Analytical cascades of enzymes (ACE) for sensitive detection of

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Analytical cascades of enzymes (ACE) for sensitive detection of structural variations in protein samples Julia Carolin Hollerweger, Isabel Josephin Hoppe, Christof Regl, Lorenz Gottfried Stock, Christian G. Huber, Urs Lohrig, Hanno Stutz, and Hans Brandstetter Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04874 • Publication Date (Web): 27 Mar 2018 Downloaded from http://pubs.acs.org on March 28, 2018

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Analytical Chemistry

Analytical cascades of enzymes (ACE) for sensitive detection of structural variations in protein samples Julia C Hollerweger†,‡, Isabel J Hoppe†,‡, Christof Regl†,‡, Lorenz G Stock†,‡, Christian G Huber†,‡, Urs Lohrig‡,§, Hanno Stutz†,‡ & Hans Brandstetter†,‡* †

Department of Molecular Biology, University of Salzburg, Salzburg, Austria

‡

Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, University of Salzburg, Salzburg, Austria

§

Phys. Chem. Characterization Biosimilars, Sandoz GmbH, Kundl, Austria

ABSTRACT. Protein function critically depends on structure. However, current analytical tools to monitor consistent higher-order structure with high sensitivity, as for instance required in the development of biopharmaceuticals, are limited. To complement existing assays, we present the analytical cascade of enzymes (ACE), a method based on enzymatic modifications of target proteins, which serve to exponentially amplify structural differences between them. The method enables conformational and chemical fingerprinting of closely related proteins, allowing for the sensitive detection of heterogeneities in protein preparations with high precision. Using this method, we detect protein variants differing in conformation only, as well as structural changes induced by diverse covalent modifications. Additionally, we employ this method to identify the nature of structural variants. Moreover, the ACE method should help to address the limited reproducibility in fundamental research, which partly relates to sample heterogeneities.

INTRODUCTION Contrasting small molecules, the function of proteins cannot be deduced from their (amino acid) composition exclusively. Specifically, while the composition of a small molecule, as determined by e.g. mass spectrometry, determines its structure, the complexity of biopharmaceuticals and the inherent conformational richness in proteins is critical for their function. Even small conformational changes that can be triggered for example by differences in pH or ligands can completely revert the functional properties of a protein, as exemplified by the balance of proteolysis and ligation1 or ion channel activity2, respectively. Given this intimate relation of protein structure and function, the exact knowledge of variations in protein structure is of utmost importance in particular for proteins that are therapeutically or diagnostically used. Lack-of-function changes render a given therapeutic protein less active, whereas gain-of-function changes can potentially elicit adverse effects. Most importantly, severe immune responses may be the result of structural changes. In such cases, patients may develop anti-drugantibodies (ADA), which neutralize the therapeutic agent, acting as inhibitors3,4, and related protein therapies can become inapplicable due to cross-reactivity5. In the worst case, also the endogenous protein homologue to the biotherapeutic is depleted6–8, with possibly serious consequences. Therefore, besides many other factors impacting the immunogenic potential of biopharmaceuticals9,10, structural heterogeneities or changes in the protein structure play a major role. The

importance of this can be illustrated by cases for which an apparent minor change in the manufacturing process changed the structural properties of the drug, which increased its immunogenicity, as exemplified by the antihemophilic factor VIII and erythropoietin11–13. Sample homogeneity is of major concern not only in the development phase of biopharmaceuticals, but also in fundamental research in academia and industry, with its low reproducibility14,15. The current attempts to monitor sample heterogeneity rely heavily on mass spectrometry. By the nature of this approach, conformational information on the target protein is hardly accessible, although indirect information can be obtained by hydrogen-deuterium exchange experiments16,17, or the identification of disulfide bonds18. Mass spectrometric analysis is complemented by an array of spectroscopic analytical techniques, including circular dichroism, Fourier transform infrared spectroscopy, or more specialized techniques like electron spin resonance19. Although these methods are able to provide information about protein structure, they do so at relatively low resolution. To obtain high resolution atomic information on protein structures, X-ray crystallography is the method of choice. However, crystallization is challenging and not always successful, in particular with heterogeneous protein samples. Most importantly, crystallization represents a selection process, which bears the intrinsic risk to exclude protein species or conformations which might be functionally important20. Similar sample selection mechanisms also apply to electron microscopy and NMR spectroscopy21,22.

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In view of the above, there is an increasing need in the art for new analytical methods, which enable the detection and identification of proteins differing from related species solely or mainly by their conformation, such as the three-dimensional (3D) structure. Several approaches have been reported that employ limited proteolysis (LiP) in combination with mass spectrometry as a tool to sample the conformational space of target proteins in solution23–25, however, they lack sufficient sensitivity for low abundant protein species. Here we present the analytical cascade of enzyme (ACE), an analytical method which combines simplicity of use and sensitivity to detect structural heterogeneities in protein samples in an unmatched manner. Samples to be investigated are subjected to up to three, preferentially sequential enzymatic modifications, using different enzymes with characteristic substrate specificities, followed by an immunoassay as readout. The ACE exponentially amplifies even small differences between protein variants, and thereby enables to both detect and identify subtle variations in protein samples, which should enable a step change in controlling sample heterogeneity and experimental reproducibility. EXPERIMENTAL SECTION Enzymes, proteins, synthetic substrates, and inhibitors. Rituximab (MabThera®) was purchased from Hoffmann-La Roche. Biosimilar recombinant human EPO (Binocrit®) and Etanercept (Erelzi®) were provided by Sandoz. Activated human wild type legumain was produced as described previously26, whereas PNGase F from Flavobacterium meningosepticum (NEB), human recombinant PADI1 (Biomol), Papain from Carica papaya (Merck), Pepsin from porcine gastric mucosa (SigmaAldrich), and recombinant microbial transglutaminase from Streptomyces mobaraensis (Zedira) were purchased. For transglutaminase-mediated labeling, Z-Gln-Gly-CADBiotin (“Gln-B”, Zedira) and Z-Gln-Gly-CAD-DNS (“GlnDNS”, Zedira) were obtained. The collagenase unit of collagenase Q (for simplicity referred to as “ColQ” in the following, UniProt B9J3S4, derived from Bacillus cereus) and the peptidase unit of collagenase T27 (“ColT” from Clostridium histolyticum) were recombinantly produced in E. coli as described elsewhere (Hoppe et al. 2017, in preparation). For protease inhibition, Ac-YVAD-cmk was purchased at Bachem, E-64 and Pepstatin A at SigmaAldrich. All other chemicals were purchased from Merck (Darmstadt). Generation of Rituximab variants and sample preparation. All buffer exchange-steps were performed using Illustra NAP-5 columns (GE Healthcare) according to the manufacturer’s instructions, resulting in a final protein concentration of 0.5 mg/ml. Depending on the enzyme cascade in use (+ / - PADI1), reaction buffers were supplied with + / - 5 mM CaCl2. Native Rituximab. Rituximab was buffer exchanged to 20 mM citric acid, 20 mM NaCl, pH 4.0. (for experiment in Fig. 3), in all other cases to 100 mM sodium acetate, 20 mM NaCl, + / - 5 mM CaCl2, pH 4.5. A-state Rituximab. Rituximab was adjusted to pH 2 by addition of 2 M malonic acid pH 0.97, followed by 20 min

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incubation at room temperature and a subsequent buffer exchange into 20 mM citric acid, 20 mM NaCl, pH 4.0. Oxidized Rituximab. 30 % H2O2 were added to Rituximab to a final concentration of 0.3 %, followed by 1 h incubation at room temperature. The sample was subsequently buffer exchanged to 100 mM sodium acetate, 20 mM NaCl, +/- CaCl2, pH 4.5. Deglycosylated Rituximab. Rituximab was buffer exchanged to 50 mM sodium phosphate, pH 7.5. Per 100 µg of antibody, 150 units of PNGase F were added, followed by incubation for 20 h at 37 °C. The sample was buffer exchanged to 100 mM sodium acetate, 20 mM NaCl, + / - CaCl2, pH 4.5. For Fig.6, sample C, 60 % of native Rituximab were mixed with 40 % of deglycosylated Rituximab. Acidic pH-stressed Rituximab. Rituximab was buffer exchanged to 25 mM sodium acetate, pH 4.0. The sample was incubated for 7 days at 45 °C (Fig. 6, sample B), in all other cases for 7 days at 40 °C, before being buffer exchanged into 100 mM sodium acetate, 20 mM NaCl, pH 4.5. Basic pH-stressed Rituximab. Rituximab was buffer exchanged to 25 mM Tris, pH 9.0. The sample was incubated for 7 days at 45 °C (Fig. 6, sample A), in all other cases for 7 days at 40 °C, before being buffer exchanged into 100 mM sodium acetate, 20 mM NaCl, pH 4.5. UV-stressed Rituximab. Rituximab was buffer exchanged to 100 mM sodium acetate, 20 mM NaCl, 5 mM CaCl2, pH 4.5 and the sample tube was subsequently exposed to UV radiation (at 302 nm, on top of a 2UV Transilluminator (UVP)) for 2 min for experiment in Fig. 4, and for 1 min, 5 min, and 10 min for MS analysis. Heat-stressed Rituximab. Rituximab was buffer exchanged to 100 mM sodium acetate, 20 mM NaCl, + / 5 mM CaCl2, pH 4.5. The sample was subsequently incubated for 5 min at 80 °C, followed by immediate cooling on ice. General enzyme cascade protocol. Whenever threestep ACEs were applied, samples were first subjected to i) citrullination by PADI1, which was skipped in two-step ACEs. PADI1 activity was hereafter inhibited by addition EDTA to a final concentration of 10 mM. In any case, samples were (subsequently for three step ACE) submitted to i) / ii) proteolytic cleavage by legumain, papain, or pepsin. Proteolytic activity was afterwards inhibited by addition of a molar excess of Ac-YVAD-cmk, E-64, or Pepstatin A, respectively. Lastly, samples were subjected to ii) / iii) transglutamination by mTG. All incubation steps were carried out at 37 °C. The exact enzyme (PADI1, protease, mTG / GlnB) to substrate (Rituximab) ratios and incubation periods of samples varied and are therefore specified for each case in the following. For enzyme cascade readout, samples were resolved on 14 % reducing SDS-PAGE (loading 5 µg of target protein, unless indicated otherwise). Gels were subsequently immunoblotted to an Amersham Protran 0.45 NC membrane (GE Healthcare) using a Trans-Blot® SD SemiDry Transfer Cell (Bio-Rad). Membranes were blocked with 1 x TBST, 5 % (w/v) nonfat dry milk. Next, membranes were incubated in 5 % milk-TBST supplied


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with 1 : 75,000 (v/v) Pierce™ Streptavidin Poly-HRP (Thermo Fisher Scientific). Chemiluminescent detection of biotinylated proteins was subsequently carried out by using Amersham ECL Prime Western Blotting Detection Reagent (GE Healthcare) in combination with an Odyssey® Fc Imaging System (Li-Cor). Native Rituximab vs. A-state Rituximab and determination of method sensitivity. i) Legumain : Rituximab, 1 : 100 (molar ratio) for 2.5 h. ii) mTG : Rituximab, 1 : 3 (molar ratio) and Gln-B : Rituximab, 200 : 1 (molar ratio) for 4h. 20 µg of each sample were subsequently analyzed. To determine method sensitivity, samples of native Rituximab were spiked with different amounts of A-state Rituximab (50 % : 50 %, 66.6 % : 33.3 %, 90 % : 10 %, and 99 % : 1 % (v/v), respectively) and were also subjected to the aforementioned mentioned protocol (Fig. 3). Detection of covalently modified Rituximab variants. Oxidized vs. native Rituximab (Fig. 4a). i) PADI1 : Rituximab, 1 : 5 (molar ratio), for 60 h. ii) Legumain : Rituximab, 1 : 50 (molar ratio), for 2.5 h. iii) mTG : Rituximab, 1 : 3 (molar ratio) and Gln-B : Rituximab, 400 : 1 (molar ratio), for 5 h. Basic pH-stressed vs. native Rituximab (Fig. 4b). i) Papain : Rituximab, 1 : 50 (molar ratio), for 2.5 h. ii) mTG : Rituximab, 1 : 3 (molar ratio) and Gln-B : Rituximab, 400 : 1 (molar ratio), for 5h. Acidic pH-stressed vs. native Rituximab (Fig. 4c). i) Legumain : Rituximab, 1 : 50 (molar ratio), for 2 h. ii) mTG : Rituximab, 1 : 5 (molar ratio) and Gln-B : Rituximab, 350 : 1 (molar ratio), for 3 h. Deglycosylated vs. native Rituximab. For experiment in Fig. 4d: i) PADI1 : Rituximab, 1 : 7 (molar ratio), for 20 h. ii) Legumain : Rituximab, 1 : 50 (molar ratio), for 2 h. iii) mTG : Rituximab, 1 : 5 (molar ratio) and Gln-B : Rituximab, 350 : 1 (molar ratio), for 3h. For Fig. 5: i) Legumain/papain/pepsin : Rituximab, 1 : 50 (molar ratio), for 2 h. ii) mTG : Rituximab, 1 : 10 (molar ratio) and Gln-B : Rituximab, 400 : 1 (molar ratio), for 12 h. UV-stressed vs. native Rituximab (Fig. 4e). i) PADI1 : Rituximab, 1 : 7 (molar ratio), for 20 h. ii) Legumain : Rituximab, 1 : 50 (molar ratio), for 2 h. iii) mTG : Rituximab, 1 : 5 (molar ratio) and Gln-B : Rituximab, 350 : 1 (molar ratio), for 3 h. Heat-stressed vs. native Rituximab (Fig. 4f). i) PADI1 : Rituximab, 1 : 7 (molar ratio), for 20 h. ii) Legumain : Rituximab, 1 : 50 (molar ratio), for 2 h. iii) mTG : Rituximab, 1 : 5 (molar ratio) and Gln-B : Rituximab, 350 : 1 (molar ratio), for 3 h. Classification of structural impurities. Samples of native, deglycosylated, oxidized, acidic pH-stressed, basic pH-stressed, and heat-stressed Rituximab were subjected to the following enzyme cascade: i) Legumain : Rituximab, 1 : 50 (molar ratio), for 2 h. ii) mTG : Rituximab, 1 : 5 (molar ratio) and Gln-B : Rituximab, 350 : 1 (molar ratio), for 3 h. Samples 2 to 7 (references) were known in identity, whereas samples A – C were shuffled and blinded. To identify the blinded samples, the respective digital western blot image was imported into the image processing software ImageJ 1.51h. The profile of each lane (with each lane corresponding to one sample after enzyme cascade treatment) was subsequently

plotted vertically (x-axis: molecular weight; y-axis: obtained signal intensities) and plot values were exported to an excel file. Data was plotted and evaluated using the Python libraries NumPy and MatPlotLib. Pearson product-moment correlation coefficients (r²) between functions obtained for reference samples 2 to 7 and A to C were calculated, highest correlation implying highest similarity between samples (Fig. 6 and Fig. S5). Evaluation of ACE reproducibility. Triplicate samples of native and A-state Rituximab were subjected to enzyme cascade treatment by i) AEP : Rituximab, 1 : 50 (molar ratio), for 2.5 h. ii) mTG : Rituximab, 1 : 5 (molar ratio) and Gln-B : Rituximab, 350 : 1 (molar ratio), for 3 h. Samples were resolved by 14 % reducing SDS-PAGE prior to immunoblotting. The digital western blot image was imported into the image processing software ImageJ 1.51h28. The profile of each lane was subsequently plotted vertically (x-axis: molecular weight; y-axis: obtained signal intensities) and plot values were exported to an excel file. Data was plotted and evaluated using the Python libraries NumPy and MatPlotLib. Pearson product-moment correlation coefficients (r²) between functions obtained for Native Rituximab and A-state Rituximab were calculated. (Figure S3). Application of the enzyme cascade to detect changes in non-Rituximab protein. Three different samples of Etanercept (stored at room temperature for an undefined amount of time, stored at 25 °C for 3 months, and stored at 40 °C for 1 month), as well as three different samples of EPO (stored at 5 °C for an undefined amount of time, stored at 25 °C for 6 months, and stored at 40 °C for 3 months) were buffer exchanged into 0.1 M sodium acetate, 20 mM NaCl, pH 4.5 + / − 5 mM CaCl2, respectively. Etanercept samples were subjected to the following enzyme cascade: i) PADI1 : Rituximab, 1 : 7 (molar ratio), for 20 h. ii) AEP : Rituximab, 1 : 50 (molar ratio), for 2 h. iii) mTG : Rituximab, 1 : 5 (molar ratio) and Gln-B : Rituximab, 300 : 1 (molar ratio), for 3 h. Samples were resolved by 14 % non-reducing SDS-PAGE prior to immunoblotting. EPO samples were subjected to the following enzyme cascade: i) papain : Rituximab, 1 : 100 (molar ratio), for 2 h. ii) mTG : Rituximab, 1 : 15 (molar ratio) and Gln-B : Rituximab, 100 : 1 (molar ratio), for 3 h. Samples were resolved by 16 % non-reducing SDS-PAGE prior to immunoblotting. Additionally, two batches of ColQ (produced in 04/2015 and 11/2016, stored at – 80 °C) and three samples of ColT (of the same batch, two samples however pretreated with either 10 mM EDTA or 1,10-phenanthroline (SigmaAldrich)) were buffer exchanged into 0.1 M MES, 20 mM NaCl, 1 mM CaCl2, 10 µM ZnCl2, 1 mM DTT, pH 6 / respectively 0.1 M MES, 20 mM NaCl, 1 mM DTT, pH 6. All samples were subjected to the following enzyme cascade: i) AEP : ColQ/T, 1 : 25 (molar ratio), for 2 h. ii) mTG : ColQ/T, 1 : 5 (molar ratio) and Gln-B : ColQ/T, 300 : 1 (molar ratio), for 3 h. Samples were resolved by 12 % reducing SDS-PAGE prior to immunoblotting. (Fig. S4). Comparison of labeling-efficiency of mTG to resincoupled mTG. 1 mg of Activa® TI Transglutaminase (Ajinomoto) was coupled to 100 µl Pierce® NHS-Activated


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Agarose Slurry (Thermo Fisher Scientific) according to the manufacturer’s instructions. To compare labelingefficiency of uncoupled mTG to resin-coupled mTG, samples of A-state Rituximab (25 µg) were incubated with a) uncoupled mTG (Zedira) : Rituximab, 1 : 3 (molar ratio) and Gln-B : Rituximab, 500 : 1, for 5 h; or b) 20 µl of resincoupled mTG and Gln-B : Rituximab, 500 : 1 (molar ratio), for 5h. Prior to sample preparation for subsequent SDSPAGE and western blotting (conducted equally to the protocol described in section “General enzyme cascade protocol”), resin-coupled mTG was removed from b) by aspirating the liquid portion with a standard micro syringe (VWR), while the resin was retained in the sample tube due to resin particle size. Sample preparation for a) was conducted as usual. (Fig. S6). Size exclusion chromatography analysis of mTGlabeled samples. Samples of native and oxidized Rituximab were subjected to transglutamination by mTG : Rituximab, 1 : 5 (molar ratio) and Gln-DNS : Rituximab, 200 : 1 (molar ratio), for 3h. mTG activity was inhibited by addition of a molar excess of MMTS (SigmaAldrich). Both samples were analyzed by size exclusion chromatography on a Superdex 200 10/300 GL column (GE Healthcare), with 100 mM sodium acetate, 20 mM NaCl, pH 4.5 as mobile phase. Peaks of both runs were fractionated and an equivalent fraction of the main peak was chosen for both runs to be analyzed for fluorescence. Triplicate measurements were performed for each sample (λEx = 335 nm, λEm = 550 nm) with an Infinite M200 Plate Reader (Tecan). Fluorescence was also measured for buffer only and served as blank. To normalize the obtained relative fluorescences to the same protein concentration, the integrals of UV absorbance (280 nm) of the respective fractions were considered. (Fig. S7). MS analysis. Rituximab variants were diluted in 175 mM ammonium acetate, 4 M GdnHCl to a final concentration of 0.1 mg / ml. Disulfide-bonds were reduced by addition of 5 mM TCEP (15 min incubation, 60 °C). For peptide mapping, free thiol-groups were subsequently alkylated by addition of 20 mM iodacetamide (for 30 min, 22 °C). Prior to digestion with Trypsin-Lys-C mixture (Promega) at a mAb to enzyme molar ratio of 1 : 10 (4h, 37 °C), all samples were buffer exchanged to 175 mM ammonium acetate with Amicon Ultra 0.5 mL Centrifugal Filters (3 kDa MWCO, Merck). Chromatographic separation of mAb digests was carried out on an UltiMate 3000 Rapid Separation system (U3000 RSLC, Thermo Fisher Scientific) at a flow rate of 60 μl / min using a Hypersil Gold aQ column (100 × 1.0 mm inner diameter, 1.9 μm particle size, 175 Å pore size, Thermo Fisher Scientific), operated at a temperature of 50 °C. Mobile phase A consisted of H2O + 0.05 % TFA, mobile phase B was composed of acetonitrile + 0.05 % TFA. The gradient applied was: 2 % B for 5 min, 5 − 10 % B in 5 min, 10 − 40 % B in 60 min. Injection was carried out in in-line split-loop mode, injection volume was 10 µl (1 µg) for each sample. UV-detection was carried out at 214 nm with a 2.5 µl flow cell. For middle-up analysis of light chain and heavy chain fragments of Rituximab variants undergone UV-irradiation for different time points, the system as well as the mobile phases were kept the same. Separation was carried out at a flow rate of 200 μl / min

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using a MAbPac RP column (150 × 2.1 mm i.d., 4 μm particle size, ~1500 Å pore size, Thermo Fisher Scientific), operated at 80 °C. The gradient applied was: 25 − 32 % B in 5 min, 32 − 40 % B in 10 min, 100 % B for 5 min, and 25 % B for 10 min. Mass spectrometry was performed on a benchtop quadrupole-Orbitrap instrument (Q ExactiveTM) equipped with an Ion MaxTM source with a heated electrospray ionization (HESI) probe (Thermo Fisher Scientific) and an MXT715-000 - MX Series II Switching Valve (IDEX Health & Science LLC). The instrument settings were as follows: source heater temperature of 250 °C, spray voltage of 3.5 kV, sheath gas flow of 30 arbitrary units, auxiliary gas flow of 10 arbitrary units, capillary temperature of 320 °C, S-lens RF level of 60.0, AGC target of 1e6 and a maximum injection time of 100 ms. Digests were analyzed with a full scan at a scan range of m/z 300 2,000 and a resolution of 70,000 was followed by ten datadependent scans (dd-MS²) upon higher energy collisioninduced dissociation at 29 % normalized collision energy at a resolution of 17,500. Precursor masses already selected for dd-MS² were excluded for ten seconds. Middle-up analysis of the light chain was carried out with a full scan at m/z 1,500 - 3,000 at a resolution of 140,000. The heavy chain was analyzed with a full scan at m/z 1,800 - 3,000 at a resolution of 17,500. Data analysis of the digests was conducted with BioPharma Finder 1.0 (Thermo Fisher Scientific) with following parameters: 5 ppm mass accuracy, minimum confidence of 0.8, specificity ‘high’ for trypsin protease, N-terminal pyroglutamate formation as fixed modification and the built-in N-glycan library for chinese hamster ovary cell lines, oxidation, deamidation, NH3loss, and H2O-loss as variable modifications. Middle-up analysis data of reduced mAb were also carried out with BioPharma Finder 1.0. The light chain was analyzed utilizing the sliding window function and the integrated Xtract algorithm for deconvolution. The heavy chain was analyzed using the sliding window function and the Respect algorithm (Tables S1 and S2 and Figure S1). CIEF and CZE. All chemicals used were of highest quality available. CIEF-UV and CZE-UV separations were performed on an Agilent CE 7100 Capillary Electrophoresis system equipped with a diode array detector, operating under 3D-CE ChemStation (Agilent Technologies, Waldbronn, Germany). CIEF. As spacer stock solutions served 500 mM L-Arg and 100 mM IDA. The anolyte consisted of 91.1 mM H3PO4 in cIEF-Gel, whereas 18.9 mM NaOH was used as catholyte. Samples for CIEF-UV analysis consisted of 0.84 % (w/v) PL 3-10 (Sigma-Aldrich), 1.8 mM iminodiacetic acid, 16.1 mM L-Arg, pI markers 7.00 and 10.0 (from pI marker kit, Beckman Coulter), 0.21 mg / ml Lectin (from Lens culinaris, Sigma-Aldrich), 0.018 mg / ml native Rituximab, and 0.018 mg / ml deglycosylated Rituximab in cIEF-gel (80.2% v/v). For CIEF-UV analyses, a 280 nm cut-off high pass filter (Agilent Technologies) that allowed for detection at 280 nm with 10 nm bandwidth was inserted in the optical path. In parallel, a reference wavelength 360 nm with 100 nm bandwidth was applied. An eCAPTM neutral capillary with 50 µm inner diameter, a total length (LT) of 32.2 cm and an effective length (LD)


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of 24.1 cm (Beckman Coulter) was used for CIEF separations. Prior to sample injection, the capillary was rinsed with anolyte solution (2 min) followed by ultrapure water(4 min). All rinsing steps were performed with 93 kPa at 20 °C. The sample was injected hydrodynamically (200 s, 93.0 kPa, 20 °C). For the subsequent two-step CIEF that included a focusing and a mobilization step, capillary ends were immersed in the anolyte (at the anode side) and catholyte (at the cathode side) solution. Focusing was done at 15.0 kV (normal polarity, 0.17 min ramp time) at 20 °C for 20 min. For subsequent pressure mobilization, 4.8 kPa were applied at the capillary inlet in combination with 21.0 kV (normal polarity, 0.17 min ramp time), all at 20 °C for 20 min. After the mobilization step the capillary was rinsed with ultrapure water (4 min). CZE. 100 mM NaH2PO4 pH 2.5 was used as background electrolyte. Both native and deglycosylated Rituximab were buffer exchanged to 30 mM sodium citrate, 20 mM NaCl, 1 mg / ml Polysorbate 80, pH 4.0. For CZE-UV separations a bare fused-silica capillary (Polymicro Technologies) with 50 µm inner diameter, a LT of 49.1 cm, and a LD of 41.0 cm was used. The capillary was rinsed with 1 M NaOH (10 min), Milli-Q (15 min), 0.10 M HCl (1 min) and background electrolyte (20 min) (all 93.0 kPa, 35 °C). 15.0 kV (with 0.17 min ramp time) were applied for 60 min with the capillary ends immersed in buffer (35 °C). Prior to each run, the capillary was treated with 0.10 M NaOH (3 min), Milli-Q (2 min), 0.10 M HCl (2 min), and BGE (3 min) (all 93.0 kPa, 15 °C). Sample injection was performed hydrodynamically at 3.5 kPa for 10 s. Separation was performed at 17.5 kV (normal polarity, 0.17 min ramp time) at 15 °C. For equilibration of the capillary surface, native Rituximab was injected 30 times prior to the performed CZE separation. Besides a change in separation temperature to 35 °C, all separation conditions were as described above (Fig S2). RESULTS Multiplicative detection of protein heterogeneities by the analytical cascade of enzyme (ACE). As implied by its name, the ACE is based on the use of enzymes as main tools to sample the conformational space of proteins. Enzymatic activity is intimately related to and dictated by substrate specificity. Consequently, a set of “analytical enzymes” can be selected, which will recognize conformational or chemical differences in the proteins of interest, i.e. the substrates, and convert them into distinct products. This inherent characteristic feature predetermines enzymes to be perfectly suitable to detect heterogeneities in recombinant protein preparations, which can result from different sources, e.g. covalent, possibly non-native posttranslational modifications (oxidation, phosphorylation, etc.), variations or absence of glycan moieties, but can also be exclusively conformational in nature. Such structural protein variations exhibit an altered susceptibility to enzymatic modifications. Typically, structural variants can affect the kinetic accessibility, i.e. exposure or shielding, of vulnerable modification sites. This may also translate into local kinetic constants, properties such as kcat or KM at a

certain modification site, which may be impacted by differences in conformation or dynamics of the protein of interest. Compared to previous LiP-based approaches, the presented method proceeds one critical step further: protein samples are subjected to multiple enzymatic modifications, leading to sequential and multiplicatively enhanced modifications. Often the modifications of the target proteins are accompanied by their successive destabilization and degradation. To illustrate the principle, two “isoforms” (e.g., different production batches) may differ slightly in their structure or dynamics, rendering one particular arginine residue stably engaged in a salt bridge in the first isoform, whereas the corresponding salt bridge would be more transient in the second isoform. Only in the second isoform, this particular arginine is accessible to a deiminase, which converts the positively charged arginine into a neutral citrulline residue. As a consequence, the salt bridge in the second isoform is permanently lost, exposing both the citrulline and the matching negatively charged residue (Asp or Glu), as well as their spatial neighborhood. The first enzymatic modification thus propagates and spreads a local difference in two isoforms and enhances the original differences. These differences can be further exploited by coupling further enzymatic reactions on the two protein isoforms, which will be differently modified with respect to kinetics, pattern, and absolute quantity. These increased differences serve as the “substrate” for a subsequent modification by a next enzymatic modification, and so forth. Initially subtle differences between protein variants are exponentially amplified, enabling a high sensitivity detection of heterogeneities in protein preparations (Fig. 1). Nested implementation of the ACE. All samples of interest are buffer-exchanged into a buffer system, which is compatible with the selected set of up to three analytical enzymes, and then sequentially subjected to enzymatic treatment. After each step in an ACE, the respective enzyme is stopped by adding a specific inhibitor, which does not interfere with the subsequently used enzymes. The choice of analytical enzymes allows for some freedom, but should be mechanistically distinct to warrant selective inhibition and may be optimized for the proteins of interest. All enzymes have to allow for testing of the 3D structure of the protein of interest. This definition first allows for a broad spectrum of enzymatic activities, e.g., protease, kinase, phosphatase, ligase, oxidase. However, in each case, enzymes with narrow substrate specificities are preferred, in order to minimize random signals and maximize differences between protein variants. In addition, the particular sequence in which modifying enzymes are applied and the “depth” of the cascade (i.e. the number of enzymes applied) are parameters that need to be tested and evaluated for optimal signal enhancement. To allow for standardization, the analytical enzymes should be readily commercially available. For streamlining the workflow, all analytical enzymes are chosen to be com-


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Figure 1. ACE mediated signal amplification. Initially small differences between conformational variants of a protein are amplified by the ACE treatment. With each step in an enzyme cascade, target proteins become ever more different compared to their initial state. Distinct amounts of enzymatic modifications are introduced as a result of exposure/ non-exposure of different modification sites to the respective attacking enzyme, depending on the conformation of the target. Such ACE-mediated signal amplification allows for a highly sensitive detection of protein variants.

patible with one buffer system. In this study, the first tier enzyme within the cascade was peptidyl arginine deiminase 1 (PADI1), a calcium-dependent enzyme which catalyzes the covalent deimination of arginines. This modification is also known as citrullination and promotes the destabilization of target proteins (Fig. 2a)29,30. As second tier modification, samples of interest were subjected to limited proteolysis by one out of a set of selected proteases (Fig. 2b), all having distinct substrate specificities. The cysteine proteases legumain and papain both display narrow substrate specificities. Legumain accepts and hydrolyzes substrates with Asn and to lesser extent Asp in P1 position (i.e. the amino acid preceding the scissile peptide bond) at its primary specificity pocket (the S1 pocket)26, whereas papain activity is dictated by strictly hydrophobic residues in P2, as well as Arg or Lys in P131. In rare cases, the aspartic protease pepsin proved to be useful, whilst displaying least substrate specificity (preference for bulky, hydrophobic residues in P132). In the third tier modification, samples underwent labeling with synthetic glutamine-derived biotin-conjugated tags by microbial transglutaminase (mTG, Fig. 2c)33,34, targeting accessible Lys residues. The biotin labeling was also exploited for chemiluminescent detection of the samples after SDS-PAGE separation and subsequent blotting. Detection of protein variants differing exclusively in conformation by the ACE. Since the aforementioned challenge of structural heterogeneities in protein preparations poses a particular threat to therapeutic proteins, we chose as a model system Rituximab, a monoclonal IgG1 anti-CD20 antibody used for treating Bcell lymphomas35, and commercially one of the most successful biotherapeutics on the market. During their purification process, antibody-based biotherapeutics typically face low-pH conditions (pH < 3, e.g. for elution from protein A affinity columns or for virus inactivation)36. However, under such acidic pH conditions, antibodies can potentially adopt a non-native, stable alternative conformation, the so-called A-state.

Figure 2. Types of analytical enzymes employed in this study. (a) PADI1 (peptidylarginine deiminase 1) catalyzes the conversion of accessible arginine (Arg) residues to citrulline (Cit) residues. This implies charge neutralization, transforming positively charged Arg to neutrally charged Cit, which results in partial unfolding of citrullinated proteins. (b) High specificity proteases were employed to hydrolyze peptide bonds in target proteins. (c) mTG (microbial transglutaminase) mediates crosslink-formation between glutamine (Gln) and lysine (Lys) residues. This enzyme was used to attach specific synthetic glutamine-derived biotintags to Lys residues, of target proteins. Likewise, lysinederived tags can be conjugated to Gln residues.

This low-pH conformation is characterized by an increase in hydrophobicity, while still displaying a high


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extent of secondary structure, and an elevated tendency towards aggregation37,38 (Fig. 3a). A-state Rituximab was produced by prolonged exposure of the drug to pH 2. Whilst differentiation of natively folded Rituximab from its A-state is not possible with conventional analytical methods like SDS-PAGE analysis (Fig. 3b) or mass spectrometry (identical amino acid sequence), the two structural variants can be easily told apart after a two-tier enzyme cascade. Although the two conformations are distinguishable already by single enzymatic modification, i.e. transglutamination, the resulting signal is drastically amplified in a two-tier cascade, including a proteolysis step prior to transglutamination. Furthermore, samples of native Rituximab were spiked with various amounts of its low-pH conformational variant before the ACE analysis, demonstrating the high sensitivity of at least 1 % (mole fraction ) of the method to detect the presence of nonnative protein variants which differ exclusively in 3D conformation (Fig. 3c).

Figure 3. High sensitivity differentiation of native Rituximab from A-state Rituximab by the ACE. (a) Schematic representation of native and A-state Rituximab (referred to as mAb in all figures). (b) Coomassie-stained reducing SDSPAGE of native mAb and A-state mAb. Both variants display an identical banding pattern (∼50 kDa: heavy chain, ∼25 kDa: light chain of mAb). (c) Reducing western blot analysis of native and A-state Rituximab after a two-tier ACE involving limited proteolysis by legumain, followed by transglutamination with mTG. Drastic differences in observed signals for lanes 2 and 3 demonstrate the capability of the method to differentiate protein variants differing exclusively in conformation. Furthermore, the presence of 1 % of a structural impurity was shown to be traceable by the enzyme cascade (by comparison of lanes 2 and 7, e.g., the 70 kDa regime).

Detection of covalently modified protein variants by the ACE. The method also enables the detection of structural changes induced by (partly) non-native, covalent modifications. The here presented examples portray very typical modifications that recombinant protein samples can differ by, and whose presence can have a deleterious effect on drug safety with regard to immunogenicity. The identity of all Rituximab variants was confirmed by mass spectrometry (Tables S1 and S2 and Figure S1). Modifications impacting protein structure and function can result from aging of samples, whereby oxidation is considered as one (of several) measures of age. Oxidation affects predominantly Cys and Met residues (and to a lesser extent His, Trp, and Tyr)39. Prolonged sample storage can also result in deamidation, which most commonly alters Asn residues and can lead to peptide bond hydrolysis40. We simulated this process by exposure of the antibody samples to H2O2 and basic pH conditions, respectively. By using the ACE, the presence of both oxidized and deamidated antibody species can be revealed (Fig. 4a and 4b, respectively), because characteristically different signals were detected in comparison to the native antibody. Similarly, changes in protein structure induced by prolonged exposure to acidic pH can be observed (Fig. 4c). Another relevant factor impacting protein integrity and function is glycosylation. Glycans are required to maintain molecular stability, as well as mediate effector functions in vivo41. Considering our model system, the removal of the N-linked glycans of antibodies results in a destabilization of the Fc-region and a decreased ability to bind to respective Fcγ receptors42,43. This deglycosylationdependent structural transition was revealed by the ACE (Fig. 4d). We could confirm the findings of the enzyme cascade by capillary zone electrophoresis and capillary isoelectric focusing analysis (Fig. S2). Frequently, damage on protein structure and function is inflicted by incorrect handling or storage, such as exposure to UV-light (e.g., day light) or elevated temperatures. Exposure of samples to UV-radiation can cause photolytic degradation of proteins44, whereas elevated temperatures can result in thermal degradation and denaturation45. We simulated these stress conditions and could detect the resulting variations in protein structure by the ACE (Fig. 4e and 4f, respectively). Given its capability to detect subtle structural heterogeneities with high sensitivity, the ACE offers the possibility to monitor and evaluate batch-to-batch variations in protein preparations. To this end, the reliability and reproducibility of the ACE method is critical. We therefore carried out the ACE analysis on the A-state of Rituximab in three independent replicates. The three experiments agreed with each other with a squared correlation coefficient (r2) of better than 90 % (Figure S3). In addition, we found a high robustness of the ACE method with respect to selection of the analytical enzymes (Fig. 5). Application of the ACE on different protein families. To test whether the sensitivity of the ACE is restricted to antibodies or applicable to target proteins of


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Figure 4. The ACE enables the detection of a broad spectrum of modifications. A number of covalently modified structural variants of Rituximab (sample 3) were compared to their unmodified counterpart (sample 2) via the ACE. All western blots were conducted under reducing conditions and identical protein amounts are loaded. The clearest results for the differentiation of (a) oxidized mAb, (d) deglycosylated mAb, (e) UV-stressed mAb, and (f) heat-stressed mAb from the native variant were obtained after ACE treatment involving three enzymes (citrullination by PADI1, proteolytic cleavage by legumain, followed by transglutamination with mTG). To detect damage inflicted by prolonged exposure of the antibody to (b) basic pH and (c) acidic pH conditions, an ACE comprising two enzymes (legumain / papain, respectively, and mTG) was applied.

different families, we investigated Etanercept, a biotherapeutic fusion protein comprising the human tumor necrosis factor receptor 2 together with an IgG1 Fc subunit; human erythropoietin (EPO) and microbial collagenases Q and T. The two marketed biotherapeutics (Etanercept and EPO) showed only minors structural changes upon different storage temperatures (Fig. S4). This is not unexpected, because therapeutic proteins are optimized for storage robustness during the development process. In addition, two batches of collagenase Q, produced at different time points, were easily differentiated by the ACE, indicating considerable differences in the structural composition of the batches. Remarkably, pronounced differences were also observed for native, EDTA-, and phenanthroline-treated collagenase T. Microbial collagenases belong to the class of metalloproteases and require Ca2+ and Zn2+ ions for proteolytic activity. Phenanthroline chelates Zn2+ only, whereby Ca2+ and Zn2+ are chelated by EDTA. The result of ACE treatment shown herein implies a structural transition in ColT, induced by the removal of catalytic Zn2+, as well as an additional, or alternatively a varying change in structure, upon Ca2+ and Zn2+ chelation. Classification of structural impurities by ACE. The ACE primarily detects the presence of differences between closely related protein variants. We further asked whether the modification patterns are characteristic for the types of heterogeneities in the samples, because these are recognized and amplified as substrates by the ACE. As a consequence, specific protein

Figure 5. High robustness of the method with respect to enzyme selection. Samples containing either native (sample 2) or deglycosylated Rituximab (sample 3) were subjected to three different two-tier ACEs, all comprising of a limited proteolysis step, followed by transglutamination with mTG. However, different proteases were applied in each case. All western blots were conducted under reducing conditions. (a) Result for differentiation of native from deglycosylated Rituximab by the ACE employing pepsin as proteolytic enzyme. Instead of pepsin, legumain (b), or papain (c) were used. In any case, the differentiation of native from deglycosylated Rituximab was feasible, indicating the high robustness of the method with respect to enzyme selection.


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Figure 6. The ACE enables the classification of heterogeneities in protein samples. (a) Western blot under reducing conditions after a two-tier ACE (proteolysis by legumain, followed by transglutamination with mTG). Lanes 2 – 7 represent a “reference library” consisting of patterns of samples of Rituximab that were subjected to different defined stress conditions prior to enzyme cascade treatment. Lanes A, B, and C represent a selection of blinded samples of Rituximab stressed under similar, but not identical conditions compared to the reference samples. (b) The observed patterns of each reference sample were transformed into functions relating the obtained signal intensities (y-axis) to the respective molecular weight (x-axis) by using an image processing program. (c) Comparing the functions obtained for the blinded samples A, B, and C to the functions of the samples in the reference library, highest similarity is observed between samples A and 6, samples B and 5, as well as samples C and 3. Evaluation is facilitated by calculation of the Pearson product-moment correlation coefficient (r²). (d) Pearson correlation matrix which numerically displays similarities between samples A, B, and C, and reference samples 2 to 7. Based on this, the identity of the blinded samples could be assigned, with sample A suffering damage from being exposed to basic pH, sample B being impaired by acidic pH, and sample C being victim of partial deglycosylation.

variants translate into characteristic patterns after ACE treatment. To this end, a “reference library” needs to be set up for each protein under investigation, which serves as a “template” to which e.g. different batches can be compared. An example for such a reference library is depicted in Fig. 6a, containing defined Rituximab variants subjected to the same ACE. Additionally, three blinded samples of the antibody stressed under similar, but not identical, conditions as those of the references, are shown. To identify the types of stresses these blinded samples were subjected to, the obtained patterns were first transformed into (mathematical) functions (Fig. 6b). These functions were subsequently used to calculate Pearson correlation coefficients for each combination of samples (Fig. 6c). To identify the stress condition, the highest similarity (i.e. the largest obtained r2) was searched for(Fig. 6d). To assess the probability of matching functions by chance, also cross-correlations within the library were determined (Fig. S5). This approach allowed correct identification the stress conditions, to which the blinded samples were subjected. However, it also demonstrates that the here shown

application is limited to some extent, as for several cases similar patterns were obtained, although initial stress conditions differed. This indicates that stress conditions, although distinct in nature, can cause similar structural transitions in proteins, resulting in comparable susceptibility of the stressed protein conformers to enzymatic modifications. DISCUSSION Protein function is not only defined by a concatenation of particular amino acids, but also dependent on their post-translational modification and their spatial arrangement, i.e. the protein fold. Consequently, biotherapeutic products have to display consistent higher-order structure in order to minimize the risk of potentially life-threatening side effects in patients. The development of biotherapeutics is, however, currently complicated by the sensitivity of analytical methods. To address this issue, we developed the ACE, a method based on sequential enzymatic modifications. Conformational changes provide different accessibility to an initial enzymatic modification, favoring the generation


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of a certain, often non-local pattern. This pattern is amplified by additional subsequent enzymatic treatments, which will be able to attack at recognition sites that become accessible due to previous enzymatic modifications. Thereby, structural heterogeneities in protein preparations are exponentially amplified and can be detected with high sensitivity. Using model proteinsRituximab and collagenase T, this method could detect protein variants differing by conformation only (native vs. A-state Rituximab and native Col T vs. EDTA- and phenanthroline-treated Col T), as well as a broad range of structural transitions induced by diverse covalent modifications (e.g. by oxidation or deglycosylation) with high sensitivity (of at least 1 %). Based on this, the method was shown to be a powerful tool to also detect batch to batch variations of protein preparations. Additionally, our approach not only detects the presence of heterogeneities, but also enables their identification based on reference experiments, since specific protein variants generate characteristic patterns after being subjected to the enzyme cascade. The ACE implementation offers several possibilities to optimize the performance with respect to sensitivity and throughput. Firstly, in the current implementation, the analytical enzymes remain with the samples and therefore also contribute to the background of the ACE readout. To circumvent this, analytical enzymes can be coupled to resins; the coupling allows their removal prior to the readout, as exemplified on the base of mTG (Fig. S6). Secondly, the current SDS PAGE-based sample separation method can be varied, e.g., by employing microfluidics or HPLC-based separation techniques. Thirdly, alternative detection methods promise improved sensitivity and throughput, most importantly direct fluorescence ACE readouts as compared to the coupled biotin-avidin-antibody detection. A fluorescence-based readout can be implemented by running the final transglutamination with fluorophore-labeled peptides rather than biotinylated peptides as in the current implementation, as has been successfully demonstrated for distinguishing native and oxidized Rituximab, see Fig. S7. Additionally, and complementary to chromatographybased detection methods, functional readouts can be employed, such as determining binding affinities of the target proteins to a known ligand after ACE treatment. While the initial unmodified protein samples are not likely to display any detectable functional difference, the ACE modification of the target proteins may well differ significantly such that functional differences are readily accessible. Along the same line, specific binding partners of the target protein variants could be included to increase the scope of application. This option would, for example, allow analysis for the presence of specific glycovariants by including appropriate lectins46 prior to ACE analysis. Finally, themost obvious type of variation within the ACE is in the choice of analytical enzymes. In fact, it is unlikely that there is a universal ACE for all situations. This means that certain protein variants of interest will require an adaption of the used enzymes. In some cases, the inclusion of an analytical enzyme conferring stability to the target protein (e.g., crosslinking enzymes such as

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lysyl oxidase47), rather than destabilization (as is the case for all enzymes used in our approach), might prove advantageous. A stabilizing modification might increase the dynamic range of the ACE on a given target protein and therefore also sensitivity of the method. Along this line, the scope of this method is not limited to proteins as molecules of interest; instead, upon using appropriate enzymes (e.g. restriction enzymes, glycosidases, lipases, etc.), other (bio-)molecules can be targeted as well. CONCLUSION & PERSPECTIVES In view of the above, the ACE can make a difference to the state of the art in a broad range of applications. Given its unmatched sensitivity and scalability ACE can serve as a valuable tool in the biopharmaceutical development, e.g. for monitoring batch-to-batch variations or evaluating biosimilarity between original (“originator molecules”) and biosimilar products. Similarly, owing to its unmatched simplicity of use with cost-saving analytical instrumentation, small academic laboratories will benefit from using the enzyme cascade. This will allow them to detect variations in sample preparations that are intimately related with poor reproducibility of complex experiments such as protein crystallization48,49. It is quite plausible that the dramatic replication crisis in biomedical sciences is partly rooted in functionally critical variations in sample preparations50,51. A potential strength of the ACE method is its applicability to complex samples such as blood plasma without the need of prefractionation. This property may allow assessment of the effect of a compound on protein structure, which may be dependent on its complex environment. There are potentially manifold applications, for instance in detecting ‘doping’ in sports. Perhaps autologous blood transfusions should be detectable due to the age sensitivity of critical proteins like erythropoietin. ASSOCIATED CONTENT Table S1: Summary of peptide mapping of Rituximab variants, Table S2: Identified Rituximab fragments after exposure to UV radiation; Figure S1: Total ion current chromatograms (TICC) and separation of Rituximab heavy and light chain fragments after UV irradiation; Figure S2: CIEF-UV and CZE-UV analysis of native and deglycosylated Rituximab; Figure S3: Reproducibility of the ACE method; Figure S4: Application of the ACE to other protein families; Figure S5: Comparison of labelling of A-state Rituximab by uncoupled and resin-coupled mTG; Figure S6: Size exclusion chromatography and subsequent fluorescence analysis of mTG-labeled native and oxidized Rituximab. This material is available free of charge via the Internet at http://pubs.acs.org. AUTHOR INFORMATION Corresponding Author: * [email protected] Author Contributions: H.B. supervised the project. J.C.H. and H.B. designed and conducted the experiments. C.R. and C.G.H. performed and evaluated HPLC-MS experiments. L.G.S. and H.S. conducted and analyzed CZE and CIEF experiments, I.J.H. conducted ACE experiments on ColQ and ColT. J.C.H., H.B., U.L., C.R., and L.G.S. wrote the manuscript.


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Notes: J.C.H. and H.B. are inventors on a patent relating to the ACE method. ACKNOWLEDGEMENT We gratefully acknowledge E. Dall (University of Salzburg) for providing activated human wild type legumain. We thank R. Reischl (University of Salzburg) for his help in conducting HPLC experiments. We are very thankful to the members of the CD laboratory for biosimilar characterization, S. Wildner, G. Gadermaier, C. Cabrele, L. Grassi, M. Schubert, M. Segl, T. Wohlschlager, and S. Senn (University of Salzburg), for valuable feedback and critical discussions. We appreciate the exceptionally helpful comments of the anonymous reviewers. The financial support by the Austrian Federal Ministry of Science, Research, and Economy and by a Start-up Grant of the State of Salzburg is gratefully acknowledged. We thank the Austrian Science Foundation FWF for support under the project W_01213.

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Analytical cascades of enzymes (ACE) for sensitive detection of

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