BD SIMULATIONS OF THE IONIC STRENGTH DEPENDENCE OF THE INTERACTIONS BETWEEN TRIOSE PHOSPHATE ISOMERASE AND F-ACTIN.

NIH Public Access Author Manuscript J Undergrad Chem Res. Author manuscript; available in PMC 2014 March 15.

NIH-PA Author Manuscript

Published in final edited form as: J Undergrad Chem Res. 2010 ; 9(4): 87–96.

BD SIMULATIONS OF THE IONIC STRENGTH DEPENDENCE OF THE INTERACTIONS BETWEEN TRIOSE PHOSPHATE ISOMERASE AND F-ACTIN Elizabeth Spanbauer Schmidt*, Neville Y. Forlemu, Eric N. Njabon‡, and Kathryn A. Thomasson† University of North Dakota, Chemistry Department, 151 Cornell St. Stop 9024, Grand Forks, ND 58202-9024

Abstract NIH-PA Author Manuscript

Functional protein-protein interactions are essential for many physiological processes. For example, the association of glycolytic enzymes to F-actin is proposed to be one mechanism through which glycolytic enzymes are compartmentalized, and as a result, play essential roles such as regulation of the glycolytic pathway and increasing glycolytic flux. Many glycolytic enzymes including fructose-1,6-bisphophate aldolase, glyceraldedhye-3-phosphate dehydrogenase, and lactate dehydrogenase bind F-actin strongly. Other glycolytic enzymes including triose phosphate isomerase (TPI) do not interact with F-actin significantly. Herein, Brownian dynamics (BD) simulations determine the energetics of the association of F-actin with the glycolytic enzyme triose phosphate isomerase as a function of ionic strength. This is the first thorough control study examining how well BD reproduces the experimental observations that the binding of TPI to Factin is very weak and falls off rapidly as ionic strength increases. The BD results confirm experimental observations that the degree of association diminishes as ionic strength increases and that the interaction of TPI with F-actin is weakly nonspecific to nonexistent.

Keywords Brownian dynamics; glycolysis; Poisson-Boltzmann; triose phosphate isomerase; computer simulation; protein-protein interactions


Introduction Protein intermolecular interactions are always occurring in biological systems and are necessary for life (1). Glycolysis, for example, occurs in nearly all cells in most organisms. Cells need energy to function, and glucose is the primary source of this energy. Glycolysis extracts the energy from glucose, so energy can be used by cells. Specifically, glycolysis is the metabolic pathway that converts one molecule of glucose into two molecules of pyruvate, which releases free energy to form ATP and NADH. A sequence of ten enzymecatalyzed reactions involving ten intermediate compounds occurs during glycolysis (Scheme 1) (2). Glycolysis is regulated by inhibiting or activating one or more of the ten enzymes that are involved at different steps in the glycolytic pathway (3, 4).

†

Corresponding author phone and E-mail: 701-777-3199, [email protected]. ‡Current Address: Chadron State College, Department of Physical and Life Sciences, 1000 Main St., Chadron, Nebraska, 69337 *Undergraduate researcher

Schmidt et al.

Page 2


There are two different viewpoints about the mechanism of action of glycolytic enzymes in the glycolytic pathway. The first viewpoint and the only viewpoint presented in most textbooks is the diffusion only model, which states that enzymes diffuse into the cell and are all randomly positioned in the cytosol (Figure 1) (5). The other viewpoint suggests that glycolytic enzymes are compartmentalized within the cell. Glycolytic enzyme interaction with cytoskeletal proteins not only compartments these enzymes, but also increases glycolytic flux, and helps to regulate the glycolytic pathway (6). The idea of compartmentation suggests that the steps in glycolysis consist of segments, each formed by a cluster of enzymes that can interact with the actin filaments of the cytomatrix (Figure 2) (7–9). Data from several studies support the concept that metabolic activities in the cytoplasm have an organized structure (8, 10–12). By binding certain enzymes and metabolites, effective compartmentation can occur within a membrane-enclosed space (12). Another supporting piece of evidence is that the rate of diffusion of proteins in the cytosol is less than that of the same proteins in aqueous solution (13). Compartmentation concentrates the glycolytic enzymes and has the potential to increase glycolytic flux and increase control of glycolysis (4).


Triose-phosphate isomerase (TPI) is the fifth glycolytic enzyme in the pathway, and it catalyzes the conversion of dihydroxyacetone phosphate to glyceraldehyde-3-phosphate. Triose-phosphate isomerase is a highly efficient enzyme that plays a very important role in glycolysis (14). TPI is necessary for efficient energy production. TPI is considered to be a “perfect enzyme”, because the rate of the reaction is only limited by the rate at which the substrate can diffuse into TPI’s active site (14). This dimeric glycolytic enzyme is essential for energy production, allowing two molecules of glyceraldehyde-3-phosphate to be produced for every glucose molecule, thus doubling the energy yield of glycolysis. Cytoskeletal structures like F-actin might play a significant role in cellular metabolism through their interaction with glycolytic enzymes. Actin is a globular, eukaryotic protein that is very highly-conserved. Actin exists in two forms: globular actin (G-actin) and filamentous actin (F-actin). G-actin subunits are individual subunits of actin, and when Gactin subunits assemble into a long filamentous polymer it is called F-actin (15, 16).


Interactions between glycolytic enzymes and F-actin may be a mechanism for compartmentation of the glycolytic pathway. Over the years, enzyme-cytoskeletal protein interactions have been evaluated by a variety of experimental techniques including affinity chromatography (17), histochemical observations (18), copelleting (16), and coelectrophoresis (19). These experiments have observed dynamic complexes between glycolytic enzymes and F-actin. These experiments have also revealed that the interaction affinity between glycolytic enzymes and F-actin varies amongst different isoforms and species (17–21). The association between cytoskeletal proteins and glycolytic enzymes has also been postulated to be mainly electrostatic, due to the strong dependence on pH, metabolites (ATP, ADP), and ionic strength: with respect to both monovalent (Na+ and K+) and divalent (Ca2+ and Mg2+) ions (21). Particularly, fructose-1,6-bisphosphate aldolase (aldolase), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and lactate dehydrogenase (LDH) are key glycolytic enzymes that have been shown to concentrate around subcellular proteins (e.g., F-actin) in muscle tissue. Many studies have observed a positive difference in activity between the bound forms and free forms of glycolytic enzymes (22, 23). For example, Lushchak and coworkers (24–26) have observed a large concentration of the bound form of seven glycolytic enzymes (hexokinase, phosphofructokinase, aldolase, glyceraldehyde-3-phosphate dehydrogenase, lactate dehydrogenase, pyruvate kinase and phosphoglucoisomerase) in the sea scorpion brain, while Laursen and coworkers (27, 28) suggest that between 70–90 % of hexokinase is found

J Undergrad Chem Res. Author manuscript; available in PMC 2014 March 15.

Schmidt et al.

Page 3


in the bound form in the brain of mice. Most recently, confocal microscopy studies depict a strong aggregation of glycolytic enzymes with the glycoprotein band 3 in human erythrocyte cells, cellular phospholipid domains and other cellular membrane structures (12). Previous studies, however, have shown a lack of direct interaction between TPI and actin directly (17). It has been suggested that TPI binds F-actin when either aldolase or GAPDH is present (29). An initial theoretical examination of TPI interacting with F-actin confirms that TPI does not interact strongly or bind specifically for a single species at a single ionic strength (30). Herein, Brownian dynamics (BD) simulations thoroughly examine the interactions between TPI and F-actin from three different species at four ionic strengths (0.01, 0.05, 0.10, 0.15 M). The application of BD simulations quantitatively estimates the relative binding energy of TPI to F-actin across the three species. BD simulates the diffusional encounter between TPI and F-actin with the goal of identifying the first encounter snapshot binding modes and to estimate the relative binding free energies.

Theory


Brownian Dynamics (BD) simulates diffusion steered by electrostatic forces. BD can be used to study reactions that may or may not be diffusion controlled (31) and may occur over longer time scales (microseconds or longer) than typical molecular dynamics simulations (femtoseconds to nanoseconds). The BD method simulates the relative translational and rotational diffusive motions of whole macromolecules (e.g., F-actin and TPI), under the influence of complex electrostatic and excluded volume interactions such as salt concentration (ionic strength) present in the solution. The electrostatic potentials are computed rigorously by iterating the solutions of the Poisson-Boltzmann equation. Brownian dynamics simulations are efficient and can access longer timescale events like protein-protein interactions, because the surrounding solvent medium is represented by a continuum. The Brownian motion of interacting biomolecules in a solvent is usually simulated as a series of small displacements governed by the diffusion equations first described by the Ermak and McCammon (E-M) (Equation 1) (32). The translational part of the E-M formalism can be written as follows: (1)


where r(t + Δt) is the new position of the molecule, r(t) is the old position, DT is the translational diffusion coefficient at infinite dilution, F is the force acting on the molecule due to other molecules, kB is the Boltzmann constant and T is absolute temperature. The term R(t) represents the stochastic component of the displacement arising from random collisions with solvent molecules and is obtained randomly using the random number generator on the computer. This random displacement factor which depends on DT, must also meet the following statistical properties (Equations 2 and 3): (2)

(3)

A similar algorithm is used to model the rotational motion of the particles (Equation 4), with the force replaced by a torque, and the translational diffusion coefficient replaced by the rotational diffusion coefficient.


Schmidt et al.

Page 4

(4)


Where ω is the angular displacement, Q is the torque and Dr is the rotational diffusion constant. The random angular displacement Ω, like its translational counterpart, R, is obtained using a random number generator on the computer.

Computational Methods Protein Structures


Crystal structures of human and rabbit G-actin (1ATN) (33), human TPI (1HT1) (34), and rabbit TPI (1R2R) (35) were downloaded from the RCSB Protein Data Bank (36). The primary sequence of zebrafish G-actin was downloaded from the UniProt database (37), (Q7ZU23) (38). Human and rabbit actin sequences, accession codes P68133 (39), and P68135 (40), respectively, had 100% sequence identity. A sequence alignment between the rabbit and the fish actin sequences in Insight®II (Accelrys, San Diego, CA) showed a high sequence identity of 98% (see reference 41). The rabbit crystal structure for G-actin in complex with gelsolin (1ATN) (33) was used as reference protein to build the fish G-actin model. The fish G-actin monomer obtained was superimposed on the previously built rabbit hexamer of the Holmes model of F-actin (42), and the coordinates transformed to form zebrafish F-actin. The F-actin hexamer was energy minimized using the same protocol in the Discover_3 module. The details for building a fish actin model have been described in greater detail elsewhere (Forlemu et al.) (41). The previously built Holmes model hexamer of rabbit F-actin (42, 43) was used in the simulations to represent human F-actin because rabbit and human actin have identical sequences.


Since a crystal structure of zebrafish TPI was not available, a model was built using the Homology module of the Insight®II modeling package (Accelrys, San Diego, CA). The model was based on the primary amino acid sequence in the Uniprot Data Base (primary accession number (PAN), Q1MTI4 (44) and known X-ray crystal structures of human and rabbit TPI. The fish sequence was then aligned with the rabbit TPI and human TPI sequences. The fish TPI sequence showed an 80.5% identity to rabbit TPI and an 80.5% identity to human TPI (Figure 3). The Homology module in Insight®II was used to perform a structural alignment of rabbit and human TPI to determine structurally conserved regions (SCRs) (Figure 3). With the structural alignment, overall, rabbit showed the best structural similarity. Atomic coordinates from the most similar reference protein were copied to the fish sequence. The fish TPI monomer was energy minimized using the Amber force field in the Discover module of Insight®II. A combination of steepest descent and conjugate gradient for 10,000 iterations was performed to minimize the energy of the fish TPI model built. The temperature was set at 298 K and the dielectric constant at 78.3 for water. The monomer was superimposed on the rabbit TPI dimer to form the tertiary structure of fish TPI. The resulting dimer was then energy minimized using the Discover_3 module of Insight®II. Protein Charges The MacroDox program (45) was used to calculate and assign charges to the proteins, determine the electrostatic potential (EP) around each molecule, and run the BD simulations (46). Charges were calculated using the Tanford-Kirkwood method with static accessibility modification (47, 48). Charges were assigned at a pH of 7.00 and a temperature of 298.15 K. Charge calculations were done at ionic strengths of 0.01 M, 0.05 M, 0.10 M, and 0.15 M. These four ionic strengths were chosen to span and evaluate a large range of electrostatics.


Schmidt et al.

Page 5


The lower ionic strengths (0.01 M and 0.05 M) were chosen to enhance the interaction, making it easier to observe statistically. The higher ionic strengths (0.10 M and 0.15 M) were chosen to mimic physiological conditions. The total charges at all ionic strengths for all species are shown in Table 1. The EP around each protein was calculated by numerically solving the linearized Poisson-Boltzmann equation on two cubic grids with dimensions 81×81×81 Å with inner and outer grid resolutions of 1.375 and 4.125 Å, respectively (Equation 5) (49–51). The average EP was determined from the charged density embedded within the molecule and the average charge density due to the ions. (5)

Where ϕ(r) is the electrostatic potential; ε(r) is the continuum dielectric constant as function of position, and ρ(r) is the fixed charge density. The parameter κ is the Debye screening constant (Debye Huckel parameter), which is the inverse of the Coulomb Debye length, (lD). This constant describes the exponential decay of the potential in the solvent and thus takes into account properties such as the ionic strength, pH and temperature of the system as evident from the Debye screening constant relation. BD Simulations


BD simulations of TPI and F-actin were performed to determine the binding site, free energy of interaction, and identify the formation of complexes. To mimic the periodic property of the actin filaments, a modified form of the BD algorithm, previously described by Ouporov et al. was used (42). With this periodic algorithm, the globular protein diffused within a truncated cylinder about F-actin; if the diffusing species moved beyond the end of the cylinder (±55 Å along the cylinder axis), it was repositioned within the cylinder with the same orientation relative to the F-actin and allowed to continue diffusing. Essentially, as the simulation proceeded, each time the enzyme center of mass (COM) moved to +55.0 Å along the Z-axis, the whole enzyme was shifted and rotated by −27.5 Å and 166.14 °, respectively. In the same way, when the enzyme COM was −55.0 Å along the Z-axis, the enzyme was subjected to a translation and rotation of +27.5 Å and −166.14°, respectively. These transformations correspond to the F-actin helical parameters and as such, the enzyme movement is limited to a cylinder (−55.0 Å < Z < +55.0 Å) simulating an infinitely long actin filament. This approximation includes the periodicity of the actin filament in the algorithm and, in effect, the F-actin is treated as being infinitely long and stationary.


Initially the center of mass (COM) of TPI was randomly positioned at a distance of about 135 Å from the helical axis of F-actin. During each trajectory, the TPI was maintained close to the F-actin by relocating it to a position 135 Å from the F-actin molecule each time the distance between the COMs of both molecules exceeded 300 Å. Each individual trajectory was terminated when TPI performed 200,000 BD steps with a distance between the COM of both proteins less than 120 Å or when the distance became greater than 250 Å (the truncation radius at which point the electrostatic interactions between the molecules is negligible). 3,000 BD trajectories took 36 hours of CPU time on an SGI Fuel workstation. A statistical analysis was performed using the MacroDox docking feature to determine potential binding modes. BD Simulations to Determine Free Energies BD simulations to determine the orientationally average free energy of the interaction of TPI and F-actin consisted of single trajectories where TPI took 1.2 × 107 diffusive steps within a cylinder of radius 250 Å around the F-actin. The reaction coordinate was defined as the distance between the TPI COM and the F-actin helical axis. The distribution of residence times of the COM of TPI in cylindrical concentric bins of 1 Å was then tallied and converted


Schmidt et al.

Page 6


to a potential of mean force (PMF) in the radial dimension by a statistical mechanical formula (Equation 6) as described previously by McCammon and Harvey (52, 53). The radial free energy of interactions was then calculated from the potential of mean force (PMF) (Equation 7). The potential of mean force constitutes the effective attractive force or radial free energy of enzyme-actin association, including Boltzmann statistical averaging over all orientational degrees of freedom. (6)

(7)

Where kB is the Boltzmann constant; T is the temperature; ρ(R) is the residence time at distance R (ρ (R) is an average distribution function), and C is the constant value of PMF when the electrostatic potential about protein is fully dissipated (zero) (116–120 Å). The radial free energy (A) was obtained by subtracting C, which set the PMF to zero since at this point the EP about F-actin is negligible or actually zero. This radial free energy is the actual driving force behind this interaction. To obtain good statistics, 10 different simulations, each of which took about 3 hours of CPU time on a SGI Fuel workstation, were performed and the results averaged to determine the free energy curves.


Results Electrostatic Potentials


The electrostatic potential (EP) maps about the proteins herein are represented by two dimensional (2D) contour maps (Figures 4–6). Each contour line represents the electrostatic potential about the molecule at a specific position from the molecular surface. This representation makes it easier to observe the effects of salt concentration or ionic strength on the interactions between TPI and F-actin. These contour maps also sample the electrostatic potential surface of the interacting molecules, and therefore help identify potential regions of charge aggregation. Clusters of similar charges enhance and extend the electrostatic potential about the surface of the molecules facilitating long range electrostatic interactions. The EP contours show that there are no significant regions with an aggregation of charges on the TPI surface. In general, that the electrostatic potential about TPI in all species does not show any considerable extension from the surface of the molecule as compared to other glycolytic enzymes (data not shown). Rabbit and human profiles (Figures 4 and 5) show very little extension of the 0.5 kcal/mol contour line at 0.01 M ionic strength. This little extension is entirely lost at higher ionic strength. The fish profile show some extension of the −0.5 kcal/mol contour line of about 5–10 Å from the surface of the molecule at 0.01 M ionic strength (Figure 6). This is considerably diminished at physiological ionic strength (0.10–0.15 M). Center of Mass Profiles The center mass profile is a distribution map of the end point of interaction for each trajectory in a multiple trajectory BD simulations. The center of mass profiles of fish, rabbit, and human F-actin and TPI show the distribution of complexes formed around each molecule (Figures 7–9). Such distribution maps can help identify specific surfaces of the proteins responsible for the interactions. For example, the profiles indicate that F-actin interacts with TPI using specific residues clustered on the surface of actin. (Figures 7–9 Left Panels). For TPI, on the other hand, the profiles vary by species. For fish, there is no characteristic binding site for F-actin on TPI (Figure 7 Right Panel). There is also no bias in putative complexes formed between the surface exposed regions of the TPI dimer indicating


Schmidt et al.

Page 7


that the interactions are just random collisions between the F-actin and TPI. For rabbit and human (Figures 8 and 9 Right Panels) there is a preferable interaction on one side of the dimer. This side of the TPI dimer possesses a group of positively charged residues, like Lys 54, 55, 5, and 19 that could be responsible in this observed bias. The bias for one side of TPI is most pronounced for rabbit. Free Energy The free energy profiles determine the orientationally averaged interaction between F-actin and TPI. The deeper the well in the free energy profile, the stronger the interaction between TPI and F-actin, and thus a higher probability for TPI and F-actin to rapidly orient in solution to form stable complexes. The minimum in the free energy curve for simulations with rabbit and human occurs when the center of mass of the TPI molecule is 60 – 70 Å from the F-actin helical axis (Figure 10) and only shows a significant well depth at 0.01 M ionic strength. The depth of the wells decreases as the ionic strength increases indicating a decrease in the interaction. The entropic curve represents the orientationally averaged random binding in the absence of electrostatic forces. The free energy curves for simulations with fish TPI do not have any wells (Figure 10 C) at any ionic strength indicating only random interactions between the two proteins.

Discussion and Conclusions NIH-PA Author Manuscript

BD simulations suggest that fish TPI and F-actin do not bind because the entropic curve falls below the free energy profiles. This can also be observed from the center of mass profiles showing only random hits between both molecules. Furthermore, the EP contours about fish TPI are highly negative as are those for F-actin, so random binding is not surprising. Rabbit and human TPI also show very little interaction at physiological ionic strengths (0.1 and 0.15 M) because the entropic curve is basically indistinguishable with the free energy profiles for both molecules. Experimental measurements at physiological ionic strength indicate that TPI does not bind F-actin, and BD is essentially agreeing with this observation (17, 29). Furthermore, BD simulations suggest that no feasible interactions occur between TPI and F-actin due to the absence of a significant and characteristic cluster of charges on the surface of the molecule.


Another major finding, peculiar to both rabbit and human TPI, is a broad well at lower ionic strength (0.01 M) indicating an interaction at ionic strengths well below physiological. This binding can be explained by observing the difference in electrostatic potential for TPI at ionic strength of 0.05 M and ionic strength 0.01 M (Figures 4 and 5). From 0.05–0.15 M there is really no extension of the 0.5 kcal/mol contour line. This implies that the pair of molecules must be much closer together for any meaningful electrostatic interactions to occur. At such distances (less that 40 Å) between the center of mass atoms of both proteins, repulsive forces become significant and reduce the extent of interaction. As a result the well depth indicating potential binding is lost at higher ionic strengths (Figure 10 A and 10 B). At 0.01 M, however, these distance restrictions are lessened by the minimal extension of the 0.5 kcal/mol contour line (Figures 4 and 5). The broadness of the well indicates that binding is not as specific or as strong as has been seen for other glycolytic enzymes like aldolase (41, 42), GAPDH (54, 55), or muscle LDH (2). TPI essentially will only bind at very low ionic strengths, and it essentially prefers a face not a narrow region of the molecule so no single dominant binding mode is seen (Figures 7–9). When complexes are formed, they are only stabilized by one or two salt bridges (Table 2). Since TPI is a small molecule, it can bind in different areas of F-actin that the larger enzymes cannot reach. Furthermore, enzymes that bind F-actin strongly even at physiological ionic strength show not only deeper free energy wells, but also multiple salt bridge stabilization and much more specific binding (41). Thus,


Schmidt et al.

Page 8

BD agrees with experiments that at physiological ionic strengths TPI will not bind F-actin directly (29).


In conclusion, BD simulations describe the energetics of interactions between TPI and Factin from three species as a function of ionic strength confirming experimental investigations that TPI does not bind F-actin directly at physiological ionic strength. TPI does not possess a characteristic binding surface for interaction with F-actin.

References

NIH-PA Author Manuscript NIH-PA Author Manuscript

1. Cho KK, Lee K, Lee KH, Kim D, Lee D. Proteins: Struct, Funct, and Bioinf. 2006; 65:593–606. 2. Njabon, NE. PhD Disseration. Deparment of Chemistry, University of North Dakota, Grand Forks; 2007. Brownian Dynamics Simulations of Interactions between Aldolase, GAPDH or LDH with Cytoskeletal Proteins; p. 5-6. 3. Volker, KW. PhD Dissertation. Department of Biochemistry, University of North Dakota, Grand Forks; 1997. A novel regulatory mechanism of glycolysis via glycolytic enzyme-tubulin interactions: modulation by protein-tyrosine kinase phosphorylation of tubulin; p. 169 4. Masters CJ, Reid S, Don M. Mol. Cell. Biochem. 1987; 76:3–14. [PubMed: 3306346] 5. Brooks SPJ, Storey KB. FEBS Lett. 1991; 278:135–138. [PubMed: 1991501] 6. Gutowicz J, Terlecki G. Cell. Mol. Biol. Lett. 2003; 8:667–680. [PubMed: 12949607] 7. Lynch RM, Paul RJ. Am. J. Physiol. Cell. Physiol. 1987; 252:C328–C334. 8. Knull HR. UCLA Symp. Mol. Cell. Bio. New Ser. 1990; 133:215–228. 9. Haanstra JR, van AT, Kessler P, Reijnders W, Michels PAM, Westerhoff HV, Parsons M, Bakker BM. Proc. Natl. Acad. Sci. U. S. A. 2008; 105:17718–17723. [PubMed: 19008351] 10. Walsh JL, Knull HR. Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol. 1988; 952:83–91. 11. Knull HR, Walsh JL. Curr. Top. Cell. Regul. 1992; 33:15–30. [PubMed: 1499331] 12. Campanella ME, Chu H, Wandersee NJ, Peters LL, Mohandas N, Gilligan DM, Low PS. Blood. 2008; 112:3900–3906. [PubMed: 18698006] 13. Carlson BE, Vigoreaux JO, Maughan DW. FASEB J. 2008; 22:31–43. 14. Velanker SS, Ray SS, Gokhale RS, Suma S, Balaram H, Balaram P, Murthy MRN. Structure. 1997; 5:751–761. [PubMed: 9261072] 15. Masters C. Interactions between glycolytic enzymes and components of the cytomatrix. J. Cell. Boil. 1984; 99:222–225. 16. Clarke FM, Masters CJ. Int. J. Biochem. 1976; 7:359–365. 17. Bronstein WW, Knull HR. Can. J. Biochem. 1981; 59:494–499. [PubMed: 7296340] 18. Nakagawa T, Nagayama F. Nippon Suisan Gakkaishi. 1989; 55:165–171. 19. Karkhoff-Schweizer RR, Knull HR. Biochem. Biophys. Res. Commun. 1987; 146:827–831. [PubMed: 3619904] 20. Knull HR, Bronstein WW, DesJardins P, Niehaus WG. J. Neurochem. 1980; 34:222–225. [PubMed: 6893841] 21. Arnold H, Henning R, Pette D. Eur. J. Biochem. 1971; 22:121–126. [PubMed: 5107301] 22. Spichenkov AV, Bagnjykova TV, Lushchak VI. Ukr. Biokhim. Zh. 1996; 68:47–52. [PubMed: 9273727] 23. Lushchak VI. Voprosy Biologicheskoi, Meditsinskoi i Farmatsevticheskoi Khimii. 2002; 1:27–31. 24. Lushchak VI, Lushchak LP. Ukr. Biokhim. Zh. 1996; 68:42–47. [PubMed: 9273726] 25. Lushchak VI, Bahnjukova TV, Storey KB. Braz. J. Med. Biol. Res. 1998; 31:1059–1067. [PubMed: 9777012] 26. Lushchak VI. Ukr. Biokhim. Zh. 1996; 68:20–28. [PubMed: 9005655] 27. Laursen SE, Belknap JK, Sampson KE, Knull HR. Biochem. Biophys. Acta. 1990; 1034:118–121. [PubMed: 2328257] 28. Knull HR, Laursen SE, Cottick AA. Mod. Cell. Biol. 1987; 5:157–161.


Schmidt et al.

Page 9

NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

29. Stephan P, Clarke F, Morton D. Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol. 1986; 873:127–135. 30. Lowe SL, Adrian C, Ouporov IV, Waingeh VF, Thomasson KA. Biopolymers. 2003; 70:456–470. [PubMed: 14648757] 31. Andrew SM, Thomasson KA, Northrup SH. J. Am. Chem. Soc. 1993; 115:5516–5521. 32. Ermak DL, McCammon JA. J. Chem. Phys. 1978; 69:1352–1360. 33. Kabsch W, Mannherz HG, Suck D, Pai EF, Holmes KC. Nature. 1990; 347:37–44. [PubMed: 2395459] 34. Mande SC, Mainfroid V, Kalk KH, Goraj K, Martial JA, Hol WG. Protein Sci. 1994; 3:810–821. [PubMed: 8061610] 35. Aparicio R, Ferreira ST, Polikarpov I. J. Mol. Biol. 2003; 334:1023–1041. [PubMed: 14643664] 36. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. Nucleic Acids Res. 2000; 28:235–242. [PubMed: 10592235] 37. Bairoch AA, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O’Donovan C, Redaschi N, Yeh LL. Nucleic Acids Res. 2005; 33:D154–D159. [PubMed: 15608167] 38. Strausberg RL, Feingold EA, Grouse LH, Derge JG, Klausner RD, Collins FS, Wagner L, Shenmen CM, Schule GD, Altschul SF, Zeeberg B, Buetow KH, Schaefer CF, Bhat NK, Hopkins RF, Jordan H, Moore T, Max SI, Wang J, Hsieh F, Diatchenko L, Marusina K, Farmer AA, Rubin GM, Hong L, Stapleton M, Soares MB, Bonaldo MF, Casavant TL, Scheetz TE, Brownstein MJ, Usdin TB, Toshiyuki S, Carninci P, Prange C, Raha SS, Loquellano NA, Peters GJ, Abramson RD, Mullahy SJ, Bosak SA, McEwan PJ, McKernan KJ, Malek JA, Gunaratne PH, Richards S, Worley KC, Hale S, Garcia AM, Gay LJ, Hulyk SW, Villalon DK, Muzny DM, Sodergren EJ, Lu X, Gibbs RA, Fahey J, Helton E, Ketteman M, Madan A, Rodrigues S, Sanchez A, Whiting M, Madan A, Young AC, Shevchenko Y, Bouffard GG, Blakesley RW, Touchman JW, Green ED, Dickson MC, Rodriguez AC, Grimwood J, Schmutz J, Myers RM, Butterfield YSN, Krzywinski MI, Skalska U, Smailus DE, Schnerch A, Schein JE, Jones SJM, Marra MA. Proc. Natl. Acad. Sci. U.S A. 2002; 99:16899–16903. 38 A. [PubMed: 12477932] 39. Hanauer, Levin M, Heilig R, Daegelen D, Kahn A, Mandel JL. Nucleic Acids Res. 1983; 11:3503– 3516. [PubMed: 6190133] 40. Collins JH, Elzinga M. J. Biol. Chem. 1975; 250:5915–5920. [PubMed: 1150665] 41. Forlemu NY, Waingeh VF, Ouporov IV, Lowe SL, Thomasson KA. Biopolymers. 2007; 85:60–71. [PubMed: 17039493] 42. Ouporov IV, Knull HR, Thomasson KA. Biophys. J. 1999; 76:17–27. [PubMed: 9876119] 43. Holmes KC, Popp D, Gebhard W, Kabsch W. Nature. 1990; 347:44–49. [PubMed: 2395461] 44. Merritt TJS, Quattro JM. Genteics. 2001; 159:689–697. 45. Northrup SH, Thomasson KA, Miller CM, Barker PD, Eltis LD, Guillemette JG, Inglis SC, Mauk AG. Biochemistry. 1993; 32:6613–6623. [PubMed: 8392365] 46. Northrup SH, Boles JO, Reynolds JCL. J. Phys. Chem. 1987; 91:5991–5998. 47. Tanford C, Roxby R. J. Am. Chem. Soc. 1957; 79:5333–5339. 48. Tanford C, Roxby R. Biochemistry. 1972; 11:2192–2198. [PubMed: 5027621] 49. Klapper I, Hagstrom R, Fine R, Sharp K, Honig B. Proteins: Struct. Funct. Genet. 1986; 1:47–59. [PubMed: 3449851] 50. Gilson MK, Honig BH. Nature. 1987; 330:84–86. [PubMed: 3313058] 51. Warwicker J, Watson HC. J. Mol. Biol. 1982; 157:671–679. [PubMed: 6288964] 52. Harvey SC. Proteins. 1989; 5:78–92. [PubMed: 2664766] 53. McCammon, JA.; Harvey, SC. Dynamics of proteins and nucleic Acids. New York, USA: Cambridge University Press; 1987. p. 220 54. Waingeh VF, Gustafson CD, Kozliak EI, Lowe SL, Knull HR, Thomasson KA. Biophys. J. 2006; 90:1371–1384. [PubMed: 16326908] 55. Ouporov IV, Knull HR, Lowe SL, Thomasson KA. J. Mol. Recog. 2001; 14:29–41.


Schmidt et al.

Page 10


Figure 1.

Cartoon Representing Free Diffusion Model in Glycolysis. Each glycolytic enzyme has to diffuse or equilibrate with bulk media before interacting with its substrate (S) to perform catalysis. Each glycolytic enzyme is represented by the black spheres, and the substrate and product (P) with grey spheres. Enzymes: hexokinase (HK), phosphoglucoisomerase (PGI), phosphofructokinase (PFK), fructose-1,6-bisphosphate aldolase (aldolase), triosephosphate isomerase (TPI), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), phosphoglycerate mutase (PGM), enolase, pyruvate kinase (PK), and lactate dehydrogenase (LDH).

NIH-PA Author Manuscript J Undergrad Chem Res. Author manuscript; available in PMC 2014 March 15.

Schmidt et al.

Page 11

NIH-PA Author Manuscript Figure 2.


Cartoon Representing Compartmentation Model in Glycolysis. Some glycolytic enzymes interact with cytoskeletal proteins like F-actin leading to compartmentation. As a result, glycolysis should proceed with little equilibration of enzyme and intermediates with bulk solvent. Abbreviations are identical to those in Figure 1.


Schmidt et al.

Page 12

NIH-PA Author Manuscript NIH-PA Author Manuscript Figure 3.

Structural Alignment to Determine Structurally Conserved Regions Between Rabbit and Human TPI. Based on good sequence similarity the coordinates of fish TPI were copied from the rabbit structure. Identical amino acids among the sequences appear in bold.


Schmidt et al.

Page 13



Two-dimensional Electrostatic Potential Contour Maps About Rabbit TPI as a Function of Ionic Strength. Solid lines represent positive electrostatic potential and dashed lines negative electrostatic potential. (A) I = 0.01 M. (B) I = 0.05 M. (C) I = 0.1 M. (D) I = 0.15 M.


Schmidt et al.

Page 14


Two-dimensional Electrostatic Potential Contour Maps About Human TPI as a Function of Ionic Strength. Solid lines represent positive electrostatic potential and dashed lines negative electrostatic potential. (A) I = 0.01 M. (B) I = 0.05 M. (C) I = 0.1 M. (D) I = 0.15 M.


Schmidt et al.

Page 15



Two-dimensional Electrostatic Potential Contour Maps About Fish TPI as a Function of Ionic Strength. Solid lines represent positive electrostatic potential and dashed lines negative electrostatic potential. (A) I = 0.01 M. (B) I = 0.05 M. (C) I = 0.1 M. (D) I = 0.15 M.


Schmidt et al.

Page 16

NIH-PA Author Manuscript Figure 7.


Center of Mass (COM) Diagram for Fish TPI/F-actin Complexes at 0.05 M. Left: The distribution of COMs of fish TPI (dots) around fish F-actin (lines). Each dot represents the COM of TPI in an encounter snapshot with F-actin. Right: The distribution of the COMs of fish F-actin (dots) around fish TPI (lines).


Schmidt et al.

Page 17


Figure 8.

COM Diagram for Rabbit TPI/F-actin Complexes at 0.05 M. Left: The distribution of COMs of rabbit TPI (dots) around rabbit F-actin (lines). Each dot represents the COM of TPI in an encounter snapshot with F-actin. Right: The distribution of the COMs of rabbit Factin (dots) around rabbit TPI.


Schmidt et al.

Page 18


Figure 9.

COM Diagram for Human TPI/F-actin Complexes at 0.05 M. Left: The distribution of COMs of human TPI (dots) around human F-actin (lines). Each dot represents the COM of TPI in an encounter snapshot with F-actin. Right: The distribution of the COMs of human F-actin (dots) around human TPI.


Schmidt et al.

Page 19



Radial Free Energy Between F-actin and Fish TPI. The reaction coordinate is the distance between the TPI COM and F-actin helical axis. (A) Rabbit TPI, (B) Human TPI and (C) Fish TPI


Schmidt et al.

Page 20


Scheme 1.

The Glycolytic Pathway. Breaking Down Glucose to Lactate. Where P is a phosphate group (PO32−).


Schmidt et al.

Page 21

Table 1

Total Charges of the Proteins


Charge (e) Protein

I=0.01 M

I = 0.05 M

I =0.10 M

I = 0.15 M

Fish TIM

−20.285

−20.861

−21.079

−21.186

Human TIM

−0.971

−0.740

−0.812

−0.802

Rabbit TIM

1.147

1.125

1.092

1.066

Fish F-actin

−59.341

−64.053

−66.011

−62.041

Human/Rabbit F-actin

−58.09

−63.626

−62.041

−65.588

NIH-PA Author Manuscript NIH-PA Author Manuscript J Undergrad Chem Res. Author manuscript; available in PMC 2014 March 15.

Schmidt et al.

Page 22

Table 2

Typical Complex Showing the Salt Bridges That Stabilize Each Complex Formed Between TPI and F-actin


Distance Å

F-actin Atom

TPI

Residue #

Atom

Residue #

2.95

OE1

GLU 99C

NZ

LYS 5A

3.56

OD2

ASP 363C

N

SER 3A

6.77

OE1

GLU 99C

OE2

GLU 38A

7.24

OE2

GLU 364C

OD2

ASP 198A

7.65

OE2

GLU 2C

NZ

LYS 159A

8.11

OE2

GLU 125C

N

SER 3A

9.09

NZ

LYS 359C

OD2

ASP 156A

9.12

NZ

LYS 359C

N

SER 3A

9.37

OE2

GLU 2C

OD2

ASP 156A

9.4

NZ

LYS 84C

NZ

LYS 32A

NIH-PA Author Manuscript NIH-PA Author Manuscript J Undergrad Chem Res. Author manuscript; available in PMC 2014 March 15.

beta barrel proteins.

Prenatal diagnosis of triose phosphate isomerase deficiency.

Simulation analysis of triose phosphate isomerase: conformational transition and catalysis.

Spectrophotometric studies on the interaction between triose phosphate isomerase and inhibitors.

Recent amplification of triose phosphate isomerase related sequences in lettuce.

Triose phosphate isomerase: interactions with ligands studied by [31P]phosphorus nuclear magnetic resonance [proceedings].

An analysis of the structure of triose phosphate isomerase and its comparison with lactate dehydrogenase.

cDNA cloning and functional expression of the Schistosoma mansoni protective antigen triose-phosphate isomerase.

A note on the two-dimensional representation of hydrogen bonding scheme in triose phosphate isomerase.

An analysis of the three-dimensional structure of chicken triose phosphate isomerase.

Evidence for the activity of immobilised monomers of triose phosphate isomerase.

The secreted triose phosphate isomerase of Brugia malayi is required to sustain microfilaria production in vivo.

Enzyme-substrate and enzyme-inhibitor complexes of triose phosphate isomerase studied by 31P nuclear magnetic resonance.

Studies of the histidine residues of triose phosphate isomerase by proton magnetic resonance and x-ray crystallography.

Chromatin models. The ionic strength dependence of model histone-DNA interactions: circular dichroism studies of lysine-leucine polypeptide-DNA complexes.

A kinetic study of the reaction between cytochrome c peroxidase and hydrogen peroxide. Dependence on pH and ionic strength.

phosphate translocator reveals the basis of substrate specificity.

Structure of chicken muscle triose phosphate isomerase determined crystallographically at 2.5 angstrom resolution using amino acid sequence data.

On the ionic-strength dependence of the intrinsic viscosity of DNA.

Ionic strength dependence of the binding of methylene blue to chromatin and calf thymus DNA.

Ionic strength dependence of the hysteresis in the polyriboadenylate-polyribouridylate system.

Association kinetics with coupled diffusion III. Ionic-strength dependence of the lac repressor-operator association.

Bridging interactions of proteins with silica nanoparticles: the influence of pH, ionic strength and protein concentration.

Performance of glutamate dehydrogenase and triose phosphate isomerase genes in the analysis of genotypic variability of isolates of Giardia duodenalis from livestocks.