Molecular Dynamics Studies of Protein Interaction with GoldCrystal Surface

Rosemary I. Braun

Preliminary Proposal for the Ph.D.

Department of Physics and Beckman Institute,University of Illinois at Urbana-Champaign,

405 N. Mathews Ave., Urbana, IL 61801, USA

Committee: Dr. R. Clegg (chairman), Dr. K. J. Schulten (advisor),Dr. P. Selvin, Dr. R. Martin

Examination Date: September 7, 2000

Abstract

The biological control of inorganic crystal morphology is of interest both to biologists study-ing hard tissue growth and to materials scientists working toward nanoscale control of crystalmorphology. Sarikaya et al. have developed a genetic system to isolate proteins which controlgold crystallization [1]. It was shown [2] that in the presence of gold binding protein (GBP)gold formed large, at hexagonal crystals displaying the f111g surface. No such crystals wereseen to form in the presence of control proteins which do not bind to gold.

It is hypothesized that GBP binds preferentially to the f111g Au surface, and that thecovering of the f111g face by the bound GBP plays a role in the mechanism by which GBPalters crystal morphology. It is not readily apparent how the GBP adheres to gold, nor why thef111g surface would be preferred to, for example, the more sparsely populated f211g face. Bothchemisorption (via GBP's methionine sulfurs) and physisorption (via polar side-chains) couldplay a role in the binding.

We have predicted structures for the three GBP sequences available using sequence similaritymethods in addition to the Holley-Karplus prediction method [3]. Of the three proteins, twoare seen to have repeating motifs which may be conducive to binding to a periodic surface. Wehave begun to carry out ab initio dynamics to study the interaction between the methionineand gold (the experimental literature is con icting on whether the methionine sulfur is likelyto form the bond). To investigate the role of physisorption, we have conducted free moleculardynamics simulations of GBP on both the f111g and f211g crystal surfaces, for both the case inwhich the methionine is bound and for which it is not. The solvated systems (� 13; 000 atoms)are simulated using NAMD[4]. The results of steered molecular dynamics [5, 6] of the unboundsystem, currently being carried out, will also be of interest.

1 Introduction

Genetically engineered polypeptides have been selected for their ability to bind to gold surfaces [1].

These gold binding proteins (GBPs) may be used to tailor the formation and assembly of nanoscale

ordered structures for use in electronic and materials applications; they may also serve as a con-

ceptual model for the macromolecular control of mineral crystallization observed in hard-tissue

growth.

The GBP sequences contain multiple repeats of a 14-30 amino acid sequence. The repeating

polypeptides retain their binding properties as part of other proteins if they contain a suÆcient

number of repeats, and aÆnity for gold increases with the number of repeats. Interestingly, none

of the GBP sequences contain cysteine, which is known to form a covalent bond to gold.

Gold crystallized in the presence of GBP exhibited the formation of large, at hexagonal crystals.

Electron di�raction [2] veri�ed that the broad surface was the f111g face. Conversely, no such

crystals were seen amongst the gold crystallized in the presence of a control protein. It is possible

that this is due to the GBP inhibiting accretion on the f111g surface by preferentially binding to

and obscuring it.

The mechanism by which GBP binds to gold is unknown. There exists some evidence [7]

that methionine is able to form a bond to gold via dissociation of the methyl group (constituting

chemisorption of the protein onto the surface). However, experiments performed on GBP in the

presence of a detergent [8] suggest that the binding is through the electrostatic interaction of the

polar side-chains with the metal surface. If GBP does bind preferentially to the f111g Au surface,

any increased aÆnity would likely be related to the interaction of the polar side-chains with the

Au atoms.

Atomic level models of biomolecules, in combination with a classical force �eld, have been

used for several decades to study proteins in their natural, solvated environments. Simulations

of biomolecular systems require a substantial investment of computational resources and yield

detailed information about the dynamics of the system on the time scale of several nanoseconds.

Such simulations have been successfully used to model physisorption of water onto gold surfaces;

similar techniques may be employed to model the physisorption of polar side chains. Although it

is possible to simulate both the chemisorbed and non-chemisorbed states of GBP on Au, ab initio

quantum mechanical methods must be used to fully understand the methionine-gold interaction.

Unlike classical molecular dynamics, ab initio simulations are severely consumptive of resources

1

even for very short calculations, making the simulation of more than a few atoms infeasible.

The proposed research will use molecular dynamics simulations and related computational tech-

niques to investigate the binding mechanism of GBP. Simulations will be based on our predicted

structures of known GBP sequences. More generally, these simulations will contribute to our un-

derstanding of the role macromolecules can play in the formation of mineral crystals.

2 Experimental Studies of GBP

There is a history of using gold surfaces as substrates to which biological molecules are attached,

eg, in the context of AFM experiments and self-assembled monolayers [9, 10, 11]. However, the

use of polypeptides as recognition elements for metal surfaces is fairly recent. Brown et al. [1]

displayed large populations of polypeptides consisting of repeating random amino-acid sequences

on the surface of bacteria, and those that bound avidly to target surfaces (gold and chromium)

were selected and analyzed. When freed from the bacteria, the polypeptides were found to retain

their binding properties. It was further found that increasing the number of repeats increased the

protein avidity for gold.

The gold-binding polypeptides addressed in [1] could be classed into two categories: those

that bound more strongly to gold after the gold surface was treated with HF to remove surface

impurities, and those which did not exhibit increased binding. It can thus be deduced that those

which bound more strongly after treatment with HF recognized the Au surface itself, rather than

a partially removed contaminant. Those that fell into the gold-recognition category had sequences

rich in the polar amino acids serine and threonine.

It was expected that the gold-binding polypeptide sequences would contain cysteine, which

is known to form disul�de bridges to gold [12]. Indeed, it is via cysteines that molecules are

customarily attached to gold surfaces for study. However, it was found that in the �rst gold-

binding polypeptide, the cysteines were buried; furthermore, other polypeptides which did display

cysteine failed to bind to Au. Later, GBPs were engineered that do not contain cysteine [8].

All the GBP sequences to date have contained methionine. Strong and Moore [7] found that

a helical oligopeptide containing three methionine residues and no cysteine residues formed a self-

assembled monolayer on Au; a non-sulfur containing control oligopeptide of similar length and

structure (Ala substituted for Met) did not form a stable monolayer. From this work, it was

deduced that methionine is able to form a bond to gold by dissociation of the methyl. In contrast,

2

Brizzolara et al. [13] found that increased presence of Met produced a greater excess of sulfur on the

Au surface originating from dissociated methyl mercaptan (S(CH3)). In this case, the methionine

itself does not adhere to gold; in fact, the methyl mercaptan on the Au surface may e�ectively

obscure it and hinder other interactions. It is unclear from these studies what role the methionine

sulfur in the GBP may play. However, because the protein was released from the surface in the

presence of a detergent, and because other polypeptides containing methionine did not bind to

gold, it is less likely that the sulfur contributes positively to the binding.

Further experiments involving the crystallization of gold in the presence of GBP were per-

formed [2, 8]. Gold, when reduced from AuCl3, changes in appearance from a pale yellow to a red

colloid, providing a simple means for monitoring the rate of crystallization. In the presence of three

of the six GBPs considered, the rate of crystallization was higher than that without GBP. The gold

crystals produced in the presence of the accelerating GBPs were broad, at, and hexagonal, unlike

the crystals produced in the presence of rate-retarding GBPs and no GBP, which were smaller and

\spherical" (equal growth on the f111g and f100g faces). The broad face of the large at crystals

was determined to be the f111g plane by electron di�raction. It may thus be deduced that the

presence of GBP either stimulates gold accretion on the f211g plane, or hinders accretion on the

f111g plane. Such e�ects may be attributable to face-speci�c binding [14].

3 Theory and Previous Simulations

Generally speaking, molecular dynamics simulations involve integrating the classical equations of

motion for a set of atoms. The potentials are described by a force�eld which models the interactions

between chemically bonded and non-bonded atoms, with terms for the bond-stretching, angle,

dihedral angle, and improper dihedral angles, as well as a 6{12 Lennard-Jones term for dispersion

forces and a Coulomb term for electrostatics. Hydrogen bonds are not explicitly modeled, but

rather are represented by the Lennard-Jones and electrostatic terms. Electrostatic charges are

�xed to individual atoms.

The force�eld used in our studies is the CHARMM26 force �eld; for a discussion of the philoso-

phy and methodology behind its development, see Ref. [15]. Because of the metal surface, additional

considerations not covered by the CHARMM26 force�eld needed to be addressed: non-bonded in-

teractions of the solvent and protein with the gold, contributions from image-charge e�ects, and

possible S-Au bond terms.

3

The dynamics of the metal-liquid interface involves interactions between the solvent and the

metal as well as between particles in the uid. A single water molecule physisorbs onto a metal

surface with an energy of between 7 and 15 kcal/mol which, while stronger than hydrogen bond

energy (5 kcal/mol), is almost as large as the interaction energy between water molecules in bulk

water under ambient conditions (19.8 kcal/mol) [16]. At the interface, one expects some compromise

between the forces leading to physisorption and solvation. Typically, the interfacial structure of the

water decays to bulklike properties within a few solvent diameters; on the metal side, the electron

density and surface structure decay to bulklike arrangements at yet shorter distances [17].

The interaction between the metal (which is rigid but with a di�use electronic structure) and

the adsorbates (which exhibit incoherent particle motion and localized electronic structure) may be

represented by a model potential in cases where the metal's properties are not the focus of interest.

The overall potential between a molecule and a metal surface, Usurf may be represented [18] by a

Lennard-Jones 10{4 potential,

Usurf = 2�1Xl=0

nl��2

"2

5

��

z + dl

�10

�

��

z + dl

�4#; (1)

where dl is the separation between the �rst and lth lattice planes, nl is the number density of

atoms in plane l, and z is the perpendicular separation of the adsorbate particle from the �rst

(l = 0) lattice plane. The parameters � and � are the Lennard-Jones 6{12 potential parameters

describing the interaction between the adsorbate and surface atoms. Because there is no signi�cant

contribution from below the second lattice plane, only the �rst two terms of the sum need be

considered. The corrugation of the more sparsely populated (eg, f211g) planes may be mimicked

by making the dl's sinusoidal functions [17]. The � and � values for these potentials for both the

Auf111g and the Auf211g surfaces may be found in Table 1.

In addition to the metal-adsorbate interactions, it is necessary to consider the electrostatic

potential contribution due to the interaction of molecular point charges with the induced image

charge \beneath" the metal plane. For a smooth surface, this contribution is given by

U imgsurf (z1; :::; zn) =

�m � �s�m(�m + �s)

2664X

i

q2i4zi

+1

2

Xi;j

i6=j

qiqjj�!r ij + 2�!n zjj

3775 ; (2)

where the �rst term describes the interaction of a charge qi at a distance zi above the image plane

4

(generally de�ned to be the plane in which the nuclei of the atoms in the topmost lattice plane lie)

with its own image charge, and the second term describes the interaction between a charge and the

images of all other charges. rij denotes the separation between real charges, �!n is the unit vector

perpendicular to the image plane, and the �'s are the dielectric constant for the surface (�s =1) and

the medium (�m = 80, water) respectively. However, the Lennard-Jones contribution dominates

the image-charge contribution close to the image plane, whereas far away from the surface, neither

term contributes signi�cantly. Furthermore, it has been observed that, for a neutral many-particle

system, the sum of charge { image charge interactions is close to zero [19, 10]; such cancellation

has been noted even with monolayer �lms of water. Thus, it is reasonable to exclude the charge {

image charge terms from the Hamiltonian.

Lastly, for the cases in which the protein is assumed to be covalently bound to the surface, bond

stretch, angle, and dihedral terms for the S-Au bond are necessary. These terms, taken from [20]

based on quantum mechanical simulations, are also summarized in Table 1.

Lennard-Jones: 4�[(�=r)12 � (�=r)6]

atom � (�A) � (kcal/mol)Au 1.8474 0.0390

Bond stretching: Ebond = kbond(r � r0)2

bond r0 (�A) kbond (kcal/mol�A2)

Au-S 2.531 198S-C 1.836 205

Angle bending: E� = k�(� � �0)2

angle �0 (deg) k� (kcal/mol(rad)2)Au-S-C 109.0 46.347

Dihedral: E� = k�[1 + cos(2� + )]

dihedral (deg) k� (kcal/mol)Au-S-C-C -180.0 0.31S-C-C-C 19 0.22

Table 1: Force�eld parameters for Au and Au-S bond [20]

4 Progress to date

Secondary structure prediction and initial simulations of solvated GBP on Au have been carried out.

Secondary structure prediction was necessary due to lack of experimental structural information;

for the GBP sequence which yielded the most reliable structure, simulations were carried out to

5

create an equilibrated protein structure on the Au surface.

Structure prediction was carried out on three sequences of (at the time) unknown aÆnity for

gold. The �rst two (GBP1 and GBP2) consisted of a 14 amino acid sequence repeated six times

(84 residues); the last (GBP3) consisted of two di�erent 14 amino acid sequences each repeated

approximately 3.5 times (94 residues). The sequences may be found in Table 2. Generally, two

types of methods are used in structure prediction: prediction of regions which might form certain

types of secondary structures such as alpha-helices, beta sheets or turns using statistical methods

(eg. Chou-Fasman, GOR, Holley-Karplus), and a comparison of the sequence of the predicted

protein to other known sequences using alignment methods (eg. BLAST, FASTA, CLUSTALW).

The Holley-Karplus method [3], used for GBP, is a neural network that identi�es three secondary

types (helix, strand, and coil). Since, in principle, neural networks can detect second- or higher-

order correlations in data, they can be more powerful than methods based on standard �rst-

order statistical treatments (such as the Chou-Fasman and GOR methods). As implemented in

QUANTA [21], the neural network is trained on 48 unrelated proteins. It uses a window of 17

residues to assign a structure to the central residue (since a residue may be a�ected by another

residue eight places away in the sequence) and the results are smoothed such that sheet regions

contain at least 2 residues, helical regions contain at least 4 residues, and shorter regions revert to

a coil.

Comparison of the sequence of interest to a database of sequences with a known structure (eg,

the Protein DataBank) can also be valuable. Sequence alignment is accomplished by using a weight

matrix to determine the similarity of non-identical amino acids (weights of 1 are assigned to identical

residues). Gaps in the alignment, if considered by the algorithm, are assigned a penalty. In this

manner, a score for the alignment of two sequences may be determined [22]. FASTA and BLAST

searches employ this methodology in their algorithms. The Biology WorkBench [23] implementation

of these algorithms was used.

For GBP1, there was good agreement between both the Holley-Karplus prediction and the

structures from the similarity searches; all indicated an anti-parallel �-sheet. For GBP2, the

Holley-Karplus prediction indicated several helical regions connected by random coils; on the other

hand, the similarity searches yielded sequences which were primarily �-sheet. For GBP3, the

Holley-Karplus prediction did not arrive at anything other than a random coil, and the similarity

searches matched sequences of globular character. The Holley-Karplus output for each sequence

6

may be found in Table 2. Because of the ambiguity of GBP2 and the lack of structure for GBP3,

we have con�ned our studies to GBP1. Later, our experimental collaborators informed us that the

sequence of GBP3 was in fact a very poor binder; if in fact it does not have a periodic structure

(like GBP1 and GBP2) as the prediction tools would indicate, this may be in agreement with

the hypothesis that higher repeats facilitate greater binding by linearly increasing the number of

favorable contacts.

GBP1 MHGKTQATSGTIQSMHGKTQATSGTIQSMHGKTQATSGTIQSMHGKTQATSGTIQSMHGK

....EEEEEEEEEEE.....EEEEEEEEE.....EEEEEEEEE.....EEEEEEEEE...

GBP2 ALVPTAHRLDGNMHALVPTAHRLDGNMHALVPTAHRLDGNMHALVPTAHRLDGNMHALVP

....HHHHH....HHHHHHHHHH....HHHHHHHHHH....HHHHHHHHHH....HHHHH

GBP3 LQATPGMKMRLSGAKEATPGMKMRLSGAKEATPGMSTTVAGLLQATPGMKMRLSGAKEAT

.....................................EEE....................

Table 2: GBP sequences and corresponding Holley-Karplus structure predictions. Data is truncated after 60amino acids. In the Holley-Karplus output, E signi�es a �-strand, H signi�es a helix, and . signi�es randomcoil. Although GBP2 appears to have helical content, similar sequences had �-sheet motifs. Con�dence inthe prediction is highest for GBP1.

Using QUANTA [21], three repeats of the GBP1 14 amino-acid sequence were mapped to the

anti-parallel �-sheet structure suggested by the prediction tools, and PROCHECK [24] was used

to check for bad angles, contacts, etc. The resulting structure was minimized using X-PLOR [25].

Two Au surfaces, displaying the f111g face and the f211g face, were created manually based on the

known gold FCC crystal structure and lattice spacing of 4.07�A. The Au surfaces were 5�A (3 layers)

deep and approximately 40�A by 80�A wide, comfortably accommodating the GBP. The completely

built protein structure was manually positioned on each of the gold surfaces. In order to consider

the case in which the methionine sulfur is bound to gold, the above structures were duplicated

and modi�ed using X-PLOR [25] to remove the CH3 and form the S-Au bond. For all four cases,

a pre-equilibrated water box was overlayed on the protein-metal system, and overlapping waters

were subsequently removed, as well as waters between the GBP and Au surface. The complete

system was comprised of 594 (582) protein atoms for the unbound (bound) case, 1457 (672) Au

atoms for the f111g (f211g) surface, and approximately 11,000 water atoms bringing the total to

approximately 13,000 atoms.

Molecular dynamics simulations were carried out using the program NAMD2 [26], using the

CHARMM26 force �eld [15] for proteins [27] with additional terms added for the Au. A timestep

7

of 1 fs was used. The system was simulated in periodic boundary conditions, with full electrostatics

computed using the particle mesh Ewald (PME) method [28] with a grid spacing on the order of

1 �A or less. The gold atoms were held �xed to speed computation.

Each system was energy minimized using the Powell algorithm, then heated for 2 ps under

Langevin dynamics at a temperature of 300 K and with a damping coeÆcient of 10 ps�1. Each

system was then equilibrated for 1 ns at constant pressure and temperature. Pressure was main-

tained at 1 atm using the Langevin piston method [29] while temperature coupling was enforced

by velocity reassignment every 2 ps.

The equilibrated structure of the protein on the Au surface may be seen in Figs. 1 and 2.

Figure 1: GBP on (a) f111g and (b) f211g Au surface, viewed from above.

Figure 2: GBP on (a) f111g and (b) f211g Au surface, viewed edge-on. Atoms near the surface are alsoshown. Coloring corresponds to residue type: polar residues are highlighted in green, charged in blue, andhydrophobic in white.

8

5 Future work

Further molecular dynamics simulations will be conducted to study the binding process of GBP.

These simulations will initially be based on the predicted GBP structures, and then on forthcoming

structural information from the experimentalists.

Free molecular dynamics of 5ns duration in the NpT ensemble as described in the previous sec-

tion is currently being carried out. The trajectories will be analyzed using X-PLOR and VMD [30]

to examine the motion and RMS deviation of the residues in contact with the Au surface, and the

accessibility to water of the corrugations in the f211g surface.

Next, steered molecular dynamics (SMD) [5] simulations will be carried out for the non-

covalently bound cases. The protein will be subject to a harmonic constraint moving across the

Au surface. Fourier analysis will be performed to check for periodic motion due to the Au surface

corrugation. The SMD simulation will also be extended to pulling GBP across a modeled grain

boundary from the f111g to the f211g surface.

Additionally, ab initio dynamics of a methionine side-chain in the close presence of a gold atom

will be carried out to explore the behavior of the Met sulfur. The results of forthcoming studies

undertaken by the experimentalists on a GBP1 mutation without methionine should also shed light

on what role, if any, the S-Au bond plays.

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