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Aptamer Affinity Maturation by Resampling and Microarray Selection Andrew Brian Kinghorn, Roderick M. Dirkzwager, Shaolin Liang, Yee -Wai Cheung, Lewis A. Fraser, Simon Chi-Chin Shiu, Marco Sze-Lok Tang, and Julian Alexander Tanner Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b01635 • Publication Date (Web): 27 Jun 2016 Downloaded from http://pubs.acs.org on June 28, 2016

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Analytical Chemistry

Aptamer Affinity Maturation by Resampling and Microarray Selection Andrew B. Kinghorn, Roderick M. Dirkzwager, Shaolin Liang, Yee -Wai Cheung, Lewis A. Fraser, Simon Chi-Chin Shiu, Marco S. L. Tang and Julian A. Tanner* School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China * Email: [email protected] Fax: +852 2855 1254 ABSTRACT: Aptamers have significant potential as affinity reagents but better approaches are critically needed to discover higher affinity nucleic acids to widen the scope for their diagnostic, therapeutic and proteomic application. Here, we report aptamer affinity maturation, a novel aptamer enhancement technique which combines bioinformatic resampling of aptamer sequence data and microarray selection to navigate the combinatorial chemistry binding landscape. Aptamer affinity maturation is shown to improve aptamer affinity by an order of magnitude in a single round. The novel aptamers exhibited significant adaptation, the complexity of which precludes discovery by other microarray based methods. Honing aptamer sequences using aptamer affinity maturation could help optimize a next generation of nucleic acid affinity reagents.

INTRODUCTION Aptamers are single-stranded nucleic acids capable of specific high-affinity epitope binding,1-4 with potential advantages over antibodies including greater thermostability, lower cost of production, ease of chemical modification and simpler integration into emerging technologies.5-6 The speed at which aptamers can be isolated for a given target via evolutionary combinatorial chemistry makes them ideal candidates as affinity reagents. Yet, aptamers selected by conventional systematic evolution of ligands by conventional exponential enrichment (SELEX) techniques2 often do not attain the affinity required for particular diagnostic, therapeutic or proteomic applications.7 Two important contributing factors to aptamer shortcomings are poor sequence space coverage in the initial library and the stochastic nature of binding capture selection.8 High throughput sequencing has been used to characterize aptamer pools.9-10 As only a few nanomoles of DNA library are typically used to represent a forty base random region, initial library sequence space coverage is less than one eighty billionth. Therefore even though high throughput sequencing characterizes the library well, there is only a 1 in 80 billion chance that the library will contain the tightest binding aptamer. New approaches are critically needed for nucleic acid selection analogous to antibody affinity maturation11 whereby an initial enriched pool of aptamers can be optimized to identify the best possible nucleic acid aptamer across a broad binding landscape. Improvement of aptamer sequences via in vitro error prone PCR12 or recombination13 can be used to search sequence space, thereby overcoming the poor sequence space coverage of initial libraries. However, these diversification approaches require many selection rounds and still rely on binding capture selection, so novel beneficial sequences may be lost.

DNA microarray technology allows for simultaneous assay via fluorescent target and selection of an aptamer library, circumventing stochastic binding capture selection. 14 A DNA microarray was previously used to explore the sequence of an immunoglobulin E binding aptamer.15 Variations of the aptamer sequence with single, double and some triple point mutations were synthesized onto a DNA microarray and assayed with fluorescent target. One variation showed mild affinity improvement (KD = 4.1 nM) when compared to the original aptamer (KD = 4.7 nM). Similarly, a recent paper showed mild affinity improvements by aptamer point mutation analysis. 16 Microarray assay was combined with in silico Closed Loop Aptameric Directed Evolution (CLADE) to select for aptamers against the fluorescent allophycocyanin target.17 This strategy was successful, isolating aptamers with nanomolar binding affinities from random sequence space in nine rounds. A later study by the same group comparing CLADE with three diversification systems mutation, recombination and statistical binding prediction, found that all three diversification techniques showed comparable enrichment of glucose-6-phosphate dehydrogenase aptamers.18 Over five rounds the highest affinity aptamer isolated had a dissociation constant KD = 245 nM. Whilst CLADE enrichment has shown success, there is a critical need to develop other more cost- and time-effective approaches. A significant advantage for aptamers over antibodies is lower cost and higher speed of production. This is especially true for the development and application of proteomics affinity reagents. An alternative diversification approach is required if aptamers are to be affinity matured in a single round, retaining the low cost and rapid development time required to produce aptamers to explore the proteome. Such an approach would not just evolve individual aptamer sequences in small increments over many rounds but instead take all available sequence information to arrive at the best possible aptamer sequence in just a single round.

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Herein, we report aptamer affinity maturation by resampling as a novel approach to successfully achieve this goal. Resampling relies on the principle that during classical SELEX it is unlikely to select the best aptamer but highly likely to select family members of the best aptamer. The sequence of the best possible aptamer is hidden within the sequences of its isolated family members. Using sequence information from all isolated family members an aptamer family motif can be created which represents an aptamer library containing the best possible aptamer sequence. In this way a single resample diversification round can be used to hone in on the sequence space containing the best possible aptamer sequence. Aptamer affinity maturation (Figure 1) incorporates two stages: the first stage is a bioinformatic approach using novel software “Resample” which we present herein to generate an aptamer library based on the aptamer family motif identified from the original standard SELEX experiment. The second stage is to synthesize this aptamer library onto a DNA microarray14 which can be screened in a massively parallel approach using fluorescently labelled target protein, similarly to Cho and coworkers19. Candidate high-affinity aptamers from aptamer affinity maturation can then be characterized using standard techniques in solution. We demonstrate the application of aptamer affinity maturation in improving the affinity of a malaria diagnostic aptamer by an order of magnitude.

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quences from the Resample result in triplicate along with the positive controls of our 31 previously isolated aptamers 20 and scrambled aptamers as negative controls. The microarray was incubated in blocking buffer (10 mg casein/ml, 137 mM NaCl, 2.7 mM KCl, 8.1 mM Na2HPO4, 1.5 mM KH2PO4, 0.1% v/v Tween-20, pH 7.4) for 1 hour before being washed in deionized H2O and dried by centrifugation. 490 µL of 50 nM Alexa Fluor® 555 PfLDH and 1 µM Alexa Fluor® 647 hLDHB in blocking buffer was loaded onto the microarray gasket slide and the microarray assembled into an Agilent hybridization chamber. The hybridization chamber was incubated at room temperature with agitation for 1 hour. The hybridization chamber was disassembled in washing buffer (0.1% v/v Tween-20, 137 mM NaCl, 2.7 mM KCl, 8.1 mM Na2HPO4, 1.5 mM KH2PO4, pH 7.4) and rinsed with washing buffer three times with decreasing concentrations of Tween-20 (0.1%, 0.01%, 0.001%). The microarray finally washed with in deionized H2O and dried by centrifugation. The microarray was scanned using an Agilent SureScan microarray scanner with 2 µm resolution. Data was extracted using Agilent Feature Extraction 12 and statistical analysis performed using Microsoft Excel and Origin 8.5.

EXPERIMENTAL SECTION Design of bioinformatics tool, Resample. The standalone version of Resample was coded in VisualBasic.NET. Resample is split into 3 major subroutines. In the first subroutine the entered family motif is checked for legality. Secondly an aptamer family library is generated from the given aptamer family motif. Thirdly the newly generated aptamer family is filtered based on any provided structural motif in the form of a base pairing motif. The web version of Resample is available online at http://resample.azurewebsites.net/. Fluorescent labelling of PfLDH and hLDHB. Two fluorophore conjugations were performed for the microarray experiments, Alexa Fluor® 555 was conjugated to PfLDH and Alexa Fluor® 647 was conjugated to hLDHB. For both reactions Life technologies Alexa Fluor® Protein Labelling Kits were used and carried out according to the manufacturer’s protocols. Briefly, 50 μL freshly prepared 1 M bicarbonate was added to 500µL of 2 mg protein/ml sample in PBS buffer. The protein solution was then transferred to the supplied vial of reactive Alexa Fluor® dye before being capped and inverted to fully dissolve the dye. The reaction mixture was incubated for 1 hour at room temperature with magnetic stirring. Gel filtration was used to separate conjugates from free nonconjugated dye. Eluted conjugates were analyzed using a Cary 300 Bio spectrophotometer. The degree of labelling was calculated using absorbance values and formulas provided by the supplier and were 1.11 for Alexa Fluor® 555 PfLDH and 1.96 for Alexa Fluor® 647 hLDHB. An electrophoretic mobility shift assay was performed to ensure that the labeled PfLDH retained binding to the 2008s aptamer (Figure S2). Microarray binding strength approximation assay. SurePrint 1x1M custom DNA microarray was ordered from Agilent which included all 186,624 novel aptamer se-

Figure 1. Aptamer affinity maturation overview. Resample uses sequence information from a SELEX experiment, in the form of an aptamer family motif, to create an aptamer family library. If available, structural information in the form of a structural motif can filter out aberrantly folding sequences and reduce library size. This novel library can be synthesized onto a DNA microarray and

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incubated with fluorescently labelled target to simultaneously measure all aptamer binding affinities.

Characterizing aptamer binding by Microscale Thermophoresis. The MST experiments were performed by 2bind (Germany). MST measurements were taken using the Monolith NT.115 Pico and performed in duplicate for all aptamer samples. Capillaries were loaded with 1 nM Alexa Fluor® 647 PfLDH and 16 concentrations of aptamer from 61 pM to 2 μM in 20 mM Tris/HCl pH 8, 100 mM NaCl, 1 mM MgCl2, 0.1% Tween-20. Data points were normalized to fraction bound and 1:1 law of mass action binding curves fitted.

tighter binding than the previously characterized 2008s1 aptamer. Taking a cut-off value of 100,000 AU (Figure 2) ten aptamer candidates were identified (Table S1). All ten candidates showed a mean normalized red fluorescence (hLDHB signal) of less than 20,000 AU despite having 20 times more fluorescent hLDHB present in the microarray incubation buffer. By simultaneously selecting for PfLDH binding and specificity against hLDHB the novel aptamers will fit desired downstream applications

RESULTS AND DISCUSSION Computational Resampling. Previously, we identified DNA aptamers against the malarial antigen PfLDH with 42 nM KD binding affinity and high specificity for PfLDH over human LDH (hLDH).20 Additionally, we demonstrated clinical application of this aptamer using aptamer tethered enzyme capture (APTEC) assay for rapid diagnosis of malaria parasites.21 We sought to demonstrate aptamer affinity maturation by improving the affinity of this aptamer. We developed the bioinformatic approach involving writing the software “Resample” to generate aptamer family libraries from sequence data (Figure 1). Resample takes an aptamer family motif, derived manually (Figure S1) from sequence alignment of aptamers isolated from any typical SELEX experiment, and generates an aptamer library representing every combinatorial chemistry variation within that family. Furthermore, known base pairing information of the aptamer family can be entered to filter out aberrantly folding sequences, reducing final library size. From a sequence alignment of our previously isolated aptamers, we demonstrated the application of this approach using the PfLDH aptamer family motif. This motif, along with the base pairing information from the aptamer crystal structure,20 was entered into Resample to generate every possible member of the PfLDH aptamer family totaling 186,624 sequences. Resample generates libraries typically 10 orders of magnitude smaller than a random library used for SELEX, allowing full representation in triplicate on a DNA microarray. Microarray selection. A custom 1 million feature DNA microarray was chosen to display the 186,624 aptamer library in triplicate along with positive controls of our 31 previously isolated aptamers and scrambled aptamer negative controls. Additionally a mutational analysis of the prototypic 2008s1 aptamer was performed. This microarray technique served as a convenient, high throughput screening method to analyze our Resample library and evaluate the utility of this approach alongside a more typical mutational analysis. To determine the binding of aptamers the array was simultaneously incubated with fluorescently labelled PfLDH as well as hLDH subunit B (hLDHB) as a negative control, before washing and array scanning. The target (PfLDH) was labelled with Alexafluor®555 whereas the control (hLDHB) was labelled with AlexaFluor®647, represented in green and red on the microarray respectively (Figure 1). Using the mean normalized green fluorescence (PfLDH signal) of each feature, the relative affinity of each aptamer was estimated (Figure 2). A total of 102 aptamers were found with

Figure 2. Histogram of mean normalized green fluorescence for microarray features. Scrambled aptamers in blue (n = 500), original aptamers in red (n = 31), and resampled aptamers in orange (n = 186,624). A selection cut-off value of 100,000 AU was imposed which is represented by the blue vertical line. All ten aptamers to the right of this line were selected for further investigation. The average standard error of fluorescent values among replicate groups from the scanned array was 0.23 which was deemed acceptable.

Interestingly, the ID numbers of the selected aptamers occur in groups (Table S1). The method of generating aptamers in Resample is sequential so closely numbered aptamers are closely related. Therefore, we hypothesized that the groups of similar aptamers represent aptamer subfamilies. To test this hypothesis we took the 38 best novel microarray aptamers and sequence aligned them with 31 original aptamers previously isolated using traditional SELEX. A phylogenetic tree was constructed from this sequence alignment (Figure 3).

Figure 3. Phylogeny of PfLDH aptamer family. The 38 best affinity matured aptamers and the 31 original aptamers from our previous selection were sequence aligned and the phylogenetic tree

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constructed. The heat map along the bottom of the phylogenetic tree represents microarray approximated binding with lower binding strength in blue and higher binding strength in red. The novel aptamers are designated with an asterisk (*).

Similarly to the observation of grouping (Table S1), the aptamers divided into several subgroups with related binding. The tightest binding aptamers from the original selection group were related to the affinity matured aptamers, whereas the lower affinity original aptamers were genetically divergent. This implied the microarray was successful in identifying lead candidates from hundreds of thousands of aptamers in a massively parallel manner.

Characterization of lead aptamers. Four representative aptamers were chosen for further characterisation. Microscale Thermophoresis (MST) was used to characterise the binding of the aptamers to PfLDH (Figure 4). Our prototypic PfLDH aptamer 2008s1 was found to have KD = 18.7±5.8 nM (Figure 4A). The 27bp 2008s1 aptamer is a shortened version of our previously published 35bp 2008s aptamer for which we had reported a isothermal titration calorimetric KD = 42nM.20 The removal of 8 bases likely uninvolved in target binding decreased the KD, consistent with other aptamers’ determination of minimal sequence for binding.22-23 The KD values for the three novel aptamers identified by aptamer affinity maturation A0179714, A0015608 and A0077761 were 7.3±3.3 nM, 4.9±2.4 nM and 2.1±0.9 nM respectively (Figure 4B, C, D). For the A0077761 aptamer, this represents an order of magnitude improvement in KD relative to the original prototypic 2008s1 aptamer. Additionally the aptamers were tested for binding against free Alexa-Fluor® 555 (Figure S3) and a panel of proteins (Figure S4) which confirmed the specific nature of binding.

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Sequence analysis of lead aptamers. The novel aptamers exhibited significant adaptation. Relative to the 2008s1 aptamer, the three characterized lead aptamers have substitutions at bases heavily involved in hydrogen bond interactions with the target (Figure 5). This suggests that the base substitutions are not merely at non-functional unconserved regions or involved in folding but form part of the surface interacting with the target. One region where base substitutions relative to the 2008s1 aptamer are involved in folding is the A13 to T20 base pair (Figure 5). Characterized aptamers A0179714 and A0077761 have this base pair substituted to G13 and C20. As the G to C base pair has a greater free energy than the A to C base pair, an aptamer with this substitution should have a more stable fold. The apparent advantage of this substitution is confirmed by its penetrance of 7 in 10 among the top 10 lead aptamers. The complexity of the novel aptamers precludes discovery by other microarray optimization methods. Of the 10 lead aptamer candidates (Table S1), aptamer A0179714 is the most divergent in sequence. In particular at positions 7 and 10 where it is the only member to have A7 and T10 when compared to all other members having G7 and A10. Mutational analysis of the 2008s1 aptamer (Figure S5) reveals that the G7 and A10 base positions are strongly conserved. Mutation at either G7 or A10 positions resulted in complete loss of binding. Indeed, any mutation to the 2008s1 aptamer resulted in a decrease in binding affinity. This illustrates that mutational scanning of a single sequence, such as that performed by Katilius and coworkers,15 is not capable of uncovering complex multi-base interactions. Moreover permutations of these complex multi-base interact-

Figure 4. Characterization of lead aptamers. Fraction bound normalized MST binding curves and calculated KD. Predicted folding and sequence of aptamers displayed. Base changes in novel aptamers when compared to the prototypic 2008s1 aptamer are shown in yellow.

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Figure 5. Aptamer protein interaction map for PfLDH aptamers. Interactions for 2008s aptamer are deduced from its crystal structure.20 Light blue dotted lines denote hydrogen bond interactions between bases and PfLDH with number of hydrogen bonds for each interaction shown. Aptamer base substitutions for A0179714, A0015608 and A0077761 shown as blue, green and yellow respectively. Interactions of the novel aptamers may not be representative.

tions may result in enhanced aptamer sequences such as our case of A0179714. Aptamer affinity maturation can uncover multifactorial interactions in such complex aptamer-target systems in ways that simple mutation of sequence space is unable to achieve.

CONCLUSIONS In summary, we developed a novel aptamer affinity maturation approach which combines the bioinformatic tool “Resample” with a microarray screen. Aptamer affinity maturation enhanced the affinity of the ‘Resampled’ PfLDH aptamers by an order of magnitude. The resampled aptamers have immediate potential application in combination with our recent use of PfLDH aptamers in the APTEC assay 21, within 3D printed devices 24, or within DNA origami25. Although studies have utilized microarrays in aptamer selection and characterization previously,15, 17, 19, 26-28 to our knowledge this is the first time aptamer sequence alignment data has been used to create a novel targeted microarray aptamer library. In vitro aptamer evolution need not be restricted to mutation and recombination with few parental sequences and the slow pace of biology. The use of high throughput sequencing9-10 means aptamer sequence information is a plentiful resource which, via ‘Resample’, can be utilized for rapid evolution. The generation of targeted yet diversified microarray libraries is critical for success of aptamer affinity maturation to isolate the tightest binding aptamers in just a single round. A single resample round should just take two days, excluding microarray shipping time. Honing aptamer sequences using the combinatorial chemistry of aptamer affinity maturation could help optimize a next generation of nucleic acid affinity reagents for diagnostics, therapeutics and proteomics.

ASSOCIATED CONTENT Supporting information Additional information as noted in text. Figure S1: Aptamer family motif derivation, Figure S2: Determination of whether fluorescent modification of PfLDH affects the binding of the aptamer by electrophoretic mobility shift assay, Figure S3: Determination of whether any of the aptamers bind to the free AlexaFluor®555 by electrophoretic mobility shift assay, Figure S4: Determination of specificity of selected aptamers by electrophoretic mobility shift assay, Figure S5: Mutational analysis of 2008s1 aptamer, Table S1: Normalized green fluorescence of 10 candidate aptamers from microarray selection. Resample is provided as both a hosted web service and standalone download at http://resample.azurewebsites.net/.

AUTHOR INFORMATION Corresponding Author * Julian A. Tanner. Email: [email protected]

Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS This work was supported by Hong Kong University Grants Council GRF grant [HKU 778813M]; and The University of Hong Kong Seed Funding Programme for Basic Research [201111159205]. We thank Dr. R. Choy and K. Wong of the Chinese University of Hong Kong for their help with microarray scanning and Dr. T. Schubert of 2bind GmbH Germany for MST analysis.

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