1 Department of Chemistry and Biochemistry, 2 Institute for Cellularand Molecular Biology and 3 Department of Chemical Engineering,The University of Texas at Austin, Austin, TX 78712, USA
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Abstract |
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Introduction |
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Antibody affinity is enhanced during the course of an immune response through somatic hypermutation (Tonegawa, 1983; Berek and Milstein, 1987
). Somatic hypermutation occurs in the germinal centers and involves a combination of antibody sequence randomization followed by selection based on affinity (Maclennan, 1994). Genetic and structural analyses have led to the conclusion that some SDR's may be changed during somatic hypermutation (Lascombe et al., 1989
), but these are not the only sites of mutation. Rather, it appears that mutations in CDR residues that do not contact antigen often result in enhanced affinity (Hawkins et al., 1993
; Chen et al., 1995
). Based on the structural characterization of a high affinity antibody that had undergone affinity maturation as well as a molecule presumed to be the corresponding germ-line antibody, it was recently suggested that the primary (germ-line) antibody repertoire may have generally flexible binding sites that can accommodate a variety of antigens with only moderate affinity (Wedemayer et al., 1997). Somatic mutations at non-SDR's then may operate to tailor the binding site by precisely stabilizing the SDR's in conformations that favor binding to a particular antigen. Stabilizing interactions in non-SDR's have also been found in a series of anti-phosphocholine binding antibodies, although here the authors proposed that increased flexibility of the CDR backbone was responsible for enhanced hapten binding affinity (Chen et al., 1995
).
The distribution of amino acids within immunoglobulin CDR's has been studied extensively. Tryptophan and tyrosine are overrepresented (Vargas-Madrazo et al., 1994; Ohno et al., 1985
) and they often display a greater solvent accessible surface area compared with other regions of immunoglobulins (Padlan, 1990
). This was taken as evidence that tryptophan and tyrosine play a major role in defining the solvent accessible interior of antibody binding pockets, and thus antigen binding (Padlan, 1990
). The special role for tryptophan and tyrosine has been attributed to the unique chemical properties of these residues, namely hydrophobicity, rigidity and the ability to participate in hydrogen bonding, that make them well-suited to interact with a variety of antigens (Padlan, 1996
). Asparagine and serine have also been identified as being overrepresented in CDR's, but since their solvent accessible surface areas were roughly the same in CDR's as in other parts of antibodies, they have been assumed to play a largely structural role in maintaining the integrity of antibody binding sites through hydrogen bonding (Padlan, 1996
). Even though the statistical analysis of amino acid utilization within CDR's is illustrative, there is relatively little experimental data on the functional roles played by the different amino acid binding pocket residues in the context of their position (Hawkins et al., 1993
; Chen et al., 1995
).
Burks et al. (1997) have demonstrated that in vitro scanning saturation mutagenesis provides a high-throughput method for systematically introducing mutations and analyzing quantitatively the binding activity of the mutant antibodies. Herein is reported the in vitro scanning saturation mutagenesis of all the SDR's in a high affinity antibody binding pocket. All 10 residues that define the binding site surface of a variant of the 26-10 anti-digoxin antibody (Huston et al., 1988; Tai et al., 1990
; Huston et al., 1991
; Near et al., 1993
; Jeffrey et al., 1993
; Schildbach et al., 1993
, 1994
; Short et al., 1995
) were subjected to in vitro scanning saturation mutagenesis and analyzed for binding to digoxin and the three related cardiac glycosides ouabain, digoxigenin and digitoxin. The 26-10 variant used possesses all of the SDR's of the parent 26-10, the only differences are found in the light chain constant regions (Francisco et al., 1993
; Burks et al., 1997
). Fourteen of the scFv antibody mutants identified by in vitro scanning saturation mutagenesis were expressed in bacteria, purified and the rate constants for association and dissociation were determined by surface plasmon resonance (SPR). The SPR data are in excellent agreement with the affinity ranking of the scFv mutants as determined from the in vitro scanning mutagenesis ELISA studies for 11 of the 14 mutants analyzed. The antigen affinity rankings of all 190 mutant scFvs were analyzed in the context of several different properties of the amino acids as quantified by various published amino acid indices. To the best of our knowledge, no previous comprehensive experimental study has examined the role played by the different CDR amino acids that make up the surface of an entire antibody binding pocket.
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Materials and methods |
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PCR mutagenesis
Mutations in the scFv(digoxin) antibody were generated as described by Burks et al. (1997). Briefly, a DNA fragment (fragment A) corresponding to the 3' portion of the scFv gene up to, and including, the desired mutation was first generated by PCR. The 5' portion of the scFv (fragment B) was amplified using a 3' primer that generated a 3' sequence complementary to the first 1520 bases on the 5' end of fragment A. The two fragments were then joined using overlap extension PCR by virtue of this 1520 base overlap region. The first round amplification was carried out using the following sequence: one cycle at 94°C for 2 min; 29 cycles consisting of 94°C for 1 min, 55°C for 2 min and 72°C for 3 min; one cycle of 94°C for 1 min, 55°C for 2 min, and 72°C for 10 min. The PCR products from the first round were gel purified and used, together with outside primers, in the overlap extension PCR reaction. The amplification sequence for the overlap extension reaction was the same as for the first round reactions except that the annealing temperature for the first five cycles was set between 48 and 55°C, depending on the melting temperature of overlapping sequence. The PCR products were ethanol precipitated and the pellets were resuspended in 100 µl of water.
In vitro transcription/translation
The coupled transcription/translation reactions were carried out in 30 µl total volume (Burks et al., 1997). 23.3 µl reaction mix was added to 6.7 µl (0.5 µg) of the DNA produced by overlap extension. For radiolabeling of the protein synthesis products, 0.083 mM of 14C-leucine (324.9 mCi/mmol, 1 Ci = 37 GBq; New England Nuclear), was added to the reaction mix. Reactions were incubated for 25 min at 37°C with gentle shaking and were stopped by placing on ice.
Preparation of digoxin analogsBSA conjugates
The digoxinBSA, digitoxinBSA and ouabainBSA conjugates used in the ELISA analysis were prepared via oxidation of the terminal sugar residues with NaIO4 followed by covalent attachment to BSA through reductive amination in the presence of NaBH4 (Burks et al., 1997; Daugherty et al., 1998
). The digoxigeninBSA conjugate was prepared from a direct reaction between BSA and 3-aminodeoxydigoxigenin hemisuccinimide (Molecular Probes) according to the manufacturer's instructions.
ELISA
Antibody capture ELISA was performed using standard procedures (Smith et al., 1970), using 1% (w/v) boiled powdered milk (Carnation) as the blocking agent. The plates were washed three times and were developed with the colorimetric horseradish peroxidase substrate 2,2'-azinobis(3-ethylbenzothiazoline)-6-sulfonic acid diammonium salt (ABTS) (Pierce). The absorbance of each well of the ELISA plates was measured at 405 nm on a microplate autoreader when the ABTS reaction was still in the linear range, a fact that was confirmed by taking several time points per plate. For each cardiac glycoside being investigated (digoxin, digitoxin, digoxigenin, ouabain) the absorbances for each mutant were linearly scaled to that of the wild-type scFv(Dig), which was assigned a value of 1.0, then plotted in the histograms of Figure 3
. Wild-type scFv(Dig) was included on every ELISA plate to provide an internal calibration for results obtained on different plates.
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Single chain Fv mutants were produced and purified according to Burks et al. (1995). The scFv genes from second round overlap extension PCR were ligated into pET25b plasmid and transformed into Escherichia coli BL21 (DE3) strain. Inclusion bodies that consist of scFvs were isolated from cell lysates and dissolved in 8 M urea. Soluble scFvs were purified by a metal chelating column (IMAC) and refolded by dialyzing against Tris buffer (50 mM TrisHCl, pH 7.4, 500 mM KCl, 10% glycerol). Protein concentrations were determined by OD280 ( = 44 850) (Harlow and Lane, 1988
). For the kinetics measurement, antibodies were further purified by a size exclusion gel filtration column Superdex®-75 (Pharmacia, Sweden) to remove any dimeric or polymeric proteins.
Surface plasmon resonance (SPR) measurement of single chain antibody mutants
The digoxinBSA, digitoxinBSA, digoxigeninBSA and ouabainBSA conjugates were immobilized on the surface of a sensor chip CM5 (approximately 400 RU) following the standard protocol from Pharmacia Biosensor. All kinetics experiments were performed in buffer containing 150 mM NaCl, 10 mM HEPES, 3.4 mM EDTA, and 0.005% P20, pH 7.4 at 25°C with a flow rate of 60 µl/min (Pace et al., 1995; Nieba et al., 1996
; Myszka et al., 1997
; Zeder-Lutz et al., 1997
). ScFv proteins over the concentration range of 25 to 400 nM were added to the chip for the association rate measurements. To prevent the rebinding of antibodies on the chip surface, soluble haptens (up to 1 mM) were added into elution buffer for the dissociation rate measurements (Burks and Iverson, 1995
). The antibody bound chip was regenerated by 50% ethylene glycol, pH 10. The values of association and dissociation rate of antibodies were calculated using BIAevaluation software from Pharmacia Biosensor. kon was determined from a plot of [ln(dR/dt)]/t versus concentration (Oddie et al., 1997
). koff values were determined from a plot of ln(R0/R) versus time.
Amino acid index analysis
The amino acid index database, more than 402 different amino acid indices corresponding to the different properties of amino acid, have been retrieved through the internet (Tomii and Kanehisa, 1996). Normalized ELISA data was plotted versus a chosen amino acid index for hydrophobicity (Cid et al., 1992
), van der Waals volume (Fauchere et al., 1988
), flexibility (Bhaskaran and Ponnuswamy, 1984
) and hydrogen bonding in an effort to establish any general trends.
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Results and discussion |
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Dramatic and reciprocal specificity changes
In vitro scanning saturation mutagenesis studies on the 26-10 antibody SDR's identified several single amino acid substitutions that displayed remarkable changes in specificity for digoxin and related cardiac glycosides. For example, the L:Pro-96Phe mutant has a striking preference, greater than two orders of magnitude, in favor of binding digitoxin over digoxin, digoxigenin or ouabain (Table I). Computer models indicate that this effect is likely the result of an unfavorable steric interaction between the L:Pro-96
Phe phenyl ring side chain and the C12-OH group of digoxin, digoxigenin and ouabain that is absent in digitoxin. The H:Tyr-50
Asn mutant shows a reciprocal specificity, namely a strong preference for binding to digoxin and digoxigenin compared with digitoxin. Interestingly, the H:50 side chain is far from the C12 position of bound digoxin in the 26-10 structure and computer models fail to provide a complete explanation for the observed digoxin versus digitoxin preference for the H:Tyr-50
Asn mutant.
Association and dissociation rates
Values for the association and dissociation rate constants (kon and koff, respectively) were measured using a BIAcore 1000 (Pharmacia) for the wild-type and 14 selected mutant scFv antibodies (Table I). The particular mutants chosen exhibited ELISA signals that indicated interesting changes in specificity (L:Pro-96Phe, H:Ser-96
Thr, H:Tyr-50
His, H:Tyr-50
Asn, H:Tyr-50
Leu, H:Tyr-50
Ala) or high affinity (H:Tyr-50
Phe, L:Val-94
Tyr, L:Val-94
Phe, H:Trp-100
Arg, H:Trp-100
His). The remaining three mutants were chosen to provide a lower range of affinities to calibrate the ELISA data (H:Trp-100
Lys, H:Tyr-50
Asp, H:Tyr-50
Gly). The selected scFv antibodies were expressed and purified following the procedure described by Burks and Iverson (1995). In each case, care was taken to insure that the kinetic values measured on the BIAcore instrument are free of artifacts due to rebinding or avidity effects arising from multimeric scFvs. In particular, gel filtration HPLC was used to confirm that each scFv sample was
95% monomeric. A high flow rate (60 µl/min) as well as up to 1 mM soluble hapten was used in the dissociation buffer to prevent rebinding during the dissociation measurements. For some of the low affinity mutants (Ka < 106 M1) the values for kon could not be determined due to rapid dissociation and practical limits on the maximum concentration of mutant scFv that could be used. The values for kon of the other mutants were obtained, all of which gave values near 106 M1 s1. This value is very close to the maximum kon for the binding of a monomeric antibody (Foote and Eisen, 1995
; Schier et al., 1996
). On the other hand, as is expected for a set of related antibodies, the mutations mostly affect the values of koff (Wells, 1996
).
Correlation between ELISA signals and SPR data
There is good agreement between the affinities measured for 11 of the 14 selected mutants by SPR and the ELISA data obtained from in vitro scanning saturation mutagenesis (Figure 3 and Table I). Thus, the ELISA assays used represent a relatively accurate method of analyzing the mutants in a high throughput manner. It is noteworthy that this correlation was observed even though some of the mutants analyzed were intentionally chosen to cover a range of affinities.
The exceptions to the ELISASPR correlation involved the three mutants with the highest ELISA signals (two L:94 mutants and one H:100 mutant), and upon further analysis, it appears as though this reflects differences in folding yield. For example, the in vitro scanning saturation mutagenesis ELISA data indicated that the L:Val-94Phe and L:Val-94
Tyr mutants should have higher affinity than wild-type 26-10, while the SPR measurements indicate they do not. Similarly, the H:Trp-100
Arg mutant displayed an ELISA signal that was twofold higher than wild-type, even though measurements of the binding kinetics of the purified antibodies showed that this mutant has a somewhat lower affinity. An enhanced folding ability and/or differential expression level may be responsible for the higher ELISA signal. Consistent with enhanced folding ability being an important factor, the soluble, expressed L:Val-94
Phe, L:Val-94
Tyr and H:Trp-100
Arg mutants exhibited significantly higher yields of active protein upon in vitro refolding (Burks and Iverson, 1995
). The issues of folding efficiency and expression levels are interesting in their own right from a protein engineering perspective: mutants that do not express or fold with a sufficiently high yield are of little interest for practical purposes whereas mutants exhibiting higher expression and/or refolding yield than the wild-type antibody can be of considerable value.
Despite the three inconsistencies found for affinity measurements, the specificities determined by the ELISA assay correlate with those measured by SPR for all 14 mutants. Not surprisingly, then, it appears as though differences in expression or refolding yields do not interfere with the identification of mutants having interesting new binding specificities.
Calibration of ELISA signals
A rough calibration can be deduced that correlates the in vitro scanning saturation scanning mutagenesis ELISA signal with mutant affinity. For example, comparison of the data for digoxin binding in Figure 3 and Table I indicates that no ELISA reading is observed for digoxin affinities that are more than three orders of magnitude below wild-type, that is, lower than 106 M1. However, an ELISA reading of 0.4 corresponds to a digoxin affinity that is approximately two orders of magnitude below wild-type and a reading of 0.70.8 corresponds to a digoxin affinity that is one order of magnitude lower than wild-type. Of course, the above calibration applies to situations in which there is little difference in folding yields between the mutants.
Plasticity and affinity involving single amino acid changes
Collectively, the data for all 190 mutants reveal several interesting trends. First, this antibody binding pocket can be thought of as being relatively plastic, tolerating a large number of amino acid substitutions at the SDRs (Burks et al., 1997). Better than 86% of the 190 mutants in the 10 SDR positions retained binding affinities for digoxin of over 106107 M1. Second, no single site SDR mutant was found to have a higher overall affinity for digoxin than the wild-type. This remarkable finding indicates that the immune system, presumably due to fine-tuning through somatic hypermutation, has found an optimum solution to the binding of digoxin when one considers only single SDR substitutions. Thus, in the context of the immune response involving single amino acid substitutions of SDR's, the binding site of 26-10 could well represent the end-point of affinity maturation. These data obtained with the scanning saturation mutagenesis of the SDR's in 26-10 are consistent with reported mutagenesis studies of phosphocholine-specific antibodies, in which the great majority of single site mutants retained binding activity (Chen et al., 1992
). Interestingly, two or more mutations in the CDR's of these anti-phosphocholine antibodies generally eliminated binding activity (Brown et al., 1996
).
Another way to look at the data for the 190 mutants is that simultaneous mutations in multiple SDRs, or perhaps non-SDR residues, would be necessary in order to increase further digoxin binding affinity for 26-10. It is highly improbable that constructive multiple mutations in SDRs would occur simultaneously in B lymphocytes undergoing affinity maturation. However, it is possible to select higher affinity antibodies with multiple mutations from combinatorial libraries in vitro. Using libraries displayed on the surface of Escherichia coli and screened by FACS, a L:Val-94Ile, L:Pro-96
Ala double mutant with threefold higher affinity than wild-type was identified (Daugherty et al., 1998
). Neither of these mutations alone showed higher affinity. Similarly, Short et al. (1995) used phage display to isolate a H:Thr-30
Pro, H:Asp-31
Ser, H:Met-34
Tyr triple mutant with about fourfold higher affinity to digoxin relative to the wild-type. In this latter case none of the mutated residues were SDRs. By combining the L:Val-94
Ile, L:Pro-96
Ala, H:Thr-30
Pro, H:Asp-31
Ser and H:Met-34
Tyr mutations we have generated an scFv antibody having a Ka for digoxin of around 1.2x1010 M1, or 12-fold better than the wild-type scFv (G.Chen, G.Georgiou and B.L.Iverson, unpublished data).
Functional roles of the SDR's
The ELISA data produced by the in vitro scanning saturation mutagenesis study of the 26-10 variant can be used to evaluate the functional roles played by the different SDRs. The goal of these analyses is to identify trends relating the observed ELISA signals to the various properties of the amino acid side chains as quantified in various indices. A positive correlation between the ELISA data at a given residue and a quantified side chain attribute can be taken as evidence that the attribute in question is important for the functioning of the wild-type residue. Amino acid indices, intended to quantify the different side chain parameters of hydrophobicity, flexibility, hydrogen bonding ability and side chain volume, were used for our correlational analyses (Tomii and Kanehisa, 1996). For the following correlational analyses, the ELISA data used was that for binding digoxin only, as this was the hapten originally used to elicit the 26-10 antibody.
Precise, quantitative correlations between the ELISA data and any one amino acid index are not necessarily to be expected, as the properties of amino acid side chains are interrelated. In other words, it is reasonable to expect general trends indicating that more than one parameter, for example, both size and hydrogen bonding ability, are important at an SDR as opposed to a correlation to any single parameter.
Overall, side chain hydrophobicity was not the dominant parameter one might have expected for binding of a hapten as hydrophobic as digoxin (Jeffrey et al., 1993). The hydrophobicity index of Cid et al. (1992) was used for this analysis because it is derived from a data set of proteins, the ßß class, that are similar to antibodies in terms of folding motifs. Figure 4
(uppermost panel) shows a significant correlation between the ELISA data of H:33 and the hydrophobicity coefficients (Cid et al., 1992
). That is, replacing a less hydrophobic amino acid with a more hydrophobic amino acid generally results in a higher ELISA signal at residue H:33. The ELISA data at H:100b also correlates somewhat to the hydrophobicity indices of the amino acid substitutions, except for the bulky aromatic amino acids (Trp, Tyr, Phe and His) that are presumably too large. The correlation is much less compelling for residue H:50, while no pattern is identifiable for H:35, H:47, H:95, H:100, L:91, L:94 and L:96 even though these residues presumably make primarily hydrophobic contacts like H:33 and H:100b. This observation transcends the exact hydrophobicity index used as over 20 different hydrophobicity indices were investigated (Tomii and Kanehisa, 1996
) with results that were no better in terms of correlations than those presented in Figure 4
using the index of Cid et al. (1992). The lack of general correlation for eight out of 10 SDRs with hydrophobicity should not be used to argue against hydrophobic desolvation being the primary driving force for binding digoxin by this antibody. Rather, the lack of hydrophobic correlation was taken as evidence that other side chain parameters such as rigidity, hydrogen bonding ability and size must also be important.
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Residue H:47 deserves special comment. H:47 is considered a framework residue and is thought to play a structural role in antibodies including important contributions to the VL/VH interface (Chothia et al., 1985). Residue H:47 only contacts the hapten in the case of 26-10 because of the unusually deep penetration of the digoxin lactone ring into the binding pocket. Residue H:47 is Trp in 93% of mouse antibodies (Kabat et al., 1991
), so it is reasonable to assume that, as pointed out previously (Jeffrey et al., 1993
), the wild-type Tyr residue at this position in 26-10 was selected on the basis of enhanced binding affinity. Consistent with this notion, Trp at this position displays a twofold lower ELISA signal, presumably due to the larger size of the Trp side chain that causes increased steric hindrance with bound hapten. There may be a hydrogen bonding explanation for the preference of H:47 Tyr as well (vida infra).
Size appears to be a key parameter for the residues at the bottom of the binding pocket, namely H:100b, L:91 and L:96. In these cases, ELISA signals increase with increasing residue size to a point, then fall off rapidly for the largest side chains. The binding pocket structure is likely robust enough at the bottom to prevent hydrophobic collapse so that considerable flexibility can be accommodated, thereby explaining why no correlation to rigidity is observed here. Interestingly, most of the `specificity' mutants occurred at these bottom positions, ex. L:96. Perhaps it will turn out to be a general feature of antibodies that fewer structural rigidity constraints at the bottom of the pocket permit more fine tuning of the fit at these SDR positions. Consistent with this notion, positions L:91 and L:96 are extremely variable from antibody to antibody in mouse light chains (Kabat et al., 1991
). Interestingly, as previously mentioned, mutants at H:100b showed a correlation with hydrophobicity, but L:91 and L:96 displayed no recognizable hydrophobicity pattern. Thus, for these latter two residues, size appears to be the clearly predominant parameter. Residue H:100b is more conserved among different antibodies (Kabat et al., 1991
) and may have constraints, i.e. hydrophobicity, based on overall antibody structural considerations along with digoxin binding.
Hydrogen bonding along with size appear to be the key parameters for residues H:35 and H:95. Presumably hydrogen bonding is required to maintain the structural integrity of the binding pocket as indicated by the presence of specific hydrogen bonds involving these side chains observed in the X-ray structure (Jeffrey et al., 1993). Interestingly, a largely hydrogen bonding structural role was previously predicted for both asparagine and serine residues in antibody binding pockets generally based on the observation that they are overrepresented in the pocket of numerous antibodies, yet have a relatively small contact surface area with bound antigen (Padlan, 1990
). H:Asn35 is often observed to take part in a hydrogen bond with the indole N-H of H:Trp47 (Roberts et al., 1994
), an interaction that is replaced in 26-10 by a hydrogen bond with the phenolic O-H group of H:Tyr47 (Jeffrey et al., 1993
). The observed preference for H:Tyr47 compared with other aromatic residues and the importance of hydrogen bonding at H:35 likely indicates the structural importance of this interaction to the integrity of the 26-10 binding pocket.
Future directions
In vitro scanning saturation mutagenesis represents an effective high throughput methodology for the rapid identification and characterization of interesting protein mutants. It should prove possible to design catalytic assays, as opposed to the binding type of ELISA assay utilized here, to allow the systematic investigation of enzyme catalytic activity. We are also using in vitro scanning saturation mutagenesis to provide for systematic in vitro antibody affinity evolution, analogous to somatic hypermutation in vivo. It is anticipated that interesting single mutants will serve as a starting point for subsequent rounds of in vitro saturation mutagenesis at other sites, enabling the identification of multiple mutations with synergistic desirable effects on binding.
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Acknowledgments |
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Notes |
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Received August 10, 1998; revised October 22, 1998; accepted October 26, 1998.