Structure of the M{alpha}2-3 toxin {alpha} antibody–antigen complex: combination of modelling with functional mapping experimental results

Catherine Tenette-Souaille1,2 and Jeremy C. Smith1,3,4

1 Section de Biophysique des Protéines et des Membranes, DBCM, CEN-Saclay, 91191 Gif-sur-Yvette, France and 3 Lehrstuhl für Biocomputing, IWR, Universität Heidelberg, Im Neuenheimer Feld 368, D-69120 Heidelberg, Germany


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Modelled structures of the acetylcholine receptor-mimicking antibody, M{alpha}2-3, both free and bound to its antigen, toxin {alpha}, are assessed in the light of new experimental mutational data from functional mapping of the paratopic region of M{alpha}2-3. The experimental results are consistent with the previously-predicted structure of the free antibody, and also demonstrate that structural particularities of the M{alpha}2-3 combining site that were identified in the models play a role in the protein association. The modelled conformations of the hypervariable loops are discussed in the context of recent new data and analyses. The new mutational data allow several previously-considered modelled structures of the complex to be rejected. Two quite similar models now remain.

Keywords: antibody-antigen complex/model validation/snake toxin/structure prediction


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Despite the increasing number of experimental antibody structures determined either isolated or complexed with their antigens (Abola et al., 1997Go), the amount of structural data available still lags far behind the amount of sequence data (Johnson et al., 1996Go). An alternative way to increase the quantity of structural information available is to use molecular modelling and simulation. The chances of successful computer prediction of the structures of antibody–antigen complexes can be enhanced by incorporating information from experiments that identify residues important for the binding interaction, so as to screen candidate structures obtained from the modelling procedures.

A combined modelling/experimental approach was used in the present work, which involves examination of the structure of the complex of the monoclonal IgG2a{kappa} antibody, M{alpha}2-3 with its antigen, toxin {alpha} from Naja nigricollis (Kd = 1.9x10–9 M; Trémeau et al., 1986). This antibody has a number of properties similar to those of the nicotinic acetylcholine receptor (AChR), a ligand-gated ion channel (Ducancel et al., 1996Go). These include (i) the recognition of all short-chain curaremimetic snake toxins; (ii) eliciting of anti-AChR antibodies, which in turn elicit anti-toxin antibodies; (iii) a number of complementary determining region (CDR) residues that are identical to those found between positions 100 and 128 of the {alpha}-subunit of AChR and (iv) binding to a snake toxin epitope that involves at least 10 residues of which eight are also present in the determinant with which the same toxin binds to AChR. These results show that M{alpha}2-3 shares some functional properties with the AChR, and may therefore be considered as an, at least partial, functional mimic of the receptor. The possibility exists then, that M{alpha}2-3 might mimic the structure of the cholinergic AChR binding site (Ducancel et al., 1996Go).

The residues contributing to the interaction between two proteins may be identified either by a functional approach based on the evaluation of the stability of the complex after local modification of one of the partners, using directed mutagenesis for example, or by a structural approach based on the inspection of the three-dimensional geometry of the complex. The residues thus identified define the functional and structural interaction sites, respectively. Comparisons between antibodies and receptors (Chanh et al., 1987Go; Pain et al., 1990Go; Williams et al., 1991Go; Davis et al., 1992Go), and anti-idiotypic antibodies and antigens (Bentley et al., 1990Go; Garcia et al., 1992Go; Evans et al., 1994Go; Leu et al., 1994Go; Fields et al., 1995Go) highlight that functional and structural mimicry are not necessarily equivalent: the recognition of the same region of a molecule by two different proteins may be achieved by similar interactions, but with non-identical binding site topologies.

However, comparative studies on antibody–antigen and hormone–receptor complexes showed that in the cases examined both sites largely overlap, the functional site being included in the structural site (Prasad et al., 1993Go; Cunningham and Wells, 1993Go; Kelley and O'Connell, 1993Go; Clackson and Wells, 1995Go). Several mutations may be required to pinpoint the role of a particular residue (Kam-Morgan et al., 1993Go). It has been suggested that an affinity change greater than 10-fold indicates that a mutated residue is energetically important for the stabilization of an antibody–antigen complex (Prasad et al., 1993Go).

In the absence of crystallographic structures for either the isolated antibody or the complex, experimental data and molecular modelling were combined to characterize the functional and structural properties of M{alpha}2-3. The early experimental studies were dedicated to the antigen, toxin {alpha}. Its solution structure was determined by nuclear magnetic resonance (NMR) spectroscopy (Zinn-Justin et al., 1992Go). The functional interaction site with the antibody—the functional epitope—was characterized using different experimental methodologies: chemical modification of Lys and Trp residues (Trémeau et al., 1986Go), the NMR determination of the H–D exchange rates of labile amide hydrogens in toxin {alpha}, free and bound to the antibody (Zinn-Justin et al., 1993Go), and binding experiments on site-directed mutants of the homologous erabutoxin a from Laticauda semifasciata (Ducancel et al., 1996Go).

In complementary structural modelling of the isolated M{alpha}2-3 variable fragment three different methods were combined (Tenette et al., 1996Go): uniform conformational sampling (Bruccoleri and Karplus, 1987Go), high temperature molecular dynamics (Bruccoleri and Karplus, 1990Go) and a hybrid algorithm (Martin et al., 1989Go). A large number of geometries were generated and clustered into 13 groups based on r.m.s. coordinate differences, from which four apparently thermally interconvertible conformations were selected. Some structural features in common that are potentially important for the binding to the antigen were identified: the relatively small solvent-accessible surface area of loop L3, the small size and poor hydrogen-bonding capabilities of the central segment of loop H3, and the presence of a ring of Tyr residues encircling loop H3.

Modelling of the three-dimensional structure of the M{alpha}2-3–toxin {alpha} complex was subsequently undertaken starting from the NMR experimental structure of the isolated toxin {alpha} and the modelled structures of the isolated M{alpha}2-3 (Tenette-Souaille and Smith, 1998Go). Orientations were generated using an algorithm based on surface complementarity and the hydrophobic effect (Wodak and Janin, 1978Go; Cherfils et al., 1991Go) and were screened for consistency with the experimental functional epitope. Eight complexes that satisfy the modelling and experimental criteria were thus found. These could not be discriminated further.

Recently, a mutational analysis has been performed on the antibody combining site so as to experimentally identify the functional residues of M{alpha}2-3 in the interaction with toxin {alpha} (Mérienne et al., 1997Go). In this work the combining site residues predicted as being solvent-exposed in the isolated antibody model were mutated. The resulting affinity data highlighted the particularly important role of 11 residues, of which two are in the framework. Most of the important residues belong to the heavy chain of the antibody.

In this paper, we first discuss the recent experimental results in the light of the modelling data and known antibody–antigen complex structures. The models of the isolated and complexed M{alpha}2-3 are compared with new results on antibody hypervariable loop conformations and with the functional mapping of the paratopic region on M{alpha}2-3. The new results and analyses allow a posteriori discussion of the adopted modelling protocol and enable the number of possible complex geometries to be considerably narrowed down.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Epitope functional mapping

Three sets of experiments have probed the functional M{alpha}2-3 epitope on toxin {alpha}: (i) H–D exchange rates of labile amide hydrogens were determined by NMR spectroscopy, on the toxin {alpha} free and bound to M{alpha}2-3 (Zinn-Justin et al., 1993Go). Eight amides exhibit significant protection factors. They define a region localized largely on loop 2 of toxin {alpha} (Figure 1Go). (ii) Chemical modifications performed on residues in toxin {alpha} and in the homologous erabutoxin a from Laticauda semifasciata revealed that the amino groups of Lys27 and Lys47 and the indole group of Trp29 participate in the interaction with M{alpha}2-3 (Trémeau et al., 1986Go). (iii) An extensive mutational analysis of erabutoxin a identified 10 residues that make a significant contribution to the stabilization of the complex with the antibody (Ducancel et al., 1996Go). These residues form a contiguous surface of 700 Å2 on the concave face of the toxin and include the three residues identified by the chemical modification experiments (Figure 1Go). This analysis highlighted the particular role of a cluster of three residues, Gln7, Trp29 and Glu38, whose mutation led to large decreases of the affinity constant. These residues might define an `energy core' (Cunningham and Wells, 1993Go; Kelley and O'Connell, 1993Go; Clackson and Wells, 1995Go; Novotny et al., 1989Go; Novotny, 1991Go; Jin et al., 1992Go; Tulip et al., 1994Go) for the interaction of M{alpha}2-3 with the short snake toxins.



View larger version (67K):
[in this window]
[in a new window]
 
Fig. 1. Space-filling representation of the functional binding site on toxin {alpha} with which it binds to M{alpha}2-3. In dark grey and medium grey are residues for which at least one mutation lowers the affinity by more than 100-fold or 10–100-fold, respectively. All figures were generated using MOLSCRIPT (Kraulis, 1991Go) and Raster3D (Merritt et al., 1997Go).

 
Paratope functional mapping

A mutational analysis has been performed on the combining site of M{alpha}2-3 (Mérienne et al., 1997Go). The residues probed in the analysis of Mérienne et al. (1997) were chosen based on their solvent exposure in models of the isolated antibody (Tenette et al., 1996Go). The roles of 18 amino acids from the CDR and two from the framework were individually probed. The results of the above analysis are that 11 residues appear to be functionally important (Figure 2Go). These belong mainly to the heavy chain and cover a contiguous area of 800 Å2 in the models of Tenette et al. (1996). Residues from loops L2, H1, H2 and H3 play a functional role in the interaction with the antigen, whereas loop L3 is excluded from the paratopic region. Two framework residues, TyrL49 and TyrL67, are also found to play a significant role in the interaction with the toxin. Two regions of loop L1, close to these critical framework residues, were also probed but the results obtained do not conclusively determine whether loop L1 plays a role in the complex.



View larger version (99K):
[in this window]
[in a new window]
 
Fig. 2. Space-filling representation of the functional binding site on M{alpha}2-3 with which it binds to toxin {alpha}. The colour code for mutations is the same as in Figure 1Go. The remaining hypervariable loops residues are in light grey. Critical residues are identified by the chain name and the sequence number.

 
Three residues caused dramatic decreases of the affinity constant irrespective of the type of the mutation: AspH31, TyrH32 and GlyH101. Similarly to what was proposed for the M{alpha}2-3 functional epitope on the toxin, these residues may also constitute an `energy core' on the antibody for the stabilization of the complex.

Modelling of the M{alpha}2-3–toxin {alpha} complex

The approach used to study the interactions between M{alpha}2-3 and toxin {alpha} was largely based on a surface complementarity method (Wodak and Janin, 1978Go; Cherfils et al., 1991Go), but allowed some conformational change. Results and details of the modelling strategy are reported in Tenette-Souaille and Smith (1998). Eighteen models were selected in the penultimate screening, and eight of these were finally retained that could not be discriminated further.


    Results and discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
M{alpha}2-3 structural models

Global fold. The solvent accessible surface areas of the individual side chains are very similar in all M{alpha}2-3 models derived (Tenette et al., 1996Go). Twenty-nine of the 52 CDR residues were predicted to be solvent exposed (side chain relative accessible surface area >20%). Two framework residues, TyrL49 and TyrL67, were also predicted to be solvent exposed and close to the combining site. Thus, based on the models of the isolated M{alpha}2-3 molecule, all these residues would be possible candidates for interaction with the toxin. Due to their high solvent accessibilities, mutations of these residues are less likely than others to induce major conformational changes in the combining site architecture. Twenty-three of these residues were experimentally probed individually or in combination. Their level of production was comparable with that of the wild type (Mérienne et al., 1997Go), suggesting that indeed none of these mutations induce a gross destabilization of the native fold.

Loop L3. In this discussion of loop L3 and the subsequent discussion of loop H3 the Numbering Scheme of Chothia and Lesk (1997) is used (Al-Lazikani et al., 1997Go). Loop L3 is eight residues long in M{alpha}2-3 with a residue at position 95 missing and has no proline at position 94. In the most recent classification of canonical structures (Al-Lazikani et al., 1997Go), this loop would belong to the L3 class 6{kappa}. This latter class was created after the elucidation to high-resolution of the structure of the antibody CRIS-1 (Guarné et al., 1996Go). The coordinates of this structure are not yet available, but the authors indicate that the L3 conformation is very similar to that found in the medium-resolution structure of the catalytic antibody 17E8 (Zhou et al., 1994Go). Modelling of loop L3 in M{alpha}2-3 was performed before the identification of class 6{kappa}, and started from the L3 region found in HyHEL05 (Sheriff et al., 1987Go). This is a eight-residue loop with a trans proline residue at position 94, belonging to canonical class 3{kappa}. It is possible that 17E8 or CRIS-1 might represent a better template structure for loop L3 for M{alpha}2-3 than HyHEL05. However, the r.m.s.d. between the L3 backbone conformations in HyHEL05 and 17E8 is only 1.4 Å, the largest structural differences occurring in the solvent-exposed central segment, L92–L94. The average deviation from 17E8 of the L3 conformation in the four M{alpha}2-3 models is 1.3 Å. This suggests that the use of an antibody loop from a different canonical class as a template for modelling L3 is unlikely to have led to serious errors.

The mutational analysis showed quite clearly that the solvent-exposed residues of loop L3 do not play a functional role in the interaction with toxin {alpha}, as mutating the whole central region of the loop (L92–L94) does not significantly affect the binding affinity to the toxin.

Loop H3. Loop H3 exhibits the highest variability in sequence and length within the six CDRs (Johnson et al., 1996Go; Rock et al., 1994Go) and no H3 canonical class has been defined (Chothia et al., 1987Go). Nevertheless, two sets of residues have been identified as critical to H3 conformation: ArgH94 and AspH101 which form a salt bridge, and the residues at position H100b and H100c, which pack against the VL domain. When the modelling of M{alpha}2-3 was initiated only two antibodies with known structure containing a H3 loop with the same length as M{alpha}2-3 (11 residues in the segment H95–H102) were available (Tenette et al., 1996Go). The reference structure from which the modelling was started was selected considering the two abovementioned regions.

Analyzing a larger structural database, Morea et al. (1998) have recently established some rules relating sequence to conformation in H3, extending from position H92 to position H104. According to this classification the loop H3 in M{alpha}2-3 is 16 residues long (Morea et al., 1998Go). Eight crystallographic structures corresponding to five different antibodies with a loop H3 of the same length were included in their database. Two subregions in the loop were defined the—`torso' and the `head'—comprising residues proximal and distal to the framework, respectively. The presence or not of a salt bridge, between a basic residue at position H94 and an Asp residue at position H101, determines the conformation of the torso. These critical residues are both present in the M{alpha}2-3 sequence, indicating that M{alpha}2-3 would belong to the `bulged torso' class (Morea et al., 1998Go). Indeed, in the M{alpha}2-3 models, the side chains of these two residues do form a salt bridge and the conformation of the torso is similar to that described in Morea et al. (1998), with a ß-bulge at position H101 (Tenette et al., 1996Go).

Within the apical region of loop H3, the segment H100–H104 is predicted to be exposed to the solvent in the four M{alpha}2-3 models. Compared with other antibodies with a loop H3 of the same length, this region contains only relatively small residues with poor hydrogen-bonding capabilities. The analysis of the mutations performed on this loop shows that the segment H100–H103 is important for the interaction with the toxin (Mérienne et al., 1997Go). Furthermore, the results suggested that the presence of small side chains is important, as mutations increasing the size of the side chain led to affinity decreases.

The ring of aromatic residues

Common structural features potentially important for the binding to the toxin were identified in the four M{alpha}2-3 models [see figure 15 in Tenette et al. (1996)]. Among these was a `ring of aromatic residues' of 15 Å diameter around H3 at the top of the combining site. This is formed by seven Tyr residues and a Trp. Five of the Tyr residues and the Trp belong to the CDRs: TyrL50 (L2), TyrL92 (L3), TyrH32 and TyrH33 (H1), TrpH50 and TyrH52 (H2). The other two Tyr are framework residues: TyrL49 (LFR2) and TyrL67 (LFR3). Three other Tyr residues are found in the combining site, at L55 (L2), L95 (L3) and H109 (H3), but are quite far from the surface, and we therefore suppose that they are unlikely to interact with the toxin.

The eight Tyr residues of the combining site, with solvent accessibility >20% in the model, were mutated both into Phe (to probe the role of the hydroxyl group) and into Ser (to probe the role of the aromatic ring) (Table IGo). As suggested from the modelling, no direct interaction was identified for TyrL55 (L2). Nor was an affinity change observed on mutation of TyrL92 (L3) or TyrH33 (H1), members of the ring of the aromatic rings. However, both mutations of TyrH32 led to a dramatic decrease of the binding constant. The data obtained for TyrL49, TyrL67 and TyrH52 suggest that they are likely to be hydrogen-bonded to the toxin, as the loss of the hydroxyl group (mutation Tyr->Phe) led to a significant affinity decrease, whereas the effect of the mutation into Ser (loss of the aromatic ring) was negligible. Similar criteria can be used to conclude that the nature of the interaction of TyrL50 with the toxin should be essentially aromatic. Thus five of the eight tested residues of the modelled aromatic ring were shown to play a direct role in the interaction with the toxin. Consequently this ring can indeed be considered as a determinant paratopic feature of M{alpha}2-3. A possible role of aromatics in the combining site may be to interact with the positively-charged residues of the toxin via cation–{pi} interactions.


View this table:
[in this window]
[in a new window]
 
Table I. Summary of the mutational analysis of Mérienne et al. (1997) of the M{alpha}2-3 combining site
 
A KabatMan analysis was performed to determine the frequency of occurrence of the aromatic ring residues, using the 2083 available complete antibody sequences (Martin, 1996Go). The results are shown in Figure 3Go. The shading illustrates the effect of mutation of the residue in M{alpha}2-3 on binding. The results suggest that the ring of aromatic residues in M{alpha}2-3 does not occur frequently in other antibodies. The involvement of L67 is particularly interesting here, as it is a framework residue of rare occurrence.



View larger version (40K):
[in this window]
[in a new window]
 
Fig. 3. Results of a KabatMan analysis of the ring of aromatic residues on M{alpha}2-3. The frequencies of occurrence of the residues in the 2083 available antibody sequences are given. The shadowing indicates the approximate effect of mutagenesis of the residue on binding.

 
Functional role of loop L1

As we mentioned in the Materials and methods section, the implication of the CDR region L1 could not be established from the mutational results in Mérienne et al. (1997). The following further considerations would suggest that this is still the case. First, AsnL31 mutation into Ala leads to an insignificant affinity decrease. Second, a residue (Ser) inserted between SerL30 and AsnL31 led to a 14-fold affinity decrease. For a mutation without insertion, this would suggest a functional role for the loop. However, the structural perturbation resulting from the insertion of a residue is likely to be more significant than the simple mutation of a solvent-exposed residue.

Consequently, following the structural rules defined in Chothia et al. (1987) and Al-Lazikani et al. (1997), we may consider that the insertion of the serine residue is equivalent to a change of canonical class, from class 2{kappa} for the M{alpha}2-3 to class 6{kappa} for the mutated antibody. Comparison of three-dimensional antibody structures shows that residues 26–29 and 32 have a common conformation in both of these canonical classes (Chothia et al., 1987Go; Al-Lazikani et al., 1997Go). Therefore, the structural perturbation due to the presence of an additional serine may be limited and the observed affinity change may stem from a functional role played by this region of loop I. However, the affinity decrease itself is close to the empirical limit for including a residue in the functional interaction site (Prasad et al., 1993Go).

As the L30–L31 region lies on the edge of the current functional site, it may be difficult to identify if it plays a role in the interaction. Comparisons between functional and structural definitions of interacting surfaces in protein complexes, such as antibody–antigen complexes (Prasad et al., 1993Go; Kam-Morgan et al., 1993Go; Jin et al., 1992Go; Tulip et al., 1994Go; Tulip et al., 1992Go; Nuss et al., 1993Go) or hormone–receptor complexes (Cunningham and Wells, 1993Go; Clackson and Wells, 1995Go; Pearce et al., 1996Go), demonstrate that the functional site covers only a portion of the structural site as defined by the analysis of the three-dimensional structure. The proportion of residues of the structural site identified by a mutational analysis can be as low as 50% (Clackson and Wells, 1995Go). So residues not identified by the mutational (functional) analysis may nevertheless be part of the structural M{alpha}2-3 paratope. The probed L1 residues are between the functional residues in the framework and those in loop L2. It is possible that they participate in the M{alpha}2-3–toxin {alpha} interface, at the boundaries of the interacting surface, though no functional role has been clearly identified to date.

M{alpha}2-3–toxin {alpha} complex models

The modelling strategy was able to roughly identify the critical regions of the combining site as determined by the experimental mutational analysis of the paratope. The mutational analysis showed that loops H1 and H3 constitute the core of the interaction between the antibody and the toxin. Eighteen models were obtained after the penultimate screening step (Tenette-Souaille and Smith, 1998Go). The analysis of the residue–residue interactions in the 18 refined models of the complex reveals that loop H3 interacts with the toxin in all models as does loop H1 in 17 of the 18 models. The experimental results demonstrate the functional role of two framework residues, TyrL49 and TyrL67. Framework regions were found to interact with the toxin in all 18 refined models. These regions involve residue TyrL49, the region around TyrL67, the N-terminal segment of the VH domain and the region H26–H30, near loop H1. Of these, only TyrL49 and TyrL67 are involved in the interaction in more than half of the models (11 for each residue).

The participation or not of loop L1 determines if the paratopic region is spread over four or five CDRs. As discussed in the preceding paragraph, the experimental work does not establish whether the functional paratopic region includes residues from loop L1. Residues L30 and L31 of loop L1 are sandwiched between the critical Tyr residues, L49 and L67 (framework) and L50 (L2). Thus it is possible that they establish some secondary contacts with the toxin with an energetic contribution to complex formation that is not large enough to be identified by the mutational analysis.

Discrimination between the eight remaining models.

Eight models were selected from the 18 refined solutions (Tenette-Souaille and Smith, 1998Go). Further analysis did not enable further reduction in the number of acceptable models. The question therefore arises as to whether the new experimental data defining the paratopic region can be used to eliminate any of the eight structures.

The mutational analysis shows the preponderant functional role of the heavy chain, particularly of CDRs H1 and H3, via residues AspH31, TyrH32, MetH100, GlyH101, AlaH102 and ThrH103 (Figure 2Go). Residues TyrH52 and AlaH54 from loop H2 were also found to play a functional role in the interaction with the toxin. The participation of the light chain is quantitatively less significant (Mérienne et al., 1997Go). The functional role of TyrL50 (loop L2) and of the framework residues TyrL49 and TyrL67 was demonstrated. The solvent-exposed residues of loop L3 do not contribute to the interaction.

The light chain is the major contributor to the complex interface in five of the eight final calculated structures (Tenette-Souaille and Smith, 1998Go). However, the functional analysis of the paratope region shows that the number of residues of the light chain contributing to the complex interface is smaller than that of the heavy chain. Therefore, these models of the complex (1, 2, 4, 8 and 12) may therefore be rejected.

Figure 4Go shows the paratopes of the three remaining models: 6, 9 and 11. The residues of M{alpha}2-3 making contacts with the residues of the toxin functional epitope are highlighted: the antibody residues making contacts with the toxin `energy core' (Gln7, Trp29 and Glu38) are in dark grey, those making contacts with the rest of the functional epitope are in medium grey. A comparative analysis of the functional and structural interaction sites of the complex between human growth hormone and its receptor showed that the functional sites, including the residues for which mutation into alanine led to a substantial loss of affinity for each complex partner, were physically interacting with each other (Clackson and Wells, 1995Go). Assuming that this is also true for the M{alpha}2-3–toxin {alpha} complex, then the region of the antibody combining site containing residues that contact at least one of the residues from the functional epitope on toxin {alpha}—the `contact region'—should include the functional paratope experimentally determined for M{alpha}2-3. As shown in Figure 4Go, this contact region extends mainly over H1 and H3 in model 6, H1, H2 and H3 in model 9, and L3 and H2 in model 11. If we compare these regions with the experimental results presented in Figure 2Go, we can see that the contact regions contain a larger number of residues than the functional paratope does. This is in agreement with the previously mentioned comparative studies which conclude that the functional interaction site makes up only part of the structurally defined site (see section `Functional role of loop L1'). Moreover, some of the residues found in the contact regions were not tested during the mutational analysis, mainly because they are partially buried in the M{alpha}2-3 model.



View larger version (75K):
[in this window]
[in a new window]
 
Fig. 4. Space-filling representation of the calculated M{alpha}2-3 paratope as proposed by the modelling. Models 6, 9 and 11 are shown. In dark grey and medium grey are residues contacting the toxin `energy core' (Gln7, Trp29 and Glu38) or the rest of the functional epitope residues, respectively. The remaining hypervariable loop residues are in light grey. Residue numbering is the same as in Figure 2Go.

 
An empirical interaction score was calculated for these three models (Table IIGo). This simple measure enables us to quantify the contacts established in the models between the functionally important residues as defined by the mutational analyses of the epitopic and paratopic regions. The score is assigned according to whether the residues interacting are from the energy core, the functional epitope outside the energy core, or outside the functional epitope. The results are presented in Table IIGo. Model 6 has the best score (23.5), followed by model 9 (19) and then model 11 (11.5).


View this table:
[in this window]
[in a new window]
 
Table II. Empirical interaction score for models of the M{alpha}2-3–toxin {alpha} complex
 
Visual inspection of the M{alpha}2-3 contact region of model 11 confirms that it does not match the functional paratope (figures 4 and Go8 in Tenette-Souaille and Smith, 1998): the interaction is indeed quite removed from loops H1 and H3, which experimentally contain the bulk of the interaction, and particularly from AspH31, TyrH32 and GlyH101, the most critical residues as defined by the mutational analysis. In contrast, these three residues are found to contact the toxin functional epitope in models 6 and 9.

The orientation of the toxin is actually quite similar in models 6 and 9 (Figure 5Go), with the tip of the toxin central loop located on one or other sides of the solvent-exposed residues of loop H3. Both models involve direct contact between the toxin and part of the experimentally-defined paratopic region. Non-contacting residues are TyrL67, TyrH52 and AlaH54 in model 6 and TyrL49, TyrL67 and MetH100 in model 9. As evident in Table IIGo, the residues constituting the energy cores of the toxin and antibody are in contact with each other in model 6 (residues in dark grey in Figure 4Go). Thus the analysis identifies model 6 as the most likely according to the experimental results available to date. Neither the structural criteria nor the energetic scores used to screen the models in the previous modelling ranked model 6 in first place (Tenette-Souaille and Smith, 1998Go).



View larger version (102K):
[in this window]
[in a new window]
 
Fig. 5. Orientation of the toxin on the M{alpha}2-3 combining site in the complex models 6 (medium grey) and 9 (dark grey). The antibody light chain is in light grey, the heavy chain is in medium grey.

 
Conclusion

In the absence of X-ray crystallographic information, the combination of modelling with experimental mutational analysis and affinity could, in principle, provide useful information on the geometries of protein–protein complexes. This procedure was followed here in modelling the variable fragment of an antibody, M{alpha}2-3, isolated and complexed with its antigen, toxin {alpha}. The only direct structural information available for this study was the NMR structure of the antigen. The results of the modelling of the isolated antibody were used to suggest residues that could be mutated on the combining site of M{alpha}2-3. In the present paper, the results of these experiments have been combined with the modelling to narrow down the range of possible structures that the complex might have. Further, an analysis is made of the earlier isolated M{alpha}2-3 modelling in the light of recent improved descriptions of the hypervariable loops.

No contradiction with the experimental data is found in the models of the isolated antibody. Moreover, structural properties of the M{alpha}2-3 combining site identified by the modelling (small residues with poor hydrogen-bonding capabilities in the solvent-exposed segment of loop H3, presence of a ring of aromatic rings) were shown experimentally to play a role in the interaction with the toxin. Furthermore, the modelling procedure of the complex was able to identify the critical regions of the interaction site on M{alpha}2-3 (importance of loops H1 and H3, role of two framework residues).

The experimental functional mapping of the paratopic region on M{alpha}2-3 considerably reduces the number of acceptable model solutions for the complex. In model 6 the most critical residues for the association, identified on the toxin and the antibody by mutagenesis experiments, interact with each other at the interface. Although model 9 does not contain pairs of interacting residues from both energy cores, we cannot rule it out at this stage.

The next logical step would be to further refine the modelling with the aid of pairs of experimental mutations, one on the antibody and one on the antigen. Indeed, an important use of the present type of modelling procedure is to suggest compensatory mutations (`swapping' experiments) of residues that are in contact in the models. Such data could provide definitive information allowing us to decide between the two remaining models. For example, two charged residues, one from each protein, may be mutated so as to inverse their charges. There is only one charged residue (AspH31) in the experimentally-determined paratope. Three positively-charged residues are included in the epitope (Lys27, Arg33 and Lys47). In model 6, AspH31 is in the vicinity of Lys27. Its side chain is oriented towards the opposite side, hydrogen-bonded with Lys15 and Tyr25, although positioning of such flexible side chains can be subject to error. In model 9, AspH31 is hydrogen-bonded to both Lys27 and Lys47. The aromatic residues of the combining site are also possible candidates for the interaction with the positive residues of the toxin, through cation–{pi} interactions. For example, the planar guanidinium group of Arg33 interacts with the aromatic ring of TyrL50 in model 6. Mutational data on TyrL50 indeed suggest that its aromatic ring is determinant in the interaction with the toxin. The same conclusion was drawn for TyrH32. Therefore compensatory mutations may also be considered between these aromatic residues and the basic residues of the toxin. This new experimental information might allow specific interactions across the antigen–antibody interface to be elucidated, and the model of the complex to be further refined.


    Notes
 
2 Present address: Biochemisches Institut, Universität Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland Back

4 To whom correspondence should be addressed at: Lehrstuhl für Biocomputing, IWR, Universität Heidelberg, Im Neuenheimer Feld 368, D-69120 Heidelberg, Germany Back


    Acknowledgments
 
We are very grateful to our colleagues at the Département d'Ingénierie des Protéines et des Membranes, CEA-Saclay, for having stimulated our interest in this project, for numerous discussions and for performing the experimental work discussed here. We thank in particular Karine Mérienne, Sophie Zinn-Justin, Frédéric Ducancel and André Ménez. We thank also one of the referees for having suggested the KabatMan analysis.


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 References
 
Abola,E.E., Sussman,J.L., Prilusky,J. and Manning,N.O. (1997) Methods Enzymol., 277, 556–571.[ISI][Medline]

Al-Lazikani,B., Lesk,A.M. and Chothia,C. (1997) J. Mol. Biol., 273, 927–948.[ISI][Medline]

Bentley,G.A., Boulot,G., Riottot,M.M. and Poljak,R.J. (1990) Nature, 348, 254–257.[ISI][Medline]

Bruccoleri,R.E. and Karplus,M. (1987) Biopolymers, 26, 137–168.[ISI][Medline]

Bruccoleri,R.E. and Karplus,M. (1990) Biopolymers, 29, 1847–1862.[ISI][Medline]

Chanh,T.C., Dreesman,G.R. and Kennedy,R.C. (1987) Proc. Natl Acad. Sci. USA, 84, 3891–3895.[Abstract]

Cherfils,J., Duquerroy,S. and Janin,J. (1991) Proteins, 11, 271–280.[ISI][Medline]

Chothia,C. and Lesk,A.M. (1987) J. Mol. Biol., 196, 901–917.[ISI][Medline]

Chothia,C., Novotny,J., Bruccoleri,R.E. and Karplus,M. (1985) J. Mol. Biol., 186, 651–663.[ISI][Medline]

Clackson,T. and Wells,J.A. (1995) Science, 267, 383–386.[ISI][Medline]

Cunningham,B.C. and Wells,J.A. (1993) J. Mol. Biol., 234, 554–563.[ISI][Medline]

Davis,S.J., Schockmel,G.A., Somoza,C., Buck,D.W., Healey,D., Rieber,E., Reiter,C. and Williams,A. (1992) Nature, 358, 76–79.[ISI][Medline]

Ducancel,F., Mérienne,K., Fromen-Romano,C., Trémeau,O., Drevet,P., Pinkasfeld,S., Zinn-Justin,S., Boulain,J.-C. and Ménez,A. (1996) J. Biol. Chem., 271, 31345–31353.[Abstract/Free Full Text]

Evans,S.V., Rose,D.R., To,R., Young,N.M. and Bundle,D.R. (1994) J. Mol. Biol., 241, 691–705.[ISI][Medline]

Fields,B.A., Goldbaum,F.A., Ysern,X., Poljak,R.J. and Mariuzza,R. (1995) Nature, 374, 739–742.[ISI][Medline]

Garcia,K.C., Ronco,P.M., Verroust,P.J., Brunger,A.T. and Amzel,L. (1992) Science, 257, 502–507.[ISI][Medline]

Guarné,A., Bravo,J., Calvo,J., Lozano,F., Vives,J. and Fita,I. (1996) Protein Sci., 5, 167–169.[Abstract/Free Full Text]

Jin,L., Fendly,B.M. and Wells,J.A. (1992) J. Mol. Biol., 226, 851–865.[ISI][Medline]

Johnson,G., Kabat,E.A. and Wu,T.T. (1996) Weir's Handbook of Experimental Immunology I. Immunochemistry and Molecular Immunology, 5th Edn. Blackwell Science, Cambridge, MA, pp. 6.1–6.21.

Kam-Morgan,L.N., Smith-Gill,S.J., Taylor,M.G., Zhang,L., Wilson,A. and Kirsch,J. (1993) Proc. Natl Acad. Sci. USA, 90, 3958–3962.[Abstract]

Kelley,R.F. and O'Connell,M.P. (1993) Biochemistry, 32, 6828–6835.[ISI][Medline]

Kraulis,P.J. (1991) J. Appl. Crystallogr, 24, 946–950.[ISI]

Leu,J.G., Chen,B.X., Diamanduros,A.W. and Erlanger,B.F. (1994) Proc. Natl Acad. Sci. USA, 91, 10690–10694.[Abstract/Free Full Text]

MacCallum,R.M., Martin,A.C.R. and Thornton,J.T. (1996) J. Mol. Biol., 262, 732–745.[ISI][Medline]

Martin,A.C.R., Cheetham,J.C. and Rees,A.R. (1989) Proc. Natl Acad. Sci. USA, 86, 9268–9272.[Abstract]

Martin,A.C.R. (1996) Proteins, 25, 130–133.[ISI][Medline]

Mérienne,K., Germain,N., Zinn-Justin,S., Boulain,J.C., Ducancel,F. and Ménez,A. (1997) J. Biol. Chem., 272, 23775–23783.[Abstract/Free Full Text]

Merritt,E.A. and Bacon,D.J. (1997) Methods Enzymol., 277, 505–524.[ISI]

Morea,V., Tramontano,A., Rustici,M., Chothia,C. and Lesk,A.M. (1998) J. Mol. Biol., 275, 269–294.[ISI][Medline]

Novotny,J. (1991) Mol. Immunol., 28, 201–207.[ISI][Medline]

Novotny,J., Bruccoleri,R.E. and Saul,F.A. (1989) Biochemistry, 28, 4735–4749.[ISI][Medline]

Nuss,J.M., Whitaker,P.B. and Air,G.M. (1993) Proteins, 15, 121–132.[ISI][Medline]

Pain,D., Murakami,H. and Blobel,G. (1990) Nature, 347, 444–449.[ISI][Medline]

Pearce,K.H., Ultsch,M.H., Kelley,R.F., deVos,A. and Wells,J. (1996) Biochemistry, 35, 10300–10307.[ISI][Medline]

Prasad,L., Sharma,S., Vandonselaar,M., Quail,J.W., Lee,J., Waygood,E., Wilson,K., Dauter,Z. and Delbaere,L. (1993) J. Biol. Chem., 268, 10705–10708.[Abstract/Free Full Text]

Rock,E.P., Sibbald,P.R., Davis,M.M. and Chien,Y.H. (1994) J. Exp. Med., 179, 323–328.[Abstract]

Sheriff,S., Silverton,E.W., Padlan,E.A., Cohen,G.H., Smith-Gill,S.J., Finzel,B.C. and Davies,D.R. (1987) Proc. Natl Acad. Sci. USA, 84, 8075–8079.[Abstract]

Tenette,C., Ducancel,F. and Smith,J.C. (1996) Proteins, 26, 9–31.[ISI][Medline]

Tenette-Souaille,C. and Smith,J.C. (1998) Proteins, 30, 249–263.[Medline]

Trémeau,O., Boulain,J.C., Couderc,J., Fromageot,P. and Ménez,A. (1986) FEBS Lett., 208, 236–240.[ISI][Medline]

Tulip,W.R., Varghese,J.R., Laver,W.G., Webster,R.G. and Colman,P.M. (1992) J. Mol. Biol., 227, 122–148.[ISI][Medline]

Tulip,W.R., Harley,V.R., Webster,R.G. and Novotny,J. (1994) Biochemistry, 33, 7986–7997.[ISI][Medline]

Williams,W.V., Kieber-Emmons,T., Weiner,D.B., Rubbin,D.H. and Greene,M. (1991) J. Biol. Chem., 266, 9241–9250.[Abstract/Free Full Text]

Wodak,S.J. and Janin,J. (1978) J. Mol. Biol., 124, 323–342.[ISI][Medline]

Zhou,G.W., Guo,J., Huang,W., Scanlan,T.S. and Fletterick,R.J. (1994) Science, 265, 1059–1064.[ISI][Medline]

Zinn-Justin,S., Roumestand,C., Gilquin,B., Bontems,F., Ménez,A. and Toma, F. (1992) Biochemistry, 31, 11335–11347.[ISI][Medline]

Zinn-Justin,S., Roumestand,C., Drevet,P., Ménez,A. and Toma,F. (1993) Biochemistry, 32, 6884–6891.[ISI][Medline]

Received June 9, 1999; revised November 23, 1999; accepted December 8, 1999.





This Article
Abstract
FREE Full Text (PDF)
Alert me when this article is cited
Alert me if a correction is posted
Services
Email this article to a friend
Similar articles in this journal
Similar articles in ISI Web of Science
Similar articles in PubMed
Alert me to new issues of the journal
Add to My Personal Archive
Download to citation manager
Search for citing articles in:
ISI Web of Science (1)
Request Permissions
Google Scholar
Articles by Tenette-Souaille, C.
Articles by Smith, J. C.
PubMed
PubMed Citation
Articles by Tenette-Souaille, C.
Articles by Smith, J. C.