- Research article
- Open Access
Binding induced conformational changes of proteins correlate with their intrinsic fluctuations: a case study of antibodies
© Keskin; licensee BioMed Central Ltd. 2007
- Received: 08 February 2007
- Accepted: 17 May 2007
- Published: 17 May 2007
How antibodies recognize and bind to antigens can not be totally explained by rigid shape and electrostatic complimentarity models. Alternatively, pre-existing equilibrium hypothesis states that the native state of an antibody is not defined by a single rigid conformation but instead with an ensemble of similar conformations that co-exist at equilibrium. Antigens bind to one of the preferred conformations making this conformation more abundant shifting the equilibrium.
Here, two antibodies, a germline antibody of 36–65 Fab and a monoclonal antibody, SPE7 are studied in detail to elucidate the mechanism of antibody-antigen recognition and to understand how a single antibody recognizes different antigens. An elastic network model, Anisotropic Network Model (ANM) is used in the calculations. Pre-existing equilibrium is not restricted to apply to antibodies. Intrinsic fluctuations of eight proteins, from different classes of proteins, such as enzymes, binding and transport proteins are investigated to test the suitability of the method. The intrinsic fluctuations are compared with the experimentally observed ligand induced conformational changes of these proteins. The results show that the intrinsic fluctuations obtained by theoretical methods correlate with structural changes observed when a ligand is bound to the protein. The decomposition of the total fluctuations serves to identify the different individual modes of motion, ranging from the most cooperative ones involving the overall structure, to the most localized ones.
Results suggest that the pre-equilibrium concept holds for antibodies and the promiscuity of antibodies can also be explained this hypothesis: a limited number of conformational states driven by intrinsic motions of an antibody might be adequate to bind to different antigens.
- Conformational Change
- Protein Data Bank
- Collective Mode
- Global Mode
- Protein Data Bank Code
Motions induced by protein-ligand interactions are controlled by the global motions of the proteins, including enzymes and antibody-antigens [1–12]. Elucidation of the mechanisms by which the proteins bind to each other or to ligands is of great importance to control and alter protein associations. Several different models have attempted to explain protein binding mechanisms. The specific action of an enzyme with a single substrate was first explained by the lock and key analogy postulated in the nineteenth century. In this analogy, the lock is the enzyme and the key is the substrate. Only the correctly sized key (substrate) fits into the key hole (active site) of the lock (enzyme). Later, it was realized that not all experimental evidence can be adequately explained by using the lock and key model. Consequently the induced-fit theory, which assumes that the substrate plays a role in determining the final shape of the enzyme and that the enzyme is partially flexible was proposed . This theory explains why certain compounds can bind to the enzyme but do not react: the enzyme has been distorted too much or the ligand is too small to induce the proper alignment and therefore cannot react. Only the proper substrate is capable of inducing the proper alignment of the active site.
Pre-existing equilibrium is another alternative model to describe the mechanisms of protein interactions [14–19]. In this model, a protein native state is defined as an ensemble of closely related conformations that co-exist in equilibrium at its binding site. The ligand will bind selectively to an active conformation, thereby biasing the equilibrium toward the binding conformation. In the pre-existing equilibrium model, one protein adapts multiple structures and, thereby, multiple active-sites and functions. Experimental evidences can increase our understanding of the model. In a recent study, pre-existence of collective dynamics of an enzyme (prolyl cis-trans isomerase cyclophilin A, CypA) was observed. Pre-sampling of conformational substates occurs before the enzyme starts its catalytic function . Another example is the aminoglycoside kinase, in which two sub-sites are formed by the motion of a flexible active-site loop . The isomerization of a tyrosine side-chain was found to be critical in the trypanosomal trans-sialidase; it allows the enzyme to have two isomers, with two distinct active-site configurations and thereby two different activities (glycosyl hydrolase and transferase) .
Computationally, Krebs et al. analyzed a set of different proteins with different binding mechanisms by applying normal mode analysis . They found that half of the proteins studied undergo conformational changes that are governed by the two or three lowest modes of the protein. Such results strongly suggest that protein movements between unbound and bound (to a ligand) structures are under selective pressure, so as to follow the lowest frequency normal modes of the protein . Flexibility of a protein allows it to adopt new conformations and, in turn, bind to distinct ligands. This ability of proteins to adopt multiple structures allows functional diversity without depending on the evolution of sequence diversity, and it can greatly facilitate the potential for rapidly evolving new functions and structures. Recently, Liu et al. found that the conformational ensemble of native conditions is determined by the network of cooperative interactions within the protein and suggested that proteins have evolved to use these conformational fluctuations in carrying out their functions . We have previously shown that Anisotropic Network Model (ANM) can be used to establish, a priori, the most likely conformational (transitional) changes of an enzyme starting from its unbound state to its three different bound states . In addition, we have shown that the degree of flexibility of the protein is important for proteins to interact with other proteins and as the species gets more complex its proteins become more flexible . Tobi and Bahar  successfully showed recently that conformational changes due to protein-protein interactions can be analyzed by the pre-existing equilibrium concept. They applied Gaussian Network Model (GNM) and ANM to study the collective motions of four proteins and indicated that motions calculated from the monomers correlate well with the experimentally observed changes upon complex formation with other proteins. Gu and Bourne's method uses GNM to identify local fluctuation changes important for protein function and residue contacts that contributes to these changes .
In this paper, previous studies are extended to investigate the multi-specificity of proteins, especially antibodies. A diverse set of proteins is analyzed with the analytical ANM to study the fluctuations of macromolecules on a coarse grained level. This also allowed us to test the suitability of the method. Pathways between experimentally known conformational changes of a macromolecule upon ligand binding are analyzed based on the pre-existing equilibrium concept for different classes of proteins, i.e. enzymes, binding and transport proteins and antibodies. The last set is studied in detail to elucidate the mechanism of antibody-antigen recognition and binding. The results show that the intrinsic fluctuations obtained by ANM correlate well with structural changes observed when a ligand is bound to the free (unbound) conformation of the protein supporting the pre-existing equilibrium concept. The decomposition of the total fluctuations serves to identify the different individual modes of motion, ranging from the most cooperative ones involving the overall structure, to the most localized ones, manifested as high-frequency fluctuations of individual residues.
It is shown that, ANM is able to find the conformational changes due to ligand binding starting from the unbound form. This suggests that the intrinsic motions of antibodies as well as the electrostatic properties of the binding site that characterize the bound form are sufficiently preserved in the unbound conformation of antibodies. Thus conformational changes of residues that are involved in binding or that are critical for binding can be identified by our method in most cases, starting from the unbound conformation.
Comparison of the global mode motions with the conformational changes upon ligand binding
Normal mode analysis has been used to study the collective motions of biological macromolecules. And, some of these normal modes of several proteins are strongly correlated with the large amplitude conformational changes of these proteins observed upon ligand binding [8, 10, 11, 33]
List of proteins studied.
Protein ID (unbound-bound)
Name of the protein
Number of residues
Rmsd between the structures (Å)
K-, R-, Orn-Binding Protein
Further, the atomic root mean square fluctuations and thus motions from individual modes, obtained by ANM are compared with the experimentally known conformational changes for the list of proteins given in Table 1. Cα atomic coordinates for the unbound and bound crystallographic structures are obtained from the PDB. The two structures (unbound and bound) are superimposed (by using their α-carbons) to calculate the individual residue displacements (ΔR = Ro - Rc) where Ro - Rc are the crystallographic coordinates of the unbound and bound structures, respectively. The theoretical atomic fluctuations of the unbound structures are obtained with Eq. 5.
Correlation coefficients between the real displacements and theoretical values
The results show that there are very high correspondences between the experimental X-ray conformational transitions and the theoretical values. These suggest that among many possible global modes driven by ANM, one or combinations of few global modes can be used to predict the directions of motions of unbound proteins when different ligands are bound. The range of functions that is in our list is very broad, from enzymes to signal transduction proteins. Note that transitions from unbound to bound conformations are mostly represented by the first, second, and third eigenvectors. We might speculate that the specific bound conformations of the proteins should be determined by the specific ligands bound to the unligated structure. And these bound forms should be among the possible conformations that the unbound structure can assume. The ligand interactions drive the structure to a new stable, functional structure among the possible conformations. These results further suggest that the structures assumed by ligand binding in all cases are driven by the pre-existing global fluctuations of the unbound forms. The bound conformation is among the conformations that the unbound protein may undergo based on its intrinsic fluctuations even in the absence of the ligand. And when there is a proper ligand in the environment, the suitable conformation that would fit the ligand might become populated.
Antibodies can bind to different antigens
Correlation coefficients between the real displacements and theoretical values for the antibody bound to three different dodecamers.
PDB ID of the Unbound-Bound Antibody
Fig 4B shows the theoretical and experimental conformational changes for the antibody (36–65 Fab). While the overall conformational changes of the unbound structure under the influence of the third mode found by ANM closely resembles the experimental displacements of the antibody, it is important to note that there are minor differences between the two sets of data. Especially, regions ~100–110, 120–130 and 175–200 display slightly different behaviors. The first region corresponds to the H3 loop of the antibody and makes contacts with the antigens. Further this loop is observed to undergo substantial movements between different conformations of the protein. Residues 120–130 connect the variable domain to the constant domain of the antibody.
One of the global modes is important for conformational changes
Investigation of the collective modes can give us insights about the mechanisms of binding processes. Comparison of the actual experimental conformational changes with the root mean square fluctuations from the collective modes indicates that individual modes are more informative than taking combined effect of all modes. Usually, only one of the slowest global modes is important to represent the ligand induced conformational changes in proteins. The results clearly show that the unbound conformation of all the proteins discussed in this study have intrinsic tendencies to reconfigure their conformations into the bound one. Therefore, the mechanism for the recognition and binding of the proteins with their ligands can be estimated a priori, by considering the conformations they undergo under the influence of the collective modes. And the proteins selectively bind to their ligands.
It has been shown that for native proteins, the very low frequency normal modes make major contributions to the conformational fluctuations at thermal equilibrium. Such motions can change the interactions of proteins with other molecules and with their environment.
Comparison of the global mode motions with the conformational changes upon ligand binding suggests a high correspondence between the normal mode directions derived from ANM calculations and the actual conformational changes. Here, intrinsic motions of eight proteins, from different classes of proteins, i.e. enzymes, binding and transport proteins and antibodies are examined. The high correlations between the experimental and computational intrinsic motions confirm that the individual eigenvectors might be useful to drive the unbound structure toward bound structures. Thus, the unbound structure can assume a set of conformations driven by the slowest modes, and ligand binding appears to introduce a new stable structure from this set of accessible conformations. The conformational changes exhibited by the proteins cannot fully be explained by the lock and key model or the induced fit model. According to the pre-existing equilibrium hypothesis proteins assume a set of conformations that are related to and in the vicinity of each other.
CheY, LAO-Binding Protein, HPPK (kinase), renin, Thymidylate Synthase are examples that show high correlations (≥ .70) between the experimentally observed structural changes and theoretically predicted conformational motions. Therefore, the unbound conformations of these proteins most probably assume a conformation that strongly resemble their bound forms. On the other hand, aspartyl protease, dihydrofolate reductase, calmodulin show lower correlations (0.63–0.66) which suggest that some further local rearrangements of the structures are needed to reach the bound conformations. These results suggest that pre-existing equilibrium is a key component in the binding process, such that it facilitates in selecting the complex forming conformation among the others but cannot exclusively explain the whole mechanism. Induced fit model should further play a role in the fine tuning of the local arrangements after the ligands are bound.
A limited repertoire of antibodies can recognize and bind to an almost infinite number of antigens. Antibodies are known with their delicate specificity for the antigens they bind; at the same time, a single antibody binding site can accommodate different, if similar, antigens . Kinetic analyses demonstrate that the pre-equilibrium states exist for an antibody to provide specific binding sites accompanied by induced fit. James et al  showed that a monoclonal immunoglobulin, SPE7 adopted at least two different conformations that were independent of antigen. Here, the intrinsic motions of a germline antibody of 36–65 Fab with three different peptides is investigated. Similar binding site is used to bind to these three peptides. The third collective mode of the unbound antibody was found to be responsible for the conformational changes undertaken with three different peptide binding. The correlations were around 0.69 for the three cases. This number might suggest that pre-existing equilibrium drives the early binding mechanism, and induced fit model later re-arranges the local arrangements after the ligands bind. The second antibody investigated is antigen binding region of SPE7. Available two pre-existing equilibrium conformations are compared with the two different antigen bound conformers. The results show that the first two modes are responsible for the global motions. The antigen binding loops are further re-arranged under the influence of higher modes. Therefore, our results on a diverse set of proteins show that: proteins undergo intrinsic collective motions driven by their structures, and the intrinsic collective motions of proteins are required for binding and these motions correlate well with the experimental conformational changes and we conclude that these intrinsic collective motions that are driven by the protein structure are required for ligand binding.
Seven proteins are analyzed in this study to test the suitability of the method. The crystal structures of both the unbound and bound conformations of these proteins are available in the Protein Data Bank (PDB) . Table 1 lists the proteins analyzed. The unbound and bound form PDB codes are given in the first column. The names of the corresponding proteins, number of residues, root mean square deviations (rmsd) between the unbound and bound forms of the proteins are listed in the following columns of the table. The ligands bound are given in the fourth column of the table. Three antibodies investigated are also listed in the same table.
The details of the analytical model (ANM) have been given in the Additional file 1 and elsewhere [6, 42]. ANM is equivalent to a normal mode analysis. In the model, Cα atoms are taken as the interaction sites for residues which are connected by virtual bonds (linear springs between the beads) to form the protein backbone. This model assumes that the protein in the folded state is equivalent to a three dimensional elastic network. Therefore, the molecule is modeled as a chain of N beads (residues) connected by N-1 springs. The bond lengths and bond angles are not constrained, which yields 3N degrees of freedom. The beads are subject to harmonic potentials from all neighboring beads regardless of backbone connections. In the calculations, only interactions between pairwise beads is considered if they are in contact, thus decreasing the spring number from N × (N - 1)/2 to a much smaller number. If the distance between two residues i and j is less than a cutoff distance then these two residues are assumed to be in contact. The cutoff distance includes all residue pairs within a first interaction shell of 15 Å and defines the range of interaction of bonded and non-bonded α carbons. The potential energy equation can be expressed in the matrix representation in terms of the 1 × 3N fluctuation vector (Δ R) and the 3N × 3N Hessian matrix (H) composed of inter-residue force constants as
The equilibrium cross-correlations between residue fluctuations is given by<ΔR i • ΔR j > = (kBT)[H-1] ij
The motions from individual modes are compared with the experimentally known conformational changes for the list of proteins given in Table 1. Cα coordinates for the unbound and bound structures are obtained from the PDB. The two structures (unbound and bound) are superimposed to calculate the individual residue displacementsΔRtheor = Ro - Rc
where Ro - Rc are the crystallographic coordinates of the unbound and bound structures, respectively. The theoretical atomic fluctuations of the unbound structures are obtained with Eq. 5. In previous similar studies, conformational changes have been compared to normal modes through the measure of scalar products. In this study Pearson correlation coefficients are used to have a consistent measure.
The author thanks Drs. Elif Ozkirimli, S. Banu Ozkan, Nevin Gerek and Ruth Nussinov for their careful reading of the manuscript and helpful discussions. O.K. has been granted with Turkish Academy of Sciences Young Investigator Programme (TUBA-GEBIP). This project has been funded in whole or in part with TUBITAK (Research Grant No 104T504).
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