Diffusion of hydrophobin proteins in solution and interactions with a graphite surface
© Mereghetti and Wad; licensee BioMed Central Ltd. 2011
Received: 31 January 2011
Accepted: 21 April 2011
Published: 21 April 2011
Hydrophobins are small proteins produced by filamentous fungi that have a variety of biological functions including coating of spores and surface adhesion. To accomplish these functions, they rely on unique interface-binding properties. Using atomic-detail implicit solvent rigid-body Brownian dynamics simulations, we studied the diffusion of HFBI, a class II hydrophobin from Trichoderma reesei, in aqueous solution in the presence and absence of a graphite surface.
In the simulations, HFBI exists in solution as a mixture of monomers in equilibrium with different types of oligomers. The oligomerization state depends on the conformation of HFBI. When a Highly Ordered Pyrolytic Graphite (HOPG) layer is present in the simulated system, HFBI tends to interact with the HOPG layer through a hydrophobic patch on the protein.
From the simulations of HFBI solutions, we identify a tetrameric encounter complex stabilized by non-polar interactions between the aliphatic residues in the hydrophobic patch on HFBI. After the formation of the encounter complex, a local structural rearrangement at the protein interfaces is required to obtain the tetrameric arrangement seen in HFBI crystals. Simulations performed with the graphite surface show that, due to a combination of a geometric hindrance and the interaction of the aliphatic sidechains with the graphite layer, HFBI proteins tend to accumulate close to the hydrophobic surface.
Hydrophobins are small (7-15 kDa) proteins produced by filamentous fungi . They perform a range of biological roles including coating of spores and surface adhesion [2, 3]. Except for Botrytis cinerea, where their function is unknown , hydrophobins lower the surface tension of water so that fungal hyphae can penetrate the air-water interface and grow outside aqueous media . To carry out these functions, they rely on unique surface/interface binding properties [1, 3, 6–8]. Besides their peculiar surface properties, which make them the most powerful surface-active proteins known , they also display unusual behaviour in solution as they form different kinds of oligomers, depending on the conditions and on the hydrophobin type [9, 10]. Hydrophobins have been divided into two classes, class I and class II, based on the hydropathy profile of the amino-acid sequence . This classification is also consistent with other properties. In particular, class I hydrophobins are more resistant to dissociation using solvents and detergents than class II hydrophobins. Furthermore, class I hydrophobins tend to form rodlet-like aggregates at interfaces, whereas class II hydrophobins do not. Although, the different types of hydrophobins show a great variability in aminoacid sequence (with sequence identity sometimes as low as 30% ), they all present a characteristic pattern of four disulfide bridges formed by eight conserved cysteines . This disulfide bridge pattern is common to all known class I and class II hydrophobins. For a recent review on hydrophobins, see ref. .
The hydrophobin HFBI from Trichoderma reesei, which will be considered in this study, belongs to class II hydrophobins and its three-dimensional structure has been determined by X-ray crystallography by Hakanpää et al. at 2.1 Å resolution . HFBI has an amphipathic structure, with a large (≈750 Å2) solvent-exposed hydrophobic patch containing aliphatic residues and a hydrophilic region composed of polar residues. It is likely that the unfavorable exposure of many hydrophobic residues to the aqueous solvent, is compensated by the four disulfide bridges [3, 9].
In the crystal structure, HFBI forms homotetramers. In solutions, HFBI forms oligomers in a concentration-dependent manner, and in particular, it has been shown that dimers and tetramers are present in aqueous solution at protein concentrations (2-20 g/L) . At protein concentrations below 2 g/L, HFBI is monomeric in solution [13, 14].
Hydrophobins adsorb on to various types of surfaces, forming regular structures (e.g. hexagonal patterns) or randomly aligned rodlets [3, 8, 10, 15–17]. In a growing number of works, hydrophobins are applied for surface modification and in biosensor development [17–20]. The surface and interfacial activity of HFBI proteins has been studied from experimental [8, 13, 16] and theoretical [21, 22] points of view. From these studies, a general model for how hydrophobins function has emerged. Hydrophobins are soluble in aqueous solution and they form different types of oligomers. Close to an interface, the oligomers dissociate and adsorb on to the surface. The secondary structure of HFBI does not change upon adsorption or self-assembly [8, 23]. However minor changes in the orientations of sidechains  or loops  do occur. It has been suggested that these local rearrangements and, in particular, the conformational change of loop 60-66, are induced by multimer formation .
The aim of this work is to shed light on the mechanisms of self-association of HFBI in solution and its adsorption onto a hydrophobic surface. A detailed understanding of these mechanisms can be relevant not only for a better understanding of the biological function of hydrophobins but also for the potential biotechnological application of these macromolecules.
In the next section, we discuss the results obtained from simulations of HFBI solutions at different concentrations. Then, results on the interaction of HFBI solutions with a graphite surface are presented.
Results and Discussion
Solution properties of HFBI
The properties of HFBI in aqueous solution were studied by means of simulations at protein concentrations of 2, 5, 10 and 20 g/L. According to the experimental conditions used in ref. , a pH of 5 and ionic strength of 50 mM were assumed. Two sets of simulations were done at each protein concentration. In one set, all proteins were in conformation A (corresponding to chain A in the crystal structure) while in the second set, a mixture of 50% conformation A and 50% conformation B (corresponding to chain B in the crystal structure) was simulated. The reason for this, as described in the Methods section, is that the four chains in the HBFI tetramer observed in the asymmetric unit of the crystal structure can be divided into two groups based on the conformation of the second β-hairpin (loop 60-66) and on their electrostatic potential .
Tetrameric encounter complexes
The tetramers identified in the simulations can be regarded as encounter complexes. The crystal and the solution structures differ due to several reasons. First, in general, the crystal structure may be different from the structure in solution because of the crystallization procedure. A structure of the tetramer of HFBI in solution was derived in the work of Kisko et al.  using small angle X-ray scattering. In that work, the authors obtained a low resolution model in which the monomeric units composing the tetramers were arranged in a somewhat different way that the crystal structure; the two pairs of dimers lay almost in the same plane and the resultant modelled tetramer was flat . Cluster analysis of the tetramers revealed that flat tetramers do not occur in our simulations. Another possibility, which explains the differences in the tetramers we found in simulations from the crystal structure, results from analysis of the inter-atomic contacts between the monomeric units as described later.
Hydrophobic surface interactions
Interaction with graphite layer
The interaction with a Highly Ordered Pyrolytic Graphite (HOPG) layer was studied by performing Brownian dynamics simulations of 16 hydrophobin molecules at 20 g/L concentration in the presence of a graphite layer. The HOPG layer was chosen, following Szilvay et al. , for its property of being flat and completely hydrophobic. Since the graphite is uncharged and hydrophobic, electrostatic interactions between proteins and the HOPG layer were not modelled; only the non-polar desolvation term and soft-core repulsion contributed to their interaction as described in the Methods Section. The oligomerization properties in simulations with and without the HOPG layer were compared. In the simulations with the surface, the fraction of higher order oligomers was higher than in the simulations without the graphite layer and the oligomers form in a layer very close to the graphite. This phenomenon can be explained by considering the distribution of hydrophobins in the simulation volume.
HFBI distribution in the simulation volume
Following the same procedure as for the protein-protein interactions, the residues involved in contact with the graphite surface were identified (see Figure 4C, D). As described in the Method Section, the parameter dc (distance cutoff) was set to 4.0 Å instead of 4.5 Å in the protein-protein case. Indeed, studies of peptide adsorption to a HOPG layer revealed that the residues can come very close to the surface (≈5 Å from the peptide backbone) [24, 25]. The interaction with the graphite surface occurs mainly via the hydrophobic region. The hydrophilic side of the protein tends to remain away from the surface due to the unfavorable desolvation energy of polar/charged residues. It has been found that the adsorption of peptides on HOPG layers is driven by the hydrophobic effect arising from the interaction of methylene groups in amino-acids sidechains with the graphite layer .
In the simulations, we identified six aliphatic residues interacting with the graphite; three leucines (Leu12, Leu26, and Leu68), one isoleucine (Ile27) one alanine (Ala66) and one valine (Val62). In an earlier molecular dynamics simulations study, five residues were found to drive the adsorption of HFBII (from Trichoderma reesei ) on a silicon surface: Leu19, Leu21, Ile22, Ala61 and Leu63 (numbering based on the sequence of HFBII) . Superimposing the structure of HFBI on HFBII, we found that Leu26(21), Ile27(22), Ala66(61) and Leu68(63) are common to the two sets (in parenthesis is the corresponding number in the HFBII sequence). In ref. , the authors also found that an aspartic acid (Asp59, HFBII numbering), which is substituted by a glycine in HFBI, determines the orientation of the adsorbed HFBII. In particular, Asp59, which sticks out from the boundary of the hydrophobic patch, drives the tilting of the hydrophobic patch to maximize the desolvation of aliphatic residues and allow solvation of Asp59 . The absence of Asp59 in HFBI leads to a different adsorption orientation. It is probable that the slightly lower binding affinity of HFBII  is related to the presence of Asp59 which reduces the contact area between the hydrophobic patch and the hydrophobic surface. From a multiple sequence alignment of class II hydrophobins , it is interesting to note that the aminoacid at position 59 (HFBII numbering) is conserved as a glycine with only two exceptions, in HFBII and MAG (from Magnaporthe grisea ), which have an aspartic acid instead.
In the simulations, some polar and charged aminoacids (Lys32, Asp30, Gln65, Gln70) were found to interact with the graphite layer, however, it is highly probable that these contacts were identified solely due to the extended conformations of their sidechains which stick out from the protein surface.
In this paper, Brownian dynamics simulations of solutions of the class II hydrophobin, HFBI, from Trichoderma reesei were performed. Simulations were carried out at several protein concentrations from 2 g/L to 20 g/L. Moreover, a graphite surface model was included in the simulations and the diffusional association of HFBI proteins to the graphite layer was studied.
In our simulations, hydrodynamic interactions were neglected since in dilute regimes (<0.1 volume fraction) it has been found that for almost spherical unconnected particles, solvent correlations do not affect the dynamic properties significantly. In particular, the experimental concentration dependent diffusion coefficients for different protein solutions can be qualitatively reproduced with Brownian dynamics simulations without hydrodynamic interactions [26–28]. The dynamics of the tetramerization process and the protein-surface adsorption may be affected by hydrodynamic interactions [29, 30].
Moreover, as discussed in the main text, the absence of explicit modelled solvent and ions may prevent exact reproduction of the bound state shown in the crystal structure. Despite these limitations, we found from the simulations that HFBI in solution exists as a mixture of different oligomeric states which form in a concentration dependent manner . The formation of stable tetramers, which is the most abundant type of oligomer seen experimentally , depends on the local structural rearrangement of a portion of the protein. Simulations which include two conformations (chain A and chain B) show a lower oligomerization propensity than simulations which include only one conformation (chain A). This suggests that the tetramerization mechanism is based on an induced fit model rather than a conformational selection.
Looking at the encounter complexes identified in the simulations, we can hypothesize the following mechanism for the formation of the tetramer. The monomeric unit exists in solutions in a chain A type conformation. The dipolar character of chain A drives the formation of the encounter complex which is stabilized by non-polar interactions between the aliphatic residues in the hydrophobic patch. Finally, a structural rearrangement at the protein interfaces of two of the monomers, allows bound tetramers to be obtained.
The simulations performed with a graphite layer show that HFBI proteins tend to remain close to the surface due to steric and hydrophobic contribution. The proximity of the HFBI proteins to the surface, in turn increases the probability of surface adsorption, explaining the higher affinity shown by hydrophobins to hydrophobic surfaces compared to solution association .
Interaction energies and forces
The first four terms in Eq. 1 are electrostatic terms, the fifth and sixth terms are nonpolar terms and the last two terms describe the soft-core repulsion. A detailed description and parameterization of Eq. 1 can be found in Refs. [26, 31]. In equations 1 and 2, Φ are the interaction potentials, q are the effective charges , A is the solvent accessible surface area and r are the atomic coordinates. For computational efficiency, all interaction potentials, Φ, as well as the soft-core repulsion, E sc , were mapped onto grids.
where 1 and 2 correspond to the surface and the protein respectively.
where r i is the center of atom i of radius a i and r is a given grid point. The term removes the singularity at |r - r i | = 0 and gives a smooth function. The value of σ can be tuned to vary the smoothness of the function, keeping its asymptotic behaviour. γ is a parameter to rescale the magnitude of the function and nexp is the exponent to set the decay. Details of these parameters can be found in ref. .
In simulations with only proteins the parameters were set to nexp = 6, σ = 3.0 Å, γ = 64.0 kcal/mol as described in ref. . For the simulations which include the surface, the smoothness of the potential was increased by rescaling the parameter σ to 1.5 Å. This was necessary because of the higher probability of the proteins to remain trapped between the surface and other proteins close to the surface, which increases the probability of clashes. To compensate the larger inter-penetrability of the proteins, which leads to a stronger short-range attraction, we adjusted the non-polar desolvation term. The non-polar interaction is due to the burial of the solvent accessible surface areas of the surface atoms of one protein by the other protein. This includes a factor β for converting the calculated buried area to hydrophobic desolvation energy , which was set to β = -0.018 kcal mol-1 Å-2 in protein only simulations and to β = -0.013 kcal mol-1 Å-2 for protein-graphite simulations. These values are within the range used in ref. [26, 31] for protein-protein interactions. One microsecond test simulations showed that a value of σ of 1.5 Å with a value of β within this range could be used for the simulations of the protein solutions and could reproduce radial distribution functions and diffusion coefficients.
The crystal structure of HFBI was taken from the Protein Data Bank (ref): 2FZ6. The crystallographic structure is tetrameric. The 4 conformationally non-identical chains in the asymmetric unit can be grouped into two types based on the conformation of the second β-hairpin (loop 60-66) . In particular, one conformation is presented by chains A and C which have a C α RMSD of 0.45 Å and the second conformation is shared by chains B and D which possess an extended conformation of the second β-hairpin as well as some other subtle differences since the C α RMSD between B and D is 1.00 Å. Superimposing molecule A or C with B or D gives an average C α RMSD of 2.53 Å.
Physical properties of monomeric HFBI
R g (Å)
R h (Å)
Polar hydrogens were added to the structures according to the specified pH and ionic strength using H++ . All simulations were performed at pH = 5 and IS = 50 mM. In these conditions, the net charge is zero for both chain A and chain B.
Partial charges and radii were assigned to all the atoms from the OPLS force field . Electrostatic potential grids Φ were computed by solving the linearized Poisson-Boltzmann equation using UHBD . The grid size was set to 100 Å with a grid spacing of 1.0 Å. In the protein-protein simulations, electrostatic and non-polar desolvation grids of HFBI were set to 80 Å, grid spacing 1.0 Å. The soft-core repulsion grid size was set to 60 Å and grid spacing 1.0 Å.
A three layer Highly Ordered Pyrolytic Graphite (HOPG) was generated using a python script . The size of the surface was set to 200 × 200 Å2. Non-polar desolvation, electrostatic desolvation and soft-core repulsion grids were set to 200 × 200 × 60 Å3 with a grid spacing of 0.5 Å.
Brownian dynamics simulations
The positions and orientations of the particles were propagated using the Ermak-McCammon  algorithm.
BD simulations were carried out using 16 or 512 proteins that were initially randomly positioned (avoiding overlaps) in a rectangular box with periodic boundary conditions. The dimensions of the box were varied according to the concentration of the protein solution. In the case of protein-surface simulations, the surface was placed at the bottom of the simulation box and we considered periodic boundaries for the sides of the box and reflective boundaries for the top of the simulation box.
Each system was subjected to 10 μs of simulation at 300 K. Equilibration was assessed by monitoring the convergence of the radial distribution function and the stabilization of the energies. In all cases, 1 μs was sufficient to obtain an equilibrated system according to these criteria and the remaining 9 μs were used for the analysis. The integration timestep was 0.5 ps. The positions and orientations of the proteins were recorded along with energy values every 0.5 ns.
For further details, see .
Oligomer analysis and clustering: computational details
An average fraction of each oligomeric species was computed by recording the occurrence of the oligomeric states at each step of the simulation and then averaging over the total number of steps. An oligomer is defined as a group of two or more proteins which are in contact with each other. A contact is defined following the "atomic contact criterion" for the definition of encounter complexes described in Ref . Namely, an encounter complex is formed when at least N ind independent contacts between two proteins occur. A contact is established when the centers of two atoms (one from each protein) are closer than a given cutoff, dc. The independence of the contacts is ensured by considering only atoms within the protein that are further from each other than a distance, d min . Following Ref. , we set N ind = 2, d c = 4.5 Å and d min = 6.0 Å. Cluster analysis was carried out to find the most favorable orientations in each oligomeric species. We first superpose all oligomers by least square fitting on one reference chain (e.g. for tetramers, chain A was used as the reference chain). A distance matrix was obtained by computing the root mean square (rms) distance between all pairs of the oligomers (e.g. all tetramers). The rmsd was computed for all atoms of the complete oligomeric structure. The most similar oligomers were grouped together using the gromos clustering algorithm from the GROMACS software  with a cutoff of 15.0 Å. In addition to describing the tendency of each atom to be involved in a contact with another protein, the number of times an atom i was found within d c = 4.5 Å (protein-protein) or d c = 4.0 Å (protein-surface) of an atom of another protein was counted . A relative atomic contact probability was then obtained as .
Particularly relevant residues involved in a protein-protein or protein-surface interaction were identified by setting a threshold in the atomic contact probability. In particular, a residue was considered relevant if any of its atoms has a probability greater than the third quartile of the atomic contact probability distribution.
The distribution of proteins with respect to the surface was measured by computing a surface-protein distribution function averaged over the x-y dimensions and normalized by the bulk density.
Highly Ordered Pyrolytic Graphite
- Φ el (r):
- Φ ed (r):
electrostatic desolvation field
- Φ np (r):
non-polar desolvation field
- E sc (r):
This work was supported by the Klaus Tschira Foundation and the Center for Modelling and Simulation in the Biosciences (BIOMS) in Heidelberg and supercomputing time at the Environmental Molecular Science Laboratory (Grant No. 30994). We gratefully thank Daria Kokh, Michael Martinez and Razif Gabdoulline for useful discussions. We also acknowledge Stefan Richter for his help in code development.
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