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2.0 Interactions of Biomolecules with Clay Minerals Randall T. Cygan |
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Almost all the dry mass of a cell is
comprised of proteins. Not only do
proteins constitute the building blocks from which cells are built, they are
critical in executing almost every function of the cell (Alberts et al.,
1998). The large number of functions is
related to the different three-dimensional shapes that the proteins can
adopt—specifically, structure and shape determine function. How a protein folds through the interaction
of its constituent amino acids to form alpha-helixes and beta-sheets and higher
order structures has only partially been determined. The ability to determine native folded state of a protein,
directly from the primary sequence of amino acids is the principal goal of
protein folding research. Understanding
the mechanisms of protein folding, and how functional groups of the polypeptide
string of amino acids interact with each other, is one of the longest-standing
problems in molecular biology (King, 1993).
Detailed information on the native folding state of a protein has
significant impact on our understanding of biochemical pathways and their role
in health disorders and disease. Improper
folding and high energy configurations for proteins often result in decreased
protein activity and cell disorders (Thomas et al. 1995; Taubes, 1996). The development of designer drugs, the
characterization and curing of genetic diseases, and other areas of biological
research that require information on protein structure and function are
impacted.
Computational chemistry methods, such as
molecular dynamics simulations, Monte Carlo simulations, and lattice modeling
techniques, have been used to address the protein folding problem. Each method uses a similar strategy by
attempting to determine the most stable, or lowest energy, conformational state
for the structure of a protein (Torshin, 2001). The early-stage interactions among amino acid groups in a polymer
string will ultimately control protein folding once the amino acids are
condensed to form the polypeptide sequence in the ribosome of the cell. Figure 2.1 and Figure 2.2 provide graphical
descriptions of an amino acid sequence and the various levels of structure
associated with a protein. The number
of possible interactions among amino acid functional groups is staggering and
cannot be simulated in any reasonable time on the best computers, even with using
the largest massively parallel computers available today. To place the computational effort into
perspective, keep in mind that the typical human cell has about 10,000
different proteins with peptide sequences (residues) that vary from less than
100 amino acids to over 2200 amino acids (Alberts, et al. 1998). Therefore, several novel strategies have
evolved to better sort through the number of ways in which the amino acids
might be arranged into a compact folded structure. Neural network and computer training algorithms (Chang et al.,
2001) and the synthesis of mini-proteins having just twenty residues (Neidigh, et al., 2001) are two recent innovations in the protein folding
research.
Figure 2.1.
Diagram of protein (oligopeptide) formation through the condensation of
amino acids (from Alberts et al., 1998).
Figure 2.2.
Schematic of a bacterial regulatory protein CAP showing the relationship
between protein domains and other structural elements (from Alberts et al.,
1998).
To date, almost the entire scientific
literature on protein folding has examined the process in vacuo without
incorporating any external influence or catalyst. Although specialized proteins referred to as molecular chaperones
are used to assist in the protein folding process in the crowded cytoplasm of
the cell, a protein chain can fold into the correct conformation by itself
(Alberts et al., 1998). However, water
solvent is omnipresent during the process and is seldom explicitly represented
in molecular simulations. Additionally,
catalysts in the form of substrates and surfaces are certain to modify the
structure and the most stable conformations of the protein. Mineral surfaces have been suggested as
providing substrates for support of the catalytic assembly of organic and
biochemical molecules (Hartman; 1998; Hazen, 2001). Specifically, clay minerals and their charged aluminosilicate
layered structure were envisioned as having the appropriate characteristics to
harbor precursor organic molecules for the synthesis of important biomolecules.
The early studies of Cairns-Smith (1966;
1992) and Friebele et al. (1980), in particular, advocated the role of clay
minerals and their surfaces as a templating unit capable of sorbing organic
material (amino acids and nucleotides) from a chaotic organic-rich aqueous solution,
followed by polymerization of the units to form life-critical
biomolecules. Although initially
considered a novel alternative origin view, this “life from rocks” theme was
resurrected in the 1990’s by several research groups who confirmed the intimate
association of biomolecules on and in clay minerals (Pitsch et al., 1995;
Violente et al., 1995; Rhode, 1999).
Moreover, there was much debate on the possibility that clay interlayers
and surfaces promoted the polymerization of isolated nucleotides into RNA-like
compounds (Ferris, 1993; Ertem and Ferris, 1998). These heterogeneous templating reactions could have led to an
increase in the possible protoncleic acids from which life ultimately emerged
(Ertem and Ferris, 1996). For a more
practical pharmaceutical—and less philosophical—application, recent work has
shown that the double hydroxide clays (anionic clays) can incorporate DNA
molecules in their interlayers and provide a suitable drug carrier for the gene
therapy treatment of leukemia (Choy et al., 2000). Intercalated clays have also been shown to be effective carriers
in the gastrointestinal release of selected cationic drugs (Fejer et al., 2001)
including chemotherapeutic treatment of colorectal cancer (Lin et al., 2002).
Speculation on the origin of life is beyond
the scope of this study, however, the ability of clay surfaces to selectively
absorb biomolecules, or the monmeric and oligomeric units of these
macromolecules, can be addressed.
Specifically, we examine our computational capabilities to perform
molecular mechanics simulations of these organic-inorganic systems based on our
experience in the simulation of clay systems (Cygan et al., 1998; Hartzell et
al., 1998; Cygan, 2002). Although there
exists only two previous studies that used molecular modeling methods to
examine the protein-amino acid-clay system, there has been no comprehensive and
systematic examination of the interactions of amino acid and protein functional
groups with the clay surface. Yu et al.
(2000; 2001) limited their simulations to a small subset of simple proteins and
oligopeptides, and used an inefficient valence forcefield that would be
difficult to extend to large-scale simulations. We feel our geochemical modeling efforts, including hardware and
software tools, have now matured to the point that we can critically examine
these complex interactions. We present
below the results of several preliminary simulations to demonstrate the ability
of our own modified set of energy parameters to model these systems. A discussion of the potential energy
functions and the modifications required to introduce the organic components
into the inorganic forcefield is presented next.
2.2 Potential Energy and Forcefields
The total potential energy of a chemical system is represented by the summation of the various contributing energy components:
(2.1)
where ECoul, the Coulombic energy, and EVDW, the van der Waals energy, represent nonbonded energy components, and the final three terms represent the explicit bonded energy components associated with bond stretching, angle bending, and torsion dihedral, respectively. These latter terms are typically used to evaluate the energy of molecule characterized by covalent bonds, such as most organic compounds. In contrast, the energy for most inorganic materials, including halides, oxides, and silicates, is represented by the first two terms and are considered as ionic-like compounds.
The Coulombic energy, or electrostatics energy, is based on the classical description of charged particle interactions and varies inversely with the distance rij:
(2.2)
Here, qi and qj represents the charge of the two interacting atoms (ions), e is the electron charge, and eo is the permittivity (dielectric constant) of a vacuum. The summation represents the need to examine all possible atom-atom interactions while avoiding duplication. Equation (2.2) will yield a negative and attractive energy when the atomic charges are of opposite sign, and a positive energy, for repulsive behavior, when the charges are of like sign. In the simple case, the Coulombic energy treats the atoms as point charges, which in practice is equivalent to spherically-symmetric rigid bodies.
Simulations involving crystalline materials or other periodic systems require the use of special mathematical methods to ensure proper convergence of the long-range nature of Equation (2.2) The 1/r term is nonconvergent except for the most simple and highly symmetric crystalline systems. In practice, it is therefore necessary to employ the Ewald method (Ewald 1921) or other alternative method (e.g., Greengard and Rokhlin 1987; Caillol and Levesque 1991) to obtain proper convergence and an accurate calculation of the Coulombic energy. The Ewald approach replaces the inverse distance by its Laplace transform that is decomposed into two rapidly convergent series, one in real space and one in reciprocal space (Tosi 1964; de Leeuw et al. 1980). The Coulombic energy in ionic solids typically dominates the total potential energy and, therefore, controls the structure and properties of the material. Purely ionic compounds such as the metal halide salts (e.g., NaF and KCl) are examples where the formal charge is used to accurately represent the electrostatics. In molecular systems where covalent bonding is more common, the Coulombic energy is effectively reduced by the use of partial or effective charges for the atoms. The Coulombic energy for non-periodic systems can be evaluated by direct summation without resorting to Ewald or related periodic methods.
The van der Waals energy represents the short-range energy component associated with atomic interactions. Electronic overlap as two atoms approach each other leads to repulsion (positive energy) and is often expressed as a 1/r12 function. An attractive force (negative energy) occurs with the fluctuations in electron density on adjacent atoms. This second contribution is referred to as the London dispersion interaction and is proportional to 1/r6. The most common function for the combined interactions is provided by the Lennard-Jones expression:
(2.3)
where Do and Ro represent empirical parameters. Although various forms of the 12-6 potential are used in the literature, the form presented here provides a convenient expression that equates Do to the depth of the potential energy well and Ro to the equilibrium atomic separation. This association would only apply for the interaction of uncharged atoms (e.g., inert gases), however, the functionality is used in practice for partial and full charge systems. Alternatively, a 9-6 function or a combined exponential-1/r6 (Buckingham potential with three fitting parameters), among other functions, can be used to express the short-range interactions. Both 9-6 and 12-6 potential functions are incorporated into published forcefields but for the present study we incorporate the 12-6 potential. In contrast to the long-range nature of the Coulombic energy, the van der Waals energy is non-negligible at only short distances (typically less than 5 to 10 Å), and, therefore in practice, a cutoff distance is used to reduce the computational effort in the evaluation of this energy.
2.3 Hybrid Inorganic-Organic Forcefield
Various forcefield models have been used to evaluate the lattice energy of simple halides and oxides, however molecular simulations of complex inorganic compounds such as aluminosilicate phases (zeolites, ternary and higher order oxides, minerals, etc.) require a more sophisticated approach (Rappé et al., 1992; Hill and Sauer, 1994). Often these forcefields are derived empirically from experimental crystal structures, physical properties, and spectroscopic measurements. Unfortunately, these energy forcefields are often limited in their application to even more complex structures such as the environmentally important clay minerals. Recently, however, Cygan et al. (2002) developed a new forcefield that specifically addresses the molecular modeling problems associated with clay minerals. Clay minerals—characterized by large unit cells, low symmetry, complex multicomponent compositions, cation order-disorder behavior, significant vacancies, and large electrostatic potentials—and other layered phases can be successfully modeled with a fully flexible clayff forcefield. Additionally, this recent forcefield development provides an accurate description of surfaces and the behavior of water interactions. The importance of simulation methods to model these hydrated phases is critical as little structural and physical property data are available due the nanoscale of these clays and the lack of quality single crystals available for characterization and analysis. The structure of clay minerals is characterized by a layered oxide structure comprised of tetrahedral-octahedral-tetrahedral (TOT) sheets, nominally represented by the mineral pyrophyllite with octahedral aluminum and tetrahedral silicon (Lee and Guggenheim, 1981). Isomorphic substitutions on either sheet with low-valence metal cations create a net negative layer charge that is balanced by interlayer cations, that are typically hydrated by water molecules, and which electrostatically maintain the net ionic nature of the lattice. Although other related clay structures exist, clays characterized by TOT layers having low charge are most common. This clay group includes the expandable smectite clays and is best typified by the mineral montmorillonite. Figure 2.3 provides a general description of the layered structure of the basic clay structure.
Figure 2.3 Schematic representation of 2:1 clay mineral such as montmorillonite, indicating locations of substitution sites on tetrahedral and octahedral layers and the hydrated interlayer cations.
Interatomic potentials were derived from parameterizations incorporating structural and spectroscopic data from a variety of simple hydrated compounds (Cygan et al., 2002). A flexible simple point charge (SPC) water model is used to describe the water and hydroxyl behavior (Berendsen et al., 1981; Teleman et al., 1987). Metal-oxygen interactions are described by a Lennard-Jones function (Equation 2.3) and a Coulombic term with partial charges derived by Mulliken and ESP analysis of DFT results (Equation 2.2). Bulk structures, relaxed surface structures, and intercalation processes were evaluated for several model phases and successfully compared to experimental and spectroscopic findings for validation. Of particular note is that the choice of the SPC water model is especially important for the present work due the derivation of the parameters with specific application to the simulations of protein-water interactions (Berendsen et al., 1981). An example of the success of clayff in modeling a complex clay structure and the expandability of a smectite is presented in Figure 2.4. The agreement of the simulations with the swelling behavior of montmorillonite observed by Fu et al. (1990)—especially with the fine structure of the swelling curve as water intercalates, forms a stable hydrogen-bonded network, and rapidly expands the clay TOT layers—provides significant validation for the use of clayff in modeling clay intercalation processes.
Figure 2.4 Swelling behavior for a smectite clay derived from molecular dynamics simulations. Expansion of clay interlayer with increasing water content is denoted directly by X-ray diffraction measurement of the basal (001) d-spacing.
For the molecular simulation of biomolecules interacting with clay surfaces, we desired an accurate and relatively simple energy forcefield that was compatible with the functionality represented in clayff, and which accurately modeled amino acids and protein structures. Moreover, it was important that the organic forcefield parameters be readily incorporated into clayff using the same energy software. We ultimately chose the organic CVFF forcefield (consistent valence forcefield; Dauber-Osguthorpe, et al. 1988) for integration with clayff. This hybrid forcefield has been successfully tested for several cases where simple organic compounds (hydrazine, trichloroethylene, tributylphosphate, etc.) have been introduced into the interlayer of smectite clays. However, this study presents the first simulations for the analysis of amino acids and other biomolecules within clays using the combined clayff-CVFF forcefields, and now referred to as clayoff.
All molecular simulations were carried out using the Cerius2 software package (Accelrys Inc., San Diego) and the OFF energy algorithm. The bulk of the modeling effort was devoted to testing the compatibility of the new clayoff forcefield for the hybrid cells. Energy minimization and molecular dynamics simulations were performed in this preliminary analysis of intercalated biomolecules within the clay. Large periodic simulation cells were created from previously optimized montmorillonite structures. These cells were expanded and the bimolecular was inserted into the interlayer. Preliminary simulations avoided the introduction of water within the interlayer, however, interlayer Na+ cations were introduced appropriately to balance the negative clay layer charge. Energy optimizations were performed allowing all atomic positions and cell parameters to vary during the minimization process, thereby simulating a system under constant pressure conditions. Several representative amino acids for intercalation were chosen in this analysis. These include representative acids having a polar, nonpolar, charged, and charged side chains: methionine, aspartic acid, leucine, and tyrosine. This amino acid sequence, referred to as an oligopeptide, is graphically presented in Figure 2.1. The individual amino acids were first created through appropriate software tools and then clayoff parameters and partial charges were assigned. Each amino acid was energy optimized to obtain the lowest energy configuration. These structures were then combined or linked together to create the representative oligopeptide that was then subjected to an energy minimization procedure. Amino and carboxylate groups are protonated and deprotonated, respectively, to represent the pH environment expected within the cytoplasm of a cell. Once fully optimized, the oligopeptide structure was then incorporated into the clay for subsequent optimization. No effort was made to examine the role of the optical activity of the central carbon of the amino acid in this preliminary study; all proteins are comprised exclusively of L-amino acids (left-handed chirality).
The results of the energy optimization for the isolated oligopeptide are presented in Figure 2.5. The structure of this mini-protein exhibits one of the stable complex conformations that can occur with the disposition of the various amino acid side chains away from the central polypeptide backbone. Nonpolar side chains such as methionine and leucine are typically directed away from the backbone and lead to congregation of similar groups at the internal fold of a protein. Typically for larger protein structures the nonpolar side chains groups occur in the core region of the protein to create a hydrophobic region. In contrast, the more polar or charged groups have more interactions with each other and with solvating water molecules. These groups are typically orient themselves on the outside of the folded protein molecule and form a relatively strong hydrogen bond network. The oligopeptide presented here shows the strong interaction of the hydroxyl of the phenol group of tyrosine with the charged carboxylate of aspartate side chain resulting in a slight fold of the polypeptide backbone. Although this result is on a small scale, the example represents the concerted efforts of larger sequences of amino acids in proteins that control complex protein folding.
Figure 2.5. Geometry optimized conformation of the test oligopeptide comprised of methionine (Met), aspartic acid (Asp), leucine (Leu), and tyrosine (Tyr). The polypeptide backbone comprised of sequential amino-hydrocarbon-carboxyl groups is highlighted.
Once intercalated within the interlayer of the clay, the molecular modeling results indicate a slightly different conformation for the oligopeptide. A montmorillonite clay having a layer charge of -0.75 per O20(OH)4 unit and sodium interlayer cations is used in the simulation. An additional interlayer cation is required to balance the negative charge associated with the aspartate side group of the oligopeptide and maintain a neutral simulation cell. The use of the clayoff forcefield allows complete flexibility for all atoms and cell parameters during the simulation. Most of the layer charge is balanced by the interlayer cations being sorbed directly onto the siloxane surfaces on either side of the oligopeptide. The tyrosine and aspartate groups remain coordinated to each other within the clay and the nonpolar methionine and leucine side chains are now more disposed subparallel to the clay layers. The terminal carboxylate group of the peptide chain is now interacting with one of the interlayer cations. Analysis of the view normal to the clay interlayer (not shown) indicates the oligopeptide structure to be more two-dimensional (flattened) relative to the isolated gas-phase calculation. This may be evidence of a denaturing process associated with clay intercalation and was similarly observed in the simulations of Yu et al. (2000).
Figure 2.6 Geometry optimized arrangement of the oligopeptide within the interlayer of a montmorillonite clay; (010) view. The green spheres represent interlayer sodium cations required to balance the charge of the clay layer and oligopeptide. All atoms and cell parameters were allowed to vary during the simulation.
The montmorillonite substrate maintains the expected clay structure after intercalation but now exhibits an expanded cell with a basal d-spacing of 18.6 Å compared to the 9.2 Å without the intercalate or water. There is some deflection of the inner hydroxyl groups associated with the sorbed sodium ions but most remain subparallel to the clay layer. The sodiums are most likely to sorb at the aluminum tetrahedral site due to the localized charge directly at the layer surface.
A related set of calculations examined the behavior the identical oligopeptide within the interlayer of pyrophyllite clay. Pyrophyllite, Al2Si4O10(OH)2, possesses no tetrahedral or octahedral substitutions and, with no layer charge, does not require charge-compensating interlayer cations. A background screening term to provide a neutral cell compensated the excess charge on the oligopeptide. No constraints were imposed allowing all atoms and cell parameters to vary during the simulation. The fully-optimized configuration is presented in Figure 2.7. Without the interlayer cations, the oligopeptide has more freedom to directly interact with the siloxane surfaces of the pyrophyllite. The oligopeptide is stabilized in a conformation that is similar to that observed for montmorillonite but with more interaction of the hydrocarbon groups and the methionine side chain with the clay surface. There is minor relaxation of the siloxane surface in response to these interactions. The basal d-spacing increases from 9.1 Å to 16.2 Å with the intercalate, which is slightly compressed relative to the d-spacing obtained for the intercalated montmorillonite. The lack of local charge on the pyrophyllite surface is less effective in controlling the localized interactions between the oligopeptide and the clay.
Figure 2.7 Geometry optimized arrangement of the oligopeptide within the interlayer of a pyrophyllite clay; (010) view. Pyrophyllite has no layer charge and no interlayer cations. All atoms and cell parameters were allowed to vary during the simulation.
Several molecular dynamics calculations for the clay-oligopeptide system were performed for simulation times up to 40 ps as an NPT canonical ensemble. In general, the results support the optimized structure presented in Figures 2.6 and 2.7 These preliminary dynamics simulations were performed primarily as test cases to determine the behavior of the clayoff forcefield for the hybrid systems and to determine the computational limits on the simulation cell size, number of atoms, time steps, and frequency of trajectory storage.
In general, the molecular mechanics simulations suggest that clay surfaces are effective in creating significant nonbonded interactions between the clay and the oligopeptide. These interactions may be competitive with the nonbonded interactions within the oligopeptide chain, and can lead to perturbation of the peptide structure once intercalated. There is some evidence of denaturing of the protein structure by the clay surface in our simulations. The oligopeptide-montmorillonite example indicates the strong influence of the interlayer cations in preventing direct contact of the oligopeptide with the clay surface. The polar and charged side chains of the peptide are most likely to interact with the interlayer cations.
Future research of these peptide-clay systems would benefit by completing a series of simulations on oligopeptides with various amino acids—basic, acidic, nonpolar, and polar side chains—in order to best categorize the general behavior of each group with the clay substrate. Additionally, it would be important to examine the role of explicit water molecules in the solvation of the protein and the influence of water in the clay interlayer. The complex competition among processes involving water solvation of the protein, the formation of hydrogen bonded water network, and the structured layering of water on the clay surfaces will ultimately influence the protein structure. The clayoff forcefield already incorporates the flexible SPC water model that accurate predicts structure and energy behavior that could be used in this effort. It is critical to examine layer charge and charge distribution by simulating a variety of clay structures, especially comparing those with charge distributed locally on the tetrahedral sheet (beidellite), octahedral sheet (montmorillonite), and with no charge (pyrophyllite). As a final thought, it is conceivable that perhaps ordering of the isomorphic substitutions (Al3+ for Si4+) in the tetrahedral sheet of a beidellite clay may be responsible for the templating of selected amino acids and that the clay surface catalyzes their condensation to form the first proteins. Molecular simulations may provide the critical tool to test this critical hypothesis.
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