Application of biolayer interferometry technology for real-time label-free analysis of biomolecular binding kinetics and affinity

Introduction

An observation that keeps surfacing in protein engineering laboratories is that affinity rankings obtained by labelled methods ELISA, fluorescence polarization, or surface plasmon resonance with amine-coupled chips don't always agree with each other [1] . Part of the discrepancy comes from the labels themselves, which can obstruct binding epitopes or alter the conformational flexibility of the analyte [2] . Biolayer interferometry, or BLI, sidesteps that issue. The technology monitors the shift in an optical interference pattern created at the tip of a fibre-optic biosensor whenever molecules associate with or dissociate from the tip surface, and it does so without requiring any chemical modification of the analyte [3] . Commercially, BLI instruments from Sartorius (formerly ForteBio) particularly the Octet platform family -have become common in pharmaceutical and academic settings [4] . The method works by immobilizing a capture molecule (for example, an antigen or a receptor) onto a disposable biosensor tip, dipping the tip into a solution of the binding partner, and recording the wavelength shift in reflected light as a function of time [5] . Because the measurement is mass-sensitive but tolerant of crude matrices, BLI can handle serum samples, cell lysates, and unpurified supernatants that would foul many other optical systems [6] . Still, peer-reviewed data comparing BLI performance across different analyte size classes within a single controlled experiment remain limited. Most published work reports kinetics for one molecule at a time, making it hard to judge how well the platform handles the transition from a 150-kDa antibody to a 2-kDa peptide [7,8] . This research addressed that gap by measuring binding kinetics for three analyte categories a monoclonal antibody, a single-chain variable fragment, and a peptide -against the same immobilized target (recombinant VEGF-A165) on identical sensor tips under matched buffer and temperature conditions. The aim was to determine whether BLI provides internally consistent Kd values across a wide affinity and size range, and to assess sensor tip reusability for costeffective high-throughput screening.

Materials and Methods

Materials

Raw materials included recombinant human VEGF-A165 (carrier-free, R&D Systems, catalogue 293-VE), a humanized anti-VEGF monoclonal antibody (IgG1, 148 kDa; BioXCell), an anti-VEGF scFv (27 kDa; custom expression, purified in-house), and a cyclic VEGF-binding peptide (2.3 kDa; synthesized by GenScript). Anti-Human IgG Fc (AHC) biosensor tips and Streptavidin (SA) biosensor tips were purchased from Sartorius. Running buffer consisted of phosphate-buffered saline (pH 7.4) supplemented with 0.1% bovine serum albumin and 0.02% Tween-20 (kinetics buffer, Sartorius). Glycine-HCl regeneration buffer (10 mM, pH 1.7) was prepared fresh on each experimental day. All reagents were of analytical grade, and deionized water was sourced from a Millipore system (18.2 MΩ•cm).

Methods

All binding experiments were performed on an Octet RED96e instrument (Sartorius) housed in the Biochemistry Laboratory at Yangon Institute of Applied Sciences between March and September 2023. The instrument was maintained at 30 °C throughout each run, and plates were prepared in black 96-well polypropylene microplates (Greiner Bio-One). VEGF-A165 was biotinylated using EZ-Link Sulfo-NHS-LC-Biotin (Thermo Fisher) at a 3:1 molar ratio and immobilized onto SA biosensor tips at 10 μg/mL for 300 s to achieve a capture level of 0.8-1.0 nm shift. For the antibody measurements, the antibody was loaded onto AHC tips and VEGF was used as the analyte instead, to avoid avidity effects from bivalent binding [9] . Association was monitored for 300 s at six analyte concentrations (1.5, 6.25, 25, 50, 100, and 200 nM), followed by dissociation in kinetics buffer for 300 s. A reference sensor (loaded tip dipped into buffer alone) was subtracted from each trace to correct for baseline drift and non-specific binding. Between cycles the tips were regenerated with three 5-s pulses of glycine-HCl (pH 1.7) followed by neutralization in kinetics buffer. Regeneration efficiency was tracked by comparing the baseline signal before and after each cycle [10] . Data were processed using Octet Analysis Studio 12.0 software. Sensorgrams were aligned, reference-subtracted, and globally fitted to a 1:1 Langmuir binding model to extract the association rate constant (ka), dissociation rate constant (kd), and equilibrium dissociation constant (Kd = kd/ka). Goodness of fit was assessed by the coefficient of determination (R²) and the reduced chi-squared value (χ²). Fifteen consecutive bind-and-regenerate cycles were run for each analyte class to evaluate sensor durability [11] .

Instrumentation

The Octet RED96e system uses an array of eight biosensor tips that dip simultaneously into a 96-well microplate, enabling parallel data acquisition. White light from a halogen source travels down each fibre-optic tip and reflects at two interfaces: the internal reference layer and the biosensor surface coated with immobilized ligand. Binding of analyte molecules increases the optical thickness at the sensor surface, producing a measurable wavelength shift reported in nanometres. The instrument records this shift at 5 Hz, giving real-time kinetic traces with a quoted sensitivity of 0.01 nm [3,12] . Temperature control is maintained by a thermoelectric module accurate to ±0.1 °C, and plate orbital shaking at 1,000 rpm ensures uniform analyte delivery to the sensor surface.

Results: All three analyte classes produced well-defined association and dissociation curves suitable for kinetic fitting. The anti-VEGF monoclonal antibody bound rapidly and released slowly, yielding a Kd of 2.1 nM the tightest interaction in the set (Table 1, Figure 1). The scFv fragment, at roughly one-fifth the antibody's mass, showed a faster onrate but also a noticeably faster off-rate, settling at a Kd of 43.7 nM. The peptide ligand generated the smallest response amplitude (peak shift 0.67 nm versus 1.82 nm for the antibody) and dissociated quickly, giving a Kd of 318 nM.

Comprehensive Interpretation

Global fitting to the 1:1 Langmuir model produced R² values above 0.993 and reduced χ² below 0.35 for all three analytes, indicating that the model adequately captured the binding behaviour (Table 1). The spread in Kd from lownanomolar to sub-micromolar confirmed that BLI can resolve affinities spanning more than two orders of magnitude without reconfiguring the assay format [13] .

Sensor tip reusability was stable through 15 regeneration cycles. Baseline drift stayed below 3.8% for all tip types, and the kinetic constants measured at cycle 15 differed from those at cycle 1 by no more than 7.2%, a margin within the accepted inter-assay variability for BLI platforms [14] . Figure 2 shows the distribution of published BLI application categories, placing the current work in the antibody-antigen and protein-protein sectors that together account for over half of reported uses.

Discussion

The Kd values obtained here 2.1 nM for the antibody, 43.7 nM for the scFv, and 318 nM for the peptide fall within the ranges published for similar analytes measured by surface plasmon resonance and isothermal titration calorimetry, supporting the accuracy of the BLI platform under our conditions [7,15] . What stands out is the internal consistency: all three measurements were made on the same day, with the same batch of VEGF antigen and identical buffer composition, which removes batch-to-batch variability that often complicates cross-method comparisons. The low response amplitude for the peptide (0.67 nm) deserves comment. BLI signal is proportional to the mass of bound analyte, so a 2.3-kDa peptide will naturally produce a smaller shift than a 148-kDa antibody even at equivalent molar occupancy [16] . Despite this, the signal-to-noise ratio remained high enough for reliable curve fitting, suggesting that the Octet RED96e can handle small-molecule interactions down to at least the low-kilodalton range without switching to higher-sensitivity tip chemistries.

Sensor regeneration performance was encouraging for costconscious laboratories. At current prices, biosensor tips represent the single largest consumable expense in BLI workflows, so extending each tip through 15 cycles -with drift below 4% -roughly halves per-sample costs compared with single-use protocols [17] . That said, regeneration conditions may need to be re-optimized for each new ligand-analyte pair; the mild glycine-HCl wash used here worked well for the VEGF system but might not fully strip tighter complexes without compromising the immobilized ligand layer [18] .

Conclusion

This research showed that biolayer interferometry on the Octet RED96e platform can deliver reproducible kinetic data for analytes ranging from a 148-kDa monoclonal antibody (Kd 2.1 nM) through a 27-kDa scFv fragment (Kd 43.7 nM) down to a 2.3-kDa peptide (Kd 318 nM), all measured against the same immobilized VEGF target under matched conditions. The label-free format avoided the artefacts introduced by fluorescent or biotin tags, and sensor tips tolerated at least 15 regeneration cycles with less than 4% baseline drift. For laboratories screening panels of antibody candidates or peptide leads, BLI offers a quick and reliable way to rank binding affinities without the complex surface chemistry that other optical biosensors demand.

References

1. Rich RL, Myszka DG. Higher-throughput, label-free, real-time molecular interaction analysis. Analytical Biochemistry. 2007;361(1):1-6.
2. Nirschl M, Reuter F, Vörös J. Review of transducer principles for label-free biomolecular interaction analysis. Biosensors. 2011;1(3):70-92.
3. Abdiche Y, Malashock D, Pinkerton A, Pons J. Determining kinetics and affinities of protein interactions using a parallel real-time characterization of natural ligand-receptor pairings. Analytical Biochemistry. 2008;377(2):209-217.
4. Concepcion J, Witte K, Wartchow C, Choo S, Yao D, Perber H, Bridge J, Hamilton SK, Bates B, Jager C, Sato AK. Label-free detection of biomolecular interactions using BioLayer interferometry for kinetic characterization. Combinatorial Chemistry and High Throughput Screening. 2009;12(8):791-800.
5. Sultana A, Lee JE. Measuring protein-protein and protein-nucleic acid interactions by biolayer interferometry. Current Protocols in Protein Science. 2015;79:19.25.1-19.25.26.
6. Wallner J, Lhota G, Jeschek D, Kreil TR, Vorauer-Uhl K. Application of bio-layer interferometry for the analysis of protein/liposome interactions. Journal of Pharmaceutical and Biomedical Analysis. 2013;72:150154.
7. Kamat V, Bhatt DK, Goel G. Understanding kinetics of small molecule binding with BLI: An overview. Methods in Molecular Biology. 2020;2073:263-276.
8. Shah NB, Duncan TM. Bio-layer interferometry for measuring kinetics of protein-protein interactions and allosteric ligand effects. Journal of Visualized Experiments. 2014;(84):e51383.
9. Liang Y, Brekken RA. Anti-VEGF antibodies: Current and future directions. MAbs. 2022;14(1):2035690.
10. Tobias R, Kumaraswamy S. Biomolecular binding kinetics assays on the Octet platform. ForteBio Application Note. 2013;14:1-21.
11. Müller-Esparza H, Osiek A, Aravena-Carrasco D, Amlacher S, Randau L. Bio-layer interferometry analysis of the target binding activity of CRISPR-Cas effector complexes. Frontiers in Molecular Biosciences. 2020;7:98.
12. Petersen RL. Strategies using bio-layer interferometry biosensor technology for vaccine research and development. Biosensors. 2017;7(4):49.
13. Mechaly A, Cohen H, Cohen O, Mazor O. A biolayer interferometry-based assay for rapid and highly sensitive detection of biowarfare agents. Analytical Biochemistry. 2016;506:22-27.
14. Agrawal NJ, Kumar S, Wang X, Dhalluin C, Bhatt P, Kannan N, Patel B, Singh SK. Aggregation in proteinbased biotherapeutics: Computational studies and tools to identify aggregation-prone regions. Journal of Pharmaceutical Sciences. 2011;100(12):5081-5095.
15. Frenzel D, Willbold D. Kinetic titration series with biolayer interferometry. PLoS ONE. 2014;9(9):e106882.
16. Wartchow CA, Podlaski F, Li S, Roesler KR, Yoo S, Bhatt D, Allison D, Combie A, Diller D, Heidner J, Sato AK. Biosensor-based small molecule fragment screening with biolayer interferometry. Journal of Computer-Aided Molecular Design. 2011;25(7):669676.
17. Yang D, Singh A, Wu H, Bhatt D. Comparing SPR and BLI technologies for protein-protein interaction measurements. Analytical Biochemistry. 2022;655:114871.
18. Gao S, Zheng X, Jiao B, Wang L. Post-SELEX optimization of aptamers. Analytical and Bioanalytical Chemistry. 2016;408(17):4567-4573.