 Driven Systems, Rheology and Nanofluidics
10. Active Matter
11. Biological and Biomimetic Fluids
For all relevant information please visit our website at http://www.fc.ul.pt/en/conferencia/liquids-2014
You can download the First Circular from http://www.fc.ul.pt/sites/default/files/fcul/public/firstcircl.pdf"
After a weekend pause, we continue to discuss "Water/Surface Model/Biological Interfaces 1", whatever that means! I took notes on the first two spectroscopy talks of the day and a later one on dewetting.
Today, Stephen Meech (East Anglia, UK) gave a talk on "Time Domain Optical Kerr Effect (OKE) Studies of Aqueous Solvation: Ions to Proteins". This is the experiment that corresponds to measuring polarisability anisotropy correlation functions (which Branka Ladanyi talked about on Day 5). The measurement results from measuring the third-order electric susceptibility (chi^(3)) through a pump-probe experiment (the second-order susceptibility, chi^(2), which plays the starring role in sum-frequency generation, is zero in the bulk medium, so chi^(3) is the lowest-order non-linear effect in the bulk). The basic idea is to get two pump signals to polarize the sample (changing its refractive index), then use the probe signal to pass through this polarized material and measure the transmitted intensity. Meech's slides have all the details (there are many), and the slides will hopefully be on the WatSurf 2013 website within a few days. Essentially, OKE, is a time domain Raman spectroscopy. You get a plot of intensity vs. delay between pump and probe (ps). In a few fs, you get electronic transitions. Within 1/4 ps, you get librations. Then for delay times > ~ 1/4 ps, you see lots of vibrations. In the long time limit, you get diffusive reorientation. If you FT the signal, you get the low wavenumber Raman spectrum (< ~200 cm^-1).
Really, what you measure is the following response function (Branka Ladanyi is the person who has worked out how to read OKE signal from simulations):
R_ijkl^(3) ~ -beta d/dt < Pi_ij(t) Pi_kl(0) >,
where Pi is the sample's polarizability tensor. You can split Pi into molecular and intermolecular part. It's impossible to measure these separately, but you can get some information about the splitting through games with polarization of the pump and probe signal. In water, the molecular polarisability is essentially isotropic, so most of the OKE signal is due to interaction-induced translational dynamics. What do you get?
The FT of the OKE signal shows dynamics of H-bond modes: bending-like motions around 50 cm^-1, stretching-like motions around 150 cm^-1 (really very collective modes), some (weak) libration-like motions around 500 cm^-1. Back in the time domain, there's a long exponential decay tail for delay times between ~0.5ps to 3-4 ps. This is related to the relaxation of the H-bond network accompanying reorientation (his explanation made a connection to the Laage-Hynes jump model).
He did some OKE measurements in solutions of urea (hydrophilic) and TMAO (amphiphilic) to look at relaxation of water in their presence. Work at low mole fractions < 0.02 to avoid OKE signal having a large component due to the osmolytes themselves. Urea seems to change the H-bonding modes significantly, but TMAO barely changes it (even at 4M TMAO). One explanation might be that TMAO is forming micellar structures, and so most waters are really in a bulk-like environment. By looking at average relaxation time of water, and decomposing it as a linear combination of "bulk-like" relaxation and "solvation-shell-like" relaxation, weighted by molar ratio of solute to solvent (very hand-wavy), find that solvation water relaxes 3-6 times more slowly than in bulk water, more so in a hydrophilic solute (urea).
Summary of his conclusions: solutes generally retard dynamics in solvation shell; retardation greatest near hydrophilic solutes; H-bond structures (as seen in bimodal THz repsonse) is persistent, especially for hydrophobic solutes.
Now switch to hydration around peptides: NAGMA (hydrophilic everywhere, hydration shell has ~30-35 water molecules) and NALMA (hydrophobic leucine residue in the middle), hydration shell with 40-45 waters. What do you see? Water structure is disrupted when half water molecules have peptide as nearest neighbour. At high concentration (3M) of NAGMA, find very slow relaxation dynamics (but may be due to peptide itself). At low concentrations, can do the previous separation of bulk- and hydration-layer-like dynamics. Find solvation shell relaxation time of 12 ps for NAGMA, 7 ps for NALMA (were ~4 ps for urea and ~1ps for TMAO). They're 10-20 slower than bulk water. Perhaps due to multiple H-bond sites?
What about larger proteins? BSA, lysozyme, trypsin. For low concetrations of peptide (few % by wt), find relaxation times of 3-4 ps for water in solvation shells around these proteins. Ordering is consistent with higher hydrophilic surface areas leading to more retardation. There may be small populations of waters that are highly retarded, but the experiment is not sensitive to them.
Now for something completely different: Time-Resolved Fluorescence. You put a fluorophore at a particular place on a molecule. Then, three measurements are possible: a) time-dependent frequency-solvation dynamics, b) t.d. population-reaction dynamics; c) t.d. orientation-rotational dynamics. These tell you something about the environment around the fluorophore, but the caveat is that the fluorophore perturbs that environment. Solvation dynamics follows the usual story: water around unexcited fluorophore at equilibrium; fluorophore excited and acquires a dipole; solvation structure slowly adjusts to new fluorophore state; so energy difference between excited and ground state changes with time. Measure that time-dependent shift in fluorescence frequency: S(t) = nu(t) - nu(infty) / (nu(0) - nu(infty)). Reaction dynamics measures the total emission as the fluorophore is changing, R(t) = int_0^t dt' nu(t'); gives some indication of the dynamics of the fluorophore change. They have lots of optical tricks to measure fluorescence response to probe with fs accuracy and blurred by ~50 fs pulse widths. Since the pulse width is known, they make an attempt at deconvoluting it. Also, they can really measure the *spectrum* of the light at any delay time, not just its intensity.
First experiment: Auramine O (goes bright to dark based on a single bond twist) confined to a 1-10 nm radius sphere of water (inside a reverse micelle). Even in a 10 nm micelle, AuO reaction is slowed down enormously -> AuO is sitting at the micelle interface, not the bulk. As you increase confinement below 3 nm diameter, reaction slows down substantially => all the water in the micelle starts looking like "interfacial" water instead of bulk water. [These reaction dynamics experiments can be fit very well with simple 1D reaction-diffusion model.] Effect of surfactant cation: only charge density (singly-charged vs. multiply-charged cations) makes a difference, the size of the cations is not very important. What about a neutral surfactant ("Igepal 5")? Essentially the same result as for singly-charged cations.
Thomas Elsaesser (Max Born Institute, Berlin, Germany) "Ultrafast Hydration Processes of DNA and Phospholipids". Focus on 2DIR of water around DNA, then energy exchange between DNA and its hydration shell, as well as hydration of reverse micelles.
H-bonding sites in DNA, in reverse order of importance: 1) outer region of phosphates (by far strongest), 2) inner region of phosphate, 3),4),5) typical H-bonding sites in ribose oxygen and carboxyl/amide groups in bases. "Fully hydrated DNA" = about 20-25 water molecules per base pair. Summary of established results about DNA hydration: A) rigid spine of water in minor groove, more flexible hydration in major groove & phosphate groups. B) H-bond lifetimes in first longer longer than in bulk (few ps - 500 ps). C) Residence times of water molecules in first shells > 50 ps. D) Water reorientation: sterically hindered in minor groove, ps timescale in major groove. E) Highly controversial: "biological water" (outrageously rigid water, "100 ps - several ns" lifetime of some waters as measured by solvation dynamics experiments, A. Zewail PNAS 100, 8113 (2003)) -> controversial because slowing down is attributed entirely to water and not to DNA response [perhaps tenable at < 10 ps, but not for long times] and because the probe chromophore affects the water around the DNA.
To test this idea of biological water, use non-invasive probe: vibrational spectra. Typical vibration periods: OH / N-H stretch = 3000 cm^-1 = ~ 0.01 ps; H-bond mode <= 300 cm^-1 = ~0.1-0.5 ps. In a fluctuating electrical environment like that provided by surrounding waters, the energy levels between vibrational modes changes all the time, which leads to dephasing, spectral diffusion ~0.05 ps to several ps. Early MD simulations from Laage & Hynes tracked changes in, say, OH stretch frequency nu_1(t). Oscillates about the average value by about +/- 250 cm^-1, and is well explained by perturbation theory (?). The time-correlation function of nu_1(t) can be measured by 2D-FTIR. But not only do you have this spectral diffusion, you have couplings between different transitions. An excitation at nu_e leads to an emmision at nu_d, where a) if the waiting time is almost zero, nu_d =~= nu_e; b) spectral diffusion at long times leads to nu_d and nu_e being mostly uncorrelated; c) coupling between transitions shows up as "jumps" between values of nu_d and nu_e without much signal at intermediate frequencies.
He then explained how you actually measure 2D-FTIR spectra using Photon Echo Spectroscopy: so many pulses!!! Sequence of three pulses, with delay tau between first two, delay T between next two. Think of two states |i> and |f>. Pulse 1 generates a coherent superposition of 2 quantum states ('coherence' or polarization). Pulse 2 transform coherence into population. Pulse 3 generates coherence in the excited sample after population time T. Coherence radiates signal field.
What are the results? At short T and 31 C, you get a fast spectral diffusion of < 100 fs due to hugh-frequency librations and resonant energy transfer between different molecules. As you lower temperature, you get an overall slowing down of spectral diffusion, spectral diffusion at ~3300 cm^-1 is slower (i.e. longer correlations between excitation and detected frequencies without jumps due to coupled transitions). Vibrational lifetimes: OH stretch 200 fs, OH bend 170 fs, Librations < 100 fs. Energy dissipation ('hot ground state'): 0.7-1ps.
Now add DNA to the system. Use 23-bp DNA with alternative A-T-A-T-... sequences, with Na+ counterions replaced by surfactant molecules (CTMA) (if you just put DNA in 55 M water, the water spectrum completely dominates, so using a surfactant allows you to make thin films of "hydrated" DNA). DNA film is placed in a humidity cell, and which allows control of water content in film. The main change as a function of relative humidity (r.h.) is that the 3000-3500 cm^-1 region of the IR spectrum shoots up (at 0% r.h., there's still signal there owing to N-H stretches in DNA itself). More detailed features: PO_3 symmetric and asymmetric stretches (1000-1300 cm^-1) change. Asymmetric stretch shifts to higher wavenumbers at higher r.h. => water interacts strongly with phosphate group.
At this point, he showed *lots* of 2D-FTIR spectra of DNA with varying r.h. His main conclusions were: there is a very 'rigid' first solvation shell, with a slow resonant energy transfer due to reduced water concentration (10M). The water around the DNA forms an efficient heat sink for excitations in the DNA. In reverse micelles, there are very slow structural fluctuations in the first hydration shells.
Alenka Luzar (Virginia Commonwealth University, USA) talked about "Tunable Hydration at the Nanoscale". Talked a bit about dewetting and summarized the usual argument for critical distance between two plates for dewetting to happen (including pressure and solid-liquid attraction). Emphasized the kinetic bottleneck to dewet a very large gap between two hydrocarbon plates. The free energy barrier to dewet scales as (separation)^2 / cos(theta_contact) => spontaneous dewetting only occurs for molecular distances. Main point: hydrophobic interaction under kinetic control. Interesting factoid: the first simulations of dewetting in proteins weren't the melittin ones by Bruce Berne's group in 2005, but by Huang, Ding, Hua, Yang, Chen, J. Chem. Phys. 121 (4), 1969 (2004). Rationalize critical distance almost quantitatively for melittin dimers between Berne & Rossky (curved vs "flattened") melittin entirely in terms of hemispheric vs flat geometry of surfaces involved. Main point: the modified Kelvin equation works down to molecular length scales.
She then looked at how dewetting is affected by the presence of surface charges (after all, this is how nature does it in protein-protein interactions). What does an electric field do? Well, in the usual two-plate setup to analyse dewetting, it adds a bulk pressure-like energy term eps_0 |E|^2 / 2 to the wet state (electrostriction: an electric field can cause water to fill pores that it wouldn't otherwise enter), which reduces the critical distance to dewetting. This is a macroscopic argument. Microscopically, however, waters at a surface have a nonisotropic orientation distribution, so the reaction to parallel and perpendicular electric fields is different. For example, when you apply an electric field perpendicular to the normal of the plate surfaces, nothing special happens. But when you apply an electric field parallel to the plate normal, the water density becomes asymmetric: waters orient along direction of E, which costs a lot of bonds on one side (the one with higher electrical potential, which, therefore, looks hydrophobic) but not so many on the other side (which, therefore, looks relatively hydrophilic). In fact, the water exerts a *torque* on the walls: water would be happier if it didn't have to make a choice between aligning with the E-field and forming lots of H-bonds, and one way to achieve that is to rotate the *walls* so that the normal is perpendicular to the E-field. The size of this torque is about twice as large as predicted by a simple continuum theory (where the water and solute have different dielectric constants), owing to these microscopic surface effects. This suggest a way to align particles in water. Reaction times to "low" applied fields (0.02 V / A) is on the order of hundreds of ps for plates of a few nanometres in size.
Finally, some publicity for the Liquids 2014 - 9th Liquid Matter Conference:
"The 9th Liquid Matter Conference will take place at the University of Lisbon, Portugal, 21-25 July 2014. Previous conferences in this series were held in Lyon (1990), Firenze (1993), Norwich (1996), Granada (1999), Konstanz (2002), Utrecht (2005), Lund (2008), and Vienna (2011). The conference is organized jointly by the Liquids Section of the Condensed Matter Division of the European Physical Society, the University of Lisbon, and the School of Engineering of the Lisbon Polytechnic Institute (ISEL).
The conference will consist of plenary lectures, topical symposia with keynote lectures and contributed oral presentations, as well as poster sessions. The highlights of the conference will be published in a special issue of the Journal of Physics: Condensed Matter.
Topics
1 Ionic Liquids and Liquid Metals
2. Water and Solutions
3. Liquid Crystals
4. Polymers, Polyelectrolytes, Biopolymers
5. Colloids
6 Films, Foams, Surfactants, Emulsions
7. Confined Fluids, Interfacial Phenomena
8. Supercooled Liquids, Glasses, Gels
9 Driven Systems, Rheology and Nanofluidics
10. Active Matter
11. Biological and Biomimetic Fluids
For all relevant information please visit our website at http://www.fc.ul.pt/en/conferencia/liquids-2014
You can download the First Circular from http://www.fc.ul.pt/sites/default/files/fcul/public/firstcircl.pdf"
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