Analytical Biochemistry 464 (2014) 73–82

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Ribosome display enhanced by next generation sequencing: A tool to identify antibody-specific peptide ligands Ewa Heyduk, Tomasz Heyduk ⇑ Edward A. Doisy Department of Biochemistry and Molecular Biology, St. Louis University Medical School, St. Louis, MO 63104, USA

a r t i c l e

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Article history: Received 6 May 2014 Received in revised form 28 June 2014 Accepted 14 July 2014 Available online 21 July 2014 Keywords: Ribosome display Next generation sequencing Antibody Peptide Ligand

a b s t r a c t Detection of antibodies in serum has many important applications. Our goal was to develop a facile general experimental approach for identifying antibody-specific peptide ligands that could be used as the reagents for antibody detection. Our emphasis was on an approach that would allow identification of peptide ligands for antibodies in serum without the need to isolate the target antibody or to know the identity of its antigen. We combined ribosome display (RD) with the analysis of peptide libraries by next generation sequencing (NGS) of their coding RNA to facilitate identification of antibody-specific peptide ligands from random sequence peptide library. We first demonstrated, using purified antibodies, that with our approach-specific peptide ligands for antibodies with simple linear epitopes, as well as peptide mimotopes for antibodies recognizing complex epitopes, were readily identified. Inclusion of NGS analysis reduced the number of RD selection rounds that were required to identify specific ligands and facilitated discrimination between specific and spurious nonspecific sequences. We then used a model of human serum spiked with a known target antibody to develop NGS-based analysis that allowed identification of specific ligands for a target antibody in the context of an overwhelming amount of unrelated immunoglobins present in serum. Ó 2014 Elsevier Inc. All rights reserved.

The immune system has an extraordinary capability to produce antibodies that recognize diverse antigens with exquisite selectivity and affinity [1]. Circulating antibodies play a crucial role in eliminating infections and are also believed to play an important role in the suppression of tumors through the process of immunosurveillance [2]. Antibodies produced by the immune system in response to infection or tumor development could be used as biomarkers for disease detection, disease prognosis, and following the progress of the treatment [3–5]. An oversensitive immune system can result in production of circulating autoantibodies against self-antigens that could give rise to an autoimmune disease where such autoantibodies target and damage healthy tissues [6,7]. Detection of autoimmune disease-specific autoantibodies has important diagnostic value [6], and blocking antigen binding sites of such antibodies has been shown to have therapeutic potential [8]. The high significance of detecting and quantifying specific antibodies emphasizes the importance of sensitive and convenient detection methodologies. Sandwich enzyme-linked immunosorbent

⇑ Corresponding author. Fax: 314 977 9205. E-mail address: [email protected] (T. Heyduk). http://dx.doi.org/10.1016/j.ab.2014.07.014 0003-2697/Ó 2014 Elsevier Inc. All rights reserved.

assay (ELISA)1 using immobilized antigen is the most commonly used antibody detection methodology in research and clinical diagnosis. Although this is a well-established methodology, it has significant drawbacks such as low throughput and the need to know the identity of the antigen of the antibody and its availability in purified form. The goal of this work was to develop a facile general experimental approach for identifying antibody-specific peptide ligands that could be used as the reagents in various improved antibody detection assay formats. For example, they could be used in the new antigen peptide-based antibody detection methodology that we recently developed [9,10]. This methodology allows simple mix-and–read format detection of antibodies with excellent sensitivity and is compatible with detection of antibodies in serum [9,10]. Antibody-specific peptide ligands could also be used for inhibition or capture of target antibodies. Peptides as antibody-specific ligands have several advantages. They can be chemically synthesized in large quantities and easily coupled to solid surfaces, beads, probes, and other signaling moieties. Furthermore, as first demonstrated by 1 Abbreviations used: ELISA, enzyme-linked immunosorbent assay; RD, ribosome display; NGS, next generation sequencing; NGSERD, next generation sequencing enhanced ribosome display; PCR, polymerase chain reaction; dNTP, deoxynucleoside triphosphate; tRNA, transfer RNA; PBS, phosphate-buffered saline; ss, singlestranded; UV, ultraviolet; BSA, bovine serum albumin; mRNA, messenger RNA.

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Geysen and coworkers [11–16], the peptides could mimic discontinuous epitopes of native proteins [15]. Such peptide mimotopes may have no sequence homology to the antigen but can specifically recognize the antibody by mimicking the essential features of the epitope [15]. Subsequent work provided ample confirmation for the peptide mimotope concept [17], suggesting that in principle effective peptide ligands for almost any antibody could be developed and used as the reagents in peptide-based antibody sensors. We chose to test whether ribosome display (RD) [18–24], enhanced by next generation sequencing (NGS) analysis of the composition of peptide libraries (NGSERD; Fig. 1), could be a facile methodology for unbiased selection, from random sequence peptide libraries, of specific peptide ligands for antibodies irrespective of the complexity of the original epitopes recognized by the target antibody. RD (Fig. 1) is a powerful in vitro evolution technology that allows directed evolution of proteins for higher binding affinity, higher stability, or other improved biophysical parameters as well as selection from the complex libraries of polypeptides the ligands with binding activity toward a specific target [23,24]. RNA encoding the peptide library (obtained by in vitro transcription of synthetic DNA template) is translated in vitro using bacterial S-30 extract under conditions where peptide products and their corresponding RNA remain associated with the ribosome. Peptide–ribosome–RNA complexes are incubated with immobilized target, unbound complexes are washed out, and RNA of specifically bound complexes is eluted. This RNA (after reverse transcription, amplification by polymerase chain reaction [PCR], and transcription by T7 polymerase) can be used to start another cycle of RD for further enrichment of the library with target-specific peptides. We expected that selection of peptide ligands by RD for antibodies recognizing simple continuous epitopes of protein antigens should be relatively straightforward. An RD-based approach should also be well suited for discovery of peptide mimotopes for antibodies recognizing complex epitopes given that RD allows more complex starting random sequence peptide libraries compared with, for example, phage display (typically 1014 vs. 109) [22,25]. The higher complexity of the starting library should enhance the probability of a successful selection of peptide mimotope ligands. The key feature of RD is that the RNA encoding the peptide library is translated in vitro under conditions where peptide products and their corresponding RNA remain associated with the ribosome, effectively tagging each peptide member of the library with its coding RNA sequence (Fig. 1). This opens up a possibility to

employ NGS for analysis of the composition of peptide libraries during the RD selection process (through analysis of the corresponding RNAs). NGS techniques revolutionized analysis of complex nucleic acid mixtures [25–28]. A single NGS analysis can reveal the identity of millions of sequences in a sample. Read count (the number of times a given sequence is read during NGS analysis) is proportional to the relative abundance of a sequence in the sample. The great utility of NGS-based analysis of corresponding nucleic acids for the analysis of the composition of polypeptide libraries has been demonstrated [29]. We speculated that implementation of NGS-based analysis of RD libraries could enable identification of rare specific ligands for target antibodies even in the presence of overwhelming excess of unrelated immunoglobins present in serum. Our long-term goal is to be able to identify peptide ligands to disease-related antibodies directly from patient serum samples without the need to isolate, express, purify, or even know the identity of the disease-specific antigens (or their corresponding antibodies). As the first step toward this goal, here we describe the data obtained with purified antibodies and serum samples spiked with the antibodies that demonstrate applicability of the NGSERD approach for identifying specific peptide ligands for antibodies with simple linear and complex epitopes and its ability to identify such ligands even when they constitute only a very minor fraction of the library. Materials and methods Materials The following anti-troponin antibodies (goat affinity purified; all from BiosPacific, Emeryville, CA, USA) were used in this work: anti-human cardiac troponin peptide 1 (recognizes amino acids 1–15 of the protein), anti-human cardiac troponin peptide 2 (recognizes amino acids 16–26 of the protein), anti-human cardiac troponin peptide 3 (recognizes amino acids 27–40 of the protein), and anti-human cardiac troponin peptide 4 (recognizes amino acids 68–86 of the protein). Mouse monoclonal anti-human cardiac phosphotroponin was obtained from Abcam (Cambridge, MA, USA), mouse monoclonal anti-p53 pAb1620 (ab-5) from Calbiochem (La Jolla, CA, USA), mouse monoclonal anti-25-hydroxyvitamin D3 from R&D Systems (Minneapolis, MN, USA), mouse monoclonal anti-phosphoserine from Millipore (Billerica, MA, USA), anti-phospho-p53 (phospho Ser46) from GenScript (Piscataway, NJ, USA), and mouse monoclonal anti-FLAG M2-peroxidase labeled from Sigma (St. Louis, MO, USA). Pooled human serum was obtained from Sigma. EZ-Link Sulfo-NHS-LC-LC-Biotin, Reacti-Bind NeutrAvidin High Binding Capacity Coated Plates (8well strips), Zeba desalting columns, and SuperSignal ELISA Femto Maximum Sensitivity Substrate were obtained from Pierce (Rockford, IL, USA). RiboMax Large Scale RNA Production System-T7, Escherichia coli S30 Extract System for Linear Templates, Wizard SV Gel and PCR Clean-Up System, Wizard SV Mini Prep DNA Purification System, RNasin, and deoxynucleoside triphosphates (dNTPs) were purchased from Promega (Madison, WI, USA), AccuScript High Fidelity Reverse Transcriptase was from Agilent Technologies (Santa Clara, CA, USA), transfer RNA (tRNA) and RED KlenTaq DV ReadyMix were from Sigma, High Pure RNA Isolation Kit was from Roche (Indianapolis, IN, USA), StrataClone PCR Cloning Kit was from Agilent Technologies, and E-Gel 2% with SYBR Safe was from Invitrogen (Grand Island, NY, USA). All other reagents were of the highest purity commercially available. Oligonucleotides

Fig. 1. Next generation sequencing enhanced ribosome display (NGSERD).

The following oligonucleotides were used throughout this project:

Next generation sequencing enhanced ribosome display / E. Heyduk, T. Heyduk / Anal. Biochem. 464 (2014) 73–82

RIB1: CAG TAT AGT CCA TGG GAC ATC ACC ATC ACC ATC AC (trimer mix)10 GAC TAC AAG GAC GAT GAC GAT AAG CTT GTG CAC CTA RIB3: AAG GAC GAT GAC GAT AAG GGT (trimer mix)15 GGT TCT GGC CTT TAT ATG GCC RIB4: AAG GAC GAT GAC GAT AAG GGT (trimer mix)25 GGT TCT GGC CTT TAT ATG GCC OV1: CTA CAA GGA CGA TGA CGA TAA GCT TTA TAT GGC CTC GGG G OV1M: GGT TCT GGC CTT TAT ATG GCC TCG GGG GCC GAA TTC GGA TCT OV2: TGA TGG TGA TGG TGA TGT CCC ATG GAT ATA TCT CCT TCT T OV2M: CCT TAT CGT CAT CGT CCT TGT AGT CCG CCA TGG ATA TAT CTC CTT CTT OV3: AAG AAG GAG ATA TAT CCA TGG GAC ATC ACC ATC ACC ATC A OV4: CCC CGA GGC CAT ATA AAG CTT ATC GTC ATC GTC CTT GTA G OV5: GAT ATA TCC ATG GCG GAC TAC AAG GAC GAT GAC GAT AAG GGT OV6: AGA TCC GAA TTC GGC CCC CGA GGC CAT ATA AAG GCC AGA ACC OV7: CCG CAC ACC AGT AAG GTG TGC GGT TTC AGT TGC CGC TTT CTT TCT OV8: ATA CGA AAT TAA TAC GAC TCA CTA TAG GGA GAC CAC AAC GG IL1: CAA GCA GAA GAC GGC ATA CGA GAT CGA AAT TAA TAC GAC TCA CTA ILIn: CCC TAC ACG ACG CTC TTC CGA TCT xxxxxx CCT TGTAGT C IL2: AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T ILIR3n: CCC TAC ACG ACG CTC TTC CGA TCT xxxxxx CGA TAA GGG T ILR3B: CAA GCA GAA GAC GGC ATA CGA GAT TTC TTC GCT GCT TCT TCC GCA. The ‘‘xxxxxx’’ corresponds to one of the 6-bp ‘‘barcode’’ sequences that was used to tag specific DNA samples so that they could be pooled together for NGS (we used a total of 48 barcode sequences; thus, ‘‘n’’ in the ILIn and ILIR3n was a number between 1 and 48). The majority of the oligonucleotides used were obtained from IDT (Coralville, IA, USA), with the exception of oligonucleotides made using codon trimer mix (Glen Research, Sterling, VA, USA), which were obtained from Keck Oligonucleotide Facility at Yale University. Ribosome display selection of peptide ligands Our protocol for RD was based on Zahnd and coworkers [19]. The steps involved are described in detail below. Biotinylation of target antibodies EZ link sulfo-NHS-LC-LC-Biotin was added to 100 ll of 0.5– 5 mg/ml antibody solution in phosphate-buffered saline (PBS) to a final concentration equal to approximately 15 molar excess of EZ Link Sulfo-NHS-LC-LC-Biotin over the antibody. Reaction mixtures were incubated for 30 min at room temperature. The excess of biotinylation reagent was removed on a Zeba spin column (Pierce) equilibrated in PBS buffer. Successful antibody biotinylation was confirmed by comparing absorption at 280 nm of a small amount of biotinylated sample before and after incubation with streptavidin magnetic beads (Dynabeads, Invitrogen). Biotinylated target antibodies were stored at –20 °C in small aliquots. For the experiments with spiked human serum, the anti-troponin peptide 3 antibody was added to human serum sample at

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approximately 0.5% of total serum proteins. Melon spin columns (Pierce) were used to remove serum albumin from spiked and control unspiked serum samples. The remaining serum proteins were biotinylated as described for purified antibodies. Preparation of starting random sequence library A double-stranded DNA fragment (OVD2) containing sequence coding for a random sequence peptide library was prepared from synthetic single-stranded (ss) oligonucleotides coding for random sequence peptides of 10-, 15-, or 25-amino-acid length (RIB1, RIB3, or RIB4, respectively) by six cycles of PCR amplification using 20 nM ss oligonucleotide as a template with OV3 and OV4 primers (RIB1) or OV5 and OV6 primers (RIB3 or RIB4). A DNA fragment (OVD1) containing T7 promoter and ribosome binding sites was prepared by PCR with OV8 and OV2 primers (when RIB1-based library was used) or with OV8 and OV2M primers (when RIB3or RIB4-based library was used) using the pRDV plasmid [30] as a template. DNA fragment (OVD3) coding for a polypeptide linker (to ensure display of the peptide library on the surface of the ribosome) downstream of the randomized peptide sequence was prepared by PCR with OV1 and OV7 primers (when RIB1-based library was used) or with OV1M and OV7 primers (when RIB4based library was used) using pRDV plasmid as a template. The final DNA template (OVD4) for in vitro transcription and translation was assembled using overlap PCR with OV8 and OV7 primers (0.5 lM) and equimolar mixture of OVD1, OVD2, and OVD3 DNA fragments (1 ll each of 100-nM solutions in 20 ll of PCR). All PCR products were purified using Wizard SV DNA Gel and PCR Clean-Up System, and their concentrations were estimated by ultraviolet (UV) absorbance at 260 nm on a Nanodrop spectrophotometer. Selection of peptide ligands by ribosome display One cycle of RD involves in vitro transcription of DNA template, in vitro translation of resulting RNA, binding of peptide–ribosome– RNA particles to immobilized target, elution of RNA from specifically bound peptide–ribosome–RNA particles, reverse transcription, and PCR amplification of resulting DNA templates coding for the library enriched in target binding peptide ligands (Fig. 1). In vitro transcription of DNA template was performed at 37 °C for 3.5–4.0 h in 25 ll of reaction mix by mixing approximately 75 ng of DNA template with 5 ll of 5 transcription buffer, 7.5 ll of 25mM ribonucleoside triphosphates (rNTPs) mix, 0.5 ll of RNasin inhibitor, 2.5 ll of T7 enzyme mix, and the RNase-free water (to 25 ll). The reaction mixture was stopped with 75 ll of ice-cold RNase-free water, and the RNA product was precipitated by adding 100 ll of ice-cold 6 M LiCl. Following a 30-min incubation on ice, RNA was collected by centrifugation at 15,000g and 4 °C for 30 min. The pellet was washed with ice-cold 70% ethanol, briefly dried in a SpeedVac, and resuspended in 100 ll of RNase-free water. The mixture was centrifuged for 5 min at 4 °C, and 91 ll of the supernatant was transferred to 250 ll of ice-cold 100% ethanol, to which 9 ll of 3 M sodium acetate was added. After a minimum incubation time of 45 min at –20 °C, the RNA pellet was collected by centrifugation for 30 min at 4 °C and then washed with 70% ethanol and dried in a SpeedVac. The pellet was resuspended in 25 ll of RNase-free water, and the concentration of RNA was determined by UV absorbance at 260 nm. The RNA was stored in small aliquots at –80 °C. To immobilize the target antibody, the wells of the NeutrAvidin-covered strips were washed with 3  250 ll of TBS buffer (50 mM Tris–HCl [pH 7.4 at 4 °C] and 150 mM NaCl) containing 1 mg/ml bovine serum albumin (BSA), and 100 ll of 150–250 nM biotinylated target antibody in the above buffer was added to each well. Wells treated analogously but with the omission of biotinylated target were used for prepanning to remove peptide sequences with an affinity for NeutrAvidin

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or microplate plastic. After a 1-h incubation with target antibody at 4 °C, the wells were washed with 3  250 ll of ice-cold WBT buffer (50 mM Tris–acetate [pH 7.5 at 4 °C], 150 mM NaCl, 50 mM MgAc, and 0.05% Tween 20). For the in vitro translation reaction, 25 ll of reaction mix was prepared by mixing 2.5 ll of complete amino acids mix, 10 ll of S30 premix, 7.5 ll of S30 extract, and approximately 2.3 lg of messenger RNA (mRNA) in RNase-free water. The translation reaction was allowed to proceed for 10 min at 37 °C and was stopped by transferring the reaction to 100 ll of cold WBT buffer containing 5 mg/ml heparin and 4 mg/ml BSA. The mixture was incubated for 2 min on ice and was centrifuged for 5 min at 14,000g and 4 °C. First, 100 ll of supernatant was subjected to a prepanning step in a NeutrAvidin-coated well (without the target antibody) for approximately 15 min at 4 °C with gentle shaking. The reaction mixture was then transferred to the targetcovered wells and incubated for 1 h at 4 °C, followed by washing with cold WBT buffer (5  250 ll). The RNA from the target-bound peptide–ribosome–RNA particles was eluted with 2  100 ll of ice-cold EB buffer (50 mM Tris–acetate [pH 7.5 at 4 °C], 150 mM NaCl, and 50 mM ethylenediaminetetraacetic acid [EDTA]) containing 50 lg/ml tRNA. The eluted RNA was purified using High Pure RNA Isolation Kit according to the manufacturer’s protocol. The purified RNA (in 50 ll of RNase-free water) was denatured at 70 °C for 10 min, chilled on ice, and stored in aliquots at –80 °C until use. To reverse transcribe the RNA, an 18-ll reaction was set up on ice by gently mixing 2 ll of 10 AccuScript buffer, 0.5 ll of 50 lM OV4 (with RIB1-based library) or OV6 (with RIB3- or RIB4-based library), 0.8 ll of 25 mM dNTPs, and 14.7 ll of purified RNA. The mixture was incubated at 65 °C for 5 min and then cooled down for 5 min at room temperature to allow the primer to anneal to the RNA. Then 0.5 ll of 400 mM dithiothreitol (DTT), 0.5 ll of RNasin, and 1.0 ll of AccuScript high-fidelity reverse transcriptase were added, and the reaction mixture was incubated at 42 °C for 60 min. The enzyme was inactivated by incubating for 15 min at 65 °C. The reaction mixture was cooled on ice and either used immediately for PCR amplification or stored at –20 °C until use. Here 2 ll of reverse-transcribed RNA was used as a template in a 20-ll PCR with 0.5 lM OV3 and OV4 (when RIB1 library was used) or with 0.5 lM OV5 and OV6 (when RIB3 or RIB4 library was used) as the primers. The PCR product was purified using the Wizard SV DNA Purification System, and its concentration was determined by UV absorbance at 260 nm. This DNA could be used as the OVD2 DNA fragment to prepare the OVD4 transcription-ready DNA template by an overlap PCR with OVD1 and OVD3 (as described for the preparation of starting random sequence library) to start the next RD selection round, or its composition could be analyzed by DNA sequencing. Binding assay to examine target binding of library Target-covered NeutrAvidin strips and in vitro translation reactions using RNA from RD selection rounds of interest were prepared as described above except for the translation reactions, which were scaled down to 10 ll. The translation reactions were stopped with 40 ll of WBT buffer containing 5 mg/ml heparin and 4 mg/ml BSA. After a 2-min incubation on ice and a 5-min spin at 14,000g, 47.5 ll from each sample was transferred to the tubes containing 52.5 ll of cold WBT. The entire contents of the tubes (100 ll) were then transferred to the target antibody-covered wells. The strips were incubated for 1 h at 4 °C and were washed at room temperature three times with 250 ll of TBST buffer (TBS with Tween) to remove unbound complexes. Then 100 ll of 1:5000 dilution of anti-FLAG antibodies in TBST buffer was added to each well and incubated for 1 h at room temperature, followed by washing three times with 250 ll of TBST buffer. A 100-ll mixture of Luminol/Enhancer and Peroxide solution (SuperSignal ELISA

Femto Maximum Sensitivity substrate) was added to each well, and luminescence was read on a plate reader (SpectrofluorPlus, Tecan, Morrisville, NC, USA). DNA sequencing of libraries DNA for the RD peptide libraries was analyzed either by standard DNA sequencing approaches or by Illumina NGS. For standard DNA sequencing, the DNA (OVD2) was cloned into a pSC-A vector using the StrataClone PCR Cloning Kit (Agilent Technologies) according to the manufacturer’s instructions. Plasmid minipreps for individual clones were prepared using the Wizard SV Miniprep DNA Purification System and were sequenced by AGCT (Wheeling, IL, USA). Custom Illumina sequencing compatible DNA samples were prepared from the DNA corresponding to RD peptide libraries by a two-step low-cycle PCR procedure essentially as described previously [31]. The first step involved amplification of the RD DNA library with IL1 and ILIn primers (when RIB1 library was used) or with ILIR3n and ILR3B (when RIB3 or RIB4 library was used), which added a 6-bp barcode sequence (to allow multiplexing many DNA sequences in a single NGS experiment) and a downstream Illumina sequencing compatible end. The DNA product of this PCR was used as a second template in the second PCR with IL1 and IL2 primers (when RIB1 library was used) or with IL2 and ILR3B (when RIB1 library was used) that added an upstream Illumina sequencing compatible end. Single end 100-bp Illumina sequencing analysis was performed at the DNA Core Facility at the University of Missouri at Columbia. Sequencing data were preprocessed using Galaxy tools (http://galaxyproject.org). Read count and read count ratios were calculated using custom scripts written in R. Sequence logos were generated using WebLogo [32]. Results and discussion The adaptation of the RD approach [18,19] for identifying antibody-specific peptide ligands is illustrated in Fig. 1. We prepared the DNA template coding for random sequence peptide library by oligonucleotide synthesis using a mixture of trimer codons at randomized positions (Fig. 2A). Templates encoding randomized peptide sequences can be alternatively obtained by randomizing individual bases, but the use of trimer codon mix avoids bias toward any individual amino acid and prevents incorporation of unwanted stop codons. DNA sequences coding either for T7 promoter or for hairpins at the ends of RNA (to increase stability of the RNA made by transcription from T7 promoter) were added by overlap PCR. The DNA template was then transcribed by T7 polymerase, and the RNA product obtained was translated in vitro using bacterial S-30 extract under conditions where peptide products and their corresponding mRNA remain associated with the ribosome [18]. Peptide–ribosome–mRNA complexes were incubated with immobilized target antibodies, unbound complexes were washed out, and specifically bound complexes were eluted. RNA from the elution step was reverse transcribed and PCR amplified. Selection rounds were repeated to enrich libraries for target binding sequences. To assess possible benefits of employing NGS analysis of RD libraries for selecting short peptide ligands for the antibodies, we first compared the outcomes of the RD selection process performed without and with NGS analysis using an antibody (anti-troponin peptide 3) recognizing a simple continuous epitope as an example. The peptide library used in this initial experiment had a 10-aminoacid long random sequence flanked by a His tag and a FLAG tag at the N and C termini, respectively (Fig. 2A). We used the FLAG tag to follow the binding of the peptide library to the target antibody using an ELISA-like assay depicted in Fig. 2B. The typical progress

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Fig. 2. RD experiment with target antibody (anti-troponin peptide 3) recognizing a linear epitope. (A) Design of DNA encoding random sequence peptide library. (B) Progress of the RD selection as measured by the target binding activity of the library after the indicated round of RD. The relative binding of the libraries was measured using an ELISAlike assay depicted in the left panel. (C) Comparison between the sequences of the known epitope of anti-troponin peptide 3 antibody and the sequence of five clones of DNA after five rounds of RD selection.

of the selection process is illustrated in Fig. 2B. The starting library had no measurable binding activity toward the target antibody. However, the binding activity of the library became observable after two rounds of selection and reached a maximum after five rounds of selection. In a standard RD protocol, the DNA library from the final selection round is cloned and a sequence of several clones is determined. Cloning and standard DNA sequencing of DNA from the fifth selection round revealed obvious sequence similarity (with the exception of one sequence) of the selected sequences with the known peptide epitope of the target antibody (Fig. 2C). We observed very similar outcomes when a different antibody recognizing simple continuous epitope (anti-troponin peptide 1) was used as the target for RD selection (data not shown). The data in Fig. 2 validated the experimental protocol that we employed for RD selection of peptide ligands for the antibodies. They also confirmed that selecting peptide ligands from a random sequence peptide library using RD is a relatively straightforward process. To test the possible advantages of NGS analysis for the RD selection process, we used DNA encoding the starting random library and DNA encoding the library from first, second, and third selection rounds from the RD experiments with anti-troponin peptide 3 antibody (Fig. 2) to prepare DNA compatible with Illumina NGS. Our design for adapting the RD libraries to Illumina NGS involves adding 6-bp barcode sequences to allow us to combine many (typically 30) samples in a single sequencing run. Fig. 3A shows a simple graphical representation of the NGS view of the progress of RD selection depicted in Fig. 2B. Normalized read counts (read counts divided by the total numbers of reads) for the 50,000 sequences that showed the highest read counts in a sample from the first selection round are plotted. Progressive enrichment of specific sequences (spikes appearing over the baseline) is well illustrated by these plots. In the starting library, 7.77 million reads out of the total 7.79 million reads (>99.9%) corresponded to unique sequences, which was consistent with the high diversity of the starting library. The first round of selection

produced a dramatic narrowing of the sequence space covered by the RD library. There were 0.99 million unique sequences in the total 8.09 million reads (i.e., the great majority of sequences were read more than once). The enrichment of selected sequences and further narrowing of sequence space continued in the second and third selection rounds, where there were 0.58 million and 0.14 million unique sequences in 7.6 million and 7.8 million total reads, respectively. A clear sequence similarity among the peptides with the highest read count after three rounds of RD selection was observed (Fig. 3B). All of these peptides also exhibited an obvious sequence homology to the known epitope sequence for the target antibody (Fig. 3B). Almost identical consensus sequence was obtained when peptides with the highest read counts after the second round were analyzed (Fig. 3C). These observations point to one important enhancement of the RD process resulting from the application of NGS analysis. NGS analysis of RD libraries reduces the number of RD selection rounds that will be required to identify antibody peptide ligands. To compare NGS analysis with standard cloning and sequencing analysis, we used NGS data to perform a virtual cloning experiment where 100 sequences were selected at random from the data at each selection round (to simulate the outcome of standard cloning and sequencing of 100 clones). We then calculated what fraction of these randomly selected sequences exhibited sequence homology to the known epitope of anti-troponin peptide 3 antibody. These fractions were 0, 0, and 57% for the first, second, and third selection rounds, respectively. Therefore, standard cloning and sequencing analysis would fail to identify antibody-specific ligands after the second selection round, whereas NGS analysis was able to readily do so (Fig. 3C), indicating that at least one less selection round would be required if NGS analysis were employed. We expect that this advantage of NGS analysis will be even more pronounced in more complicated RD selection experiments. Reducing the number of RD rounds will reduce the time, effort, and cost of antibody peptide ligand selection. NGS analysis produces exhaustive data regarding relative abundance of thousands of sequence variants of selected sequences.

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Fig. 3. NGS analysis of the RD experiment depicted in Fig. 2. (A) Relative read counts for each sequence are plotted for the RD selection round indicated by the number in the top right corner. Number 0 corresponds to the starting random sequence library. A total of 50,000 sequences with the highest read counts after the first selection round were chosen for plotting. (B) Sequences of 5 peptides with the highest read counts (the number next to the sequence) after the third selection round. Green font depicts the sequence of the epitope recognized by anti-troponin peptide 3 antibody. Sequences of 25 peptides with the highest read counts after the third selection round are listed in the Supplementary material. (C) Sequence logos derived from 100 peptides with the highest NGS read counts after the second and third selection rounds. (For interpretation of the reference to color in this figure legend, the reader is referred to the Web version of this article.)

This information could be used to assess relative importance of various residues of identified peptide ligands for target antibody binding. For example, sequence alignment of the peptides with the highest read counts could be used to identify the critical residues that define the epitope recognized by the antibody (Fig. 3C). We are currently working on developing tools to allow the use of NGS data for quantitative assessment of relative binding affinity of large numbers of peptide ligands. To confirm that the NGSERD approach could be used for rapid selection of peptide mimotopes for antibodies with complex or discontinuous epitopes, we used purified antibodies that recognized complex epitopes as the targets in NGSERD experiments. Disease-related antibodies in human serum will often recognize complex epitopes such as epitopes containing posttranslational modifications or conformational epitopes where residues distant in the primary sequence of the antigen are brought together by folding of the antigen to its native conformation. We performed NGSERD selection experiments using as the targets two antibodies with complex epitopes: an antibody specific to a phosphorylated protein (human troponin phosphorylated at Ser22/23) and an

antibody with a well-described conformational epitope (anti-p53 pAB1620 [33]). Fig. 4 illustrates the NGS analysis of the RD experiment using anti-phosphotroponin antibody as a target. NGS analysis (Fig. 4A) revealed enrichment of specific peptide sequences concomitant with the progress of the RD selection process. Compared with an antibody with a simple continuous epitope (Fig. 3A), the population of enriched peptide sequences selected was more heterogeneous. For example, after the third round of selection, there were 0.29 million unique sequences in the total 7.75 million reads, which was approximately 2 times greater than the amount seen for the third RD selection round for the anti-troponin peptide 3 antibody. Peptide libraries obtained in RD experiments on anti-troponin and anti-phosphotroponin antibodies were highly specific for their respective antibody targets (Fig. 5). Sequences with the highest read counts (Fig. 4B) exhibited no similarity to the human troponin sequence, but they bound antiphosphotroponin antibody with high specificity (Fig. 5). Analysis of target antibody binding of selected sequences identified by NGS analysis demonstrated that they indeed were responsible for target antibody binding activity of the entire library (see

Fig. 4. NGS analysis of the RD experiment with the target antibody, anti-phosphotroponin, recognizing an epitope containing a posttranslational modification. (A) Relative read counts for each sequence are plotted for the RD selection round indicated by the number in the top right corner. Number 0 corresponds to the starting random sequence library. A total of 50,000 sequences with the highest read counts after the first selection round were chosen for plotting. (B) Sequences of 5 peptides with the highest read counts (the number next to the sequence) after the third selection round. Central E residue is highlighted in red. Sequences of 25 peptides with the highest read counts after the third selection round are listed in the Supplementary material. (C) Sequence logos derived from 100 peptides with the highest NGS read counts after the second and third selection rounds. (For interpretation of the reference to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 5. Binding specificity of peptide libraries from RD experiments with antitroponin peptide 3 (top) and anti-phosphotroponin (bottom) as the targets. Binding to the indicated immobilized samples is expressed as percentage of the binding observed with specific target.

Figs. S1B and S1C in the online Supplementary material). Sequences identified by NGS analysis had in common a central glutamic acid residue flanked on the C-terminal side by a nonpolar residue (Y, L, or F) (Fig. 4C). The same amino acid preferences were observed when the data after the second and third selection rounds were examined (Fig. 4C). Glutamic acid is a known mimic of phosphoserine; thus, it was likely that it played an important role in the peptide ligand binding to the target antibody. Direct binding measurements demonstrated that mutating this Glu residue abolished binding of the peptide ligand (Fig. S1C), confirming the crucial role of the central Glu residues for binding to anti-phosphotroponin antibody. However, the remaining residues of peptide ligands provide specificity toward troponin given that no binding of peptide ligand to a broad specificity anti-phosphoserine antibody or unrelated anti-phosphoprotein antibody (anti-phosphop53) was observed (Fig. S1D). We concluded that RD-derived ligands for anti-phosphotroponin antibody recognized the target antibody by mimicking the structure of the original epitope. RD experiments with an anti-phospho antibody demonstrated another important benefit of applying NGS analysis to the RD process. We often observed in RD experiments that, in addition to target-specific peptide ligands, peptide ligands that bound to microplates (or NeutrAvidin that was used to cover microplate wells for immobilizing biotinylated targets in most of our experiments) were also significantly enriched and on occasions could be a dominating fraction of sequences in the RD library. These spurious sequences can be carried over from one RD experiment to another even if good practices to prevent cross-contamination between PCR amplification reactions are observed. In such cases, performing RD selection experiments without the benefit of the insights provided by NGS analysis will likely fail to identify

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target-specific ligands. This is because when RD libraries are cloned and sequenced using standard DNA sequencing, in which case only a limited number of members of RD library are sequenced, these spurious sequences could constitute the majority (or in some cases all) of the cloned and sequenced members of the RD library, making identification of authentic peptide ligands difficult or impossible. For example, the sequence LMYATFNSTL, corresponding to the highest peak after the second selection round (Fig. 4A), was such a spurious sequence that it was a dominating component of the RD library at the second round of this RD experiment and remained a significant component of the library at the third round. NGS analysis allows detailed and quantitative insight into the composition of RD libraries; thus, it greatly facilitates discrimination between spurious sequences and authentic target-specific peptide ligands. In the case of spurious sequence in Fig. 4A, its identification as spurious was straightforward given that we observed the same sequence in another RD experiment with a different target (clearly identifying it as a nonspecific library component). In less obvious cases, identification of spurious sequences could be facilitated by adding the RD selection round performed with the target immobilized on a different solid support (e.g., streptavidin-covered plates in place of NeutrAvidin-covered plates). Relative read counts for spurious sequences with affinity to NeutrAvidin should be greatly decreased in the experiment where the target was immobilized on streptavidin (as we observed experimentally; data not shown). In the next experiment, we performed NGSERD selection on a target antibody that recognizes a discontinuous conformational epitope (anti-p53 pAB1620 antibody that recognizes a welldescribed conformational epitope of p53 [34]). The residues of p53 that are recognized by this antibody are distant in the primary structure of the protein but are brought together when the protein folds into its native conformation. Thus, this antibody binds to native p53 but is unable to recognize the denatured protein [34]. For the RD experiment with this antibody, we used a longer (25amino-acid) random sequence peptide library, reasoning that longer peptide ligands could better mimic the conformational epitope. As in the previous RD experiments, NGS analysis revealed progressive enrichment of the library with specific peptide sequences (Fig. 6). The peptide library after the final selection round bound target antibody with high specificity (see Fig. S3 in Supplementary material). Sequences exhibiting the highest reads in NGS analysis were quite diverse, and no obvious sequence homology with the sequence of the p53 protein could be detected. Analysis of target antibody binding by selected sequences identified by NGS analysis and their scrambled variants demonstrated that these sequences were indeed responsible for target antibody binding activity of the entire library (Fig. S2). We concluded that peptide ligands identified by NGSERD analysis specifically recognized anti-p53 pAB1620 antibody by mimicking the structure of the original epitope. Interestingly, it appears that the structure of the original epitope could be mimicked by more than one peptide ligand sequence given that ligands with quite different sequences could recognize the target antibody. Systematic exhaustive analysis of sequence and structural determinants of recognition of target antibodies by identified peptide mimetope sequences will be required to obtain full molecular understanding of the molecular mimicry employed by these ligands. Such analysis is beyond the scope of this article, but we are actively pursuing these questions and will describe the outcomes of this analysis elsewhere. The data in Figs. 1–6 validated the applicability of NGSERD for facile identification of peptide ligands using purified antibodies as the targets. However, we believe that the unique utility of NGSERD approach will be for identifying peptide ligands for specific antibodies in serum samples when it would be impossible to purify the target antibodies due to lack of suitable antigen for antibody purification or because the exact identity of the

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Fig. 6. NGS analysis of the RD experiment with the target antibody, anti-p53 pAB1620, recognizing a conformational epitope. (A) Progress of the RD selection as measured by the target binding activity of the library after the indicated round of RD. (B) Normalized read counts for 50,000 sequences are plotted at the indicated RD selection round. A total of 50,000 sequences with the highest read counts after the second selection round were chosen for plotting. (C) Sequences of 5 peptides with the highest read counts (the number next to the sequence) after the third selection round. Sequences of 25 peptides with the highest read counts after the third selection round are listed in the Supplementary material.

antigen/epitope would not be known. We propose that in such cases computational analysis of NGS data for RD selection experiments using the target serum and the appropriate control serum could enable identification of the correct target antibody-specific ligands in the presence of an overwhelming amount of spurious nonspecific sequences. By analyzing the binding properties of a large number (thousands) of peptides, target-specific ligands would not be missed even if their relative amounts in the library were very small. To test and validate the applicability of NGSERD for analysis of complex serum samples, we performed RD experiments with human serum samples spiked with a single antibody. To be able to compare the data obtained with serum with the data obtained with purified antibodies, we used anti-troponin peptide 3 antibody (which was used as the target in the experiments in Fig. 3) to spike the serum. At each of the RD selection rounds with spiked serum as the target, we performed prepanning on the unspiked serum to enhance the probability of selecting peptide ligands specific for the spiked samples. Because experiments with purified antibodies as the targets demonstrated that target-specific ligands could be detected by NGS analysis already after very few rounds of selection, we took the library from the fourth selection and performed in parallel another RD selection round using the spiked or unspiked control serum as the target. We then examined how the read count for sequences present in the RD library from the fourth selection changed after binding to spiked and control unspiked serum samples by calculating for each sequence the ratio of read count in a sample that bound to spiked serum to the corresponding read count in a sample that bound to the control serum. We expected that this ratio for peptide ligands that bound targets common to spiked and unspiked serum (i.e., the great majority of the ligands) would be approximately 1, whereas for the ligands specific to the target antibody the ratio would be 1 (because they

should be enriched on binding to the spiked serum and depleted on binding to the control unspiked serum). Fig. 7A shows that within the first 1500 most abundant peptides in the library, 2 peptides had read count ratios 1. We plotted in Fig. 7A the 1500 peptides with the highest NGS read counts because the discrimination between specific and unspecific peptides is most obvious for peptides where the read counts are sufficiently high. When the read count is low, the noise of the data becomes too high for a reliable calculation of the read count ratio. Sequences of these two peptides (peptides 519 and 1312; Fig. 7A) exhibited a clear similarity to the known epitope of the target antibody and were also very similar to sequences of peptide ligands identified by RD using purified target antibody as a target (Fig. 3). There were no sequences with read ratios 1 when the two repeats of an RD experiment using control unspiked serum as targets were compared (Fig. 7B). Both peptides 519 and 1312 were enriched up to the fourth selection round, after which their abundance leveled off or decreased (Fig. 8). These data demonstrate that it would be counterproductive to perform many rounds of RD selection with the serum sample as the target. Based on the data in Fig. 7, we concluded that NGS analysis of RD data on complex serum sample enables identification of correct specific peptide ligands for the target antibody in the serum in the context of an overwhelming excess of other immunoglobins. Our data demonstrate that RD enhanced by NGS is an effective tool for facile development of specific peptide ligands for the antibodies. RD-derived peptide ligands for the antibodies recognizing simple continuous epitopes were sequence homologues of the original epitopes, whereas ligands for the antibodies with complex epitopes were mimotopes that had no sequence homology to the original antigens but specifically bound target antibodies by mimicking the structure of the original epitope. Although the primary use for antibody-specific peptide ligands will be in assays for

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Fig. 7. (A) Read count ratios for approximately the top 1500 sequences from the RD library after the fourth round of selection on spiked serum sample after binding to spiked sample and unspiked control. (B) Read count ratios for approximately the top 1500 sequences from the RD library after the fourth round of selection on spiked serum sample after binding to two unspiked control samples.

Fig. 8. Read counts at indicated RD selection rounds for two sequences identified by the analysis illustrated in Fig. 7.

detecting the antibodies for diagnosis of infectious diseases, autoimmune diseases, or cancer, there is an abundance of additional high-impact applications of peptide ligands for antibodies. Peptide mimotopes for autoantibodies involved in the pathogenesis of autoimmune diseases could serve as prototypes of therapeutic agents that block the activity of these autoantibodies. Peptide mimotopes for antibodies produced in patients in response to infection or tumor development could be used in the development of vaccines, especially in cases where the use of the original antigens for vaccine development failed (e.g., the case of HIV vaccine [35,36]). NGSERD, by including NGS-based analysis of the composition of peptide libraries, improves the peptide ligand selection process by reducing the number of RD selection rounds that will be required to identify specific ligands and by facilitating discrimination between specific and spurious nonspecific sequences. NGS is typically perceived as expensive technology. However, the cost of the analysis is greatly reduced by barcoding the DNA samples, which allows combining DNA samples from many experiments in one NGS run. This way, NGS analysis can be done with effectively similar cost as the cloning and standard DNA sequencing, but with

greatly enhanced insight into the RD selection process. The most impactful advantage of the NGSERD approach, derived from the analytical power of NGS data and computational analysis of such data from target sera and appropriate control sera, is the possibility of fleshing out rare peptide ligands for the target antibodies in serum from patients. Based on the data on serum spiked with known antibody (Fig. 7), we propose that for the analysis of real serum samples, RD selections should be performed on patient sera samples and the last selection round should be performed in parallel on the target patient serum sample and in samples from healthy controls. This way, the analysis illustrated in Fig. 7 could be performed and the candidate patient serum-specific peptides could be identified. Performing this analysis on many pairs of patient–healthy control samples should allow identification of an optimal set of peptide ligands specific for patient sera. Although prepanning on healthy control samples could be included in this process, in our opinion this will not be necessary (or it could even be counterproductive) because the NGS-based analysis illustrated in Fig. 7 will be a much more effective way of identifying patient serum-specific ligands. NGSERD, by leveraging the remarkable analytical power of NGS technology (normally reserved for the analysis of nucleic acids) for the analysis of peptide ligands, may allow development of specific reagents for detecting diseaserelated antibodies in serum without the need to isolate, express, purify, or know the identity of the disease-specific antigens (or their corresponding antibodies). This could provide an experimental path to disease-specific detection reagents without the need for dissecting the molecular mechanisms of the disease. Such a capability of NGSERD is suggested by our data on a model of serum spiked with a known antibody. This potential needs to be verified by large-scale experiments with patient samples. We are currently undertaking such an experiment using lupus patient samples as the model. Acknowledgments We thank Andreas Pluckthun (University of Zurich) for the gift of pRDV plasmid and David Gohara (Saint Louis University) for the script to obtain read counts from NGS files for a large number of sequences of interest. This work was supported by the St. Louis University President’s Research Fund.

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