Antibody Modeling: Implications for Engineering and Design

METHODS 20, 267–279 (2000) doi:10.1006/meth.1999.0921, available online at http://www.idealibrary.com on

Antibody Modeling: Implications for Engineering and Design Veronica Morea,* ,† Arthur M. Lesk,‡ and Anna Tramontano* *IRBM “P. Angeletti,” Via Pontina Km. 30.600, 00040 Pomezia (Rome), Italy; †Istituto di Chimica del Farmaco, Universita´ “G. D’Annunzio,” Via dei Vestini, Chieti, Italy; and ‡University of Cambridge Clinical School, Wellcome Trust Centre for the Study of Molecular Mechanisms in Disease, Cambridge Institute for Medical Research, Wellcome/MRC Building, Hills Road, Cambridge CB2 2XY, United Kingdom

Our understanding of the rules relating sequence to structure in antibodies has led to the development of accurate knowledgebased procedures for antibody modeling. Information gained from the analysis of antibody structures has been successfully exploited to engineer antibody-like molecules endowed with prescribed properties, such as increased stability or different specificity, many of which have a broad spectrum of applications both in therapy and in research. Here we describe a knowledge-based procedure for the prediction of the antibody-variable domains, based on the canonical structures method for the antigen-binding site, and discuss its expected accuracy and limitations. The rational design of antibody-based molecules is illustrated using as an example one of the most widely employed modifications of antibody structures: the humanization of animal-derived antibodies to reduce their immunogenicity for serotherapy in humans. © 2000 Academic Press

Key Words: antibody modeling; canonical structures; CDRs; loop grafting; protein engineering.

The function of antibodies is to recognize a theoretically unlimited set of foreign substances and to defend the vertebrate body against them. Because of their extraordinary affinity and specificity toward these antigens, antibodies are invaluable tools both in therapy and in research. Since their first description (1), monoclonal antibodies (a homogeneous population of molecules with a binding site specific for a unique antigen) have been produced toward a number of different antigens and even endowed with catalytic activities (2, 3). These monoclonal antibodies have many applications both in research and in the diagnosis and treatment of diseases. However, in spite of their advantages (possibil1046-2023/00 $35.00 Copyright © 2000 by Academic Press All rights of reproduction in any form reserved.

ity to be raised in large amounts against any type of antigen), the usefulness of rodent monoclonal antibodies in human therapy is limited because of problems arising from their nonhuman origin (e.g., immunogenicity, variable efficiency in fixing complement (4, 5). On the other hand, human monoclonal antibodies that would represent ideal candidates for therapeutic applications are difficult to produce (6, 7). The engineering of antibody-encoding genes and, therefore, the generation of antigen-binding specificity in vitro, independently of animal immunization (5, 7–11) have been made possible by the recent advances in recombinant DNA technology. Engineered human antibodies could overcome some of the problems associated with the use of rodent monoclonal antibodies and lead to more effective antibody-based diagnostic and therapeutic agents. They are indeed being introduced in clinical medicine and are employed in clinical trials more and more frequently (12). Large collections of antibodies with different specificity can be generated (e.g., from phage-display libraries) and selected based, for example, on specific antigen-binding activity, high-level expression, and expression in specific cellular environments (13–15). This procedure mimics the natural mechanisms of antibody maturation in animals. Alternatively, one can rationally modify antibody structures, with molecular modeling techniques, to endow them with new properties (e.g., improved stability or affinity for the antigen). This can also be used to improve properties of antibodies obtained by random selection. Modeling techniques have been successful, for example, in designing smaller antibody fragments (7) still able to bind the antigen (16 –18) or endowed with effector functions. As antibodies are very large molecules, the use of small fragments can be advantageous 267

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for both research and in vivo applications, due, for example, to their increased ability to penetrate tissues and to their faster clearance from tissues and serum. Other examples of rational modifications of antibody molecules are the engineering of fusion proteins, where antibody fragments are joined to fragments coming from different proteins and bearing specific activities (e.g., toxins, enzymes, ligands) to deliver them to specific cellular targets (19); the design of antibody-based scaffolds into which tailored functions can be introduced (20); and the modification of antibodies or antibody-based molecules to increase their stability (21–23). Also, the antigen-binding site can be modified, for example, to increase the affinity or specificity toward a specific antigen (24); to introduce novel functions, like the ability to induce a response against a specific antigen (25); to add new binding sites for metals (26); and to improve the efficiency of catalytic antibodies (27). Finally, modeling techniques are instrumental for the humanization of animal-derived antibodies, aimed at reducing their immunogenicity for serotherapy in humans. This is obtained by joining a portion of the nonhuman antibody bearing the antigen-binding activity to the remaining portion of a human antibody (28). The success of protein design, that is the construction of proteins endowed with desired properties, relies on our understanding of how these properties are related to protein structures and sequences. The structural analysis of antibodies has provided us with a wealth of information on the rules governing their structure, especially with respect to their antigenbinding sites (29 –32), and on the structural roles of individual amino acid residues (33). These results can be exploited to gain a deeper insight into the mechanisms of antibody–antigen recognition and, more generally, those of protein–ligand interactions and to develop prediction methods (34, 35). Moreover, they provide a rational basis to interpret experimental results and to design new experiments (36, 37). As will be described below, the information derived from the structural analysis of antibodies represents an invaluable guide to antibody design and provides the rational basis for any antibody prediction method. On the other hand, antibody engineering can help to elucidate the relationships between antibody structure and properties. Here we will describe a procedure to build a model of the complete variable fragment of antibodies based on the canonical structures method, which is the most accurate method developed until now for the prediction of the antigen-binding-site structure. We will outline alternative methods that can be used when the first one is not applicable. We will also illustrate the implications of the rules determining antibody structure for

antibody engineering and in particular for the humanization of antibodies through loop grafting.

DESCRIPTION OF METHOD Antibody Structure: Implications for Modeling and Engineering Antibodies or immunoglobulins are multichain proteins, consisting of two pairs of light chains (either ␬ or ␭ isotype) and two pairs of heavy chains (isotype ␥, ⑀, ␦, ␣, or ␮). Both chains are composed of multiple variants of a basic domain of about 100 residues in length. The light chain is formed by two of these domains, called the variable (V L) and the constant domain (C L), from the variability of their amino acid sequences. The heavy chains contain a variable domain (V H) and three or four constant domains (C H1, C H2, C H3, C H4), depending on the heavy-chain isotype (38). Each domain is formed by two ␤ sheets packed face to face, linked together by a conserved disulfide bridge, and by interstrand loops (39). Their modular nature makes antibody molecules particularly suitable candidates for protein engineering. In fact, by limited proteolytic digestion, it is possible to obtain smaller antibody fragments containing only a subset of the domains of a complete antibody, of which they maintain either the antigen-binding ability [F(ab⬘) 2, Fab, Fv, ScFv] or the effector functions (Fc, hinge) (7). The antigen-binding sites of most antibodies are formed primarily by six loops, three from the V L domain (L1, L2, L3) and three from the V H domain (H1, H2, H3) (31) (Fig. 1). The regions of the variable domains outside these loops are called the framework. In known immunoglobulins, the framework regions are highly conserved in both sequence and main-chain conformation, and they can be accurately predicted using standard homology modeling techniques (40). The residues determining the structure of the common core of V L and V H domains have been described (33, 41) and they should be kept into account in any attempt to engineer the framework regions, for example, to increase the stability of antibody fragments. Due to the presence of conserved residues at the interface between the variable domains of the light and the heavy chain (V L and V H), the relative geometry of these domains is also relatively well conserved, creating a scaffolding of conserved structure on which the antigen-binding site is erected (41). The six loops of the antigen-binding site are even more variable in sequence than the rest of the variable domains. Based on this observation, Kabat and coworkers correctly predicted that these hypervariable regions were involved in antigen binding and called them “complementarity determining regions” or CDRs (42, 43). This sequence-based definition is largely over-

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lapping with the structural definition subsequently provided by Chothia and Lesk (29), who include in the antigen-binding loops only those residues which are not part of the conserved ␤-sheet framework (Table 1). In spite of their high sequence variability, five of the six loops of the antigen-binding site can assume just a small repertoire of main-chain conformations, called “canonical structures” (29 –31). These conformations are determined by the length of the loops and by the presence of key residues at specific positions in the antibody sequence (either within the loops or in the framework regions) that determine the conformation of the loops through their packing, hydrogen bonding, or the ability to assume unusual main-chain conformations (Table 1). The other loop residues are free to vary to modify the topography and physicochemical properties of the antigen-binding site. Most of the hypervariable regions of known structures have conformations very close to the described canonical structures (31, 44).

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The identification of the structural determinants of the antigen-binding loops is a necessary requirement for the successful engineering of antibodies with prescribed specificity. In any design involving modifications and/or transplant of the antigen-binding site loops (aimed, for example, at varying the antibody affinity or specificity toward the antigen, at introducing metal-binding sites, or at generating large repertoires of antibody molecules through the use of libraries) it is indeed necessary to keep into account that mutations of residues at most positions of the hypervariable loops will determine only local variations of the antigenbinding site, without affecting its main-chain conformation. On the other hand, mutations at key sites (Table 1) will, in most cases, also affect the main-chain conformation of the antigen-binding site loops and are likely to have a larger impact on the affinity toward the antigen. The specific pattern of residues that determines each canonical structure forms a “signature” whereby a ca-

FIG. 1. Ribbon representation of the antibody McPC603 Fab fragment (a) (112). The antigen-binding site is shown in (b) and (c). The view in (c) is roughly perpendicular to that in (b).

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TABLE 1 Canonical Structures for the Hypervariable Loops of Immunoglobulins Loop

Canonical r.m.s.d. structure (Å) Length

L1 (␬)

1

L1 (␬)

2

L1 (␬)

Key residues in known structures

Structural numbering

6

25 26–30

32 33

29 V

0.4–0.9

7

25 26–30

31 32 33

I V

2A

0.4

7

25 26–30

31 32 33

I V

L1 (␬)

2B

0.5

7

25 26–30

31 32 33

I V

L1 (␬)

3

0.3–0.5

13

25 26–30 30a 30b 30c 30d 30e 30f 31 32 33

L V

L1 (␬)

4

0.5–0.6

12

25 26–30 30a 30b 30c

30e 30f 31 32 33

I L

2 25 33 71 30e I A L Y M I A I F L Y V I A I F L V I A I Y L V I S L F E Q S L S F F G V P L

11 8

25 26–30 30a 30b 25 26–30 30a

30e 30f 31 32 33 31 32 33

V

N

10

24 25–27 28 29 30 30a 30b

11

24 24 24 24 24

L1 (␬) L1 (␬)

5 6a

L1 (␭)

1

L1 L1 L1 L1 L1

(␭) (␭) (␭) (␭) (␭)

a

0.3

2 3 3A 3B 4

0.2–0.8 0.2

11 11 9

25–27 25–27 25–27 25–27 25–27

28 28 28 28

29 29 29 29

30 30 30 30 30

30a 30a 30a 30a 30a

30b 30b 30b 30b 30b

31 32 33 30c 30c 30c 30c 30c

L2 (␬, ␭)

1

0.1–0.5

3

49 50 51 52 53

L3 (␬)

1

0.3–0.7

6

90 91 92 93 94 95 96

97

L3 (␬)

2

6

90 91 92 93 94 95 96

97

L3 (␭)

3a

5

90 91 92 93 94 95

97

L3 (␬)

4

a

4

90 91 92 93 94

97

L3 (␬)

5a

7

90 91 92 93 94 95 96 96a 97

L3 (␬)

6

5

90 91 92 93 94 95 90 90 90 90 90

L3 L3 L3 L3 L3

(␭ (␭) (␭ (␭) (␭)

31 31 31 31 31

32 32 32 32 32

97

1 1A 1B 1C 2

0.4–1.1 0.4

0.8

6 6 6 6 8

H1

1

0.1–1.2

7

25 26 27 28 29 30 31

H1 H1

2a 3a

8 9

25 26 27 28 29 30 31 31a 32 33 25 26 27 28 29 30 31 30a 30b 32 33

91 91 91 91 91

92 92 92 92 92

93 93 93 93 93

94 94 94 94 94

95 95 95 95 95 95a 95b

96 96 96 96 96

33 33 33 33 33

A

L

Y

29, 31, 35

29, 31, 35, 56

31

31

29, 31, 35, 56

31, 35, 56 31 31, 56

25 28 30 33 71 66 90 31 29, G I V A G I V A 29, 31 31 S G V A A L L N 31, 31,

31 31

113 44

48 64 I G V

29, 31, 35, 56

90 95 97 Q P S N T H 90 94 97 Q P T 90 95 97 Q P T 90 97 Q S 90 96 97 Q P T 90 94 95 97 Q L Y T

29, 31, 35, 56

29, 31, 35, 56 31, 35, 56 31, 56, 114 31, 56, 115 116 29, 31 31 31 31 29, 31

97 97 97 97 97 32 33

Refs.

26 27 29 34 94 24 G F F M R A Y L V G V S I I N S D Y K T W S G Y I A R V G F L M Q F I Y R V

29, 31, 35, 57

31, 57, 115 31, 57

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ANTIBODY ENGINEERING TABLE 1—Continued

Loop

Canonical r.m.s.d. structure (Å) Length

H2

1

H2

2

H2 H2 H2

2A 2B 3

H2 H2 H2 H2

3A 3B 3C 4

H3 b

0.1–0.2

0.3–0.5 0.2–1.5

0.2–0.3

TB

Structural numbering

3

52

53 54 55 56

4

52 52a

53 54 55 56

4 4 4

52 52a 52 52a 52 52a

53 54 56 53 54 55 56 53 54 55 56

4 4 4 6

52 52 52 52

53 53 53 53

52a 52a 52a 52a 52b 52c

54 54 54 54

55 55 55 55

56 56 56 56

Key residues in known structures 55 71 G K D R V I 52a 55 71 P G A T N L A D T S

54 71 G R S K N D

54 55 71 K Y R N S

Refs. 29, 31, 35, 57

29, 31, 35, 57

31 31 29–31, 35, 57

31 31 31 29, 31, 35, 57

ⱕ1.2 10

29, 31, 32, 45, 46

32, 45, 46

H3

TNB

10

H3

TNB-1

10

H3

TNB-2

10

91 92–95 96–100 100a– i 100x 100y 101–104 105 R nR K nK 91 92–95 96–100 100a–i 100x 100y 101–104 105 94 101 D nR nK 91 92–95 96–100 100a–i 100x 100y 101–104 105 94 101 96 99 D Ar nAr 91 92–95 96–100 100a–i 100x 100y 101–104 105 94 101 96 99 D nAr Ar

32, 45 32, 45

Note. The canonical structures have been observed in high-resolution (1.6 –2.3 Å) antibody structures, except where specified. The r.m.s. deviation values obtained after optimal superposition of the main-chain atoms of loops in high-resolution (1.6 –2.3 Å) structures are reported. Residues within the hypervariable loops are numbered according to the structural numbering (31) given in this table. For all the other residues, Kabat numbering (65) is used. The numbering of residues also includes one residue on each side of the loop. The subscript “n” before residue X ( nX) means “any residue but X.” Ar, aromatic residue. Conservative substitutions of residues at key positions in human germlines and expressed sequences have been described in (56, 57). a Structure determined at medium to low resolution (2.7–3.0 Å). In these cases, it can be difficult to distinguish carbonyl oxygen atoms from main-chain atoms; therefore, hypervariable loop conformations observed only in these structures should be considered as possible, but not certain, canonical structures. b There are no canonical structures defined for the whole H3 loop. Here we only refer to the torso residues (underlined), that is, the 4 N-terminal residues (92–95) and the 6 C-terminal residues (101–104 and the two residues preceding 101) of the loop. The r.m.s.d. in this case is calculated between the torso region of known structures with a resolution of 2.8 Å or better. Only the loops of 10 residues show a common overall conformation (r.m.s.d. ⫽ 0.44 – 0.74 Å). Exceptions to the rules described are discussed in (32, 46).

nonical structure can be recognized in the sequences of an immunoglobulin of unknown structure and can therefore be predicted from sequence alone. This has been demonstrated in “blind tests” where the conformation of the loops was correctly predicted with high accuracy before the experimental structure was published (34, 35). The relative position of these loops is also predicted with good accuracy if framework struc-

tures with high sequence similarity are used as a template to model the target antibody (35). The H3 loop is a special case. This loop is the most variable in length, sequence, and structure and its variability in conformation has not been circumscribed by a suitable catalog of canonical structures. However, recent studies have revealed the existence of relationships between the main-chain conformation of the 10

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residues of the loop proximal to the framework (the “torso” of the loop) and the sequence of the antibody (32, 45, 46) and have demonstrated that good predictions can be made for the torso region of the H3 loop. The remaining portion (the “head” region) of short H3 loops (up to 14 residues), with a specific conformation of the torso region, has been shown to behave like standard hairpins (47–55) and, in these cases, the complete main-chain conformation can be predicted from the sequence alone (32, 45, 46). The main-chain conformation of many of these regions is not unique to antibody H3 loops (32) and specific database search procedures can be used to predict them (32, 45, 46). Although in general the main-chain conformations of the other hypervariable loops do not vary on antigen binding, variations in the main-chain conformation of the H3 loop occur in a few cases, but it has been shown that, in these cases, the ligated conformation rather than the nonligated one follows the described sequence–structure relationships and therefore would be predictable from the sequence (32). As a result of these studies, the uncertainty in the conformation of the H3 loop has been restricted to a few residues in the head region of some loops. The sequence pattern of known canonical structures can be identified for the large majority of the L1, L2, L3, H1, and H2 loops of the expressed human and mouse V L and V H repertoire (29, 35). Moreover, a thorough analysis of the human germline genes of antibody V ␬ (56) and V H (57) sequences has revealed that nearly all of these sequences have hypervariable regions corresponding to a known canonical structure. The signature patterns identified for the H3 loop are also present in the large majority of the known expressed sequences (46). The mature human antibody sequences preserve the canonical structures for the hypervariable loops that were selected during the primary response (56, 57). In fact, in these rearranged sequences, nonconserved mutations are rarely present at those positions, where they could change or even disrupt the canonical structure of the loops. The observed conservative mutations are not expected to affect significantly the canonical structure but might nevertheless produce small shifts in their geometry and relative position, thus contributing to affinity maturation (56). The canonical structure determinants for L1, L2, L3, H1, and H2 are also present in cartilaginous fishes, the species known to have an immune system which is most distantly related to humans. Taken together, these observations imply that, given an unknown antibody sequence, there is a very high probability that the hypervariable loops L1, L2, L3, H1, and H2 have a main-chain conformation that corresponds to a known canonical structure and that the torso region of H3 has a conformation corresponding to one observed in known structures and predictable from

the sequence. Consequently, except for the head region of some H3 loops, very accurate 3D models can be built for most of the antibody sequences. Antibody Modeling: Canonical Structures Method We describe here the complete model-building procedure for the variable domains of immunoglobulins, based on our understanding of the determinants of the conformations (canonical structures) of the antigenbinding-site-hypervariable loops (29 –31, 34, 35). The results obtained in blind tests, comparing experimentally determined structures with models built using this procedure, are very satisfactory (34, 35). The model-building procedure consists of the following steps: 1. The sequence of each variable domain (V L and V H) of the immunoglobulin of unknown structure to be modeled (target) is aligned with the corresponding variable domain sequences of all the immunoglobulins of known structure. For this step, standard database searching [e.g., FastA (58), BLAST (59), SSEARCH (60)] and multiple sequence alignment [e.g., Pileup, of the GCG package (61) or Clustalw (62), Maxhom (63)] programs can be used. The conserved residues at the V L–V H interface (Table 2), the hypervariable loops and CDRs (Table 1), and the determinants of the canonical structures for the hypervariable loops in the target sequence and of the recurrent conformations of the torso region for H3 (Table 1) can be identified from the alignment. 2. The main chain of the framework region of each domain is modeled using as a template the domain of known structure with the highest sequence identity with the target domain. The rationale for this is that, in general, the higher the residue identity in the core of two proteins, the more similar the conformation in this region (64) and, hence, the higher the quality of the model. However, if another known structure exists that has been solved to significantly higher resolution, and that has only slightly lower sequence identity (ⱕ5%) with the target, the higher resolution structure is selected as the parent domain. 3. If the parent domains for the V L and the V H domains come from different immunoglobulins, they are packed together by a least-squares fit of the mainchain atoms of residues conserved in the V L–V H interface (Table 2) (41). The root-mean-square (r.m.s.) devi-

TABLE 2 Conserved Residues at the V L–V H Interface (41, 35) VL VH

33–39 34–40

43–47 44–48

Note. Kabat numbering is used (65).

84–90 88–94

98–104 103–109

ANTIBODY ENGINEERING

ation of the main-chain atoms in these sets of residues for different structures is usually below 1.0 Å and often below 0.6 Å. If the r.m.s. deviation is higher than 1.0 Å, the atoms that are farthest apart are deleted from the set, the r.m.s. deviation is recalculated, and the interface residues are superposed again. The procedure is repeated, if necessary, until the r.m.s. deviation is below 1.0 Å. 4. The target sequence is examined to determine whether a canonical structure can be identified for its hypervariable loops L1, L2, L3, H1, and H2, by checking for the residues that form the signatures of the canonical structures (Table 1) (35). The canonical structures identified in the known immunoglobulin structures account for most of the loops in most immunoglobulin sequences (29, 35, 56, 57); therefore, it is generally possible to predict the main-chain conformations of the loops in an unknown structure. 5. For the H3 loop, the recently described rules (32, 45, 46) allow the prediction of the main-chain conformation of the torso region (10 residues of H3 proximal to the framework: 4 at the N-terminus, starting from Cys-92, and 6 at the C-terminus, ending with Gly-104) (Table 1). The sequence of the target antibody is therefore inspected to identify the conformation of the torso region for H3. The determinants of the main-chain conformations of the torso region of H3 can be identified in almost all known sequences (46, 65); therefore, this region can be predicted in most cases. 6. The head regions of short H3 loops (up to 14 residues) with a nonbulged conformation of the torso region follow rules of standard hairpins (32, 45) and can therefore be modeled using standard techniques (47–55). In some cases, the head region of H3 loops with a bulged torso conformation can also be predicted using a database search procedure (32, 45). This consists in searching the database of well-determined protein structures (resolution ⱕ2.0 Å) for regions matching the last two residues of the torso from the N-terminus of the H3 loop and the last three residues from the C-terminus (to take into account the hydrogen-bonding pattern typical of the bulged conformation) and separated by the correct number of residues. If loops with the same pattern of glycines and prolines as the region to be modeled are found, they are used as templates. 7. If the canonical structure identified for a loop is different from that of the structure selected as a parent for the framework regions, then a loop from another known immunoglobulin having the same canonical structure of the target, if present, is grated in. The residues adjacent to the loop in the model and in the selected structure are superimposed by a weighted least-squares fit of the main-chain atoms (66) and the loop is thereby transferred to the model. The four residues before the N-terminus of the loop and the four

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residues after its C-terminus are used to superpose the structures; the residue immediately preceding the loop and the residue immediately after have weight 1.0 and the weight of each successive residue is decreased by a factor of 0.8. The value of the weighted r.m.s. deviation is usually below 1.0 Å. The same procedure is used to import in the model the torso region of H3 if coming from a different immunoglobulin. For the head regions of H3 loops with a bulged torso that can be predicted using the database search procedure described above, the same residues used for the database search (last two of the torso from the N-terminus and last three from the C-terminus) are used for the superposition between the selected structure and the model. 8. If a canonical structure for the loops— or a template conformation for the head region of H3— cannot be identified, the corresponding region from the parent structure is temporarily retained. Any “temporary loops” will be deleted at the end of the procedure, and no predictions will be recorded for them. 9. The conformations of the side chains are then modeled. At sites where the parent structure and the model have the same residue, the conformation of the parent structure is retained. At sites where the residues are different, the alignment is inspected to find the immunoglobulin with the highest sequence identity with the target having the same residue in the corresponding position. In this case, its conformational angles are transcribed into the model. Alternatively, the conformation of the side chains in the parent structure is retained as far as the relative lengths of the side chain permit. Standard rotamer libraries are used in other cases (67). If the substituted residue is part of a hypervariable loop, then the side-chain conformation is taken only from a loop with the same canonical structure. 10. The model is refined by a few cycles of energy minimization. This step is only performed to improve the stereochemistry, especially in those regions where segments of structures coming from different immunoglobulins have been joined and not to refine the models significantly. In fact, the r.m.s. difference between the backbone atoms of the initial and final structures are typically only 0.1– 0.2 Å. Given the limited number of minimization steps, the choice of the program used for the minimization should not significantly affect the results. We tested the effect of three different minimization programs [ENCAD (68), GROMOS (69), and DISCOVER (MSI, San Diego, CA)] on the NQ10/12.5 model described in (35). In all cases default parameters were used. The r.m.s. deviation of the backbone atoms between the three different final models after optimal superposition is lower than 0.2 Å. These differences are significantly lower than the r.m.s. deviations between our models and the experimental structures and are comparable with typical experimental error. Moreover,

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the visual inspection of the three models does not show any systematic differences between them, suggesting that the r.m.s. difference is the effect of small deviations uniformly spread in the models. This confirms that the choice of the program, among those used, does not substantially affect the coordinates of the final model. 11. The temporary loops, if any, are removed from the final coordinate set. Sets 3, 5, and 7 can be performed using programs written by one of the authors (A.M.L.) (70). Standard software for protein structure visualization, structure superposition, and surface calculations, such as INSIGHT (71) and WHATIF (72), can also be used to perform these steps, with only minor differences. Expected Accuracy of the Canonical Structures Method Because this model-building procedure is based on structural similarity between different immunoglobulins and on rules derived from the analysis of known structures, its success depends on the extent of the similarity between different structures and on the completeness of our understanding of where differences can be expected and what their determinants are. When a target loop matches a known canonical structure, the expected r.m.s. deviation of the mainchain atoms between the modeled loop and the real structure is expected to be in the range of the r.m.s. deviation values measured for that canonical structure in different antibody structures. [For an illustration of the relationship between the value of the r.m.s. deviation of the main-chain atoms and the extent of structural similarity between hypervariable loops see (73).] In different high-resolution antibody structures, the local conformation of the canonical structures is highly conserved (31). Loops with the same canonical structures and identical residues at key sites have virtually identical main-chain conformations (r.m.s. deviation 0.2– 0.5 Å) (73). Loops with the same canonical structures and conservative substitutions at key sites still have very similar main-chain conformations, but they show small differences in their geometry that could affect affinity (r.m.s. deviation closer to 1.0 Å) (29, 31, 35). Differences in the main-chain conformation increase slightly with the size of the loops: the r.m.s. deviation is generally ⬍0.5 Å for small and medium size loops and ⬍0.8 Å for longer loops. In some cases, hydrogen bond interactions with framework residues determine rotations of peptide groups within the loops resulting in higher r.m.s. deviation values (⬃1.0 Å) between loops with the same canonical structure. The head regions of the H3 loops that can be predicted using the database search procedure are generally within 1.0 Å r.m.s. deviation of the real structure

(32, 45). It is worth noting that, although in many cases this database search procedure does not provide a template loop for the head region, in no case does it provide incorrect results (32, 45). In general, loops with the same canonical structures have very similar local conformations in different antibodies; however, their position relative to each other and to the framework can vary, as a result of variations of residues at the key sites, at neighboring positions in the framework and at the interface between V L and V H domains (29, 30, 35). Differences in the sequence of the framework regions and differences in the relative orientation of the variable domains, which can be up to 15° (74 –76), can shift the relative positions of the hypervariable loops by 1.0 –2.0 Å in most cases and by 3.0 Å in a few (29, 56). To build an accurate model of the framework regions of the variable domains is therefore essential to obtain a correct model of both the packing of the two domains and the positioning of the hypervariable loops relative to each other and to the framework. For homologous proteins, structural similarity is a function of sequence identity, the r.m.s. deviation being about 1.0 Å for the main-chain atoms of the core residues between proteins with about 50% sequence identity (64). As the percentage of residue identity in the framework between the target domain and the parent structure usually ranges between 45 and 85%, the expected r.m.s. deviation is between the main chain of the model framework, and that of the real structure is ⱕ1.0 Å Superposition of the backbone of the framework residues as defined in (29), for the known structures, gives r.m.s. deviations usually ranging between 0.9 and 1.5 Å. If the frameworks of V L and V H domains are superposed separately, the r.m.s. deviation is on the average lower implying that the differences in the structures are not only local, but also depend on the variation in the V L–V H interdomain geometry. Additional errors in the predicted structure may arise from noise and experimental errors in the determinations of the parent structure (the largest component being crystal packing). It is known that the expected value of the r.m.s. deviation of the backbone for independent experimental determinations of the same structure is about 0.3 Å for well-refined structures (64) and can be as high as 0.8 Å for medium resolution structures (77). Given this background, how good a model can we expect from our procedure, on the hypothesis that a model for all the hypervariable loops can be provided? Differences between predicted and experimental structures can arise from errors in the prediction of the conformation of each loop, errors in positioning the loop with respect to the framework, and errors in the relative position of the light- and heavy-chain variable

ANTIBODY ENGINEERING

domain, in addition to any inaccuracies in the parent structures themselves. As it has been discussed above, if the structures used a parent structures for the two domains and the loops are high-resolution, well-refined structures, we can expect the backbone of the framework to be correct within 1.0 Å r.m.s. deviation; the backbone of the predicted loops to differ by about 0.7 Å r.m.s. deviation on the average, and no more than 1.0 –1.2 Å in all cases; and the relative position of the loops to differ, in most cases, by about 1.0 –2.0 Å. As the current library of canonical structures covers a high proportion of the conformations that occur in the hypervariable loops L1, L2, L3, H1, and H2 (29, 35, 56, 57), these regions can be predicted in most cases. Also the determinants of the conformations of the torso region of the H3 loop are present in almost all sequences (46), and therefore the main-chain conformation of this region can be predicted accurately. On the other hand, some loops do not show the signature pattern corresponding to any known canonical structures and accurate predictions can so far be made for only a small proportion of the H3 head regions, either from sequence or through database search procedures (32, 45, 46). However, with the continuous increase in the number of the available antibody structures, the probability of observing the few unknown canonical structures will become higher and higher and the availability of more examples of H3 conformations will hopefully lead to the elucidation of other sequence–structure rules for the head region of this loop and to the development of a more complete prediction method. Alternative Procedures As discussed above, the canonical structures method has been shown to be successful in “blind” tests and it is, to the best of our knowledge, the most accurate and reliable way to predict the antigen-binding sites of antibodies (78). For this reason, whenever a canonical structure for a loop is present in the database, this is by far the best method to model the main-chain conformation of these loops. A limitation of this approach is the lack of a structural representative for a few canonical structures and of a general method to predict the conformation of the head region of the H3 loop. In these cases, alternative methods should be used. The combination of the canonical structure procedure described here with other computational approaches may offer the promise of an improved combined system for predicting antibody-combining sites. Framework regions are generally built using homology modeling procedures similar to the one described above. In some cases, portions of template structures with unusually high temperature factors have been rejected and the corresponding regions have been selected from other structures (78). Alternatively, “aver-

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age” frameworks have been used (79). The packing of the V L and V H domains are generally modeled on the basis of those present in known structures (78, 80). Several procedures for side-chain modeling have also been described (81). Loop modeling procedures that have been described in the literature (reviewed in 73, 78, 80) can be classified as knowledge-based, when they exploit information contained in known structures, and as ab initio, when they are based on a priori physicochemical principles, including conformational searching and evaluation techniques (82– 87). Besides the canonical structures approach described above, there are knowledge-based methods which use other antibody-hypervariable loops as templates, selecting them on the basis of their length and maximum sequence identity (rather than on the presence of key residues) (88, 89) or loops taken from either antibodies or other proteins selected on the basis of their length and of the structural similarity with the regions flanking the loop (database screening methods) (90). Ab initio methods do not rely on the presence of a template loop in the database, and therefore they are of general use. Their major limitation is that, due to our limited comprehension of the physicochemical principles governing protein structures, the energy functions used to evaluate the different conformations often do not distinguish a correct prediction from an incorrect one, even when additional criteria are used to filter the results. Approaches combining knowledge-based and ab initio methods have also been described (91, 92). Antibody Humanization through Grafting and Resurfacing The understanding of sequence–structure relationships in antibodies is of central importance for the engineering of antibodies with prescribed specificity and properties. One of the most important applications of antibody engineering is the “humanization” of animal-derived antibodies, aimed at reducing the immune response associated with the use of nonhuman antibodies in human therapy. Strategies for antibody humanization used up to now include chimerization, reshaping, and resurfacing. In chimerization experiments, variable domains from nonhuman antibodies are linked to human constant domains (93, 94). The immune response is reduced with respect to murine antibodies (95) but it is still relevant (96). Reshaping an antibody consists in grafting antigen-binding sites from nonhuman antibodies into human frameworks (28, 97–99). This has been shown to elicit a considerably reduced immune response with respect to completely nonhuman antibodies and to antibodies humanized through chimerization, and there have been clinical trials with prom-

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ising results (100). Variable domains from mouse can be resurfaced by replacing surface residues in the framework regions with human residues (101, 102). No clinical data on the immune response to these antibodies are available yet. The humanization of nonhuman antibodies (nhAbs) through antigen-binding-site grafting and variable domain resurfacing is a multistep process. First, a suitable human framework must be selected. In reshaping experiments, as the relative position of the hypervariable loops is affected by the relative position of the V L and V H domains and of the ␤ sheets within each domain, the selection of the framework is a critical step. The human frameworks can be those with the highest sequence identity with each of the variable domain frameworks of the nhAbs (103–105), therefore not necessarily coming from the same human antibody, or one can select the human variable sequence with the highest overall sequence identity with the V L and V H domains of the nhAb (102). A comparison between reshaped antibodies has shown that the affinity toward the antigen increases with the percentage of sequence identity between the human and murine V H frameworks (106). Alternatively, consensus sequences for human V L and V H frameworks can be used (105, 107). In resurfacing experiments the human frameworks are selected on the basis of the overall sequence identity of the nhAb with the V L and V H frameworks coming from a database of clonally derived heavy- and light-chain pairs (101) or of the sequence identity between the pattern of surface-exposed residues (108) of human V L and V H sequences and that of the nhAb (101). The frameworks with highest sequence identities can be identified using standard database search programs (58 – 60). The human consensus sequences for the V L and V H can be found in (65). In “reshaping” experiments (28, 97–99), the antigenbinding site is grafted from the nhAbs in the selected human framework, thus obtaining a “reshaped” antibody. To endow the reshaped Ab with antigen specificity and affinity as close as possible to those of the nhAb donor of the antigen-binding site, it is necessary to ensure that the conformation of the grafted antigenbinding site in the reshaped Ab is similar to that of the original nhAb. The knowledge of the determinants of antigen-binding-site loop conformation (Table 1) is extremely helpful in that residues at key sites should also be grafted to avoid a loss in binding affinity. Much experimental evidence shows that the presence of nonconserved residues at key positions can diminish antigen-binding affinity and that this can be restored by grafting of key-site residues (98, 107). In fact, changes in the loop conformation can be considerable when the replaced amino acid is incompatible with the same canonical structure (56, 57). However, even small variations in the conformation of the antigen-binding-

site loops might have very large effects on affinity (57, 107). A very important issue in antigen-binding-site grafting procedures is the exact definition of the boundaries of the antigen-binding site. The structural definition of the hypervariable loops (Table 1) and the definition based on sequence analysis (CDRs) (65), although largely overlapping, do show some differences. In most cases grafting of the CDRs only produces a large decrease or a complete loss in affinity and the grafting of additional residues (in most cases, the key residues determining the loop conformation), both within the hypervariable loops and in the framework, is required to restore affinity. Contacts of the antigen with framework residues outside of the hypervariable loops and of the CDRs are rarely observed in known structures (109). The best approach consists therefore in grafting all CDR residues (65), all hypervariable loop residues, and all residues at key sites necessary to maintain the conformation of the hypervariable loops (Table 1) (110). In “resurfacing” experiments (101, 102) the sequences of the variable domains of the nhAb and of the selected frameworks are compared and surface framework residues (108) of the nhAb are substituted with the corresponding human residues. As discussed above, to maintain the antigen specificity and affinity of the nhAb in the “resurfaced” one, key-site surface residues should not be changed. Obviously if the structure of a complex between the nhAb and the antigen is known, and residues outside of the antigen-binding site are in contact with the antigen, these should also be transferred to the humanized Ab. It is important to explicitly build and inspect a model of the reshaped or resurfaced Ab to analyze all the contacts of the mutated residues in the original nhAb and in the humanized Abs in order to identify residues that might affect the structure of the grafted antigenbinding site. It is worth mentioning that our understanding of the determinants of the antigen-binding loop conformation is based on the structural analysis of natural antibodies. Grafting and resurfacing procedures might generate contacts between antigen-binding site and framework residues not previously observed in natural antibody structures that could affect the conformation of the antigen-binding site in a fashion not predictable on the basis of the nhAb and humanized Ab sequences only. The advantage of the resurfacing procedure over grafting is that the design of the humanized antibody is much simpler and that, as only surface residues are mutated, the core of the nhAb-variable domain is unchanged and consequently it is more likely that both the antigen-binding loop conformations and their relative position in the resurfaced Ab are similar to those of

ANTIBODY ENGINEERING

the starting nhAb. On the other hand, while antibodies humanized through grafting have been demonstrated to have low immunogenicity in clinical trials for infectious, autoimmune, and neoplastic diseases, there are still no clinical data available on the immunogenicity of antibodies humanized through resurfacing. The rationale for the resurfacing strategy is that exposed residues are thought to play a major role in protein antigenicity. However, resurfaced antibodies have a larger portion of nonhuman residues, with respect to reshaped ones, which could determine a greater antigenicity, due for example to antigen processing (101).

CONCLUDING REMARKS The design of an antibody structure potentially endowed with a desired property is a key goal in antibody engineering. On the other hand, since the number of known antibody structures (111) (http://pdb.pdb.bnl. gov/) is much lower than the number of known sequences (65) (http://immuno.bme.nwu.edu/), advances in our understanding of the sequence–structure relationship in this important class of molecules, especially with respect to their antigen-binding site, have considerable practical applications. The method for modeling the variable fragment of antibodies described here is based on the understanding of the sequence–structure relationship in the antigen-binding site of antibodies and is applicable to a very large proportion of known sequences. We have also outlined alternative strategies that can be used in the remaining cases, but it is worth mentioning that, given the impressive pace at which new structures are determined, the range of applicability of the canonical structure method is bound to increase and one might expect it to be used on a regular basis as an integral part of antibody redesign and humanization experiments.

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