proteins STRUCTURE O FUNCTION O BIOINFORMATICS

Increasing stability of antibody via antibody engineering: Stability engineering on an anti-hVEGF Shuang Wang,1 Ming Liu,1 Dadi Zeng,1 Weiyi Qiu,1 Pingping Ma,2 Yunzhou Yu,1 Hongyan Chang,1 and Zhiwei Sun1* 1 Laboratory of Protein Engineering, Beijing Institute of Biotechnology, Beijing, China 2 Department of Life Science, Beijing Institute of Technology, Beijing, China

ABSTRACT Antibody stability is very important for expression, activity, specificity, and storage. This knowledge of antibody structure has made it possible for a computer-aided molecule design to be used to optimize and increase antibody stability. Many computational methods have been built based on knowledge or structure, however, a good integrated engineering system has yet to be developed that combines these methods. In the current study, we designed an integrated computer-aided engineering protocol, which included several successful methods. Mutants were designed considering factors that affected stability and multiwall filter screening was used to improve the design accuracy. Using this protocol, the thermo-stability of an anti-hVEGF antibody was significantly improved. Nearly 40% of the single-point mutants proved to be more stable than the parent antibody and most of the mutations could be stacked effectively. The T50 also improved about 7 C by combinational mutation of seven sites in the light chain and three sites in the heavy chain. Data indicate that the protocol is an effective method for optimization of antibody structure, especially for improving thermo-stability. This protocol could also be used to enhance the stability of other antibodies. Proteins 2014; 82:2620–2630. C 2014 Wiley Periodicals, Inc. V

Key words: computer-aided; molecule design; thermo-stability; structure optimization; antibody mutant.

INTRODUCTION Protein therapeutics, specifically therapeutic monoclonal antibodies, have become a significant addition to the pharmaceutical repertoire over the past 20 years and will likely to play an even more significant role in future pharmaceutical intervention for a variety of diseases. In total, 33 monoclonal antibodies (mAb) had been approved by the FDA and EMA for therapeutic uses as of 2012 and these technologies are billion-dollar products in the US. One of the challenges of the therapeutic antibody development process is improving the structural and/or functional properties of the antibody. In addition to exhibiting high antigen specificity and affinity, the thermodynamic stability of the antibody variable (Fv) or single-chain variable (scFv) fragment is of significant importance. Generally speaking, a thermodynamic stability enhanced antibody is more stable against elevated temperatures, high denaturant concentrations, aggregation, and even proteases. Therefore, such an improvement can increase in the life span of the antibody, allowing it to be applied in numerous fields such as immunotherapy, diagnostics, and proteomics, as well as environmental applications.1–4 Interestingly, stability engi-

2620

PROTEINS

neering can also compensate for the loss of two intra-chain disulfide bonds, one in each variable domain VH and VL, thus enabling antibodies to functionally express as intrabodies in the reduced environment of the cellular cytoplasm.5 This provides a novel, prospective type of therapeutics due to intrabody’s favourable membrane penetration.6,7 With the development of experimental and computational technologies, computer-aided antibody design enables easier optimization of the antibody’s properties.8 Many studies have focused on engineering the stability of scFv or Fab Additional Supporting Information may be found in the online version of this article. Funding: This study was funded by the National Natural Science Foundation of China (31170883). Shuang Wang, Ming Liu, and Dadi Zeng contributed equally to this work. This article was published online on 5 July 2014. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected 25 July 2014. *Correspondence to: Shuang Wang, Beijing Institute of Biotechnology, 20 Dongdajie Street, Fengtai District, Beijing 100071, China. E-mail: wangsshuang@gmail. com or Zhi-Wei Sun, Beijing Institute of Biotechnology, 20 Dongdajie Street, Fengtai District, Beijing 100071, China. E-mail: [email protected] Received 24 January 2014; Revised 28 May 2014; Accepted 5 June 2014 Published online 11 June 2014 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/prot.24626

C 2014 WILEY PERIODICALS, INC. V

Antibody Stability Engineering on an Antibody

fragments,9 typically using knowledge-based methods10 which are the easiest to use. Structure-based methods are more effective and creditable, however these methods are not as commonly used because prior knowledge of the 3Dstructure of the engineered antibody is usually inaccessible, particularly when working with a novel antibody (e.g., the antibody obtained from hybridoma or phage-display). The situation becomes more complicated when keeping high affinity and specificity is important, because this may require 3D information regarding the antibody-antigen complex. This is much more difficult than obtaining the 3D-structure of the antibody only, both computationally and experimentally. To gain insight into the 3D information of the antibody-antigen complex computationally, the free structures for both the antibody and the antigen are required and then they must be docked together. According to many computational studies, the docking accuracy is marginal when the free structures for one or both of the docking partners have not been experimentally determined. Therefore, the structure-based approach is limited for stability engineering of antibodies and other proteins due to the difficulty of predicting complex structure and the theory of protein folding behind it. There are many successful examples of antibody structure reformation using computational technologies; however, antibody stability is affected by many factors, so much work is required to improve it and success is low when only one method is used. Previously, we identified an anti-hVEGF mAb, AMMSV6 (AV6), from a large, human antibody library. AV6 can bind hVEGF with a KD of 0.1 nM and significantly inhibits the growth of HUVECs induced by hVEGF, but it is insufficiently stable, and the temperature at which half of it is inactivated (T50) is no greater than 57 C. Therefore, practical development of AV6 may be severely affected by its low stability. To enhance the potency of AV6 for prospective usage as an antitumor therapy, we improved its thermo-stability with an integrated stability engineering protocol that combines several techniques. Using the integrated protocol, as well as a single protocol, 60 single point mutants were designed and validated by subsequent experimental methods. Results indicate that nearly 40% of the single-point mutants were more stable than the parent antibody and that the T50 of mutant, which contained seven sites mutation in VL and 3 sites mutation in VH, was elevated 7 C. In conclusion, an integrated protocol for antibody stability engineering was developed using multiwall filter screening that increased efficiency and success. This protocol may be an effective tool for enhancing the stability of other antibodies.

Step-1: Antibody structure modeling

the Rosetta-Antibody protocol (Rosetta 2.3) was used to model antibody variable regions of AV6 and its derivative—AV6QP. This protocol combines comparative modeling of canonical complementarity determining region (CDR) loop conformations and de novo loop modeling of CDR H3 conformation with simultaneous optimization of VL-VH rigid-body orientation and CDR backbone and side-chain conformations.12,13 In this work, 5000 decoys were generated for both AV6 and AV6QP and the best scoring decoy was selected as the final model for each antibody. Step-2: Complex structure prediction

After obtaining the antibody models, the molecular docking method was used to build the AV6-VEGF complex model. A two-step docking strategy was employed in order to get better results. In the first step, the ZDock program was used to search the conformational space, without considering any conformational changes, for each residue. The searching was limited to a region predefined by an alanine-scan (in house data). Altogether, 54,000 decoys were obtained and 2000 decoys with the best scores were rescored and clustered. After obtaining all relevant data, several possible models were synthesized and used in the refinement-docking step. In the second step, the Rosetta Snug Dock protocol14 the flexibility of residues in the CDR and VL-VH interface was used to refine these complex models. The scores for the whole model and the interface are illustrated in Supporting Information Figures S1A and B. Step-3: Virtual saturation-scan

To determine more possible design positions, a python script, for running and parsing the output of the FoldX program,15,16 was written to evaluate the DDGf (folding free energy difference between the mutant and the WT) for all substitutions. First, the script asks FoldX to build all possible substitutions for each position. Then, the script calls on FoldX to calculate the folding free energy for each mutant. Finally, the script processes the output data of FoldX to determine which positions are suitable for stability engineering. Step-4: Design

MATERIALS AND METHODS 1-The structure-based stability elevation protocol

We proposed a structured protocol to increase antibody stability using structure modeling, complex structure prediction, virtual scan, and mutant design.

Basically, three parameters, folding free energy, local structure entropy (LSE), and residue using frequency, were used as filters to design mutants. First, all mutants needed to have reasonable free energy changes. The folding free energy of each mutant was retrieved from the list generated by the previous energy scan step. Mutants were not submitted for further analysis if the difference between the PROTEINS

2621

S. Wang et al.

folding free energy of the mutant and the parent antibody was less than 20.5 kcal mol21. It should be noted here that the first filter was not obligate because the rigid backbone method used for energy evaluation was only an approximation of the fact of the case. The second filter, the LSE, was first proposed by Chan et al.17 According to Chan’s results, stable proteins normally have small LSE values. Therefore, LSE was chosen as the second design filter. Similarly, this filter is not obligate due to the intrinsic statistical error and uncertainty embedded in the LSE method. To obtain higher accuracy and to guarantee that all designed mutants were human-sourced for fear of higher immunogenicity, the residue using frequency was introduced as the third filter. 2-Unfolding pathway prediction

The iterative version of Gaussian network model (GNM) was employed to predict the unfolding pathway of the monoclonal antibody AV6 as the traditional GNM can only explore the dynamics of a target protein with a specific conformation. By breaking "weak" contacts between nodes step by step, we have shown that the unfolding processes of proteins can be predicted by the iterative GNM with a reasonable accuracy.18,19 In this study, we tested the unfolding order of all designed positions. We hypothesized that weak positions were likely to unfold before strong ones. It should be noted that we did not take the unfolding order as a stick filter for stability elevation because those designable positions are not necessarily located at the predicted weak region. When applying the iterative GNM, the interactions between residues were divided into covalent and noncovalent groups. In contrast to the traditional GNM, the interactions between the covalently and the noncovalently bonded residues were treated differently. The spring constants between all pairs of nonbonded residues within the cutoff distance were treated equally; a single force constant g is employed. The strengths of the interactions between all covalently bonded pairs along the chain backbone were defined by cg, where c was the coefficient and could be determined by fitting predicted fluctuations against the crystallographic B-factors.18 The modified Kirchhoff matrix for the iterative GNM is given by 8 2c if ji2jj51 > > > > > > 21 if ji2jj > 1 and Rij  rc > < C5 0 if ji2jj > 1 and Rij > rc > > > X > > > Cij if i5j > :2 j;j6¼i

where Rij and rc are defined as the same of those in the traditional GNM.

2622

PROTEINS

The mean-square fluctuation in the distance vector Rij, between the residues i and j, can be calculated by with20 

2

h DRij i5



Rij 2Rij0

2   2 5h DRi 2DRj i

5hDRi  DRi i1hDRj  DRj i22hDRi  DRj i   3kBT  21  21 C ii 1 C jj 22 C21 ij 5 g where Rij and R0ij stand for the instantaneous and equilibrium separation vectors between residues i and j, respectively. As the temperature of the system is gradually increased, the native contacts among residues were expected to break in a fluctuation-dependent manner.18 Therefore, this method is based on the concept that the weakest interaction will be broken first. The native contact to be removed in each step was randomly selected from contacts with the first three largest fluctuations, and the calculations were performed 100 times for each AS to get more truly results. 3-Clone, expression, and purification

The vectors of pABj-Av6 (constructed by our lab, containing the light chain variable region of AV6 and the Cj gene) and pABG1-Av6 (constructed by our lab, containing the heavy chain variable region of AV6 and the CH domain gene of IgG1) were used for the expression of whole IgG from antibody Av6, just as described.21 The recombinant vectors of light and heavy chain variants were constructed using a site-specific mutagenesis kit (Transgen biotech. Co, Beijing, CHN). The recombinant light and heavy vectors were co-transfected into FreeStyle HEK293T cells (Invitrogen, Carlsbad, CA) for instantaneous expression of different variants. Expression supernatants, containing different IgG proteins, were collected and purified by using HiTrap protein A FF 1-mL column (GE Healthcare, Uppsala, Sweden). 4-Affinity analysis

The affinities of different variant antibodies were detected using the ForteBIO QKE system. Briefly, relative affinities of multiple mutants were detected and compared with the parent. In detail, the bio-VEGF was loaded on the surface of the SA sensor of the QKE system (ForteBIO, a division of Pall Life Sciences). Variant antibodies and Av6 or Av6QP were diluted to 50 nM with HEPES EP and added into the 96-well black plate. The sensors loading VEGF were transferred to detected wells of different antibodies, associating for 600 s, and then be transferred to another wells, which contained HEPES EP only, for dissociating for 600 s. The

Antibody Stability Engineering on an Antibody

Table I Affinity Analysis of AV6 and AV6QP Kon (105 1/MS)

Koff (1025 1/s)

KD (nM)

2.38 1.76

4.74 1.91

0.199 0.107

AV6 AV6QP

association and dissociation rates were then analyzed using analysis software, which was provided in the system itself. Affinity of Av6 and Av6QP were further confirmed by SPR on BIAcore 3000 system (detail in Supporting Information Fig. S2).

5-Stability evaluation

To detect thermo-stability, antibodies (final concentration of 5 mg/mL), were exposed to heat of different temperatures for 2 h with PCR. VEGF was coated on 96-well ELISA plates and antibodies were diluted to 50 ng/mL for ELISA. HRP conjugated sheep-anti-human IgG (Sigma-Aldrich, St. Louis, MO) was used to measure the binding activity of different mutants and the mutant binding curve of mutants at different temperatures was created, and the residual affinity was obtained as follows: Affinity residual 5ðOD492nm-heated 2OD492nm-control Þ= ðOD492nm-unheated 2OD492nm-control Þ

RESULTS 1-First round design: from AV6 to AV6QP

The anti-VEGF antibody, AV6, was obtained by randomly screening the phage display library. AV6 is composed of j3 type light chains and h3 type heavy chains. After conducting sequence alignment with a consensus sequence of k3 type light chains, we found that some residues on the L3 loop of AV6 did not fit well with the consensus ones. Empirically, carefully substituting a nonconsensus residue with the consensus enhances stability of the target antibody.22 In addition, results from molecular docking and Ala-scan (data not shown) indicate that K89 and L95 of AV6 light chain do not interact with the antigen. Therefore, these two residues with consensus ones were substituted. The designed double-point mutant named AV6QP. According to the results of Biacore3000, the affinity of AV6QP is about 2-fold that of AV6. The on-rates of the AV6 and its mutant, AV6QP, are comparable. The elevated affinity of the target antibody is mainly attributed to the decreased off-rate (Table I, Supporting Information Fig. S2). In principle, a slower off-rate is a reflection of a better binding interface. Therefore, it is speculated that K89Q and L95P

substitutions may adjust the conformation of the L3 loop and thus refine the binding interface. A local version of Rosetta-Antibody13,23 was used to build the structure of AV6 and AV6QP. The best scoring decoys for the two antibodies were checked using the Ramachandran plot and the 3D profile method.24 According to Ramachandran plots, 96.6% and 96.3% of AV6 and AV6QP residues are located at the favorable region, respectively. The Ca root mean square deviation (RMSD) between AV6 and AV6QP is 0.9 A˚, which can mainly be attributed to different conformations of the L3 and H3 loops of the two antibodies. On the basis of structural comparison, the L3 loop conformation of AV6QP resembles that of AV6 more than that of bevacizumab (the L3 sequence information of AV6, AV6QP, and bevacizumab is tabulated in Supporting Information Table S1). In previous work, Luo et al. reported that a nonconsensus sequence may result in an unstable noncanonical structure.11 According to the structural model of AV6, K89 is buried deep inside the core of the Fv fragment and its sidechain forms no H-bond with the surrounding residues [Fig. 1(A)]. Burying a polar amino acid residue with unsaturated H-bond donors or acceptors takes a huge amount of desolvation energy. Therefore, K89 may adversely affect the stability of the antibody. In the structural model of AV6QP, it was found that Q89 forms an H-bond with the highly conserved Y36 [Fig. 1(B)], thus decreasing the folding free energy. Based on the Ramachandran plot, the backbone dihedral of L95 lies at the region allowable to proline residue (Supporting Information Fig. S3). L95P substitution may improve the stability of the antibody by increasing the rigidity of the L3 loop. Since L95 of AV6 are located at the interface between VL and VH, L95P may also stabilize the interface between VL and VH. To evaluate the stability of AV6 and AV6QP, we determined the half-inactivation temperature, or the temperature at which the antibody loses half of its activity (T50), as described in Methods. The T50 value for AV6 is around 57 C and K89Q/L95P substitutions elevate the T50 of AV6QP by 2 C (Fig. 2). Because of the apparent elevation in stability, AV6QP was chosen as the starting point for the following systematic stability engineering work. 2- Second round design: Light Chain

For most therapeutic antibodies, target antigen affinity is the major factor that determines effectiveness. The key in stability engineering is to leave the affinity untouched. To prevent negative effects on the interface between AV6QP and VEGF, the AV6QP-VEGF complex model (Fig. 3) was constructed based on the molecular docking protocol described in Methods. The interface between AV6QP and VEGF was determined according to the PROTEINS

2623

S. Wang et al.

Figure 1 The local structure around the 89th residue in light chain of AV6 (A) and AV6QP (B). All figures that depict structure were generated by Discovery Studio (accelrys software Inc.) if not stated otherwise.

complex model. Residues involved in interacting with VEGF were excluded from subsequent virtual scanning. Scanning the AV6QP light chain identified a total of 28 designable positions for the light chain of AV6QP. First, a design was made only if it passed one of the three filters described in Methods. In total, 60 single-site mutants were designed according to the three methods. The full list of these mutants is given in Supporting Information Table S2. All designed mutants were then expressed and purified for the following analyses. Previous studies have demonstrated that the functional expression of an antibody is, to a certain extent, related to its stability. Therefore, expression of AV6QP and each mutant was roughly evaluated using standard polyacrylamide gel electrophoresis. Many mutant’s expressions were elevated compared to AV6QP (Supporting Information Table S2), implying that the stability of these

mutants may also have improved. The affinity and stability for each light chain mutant was determined as described in Methods (see Supporting Information Tables S2 and S3). Most of these substitutions did not affect the affinity of the parent antibody, suggesting that the predicted complex model of AV6QP-VEGF was reasonable. Results from the stability assay indicated that most of the mutants with elevated expression were also prone to have a higher T50. Among all of the light chain mutants, only seven of them passed multiple filters. Unsurprisingly, only these mutants had remarkably elevated stability [Table II and Fig. 4(A,B)]. The complete consistency between predictions and experimental results validated the high efficacy of the integrated design protocol used in this work. In spite of the success, the T50 of these single-site mutants was still much lower than that of bevacizumab. To obtain more stable mutants, the seven most promising mutants were chosen to be the building blocks for the combinational mutants. Details regarding the design process for the seven mutants given in Supporting Information.

Figure 2 Inactivation curves for the AV6, AV6QP, and bevacizumab. The lines show the relative binding activity of the antibodies after being heated for 2 h at different temperatures compared with binding activity without heat. AV6QP is the mutant of AV6, after replacement of the 89th Lys in VL with Gln and the 95th Leu in VL with Pro. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary. com.]

2624

PROTEINS

Figure 3 Complex model of VEGF and the Fv of AV6QP.

Antibody Stability Engineering on an Antibody

Table II Seven Mutants with Elevated Stability Selected For the Light Chain

Mutants

DDGsa (kcal mol21)

DDGf (kcal mol21)

L21I V29I A51G S53N T56P A60D G96P

21.03 10.02 11.89 20.82 10.99 20.28 20.92

20.33 20.62 20.70 20.53 20.80 20.49 21.35

DLSE

DT50 ( C)

20.008 0.000 20.005 20.002 20.006 20.013 20.009

1.6 1.6 1.2 1.1 1.4 1.2 2.2

a DDG15IRT ln ðfw Þ where R is the Boltzmann factor; fm and fw are the residueusing frequency of the mutants and the WT antibody, respectively. fs

Previously, Stephen et al. suggested that substituting less frequently used residues with ones that are used more frequently is sometimes helpful for antibody stability.22 One can calculate the free energy changes with the following equation: DG s 5IRT ln ðfw Þ fs

On the basis of our results, we found that DDGs is not a suitable indicator for stability (Table II). Although Val is the consensus residue at the 29th position of the human j-chain (based on Steipe’s results,25 V29I substitution was one of the successful designs, improving the T50 of AV6QP by 1.6 C. According to the data given by V-base, Val and Ile are the consensus residues for the 29th position of human j3 and j1, respectively. The light chain of AV6QP is of the j3 type and is characterized by the typical triad of serine residues, S30, S30A, and S31. On the basis of known antibodies, j1 is very close to j3 in sequence and in structure. Therefore, the

success of V29I demonstrates that (1) the consensus sequences of antibodies are not perfect as far as stability is concerned and (2) some sequence elements of j3 can be perfectly substituted by corresponding elements of j1. The latter point is exemplified by L21I substitution, for which Leu and Ile are consensus residues of j3 and j1 types, respectively. T56P and A51G are another two obvious examples contradicting the famous, consensus design method. The frequency of Pro at 56th position is one-fifth that of Thr. Gly also rarely occurs at the 51st position of the j chain. The success of T56P and A51G strongly indicate that the consensus antibody sequence is far from perfect in terms of stability. 3-Third round design—Heavy Chain

Following the same design protocol, 19 single-site mutants were designed for the heavy chain of AV6QP. Except for E6Q and V105F, the expression level was improved to some extent in all other mutants (Supporting Information Table S2). We substituted the buried E6 with Gln, the second most frequently used residue in this position, to decrease the solvation energy caused by Glu at 6th position. According to the free energy calculation, the solvation energy of E6Q had indeed decreased, whereas the total energy had increased by about 0.6 kcal mol21 because Q6 did not form H-bonds with the surrounding residues. According to Honegger et al., the framework of the AV6QP heavy chain belongs to the Type II. E6Q substitution will therefore conflict with other typical residues of the Type II framework. Next, we expressed the E6Q mutant several times, under different expression conditions, but were unsuccessful. V100kF was designed based solely on the consensus method. The

Figure 4 Inactivation curves for two sets of L-chain single-site mutants. The lines show the relative binding activity of the antibodies after being heated for 2 h at different temperatures compared to binding activity without heat. All mutations are in the light chain of AV6QP. L-L21I 5 replacing the 21st Leu with Ile, L-V29I 5 replacing the 29th Val with Ile, L-A51G 5 replacing the 51st Ala with Gly, L-S53N 5 replacing the 53rd Ser with Asn, LT56P 5 replacing the 56th Thr with Pro, L-A60D means replacing the 60th Ala with Asp, L-G96P 5 replacing the 96th Gly with Pro. All sequence indices are defined according to Chothia’s numbering scheme. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

PROTEINS

2625

S. Wang et al.

Table III Selected Single-Site Mutants for the H-Chain Mutant

DDGs (kcal mol21)

DDGf (kcal mol21)

DLSE

DT50 ( C)

S31D G52aP A84P

0.92 20.21 0.60

20.63 2.36 22.00

20.006 20.001 0.005

1.5 0.6 0.7

total expression of V100kF was comparable with that of AV6QP, whereas the functional component of expression is much less. Multiple bands were found in the SDSPAGE, suggesting that folding in this mutant was affected. Unlike the light chain design, the affinity for many heavy chain mutants was affected. This observation reflects the fact that AV6QP mainly interacts with VEGF through the heavy chain. Among all of the 19 single-site mutants, only a few of them had increased stability. All mutants and corresponding results are given in Supporting Information Table S3 and the detailed information regarding the design process is described in Supporting Information. Based on calculations, the LSE value of AV6QP_VH (representing the VH fragment of AV6QP) is comparable with that of bevacizumab_VH (data not shown). This suggests that the intrinsic stability of AV6QP_VH is similar with that of bevacizumab_VH, which means that there is really no need to improve the intrinsic stability of AV6QP_VH. However, there is also inter-space to make it better. For example, in terms of affinity and stability, S31D, G52aP, and A84P were selected for the following combinational mutagenesis. The detailed information for the three mutants is listed in Table III, and the sequences information of VH and its mutants is shown in Supporting Information Figure S4B. Inactivation curves for these mutants are illustrated in Figure 5. S31D shows outstanding improvement in heat resistance (DT50 > 1.5 C), whereas G52aP and A84P exhibit moderate improvements (0.6 and 0.7 C, respectively). The affinities of S31D and G52aP are comparable with AV6QP and the affinity of A84P is marginally better than AV6QP. According to the predicted complex model, none of these three positions is located at the antibody-antigen interface. Thus, careful substitutions at these positions will not seriously affect the binding property. A84 is situated at the turn between b7 and b8. Since the loop between b8 and b9 constitutes the functionally critical H3, the stable A84P may help H3 have a better conformation for antigen recognition.

heavy chain single-site mutations (S31P, G52aP, and A84P) were selected as building blocks for the creation of a thermo-stable antibody. Figure 6(A,B) illustrates the inactivation curves for light chain and heavy chain combinational mutants, respectively. On the basis of repeated assays, nearly all two-site and three-site mutants, whether light chain or heavy chain, are of good additivity (data not shown). Thus, the effect on stability of any combinational mutant is comparable when taking the sum of the stability effects of each single-site mutant in the combination. This indicates that the selected mutation positions are independent of each other so that a substitution at one position has little influence on the folding at other positions. This is not surprising because that nearly all positions, except A51G and S53N of the light chain, are more than 5 A˚ from each other. The remarkable additivity found in the two-site and three-site mutants was not found in five-, six-, and seven-site mutants of the light chain. We hypothesized that this phenomenon was caused by the accumulation effect of other substitutions. In principle, every mutation has an influence on the local structure and the influence will be rapidly augmented as the changed positions increase. This theory could explain the paradox observed. Alternatively, it is likely that the engineered VL fragment reached its maximum intrinsic stability after four or more substitutions. The total stability of a single-chain protein is exactly equal to its intrinsic stability and the total stability of a multi-chain protein is determined by the intrinsic stability of each chain and the interactions (i.e. the extrinsic stability) among them. The sequences information of VL (AV6QP) and its

4-The final round design: Combination

Figure 5

The success of the designs described above has validated our design protocols. In spite of this, the stability of the single-site mutants was not satisfactory yet. Therefore, we gradually stacked these mutant points to further increase the stability. In summary, seven light chain (L21I, V29I, A51G, S53N, T56P, A60D, and G96P) and 3

Inactivation curves for the H-chain single-site mutants. The lines show the relative binding activity of the antibody after being exposed to heat of different temperatures for 2 h compared with binding activity without heat. All mutations were to the heavy chain of AV6QP. HS31D5replacing the 31st Ser with Asp, H-G52aP 5 replacing the 52nd a Gly with Pro, H-A84P 5 replacing the 84thAla with Pro. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

2626

PROTEINS

Antibody Stability Engineering on an Antibody

Figure 6 Inactivation curves for light/heavy chain combinational mutants (A) and (B). The lines show the relative binding activity of the antibody after being exposed to heat of different temperatures for 2 h compared with binding activity without heat. A: The combinational mutants on VL of AV6QP. Two single-site mutants L-A60D and L-G96P are used as controls. L21I60D 5 combinational mutant of the 21st and 60th. LGPP 5 combinational mutant of the 51st Ala to Gly, 56th Thr to Pro, 96th Gly to Pro. L-IGPD 5 combinational mutant of the 21st Leu to Ile, 51st Ala to Gly, 56th Thr to Pro and 60th Ala to Asp. L-IIGPD 5 combinational mutant of the 21st Leu to Ile, 29th Val to Ile, 51tst Ala to Gly, 56th Thr to Pro and 60th Ala to Asp. L-IIGPDP 5 combinational mutant of the 21st Leu to Ile, 29th Val to Ile, 51st Ala to Gly, 56th Thr to Pro, 60th Ala to Asp and 96st Gly to Pro. L-IIGNPDP 5 combinational mutant of the 21st Leu to Ile, 29th Val to Ile, 51st Ala to Gly, 53rd Ser to Asn, 56th Thr to Pro, 60th Ala to Asp and 96th Gly to Pro. B: The combinational mutants on VH of AV6QP. H-DP 5 combinational mutant of the 31st Ser to Asp and 84th Ala to Pro. H-DPP 5 combinational mutant of the 31st Ser to Asp, 52nda Gly to Pro and 84th Ala to Pro. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

mutants was shown in Supporting Information Figure S4A. The T50 of S53N and G96P were 1.1 and 2.2 C higher than that of AV6QP [Fig. 6(A,B)], respectively. Based on Figure 6(A), the stability of the five-site mutant AV6QP(L)IIGPD was slightly weaker (DT50 < 0.5 C) than that of AV6QP-(L)IIGPDP and AV6QP-(L)IIGNPDP. This was similar for the heavy chain mutants. As illustrated in Figure 6(B), the stability of AV6QP-(H)DPP was no better than that of AV6QP-(H)DP (refer to S31D/A84P double-site mutant of heavy chain). All selected mutations, except G96P of the light chain, were located far away from the VL/VH interface. Therefore none of the mutations, except G96P, influenced the extrinsic stability of the antibody. In other words, reinforcement in the light or heavy chain alone only enhanced intrinsic stability if G96P was excluded from consideration. Apparently, when one chain was enhanced, the determinant of the total stability was shifted to the weaker chain. This may be another reason why the stabilities of AV6QP(L)IIGPDP and AV6QP-(L)IIGNPDP were comparable with that of AV6QP-(L)IIGPD. Therefore, engineering a single chain was not adequate. We postulate that it is necessary to synchronously improve the intrinsic stability of both the light and heavy chains otherwise the weaker chain will decrease the total stability. To test this theory, a series of light chain mutations were combined with heavy chain mutations. Each combination should be more stable than its corresponding light or heavy chain components. As illustrated in Figure 7, all light/heavy chain combinational mutants were much more stable than all individual light or heavy chain mutants. The three light/heavy chain combinational mutants,

Figure 7 Inactivation curves for L and H-chain combinational mutants. The lines show the relative binding activity of the antibody after being exposed to heat at different temperatures for 2 h compared with binding activity without heat. The L-GPP 5 combinational mutant of the 51st Ala to Gly, 56th Thr to Pro, 96th Gly to Pro. L-IGPD 5 combinational mutant of the 21st Leu to Ile, 51st Ala to Gly, 56th Thr to Pro and 60th Ala to Asp. L-IIGPD 5 combinational mutant of the 21st Leu to Ile, 29th Val to Ile, 51st Ala to Gly, 56th Thr to Pro and 60th Ala to Asp. LIIGPDP 5 combinational mutant of the 21st Leu to Ile, 29th Val to Ile, 51st Ala to Gly, 56th Thr to Pro, 60th Ala to Asp and 96th Gly to Pro. L-IIGNPDP 5 combinational mutant of the 21st Leu to Ile, 29th Val to Ile, 51st Ala to Gly, 53rd Ser to Asn, 56th Thr to Pro, 60th Ala to Asp and 96th Gly to Pro. All of the mutations were on VL. HDPP 5 combinational mutant of the 31st Ser to Asp, 52nda Gly to Pro and 84th Ala to Pro. The mutations of L-IGPD, L-IIGPDP, and LIIGNPDP on the light chain were further combined with H-DPP, producing L-IGPD/H-DPP, L-IIGPDP/H-DPP, and L-IIGNPDP/HDPP, respectively. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

PROTEINS

2627

S. Wang et al.

Figure 8 Unfolding process of AV6QP. Panels A–D represent the contact plot of AV6QP in the native state, loss-number-of-native contact (LNNC) 5 50, LNNC 5 100, and LNNC 5 200, respectively.

AV6QP-(L)IGPD(H)DPP, AV6QP-(L)IIGPDP(H)DPP and AV6QP-(L)IIGNPDP(H)DPP, were named Mut_1, Mut_2 and Mut_3, respectively. AV6QP and bevacizumab were totally inactivated 61.2 and 64.5 C. In contrast, Mut_1, Mut_2, and Mut_3 kept 77%, 89%, and 85% of binding activity after 2 h incubation at 61.2 C. After incubating for 2 h at 64.5 C, the three mutants had 54%, 66% and 68% residual activity, respectively. Despite of the different inactivation curves, the T50 of the three light/heavy chain combinational mutants were at the same level (i.e., nearly 7 C higher than that of AV6QP). All of the three combinational mutants had slightly better affinity than AV6QP (data not shown). 5-Unfolding pathway analysis

A thermodynamically stable material may be kinetically unstable.12 Kinetic stability, in the context

2628

PROTEINS

discussed here, is a measure of how rapidly a protein unfolds. It is a particularly important consideration for proteins that unfold very slowly or denature irreversibly, which is not uncommon in experimental work. A protein can denature irreversibly if the unfolded protein rapidly undergoes some permanent change, such as aggregation or proteolytic degradation. In cases such as these, it is not the free energy difference between the folded and unfolded state that is important as this only affects the equilibrium and this is not an equilibrium process. The important thing is the free energy difference between the folded and the transition states (activation energy) since it is the magnitude of this difference that determines the rate of unfolding (and hence inactivation). A kinetically stable protein will unfold more slowly than a kinetically unstable protein. In a kinetically stable protein, a large free energy barrier to unfolding is

Antibody Stability Engineering on an Antibody

required, and the most important thing is the relative free energies between the folded (Gf ) and the transition state (Gts) of the first committed step on the unfolding pathway. The free energy barrier between the native and the unfolded states is difficult to calculate because it is hard to describe the corresponding transition state. In contrast, to calculate the free energy barrier explicitly, we used iterative GNM method to predict the unfolding pathway for AV6QP. By doing this, the kinetically weak regions of the target protein can be determined. On the basis of our calculations, the kinetically weakest region of AV6QP was located at the VL/VH interface (Fig. 8). Interactions between L-b9 (including the last two residues of L3)/L-b4 and H-b4/H-b9 (including V100a of H3) in particular, were broken at a very early stage. As mentioned previously, L_G96P was designed according to our design protocol. L_G96P was much more stable than AV6QP and the folding free energy of L_G96P was significantly lower than that of AV6QP, and our calculations suggested that careful engineering on the interface was helpful for the stability of AV6QP. Furthermore, we tested the unfolding properties of the other nine experimentally validated mutations. Of the nine mutation sites, only L21, A51, and S53 were located at regions that unfolded slowly, suggesting that taking predictions of unfolding pathways into consideration prior to mutant design is helpful for design accuracy. Following the breakdown of the VL/VH interface, both light and heavy chains began to unfold in an asynchronous manner. As a whole, the heavy chain unfolded much more quickly than the light chain.

DISCUSSION AND CONCLUSIONS We applied an integrated design protocol to elevate the stability of the neutralizing anti-human VEGF antibody, AV6. Using this protocol, we designed 60 singlesite mutants with an overall success rate of 40%. The introduced mutants in most cases did not affect the affinity of the target antibody although some substitutions were located at CDRs. These results indicate that the design protocol is practical and can be safely used for antibody development. From to our experience on antibody engineering, the prediction accuracy of using a single method mentioned in this work is low and therefore, is not suitable for industrial application. To improve the design success rate, a combination of methods was used to develop an integrated design protocol. Based on experimental results, we had more success using our newly developed design protocol and this could be improved by incorporating the unfolding pathway prediction into the design protocol. In general, success is proportional

to the number of filters used in the design protocol, although this does carry a risk that some positive designs will be missed. Antibody expression and purification are both time consuming and costly, and the success rate is usually of the utmost importance for most libraries. To improve the success rate, one should use as many filters as possible so that the risk of obtaining false positive decoys is minimized. Rarely, the number of filters used in the design process could be gradually reduced to obtain more design possibilities, however this is quite costly in time and cost of experiment verification. REFERENCES 1. Holliger P, Hudson PJ. Engineered antibody fragments and the rise of single domains. Nat Biotechnol 2005;23:1126–1136. 2. Hoogenboom HR. Selecting and screening recombinant antibody libraries. Nat Biotechnol 2005;23:1105–1116. 3. Wingren C, Steinhauer C, Ingvarsson J, Persson E, Larsson K, Borrebaeck CA. Microarrays based on affinity-tagged single-chain Fv antibodies: sensitive detection of analyte in complex proteomes. Proteomics 2005;5:1281–1291. 4. Harris B. Exploiting antibody-based technologies to manage environmental pollution. Trends Biotechnol 1999;17:290–296. 5. Stocks M. Intrabodies as drug discovery tools and therapeutics. Curr Opin Chem Biol 2005;9:359–365. 6. Lo ASY, Zhu Q, Marasco WA. Intracellular antibodies (intrabodies) and their therapeutic potential. In: Chernajovsky Y, Nissim A, editors. Therapeutic Antibodies: Handbook of Experimental Pharmacology, Vol 181. pp 343–373. 7. Chen SY, Bagley J, Marasco WA. Intracellular antibodies as a new class of therapeutic molecules for gene therapy. Hum Gene Ther 1994;5:595–601. 8. Kuroda D, Shirai H, Jacobson MP, Nakamura H. Computer-aided antibody design. Protein Eng Des Sel 2012;25:507–521. 9. Wang T, Duan Y. Probing the stability-limiting regions of an antibody single-chain variable fragment: a molecular dynamics simulation study. Protein Eng Des Sel 2011;24:649–657. 10. Monsellier E, Bedouelle H. Improving the stability of an antibody variable fragment by a combination of knowledge-based approaches: validation and mechanisms. J Mol Biol 2006;362:580–593. 11. Luo J, Obmolova G, Huang A, Strake B, Teplyakov A, Malia T, Muzammil S, Zhao Y, Gilliland GL, Feng Y. Coevolution of antibody stability and Vj CDR-L3 canonical structure. J Mol Biol 2010; 402:708–719. 12. Laurie SH. Kinetic stability versus thermodynamic stability. J Chem Educ 1972;49:746. 13. Sivasubramanian A, Sircar A, Chaudhury S, Gray JJ. Toward highresolution homology modeling of antibody Fv regions and application to antibody-antigen docking. Proteins 2009;74:497–514. 14. Sircar A, Gray JJ. SnugDock: paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models. PLoS Comput Biol 2010;6:e1000644. 15. Schymkowitz JWH, Rousseau F, Martins IC, Ferkinghoff-Borg J, Stricher F, Serrano L. Prediction of water and metal binding sites and their affinities by using the Fold-X force field. Proc Natl Acad Sci USA 2005;102:10147–10152. 16. Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 2002;320:369–387. 17. Chan CH, Liang HK, Hsiao NW, Ko MT, Lyu PC, Hwang JK. Relationship between local structural entropy and protein thermostabilty. Proteins 2004;57:684–691.

PROTEINS

2629

S. Wang et al.

18. Su JG, Li CH, Hao R, Chen WZ, Wang CX. Protein unfolding behavior studied by elastic network model. Biophys J 2008;94:4586–4596. 19. Su JG, Xu XJ, Li CH, Chen WZ, Wang CX. An analysis of the influence of protein intrinsic dynamical properties on its thermal unfolding behavior. J Biomol Struct Dyn 2011;29:105–121. 20. Chennubhotla C, Rader AJ, Yang LW, Bahar I. Elastic network models for understanding biomolecular machinery: from enzymes to supramolecular assemblies. Phys Biol 2005;2:S173–S180. 21. Demarest SJ, Chen G, Kimmel BE, Gustafson D, Wu J, Salbato J, Poland J, Elia M, Tan X, Wong K, et al. Engineering stability into

2630

PROTEINS

Escherichia coli secreted Fabs leads to increased functional expression. Protein Eng Des Sel 2006;19:325–336. 22. Sircar A, Kim ET, Gray JJ, Rosetta. Antibody antibody variable region homology modeling server. Nucleic Acids Res 2009;37: W474–W479. 23. Gribskov M, McLachlan AD, Eisenberg D. Profile analysis detection of distantly related proteins. Proc Natl Acad Sci USA 1987;84:4355–4358. 24. Steipe B, Schiller B, Pluckthun A, Steinbacher S. Sequence statistics reliably predict stabilizing mutations in a protein domain. J Mol Biol 1994;240:188–192.