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Antibody design: beyond the natural limits

Anthony R. Rees, David Staunton, David M. Webster, Stephen J. Searle, Andrew H. Henry and Jan T. Pedersen

Dissection of antibody-antigen interactions requires a knowledge of antibody

structure, the ability to model accurately the conformation of antibody-combining

sites, and an understanding of the energetic factors governing the interactions.

When this understanding has reached the point where the molecular shape and

chemical character of a combining site necessary to define a particular specificity

and binding requirement can be designed, the antibody repertoire will have been

extended 'beyond the natural limits'.

At the time of writing, the Brookhaven Protein Struc- tural Database (established by Bernstein et al. ~ and continuously updated) contains 23 structures of anti- bodies or their fragments, while almost 2000 variable- region sequences have been determined (Kabat database2). The rate of sequence acquisition continues to outstrip the rate of structure determination and, although X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy are the preferred methods for producing structures at atomic resolution, the need for good three-dimensional models to guide antibody engineering has become essential. Such models will be increasingly useful for assessing the structural effects of point mutations within the anti- body-combining site, for designing minimal pertur- bation strategies for grafting complementarity-deter- mining regions (humanization), and for rationally selecting residues on the basis of their likely accessi- bility to antigen that can be targeted for random mu- tation by gene library methods (e.g. 'phage libraries'). Where antibody specificity is modified 'by design', the three-dimensional structure or model will be not so much useful as essential.

Antibody structure The 'immunoglobulin fold'

Both the heavy and light chains of an antibody con- sist of units termed domains: each light chain consists of one variable domain (VL) and one constant domain (CL); and each heavy chain consists of one variable domain (VL) and three (or four, depending on the antibody class) constant domains (CH1, CH2 and CH3 ) (Fig. la). Each domain is folded into a characteristic

A. R. Rees, D. Staunton, D. M. Webster, S. J. Searle, A. H. Henry and J. T. Pedersen are at the School of Biology and Biochemistry, University of Bath, Claverton Down, Bath, UK BA2 7A Y.

structure, termed the 'immunoglobulin fold', and the nature of this three-dimensional structure was estab- lished when Poljak and co-workers determined the first structure of an antibody Fab fragment 3. The immunoglobulin fold consists of a 'sandwich' of two beta sheets, each of which is made up of antiparallel beta strands (Fig. lb). Although the variable and con- stant domains have a largely similar structure, there are slight differences: the variable domain beta-sheet 'sandwich' has an extra pair of beta-sheet strands and an extra loop connecting these strands (Fig. lb). The immunoglobulin fold has now been found in a large superfamily of immunoglobulin-like molecules (see Ref. 3 and references therein). The two sides of the sandwich are covalently linked by a disulphide bond found in most, but not all, proteins containing the immunoglobulin fold 4.

Domain packing:formation of the framework The sequence variability within the V H and V L

domains is concentrated in several hypervariable regions, which form the antigen-binding site of the molecule, and are also therefore termed the complementarity-determining regions (CDRs). The remainder of the variable domains are referred to as the framework region. The position of the three CDRs with respect to the beta-sheet framework of a variable domain is shown in Fig. lb. Each C D R is a loop connecting two beta strands, and has a fixed orien- tation on the framework, depending on its length and sequence motifs characteristic of the individual domain. The V L and V H domains associate non- covalently to form a beta-barrel structure (Fig. 2). This places the six CDRs close to each other at the amino- terminal end of the VL-V H dimer. This dimer, also known as the Fv region, associates with a high affinity that is due almost entirely to the close complemen- tarity of conserved residues at the dimer interface 5.

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b

Fc = (C.2 + C. 3)=

a

C,3

Fv = V, + V L

ab V.+VL+CL+C.1

CDR 1

CDR 3

CDR 2

\

Figure 1 (a) Cartoon of the antibody four-chain arrangement, showing the rela- tive positions of the variable (V) and constant (C) domains. (b) The domain structure of immunoglobulins, showing the ~-sheet sandwich structures characteristic of all members of the immunoglobulin superfamily. The top diagram is of a variable domain, consisting of nine 13-strands packed to form a ~-sheet sandwich. The loops con- necting the strands form the complementarity determining regions (CDRs) shown generically as CDR1, CDR2 and CDR3. The bottom diagram is of a constant domain, consisting of only seven ~-sheet strands, packed similarly to the variable domain, but lacking the two additional strands C' and C".

O f course, the V c and V H C D R loops are also brought into contact by this dimer formation, and this may either add to, or subtract from, the binding energy. Although many of the interface positions are con- served, variations do occur 6. When procedures, that result in random pairings of V L and V M domains are used, such as screening random libraries, incompati- bilities across the interface may be encountered, which result in abortive pairing and a consequent reduction in library diversity.

Ant ibody modell ing The framework region

Although the three-dimensional structure of Fv regions is well conserved, not all framework regions are structurally identical and, when new sequences are modelled on existing X-ray structures, the differences must be taken into account. An analysis of 12 anti- body structures has determined where these differ- ences occur and has shown that, of the eight strands that form the central [3-barrel of the Fv region, only parts of each strand exhibit high positional conser- vation 6,7. This has led to the development of an auto- mated VL-V H docking program 6-s that allows any two variable domains to be correctly paired, using an aver- age [3-barrel structure that contains only the most conserved regions of the strands.

CDR conformation The combining site of an antibody is derived from

the juxtaposition of six interstrand loops (the CDRs), three derived from the hea W chain (H1, H2 and H3) and three from the light chain (L1, L2 and L3). Despite considerable variability in these C D R se- quences from one antibody to another 2, there is some structural similarity among many of the CDRs. The structural conservation of H1, H2 and L2 loops has previously been highlighted and a relationship be- tween C D R length and conformation has been proposedg, 10.

The present database of antibody structures 1 con- tains combining sites with various combinations of C D R length and sequence. The structures of these CDRs often vary considerably with length. For example, four of the CDRs (L2, L3, H2 and H3) are hairpin loops. The preferred conformations of such loops have been extensively studied for other pro- teins11.12, but the high variability in length of the anti- body loops has complicated the development of a rigorous structural classification. The most compre- hensive attempt at C D R classification is by Chothia et aI. 13qs, who have descibed 'canonical' families for CDRs L1, L2, L3, H1 and H2. These families arise from the presence of 'key' residues at defined positions that dictate C D R conformation through hydrogen bonding, packing or torsional preferences.

While the canonical method of classification is use- ful for those CDRs that obey the predefined 'rules' exactly, further development is desirable in two areas. First of all, refinement of these rules is necessary for the structural classification of those CDRs that lack

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the appropriate key residues in positions that define a particular canonical class. The second issue relates to the question of whether all permutations of heavy and light chain CD1Ks should be allowed - a particular canonical conformation in one V c or V H domain may be unfavourable because, in the presence of its neigh- bouring CDtKs, correct domain assembly is hindered. This possibility has already been discussed by Steipe et al. 16. If particular canonical combinations are dis- allowed, this will lead to 'mutual exclusion' for some VL-V H pairings. That some restriction exists is suggested by the observation that recombinatorial gene libraries of light and heavy chains are usually less diverse than would be predicted on the basis of ran- dom recombination. Clearly, where site-directed mu- tations target canonical positions and convert one ca- nonical structure to another, loss of antigen binding may arise from perturbation of the entire Fv structure.

The special problem of C D R H3 However comprehensive the canonical hypothesis

turns out to be, it is, at present, incapable of defining the conformations o f CDIL H3 loops. This CD1L occupies a central position in the combining site and has a critical effect on the combining-site topography. One of the factors determining CDtL conformation is the loop 'take-off' angle (i.e. the angle at which the loop lies relative to the framework [3-strand) (Fig. 3a). This positional variability may be important for the binding of an antigen when induced-fit is required (see below). However, in the unbound state, each C D R would be expected to exhibit a preference for one or a small number of energetically favourable conformers. An analysis of the take-off angles of all six CD1Ks (Ref. 6) has shown that CD1K H3 has the highest variability and can vary by up to 90 ° between different antibodies. On the basis of this analysis, we have defined seven classes of H3 loops, which are illustrated in Fig. 3b. This has now made possible the modelling of the complete antibody-combining site with an accuracy approaching that of the medium- resolution X-ray structure (2.5-3A) and, for some regions, at considerably higher accuracy (1.5-2 A res- olution). (Examples of predicted structures can be found in ILefs 6-8.)

The antibody surface ' phenotype ' Combining site topography - the CDR surface

Three types of combining-site topography can be defined 17,1s on the basis of an analysis of antibody X-ray structures: cavity, groove and planar. Unfortu- nately, there is no totally satisfactory way of predicting which C D R combinations give rise to which surface topography. Table 1 shows that different CDIL-length combinations can give rise to the same types of top- ography. Clearly, more structures are required to test the validity of this type of classification.

The framework surface The classification 'variable region' arose from the

analysis of antibody sequences by Wu and Kabad 9.

a

b \

Figure 2 The pairing of V L and V H domains to form an Fv region. (a) Plan and (b) side views are shown to indicate the tight, noncovalent packing of aromatic and other hydro- phobic residues at the Vc-V H interface. Close inspection reveals the manner in which the two conserved Trp residues, one from each domain, are packed.

However, such sequence variability does not predict exactly which segments of the variable region are con- served at the structural level. As has been shown, the [3-sheet core is well conserved, and even where the greatest variability occurs (in the CDRs), patterns of conservation are also seen. Variable regions show structural conservation in one further respect: the pos- itions and types of residues exposed at the surface.

When the solvent accessibility is calculated for all framework residues (i.e. minus the C D R residues) in the Fv region using a set of high-resolution crystal structures of Fab fragments, the alignment positions of 'surface' residues 2° is conserved with 98% fidelity. One of the most striking features of this analysis has been the fact that the identical V L and V H sequence families 2, previously classified on the basis of contigu- ous sequences, can be generated using the surface- residue profiles alone. This has suggested that the sur- face patterns of Fv regions may be highly conserved.

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a l

I \ T;-' !

b

H3a H3b H3c H3d

H3f H3e H3g

Figure 3 The effect of the 'take-off' angle of a complementarity-determining region (CDR) on its orientation on the Fvframework. (a) The differences in CDR position on the framework result from the different angles at which the residues at the base of the CDR emerge from the B-strand of the framework. (b) The effect of this difference in take-off angle on the conformations of the seven different H3 CDR classes H3a-g, defined below. The backbone positions of the framework f~-strands are shown for each CDR to illustrate the identity of these positions, seen as overlapping white, light grey, dark grey and black regions at the base of each of the loop clusters. Within classes, similar take-off angles are seen, whereas between classes the differences in take-off angles leads to markedly different CDR conformations. H3a loops are shorter than seven residues; H3b loops are seven residues long; H3c-g loops have 8, 9, 10, 11 or 12 residues, respectively, and all have a conserved Arg or Lys at position 94 that interacts with a Gly, Ala or Asp residue at position 101. (H3 loops with more than 13 residues have the same motif seen in classes H3c-g, but because of their extra length, confidence in their prediction is somewhat lower than for loops of 12 residues or less.) Each group contains H3 loops taken from antibody (or fragment) structures clustered so that they differ from each other by no more than 35 °, The angular difference is calculated as the angle between the planes defined by the amino-terminal Ce~, the centre of geometry and the carboxy-terminal Ca, for each pair of structures. (A detailed description of these H3 classes can be found in Ref. 8; residue numbering is as defined in Ref. 7.)

The main conclusions of the analysis are: (1) that V L and V H surface residue positions are conserved; (2) within each species there appears to be a preference for particular residues at certain positions; (3) V L and V H sequences can be classified into families according

Table 1. Lengths of complementarity-determining regions (CDRs) found in antibodies of the three different topographic

classes

Antibody L1 L2 L3 H1 H2 a H3 Topography Ref. ,,?

HyHel 10 11 7 9 5 9 5 Planar 39 D1.3 11 7 9 5 9 8 Planar 42

B1312 16 7 9 5 10 10 Groove 41 Gloop2 11 7 9 5 10 5 Groove b

36-71 11 7 9 5 9 8 Cavity 43 ¢ 4 - 2 0 16 7 9 5 12 7 Cavity 40

aThe CDRs are defined according to Kabat et al. 2, with the exception of H2, which is defined on a structural basis according to Pedersen et aLL b p. D. Jeffrey, G. L. Taylor and A. R. Rees, unpublished.

to their surface-residue profiles; and (4) although a par- ticular surface residue profile recurs within a species, it is never seen in both mice and humans. This study allowed the development of a novel method of humanization in which the surface pattern of a routine Fv region is replaced by its nearest human Fv surface pattern 21 (Fig. 4).

What is the significance of the surface conservation? One possibility is that the Fv-ffamework surfaces con- tain recognition codes for other molecules (e.g. B-cell superantigens), in which case, the available repertoire of antibody reactivity may be more diverse than is possible through C D R interactions alone.

Ant ibody design Affinity maturation in vitro

The great majority of antibodies has been raised against protein or peptide antigens. The first attempt to improve the affinity of a monoclonal antibody in vitro used site-directed mutagenesis guided by crude models, and achieved an improvement in K~ of an order of magnitude = . Since these early exper- iments, modelling has improved to the point where

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Rodent F v

i I

+

l

I e

t Human Fv

Rodent Fv with human surface

t Rodent F v with human surface and mouse CDR-supporting residues

Figure 4 Cartoon illustrating how antibody humanization by resurfacing occurs. In the first stage, the mouse framework (white)is retained and only the surface residues are changed from mouse (dark-grey circles) to the closest human pattern (light-grey circles). In the second stage, surface residues that are within 5A of the complementarity-determining regions (CDRs) (indicated by loops) and that are predicted to perturb CDR conformation, are replaced with the original mouse residues.

the positions of C D R residues at or near the putative antigen-binding region can be more accurately speci- fied, as exemplified by the work of Sharon 23, Denzin et al. 24, and Kelly and O'Donnell 2s. Given such method-ological improvements, can the effects of mutations on affinity be predicted?

Inspection of antibody-protein crystal structures suggests that there is no obvious correlation between the number or type of interaction seen (such as van der Waals or hydrogen bonding or salt bridges) and affinity (see Table 2). In addition, the buried surface area is not a good indicator of affinity, although this might be the case if burial ofhydrophobic residues and release of water molecules were the major contributor to the free energy of binding. In fact, recent work by Poljak and co-workers 26 suggests the reverse. During measurements of the enthalpic and entropic contri- butions to the association of hen's egg lysozyme (HEL) with D1.3 using microcalorimetry, it was found that the binding was substantially enthalpically driven, while the entropy actually decreased. While this may not always be so, it is clear that the differing affinities observed in different complexes may have their origins in rather different profiles of interaction. A good fit of the two surfaces is clearly important for high affinity, but after this has been achieved, the thermodynamics may be driven by many different combinations of hydrophobic and electrostatic inter- actions. There is probably more of a balance between these two types of interaction than is evident in other protein-protein complexes since, for much of its life, the antibody must retain stability in the absence of any bound antigen.

Where does all this leave the antibody engineer? Clearly, any engineering strategy must target both polar and non-polar residues in a way that, ideally, will lead to both enthalpic and entropic improvements in the interaction interface. Where entropy-enthalpy compensation occurs 27, more drastic changes may

Table 2. Some physicochemical characteristics of antibody-hapten, antibody-peptide and antibody-protein complexes a

Buried surface vdW Salt Antibody Complex Hapten area (A 2) contacts H-bonds bridges K a (M -1) name

Antibody- Phosphocholine 137 30 2 3 1.7 x 105 McPC603 hapten Phenyloxazalone 170 >15 b >1 0 1.0 x 108 NQ10/12.5

Fluorescein 266 65 5 1 3.4 x 10 lo 4-4-20

Antibody- Influenza virus 400 74-81 13-15 1 5.0 x 107 anti-HA1 peptide haemagglutinin

residues 100-108

Antibody- Hen-egg lysozyme 680 75 15 0 1 x 109 D1.3 protein Hen-egg lysozyme 774 111 14 1 5 x 109 HyHEL

Hen-egg lysozyme 750 74 10 3 2 x 10 lo HyHEL5 Influenza virus 879 108 23 1 - NC41

neuraminidase D1.3 800 99 c 9 1 - anti-D1.3

Data for antibody complexes McPC603, 4-4-20, D1.3, HyHEL5, HyHELIO and NC41 are from Ref. 44; data for anti-D1.3 are from Ref. 45; data for NQ10/12.5 are from Ref. 46; and data for anti-HA1 are from Ref. 50. b Not yet fully refined. c Author's estimate.

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Figure 5 Stereo view of the modelled zinc-binding site in antibody HyHEL5, showing the Fv region (blue), the mutated complementarity-determining region L1 (yellow; Ala25 and Va129 to Cys, Ser27 to Glu and Ile12 to His), and the position of the metal ion (pink) (predicted by the method of Gregory et al.37).

have to be incorporated (e.g. changes in the length of the CDP,_ backbone or multiple mutations). In situ- ations where a conformational change accompanies binding 2s,29, the problem of accurate design will become even more acute. In such circumstances, the creation o f artificial repertoires (e.g. via 'phage libraries'), in which both CD1K length and sequence is scrambled, may be the only effective way to engin- eer affinity and specificity in a timely fashion 3°. Such approaches, when combined with accurate mod- elling 8, and optimized mutagenesis methods 31 will lead to the rapid accumulation of engineering 'rules'.

N e w properties How far can the diversification of antibody function

go? The natural function of an antibody is to bind. However, in 1986, immunization with transition~state analogti'~s generated antibodies that could also act as catalysts 32. In 1990, metal-binding sites were engin- eered into the variable regions of two antibodies, using either framework 33 or C D R 34 residues to create metalloantibodies. As yet, it has not been possible to design catalytic activity, but the considerable success with the design of metal-binding sites may be a prel- ude to the creation of a new generation of catalytic proteins. Design of metal-binding sites can be approached in two ways. Existing residues in the com- bining site that conform spatially to a template appro- priate"for a set of coordinating ligands, but that are

sequentially discontinuous, may be changed to His, Cys or Glu residues, or a combination thereof, de- pending on the metal preferences. An example of this approach, targeted at framework residues, has been described by Roberts et al. 3~ and Wade et al. 36.

We have developed an alternative computational method that identifies sites within a protein with good potential to bind metal ions 37 and, in combination with the variable-region modelling program AbM T M

(see 1Kef. 8, and footnote*), enables such sites within CDRs to be designed. In one experimental example of this method, the CD1K L1 loop of the anti- lysozyme antibody HyHEL5 was modified by intro- ducing three potential Zng+-liganding residues. A fourth ligand was introduced at the amino terminus of the light chain to complete construction of the tetrahedral shell (Fig. 5) and the mutant antibody was expressed in E. coil As predicted by the model, the mutant (apo-)antibody had affinity for its antigen (lysozyme) that was identical to that of the wild-type antibody. When Zn 2+ was present in the medium, it bound to the mutant antibody (K a = 108M-1), but had no effect on the binding affinity of the antibody-anti- gen interaction. However, when cobalt ions bound ( K a = 109Ivl 1), the antigen affinity was 'allosterically'

* AbM TM, a program for modelling variable regions of antibodies (avail- able from Oxford Molecular, Oxford, UK).

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regulated and reduced by a factor of 12 (D. Staunton, and A. R. Rees, unpublished).

The recent incorporation of genes encoding a simi- lar metalloantibody variable region into the germline of the mouse 38 may lead to an extension of the nor- mal murine repertoire in vivo but, for the moment, such in vitro allosteric metalloanfbodies remain be- yond the present natural limits.

Future prospects It is now possible to produce three-dimensional

models of the antibody combining site from C D R sequences alone. However, to generate an antibody specificity de novo requires that a particular set of C D R sequences must be predicted to give rise to the shape of a particular antibody-combining site. This process is by no means simple and is at the heart of the design concept. Our analysis of more than 70 antibodies for which the antigens are known has suggested that there is no strong relationship between particular combi- nations of C D R lengths and antigen size (J. T. Pedersen, Ph.D. thesis, University of Bath, UK, 1993). However, the coarse topography of a combining site (cavity, groove or planar class) will be dictated by the backbone conformation, while the fine specificity for the epitope will largely be determined by the side chains. This leads to the hypothesis that any antibody in a given topographic class that is stripped of all those side chains that do not play a structural role (e.g. canon- ical residues13-1s), should provide a combining site that is generic for a large number of different antigens.

These assumptions have led us to a design strategy that is now being tested. The concept is illustrated in Fig. 6 and follows the sequence set out in the legend. This design process can be tested by synthesis and expression of candidate designs or by phage-library methods. Sequences identified as new antigen binders

Figure 6 Cartoon of the process described in the text by which an antibody specific for one antigen can be remodelled to fit a different antigen. In the example shown, the new antigen is morphine, a binding site for which is being created within the combining site of the anti-peptide anti- body, Gloop2 (P. D. Jeffrey, G. L. Taylor and A. R. Rees, unpublished). The process shown is as follows: (a) Select a new antigen, for which an X-ray (and preferably an NMR) structure is known, and define the required antibody class: cavity for hapten; groove for peptide; or planar for protein. Then select an antibody of the required class from the Protein Databank (cavity, groove or planar) whose natural antigen is most similar to the new antigen. (b) Remove the side chains (down to the 13-carbon) from all the residues of the complementarity deter- mining region that are not required to define backbone structure (cir- cled in red). (c) Generate an 'aianine cushion' using an extended van der Waais radius for the truncated residues described in (b), and dock the new antigen on to the alanine cushion using a Monte Carlo simu- lated annealing method 47, (Other methods such as functionality map- ping 48 or pair-preference analysis 49 can also be used.) Restore all original side chains predicted to be distant from the docked antigen. (d) Reconstruct predicted contact side chains (shown in red), using torsional search. (e) Evaluate candidate side-chain sets using knowledge-based and conformational search methods. (Hydrogen bonds are shown in blue.)

a

b

C

d

e

H 2 N / L ' ~ OH

",, H 2 N ~ OH

H2N ~ OH

O / ~dH L,

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can then be analysed and fed back into the design process. When design algorithms like these are fully developed, they will take their places alongside the more traditional immunization and gene library methods in vivo and will probably in time become the method of choice where antigen structure is known.

Acknowledgements We thank the Danish Research Foundation (JP),

Amersham International (DS) and SERC, British Biotechnology and Wellcome Research for a LINK grant.

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