Helical crystallization on lipid nanotubes: Streptavidin as a model protein

Journal of

Structural Biology Journal of Structural Biology 150 (2005) 90–99 www.elsevier.com/locate/yjsbi

Helical crystallization on lipid nanotubes: Streptavidin as a model protein Thanh X. Dang a,1, Sammy J. Farah b,1, Alice Gast c, Channing Robertson e, Bridget Carragher a, Edward Egelman d, Elizabeth M. Wilson-Kubalek a,* a

The Department of Cell Biology, The Scripps Research Institute, La Jolla, CA 92037, USA b Vaccine Technology and Engineering, Merck and Co, Inc., West Point, PA 19486, USA c The Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA d The Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22904, USA e The Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA Received 20 November 2004, and in revised form 26 January 2005

Abstract In this study, we use streptavidin (SA) as a model system to study helical protein array formation on lipid nanotubes, an alternative to 2D studies on lipid monolayers. We demonstrate that wild-type and a mutant form of SA form helical arrays on biotinylated lipid nanotubes. 3D maps from helical arrays of wild-type and mutant SA were reconstructed using two different approaches: Fourier-Bessel methods and an iterative single particle algorithm. The maps show that wild-type and mutant streptavidin molecules order differently. The molecular packing arrangements of SA on the surface of the lipid nanotubes differ from previously reported lattice packing of SA on biotinylated monolayers. Helical crystallization on lipid nanotubes presents an alternative platform to explore fundamentals of protein ordering, intermolecular protein interaction and phase behavior. We demonstrate that lipid nanotubes offer a robust and reproducible substrate for forming helical protein arrays which present a means for studying protein structure and structure–function relationships. Ó 2005 Elsevier Inc. All rights reserved. Keywords: Streptavidin; Electron microscopy; Lipid nanotubes; 2D crystals; Helical arrays; 3D density maps

1. Introduction Lipid surfaces are particularly versatile as substrates for forming 2D protein arrays because of the possibility of modifying the lipid substrate to facilitate protein binding (Uzgiris and Kornberg, 1983). By incorporating natural lipid ligands, charged lipids or chemically modified lipids with high-affinity ligands, protein molecules can be adsorbed from an aqueous solution and concentrated at the lipid–water interface. The lateral diffusion

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Corresponding author. Fax: +1 858 784 2749. E-mail address: [email protected] (E.M. Wilson-Kubalek). These authors contributed equally.

1047-8477/$ - see front matter Ó 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jsb.2005.02.002

of lipids in the fluid film allows enough translational and rotational freedom for the bound molecules to pack into ordered 2D arrays. This approach, combined with electron microscopy (EM) and image analysis, has been used to determine the structure of a variety of protein molecules (reviewed by Chiu et al., 1997; Jap et al., 1992; Kornberg and Darst, 1991). Studies of proteins bound to lipid substrates via specific interactions can also be used to further our understanding of intermolecular protein interaction, phase behavior, and the principles of 2D biomolecular ordering (Blankenburg et al., 1989; Darst et al., 1991; Wang et al., 1999a). The intrinsic properties of different proteins and their interactions with the lipid surface govern their ability to form ordered arrays.

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A method for promoting the formation of helical arrays of proteins has been developed (Wilson-Kubalek et al., 1998; Wilson-Kubalek, 2000). By combining the advantages of 2D lipid-layer crystallography with the tube-forming properties of galactosylceramide (GalCer) glycolipids, functionalized unilamellar lipid nanotubes bind proteins in a specific manner via high-affinity interactions and thus act as a substrate scaffold for helical crystallization. Helical arrays have an advantage over 2D crystals in that they contain a complete range of equally spaced molecular views of the protein molecules and yield a 3D density map without tilting the specimen with respect to the electron beam. Once the helical symmetry of the specimen has been determined, semi-automatic helical analysis and averaging software allows rapid calculation of three-dimensional maps (Carragher et al., 1996; Egelman, 2000; Owen et al., 1996). Helical image analysis has been successful in the elucidation of conformational changes of proteins that adopt helical ordering, as with myosin on F-actin and kinesin on microtubules (Jontes et al., 1995; Rice et al., 1999; Whittaker et al., 1995), and of tubular crystals of acetylcholine receptor (Unwin, 1996), dynamin (Sweitzer and Hinshaw, 1998), and Escherichia coli RNA polymerase (Opalka et al., 2000). Well-ordered helical specimens such as tobacco mosaic virus (Jeng et al., 1989), bacterial flagella (Morgan et al., 1995; Yonekura et al., 2003), the Ca-ATPase ion pump (Zhang et al., 1998), and the acetylcholine receptor (Miyazawa et al., 1999, 2003) have allowed visualiza˚ resolution range. Furtion of structure in the 4–11 A thermore, to provide insight into the structure– function relationships of macromolecular complexes, previously determined atomic models of single protein molecules have been accurately docked into 3D molecular envelopes determined by helical image analyses. (Nogales et al., 1999; Orlova et al., 2001; Sosa et al., 1997; VanLoock et al., 2003). SA is a well-characterized protein and several atomic models of SA are known (Freitag et al., 1997). SA exists as a homotetramer with a molecular weight of approximately 58 kDa (Green, 1990). The individual SA monomers possess a high affinity for the vitamin biotin (KD = 10
15 M) (Bayer et al., 1990), and the overall molecule displays 222 point group symmetry such that the two pairs of biotin binding sites reside on opposite faces of the protein (Weber et al., 1989). Studies of 2D SA crystals grown on biotinylated lipid monolayers show that two of the SA molecules in the tetramer bind to the lipid while the other two are free to interact with molecules in the aqueous solution (Darst et al., 1991). After binding biotin, conformational changes in the protein evoke anisotropic crystal growth in two dimensions (Ku et al., 1993; Wang et al., 1997, 1999a). A recent study determined the crystal structure with two biotinbound subunits and two biotin-free subunits (Freitag

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et al., 1997) corresponding to the state of SA when bound to a biotinylated lipid monolayer. Protein crystallography provides a means for observing intermolecular interactions between identical molecules and for studying the underlying mechanisms that give rise to long-range molecular ordering. Such interactions, between identical protein molecules, are thought to play a role in protein aggregation diseases including AlzheimerÕs, HuntingtonÕs, ParkinsonÕs, and prion diseases (reviewed in Horwich, 2002). Sickle-cell disease, one of the first diseases to be characterized at the molecular level, is the consequence of a single point-mutation that causes aberrant polymerization of hemoglobin under oxygen stress (Ingram, 1956). Likewise, point-mutations, introduced by directed mutagenesis, alter intermolecular contacts in protein crystals (Farah et al., 2001; Lawson et al., 1991; Qu et al., 1997; Wang et al., 1999b). Crystallization of proteins in 2D at surfaces affords an opportunity to study intermolecular interactions within a single plane of molecules. This reduced dimensionality simplifies matters by limiting the number of interactions that can occur. Along these lines, 2D crystals of SA, bound to lipid monolayers through biotin or via metal-ion coordination, have been thoroughly studied (Blankenburg et al., 1989; Darst et al., 1991; Frey et al., 1996a,b, 1998; Ku et al., 1993; Scheuring et al., 1999; Wang et al., 1997, 1999a,b, 2000). Most notably, this approach has yielded correlations between apparent intermolecular contacts and macroscopic crystalline morphologies. Functionalized lipid nanotubes produced from synthesized DODA-EO2-biotin lipid (Ringler et al., 1997) and hydrated mixtures of GalCer and biotin-X-DHPE lipid (Wilson-Kubalek et al., 1998) have been used to form helical arrays of SA. To date there are no 3D models of SA on lipid nanotubes using either of these methods. In a study of helical arrays of SA formed on carbon nanotubes, the orientation and the molecular contacts of the SA molecules were reported to be similar to 2D SA crystals grown on lipid monlayers (Balavoine et al., 1999). SA was used as our model system to evaluate the potential of functionalized lipid nanotubes as an alternative substrate to lipid monolayers to study protein self-assembly into ordered arrays. We used the mixed lipid approach (Wilson-Kubalek et al., 1998; WilsonKubalek, 2000) to produce biotinylated nanotubes. The biotinylated nanotubes were used to form helical arrays of wild-type and a mutant form of SA, having aspartate 36 replaced with lysine (D36K) (Wang et al., 1999b). The helical arrays of SA were analyzed separately with two independent analysis programs (Carragher et al., 1996; Egelman, 2000). The resulting 3D density maps of the wild-type and mutant SA show that the SA tetramers pack in a unique and specific molecular arrangement on biotin functionalized lipid nanotubes.

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We have shown that the atomic structure of SA can be docked into the molecular envelopes of the density maps. By confirming the use of this approach to produce a 3D map for the SA protein we have established a means for studying a variety of protein structures by this general helical crystallization method. Furthermore, lipid nanotubes can serve as an alternative substrate to study protein ordering by introducing new constraints and conditions that, in turn, affect the process of protein organization. Manipulating protein arrays on different surfaces will further our understanding of the self-assembly process and it has important applications in the development of biosensor technology and medical diagnostics (Higson and Vadgama, 1994; Moll et al., 2002; Nicolini, 1995).

the aqueous suspensions were vortexed for 30 s and then sonicated for two minutes in a water bath at room temperature to promote nanotube formation. The best ratio of biotinylated lipid to GalCer was determined empirically. Five microliters of each of the above nanotube preparations was applied to a carbon-coated EM grid. After blotting and negatively staining with 1% uranyl acetate the grids were observed with a FEI CM208 Electron Microscope (EM). The quality of the preparations was assessed on the basis of a high abundance of nanotubes of uniform diameter, lengths generally exceeding 1 lm, and a low abundance of other lipid structures, such as liposomes. Nanotube preparations were also compared based on their propensity to yield SA arrays. 2.4. Crystallization conditions

2. Materials and methods 2.1. Specimen Wild-type streptavidin was purchased from Prozyme (San Leandro, CA). The mutant streptavidin (D36K) is described elsewhere (Wang et al., 1999a,b). 2.2. Lipids Synthetic D-galactosyl-b1-1 0 -N-nervonoyl-D-erythrosphingosine (GalCer) was purchased from Avanti Polar Lipids (Alabaster, AL) and stored at 10.0 mg/mL in 50:50 chloroform/methanol. Biotinylated lipid, N-((6(biotinoyl)amino)hexanoyl)-1,2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine (Biotin-X-DHPE), was purchased from Molecular Probes (Eugene, OR) and stored at 1.0 mg/mL in chloroform. Biotinylated lipid, N-(6-(biotinoylamino)hexanoyl)-1,2,distearoyl-sn-glycero3-phosphoethanolamine (Biotin-X-DSPE) was purchased from Northern Lipids (Vancouver, Canada) and stored at 5.0 mg/mL in chloroform. 1,2-Dioleoyl-sn-glycero-3phosphoethanolamine-N-(Cap biotinyl) (N-Cap Biotinyl-PE) was purchased from Avanti Polar Lipids and stored at 5.0 mg/mL in chloroform. All lipids were stored in a
20 °C freezer. 2.3. Nanotube preparation We used the previously described protocol to produce lipid nanotubes (Wilson-Kubalek et al., 1998; WilsonKubalek, 2000). In brief, biotin lipids biotin-X-DHPE, biotin-X-DSPE, and N-Cap biotinyl-PE were mixed with GalCer at varying ratios (5:95, 10:90, 20:80, 30:70, 40:60, and 50:50 wt:wt) in the above organic solvents. The organic solvents from the different lipid mixes were removed by drying under a steady stream of argon and after rehydration in 50 mM Tris–HCl, 200 mM NaCl, pH 7.0, final lipid concentration 0.5 mg/mL,

Lipid nanotubes containing any of the three biotinylated lipids tested, biotin-X-DHPE, biotin-X-DSPE, or N-Cap biotinyl-PE, permitted the formation of SA arrays. To optimize the crystallization conditions we used biotin-X-DHPE:GalCer, which consistently yielded the most uniform nanotubes. Crystallization trials were carried out using all of the above ratios of biotin-XDHPE:GalCer (in 50 mM Tris–HCl, 200 mM NaCl, pH 7.0 at a final lipid concentration 0.45 mg/mL). Twenty microliter aliquots (in microcentrifuge tubes) of the aqueous nanotube suspensions were mixed with different protein concentrations (between 0.05 and 0.20 mg/mL) of both wild-type and mutant SA. The protein/nanotube mixtures were incubated at room temperature or at 4 °C for various time intervals (5 min—24 h). Helical array formation could be directly observed by EM as described above. Helical analysis was carried out on SA (wild-type and mutant) arrays formed by incubating SA (final protein concentration 0.15 mg/mL) with nanotubes containing biotin-X-DHPE:GalCer (20:80 wt:wt final lipid concentration 0.45 mg/mL) at room temperature for one hour. 2.5. Electron microscopy Images of the SA (wild-type and mutant) helical arrays, preserved by negative stain (1% uranyl acetate), ˚ 2) were recorded under low-dose conditions (<10 e/A with a Philips CM120 electron microscope at a nominal magnification of 35 000 operating at 100 kV. Electron micrographs were evaluated using an optical diffractometer to identify tubes with appropriate defocus and astigmatism and having strong diffraction signatures. The selected images were digitized with a Perkin-Elmer 1010G flat bed microdensitometer (Wellesley, MA) using a step size of 20.0 lm, corre˚ /pixel at the sponding to a sampling rate of 5.76 A specimen.

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2.6. Image analysis A total of 34 wild-type and 26 mutant SA nanotubes were scanned. Twenty-six of the wild-type tubes and 11 of the mutant SA tubes were selected for further processing based on the similarity of the tube diameters. Segments from the SA (wild-type and mutant) arrays were selected based on their diffraction pattern quality. The diameter of the selected tube segments that were ˚ and the pofurther processed ranged from 360 to 390 A sition of the identified layer lines in the helical diffraction patterns varied by less then 1 pixel between tubes. The large diameters of the nanotube arrays of SA enabled the helical symmetry of the tubes to be determined by constructing a 2D reciprocal lattice that best fit the helical diffraction patterns (Fig. 1) (Klug and DeRosier, 1966). The images were analyzed using both Phoelix, a Fourier based helical image analysis method (Carragher et al., 1996), and by an iterative helical real space reconstruction (IHRSR) method (Egelman, 2000).

Fig. 1. Images of wild-type and mutant SA nanotubes (A and B) with their corresponding Fourier transforms. The Fourier transforms show the lattice corresponding to the selected indexing scheme. Arrows point ˚ in the wild-type SA and 27 A ˚ in the to diffraction peaks at 25 A mutant SA diffraction patterns.

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Analysis using the Phoelix software package proceeded essentially as described elsewhere (Carragher et al., 1998). Layer line data were collected for Bessel or˚ ) for the ders 632 out to the 18th axial layer line (26 A wild-type SA and Bessel orders 628 out to the 13th axial ˚ ) for the D36K mutant SA. These data layer line (27 A were used to determine and correct for any tilt of the tube out of the plane of the image and any shift of the tube axis. Typical tilts and shifts were 0.0°–1.0° and 0.0–1.7 pixels. Tubes exhibiting a tilt >5° or a shift >2 pixels were rejected. A total of 18 for wild-type and 10 ˚ were selected for furmutant tube segments of 3000 A ther analysis using these criteria and on the basis that they belonged to one helical family. The tube segments were moved to a common phase origin and averaged using one of the individual tubes segments as a template for the initial round of fitting and averaging. Averaging and fitting was iterated three times with the averaged data set from the previous round acting as the new template in each successive round. Tube segments with phase residuals >25° and radial scaling >1.1 were not included in the average. The averaged layer lines were ˚ and used to compute a three-dimentruncated to 25 A sional map using Fourier Bessel inversion. The set of images selected for final averaging using the Phoelix analysis described above was also processed using IHRSR (Egelman, 2000), an iterative real-space/ helical approach based on the SPIDER single particle image processing package. Individual tubes were divided ˚ long segments with a 34.6 A ˚ shift into overlapping 576 A between adjacent segments, and treated as single particles. The orientation and relative translational displacement of each of the segments is determined by finding the best match between the segment and a set of re-projections of a 3D model of the tube. As an initial model for the wild-type SA we used two of the layer lines from the 3D map, which are re-projected around the helical axis in increments of 2°. The maximum cross-correlation match is used to determine the appropriate translational and rotational parameters with which to back project the individual segments to determine a 3D density map. The density map thus formed is searched to determine the helical symmetry in the structure. This process was repeated for the mutant SA tubes. The initial model in this case was the complete initial wild-type 3D map. The axial twist and rise parameters calculated from the original indexing scheme became the starting point of the helical symmetry search. In the case of wild-type SA the starting point had an axial twist of
9.6° and ˚ . For the mutant data set it was an axial rise of 52 A ˚ , respectively. The search range for
130.2° and 6.87 A the axial twist and axial rise was in increments of 0.1° ˚ for the wild-type, a narrower range of and 0.1 A ˚ rise and 0.01° twist led to convergence on a sen0.01 A sible map for the mutant SA helical crystals. This search results in new helical parameters that best match the

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symmetry of the calculated map, which is helically averaged based on these parameters to form a new helically symmetric 3D map. This takes the place of the initial 3D template and the procedure is iterated. We performed a total of 20 iterations although the procedure stabilized after 3–4 iterations. For the wild-type data set, the final axial rise and twist for the structure was
9.95° and ˚ . For the mutant data set, it was
130.21° and 52.94 A ˚ 6.77 A, respectively. 2.7. Maps 3D density maps were reconstructed using both Phoelix (Carragher et al., 1996) and IHRSR (Egelman, 2000) software packages. Side and tilted surface representations and projected views of the final 3D density maps were produced using UCSF Chimera visualization software package (Pettersen et al., 2004) and AVS (Advanced Visual Systems, Waltham, MA). Density within a radius ˚ from the center, corresponding to noise from of 115 A extraneous lipid and stain, was removed from the tilted isosurface representation of the 3D map. Density within ˚ was dissected from the projected view a radius of 92 A (middle section of tube). A 90° wedge of the 3D map at ˚ was removed and flattened to generate a radius of 178 A a 2D projected view using MATLAB 6.5 (The Math Works, Natick, MA) with the mrc3d subprogram (http://ami.scripps.edu/publications/techreports/01-015/ .pdf), under the tool ContourCylSec. 2.8. Docking the crystal structure into the 3D maps The atomic structure of streptavidin with two bound biotins (SA), (PDB 1swd Freitag et al., 1997) was docked as a rigid body into the SA (wild-type and mutant) 3D density maps. The docking of the atomic structure into the 3D SA density maps was accomplished interactively on a Mac workstation. The position and orientation of the crystal structure in the wild-type and mutant 3D density maps were determined manually using the program PYMOL (DeLano Scientific LLC (http://www.delanoscientific.com)). The biotin molecules were oriented so they faced the lipid surface face. The quality of the docking was estimated visually, using the biotin molecules as a marker for the protein orientation on the lipid substrate, and the positioning was judged satisfactory if the SA tetramer filled the volume of the 3D density map.

3. Results 3.1. Helical arrays Using the methods previously described (Wilson-Kubalek et al., 1998; Wilson-Kubalek, 2000) we produced

helical arrays of both wild-type and mutant SA (D36K). SA bound and began ordering 5 min after incubation on all biotinylated lipid nanotubes tested in this study. The lipid mix of 20:80 biotin-X-DHPE lipid/GalCer wt:wt, provided the most uniform biotinylated lipid nanotubes with the least amount of background structures, such as liposomes or other aggregated lipid structures, and were subsequently used to form arrays of both wild-type and mutant SA. Well-ordered SA (wild-type and mutant) arrays formed on the biotin-XDHPE/GalCer nanotubes after one hour incubation at room temperature. An example of the wild-type and the mutant SA tubes and their respective computed diffraction patterns are shown in Fig. 1. The diffraction patterns display a series of layer lines characteristic of a helical structure and extend axially to a resolution of ˚ for the wild-type SA (Fig. 1A) and 27 A ˚ for 25 A the mutant SA (Fig. 1B). The averaged data set for the wild-type SA contained 12 nanotube segments with a total of 23 near and far sides corresponding to 11 000 molecules. The averaged data set for the mutant SA contained 5 nanotube segments with a total of 10 near and far sides corresponding to 5000 molecules. The number of helical segments selected and analyzed from each data set was sufficient to produce the respective 3D density maps. 3.2. Final maps 3D density maps of wild-type and mutant SA reconstructed using both Phoelix and IHRSR software processing packages produced essentially identical results. Surface representations and projected views of the final 3D density maps (reconstructed using PHOELIX) are shown in Figs. 2 and 3. Surface representations of the final 3D density maps (reconstructed using IHRSR) are shown in a supplemental figure. The surface representations and projected views of the 3D density map of the wild-type SA helical arrays (Fig. 2) show that the molecules appear to organize as alternating helical rows. Within each row, neighboring molecules are oriented with their long axes parallel. Between rows, the long axes of neighboring molecules are approximately perpendicular. Under the same crystallization conditions, mutant SA adopts a different molecular packing motif on the lipid nanotube substrate. The surface representations and projected views of the 3D density maps of the mutant SA helical arrays (Fig. 3) show that the long axis of each molecule is oriented roughly perpendicular to the long axis of each of its four nearest neighbors. Introducing a single amino acid substitution (aspartate 36 to a lysine 36) weakens the interaction between the molecules within the axial rows while the contacts with the molecules in the neighboring row become relatively stronger. We find a similar behavior for this mutant in lipid monolayer studies (Wang et al., 1999b); here

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Fig. 2. Views of the three-dimensional reconstruction of wild-type SA. The side view (A) of the surface rendered 3D map has been thresholded to show the continuous densities of the SA molecules and the underlying lipid substrate (black lines indicate organization of molecules). To view the ˚ , corresponding to noise from extraneous lipid and stain, inner and outer surface of the tube a tilted view (B) has density with a radius of 115 A ˚ , b = 57 A ˚ , and c = 120°) with pseudo 2-fold removed. 2D side projected view (C) from the 3D map (see Section 2) depicts a unit cell (a = 119 A ˚ ˚. symmetry. The projected view (D) has a density within a radius of 92 A cut out of the center. Scale bars = 100 A

the lysine–lysine repulsions forbid the crystal structure found in the wild-type and produce a lower density crystal instead. Ninety degrees wedges from the 3D map of the wild-type and mutant SA maps have been flattened and projected in 2D (Figs. 2C and 3C). The asymmetric ˚ , b = 57 A ˚ , and unit of the wild-type (a = 119 A c = 120°) shows that it has pseudo P2 symmetry whereas ˚, the asymmetric unit of the mutant SA (a = 92 A ˚ , and c = 87°) has pseudo P4 symmetry. This b = 77 A P4 symmetry is not observed in SA mutant crystals on spread lipid monolayers. The molecular packing arrangements of both the wild-type SA and the mutant SA on the biotinylated lipid nanotube scaffold are different from all reported studies of SA ordered via biotin or metal-ion coordination on lipid monolayers. To better understand the differences between the packing arrangement of the wild-type SA and the mutant SA we docked the atomic structure of SA into the respective density maps.

3.3. Comparison of SA packing arrangement We used the X-ray atomic structure of streptavidin (Freitag et al., 1997) with two bound biotin molecules since we predicted that only one pair of SA monomers could bind to the lipid nanotube surface. Side views of the fit into the 3D density maps of SA (wild-type and mutant) are shown in Figs. 4A and B. The biotin molecules (magenta) are oriented so they face the lipid surface. Inspection of the position of residue 36, aspartate in wild-type SA and lysine in the mutant (indicated by blue space-filling in Fig. 4), suggests that upon mutation this basic residue can electrostatically interact with Glu101 (indicated by red space-filling in Fig. 4) on a neighboring protein molecule. Glu101 and Asp36 occur on surface loops with high b-factors in the atomic model. Presumably these surface loops adopt a conformation that facilitates the Lys36–Glu101 interaction. The distance between the a-carbons of these residues in the

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Fig. 3. Views of the three-dimensional reconstruction of mutant SA. The side view (A) of the surface rendered 3D map has been thresholded to show the continuous densities of the SA molecules and the underlying lipid substrate (black lines indicate organization of molecules). To view the inner and ˚ , corresponding to noise from extraneous lipid and stain, removed. 2D outer surface of the tube a tilted view (B) has density within a radius of 115 A ˚ , b = 77 A ˚ , and c = 87°) with pseudo 4-fold symmetry. The side projected view (C) from the 3D map (see Section 2) depicts a unit cell (a = 92 A ˚ cut out of the center. Scale bars = 100 A ˚. projected view (D) has a density within a radius of 92 A

Fig. 4. Docking of the atomic structure of SA into the 3D density maps. The atomic structure of SA (PDB 1swd, Frietag et al., 1997) was docked as a rigid body into side views of SA wild-type (A) and mutant (B) 3D density maps so that the biotin molecules (magenta) face the lipid surface. Asp 36, is indicated (by space-filling) in blue in both (A) and (B). Glu 101, present on a surface loop is indicated (by space-filling) in red in both (A) and (B). This arrangement suggests that upon mutation of aspartate 36 to lysine (in the mutant SA map) this basic residue can electrostatically interact with Glu101 in a neighboring protein molecule.

˚ . Flexible side surface loops, as depicted in Fig. 4B, is 9 A loop extension and the addition of a longer lysine side chain would shorten this distance sufficiently to allow

for the proposed electrostatic interaction. On the other hand, the distance between Asp36 and Glu101 in the fit ˚ is too of neighboring wild-type SA molecules of 19 A

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large to permit an electrostatic interaction. Using the biotin molecules as markers for the proteinÕs orientation on the lipid scaffold, the atomic structure of SA fits remark˚ molecular envelopes of the SA ably well into the 25 A (wild-type and mutant) 3D density maps.

4. Discussion In this study, we used streptavidin as a model protein to demonstrate that lipid nanotubes are a practical and valuable substrate for protein structural studies. We established that helical protein arrays on lipid nanotubes can be used as a tool for investigating the physical-chemical behavior of protein ordering, intermolecular protein interactions and phase change behavior of protein arrays. Streptavidin formed helical arrays within minutes after incubation with the biotinylated lipid nanotubes. Efforts to slow down the crystallization process and obtain a well-ordered array along the entire length of the tube were unsuccessful. Averaging several small well-ordered segments of the nanotubes preserved in negative ˚ can provide strucstain, showed that 3D maps at 25 A tural details of the molecular packing arrangements of wild-type SA and a mutant form of SA where the single amino acid, aspartate 36, is substituted with lysine. One of the difficulties faced in using the methods of helical crystallization for determining macromolecular structure is assessing the helical symmetry of the tube prior to beginning the reconstruction procedures. This is a requirement in using the Phoelix software package (Carragher et al., 1998) as well as most other helical processing packages. The IHRSR (real space/helical hybrid method) (Egelman, 2000) offers an alternative approach requiring only an initial crude model of the structure. An estimate of the helical parameters, in terms of axial rise and rotation per subunit, can be made by examining the convergence of the procedure from different initial points. Starting with the helical parameters calculated with the conventional indexing methods we established the applicability of the IHRSR programs to calculate 3D maps for both the wild-type and mutant SA helical arrays. The helical parameters that converged to a sensible map for both the wild-type and mutant SA using IHRSR were virtually identical to those calculated using Phoelix, and the maps generated by the two approaches were similar. The IHRSR programs provided further confirmation that our indexing schemes for both wildtype and mutant SA were correct. This suggests that given the appropriate initial indexing, both programs are equivalent at determining macromolecular ordering of helical arrays on lipid nanotubes at the resolution in this study. It should be noted that the resolution is not limited by the helical crystallization method but rather by the negative stain and the amount of defocus in the images. In a separate study, using Cryo-EM techniques,

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helical protein arrays of a toxin molecule formed rela˚ ) that diftively small ordered segments (3000–6000 A ˚ fract to a resolution of 15 A (to be published). The 3D density maps of the wild-type and mutant SA on the biotinylated lipid nanotubes show that the macromolecular packing arrangement can be changed by a single point mutation in the SA monomers. Docking the crystal structure of SA into the 3D density maps (Figs. 4A and B) provided insight into how the intermolecular connections between the SA tetramers influence protein ordering. Therefore, rational packing/protein ordering may be designed and tested using the nanotube/helical array systems. Small perturbations in surface residues may yield new useful or instructive packing arrangements without disrupting core functional attributes, such as biotin or ligand binding, of proteins. Mixtures of mutant and wild-type proteins can be made to create different packing arrangements (Farah et al., 2001). This method offers what may be a general and widely applicable approach for investigating physical-chemical properties of protein crystals, as well as exploring protein structure and structure–function relationships. The lipid nanotube substrates can be produced consistently and perform reproducibly making this system an attractive alternative or complement to other traditional methods of forming protein arrays. And perhaps most importantly, helical protein arrays formed on lipid nanotubes afford simplified data collection and subsequent computer processing schemes for obtaining 3D protein structural information since all the views required are captured in a single image. The hope is that by using a well-characterized protein such as streptavidin to test and make further inquiries into this system, we may not only learn more about the fundamentals of protein ordering, but further develop a general strategy for the crystallization of a large variety of proteins. Functionalized lipid nanotubes may prove to be useful scaffolds for the crystallization of a large variety of proteins and could serve as a tool in efforts to automate protein crystallization.

Acknowledgments We thank Craig Yoshioka for assisting with the program PYMOL. This project was funded by NIH Grants GM61938 (to E.W-K.), GM61939 and RR17573 (to B.C.), and EB001567 (to E.E.). Additional support was provided (to S.F., A.G., and C.R.) by the National Science Foundation (BES-9729950).

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jsb.2005. 02.002.

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