Precise and efficient antibody epitope determination through library design, yeast display and next generation sequencing Thomas Van Blarcom, Andrea Rossi, Davide Foletti, Purnima Sundar, Steven Pitts, Christine Bee, Jody Melton Witt, Zea Melton, Adela HasaMoreno, Lee Shaughnessy, Dilduz Telman, Lora Zhao, Wai Ling Cheung, Jan Berka, Wenwu Zhai, Pavel Strop, Javier Chaparro-Riggers, David L. Shelton, Jaume Pons, Arvind Rajpal PII: DOI: Reference:

S0022-2836(14)00519-1 doi: 10.1016/j.jmb.2014.09.020 YJMBI 64575

To appear in:

Journal of Molecular Biology

Received date: Revised date: Accepted date:

7 August 2014 17 September 2014 26 September 2014

Please cite this article as: Van Blarcom, T., Rossi, A., Foletti, D., Sundar, P., Pitts, S., Bee, C., Witt, J.M., Melton, Z., Hasa-Moreno, A., Shaughnessy, L., Telman, D., Zhao, L., Cheung, W.L., Berka, J., Zhai, W., Strop, P., Chaparro-Riggers, J., Shelton, D.L., Pons, J. & Rajpal, A., Precise and efficient antibody epitope determination through library design, yeast display and next generation sequencing, Journal of Molecular Biology (2014), doi: 10.1016/j.jmb.2014.09.020

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT TITLE Precise and efficient antibody epitope determination through library design, yeast display and next

PT

generation sequencing

RI

AUTHORS

SC

Thomas Van Blarcom1 † *, Andrea Rossi1 †, Davide Foletti1 †, Purnima Sundar1, Steven Pitts1, Christine Bee1, Jody Melton Witt1, Zea Melton1, Adela Hasa-Moreno1, Lee Shaughnessy1 2, Dilduz Telman1, Lora

MA

L. Shelton1, Jaume Pons1 and Arvind Rajpal1

NU

Zhao1, Wai Ling Cheung1, Jan Berka1 2, Wenwu Zhai1 2, Pavel Strop1, Javier Chaparro-Riggers1 *, David

1

Rinat, Pfizer Inc., 230 East Grand Avenue, South San Francisco, CA 94080, USA

2

TE

D

Present addresses: Lee Shaughnessy, Stratatech Corporation, 505 South Rosa Road, Madison, WI

53719, USA; Wenwu Zhai, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; Jan

AC CE P

Berka, Roche Molecular Systems, 4300 Hacienda Drive, Pleasanton, CA 94588. †T.V.B., A.R. and D.F. contributed equally to this work. *Corresponding authors: [email protected], [email protected]

ABSTRACT

The ability of antibodies to bind an antigen with a high degree of affinity and specificity has led them to become the largest and fastest growing class of therapeutic proteins. Clearly identifying the epitope at which they bind their cognate antigen provides insight into their mechanism of action and helps differentiate antibodies that bind the same antigen. Here we describe a method to precisely and efficiently map the epitopes of a panel of antibodies in parallel over the course of several weeks. This method relies on the combination of rational library design, quantitative yeast surface display and next generation DNA sequencing and was demonstrated by mapping the epitopes of several antibodies which neutralize alpha toxin from Staphylococcus aureus. The accuracy of this method was confirmed by comparing the results 1

ACCEPTED MANUSCRIPT to the co-crystal structure of one antibody and alpha toxin and was further refined by the inclusion of a lower affinity variant of the antibody. In addition, this method produced quantitative insight into the

PT

epitope residues most critical for the antibody-antigen interaction and enabled the relative affinities of each antibody toward alpha toxin variants to be estimated. This affinity estimate serves as a predictor of

RI

neutralizing antibody potency and was used to anticipate the ability of each antibody to effectively bind

SC

and neutralize naturally occurring alpha toxin variants secreted by strains of S. aureus, including clinically relevant strains. Ultimately this type information can be used to help select the best clinical

MA

NU

candidate among a set of antibodies against a given antigen.

ABBREVIATIONS

FACS, fluorescence-activated cell sorting; S. aureus, Staphylococcus aureus; YD Score, yeast display

AC CE P

INTRODUCTION

TE

D

score; Fab, antibody fragment; r, Pearson’s correlation coefficient

Monoclonal antibodies have become an integral therapeutic option in a variety of disease areas and represent a fast growing modality to address unmet medical needs1; 2; 3. To a large extent the recent and growing success of antibodies as drugs is due to their exquisite specificity for their antigens and their pharmacokinetic properties. A detailed understanding of the epitope, the exact binding site of an antibody on its antigen, is extremely valuable to gain insight into the therapeutic mechanism of action4, crossreactivity to other species, and potentially the binding properties to naturally occurring variants of the antigen. These variants may arise both in the context of allelic expression within an individual and in the context of population level genetic variation when the antigen is a human protein or due solely to the genetic variability among different strains when the antigen derives from a viral or bacterial pathogen. Finally, in a competitive environment where multiple companies are pursuing the same set of antigens 5, antibody differentiation based on precise binding epitope is often invaluable to establish intellectual property and obtain patent protection. For these reasons it is highly desirable to obtain precise epitope 2

ACCEPTED MANUSCRIPT information for multiple lead antibodies early in the discovery process to help select the optimum

PT

candidate for clinical development.

A variety of techniques that have been developed to characterize protein-protein interactions have been

RI

applied to the determination of antibody epitopes6; 7; 8; 9. These techniques offer different advantages and

SC

disadvantages and provide various levels of information. X-ray crystallography is the recognized gold standard since it provides the atomic details of the antibody-antigen complex. Unfortunately, it can be

NU

labor-intensive, low-throughput, requires large amounts of highly pure reagents and its success is

MA

unpredictable. Epitope binning on label-free biosensors is a higher throughput and relatively low cost technique that provides an assessment of the epitopes of all antibodies relative to each other, but the

TE

binding location of the antibodies10.

D

results do not offer the same degree of precision as crystal structure determinations nor provide the exact

AC CE P

While not as definitive as crystallography or as high throughput as epitope binning, mutational scanning in conjunction with analysis of previously obtained structural information of the antigen is a technique that can also be used to determine the epitope of antibodies11; 12. With this approach, individual point mutants of the antigen are expressed and purified and the binding interaction with the antibody is measured. Mutated positions that result in a decrease in binding are mapped onto the structure of the antigen to determine the functional epitope for the antibody13; 14; 15. The functional epitope encompasses amino acids that contribute to the energetics of the protein-protein interface and is complementary to the structural epitope determined through X-ray crystallography. The most fundamental approach is alanine scanning and involves mutating residues to alanine. Although this method has been used to map a variety of protein-protein interactions including those involving antibodies11; 12; 16, it is low throughput and the effect of an alanine mutation on binding may not be detected even when the residue is within the epitope or it may be disruptive even when it is not in the structural epitope because it destabilizes the overall fold of the protein or it induces a conformational change6. As a result, interpreting the results from a 3

ACCEPTED MANUSCRIPT mutational scanning is not always straightforward and a comprehensive understanding of the functional

PT

epitope is rarely achieved.

One of the throughput limitations of mutational scanning directly results from the need to individually

RI

clone, express and purify the point mutants prior to the analysis. Display technologies, including phage,

SC

cell surface and ribosome display, have been exploited to reduce this limitation by providing a link between the protein being interrogated and the DNA sequence that encodes it. This enables a large library

NU

of protein variants (up to 1012) to be simultaneously generated, tested and selected for particular

MA

properties such as binding to its cognate partner. This process typically requires rounds of selection and expansion to obtain a purified population of variants with desired binding characteristics. These variants are isolated, individually sequenced and their binding properties confirmed through various techniques.

D

Although pioneering work on this approach utilized phage display to map a variety of protein-protein

TE

interactions17; 18; 19, yeast surface display is better suited to isolate variants with diminished binding

AC CE P

properties since these populations are more easily identified and isolated by quantitative, multi-parameter FACS20. Antibody-antigen interactions have commonly been determined using yeast surface display, but the combination of libraries randomly generated using error-prone PCR and analysis by Sanger sequencing frequently result in an incomplete assessment of both the positions within the epitope and the permissible amino acid substitutions unless follow-up experiments are performed with additional mutants 6; 21; 22

. Furthermore, this iterative approach does not scale well when attempting to determine the epitope

for multiple antibodies in parallel as is desirable during the early phase of drug discovery.

Here we extend recent exploitations of next generation DNA sequencing technologies for comprehensive and quantitative assessments of protein-protein interaction to that of antibody epitope mapping23; 24; 25; 26. We describe how the combination of a rationally designed antigen library, quantitative selection through yeast surface display and in-depth computational analysis of the enriched populations through high throughput DNA sequencing was used to determine the epitope of multiple antibodies in parallel in an 4

ACCEPTED MANUSCRIPT efficient and comprehensive manner. We apply this methodology to four neutralizing antibodies against

PT

alpha toxin, a well-characterized virulence factor of S. aureus.

S. aureus is a versatile pathogen which can cause infections that range in severity from skin and soft

RI

tissue infections to more invasive and life-threatening diseases such as sepsis and pneumonia, conditions

SC

often aggravated by the emergence and spread of multi-drug resistant strains27; 28; 29. With antibiotic resistance on the rise, there is an urgent need for development of novel therapeutic modalities including

NU

vaccination and immunotherapy. The therapeutic potential of alpha toxin neutralizing antibodies has been demonstrated in animal models of staphylococcal infection30; 31; 32; 33 and such antibodies are being

MA

pursued clinically34. The successful clinical use of antibodies depends on their ability to effectively bind naturally occurring and potentially drug-induced variants of the antigen, an issue that is particularly acute

TE

D

given the high number of constantly evolving S. aureus strains. Therefore, in addition to defining the epitopes of the four antibodies, we apply statistical techniques to generate a quantitative and nearly

AC CE P

comprehensive analysis of the contribution of each amino acid to the antibody-antigen interactions. This information can be exploited to predict the effect of antigen variability on antibody binding and neutralizing activity and ultimately guide therapeutic antibody selection.

RESULTS

Overall epitope mapping process The overall epitope mapping strategy is depicted in Fig. 1 and includes four steps: (1) library design and synthesis; (2) display of the library in yeast and subsequent FACS; (3) deep sequencing of individually sorted libraries and bioinformatics analysis; (4) structural mapping of enriched positions and epitope determination. The process began with the rational design and synthesis of a library of alpha toxin point mutants which were displayed on the surface of yeast. The library was incubated with a fluorescently labeled alpha toxin specific antibody and yeast expressing alpha toxin variants with a severe (low gate), a moderate (medium gate) or no (high gate) decrease in antibody binding were isolated by FACS. These 5

ACCEPTED MANUSCRIPT individually selected populations were expanded, labeled with antibodies and sorted by FACS again using the gate of origin for three consecutive rounds resulting in a total of nine unique populations. Roche 454

PT

deep sequencing was applied to each population and the sequence data was analyzed to determine the frequency of each alpha toxin variant in each population. The frequencies of each mutant following FACS

RI

were compared to the original library in order to calculate the enrichment ratios. Positions with positive

SC

enrichment ratios in the low and medium gates were considered potential contributors to the epitope and mapped on the crystal structure of alpha toxin. Finally, the predicted high-resolution epitope of a given

NU

antibody was carefully inspected, validated and refined as needed to satisfy the structural features of an

Rationally designed library and analysis

MA

antibody footprint.

TE

D

A library of alpha toxin variants with point mutations was designed to include positions that uniformly cover the protein surface without affecting the structural integrity of the protein. Of the numerous

AC CE P

naturally occurring alpha toxin variants that exist, the reference sequence we chose to build the library based on the sequence of the methicillin-resistant S. aureus (MRSA) strain USA300 in light of its clinical importance. Since the alpha toxin exists as both a soluble monomer and a membrane bound heptamer, the structures of the soluble monomer and a subunit from the heptamer (heptamer subunit) were used to guide the rational design of the library (see Methods). Initially, potential positions to include in the library were determined using side chain solvent accessibility cutoffs adjusted for the size of the side-chain to avoid biasing the library toward amino acids with larger side chains (Table S1). For example, 20 A2 was used for alanine and 50 A2 was used for tryptophan. All positions with side chain solvent accessibility larger than their respective cutoff in either the soluble monomer or heptamer subunit were selected. Subsequently, selected positions were visually inspected in the context of the structures and manually refined to reduce the size of the library while still ensuring uniform coverage of the protein surface. This manual curation avoids biasing the library toward convex over concave surfaces which will occur if solvent accessibility cutoffs are used exclusively. All possible amino acids substitutions at each position 6

ACCEPTED MANUSCRIPT were incorporated into the library except for proline, glycine and cysteine to obtain a nearly comprehensive assessment of amino acid substitutions while minimizing the chance of introducing a

PT

gross conformational change, instability or unwanted disulfide pairings, respectively. A single codon for each amino acid was used to minimize the library size. The resulting library targeted 108 positions that

SC

49% (8635 A2 out of 17526 A2) of the heptamer subunit (Fig. 2).

RI

account for 55% of the solvent accessible surface (8234 A2 out of 14779 A2) of the soluble monomer and

NU

The library of alpha toxin variants was cloned into the yeast display vector resulting in 3 X 106

MA

transformants. This is over 1000-fold coverage of the theoretical diversity which is 1736 variants (100 non-Gly/Pro/Cys residues each mutated to 16 amino acids plus 8 Gly/Pro residues each mutated to 17 amino acids) plus the reference sequence. Library quality was assessed by sequencing using Roche 454

TE

D

GS FLX. Two overlapping amplicons were required to cover the entire alpha toxin gene resulting in a minimum of 151,061 reads per position. Reads were trimmed and filtered as discussed in Methods to

AC CE P

avoid short reads, reads with stop codons and multiple mutations. An average of 93% passed filtering indicating the library is comprised of primarily single point mutants. While the assumption of having only single mutants in the library is not strictly necessary, it simplifies the analysis to independently characterize the contribution of each position to the epitope. Of the 1736 expected variants, 1663 were found at least once resulting in at least 96% coverage of the theoretical library diversity (Table S2). All library positions contained at least 6 of the desired variants with an average of 15.4 ± 1.9 per position (Table S2). Mutations were also found at non-targeted library positions, but 10 times less frequently than those at the targeted positions (data not shown). Although the library was designed to exclude cysteine, proline and glycine, they were still present albeit at frequencies lower than each of the desired amino acids and similar to the incorporation of mutations at positions outside of the library design (Table S3). Overall, the library contains the majority of the desired mutants with a small frequency of undesired ones which suggests the library is of good quality and fully appropriate for its intended use.

7

ACCEPTED MANUSCRIPT Yeast display, FACS and deep sequencing The rationally designed library was used to determine the epitopes of four anti-alpha toxin antibodies that

PT

neutralize the lytic activity of alpha toxin in vitro and in vivo30. The antibodies were chosen since they bind to four distinct non-overlapping epitopes as determined by a biosensor binning assay and vary in

RI

their affinity as well as their neutralization potency (Table S4 and Fig. S1). LTM14 is an ideal benchmark

SC

to assess the quality of the epitope determination in our study since the co-crystal structure of LTM14 with alpha toxin has been reported30. A fifth antibody, LTM14.3, is a lower affinity point mutant of

MA

NU

LTM14 and was included to assess the impact of antibody affinity on the epitope prediction.

The library of alpha toxin variants was displayed on the surface of yeast and incubated with antibody directly conjugated with Alexa Fluor® 64735. A concentration of antibody corresponding to 67% of the

TE

D

maximum binding signal was chosen in order obtain high sensitivity for detecting binding perturbations while maintaining a near maximum fluorescent signal for antibody binding (Fig. S2). The Fab format was

AC CE P

used for all antibodies besides 14B9 since monovalent binding avoids avidity effects associated with bivalent IgG thus allowing higher sensitivity for detecting changes in affinity. The IgG format was used for 14B9 since it maintained alpha toxin binding following fluorophore conjugation which was not the case for the 14B9 Fab. After the interaction approached equilibrium, the cells were maintained on ice in order to minimize antibody dissociation while they were incubated with a FITC conjugated antibody specific for the N-terminal V5 epitope tag in order to monitor alpha toxin display levels and normalize antibody binding. The entire population of cells displaying alpha toxin variants was divided using three gates in order to identify mutants that bound to each antibody with varying affinities (Fig. 3 and Fig. S3). The gates were designed using the results from the antibody titration experiments (Fig. S2). For example, the library was incubated with the LTM14 Fab at 3.2 nM since this is the concentration that correlates to 67% of the maximum binding signal. The medium gate was designed to isolate variants that LTM14 binds at concentrations between 0.32 and 3.2 nM which correspond to a 10-fold maximum decrease in the apparent affinity (KDApp). The low and high gates were designed around this gate to capture the 8

ACCEPTED MANUSCRIPT remaining cells displaying alpha toxin variants with either further diminished binding properties (~100-

PT

fold decrease) or no decrease (~1-fold decrease), respectively.

All three gates were simultaneously collected and the individual populations expanded for two additional

RI

rounds of FACS to enrich for these three separate populations of mutants. Although three rounds of

SC

FACS were performed, significant enrichments were observed in the low and medium gates following a single round of FACS (Fig. 3 and Fig. S3). Each population (i.e. antibody, gate and round) was sequenced

NU

by Roche 454 GS FLX using the same conditions used for the original library. Less depth was targeted

MA

for these samples compared to the original library since enrichment in the low and medium gates was anticipated. An average of depth of 8,696 counts per position (range of 302 to 61,506) was seen across all

TE

D

samples.

Sequencing data analysis for epitope determination

AC CE P

Variants in the library that are overrepresented in the low and medium gates compared to the original library are expected to contain a mutation that decreases antibody binding. Based on this assumption, the enrichment ratio for all positions included in the library for each antibody, gate and round of selection was computed (Table 1 and Fig. 4a). This analysis was performed at all positions by taking the cumulative frequency of all amino acid substitutions found following selection and dividing by their frequency in the original library. Enrichment ratios up to 384 were calculated and typically changed during additional rounds of selection. Some positions found to be moderately enriched in early rounds were no longer enriched following subsequent rounds of selection. These positions are presumably either epitope false positives or less significant binding contributors that were out-competed during additional rounds of selection.

A critical part in identifying potential epitope positions is determining the minimum enrichment ratio value that identifies the majority of the positions while minimizing false positives. For example, the 9

ACCEPTED MANUSCRIPT inclusion of all positions with an enrichment ratio greater than one identifies positions that are scattered across the surface of the molecule while inclusion of just the highest values only identifies a subset of the

PT

epitope residues. The optimal enrichment ratio cutoff was determined empirically by adjusting the enrichment ratios up and down and viewing the results in the context of the crystal structure until most of

RI

the residues clustered in a region compatible with the footprint of an antibody. Through this it was

SC

determined that only positions with an enrichment ratio greater than 3 in at least one gate and round were robust predictors of a significant contribution to the epitope. This prediction was further refined by only

MA

NU

including positions with at least 50 counts.

The potential epitope positions (Fig. 4b) for each antibody were identified by subjecting the enrichment ratios from all gates and rounds of selection for each position to a weighted geometric mean (Methods).

TE

D

We refer to this as the yeast display score (YD Score). The YD Score is not only a function of the enrichment ratios, but also the approximate fold-decrease in affinity of the variants associated with each = 1-fold, medium gate

= 10-fold and low gate

= 100-fold). This approach

AC CE P

gate (high gate

provides a single value for each position on a scale from 1 to 100 where 1 indicates a position not involved in antibody binding and 100 indicates a position critical for binding. For example, variants at positions critical for binding should only be identified by the low gate during each round of selection. This results in a normalized enrichment ratio of 1 for the low gate and 0 for the medium and high gate resulting in a YD Score of 100.

Epitope of LTM14/LTM14.3 Fab and structural validation The crystal structure of the LTM14 Fab in complex with the alpha toxin monomer was determined while this work was in progress 30 and represents an ideal benchmark to evaluate the accuracy of the process described here (Fig. 5a). We analyzed the crystal structure and utilized the change in solvent accessibility30; 36 to define the residues that make up the structural epitope. Out of the 24 positions in the

10

ACCEPTED MANUSCRIPT structural epitope, 11 are present in the designed library (K30, E31, N32, Q64, R66, V67, E70, E71, D208, D212 and W274; Fig. 5b). Seven of these positions gave a YD Score greater than one following

PT

selection with LTM14 Fab and were further evaluated (Fig. 4b and Table 1). Five of these positions (E31, R66, V67, E70 and W274) have a significant enrichment in Round 3 and are all in the structural epitope

RI

(Fig. 5b, Fig. S4 and Table 1). Of these five positions, R66 and E70 are greatly enriched in the low gate

SC

suggesting a significant role in binding. R66 was shown to lie in the center of the LTM14 epitope and appears to be critical for LTM14 binding30. Neighboring residue V67 is enriched almost equally in both

NU

the low and medium gates; and E31 and W274 are enriched only in the medium gate. The same five

MA

positions were identified following analysis of the enrichment ratios from Round 2. The remaining two positions (K273 and P103) were identified from Round 1 (Table 1) and further improved the resolution of the epitope. K273 is found on the periphery of the structural epitope (Fig. 5b). The fact that K273 is

TE

D

identified in Round 1, and is not observed in later rounds suggests that the impact of mutants at this position is minimal which may be expected for residues on the periphery of the structural epitope.

AC CE P

Residue P103, which has a low enrichment ratio, is located on the opposite side of the alpha toxin monomer compared to the other identified epitope residues (Fig. S4b), and was also identified as possible contributor to the epitope of other antibodies. Altogether, this data suggests that although P103 might have an important structural role in that mutations at this position likely affect the overall fold of the toxin, it is unlikely to be part of the epitope for any of the tested antibodies.

The experiment was repeated with LTM14.3, a lower affinity variant of LTM14, since the high affinity of LTM14 may prevent the detection of positions with smaller contributions to antibody binding. Thirteen positions were predicted to be part of the epitope using the YD Score compared to just 7 for LTM14 (Fig. 4b). Ten of these positions were identified in Round 3 and all of them are part of the functional epitope (Fig. 5c, Fig. S5 and Table 1). Four of these positions were identified during the LTM14 analysis (E31, R66, V67 and W274), three are in the structural epitope (Q64, D208 and D212) and three are on the periphery of the structural epitope (D29, K75 and R251). When Round 1 and 2 for LTM14.3 are included, 11

ACCEPTED MANUSCRIPT three additional positions are identified. Of these three positions, E71 and N32 are in the structural

PT

epitope, while A73 is adjacent to the structural epitope.

The epitope determined with the method described here is in good agreement with the crystal structure

RI

which indicates the antibody binds to the rim domain. It appears that when a very high affinity antibody is

SC

used and multiple rounds of selections are employed, only the most disruptive residues are identified. However, a more precise definition of the epitope can be obtained by analyzing the results from earlier

MA

NU

rounds of selection or performing the selection with a lower affinity variant of the antibody.

Epitope of 5D10 Fab

Twelve positions were predicted to be part of the 5D10 epitope using the YD Score (Fig. 4b and Table 1).

TE

D

Excluding P103 discussed above, the 7 positions identified in Round 3 (W179, P181, W187, P189, V190, R200 and K266) map together on the rim domain of alpha toxin in a location proximal to the cell

AC CE P

membrane and enclose a region compatible with the footprint of an antibody (Fig. 5d and Fig. S6). The two positions with highest enrichment in the low gate correspond to W187 and P189. R200, V190 and K266 have significant enrichments both in the low and medium gates and P181 and W179 have significant enrichments in the medium gate only. Analysis of Round 1 and Round 2 revealed four positions (D183, V175, D212 and I43). Three of these four positions cluster together with the Round 3 positions and are therefore in the epitope or on its periphery. I43 lies on the opposite side of the molecule and it is buried in the heptamer subunit (Fig. S6). Its position relative to the rest of the epitope, the presence only in Round 2, and modest enrichment of 3.2 strongly suggests that I43 is a false positive residue and is not part of the 5D10 epitope.

Epitope of 10G7 Fab For 10G7, all eleven positions predicted to be part of the epitope using the YD Score have significant enrichments in Round 3 (I7, T11, V20, F39, I43, L116, T125, G143, H144, T145 and L146; Fig. 4b and 12

ACCEPTED MANUSCRIPT Table 1). When mapped on the structure of the soluble monomer, all 11 positions map to a distinct cluster encompassing parts of the stem and the amino latch on the upper part of the alpha toxin -sandwich

PT

domain (Fig. 5d and Fig. S7). Two positions (F39 and I43) map on the –sandwich domain, three positions (I7, T11 and V20) map on the amino latch, while six positions (L116, T125, G143, H144, T145

SC

RI

and L146) map to the stem.

Epitope of 14B9 IgG

NU

Of the 13 positions predicted to be part of the 14B9 epitope using the YD Score, 11 were identified in

MA

Round 3 (P91, E94, R236, A238, K240, Q242, N244, R281, E287, K288 and T292; Fig. 4b and Table 1). These positions all cluster to the upper part of the alpha toxin -sandwich domain, opposite to the epitope

D

for 10G7 (Fig. 5d and Fig. S8). Unlike the pattern of positions observed for the other antibodies, all 11

TE

positions have enrichments both in the low and medium gates with enrichment in the low gate ranging from 5.1 to 11.3 and enrichment in the medium gate ranging from 6.0 to 15.3. This data might suggest

AC CE P

that the energetic contributions to binding are distributed across multiple positions rather than being concentrated in a few residues (such as R66 for LTM14). However, this result may also be a consequence of using a bivalent full-length antibody in this particular assay and the associated avidity (the other antibodies were tested in the monovalent Fab format). Two additional positions (F39 and R277) which cluster in the same area are found in Round 1 and Round 2.

Biosensor confirmation of the epitopes The epitope mapping results predict that all antibodies have non-overlapping epitopes except for LTM14 and LTM14.3, which as expected share the same binding site on alpha toxin since they only differ by a single amino acid substitution (Fig. 5d). This data is in full agreement with the epitope binning information generated by biosensor analysis (Table S4). To further confirm these results, alpha toxin point mutants were generated based on the predicted epitope position for each antibody. Two or more

13

ACCEPTED MANUSCRIPT single point mutants per epitope were cloned and expressed in E. coli and the binding affinity of each antibody was measured using a label-free biosensor assay. Each antibody showed a significant decrease in

PT

binding affinity toward alpha toxin mutants at their predicted epitope positions while maintaining a similar affinity toward mutants at the other positions (Fig. 5e). This indicates that loss of binding results

RI

from a disruption in the epitope-paratope interaction and not from improper folding of the mutants and

SC

confirms the epitope prediction.

NU

Correlation between enrichment ratios, binding affinity and neutralization activity

MA

Further insight into the epitope-paratope binding interaction can be obtained with a detailed analysis of the enrichment ratios of individual amino acid substitutions across positions identified to be part of the epitope of a specific antibody. Comparing the Round 3 enrichment ratios from the low and medium gates

TE

D

for individual amino acid substitutions at selected positions in the epitope for LTM14.3 illustrates how at certain positions, such as R66, a number of mutants were mostly enriched in the low gate and therefore

AC CE P

were predicted to have a strong impact on the antibody-antigen binding affinity (Fig. 6a). At other positions, such as E31 and V67, the enrichment of individual mutants spans a broader range between the low and medium gates, predicting various degrees of impact on the binding affinity.

The effect of these individual amino acid substitutions can be assessed through the YD Score. Here, the same minimum enrichment ratio of 3 that was used to identify potential epitope positions was applied to individual substitutions, but the minimum number of counts was reduced from 50 to 5 to account for the rarity of the unique substitutions. To assess the ability of the YD Score to estimate the actual effect on the binding affinity of the interaction, the affinity of LTM14.3 for a set of alpha toxin mutants with amino acid substitutions at selected positions within the epitope was experimentally determined. LTM14.3 was chosen instead of LTM14 since it has a faster dissociation rate that allows detection of small changes in affinity in a convenient assay format. A total of 18 alpha toxin point mutants with a range of YD Scores were selected for this comparison along with the alpha toxin reference molecule. LTM14.3 binding was 14

ACCEPTED MANUSCRIPT strongly reduced by all mutants at position R66 with estimated KD values above 10 M (Fig. S9). The affinity for the remaining mutants was more precisely measured and the results indicate a robust

PT

correlation between their YD Scores and affinities (Pearson’s correlation coefficient r=0.76; Fig. 6b).

RI

This correlation was also true for other antibodies tested and will be described below.

SC

Previous work has demonstrated a correlation between the affinity and potency of neutralizing antibodies37; 38; 39. If this is also true for alpha toxin neutralizing antibodies, the YD Score could serve as a

NU

predictor of antibody potency. We therefore measured the blocking activity of LTM14.3 against a subset

MA

of these 18 alpha toxin mutants and reference molecule in an alpha toxin mediated rabbit erythrocyte lysis assay. In the absence of antibody, all mutants except for R66W maintain lytic activity nearly equivalent to that of the reference toxin (data not shown). In the presence of antibody, there is a very good correlation

TE

D

between the KD and the EC50 (Pearson’s correlation coefficient r=0.79) for the 10 mutants with welldefined binding affinity (Fig. 6c). Since the YD score is strongly correlated to the KD, these results

AC CE P

demonstrate that by predicting the effect on the binding affinity of a given amino acid substitution, the YD Score also predicts the neutralizing potency of the antibody toward that variant.

Antibody susceptibility to variants of alpha toxin The potential therapeutic use of an alpha toxin neutralizing antibody against S. aureus infections is contingent on its ability to effectively bind and block the pathogenic activity of alpha toxin variants produced by the multitude of S. aureus strains, in particular strains that are currently or may become clinically relevant in the future. We analyzed an internal strain collection and searched a public database of S. aureus genomes (see Methods) to identify the amino acid sequence of alpha toxin from a total of 642 S. aureus strains. The sequences were aligned using alpha toxin from S. aureus USA300 FPR3757 as a reference and positions of naturally occurring variability within the epitopes of LTM14/LTM14.3, 10G7, 5D10 and 14B9 were identified (Table S5). We also included positions adjacent to the epitope that were not identified using our method to further validate our results. We next used the calculated YD Score for 15

ACCEPTED MANUSCRIPT the natural variants at these positions to predict the effect they would have on the antibody binding affinities and therefore on their ability to block the activity of the toxin. A number of these variants have

PT

low YD Scores and are therefore predicted to have no or minimal impact on binding (Table 2). Interestingly, we found naturally occurring alpha toxin variants with high YD Scores within the epitopes

RI

of LTM14/LTM14.3, 5D10 and 14B9. While, not surprisingly, a number of these variants are found in the

SC

alpha toxin encoded in the genome of the early-branching S. aureus MSHR1132 strain40, we also found

NU

variants carried by clinical isolates including another USA300 strain (USA300 TCH959; Table S5).

MA

A panel of these naturally occurring alpha toxin variants within specific epitopes was expressed in E. coli and the affinity of the cognate antibody was measured. As expected, antibodies toward variants with low YD Scores did not show any significant loss in affinity compared to the reference alpha toxin, while the

TE

D

affinity of antibodies toward variants with high YD Scores were significantly affected with fold losses ranging from one to two orders of magnitude (Table 2, Fig. S10). To confirm that the reduction in affinity

AC CE P

was not due to improper folding of these alpha toxin variants, we tested the binding of antibodies with non-overlapping epitopes and found it to be unaffected. Additionally, we assessed the lytic activity of these variants on a blood agar plate assay and found it to be equivalent to the lytic activity of the alpha toxin from the reference strain USA300 FPR3757 (data not shown).

Analysis of the alpha toxin variants not located in the functional epitope further confirms how this epitope mapping method provides complementary information to what is obtained from a crystal structure of the antibody in complex with the alpha toxin. For example, although K30 is considered to be part of the structural epitope for LTM14 based on the analysis of the structure of LTM14 in complex with the alpha toxin (Fig. 5b), the overall YD Score for this position was 1 for both LTM14 and LTM14.3 which indicates this position is highly tolerant of amino acid substitutions and therefore has minimal importance to the overall binding interaction. Analysis of the individual variants at this position further supports this conclusion since the majority of them had a YD Score of 1 while the others had a low YD Score of 4. 16

ACCEPTED MANUSCRIPT This result was corroborated by biosensor analysis of the binding between these antibodies and this variant which showed that LTM14 has nearly identical binding affinity to this variant and the reference

PT

alpha toxin while the binding affinity between LTM14.3 and K30I decreased a mere 1.7-fold (Table 2). Similarly, although S239 would likely be considered part of the structural epitope of 14B9 given its

RI

proximity to two identified positions (A238 and K240; Table 1), it was not identified by the epitope

SC

mapping method presented here (YD Score = 1). As for LTM14 and the variant K30I, biosensor analysis of 14B9 binding to the S239T and S239Q variants, both of which had YD Scores of 1, showed a

MA

NU

negligible change in affinity compared to the reference alpha toxin.

The YD scores associated with each substitution for all epitope positions for a given antibody also allow us to predict the impact of as yet undetected and possible future variants. For example, while we have not

TE

D

found naturally occurring variants at position R66, we know from the structural analysis described above that this is a critical residue in the epitope of LTM14. YD Scores for almost all possible variants at this

AC CE P

position are very high (Fig. 6a) and would very likely cause losses in binding affinity and neutralizing potency far greater than the naturally occurring E31V variant. This also assumes that the variants maintain lytic activity which is the case for the majority of point mutants at R66 (data not shown). Similarly, while 10G7 does not lose affinity toward the naturally occurring variant V20I (as expected because of its YD Score of 1), a significant loss in affinity and neutralizing potency would take place if the V20W and V20R (YD Scores of 100) variants occurred.

Comparison to previous methods To compare this method to previous epitope mapping approaches that relied on random mutagenesis libraries, FACS using a single selection gate and Sanger sequencing 6; 22, a random mutagenesis alpha toxin library was generated by low error-rate PCR 41 and cloned into the yeast display vector resulting in a library containing 7 X 106 transformants. Library quality was assessed by deep sequencing as described above for the designed library. The average number of amino acid mutations per gene was estimated to be 17

ACCEPTED MANUSCRIPT 0.29 and results in the library containing approximately 22% non-parental genes. Only 34% of the 5567

PT

potential single amino acid substitutions across all 293 positions were observed.

The LTM14 antibody mapping effort was repeated using this library with the same conditions and gates

RI

used for the designed library. Following three rounds of FACS, the 5D10 antibody, which binds a non-

SC

overlapping epitope, was used to assess the proper conformation of the selected variants42. Similar to previous reports 42, only 5% of the low gate population maintained binding toward 5D10, suggesting that

NU

a large fraction of LTM14-enriched clones diminish binding by perturbing the conformation of alpha

MA

toxin rather than by directly interacting with LTM14 (Fig. S11). In comparison, under identical experimental conditions, more than half of the mutants (55%) from the designed library maintained binding toward 5D10 which suggests the designed library dramatically reduced this issue as expected per

TE

D

the design rationale. Both libraries were subjected to a fourth round of FACS to reduce the number of

AC CE P

these undesired mutants by isolating mutants that maintained binding toward 5D10.

Traditional sequencing was used to assess the populations following the fourth round of FACS. Although 55 clones out of the 96 sequenced from the random library (57%) contained a single mutation, only 5 unique mutants were found across 4 positions (R66, E70, A79 and D255; Fig. 7a). In comparison, a total of 17 unique mutations across 4 positions (R66, V67, E70 and D255) were identified when the equivalent population was sequenced from the designed library selection. Deep sequencing was also performed to determine if sequencing depth alone can increase the precision of the random library epitope prediction. A total of 13 mutants were identified (R66, V67, E70, G77, A79, W80, P81, V165, A206, D254, D255, L258 and G267) including all 4 identified from traditional sequencing (Fig. 7). Almost twice as many positions were identified with the random library compared to the designed library (13 versus 7), but analysis of the co-crystal structure suggests only 4 residues (R66, V67, E70 and A79) interact directly with the antibody and one (D255) is on the periphery of the epitope (Fig. 7b). The remaining 7 positions (G77, W80, P81, A206, D254, L258 and G267) are mostly buried and hydrophobic and form a network 18

ACCEPTED MANUSCRIPT of residues below the surface of the structural epitope (Fig. 7b). Replacement of any of these positions is likely to cause a local perturbation affecting the conformation of the structural epitope. It is interesting to

PT

note that none of the buried positions on the other side of the rim domain (far from the structural epitope) were enriched which could be the result of the positive selection with 5D10 (Fig. 7b). The last position

SC

RI

(V165) identified with the random library approach is not near the structural epitope.

Although the use of deep sequencing with the randomly generated library yielded a more detailed epitope

NU

than determined through traditional sequencing of a limited set of clones, the designed library increased

MA

the epitope resolution, improved the substitution coverage at each position (only 19% of the individual mutants identified from the designed library were identified by the random library; Fig. 7c) and most importantly reduced the number of false positives. This demonstrates that the designed library provides

TE

D

substantially more information about tolerated mutations at each position and the overall method outlined

DISCUSSION

AC CE P

above provides a higher resolution functional epitope map than determined in previous work.

In this study, we demonstrate that the combination of a rationally designed antigen library based on detailed crystal structure examination coupled with quantitative yeast surface display selection by FACS, deep sequencing and statistical analysis enables the precise determination of the epitopes of multiple antibodies in parallel in a single round of experiments. Unlike previous methods6; 22, this approach circumvents the need to perform time consuming follow-up experiments on individual antigen variants to verify the results which becomes cumbersome when dealing with multiple antibodies that bind different epitopes. We also determined that the definition of an epitope can be further refined by using a lower affinity variant of an antibody. Furthermore, we show that this approach generates a detailed assessment of the contribution of the majority of amino acid variants within the identified epitope positions. This information can be used to predict the affinity and efficacy of antibodies toward antigenic variants as may be encountered by an immunotherapy toward pathogens or human targets with allelic variations. 19

ACCEPTED MANUSCRIPT

Previous yeast display based epitope mapping work utilized low error rate mutagenesis to generate the

PT

library of single point mutants6; 21; 22. Although this is a quick and cost effective method, it comes with several drawbacks. First, both surface exposed and buried positions are randomly targeted and this can

RI

results in the selection of mutations that are not part of the epitope, but instead destabilize the protein

SC

structure. Second, since single nucleotide mutations are typically targeted per gene, only amino acids that are one nucleotide away from the original sequence are sampled and the frequencies of these substitutions

NU

are difficult to predict. Third, the majority of these libraries contain variants with zero or multiple

MA

mutations which are not included in the epitope prediction analysis. Larger libraries are thus required to sample the majority of the desired single point mutant variants. The size of these libraries approaches or exceeds the limits of reasonable sequencing depth which prevents an accurate assessment of the

TE

D

frequency distribution of the different mutants. This limits the accuracy of the enrichment ratios calculated following selection and used to determine the relative importance of each position within the

AC CE P

epitope. Furthermore, these large libraries require longer FACS run times, which in turn limit the number of antibodies that can be handled in parallel. All of these issues are resolved through the use of a rationally designed library.

The designed library approach described here is a general method that can be applied to any antigen that can be displayed on yeast and has a known structure or available homology model. An amino acid specific solvent accessibility cutoff was used to select positions that have the potential to disrupt antibody binding while minimizing the chance of perturbing the antigen conformation. This approach was chosen since using a total solvent accessibility area cutoff will penalize small residues while using a percentage of solvent accessibility will penalize large residues and result in their under representation in the library36. The subsequent manual curation of the selected positions in the context of the structure ensured optimal coverage of protein surface within the limits of the desired library size. Although most amino acid substitutions were included to obtain a nearly comprehensive assessment of amino acid substitutions 20

ACCEPTED MANUSCRIPT while minimizing the impact on protein folding, this library synthesis strategy is more expensive. A more comprehensive and cost effective strategy is to use a single degenerate primer to incorporate the genetic

PT

diversity then remove undesired substitutions (i.e. proline, cysteine and glycine) during the subsequent

RI

sequencing analysis.

SC

The advantages of the rational library approach compared to the randomly generated library were apparent after the first round of FACS. The percentage of events that were found to disrupt binding in

NU

both the low and medium gates for LTM14 was 5 times higher for the designed library compared to the random library. This allowed the designed library to be sorted in 30 minutes with 10,000-fold coverage of

MA

the library’s theoretical diversity while the random library required 2 hours of sorting in order to obtain just 10-fold coverage of the library size. Deep sequencing also revealed significant enrichment after a

TE

D

single round. 86% of the epitope positions identified for all antibodies over all rounds of FACS were identified in the first round alone. If the overall goal was to quickly determine the epitope of a panel of

AC CE P

antibodies with less quantitative results, a single combined low and medium gate could be used and 24 unique antibodies processed by a single user with just one day of FACS followed by another day to recover the genes for deep sequencing. This level of throughput would not be possible using a randomly generated library.

The enormous amount of sequencing data generated for each antigen position across all gates and rounds for each antibody can be summarized through a single value which we refer to as the yeast display score (YD Score). This score can then be used to identify potential epitope positions and quantify their relative importance for antibody binding in a facile manner. A weighted geometric mean was chosen since it accounts for the affinity fold-decrease each gate was designed for and the frequency distribution of positional enrichment ratios across these gates. The latter provides an additional means to quantify the relative importance of each position and account for sequencing and other experimental errors. Across all antibodies, 91% of the positions predicted to be part of the epitope using this method were localized in a 21

ACCEPTED MANUSCRIPT unique region of the alpha toxin monomer, which is consistent with the epitope binning results (Table 1, Table S4). This is in contrast to previous work where the majority of mutations were scattered throughout

PT

the molecule 6. The YD Score also provides a quantitative assessment at the amino acid level. Here, individual amino acid substitutions in the identified epitope positions are analyzed in the context of the

RI

same equation. The resulting values showed a robust correlation to both the binding affinity, and

SC

consequently the neutralizing activity, of an antibody toward alpha toxin variants within its epitope.

NU

One significant advantage of having detailed epitope information for a set of lead antibodies early in the

MA

development program is that it sheds light onto their mechanism of action, which can help in the selection of the best therapeutic candidate. Alpha toxin monomers oligomerize on the surface of susceptible cells in a series of steps that eventually lead to the formation of the heptameric lytic pore. The antibodies

TE

D

described in this work all have the ability to block the lytic activity of alpha toxin, yet they may accomplish this in different ways. The antibodies could bind the toxin in solution and prevent its direct

AC CE P

interaction with the target membrane or its binding to membrane receptors such as ADAM1043; 44. Alternatively, the antibodies could block the lytic process by binding the interface between monomers and prevent oligomerization, or by interfering with the required conformational changes.

Our epitope mapping data suggests that LTM14 and 5D10 might function by a similar mechanism where they interact with the monomer in solution and prevent binding to the cell membrane, a mechanism previously described for LTM1430 (Fig. S4, Fig. S5 and Fig. S6, respectively). The epitopes of 10G7 and 14B9 lie on opposite sides of the -sandwich domain at a location distant from the cell surface when analyzed in the context of the membrane embedded heptamer conformation of the toxin. This suggests that antibody binding is unlikely to interfere with direct membrane association, although it could disrupt interactions with membrane receptors such as ADAM10 (Fig. S7 and Fig. S8, respectively). Residues in the 10G7 epitope are grouped together when mapped on the soluble monomer structure, however, they are segregated in two different clusters when mapped on the heptameric subunit structure (Fig. S7). Since 22

ACCEPTED MANUSCRIPT the epitope of 10G7 encompasses positions both in the amino latch and the stem in its folded back conformation, it is conceivable that 10G7 neutralizes alpha toxin by preventing the conformational

PT

changes required for oligomerization and pore formation.

RI

In an effort to characterize a panel of alpha toxin neutralizing antibodies and select the best molecules to

SC

move toward clinical development, the epitope mapping strategy described in this work not only defined their individual binding sites on alpha toxin, but also provided detailed quantitative data to assess the

NU

impact of individual mutations within the identified epitope positions for each antibody. The variability at

MA

the amino acid level of bacterial targets, like alpha toxin, represents a critical potential problem as antibody coverage and efficacy can be compromised or even abrogated by sequence variability in the antibody epitope. A multitude of new S. aureus genome sequences are constantly released into the public

TE

D

domain and the data generated with this method allows one to predict the efficacy of each antibody against these variants as well as assess their liability towards loss of therapeutic value against yet

AC CE P

uncovered variants. If one or more therapeutic antibodies against staphylococcal virulence factors reach clinical use, one can envision using the type of detailed quantitative epitope analysis described here in conjunction with the growing genomic knowledge of antigenic variability to set up a monitoring system to ensure that the antibody with the best breadth of coverage and efficacy is always clinically available.

In this study, we demonstrated how information from orthogonal techniques can be used to quickly and accurately map the epitopes of small panels of antibodies in parallel over the course of just several weeks. This approach produced quantitative insight into the epitope residues most critical for the antibodyantigen interaction which enabled the relative affinity of each antibody toward different alpha toxin variants to be estimated. This affinity estimate serves as a predictor of neutralizing antibody potency and can be used to predict strain cross-reactivity. The data we generated indicates there are known naturally occurring variants of alpha toxin that can decrease the binding affinity and therefore the neutralizing potency of the antibodies tested in this study. It is likely that additional variants will emerge, possibly 23

ACCEPTED MANUSCRIPT encoded by clinically important S. aureus strains, that could affect not only this set of antibodies but antibodies directed against any other epitope on alpha toxin. Therefore, an oligoclonal approach, a

PT

combination of multiple antibodies against different epitopes, should be considered when pursuing alpha toxin as a target for immunotherapy for staphylococcal infections to maximize the chance of maintaining

RI

efficacy across known and unknown strains of S. aureus. Similarly, others have recommended that

SC

several variants of multiple targets should be included in vaccine cocktails against S. aureus infection to

NU

achieve good coverage and efficacy45.

MA

MATERIALS AND METHODS Antibody expression, purification and labeling

Five different alpha toxin antibodies were included in this study. Antibody LTM14 was obtained by

TE

D

panning a naïve human phage display library and LTM14.3 is a lower affinity point mutant of LTM14. Antibodies 5D10, 10G7 and 14B9 are mouse monoclonal antibodies generated by standard hybridoma

AC CE P

technology. Anti-alpha toxin antibodies (Fab and IgG) were produced by transiently transfecting HEK293 cells using Lipofectamine™ (Invitrogen) following the manufacturer's instructions. All Fabs were purified from conditioned media using a Ni column (Qiagen) using standard techniques. 14B9 IgG was purified from conditioned media using a Protein A column (Mab Select, Amersham Bioscience) based on standard techniques. Anti-alpha toxin antibodies were conjugated to Alexa Fluor® 647 (Alexa Fluor® 647 Protein Labeling Kit, A-20173, Molecular Probes) and purified using NAP-10 gel filtration columns (GE Healthcare, Uppsala, Sweden) as previously described46.

Yeast surface display The yeast display vector pRNYD was generated by modifying a previously described vector47 to include an N-terminal V5 epitope tag (GKPIPNPLLGLDST) followed by SfiI and NcoI restriction sites to facilitate cloning of the gene of interest. The reference sequence for alpha toxin was derived from the genomic sequence for the S. aureus USA300 FPR3757 strain corresponding to the mature alpha toxin 24

ACCEPTED MANUSCRIPT protein (the reverse complement of bases 1156370-1157248 on Genbank accession NC_007793.1) where silent mutations were introduced to remove all DNA homopolymeric stretches of 4 or more base pairs to

PT

decrease Roche 454 GS FLX sequencing errors. This gene was subcloned into pRNYD via the SfiI and NcoI restriction sites. The protein was displayed on S. cerevisiae strain BJ5465 following previously

RI

described protocols 35. Briefly, S. cerevisiae BJ5465 was transformed with plasmid and selected on CM

SC

Glucose minus Tryptophan agar plates (Teknova Inc.) for 2 days at 30 °C. An individual colony was cultured in SDCAA media and grown overnight at 30 °C and 250 RPM then shifted to SGCAA media to

NU

induce protein expression for 16 hrs at 20 °C and 250 RPM. Cells were harvested, washed with ice cold

MA

PBS supplemented with 5% BSA (PBSB) and resuspended to a final concentration of 3 X 108 cells/mL.

Flow cytometry

TE

D

Yeast cells displaying USA300 alpha toxin were incubated with the anti-alpha toxin antibody conjugated to Alexa Fluor® 647 in PBSB for 2 hours at room temperature with gentle agitation in the dark. The

AC CE P

antibody concentration was titrated while maintaining at least a 10-fold molar excess compared to the surface displayed alpha toxin concentration based on an estimate of 105 alpha toxin molecules per cell 35. The cells were washed with PBSB then incubated with anti-V5-FITC (V5 mouse Monoclonal Antibody FITC Conjugate, Novex, R963-25) in ice cold PBSB in the dark for 40 minutes at 4 °C with intermittent agitation. Cells were washed with ice cold PBS prior to analysis or FACS. Analysis was performed on an LSR II with a high-throughput plate sampler (Becton Dickinson, Franklin Lakes, NJ). For library selection by FACS, anti-alpha toxin antibodies were incubated at a concentration resulting in 67% of maximum binding as determined by a titration analysis as previously described (Fig. S2)35. The results from the antibody titration experiment were used to divide the entire population of cells over three gates to isolate alpha toxin variants in which the antibody had different apparent affinities (KD App). The gates used and the approximate affinities targeted are as follows: high ≤ USA300 KD App; USA300 KD App < medium < USA300 KD App X 10; USA300 KD App X 10 ≤ low (Fig. S2). At least 100-fold the library theoretical diversity was sampled and each gate individually collected. FACS was performed on a 25

ACCEPTED MANUSCRIPT FACSAria (Becton Dickinson, Franklin Lakes, NJ). Following FACS, each population of cells was individually recovered overnight in SDCAA at 30 °C and 250 RPM then a portion shifted to SGCAA

PT

media to induce protein expression for 16 hrs at 20 °C and 250 RPM. Each population was fluorescently labeled as described above and cells collected by FACS using the same gate from which the population

RI

was derived in the previous round. Prior to protein induction, an additional 5 X 108 cells were removed,

SC

centrifuged at 2500 x g for 2 minutes and the pellet stored at -20 °C until being processed for deep sequencing. Data analysis was performed using FlowJo (Tree Star Inc., Ashland, OR) and Excel

MA

NU

(Microsoft, Redmond, WA).

Protein structure modeling and structure visualization Alpha toxin is known to adopt at least two distinct conformations, a monomeric conformation in solution

TE

D

(soluble monomer) and a heptameric conformation with the amino latch and the heptamerization loop in extended conformation. The crystal structure of the soluble monomer was not available at the time of

AC CE P

designing the library so a structural model of the alpha toxin soluble monomer was generated based on the crystal structure of LukF (PDB ID: 1LKF)48 by standard homology modeling using MODELLER49 (Fig. 1 and Fig. 2). This model was utilized for designing the library. For all subsequent structural analysis we utilized the crystal structure of alpha toxin in complex with antibody LTM14 (PDB ID ID: 4IDJ)30 and modeled the disordered regions by standard loop modeling with MODELLER. Finally, a subunit from the crystal structure of the heptameric form of the alpha toxin (heptamer subunit, PDB ID: 7AHL, chain A)50 was utilized as representative of the heptameric conformation (Fig. 2). Solvent accessibilities for library design were calculated using MODELLER. All protein structure figures have been generated using UCSF Chimera51.

Rationally designed library generation The library, generated by Genscript (Piscataway, NJ), was amplified using Phusion Hot-Start High Fidelity DNA Polymerase (Thermo Fisher Scientific, Waltham, MA) with primers located 40 bp upstream 26

ACCEPTED MANUSCRIPT and downstream of the SfiI and NcoI restriction sites, respectively. The PCR product was column purified (DNA Clean and Concentrator, Zymo Research, Irvine, CA) as was pRNYD following digestion with SfiI

PT

and NcoI. One ug of each was combined and transformed into S. cerevisiae BJ5654 using a lithium acetate method to facilitate assembly through homologous recombination resulting in 3 X 106

RI

independent clones 52. After transformation, cells were grown overnight in SDCAA media at 30 °C and

SC

250 RPM, 2 X 108 cells were removed, centrifuged at 2500 x g for 2 minutes then resuspended in SGCAA media to induce protein expression for 16 hrs at 20 °C and 250 RPM. Prior to protein induction,

Random mutagenesis library generation

MA

20 °C until being processed for deep sequencing.

NU

an additional 5 X 108 cells were removed, centrifuged at 2500 x g for 2 minutes and the pellet stored at -

TE

D

The alpha toxin gene in pRNYD was subjected to random mutagenesis by error-prone PCR 41 with primers located 40 bp upstream and downstream of the SfiI and NcoI restriction sites, respectively. The

AC CE P

library was transformed as described above, but in duplicate resulting in 7 X 106 independent clones.

Deep sequencing

5 X 108 cells stored at -20 °C were thawed at room temperature, resuspended in 250 uL Qiagen P1 Buffer, lysed by vortexing for 5 minutes in the presence of 50 uL acid washed glass beads (Sigma-Aldrich) and the plasmid purified using QIAprep Spin Miniprep Kit according to the manufacture’s protocol (Qiagen, Hilden, Germany). The alpha toxin genes were recovered by PCR using two different molecular barcoding strategies for Roche 454 GS FLX sequencing. The majority of samples were processed using the first method, termed as “Ligation-PCR”. For this method amplification was performed (20 cycles, 94 °C 15 s, 59 °C 15 s, 68 °C 40 s) using Platinum High Fidelity DNA Polymerase (Invitrogen). A 652 bp 5’ fragment and a 687 bp 3’ fragment that overlapped by 366 bp were used to recover the entire alpha toxin gene during Roche 454 GS FLX sequencing. 454 Rapid library Adaptors and 12 RLMIDs required for bidirectional Roche 454 GS FLX sequencing were incorporated by ligation onto both the 5’ and 3’ 27

ACCEPTED MANUSCRIPT fragments. In the second method, termed as “Flap-PCR”, amplification of the 5’ and 3’ fragments were performed using primers containing the target-specific amplification sequence, the 454 Lib-A Adaptor

PT

and 48 unique GSMID sequences. Samples generated using both Ligation and Flap-PCR methods were purified using AMPure XP (Beckman Coulter, Inc), normalized into respective multiplexed library pools,

RI

quantified using qPCR (Kapa Biosystems) and subjected to emulsion PCR and bidirectional Roche 454

SC

GS FLX sequencing on two 2-region sequencing runs. The original phage library was sequenced at higher sequencing depth with 250K-500K reads per sample per position, compared to the panned libraries at

MA

NU

2000-5000 reads per sample per position.

Analysis of sequencing results

Signal processing was performed using the Roche/454 instrument software and reads were subsequently

TE

D

demultiplexed using the molecular barcode identifiers and the Roche/454 utility program sfffile. Following demultiplexing, reads were assigned to amplicons by matching read ends to primer sequences.

AC CE P

Prior to alignment, reads containing Ns were filtered out and the 3’-ends of reads were trimmed back using a sliding 50 nucleotide window to the last point where the mean quality score in the window was >= 35 using PRINSEQ-lite53. The remaining trimmed reads assigned to a given amplicon were subsequently aligned to it using BWA-SW54. Using proprietary scripts the BWA pairwise alignments were converted from the CIGAR format to a multiple sequence alignment format. Aligned nucleotide reads were converted to amino acid reads and these were filtered by removing: (i) reads shorter than 80 amino acids; (ii) reads with stop codons; (iii) reads with two or more mutations. For each position in the filtered multiple sequence alignment the absolute count and relative frequency of each amino acid was computed. More specifically, for a given sample s (either the library or a specific antibody/gate/round combination) let

be the number of times a given amino acid occurs at position p. Then the relative frequency f is

defined as

28

ACCEPTED MANUSCRIPT where the sum in the denominator is extended to all 20 amino acids. To determine if a position p is affected by the selection with an antibody, we used the position-dependent frequency of all possible

indicates the frequency for the wild type (reference sequence) amino acid. At each position,

SC

where

RI

PT

substitutions:

we are interested in comparing the frequency of a given sample s with the original unbiased library. We

MA

where

NU

define the enrichment ratio E as follows.

can either be the amino-acid-dependent frequency

D

defined earlier. For a given antibody, we have

or position dependent frequency

round of selection and

gates. The enrichment ratios

AC CE P

TE

were normalized to 1 across all gates g for each round:

Finally we generated a cumulative yeast display score SYD to estimate the relative amino acid variant by taking the geometric mean of the gate-specific

for a particular

weighted by their normalized

enrichment ratios and averaged over all rounds of selection:

where

correspond to the fold loss in binding associated to a given gate g. We empirically estimated

that the medium gate

= 10-fold and low gate

= 100-fold.

29

ACCEPTED MANUSCRIPT

Generation of Alpha Toxin Point Mutants and Lysates

PT

A plasmid containing the alpha toxin gene from Staphylococcus aureus USA300 FPR3757 cloned into the pET-46 vector (EMD Millipore) was used as template in all mutagenic PCR reactions. Individual point

RI

mutations of alpha toxin were generated using QuikChange site-directed mutagenesis (Agilent

SC

Technologies) and transformed into E. coli Rosetta DE3 cells for expression. All mutations were confirmed by sequencing. Crude lysates of S. aureus USA300 FPR3757 and single-site mutants of alpha

NU

toxin were generated as follows. E. coli Rosetta DE3 cells harboring native and mutant alpha toxin were

MA

grown to mid-log phase in Luria Broth LB containing carbenicillin at 37°C with constant shaking. Alpha toxin expression was induced with 500 µM IPTG for 3 hours. The cultures were spun down at 3500 rpm for 10 minutes at 4°C and resuspended in 1 ml LB. The cells were disrupted with three rounds of freeze

TE

D

thawing. Freezing was carried out in liquid nitrogen and thawing was conducted at 37°C. The lysates were centrifuged at 13,000 rpm for 10 minutes to pellet debris and filtered through a 0.2 µm syringe filter

Biosensor data

AC CE P

prior to analysis.

Epitope binning of alpha toxin antibodies was performed at 25°C in the “classical sandwich” orientation 55

on an Octet Red 384 biosensor using streptavidin sensors (ForteBio Menlo Park, CA, USA) and HBST

running buffer (10 mM HEPES pH 7.4, 150 mM NaCl, 0.05% (v/v) Tween-20) supplemented with 1 g/L BSA. Antibodies were biotinylated using NHS-LC-LC-biotin linker (Thermo Fisher Scientific, IL, USA), captured onto streptavidin sensors and then bound to 100 nM alpha toxin (Calbiochem). Unbiotinylated antibodies (67 nM binding sites) were then tested for pairwise binding. The affinities of Fabs for different alpha toxin variants were determined relative to the reference sequence on a Biacore T200 instrument (GE Healthcare Life Sciences) at 25°C by capturing alpha toxin and flowing the Fabs as analytes. From the epitope binning studies, a non-competing mAb was chosen and amine-coupled to a CM4 sensor chip (GE Healthcare Life Sciences) using a standard procedure except that surfaces were blocked using 0.1 M 30

ACCEPTED MANUSCRIPT ethylenediamine in 150 mM sodium borate pH 8.5 instead of ethanolamine. Alpha toxin variants from crude lysates were diluted 1/100 to 1/1000 into running buffer (HBST with 1 g/L BSA) and captured onto

PT

flow cells 2, 3 and 4, leaving flow cell 1 unmodified to serve as a reference surface. Fab analytes were injected at two to five different concentrations within the range 1nM - 12.5M over all flow cells and the

RI

capture surfaces were regenerated with three 20-sec injections of 75 mM phosphoric acid. Double-

SC

referenced sensorgrams for each Fab analyte concentration series 56 were fit globally to a 1:1 Langmuir

NU

binding model with mass transfer using the Biacore T200 evaluation software version 1.0.

MA

Rabbit erythrocyte lysis assay

Antibody serial dilutions were pre-incubated with 0.5 nM (EC50) purified alpha toxin (Calbiochem) or an appropriate dilution of crude lysates containing alpha toxin mutants prepared as described above in a final

D

volume of 0.1 mL at RT for 30 minutes. This was combined with 0.1 ml of 1% rabbit erythrocytes (RE)

TE

suspension and incubated at 37oC for 1 hr in a 96-well plate. The plate was subsequently spun at 2,400

AC CE P

rpm for 5 min to pellet intact and lysed RE. The absorbance of the supernatant was read at 405 nm to quantify the amount of released hemoglobin as a measure of lysis. Data analysis and EC50 calculations were done with GraphPad Prism.

Alpha toxin lytic activity test on blood agar plates Colonies of Rosetta DE3 E. coli strains harboring USA300 alpha toxin or the desired alpha toxin point mutants on the pET-46 expression plasmid were used to inoculate 500 µl LB containing 100 µg/ml carbenicillin and grown overnight at 37°C with shaking. The following day, the cultures were used to streak onto tryptic soy agar plates containing 5% rabbit erythrocytes (BD Diagnostic Systems). Prior to plating, 100 µl of carbenicillin (25 mg/ml) was spread onto the surface of the plate and allowed to dry at room temperature for 1 hour. Once dry, a 10 µl sterile loop was used to streak either USA300 or mutant strains across the plate in a single line. Each plate contained one streak of the USA300-harboring strain and two of the mutant-containing strains. Plates were allowed to dry and placed at 37°C overnight. Plates 31

ACCEPTED MANUSCRIPT were examined the next day and the lytic activity of the mutant-containing strains was compared to the

PT

lytic activity of the USA300 strain on the same plate.

Analysis of naturally occurring alpha toxin variants

RI

Natural sequence variation in alpha toxin was assessed using a combined set of 642 sequences obtained

SC

from public databases and sequencing of additional clinical isolates. Public sequences were extracted from the set of 458 S. aureus isolates whose genome sequences were found in the February 2014 release

NU

of the PATRIC database57 and have alpha toxin precursor gene annotations. The 425 such annotated gene

MA

sequences whose length would generate an amino acid sequence within 10 residues of the predominant length (319 a.a.) were combined with 217 sequences obtained by targeted sequencing of alpha toxin in additional clinical isolates. Multiple sequence alignments at both the nucleotide and amino acid level

D

were computed using MUSCLE58. Variant frequencies relative to the USA300_FPR3757 strain were

TE

computed from the multiple sequence alignments using a custom R59 script making use of the Biostrings 61

.

AC CE P

package60 from the Bioconductor project

CONFLICT OF INTEREST STATEMENT All authors were employees and shareholders of Pfizer Inc. at the time the study was conducted.

ACKNOWLEDGEMENTS We would like to thank Mark Gilbert for assistance with FACS experiments; Jeanette Dilley and Jessica Yu for mouse antibody generation; Jan Berka and Shobha Potluri for helpful discussions regarding next generation sequencing experimental design and analysis; Roche 454 with assistance in testing their 454 GS FLX XL platform; Kevin Lindquist for help generating the heatmap; and Annaliesa Anderson (Pfizer Vaccines, Pearl River, New York) for sharing the S. aureus strains from their collection. We would also like to thank Yik Andy Yeung for careful review of the manuscript.

32

ACCEPTED MANUSCRIPT

TABLES

PT

Positional deep sequencing data summary of predicted epitope positions for each antibody. Summary of the deep sequencing results used to predict the epitope positions of each antibody. Counts,

RI

frequency (Freq.) and enrichment ratio (E.R.) for the low and medium gates over the three rounds of

SC

FACS are reported as well as the YD Score for each round (Score) and the overall yeast display score (YD Score). Positions in which at least one round and at least one gate contain at least 50 non-reference

NU

strain amino acid counts and an enrichment ratio larger than 3 compared to the library resulting in an YD

MA

Score >1 are included. Values for the high gate are not reported because the counts and enrichment ratios were all below these cutoffs. Positions determined to be part of the proposed epitope following visual

TE

D

inspection of the crystal structure are indicated (Y).

Antibody susceptibility to genetic variation of alpha toxin. YD Score and decrease in affinity

AC CE P

for naturally occurring alpha toxin variants at epitope positions. YD Score of 1 given for mutants not enriched during FACS. Affinities determined using a Biacore T200 instrument.

FIGURES

Fig. 1. Overall epitope mapping process. (1) library design and synthesis; (2) display of the library on yeast and subsequent FACS; (3) deep sequencing of individually sorted libraries and bioinformatics analysis; (4) structural mapping of enriched positions and epitope determination.

Fig. 2. The positions included in the alpha toxin library are highlighted in green on different structural representations of alpha toxin (a, b and c) and the linear sequence (d). (a) Monomer model based on the structure of LukF (soluble monomer), (b) heptamer and (c) subunit from the crystal structure of the alpha toxin heptamer (heptamer subunit).

33

ACCEPTED MANUSCRIPT

Fig. 3. FACS enrichment of alpha toxin mutants from the designed library using LTM14 Fab. The library

PT

of alpha toxin mutants was displayed on the surface of yeast, incubated with LTM14 Fab conjugated to Alexa Fluor® 647 to detect binding (x-axis) followed by a FITC conjugated antibody specific for an N-

RI

terminal V5 epitope tag to normalize for alpha toxin mutant expression (y-axis).Three gates were

SC

generated to enrich for alpha toxin mutants where the antibody exhibited a severe (Low in blue), moderate (Medium in green) or no (High in red) decrease in binding. Each population was individually

NU

selected by FACS, expanded, labeled and selected again using the gate of origin for a total of 3

MA

consecutive rounds of FACS resulting in 9 unique populations. Data shown is a subset previously gated by size (forward scatter and side scatter) and mutant surface expression (FITC) based on the N-terminal

TE

D

V5 epitope tag. Percentage of events in the gates used for each round of FACS is indicated.

Fig. 4. Epitope prediction based on enrichment ratios following FACS. (a) Heat map representation of the

AC CE P

yeast display enrichment ratios for non-reference strain amino acids per library position. Data is plotted as the Log10 of the enrichment ratio based on antibody, gate and round of selection using (individual scales for each antibody). (b) Predicted epitope positions for each antibody based on the yeast display enrichment ratios.

Fig. 5. Epitope confirmation and correlation with co-crystal structure. Positions significantly enriched during yeast display selection were mapped in the context of the alpha toxin monomer crystal structure. (a) Epitope mapping of the LTM14 antibody on the co-crystal structure LTM14 Fab and the alpha toxin (shown in surface representation). The library (green) and LTM14 epitope positions determined from the Low gate (red) and Medium gate (orange) are highlighted. The heavy chain (blue) and light chain (cyan) of the LTM14 Fab are shown as a ribbon model. (b-c) Positions determined to be part of the LTM14 and LTM14.3 functional epitopes, respectively. The structural epitope determined from the co-crystal structure is outlined in cyan. (d) The epitopes of LTM14 and LTM14.3 (red), 5D10 (purple), 10G7 34

ACCEPTED MANUSCRIPT (yellow) and 14B9 (blue) are shown on the crystal structure of the alpha toxin monomer along with library positions that were not determined to be part of any of these antibodies epitope (green). (e)

PT

Biosensor confirmation of the epitopes by determining the affinity of each antibody toward a panel of alpha toxin point mutants expressed in E. coli. The fold decrease in affinity of each mutant compared to

SC

RI

the reference strain is shown. NB indicates no detectable binding at the concentrations tested.

Fig. 6. YD Score, correlation to affinity and neutralizing activity. (a) The round 3 low and medium gate

NU

enrichment ratios (E.R.) for three positions on the alpha toxin molecule that are part of the LTM14.3 epitope. Red points represent mutants used in the correlation experiments described below. Enrichment

MA

ratios only calculated when the counts are >4. (b) Pearson’s correlation between the YD Score and the effect of the mutations on the affinity of LTM14.3 towards alpha toxin. Mutants at position R66 (green)

TE

D

were excluded from the Pearson’s correlation analysis since their affinities were below the sensitivity of the assay. The reference alpha toxin was included as a control. (c) Correlation between antibody affinity

AC CE P

and neutralizing activity of LTM14.3 toward a panel of mutants in the rabbit erythrocytes lysis assay.

Fig. 7. Epitope mapping with the random library and comparison to the designed library. (a) Structural mapping of enriched positions for antibody LTM14 with the random library. (b) Enriched positions mapped on the structure and visualized using stick and ribbon representation. Most of the enriched positions are in the hydrophobic core in proximity to the epitope but do not interact directly with the LTM14. (c) Heat map displaying the frequency of each amino acid for the three positions that were identified to be part of the LTM14 epitope using both methods. The amino acid frequency is based on the deep sequencing data from the Round 4. The total number of unique amino acids found is shown. Data is displayed as the Log10 frequency.

35

ACCEPTED MANUSCRIPT

SUPPLEMENTAL DATA

PT

Table of selected position for the designed library. Selection column indicate if positions have been selected algorithmically by solvent accessibility cutoff analysis (A) or during manual inspection of

RI

the protein structure (M). Positions in dark green are part of the final library. Positions in light green were

SC

algorithmically selected but not included in the library to decrease the overall size of the library while maintaining an evenly distributed coverage of the surface. Area cutoffs used for an initial selection of

NU

library positions (A2): Gly=10; Ala, Cys, Ile, Leu, Met, Ser, Thr, Val=20; Asp, Glu, Phe, His, Lys, Asn,

MA

Pro, Gln, Arg, Trp, Tyr = 50. For Glycine C alpha was used as representative of the side chain.

Rationally designed library variant analysis. Indicated is the number of amino acid counts

TE

D

across all designed library positions identified through Roche 454 sequencing of the designed library. Amino acid counts are divided between the reference strain and non-reference strain amino acids found at

AC CE P

each position. The total counts of all non-reference strain amino acids per position is indicated. Library positions were expected to be mutated at a frequency of 0.9% (1/108) and were found to be occupied by a non-reference strain amino acid at a frequency of 0.63% ±0.51%, which is over 10-times more frequent than non-targeted positions (0.06% ±0.04%, data not shown). The non-targeted mutations likely results from of combination of errors associated with library synthesis, PCR and sequencing. Unique variants indicates the total number of unique non-reference strain amino acids identified at each position (All) and the subset of these that were included in the library design (Designed).

Amino acid incorporation in the designed library. Frequency of amino acid substitution at the 108 positions targeted in the designed library and the remaining 185 positions not targeted for mutagenesis. Mutations for each amino acid (Counts per Amino Acid) were only measured for positions where that mutation was possible (Total Counts). For example, there were no cysteines in the reference

36

ACCEPTED MANUSCRIPT sequence targeted for mutagenesis in the library so all 31,146,649 counts across all 108 positions have a

PT

chance to be mutated to cysteine of which 251 were found.

Affinity measurements and epitope binning of the five different alpha toxin antibodies

RI

determined by Biosensor analysis along with their IC50 based on an in vitro rabbit erythrocyte lysis

SC

assay. All values determined exclusively by Biacore with the exception of LTM14.

NU

Naturally occurring variants of alpha toxin. Epitope positions with naturally occurring

MA

sequence variability compared to the alpha toxin sequence of the reference strain (USA300 FPR3757) were identified by alignment of a set of 642 individual sequences. The position with variability is listed along with the number of occurrences of the reference and variant amino acid. The S. aureus strain(s) in

TE

D

which the variant was identified is also listed.

AC CE P

Antibody in vitro neutralizing activity. The five antibodies block the lytic activity of alpha toxin on rabbit erythrocytes in a dose dependent manner.

Titration analysis of all antibodies. Yeast cells displaying reference alpha toxin on the cell surface were incubated with serial dilutions of each antibody conjugated to AF-647 (x-axis) followed by a FITC conjugated antibody specific for an N-terminal V5 epitope tag to normalize for alpha toxin expression (y-axis). The flow cytometry data for one of two replicates is shown in each dot plot along with the antibody concentration that produces an AF-647 mean fluorescence intensity (MFI) equivalent to 67% of the maximal binding signal. The insert contains the normalized fraction bound as determined by the AF-647 MFI from both replicates and a cumulative fit of the data. The black dashed line corresponds to the intersection of the concentration producing 67% of maximal binding as measured by AF-647 MFI. All plots are on the same scale for comparison. Analysis was performed on an LSR II flow cytometer

37

ACCEPTED MANUSCRIPT with a high-throughput plate sampler. The FACS gating strategy is shown for LTM14 where the red,

PT

green and black outlines correspond to the high, medium and low affinity gates used for library selection.

FACS enrichment for all antibodies. FACS data of the designed library with each antibody and

RI

the random library with LTM14. The library labeling and unique gates used for each antibody are shown

SC

as dot plots and the enrichment per round of FACS are shown as overlay dot plots by round where the events are color coded by the gate from which they events originated (High in red, Medium in blue and

MA

NU

Low in Green). FACS was performed on a FACSAria.

Structural mapping of enriched library positions for antibody LTM14. For all 6 panels, nonenriched library positions are in green and enriched positions are in red (Round 3 only) and orange

TE

D

(Round 1 and 2). (a-b) Soluble monomer; (c-d) heptamer subunit; (e) focus on the epitope area from panel (a) with enriched residues labeled; (f) one heptamer subunit in the context of a pore-forming heptamer.

AC CE P

Combination of surface (a, c, f) and ribbon (b, d, f) representations with the enriched residues in space fill rendition. All 6 structures have the same orientation.

Structural mapping of enriched positions for antibody LTM14.3 (see legend Fig. S4).

Structural mapping of enriched positions for antibody 5D10 (see legend Fig. S4).

Structural mapping of enriched positions for antibody 10G7 (see legend Fig. S4).

Structural mapping of enriched positions for antibody 14B9 (see legend Fig. S4).

Biosensor sensorgrams of LTM14.3 Fab binding a panel of alpha toxin point mutants with varying YD Scores. Sensorgrams are normalized by alpha toxin capture level such that the maximum 38

ACCEPTED MANUSCRIPT theoretical binding response for every mutant is 100. LTM14.3 was injected either in a 4-membered, 5fold dilution series with a top concentration of 12.5 uM or in a 3-membered, 5-fold dilution series with a

PT

top concentration of 250 nM. Sensorgrams are shown in black and fits are shown in grey. The

RI

sensorgrams for V67F are truncated to improve fit quality.

SC

Antibody susceptibility to genetic variation of alpha toxin and correlation with YD Score. YD Score and fold decrease in affinity compared to the reference strain for naturally occurring alpha toxin

MA

determined using a Biacore T200 instrument.

NU

variants at epitope positions. YD Score of 1 given for mutants not enriched during FACS. Affinities

FACS for LTM14 using an antibody that binds a non-overlapping epitope as a conformational

TE

D

control. Flow cytometric comparison of the designed library and random library following 3 rounds of FACS isolating variants with the most diminished LTM14 binding (Low gate) and a fourth round of

AC CE P

FACS selecting for variants that maintain binding toward 5D10. Populations are labeled with the indicated antibody as described in Fig. 3.

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8.

9.

Sliwkowski, M. X. & Mellman, I. (2013). Antibody therapeutics in cancer. Science 341, 1192-8. Chan, A. C. & Carter, P. J. (2010). Therapeutic antibodies for autoimmunity and inflammation. Nat Rev Immunol 10, 301-16. Scott, A. M., Wolchok, J. D. & Old, L. J. (2012). Antibody therapy of cancer. Nat Rev Cancer 12, 278-87. Schmitz, K. R. & Ferguson, K. M. (2009). Interaction of antibodies with ErbB receptor extracellular regions. Exp Cell Res 315, 659-70. Shim, H. (2011). One target, different effects: a comparison of distinct therapeutic antibodies against the same targets. Exp Mol Med 43, 539-49. Chao, G., Cochran, J. R. & Wittrup, K. D. (2004). Fine epitope mapping of anti-epidermal growth factor receptor antibodies through random mutagenesis and yeast surface display. J Mol Biol 342, 539-50. Russell, R. B., Alber, F., Aloy, P., Davis, F. P., Korkin, D., Pichaud, M., Topf, M. & Sali, A. (2004). A structural perspective on protein-protein interactions. Curr Opin Struct Biol 14, 313-24. Schneidman-Duhovny, D., Rossi, A., Avila-Sakar, A., Kim, S. J., Velazquez-Muriel, J., Strop, P., Liang, H., Krukenberg, K. A., Liao, M., Kim, H. M., Sobhanifar, S., Dotsch, V., Rajpal, A., Pons, J., Agard, D. A., Cheng, Y. & Sali, A. (2012). A method for integrative structure determination of protein-protein complexes. Bioinformatics 28, 3282-9. Paus, D. & Winter, G. (2006). Mapping epitopes and antigenicity by site-directed masking. Proc Natl Acad Sci U S A 103, 9172-7.

39

ACCEPTED MANUSCRIPT

17. 18.

19.

20. 21.

22. 23.

24. 25.

26. 27.

28. 29. 30.

31.

32. 33.

PT

RI

SC

16.

NU

14. 15.

MA

13.

D

12.

TE

11.

Abdiche, Y. N., Miles, A., Eckman, J., Foletti, D., Van Blarcom, T. J., Yeung, Y. A., Pons, J. & Rajpal, A. (2014). Highthroughput epitope binning assays on label-free array-based biosensors can yield exquisite epitope discrimination that facilitates the selection of monoclonal antibodies with functional activity. PLoS One 9, e92451. Jin, L., Fendly, B. M. & Wells, J. A. (1992). High resolution functional analysis of antibody-antigen interactions. J Mol Biol 226, 851-65. Pons, J., Rajpal, A. & Kirsch, J. F. (1999). Energetic analysis of an antigen/antibody interface: alanine scanning mutagenesis and double mutant cycles on the HyHEL-10/lysozyme interaction. Protein Sci 8, 958-68. Sidhu, S. S., Fairbrother, W. J. & Deshayes, K. (2003). Exploring protein-protein interactions with phage display. Chembiochem 4, 14-25. Wells, J. A. (1991). Systematic mutational analyses of protein-protein interfaces. Methods Enzymol 202, 390-411. Sutherland, J. N. & Maynard, J. A. (2009). Characterization of a key neutralizing epitope on pertussis toxin recognized by monoclonal antibody 1B7. Biochemistry 48, 11982-93. Cunningham, B. C. & Wells, J. A. (1989). High-resolution epitope mapping of hGH-receptor interactions by alaninescanning mutagenesis. Science 244, 1081-5. Weiss, G. A., Watanabe, C. K., Zhong, A., Goddard, A. & Sidhu, S. S. (2000). Rapid mapping of protein functional epitopes by combinatorial alanine scanning. Proc Natl Acad Sci U S A 97, 8950-4. Vajdos, F. F., Adams, C. W., Breece, T. N., Presta, L. G., de Vos, A. M. & Sidhu, S. S. (2002). Comprehensive functional maps of the antigen-binding site of an anti-ErbB2 antibody obtained with shotgun scanning mutagenesis. J Mol Biol 320, 415-28. Skelton, N. J., Koehler, M. F., Zobel, K., Wong, W. L., Yeh, S., Pisabarro, M. T., Yin, J. P., Lasky, L. A. & Sidhu, S. S. (2003). Origins of PDZ domain ligand specificity. Structure determination and mutagenesis of the Erbin PDZ domain. J Biol Chem 278, 7645-54. Boder, E. T. & Wittrup, K. D. (1997). Yeast surface display for screening combinatorial polypeptide libraries. Nat Biotechnol 15, 553-7. Han, T., Sui, J., Bennett, A. S., Liddington, R. C., Donis, R. O., Zhu, Q. & Marasco, W. A. (2011). Fine epitope mapping of monoclonal antibodies against hemagglutinin of a highly pathogenic H5N1 influenza virus using yeast surface display. Biochem Biophys Res Commun 409, 253-9. Levy, R., Forsyth, C. M., LaPorte, S. L., Geren, I. N., Smith, L. A. & Marks, J. D. (2007). Fine and domain-level epitope mapping of botulinum neurotoxin type A neutralizing antibodies by yeast surface display. J Mol Biol 365, 196-210. Forsyth, C. M., Juan, V., Akamatsu, Y., DuBridge, R. B., Doan, M., Ivanov, A. V., Ma, Z., Polakoff, D., Razo, J., Wilson, K. & Powers, D. B. (2013). Deep mutational scanning of an antibody against epidermal growth factor receptor using mammalian cell display and massively parallel pyrosequencing. MAbs 5, 523-32. Fowler, D. M., Araya, C. L., Fleishman, S. J., Kellogg, E. H., Stephany, J. J., Baker, D. & Fields, S. (2010). High-resolution mapping of protein sequence-function relationships. Nat Methods 7, 741-6. Whitehead, T. A., Chevalier, A., Song, Y., Dreyfus, C., Fleishman, S. J., De Mattos, C., Myers, C. A., Kamisetty, H., Blair, P., Wilson, I. A. & Baker, D. (2012). Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat Biotechnol 30, 543-8. Hietpas, R., Roscoe, B., Jiang, L. & Bolon, D. N. (2012). Fitness analyses of all possible point mutations for regions of genes in yeast. Nature protocols 7, 1382-1396. Klevens, R. M., Morrison, M. A., Nadle, J., Petit, S., Gershman, K., Ray, S., Harrison, L. H., Lynfield, R., Dumyati, G., Townes, J. M., Craig, A. S., Zell, E. R., Fosheim, G. E., McDougal, L. K., Carey, R. B., Fridkin, S. K. & Active Bacterial Core surveillance, M. I. (2007). Invasive methicillin-resistant Staphylococcus aureus infections in the United States. JAMA 298, 1763-71. Lowy, F. D. (1998). Staphylococcus aureus infections. N Engl J Med 339, 520-32. Otto, M. (2012). MRSA virulence and spread. Cell Microbiol 14, 1513-21. Foletti, D., Strop, P., Shaughnessy, L., Hasa-Moreno, A., Casas, M. G., Russell, M., Bee, C., Wu, S., Pham, A., Zeng, Z., Pons, J., Rajpal, A. & Shelton, D. (2013). Mechanism of action and in vivo efficacy of a human-derived antibody against Staphylococcus aureus alpha-hemolysin. J Mol Biol 425, 1641-54. Kennedy, A. D., Bubeck Wardenburg, J., Gardner, D. J., Long, D., Whitney, A. R., Braughton, K. R., Schneewind, O. & DeLeo, F. R. (2010). Targeting of alpha-hemolysin by active or passive immunization decreases severity of USA300 skin infection in a mouse model. J Infect Dis 202, 1050-8. Ragle, B. E. & Bubeck Wardenburg, J. (2009). Anti-alpha-hemolysin monoclonal antibodies mediate protection against Staphylococcus aureus pneumonia. Infect Immun 77, 2712-8. Tkaczyk, C., Hua, L., Varkey, R., Shi, Y., Dettinger, L., Woods, R., Barnes, A., MacGill, R. S., Wilson, S., Chowdhury, P., Stover, C. K. & Sellman, B. R. (2012). Identification of anti-alpha toxin monoclonal antibodies that reduce the severity of Staphylococcus aureus dermonecrosis and exhibit a correlation between affinity and potency. Clin Vaccine Immunol 19, 377-85.

AC CE P

10.

40

ACCEPTED MANUSCRIPT

40.

41. 42.

43.

44. 45. 46.

47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57.

58.

PT

RI

SC

39.

NU

38.

MA

37.

D

36.

TE

35.

MedImmune LLC. A Phase 1 Study to Evaluate the Safety, Tolerability, and Pharmacokinetics of MEDI4893 in Healthy Adult Subjects. In: ClinicalTrials.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2013 - [cited 2014 June 17]. Available from: http://clinicaltrials.gov/show/NCT01769417 NLM Identifier: NCT01769417. Chao, G., Lau, W. L., Hackel, B. J., Sazinsky, S. L., Lippow, S. M. & Wittrup, K. D. (2006). Isolating and engineering human antibodies using yeast surface display. Nat Protoc 1, 755-68. Jones, S. & Thornton, J. M. (1997). Analysis of protein-protein interaction sites using surface patches. J Mol Biol 272, 121-32. Maynard, J. A., Maassen, C. B., Leppla, S. H., Brasky, K., Patterson, J. L., Iverson, B. L. & Georgiou, G. (2002). Protection against anthrax toxin by recombinant antibody fragments correlates with antigen affinity. Nat Biotechnol 20, 597-601. Wu, H., Pfarr, D. S., Tang, Y., An, L. L., Patel, N. K., Watkins, J. D., Huse, W. D., Kiener, P. A. & Young, J. F. (2005). Ultrapotent antibodies against respiratory syncytial virus: effects of binding kinetics and binding valence on viral neutralization. J Mol Biol 350, 126-44. Rani, M., Bolles, M., Donaldson, E. F., Van Blarcom, T., Baric, R., Iverson, B. & Georgiou, G. (2012). Increased antibody affinity confers broad in vitro protection against escape mutants of severe acute respiratory syndrome coronavirus. J Virol 86, 9113-21. Holt, D. C., Holden, M. T., Tong, S. Y., Castillo-Ramirez, S., Clarke, L., Quail, M. A., Currie, B. J., Parkhill, J., Bentley, S. D., Feil, E. J. & Giffard, P. M. (2011). A very early-branching Staphylococcus aureus lineage lacking the carotenoid pigment staphyloxanthin. Genome Biol Evol 3, 881-95. Fromant, M., Blanquet, S. & Plateau, P. (1995). Direct random mutagenesis of gene-sized DNA fragments using polymerase chain reaction. Anal Biochem 224, 347-53. Mata-Fink, J., Kriegsman, B., Yu, H. X., Zhu, H., Hanson, M. C., Irvine, D. J. & Wittrup, K. D. (2013). Rapid conformational epitope mapping of anti-gp120 antibodies with a designed mutant panel displayed on yeast. J Mol Biol 425, 444-56. Inoshima, I., Inoshima, N., Wilke, G. A., Powers, M. E., Frank, K. M., Wang, Y. & Bubeck Wardenburg, J. (2011). A Staphylococcus aureus pore-forming toxin subverts the activity of ADAM10 to cause lethal infection in mice. Nat Med 17, 1310-4. Powers, M. E., Kim, H. K., Wang, Y. & Bubeck Wardenburg, J. (2012). ADAM10 mediates vascular injury induced by Staphylococcus aureus alpha-hemolysin. J Infect Dis 206, 352-6. McCarthy, A. J. & Lindsay, J. A. (2010). Genetic variation in Staphylococcus aureus surface and immune evasion genes is lineage associated: implications for vaccine design and host-pathogen interactions. BMC Microbiol 10, 173. Van Blarcom, T. J., Sofer-Podesta, C., Ang, J., Boyer, J. L., Crystal, R. G. & Georgiou, G. (2010). Affinity maturation of an anti-V antigen IgG expressed in situ through adenovirus gene delivery confers enhanced protection against Yersinia pestis challenge. Gene Ther 17, 913-21. Ballance, D. J. B., Prior, C. P. R., Sadeghi, H. D. & Turner, A. J. K. o. P. (2011). Exendin Fusion Proteins, Vol. 12956623, US. Olson, R., Nariya, H., Yokota, K., Kamio, Y. & Gouaux, E. (1999). Crystal structure of staphylococcal LukF delineates conformational changes accompanying formation of a transmembrane channel. Nat Struct Biol 6, 134-40. Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234, 779815. Song, L., Hobaugh, M. R., Shustak, C., Cheley, S., Bayley, H. & Gouaux, J. E. (1996). Structure of staphylococcal alphahemolysin, a heptameric transmembrane pore. Science 274, 1859-66. Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C. & Ferrin, T. E. (2004). UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem 25, 1605-12. Gietz, R. D. & Schiestl, R. H. (2007). Large-scale high-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat Protoc 2, 38-41. Schmieder, R. & Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863-4. Li, H. & Durbin, R. (2010). Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589-95. Abdiche, Y. N., Malashock, D. S., Pinkerton, A. & Pons, J. (2009). Exploring blocking assays using Octet, ProteOn, and Biacore biosensors. Anal Biochem 386, 172-80. Myszka, D. G. (1999). Improving biosensor analysis. J Mol Recognit 12, 279-84. Wattam, A. R., Abraham, D., Dalay, O., Disz, T. L., Driscoll, T., Gabbard, J. L., Gillespie, J. J., Gough, R., Hix, D., Kenyon, R., Machi, D., Mao, C., Nordberg, E. K., Olson, R., Overbeek, R., Pusch, G. D., Shukla, M., Schulman, J., Stevens, R. L., Sullivan, D. E., Vonstein, V., Warren, A., Will, R., Wilson, M. J., Yoo, H. S., Zhang, C., Zhang, Y. & Sobral, B. W. (2014). PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res 42, D581-91. Edgar, R. C. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792-7.

AC CE P

34.

41

ACCEPTED MANUSCRIPT

PT

RI SC NU MA D

61.

TE

60.

R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing. R Core Team, Vienna, Austria. Pages, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: String objects representing biological sequences, and matching algorithms (2.30.1, R. p. v., ed.). Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A. J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J. Y. & Zhang, J. (2004). Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5, R80.

AC CE P

59.

42

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

43

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

44

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

45

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

46

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

47

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

48

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

49

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

50

AC

CE

PT ED

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

51

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

52

ACCEPTED MANUSCRIPT Highlights Current antibody epitope mapping techniques are limited by throughput or resolution



This improved method uses library design, yeast display and next-gen DNA sequencing



It was used to map the epitopes of a panel of antibodies in parallel within weeks



It quantitatively predicts the effect of antigen variability on antibody binding



This method is faster, more accurate and provides more quantifiable results

AC CE P

TE

D

MA

NU

SC

RI

PT



53

Precise and efficient antibody epitope determination through library design, yeast display and next-generation sequencing.

The ability of antibodies to bind an antigen with a high degree of affinity and specificity has led them to become the largest and fastest growing cla...
2MB Sizes 9 Downloads 14 Views

Recommend Documents


Epitope-Specific Binder Design by Yeast Surface Display.
Yeast surface display is commonly used to engineer affinity and design novel molecular interaction. By alternating positive and negative selections, yeast display can be used to engineer binders that specifically interact with the target protein at a

Epitope mapping with random phage display library.
Random phage display library is used to map conformational as well as linear epitopes. These libraries are available in varying lengths and with circularization. We provide here a protocol conveying our experience using a commercially available pepti

REAL-Select: full-length antibody display and library screening by surface capture on yeast cells.
We describe a novel approach named REAL-Select for the non-covalent display of IgG-molecules on the surface of yeast cells for the purpose of antibody engineering and selection. It relies on the capture of secreted native full-length antibodies on th

Functional characterization of a monoclonal antibody epitope using a lambda phage display-deep sequencing platform.
We have recently described a method, named PROFILER, for the identification of antigenic regions preferentially targeted by polyclonal antibody responses after vaccination. To test the ability of the technique to provide insights into the functional

Broad epitope coverage of a human in vitro antibody library.
Successful discovery of therapeutic antibodies hinges on the identification of appropriate affinity binders targeting a diversity of molecular epitopes presented by the antigen. Antibody campaigns that yield such broad "epitope coverage" increase the

Assessment of antibody library diversity through next generation sequencing and technical error compensation.
Antibody libraries are important resources to derive antibodies to be used for a wide range of applications, from structural and functional studies to intracellular protein interference studies to developing new diagnostics and therapeutics. Whatever

Diversity of the antibody response to tetanus toxoid: comparison of hybridoma library to phage display library.
Monoclonal antibodies are important tools in research and since the 1990s have been an important therapeutic class targeting a wide variety of diseases. Earlier methods of mAb production relied exclusively on the lengthy process of making hybridomas.

Isolation and Validation of Anti-B7-H4 scFvs from an Ovarian Cancer scFv Yeast-Display Library.
B7-H4 (VTCN1, B7x, B7s) is an inhibitory modulator of T-cell response implicated in antigen tolerization. As such, B7-H4 is an immune checkpoint of potential therapeutic interest. To generate anti-B7-H4 targeting reagents, we isolated antibodies by d

Improving GLONASS Precise Orbit Determination through Data Connection.
In order to improve the precision of GLONASS orbits, this paper presents a method to connect the data segments of a single station-satellite pair to increase the observation continuity and, consequently, the strength of the precise orbit determinatio

The epitope analysis of an antibody specifically against Vibrio cholerae O1 Ogawa by phage library study.
To prevent epidemic and pandemic cholera disease, an indispensible approach is to develop cholera vaccines based on comprehensive epitope information of this pathogen. This study aimed to utilize our previously raised monoclonal antibody IXiao3G6, wh