Interaction of C60-Fullerene and Carboxyfullerene with Proteins

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Bioconjugate Chem. 2006, 17, 378−386

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Interaction of C60-Fullerene and Carboxyfullerene with Proteins: Docking and Binding Site Alignment Hadar Benyamini,*,†,§ Alexandra Shulman-Peleg,‡ Haim J. Wolfson,‡ Bogdan Belgorodsky,§ Ludmila Fadeev,§ and Michael Gozin*,§ Bioinformatics Unit, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel, School of Computer Science, Beverly and Raymond Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel, and School of Chemistry, Beverly and Raymond Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel. Received October 10, 2005; Revised Manuscript Received January 8, 2006

The unique properties of fullerenes have raised the interest of using them for biomedical applications. Within this framework, the interactions of fullerenes with proteins have been an exciting research target, yet little is known about how native proteins can bind fullerenes, and what is the nature of these interactions. Moreover, though some proteins have been shown to interact with fullerenes, up to date, no crystal structure of such complexes was obtained. Here we report docking studies aimed at examining the interactions of fullerene in two forms (C60 nonsubstituted fullerene and carboxyfullerene) with four proteins that are known to bind fullerene derivatives: HIV protease, fullerene-specific antibody, human serum albumin, and bovine serum albumin. Our work provides docking models with detailed binding pockets information, which closely match available experimental data. We further compare the predicted binding sites using a novel multiple binding site alignment method. A high similarity between the physicochemical properties and surface geometry was found for fullerene’s binding sites of HIV protease and the human and bovine serum albumins.

INTRODUCTION Since the discovery of fullerenes (1, 2), these molecules and their derivatives (3) have been extensively explored for biomedical applications. Examples of fullerene bioactivity includes antibacterial activity (3), neuroprotection (4, 5), DNA cleavage (6), apoptosis (7), ion channel inhibition (8), and amyloid formation inhibition (9). Enzyme inhibition by fullerene-based compounds was found for nitric oxide synthases (10) and glutathione reductase (11). Antiviral activity of fullerenes was demonstrated in the inhibition of the HIV protease, initially by modeling and subsequently by in vitro studies (12-15). Namely, the prediction that fullerene derivatives can potentially fit into the catalytic cavity of HIV protease was supported by binding and inhibition experiments. Interactions of fullerenes with some other proteins were also documented, including a fullerenespecific antibody (16, 17), human serum albumin (HSA), and bovine serum albumin (BSA) (18-20). However, in all cases, the reported information provides only partial data regarding structures of these fullerene-protein complexes. We have recently reported the preparation, characterization, and a docking model of a stable complex of HSA with a C3-isomer of carboxyfullerene (CF) (18). The agreement of the docking predictions with the experimental results has encouraged us to generate docking models of fullerenes with additional proteins. Characterizing protein binding sites of fullerenes is an important step toward better understanding of mechanisms by which various enzymes and receptors are interacting with fullerenes. * To whom correspondence should be addressed. Hadar Benyamini, current address: Department of Organic Chemistry, The Hebrew University of Jerusalem, Givat Ram, Jerusalem 91904, Israel. Tel. +972-2-6584854, Fax. +972-2-6585345, E-mail: hadar@ chem.ch.huji.ac.il. Michael Gozin: Tel. +972-3-6405878, Fax. +9723-6405879; E-mail: [email protected]. † Bioinformatics Unit. ‡ School of Computer Science. § School of Chemistry.

Here, using leading computational tools for protein structure analysis, we first provide docking models for the interactions of C60 nonsubstituted fullerene (NS-C60) and CF with four selected proteins: HSA, BSA, HIV protease, and the fullerenespecific antibody. Our models, obtained using the PatchDock algorithm (21-23), are consistent with previously reported experimental results and hence are highly reliable. The predicted binding sites were further compared using a novel method for multiple binding site alignment, MultiBind (24, 25), which revealed a significant similarity between fullerene’s binding sites of HSA, BSA, and HIV-protease.

METHODS Protein Structures. For docking studies we used the following structures: (i) HSA. We used the structure of free HSA (PDB: 1ao6, (26)). (ii) BSA. Using the Swiss-model server (27), the HSA structure was used as a template in order to build a structural model for the BSA protein. The pairwise sequence alignment has only one gap over all the 578 residues of the BSA sequence, with 75% identity and 87% similarity shared between the human and bovine sequences (Figure 1). Thus, our model for BSA is reliable and is likely to resemble the native three-dimensional structure. (iii) HIV-protease. We used the structure of PDB: 1aid (28). (iv) Fullerene specific antibody. We used the structure of PDB: 1emt (16). Docking. Docking models were obtained using the PatchDock algorithm (21). PatchDock takes as input two molecules and computes three-dimensional transformations of one of the molecules with respect to the other with the goal of maximizing surface shape complementarity while minimizing the number of steric clashes. Complexes may be of the types: proteinprotein, protein-small molecule, antibody-antigen, or proteinnucleic acid. The program was tested and shown to successfully predict protein interactions for many examples (21, 22, 29, 30). Given two molecules, PatchDock first divides their surfaces into patches according to the surface shape (concave, convex, or flat).

10.1021/bc050299g CCC: $33.50 © 2006 American Chemical Society Published on Web 02/22/2006

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Figure 1. Sequence and structure alignment of human and bovine serum albumins. Left: sequence alignment. In the middle line of each three lines, identical residues are detailed; ”+” stands for similar residues. Right: Structure alignment of HSA (PDB: 1ao6) with the model for BSA, built using the Swiss-model server (27). Structure alignment was performed using the C-R match program (http://bioinfo3d.cs.tau.ac.il). The gap region is marked in both alignments. Bovine serum albumin has one less amino acid residue, lacking a corresponding residue to Val112, using PDB: 1ao6 (HSA) numbering.

Then, it applies the Geometric Hashing algorithm to match concave patches with convex patches and flat patches with flat patches and generates a set of candidate transformations. Each candidate transformation is further evaluated by a set of scoring functions that estimate both the shape complementarity and the atomic desolvation energy (31) of the obtained complex. Finally, redundant solutions are discarded by the application of an RMSD (root-mean-square deviation) clustering. PatchDock is highly efficient, since it utilizes advanced data structures and spatial pattern detection techniques which are based on matching of local patches. The local shape information is then extended and integrated to achieve global solutions. The algorithm implicitly addresses surface flexibility by allowing minor penetrations. Binding Sites Alignment. The alignment of the predicted binding sites of the modeled complexes was performed using the MultiBind algorithm (24, 25). MultiBind considers protein binding sites represented by a set of three-dimensional points with assigned physicochemical and geometrical properties important for protein-ligand interactions. It performs multiple alignment of binding sites and allows recognition of the structurally conserved physicochemical and geometrical patterns that may be responsible for the binding and biological effect. The goal of MultiBind is to find a set of transformations that will superimpose the binding sites in a manner that will maximize the physicochemical score of the matched properties. Five physicochemical properties are considered: hydrophobic aliphatic, aromatic, hydrogen bond donors, hydrogen bond acceptors, and mixed donor/acceptors. The scoring function compares the similarity of the attributes, which are more important for the specific property. The molecules representation is based on the model of Schmitt et al. (32). According to this model, each amino acid is represented by a set of three-

dimensional points in space, denoted as pseudocenters. Each pseudocenter represents one of the five considered properties. The pseudocenters are extracted from the side-chains, as well as from the protein backbone. For example, the side chain of arginine is represented by three donors (nitrogen atoms) and an aliphatic pseudocenter (located at the center of mass of its three carbons), while the side-chain of phenylalanine is represented by an aromatic pseudocenter, located at the center of its ring. The MultiBind algorithm has three major computational steps. The first one is a generation of three-dimensional transformations that would align the binding sites. The second step is a search for a combination of three-dimensional transformations that would give the highest scoring common three-dimensional pattern of physicochemical properties. The final step is a computation of matching between points under multiple transformations. The MultiBind algorithm was applied to some well studied biological examples such as estradiol, ATP/ANP, and transition state analogues binding sites, providing results which agree with the available biological data (24, 25). Evaluation of Common Binding Patterns. To calculate the frequency of random occurrence of structural patterns recognized by MultiBind, we searched their occurrence on the complete surfaces of proteins in the nonredundant PDB representation by the ASTRAL dataset (33, 34) (release 1.65, less than 40% sequence identity). The searched structural pattern was represented by the pseudocenters of the first molecule (in the alignment input order). Each pattern that was recognized on the surface of some protein was scored by the above-described physicochemical scoring function of MultiBind. The frequency of occurrence was calculated as the ratio between the number of times that a pattern with a score higher than a reference score was observed, relative to the total number of searched proteins.

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Figure 2. Distribution of the two fullerene binding sites on HSA and BSA among the top 20 ranking solutions. X axis: Rank of docking solution, from 0 to 20. The ranking of docking solutions is determined by their score, which optimizes geometric complementarity. Y axis: Type of binding site on human or bovine serum albumin. Note, that this axis has no numerical value and numbers 1 and 2 stand for the different binding sites (Figures 3 and 4 for type 1 and type 2 binding sites, respectively). The upper and lower graphs depict the distribution of HSA and BSA binding sites, respectively. The left and right graphs depict the sites distribution for carboxy- and nonsubstituted fullerene, respectively. For example, the top two ranked docking solutions for the binding of HSA to carboxyfullerene (up, left) are on the binding site type 1.

The reference score is defined to be the score of the outlier, i.e., lowest score of the most different binding site that participated in the pattern construction with MultiBind. The obtained ratio represents the estimation of the chances for a random occurrence of the recognized pattern. Using the score of the outlier as a reference score provides the highest possible ratio and the worst case estimation of the most frequent pattern.

RESULTS AND DISCUSSION 1. Docking Models of Nonsubstituted C60 Fullerene and Carboxyfullerene with Four Proteins. We docked nonsubstituted C60 fullerene (NS-C60) and carboxyfullerene (CF) to four biomolecules: human serum albumin (HSA), bovine serum albumin (BSA), HIV protease (HIVP), and a fullerene-specific antibody. For three of the examined proteins, previously published data exists regarding fullerene binding sites: HIVP (12, 15, 35), fullerene-specific antibody (16, 17), and HSA (18). PatchDock successfully detected surface pockets that are in agreement with previously suggested binding sites. In all the reported docking models, no a priori information was used as an input for the PatchDock algorithm, i.e., the surface pockets on the receptor molecules were detected fully automatically. Being able to reconstruct known fullerene binding sites makes PatchDock a reliable tool for prediction of fullerene binding sites in other proteins. Docking of Fullerenes to HSA and BSA. The binding of HSA and BSA to fullerenes was observed experimentally (1820). The location of CF binding site in HSA was suggested to be adjacent to the Trp214 residue, based on time-resolved FRET measurements (18). The results of our docking studies suggest that both proteins are capable of fullerene binding by two distinctive binding sites, located on the opposite faces of the albumin molecules and for clarity termed binding sites “type 1” and “type 2”. For each of these proteins and for both ligands (NS-C60 and CF), the 20 top scored docking solutions were

found to be distributed over these two sites (Figure 2). In HSA, the type 1 binding site docking solution positions the fullerene molecule in a short distance from Trp214 residue (Figure 3). In general, this binding pocket was found to be almost identical for both albumins and for HSA can be marked by Trp214, His288, and His440 residues (the complete list of residues in radius of 6 Å from the fullerene ligand is detailed in the Supporting Information). The type 2 binding site places the fullerene ligand in a pocket on the opposite face of the albumin molecules (Figure 4). In HSA, this binding pocket can be marked by residues Lys190, Glu425, and Arg428 (the complete list of residues in radius of 6 Å from the fullerene ligand is detailed in the Supporting Information). Figure 2 presents the distribution of the type 1 and type 2 binding sites among the top 20 docking solutions. As can be seen in the figure, although both type 1 and type 2 binding sites appear in the top 20 ranking docking solutions for HSA and BSA, HSA protein displayed preferential binding of fullerene toward type 1 site, while the BSA protein displayed preferential binding toward type 2 site (Figure 2). The correlation between binding preferences and clustering of docking solutions was recently discussed (36). We estimate that these differences can be mostly attributed to the fact that type 2 binding site of BSA lacks an amino acid corresponding to the Val112 residue, which is present in type 2 binding site of HSA. This difference appears to make the type 2 site of BSA more compatible and less sterically crowded for accommodation of fullerene, in comparison to the similar site in HSA. Comparison between Binding of Fullerenes and Other Known HSA Ligands. The Protein Data Bank (37) currently holds 22 complexes of HSA with 14 different ligands, uniquely located at multiple various binding sites. Although, due to the substantial differences in ligands shapes, fullerene binding sites of types 1 and 2 are not identical to pockets of other known serum albumin ligands, these sites share many common amino

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Figure 3. Predicted binding site (type 1) for carboxyfullerene on human serum albumin. Left: carboxyfullerene is represented in a spacefill model. HSA is represented in cartoon, except for His288, Trp214, and His440 which are represented in spacefill and colored green. Right: Fullerene is represented in sticks. HSA is represented by its surface and colored light blue except for His288, Trp214, and His440 which are colored green. This Figure, as well as Figures 4, 5, and 6 were prepared using PyMOL (44).

Figure 4. Predicted binding site (type 2) for carboxyfullerene on bovine serum albumin. Leu190, Glu425 and Arg428 are marked. Coloring and representation are the same as in Figure 3.

acid residues with other reported binding pockets of HSA. For example, the predicted type 1 binding site of fullerene consists of residues that participate in binding of the following ligands: tribenzoic acid (PDB: 1bke); 2-bromo-2-chloro-1,1,1-trifluoroethane (PDB: 1e7c); decanoic acid (PDB: 1e7e); lauric acid (PDB: 1e7f); myristic acid (PDB: 1e7g, 1hk4, 1hk5, 1o9x); palmitic acid (PDB: 1e7h); stearic acid (PDB: 1e7i); oleic acid (PDB: 1gni); arachidonic acid (PDB: 1gnj); R-wafarin (PDB: 1h9z); S-wafarin (PDB: 1HSA-2); 3,5,3′,5′-tetraiodo-L-thyronine (PDB: 1hk1, 1hk2, 1hk3); citric acid (PDB: 1tf0). The predicted type 2 binding site of fullerene consists of residues that participate in binding of the following ligands: tribenzoic acid (PDB: 1bke); myristic acid (PDB: 1bke, 1e7c, 1e7g, 1h9z, 1HSA-2, 1hk4,1hk5); 2-bromo-2-chloro-1,1,1-trifluoroethane (PDB: 1e7c); decanoic acid (PDB: 1e7e); lauric acid (PDB: 1e7f); palmitic acid (PDB: 1e7h); stearic acid (PDB: 1e7i); oleic acid (PDB: 1gni); arachidonic acid (PDB: 1gnj); 3,5,3′,5′-

tetraiodo-L-thyronine (PDB: 1hk4, 1hk5); Fe-containing protoporphyrin IX (PDB: 1o9x, 1n5u). Figure 1 in the Supporting Information displays side-by-side the docking model of HSA-CF complex aligned with the structure of the HSA complex with citric and decanoic acids (PDB: 1tf0, (38)). Coloring the fullerene’s type 1 site residues on both the HSA-CF docking model and on the crystal structure of HSA complex with citric acid clearly demonstrates the similar residues that participate in the binding of both ligands. Docking of Fullerenes to HIV Protease. The binding of fullerene to HIV protease (HIVP) was first demonstrated by Friedman and co-workers (12, 15). Docking of fullerene to HIVP (PDB: 1aid, (28)), by using PatchDock, suggests the same binding pocket for NS-C60 and CF ligands (Figure 5). This cylindrically shaped, symmetric binding pocket can be marked by residues Asp25, Asp25′, Ile50, and Ile50′ (i.e. residues 25 and 50 on chains A and B; the complete list of residues is

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Figure 5. Predicted binding site for carboxyfullerene on HIV protease. Residues Asp25, Asp25′, Ile50, and Ile50′ are marked. Coloring and representation are the same as in Figure 3.

Figure 6. Predicted binding site for carboxyfullerene on a fullerene specific antibody. L chain residues Tyr32 and Phe96, as well as chain H residue Trp33 are marked. Coloring and representation are the same as in Figure 3.

detailed in the Supporting Information), which were reported to participate in various ligands binding and, in particular, in fullerene binding by HIVP (39). Docking of Fullerene to a Fullerene-Specific Antibody. Erlanger and co-workers have prepared a monoclonal fullerenespecific antibody and determined its structure by X-ray crystallography (16, 17). The suggested fullerene binding site was further subjected to molecular dynamics calculations (40), supporting the involvement of chain L residues Asn34, Leu46, Tyr49, Gln89, Tyr91, and Phe96, along with chain H residues Trp33, Trp47, Arg50, Tyr52, Ala97, and Ala101. In this work, by using the PatchDock algorithm with a set of parameters designed for antibody-antigen interactions prediction, we found the same pocket as the most likely site for fullerene binding by this antibody molecule (PDB: 1emt, (16)). Figure 6 displays the predicted binding site for fullerene on the fullerene-specific antibody. This binding pocket can be marked by chain L residues Tyr32 and Phe96, as well as chain H residue Trp33 (the complete list of residues is detailed in the Supporting Information).

2. Binding Sites Alignment and Analysis. After obtaining the docking models, our goal was to compare between the predicted binding sites and recognize the common features that may be related to the binding of fullerenes. Thus, we analyzed the similarities between the fullerene binding sites of HSA type 1, HSA type 2, HIVP, and the fullerene-specific antibody. The predicted binding sites of BSA were excluded from the multiple binding sites alignments, mainly due to these sites close resemblance to the corresponding HSA type 1 and type 2 sites. In addition, besides the inherent deviation of a docking model from the bound complex, additional deviations could exist due to the structure model building of BSA. The four compared binding sites have different overall sequences and tertiary structures. Specifically, the sequence identity between the HIVP and HSA proteins is only 8% and between HSA and the fullerene-specific antibody is 15%. Obviously, HSA, HIVP, and the fullerene-specific antibody are also structurally nonrelated. Due to the above-mentioned differences, the proteins in our study cannot be aligned by standard alignment methods, that assume similarity of either sequence

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Table 1. Details of the Common Pattern Calculated between the Three Most Similar Fullerene Binding Sites HSA, type 2

HSA, type 1

residue number

residue type

featurea

111 111 112 112 112 114 221 221 422 425

Asn Asn Lys Lys Lys Arg Pro Pro Thr Glu

Pii acceptor donor Pii acceptor donor Pii acceptor mixed donor-acceptor acceptor

HIV-protease

residue number

residue type

feature

292 292 293 293 293 294 447 447 448 451

Glu Glu Val Val Val Glu Pro Pro Cys Asp

Pii acceptor donor Pii acceptor donor Pii acceptor acceptor acceptor

residue

numberb

49 (A) 49 (A) 49 (A) 48 (A) 48 (A) 48 (A) 27 (A) 27 (A) 25 (B) 25 (A)

residue type

featurea

Gly Gly Gly Gly Gly Gly Gly Gly Asp Asp

Pii acceptor donor Pii acceptor donor Pii acceptor acceptor acceptor

a The common features detected by Multibind contain either Pii (aromatic) interactions, hydrogen bond donor or acceptor, or mixed donor-acceptor (can use as both). b The chain ID is in parentheses.

or backbone patterns (41-43). Thus, to compare between the predicted binding sites of the modeled complexes, we used a recently developed method, MultiBind (24, 25), which performs a multiple structure alignment between protein binding sites, in the absence of overall sequence, fold, or binding partner similarity. The MultiBind method recognizes the common spatial arrangements of physicochemical properties shared by the compared binding sites. This method is suitable for the comparison of the diverse studied proteins due to the utilized physicochemical representation of the protein molecules, where each amino acid is represented by a set of three-dimensional points in space, denoted as pseudocenters (see Methods for details (32)). Additionally, MultiBind does not consider the location of the binding partners, and therefore it is most appropriate for comparison of docking models, which provide only an approximate representation of the bound complex. Here, we describe the patterns of pseudocenters which were recognized as shared by the compared binding sites. The predicted binding sites of the modeled protein-fullerene complexes were compared with both NS-C60 and CF ligands. We analyzed the results of the alignments between a set of all four binding sites in addition to its different subsets (two and three binding sites). Then, we evaluated the quality and the significance of the resulted patterns recognized by MultiBind by calculating their frequency of occurrence on the surfaces of complete proteins in the nonredundant PDB representation by the ASTRAL dataset (33, 34). The ratio of the number of observed patterns, relative to the size of the searched dataset, provides an estimation regarding the probability of observing such a pattern by chance, on a randomly selected protein. The lower the frequency of occurrence, the more significant and rare is the pattern. Additional details regarding the exact calculations are provided in Methods. Multiple Alignments between Four Binding Sites (HSA type 1, HSA type 2, HIVP, and the fullerene-specific antibody). Multiple alignment between the four predicted binding sites of NS-C60 recognized a pattern of seven physicochemical properties. The pattern contained one aromatic property, one hydrogen bond donor, and five hydrogen bond acceptors. Although the NS-C60 ligands were not taken into account during any of MultiBind computational steps, transformations calculated by MultiBind for the binding sites superimpose the NS-C60 molecules to similar locations in space, supporting the correctness of the alignment. However, when the recognized pattern was searched in the proteins of the ASTRAL dataset, it was found in 36% of the cases. This suggests that the detected pattern is not likely to be specific enough to distinguish between proteins that can bind NS-C60 and those that cannot. The application of MultiBind to the four predicted binding sites of CF recognized a pattern of six physicochemical properties. The pattern consisted of three aromatic pseudocenters

and three hydrogen bond acceptors. Two properties, one acceptor and one aromatic, were created by backbone atoms in all of the compared binding sites. The frequency of occurrence of this pattern in the ASTRAL dataset was 37%, which is also too high to be statistically significant. Multiple Alignments between Three Binding Sites. The alignment between all four binding sites has shown that the binding site of the fullerene-specific antibody was the most different from all the rest, since it received the lowest score in the alignment. The inclusion of the antibody in the multiple alignment significantly reduced the size, the score, and the statistical significance of the common pattern. Therefore, in the alignments below, we removed this binding site from the aligned ensemble and compared only between the three binding sites of HSA type 1, HSA type 2, and HIVP. It must be noted that since the sequence and the fold of HIVP is very different from those of HSA, these three binding sites cannot be compared by standard sequence and structural alignment methods. The binding sites of HSA type 1 and HSA type 2 are also very different. They are located at opposite faces of HSA and are created by different noncontiguous sequences of HSA. Three NS-C60 Binding Sites. Comparison between the three predicted binding sites of NS-C60 revealed an interesting pattern of 10 common physicochemical properties. Table 1 presents the details of the recognized pattern. Figure 7 presents the spatial arrangement of the recognized features and the superimposition between the proteins and the fullerene ligands, according to the transformations suggested by MultiBind. The recognized pattern contained three aromatic properties, two donors, and five hydrogen acceptors. The frequency of occurrence of this pattern was 1%, which shows its relative rarity. Three CF Binding Sites. Interestingly, the pattern recognized by the alignment of the three CF binding sites was almost the same as the pattern recognized by the alignment of the three NS-C60 binding sites. This pattern contained 10 features with exactly the same properties. The contribution of the backbone and side-chain atoms has also remained the same. The slight differences in the exact alignment correspondence details may be explained by the different boundaries of the binding sites and by the approximation algorithms of the MultiBind method (see Methods). In general, we can summarize that the same binding pattern was recognized for the predicted binding sites of both NS-C60 and CF. Pairwise Alignments. To evaluate the similarity between the compared binding sites, we also performed all-against-all pairwise surface alignments (Table 2). Naturally, the patterns detected between pairs of binding sites contained more features than the patterns common to larger sets (three or four binding sites). The frequency of occurrence of the recognized pairwise patterns in the ASTRAL dataset was very low and ranged from 0 to 1.4%. The size of the pairwise patterns ranged from 11 to 20 (mean ) 17) properties for the predicted NS-C60 binding

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Figure 7. Surface alignment between the predicted binding sites of human serum albumin (types 1 and 2) and HIV protease. Left: the structures of human serum albumin (HSA) and HIV protease (HIVP) are represented by cartoons. HSA- type 1 is colored gray, HSA- type 2 is colored light gray and HIVP is colored according to secondary structure (β-strands are colored yellow and R-helices are colored pink). Fullerenes are represented as sticks and colored gray. The ligand molecules are presented for verification purpose only and are not a part of the input to MultiBind. Right: close-up on the aligned binding sites. Matched pseudocenters are represented by balls. Hydrogen bond donors are blue, acceptors are red, donors/ acceptors are green, and aromatic is white. The surface of human serum albumin (type 2 binding site) is represented in dots and colored cyan. The figure was prepared using RasMol. Table 2. Summary of Pairwise Alignments of Fullerene Binding Sites compared proteinsa

no. detected features

score

Ligand: Nonsubstituted [C60] Fullerene HSA-1, HSA-2 20 521.1 HIVP, HSA-2 19 489 HSA-1, HIVP 19 426.4 HSA-2, C60Ab 16 337.0 HSA-1, C60Ab 17 291.9 HIVP, C60Ab 11 238.4 Ligand: Carboxyfullerene HIVP, HSA-2 21 454.3 HSA-1, HSA-2 20 446.7 HSA-1, HIVP 19 442.7 HSA-2, C60Ab 15 318.4 HSA-1, C60Ab 12 291.1 HIVP, C60Ab 14 255.8

solution rank 1 1 1 1 1 1 1 1 1 1 1 1

a HSA-1 ) human serum albumin, type 1. HSA-2 ) human serum albumin, type 2. HIVP ) HIV protease. C60Ab ) fullerene specific antibody (16, 17). The columns containing the number of detected features, the score, and the solution rank are the output of the MultiBind algorithm.

sites and from 14 to 21 (mean ) 18) for the CF binding sites. In both cases the alignments that involved the fullerene-specific antibody recognized smaller patterns, with lower score and with higher frequency of random occurrence than in the rest of the cases. Figure 2 in the Supporting Information presents the similarity relationships between the compared binding sites, according to the score of MultiBind, plotted using the statistical toolbox of the Matlab program. The two binding sites that were recognized to be most similar to each other are those of HSA type 1 and HSA type 2. As can be seen in this figure, the binding site of the fullerene-specific antibody was indeed the most different from all the rest. Summary and Discussion of the Alignment Results. We have observed that patterns common to all the four binding sites are not specific enough and are not likely to represent the features essential for the binding of fullerenes. This can be explained by several factors. First, as shown by the pairwise

comparisons, the binding site of the fullerene-specific antibody is very different from all the rest. Second, in all of the cases the binding sites were actually extracted from unbound complexes (i.e. the molecules are given in their structures that were determined separately). Both the docking and the alignment algorithms are rigid and address protein flexibility only implicitly through a set of thresholds that allow certain variability in locations. In practice, both the side-chains and the protein backbone can undergo conformational changes upon ligand binding. The alignment results suggest that either the fullerene binding sites are different on different proteins, or that the fullerene binding sites undergo more significant conformational changes. A more interesting and significant pattern of shared physicochemical properties was recognized between the three binding sites of HSA type 1, HSA type 2, and HIVP. From the computational standpoint, the high similarity of the patterns recognized for the binding sites of NS-C60 and CF is encouraging and shows consistency of the docking and alignment methods. However, from the biological standpoint, we would have expected these patterns to be different. Specifically, NSC60 is a hydrophobic ligand and we would expect the pattern that characterizes its binding to be more hydrophobic. On the other hand, the CF ligand has chargeable groups, and we would expect its binding pattern to be less hydrophobic. The same pattern obtained for NS-C60 and CF cases could be explained by the fact that we have used the same unbound protein structures to construct the docking models with both ligands. The observation that most of the properties that constitute the recognized pattern are contributed by backbone atoms suggests the differences in side-chain orientations and conformations. In reality the side-chains of the considered binding sites may rearrange and undergo conformational changes to accommodate each of the considered ligands in a slightly different manner. However, only solving the structures with these ligands will provide an exact answer to this puzzle.

Protein−Fullerene Complexes

CONCLUSIONS Here we have employed a computational approach in order to address the subject of fullerene interactions with proteins. We suggested docking models consistent with available experimental data for the interactions of fullerenes with four proteins. We further compared between the surface spatial and physicochemical properties of the suggested binding sites. A similarity was found between the (human and bovine) serum albumins and the HIV-protease binding sites, while the binding site of the fullerene-specific antibody was found to be different from the others.

ACKNOWLEDGMENT We would like to thank Dina Schneidman-Duhovny for software support and critical reading of this manuscript. We would like to thank Maxim Shatsky for useful suggestions. This work was supported by the Israel Science Foundation. Supporting Information Available: Detailed lists of amino acids that are within 6 Å distance from the docked ligand for each of the predicted binding sites. Two figures that depict (i) the similarity of the predicted binding site of carboxy-fullerene on HSA to binding sites of other known ligands, demonstrated for the case of citric acid (Figure S1) and (ii) similarity relationships between the four predicted binding sites of HSAtype 1, HSA-type 2, HIVP and a fullerene-specific antibody (Figure S2). This material is available free of charge via the Internet at http://pubs.acs.org.

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