Blog What is a Gator XT biosensor? XT optics is what sets gator apart from the competition Author: Benjamin Osborn, PhD; Anuja Patil, MS; Indrani Chakraborty, PhD What is a BLI biosensor? A biosensor is a detection instrument that uses a biological molecule to detect a specific target. These detector biomolecules (called “capture molecules” in this article) can include bacterial proteins like Protein A, antibodies/VHH and aptamers, each of which target molecules selectively. This allows biosensors to discriminate between samples that do contain the target molecule from samples that do not. Biolayer Interferometry (BLI) is how selective binding is transduced into a meaningful signal. The inventor of BLI technology, Gator Bio’s CEO Hong Tan, discovered that when white light travels to the end of an optical fiber, a small layer of biomolecules (a biolayer) there reflects the light back into the fiber. A spectrometer can then observe that reflection. A reflection isn’t a signal, but with the addition of a reference layer at the distal end of the biosensor, the two reflections (biolayer and reference) interfere with one another, changing the reflected light. The starting interference looks something like what’s pictured in Figure 1 in light grey. We already see some wavelengths amplified because the distance to and from the biolayer aligns with those wavelengths, creating constructive interference. Similarly, destructive interference occurs where that round-trip distance comes out to a half wavelength. Figure 1: Exaggerated schematic representation of how antibody binding to a biosensor alters the interference spectrum in the visible range. Starting from a reference interference spectrum (grey), further biomolecule binding moves the peaks in the interference spectrum toward the infrared. This shift is plotted on a sensorgram as a function of time. Examples of sensorgrams are included in Figures 3 and 5. As ligands bind to the surface, the peaks in the interference spectrum slide toward longer wavelengths (reds). Binding increases the optical thickness of the biolayer, which is its physical thickness multiplied by its refractive index change, so the reflected light completes a slightly longer round trip. The instrument reports that change in optical thickness in nanometers as the “nm shift.” Because optical thickness scales with the amount of material on the surface, the nm shift is a direct measure of how much biomolecule has accumulated. How are Gator XT biosensors different? XT biosensor optics is what sets Gator BLI apart from existing BLI technologies with dramatically improved sensitivity and performance. When Dr. Tan revisited BLI technology and founded Gator Bio, he wanted to address a problem he had noticed early on in its development. He was getting far less signal than he had expected given the sizes of molecules that were binding to the surface. This is because traditional biosensor design captures less light from the biolayer than from the reference layer. While reduced sensitivity wasn’t a problem for a first-of-its-kind instrument that mostly focused on large molecule quantitation and binding kinetics, first-gen BLI biosensors clearly still had room for improvements. So when Dr. Tan returned to the problem and founded Gator Bio, the Gator team revisited this discrepancy. Could our engineers capture more light from the biolayer? Could we improve performance of a BLI biosensor solely by improving the optics? Over the following years, the Gator team completely redesigned the surface of the biosensor, culminating in a proprietary process that improved the reflection from the biolayer back into the instrument. Without getting too technical, these improvements allow more of the light from the biolayer reflection into the optical fiber to reach the spectrometer, increasing the signal intensity (see Figure 2). Figure 2: Schematic representation of a traditional biosensor (top) vs a Gator XT biosensor (bottom). Note how the total reflection from the protein layer (orange) has a much greater amplitude from XT biosensors which capture more of the reflected light, resulting in a much larger phase shift (nm shift). For this reason, XT biosensors give more signal per molecule Now that the reflection from the biolayer is more intense, the resulting nm shift in the interference spectrum is approximately 3x greater relative to a traditional BLI biosensor. Figure 3 shows a head-to-head comparison of a traditional streptavidin BLI biosensor against a Gator SA XT biosensor featuring these new optical improvements. The difference in sensitivity and performance is clear just from this single experiment – a 3-4 nm loading shift was observed with a traditional BLI optical design, versus a 12 nm loading shift using Gator XT optics. This improvement was observable on the base model Gator Prime instrument (launched several years before any biosensors with XT optics were available), without requiring any redesign of the instrumentation or software. Figure 3: Traditional BLI streptavidin sensors vs Gator Streptavidin XT. While the response is much greater on a Streptavidin XT biosensor, the antigen binding kon remains unchanged. Ligand Load (nm)koff (s-1)kon (M-1 s-1)KD (M)First-Gen SA3.65.47*10-51.09*1055.03*10-10SA XT12.58.47*10-51.01*1058.42*10-10 Does increased sensitivity also mean increased noise? Noise from biosensors with XT optics is still well beneath the instrument spec of 4 picometers of RMS noise, and the improvement to sensitivity dwarfs that small increase. As an example, across a panel of twenty-plus His-tagged proteins, we tested our anti-His XT (HIS XT) biosensor against both First-Gen BLI anti-his and our own traditional Gator Anti-His biosensor (See Figure 4 – full dataset here). The jump from First-Gen BLI to HIS XT is over 11x total signal on average. Moreover, some proteins did not bind to the First-Gen BLI’s biosensor at all, meaning that it’s not just a question of signal to noise, but whether you see anything at all. Figure 4: Comparison in binding activity in a typical two-minute binding experiment across twenty different his-tagged proteins on each biosensor. BiosensorAvg. signal loadedvs. Legacy BLIFirst-Gen Anti-His0.17 nm1x (some showed no binding at all)Gator Anti-His0.40 nm2.4xGator HIS XT1.93 nm11.3x That sensitivity comes from two upgrades in parallel: the XT optics themselves, plus a better capture molecule using Monod Bio’s anti-His NovoBody™ technology. Together, Gator’s HIS XT offers an 11.3x signal improvement without a meaningful increase in noise. Importantly: the signal gain from Gator XT biosensors comes by using improved optical physics and capture molecules, not sample rates and smoothing algorithms. Why isn’t the noise increased as much as signal? A meaningful shift in the interference spectrum comes from a uniform layer of biomolecules across the surface, which produces a clean, consistent change in path length. A major source of noise, by contrast, is transient, non-uniform events at the surface and since these are incoherent, they couple weakly into the interference shift.This is an important advantage of the XT format: increasing the gain from the biolayer amplifies the true binding signal without proportionally amplifying surface-level noise. The result is a stronger signal-to-noise ratio and better resolution of weak or low-level binding events. We’ll explore this mechanism in greater detail in a future article on the optical physics behind the XT platform. Does XT optics allow you to study smaller analytes? Although noise is incoherent, the signal amplification from XT optics does amplify coherent increases Figure 5 is a dataset with a 2 kDa peptide as analyte on a Gator Pivot instrument which used Peltier cooling to run the 16 channel assay at 20°C, 400 RPM with an SA XT biosensor. The XT sensitivity improvement results in a signal:noise ratio of over 200 – for a 2 kDa analyte! Figure 5A: Full sensorgram showing binding by 150 kDa dimer as ligand and 2 kDa peptide as analyte. B: Analyte binding and kinetics fitting data. Analyte signal of 0.8 nm shift translates to over 200x signal:noise in a peptide binding experiment. XT optics increases response signal without also increasing noise. In addition to the added sensitivity for smaller analytes, SA XT biosensors paired improved optics with a unique capture molecule design optimized for larger molecules. On SA XT, we use crosslinked streptavidin on the surface of the biosensor, removing the 3D matrix from the surface and that might hinder larger analytes, like lipid nanodiscs, from penetrating into the biosensor surface. This improved sensitivity and wider compatibility range across both larger and smaller molecules make SA XT a versatile troubleshooting sensor. It became one of our best sellers almost immediately. SA XT biosensors have been cited for numerous applications like nucleic acids [1], AI-driven protein engineering [2], and polysaccharide-protein interactions [3], among others. For more examples, you can search our citations page for Streptavidin XT or SA XT. What are some other examples of XT biosensors? Gator Bio’s XT optics innovation doesn’t increase the number of binding sites on the sensor – it generates more signal per molecule. This means that XT optics can be applied to any capture molecule on the biosensor. Gator has developed many different biosensors that take advantage of XT optics, so whatever your workflow, we likely have a biosensor with XT optics that meets your needs. Strep-Tactin XT Gator’s Strep-Tactin XT biosensor uses an engineered Streptavidin molecule, known as Strep-Tactin XT licensed from IBA Lifesciences, as a capture molecule to immobilize twin-strep-tagged proteins (tag sequence: SAWSHPQFEKGGGSGGGSGGSAWSHPQFEK). The twin-strep tag is two copies of the Strep-Tag II sequence (SAWSHPQFEK) which binds to the Strep-Tactin XT tetramer with picomolar affinity, making it an excellent choice for rapid capture and kinetics applications. They also regenerate up to 10 times, making them an excellent choice to run complex, multi-cycle assay formats. Note: proteins tagged with only a single copy of the Strep-tag II sequence will not remain tightly bound to Strep-Tactin XT. Gator offers an anti-Strep-Tag II antibody sensor for molecules that use a single Strep-Tag II tag.Strep-Tactin XT biosensors originated from our custom biosensor program back in 2023. Built on the XT optical improvements, the Strep-Tactin XT biosensors can capture ligands from cell-free expression media, making them an ideal choice for AI-driven discovery workflows, where the XT optical improvements improve sensitivity for smaller targets. Recent AI-driven discovery and binder-characterization pre-prints have used Strep-Tactin XT in their workflows [4-6]. For more examples, you can search our citations page for Strep-Tactin XT. Mouse Fc XT (MFC XT) Mouse Fc XT also represents a significant improvement over traditional Mouse Fc biosensors. Not only does it have the same XT optical improvements, but it also includes a nanobody capture molecule with much faster on-rate and higher affinity, allowing faster screening of hybridoma pools for high throughput kinetics. Because MFC XT gives more signal per molecule, low-expressing clones that produce little or no signal on traditional Mouse Fc sensors often show a clear, positive signal on MFC XT, so you catch weak positives you would otherwise miss. For more information about the MFC XT biosensor, download this application note on using Mouse Fc XT biosensors for hybridoma screening. Anti-His XT (HIS XT) We’ve already talked quite a bit about HIS XT biosensors and their development journey in this previous blog post. HIS XT Biosensors combine XT optical improvements with Monod Bio’s new HIS XT NovoBody™, designed using AI to bind to a wide variety of His-tagged proteins with high affinity. HIS XT biosensors are also the best biosensor on the market for high throughput Fc receptor studies for two reasons: The capture molecule has no Fc (and therefore doesn’t cause any Fc interference). The sensitivity improvement derived from the XT optics (more signal per molecule) means that you can load fewer molecules on the surface, thereby improving the accuracy of kinetics experiments on this notoriously difficult interaction pair. For a detailed protocol on how to use HIS XT for Fc receptor studies, download our application note studying CD32/FcγRIIa interaction here. Regen SA (RSA) Regen SA is our newest biosensor, employing an engineered streptavidin surface that can be completely regenerated using a simple two-step process. RSA delivers up to 4x higher signal and cuts cost per interaction by up to 8x versus non-reusable SA sensors. This makes it an ideal choice for high throughput screening for workflows featuring biotinylated molecules or instances where re-using an identical binding surface are important for consistency. Other biosensors with XT optics While many of our products are clearly labeled with the XT on the label, several of our other biosensors also incorporate XT optics into their biosensor design. Below is a complete list of biosensors that use Gator XT optics: BiosensorCapture MoleculeKey AdvantageBest Use CaseSA XTCrosslinked streptavidinOptimized surface for large molecules/complexes, sensitive enough for analytes as small as 2 kDaTroubleshooting sensor; nucleic acids, AI-driven protein engineering, lipid nanoparticles (LNP, VLP, nanodisc)Regen SA (RSA)Engineered StreptavidinRegenerates up to 10 times with our proprietary regeneration solutionAptamers, Peptides, and biotinylated proteins of all types. Best with long-chain linkers (LC-LC; PEG4)SMAP (Small Molecule and Peptide)StreptavidinHigh-density surface allows for visualization of small peptide and small molecule bindersPeptides, small molecules, and other low MW analytesStrep-Tactin XTStrep-Tactin XT (licensed from IBA Lifesciences), binds twin-Strep-tagRegenerates up to 10x; compatible with cell-free expression media; lower ligand loading for cleaner kineticsCell-free capture, AI-driven discovery and binder characterization workflowsMFC XTAnti-Mouse Fc nanobody (fast on-rate)Faster screening of hybridoma pools; lower ligand loading for cleaner kineticsHigh-throughput hybridoma screening; quality Mouse Fc kineticsHIS XTHIS XT NovoBody™, AI-designed by Monod Bio (no Fc domain)No Fc interference; supports lower ligand loading for cleaner kineticsCell-Free capture; Fc receptor studies (e.g., CD32/FcγRIIa); His-tagged protein kineticsAnti-VHHPolyclonal anti-VHH cocktailBinds to no-tag VHH; supports capture from cell-free mediaHigh throughput screeningAnti-human FABAnti-FAB antibodyBinds to untagged human FAB fragmentsFAB screening, FcRnAAVX/AAV9CaptureSelect AAVX/AAV9 nanobodyBinds only to intact capsids, giving viral particle titerUpstream/downstream viral titer determinationAnti-PEGAnti-PEG antibodyBinds to PEG of any length, capture LNPs with PEGylated lipidsLNP characterization for selective CAR-T mRNA deliveryAmine Reactive (AR)Classical label-free chemistryIncreased sensitivity using amine-reactive formatNo-tag label-free experiments, reduced NSB for C1q assays over First-Gen BLI In addition to the list above, custom biosensors can be made with XT optics on request. For any questions about our custom biosensors program contact our team. Should I approach my experiments differently with XT optics? The same best practices of BLI still apply. However, XT biosensors give you a leg up in four different ways: Common best practice is to load as few ligand molecules as possible while obtaining a usable amount of signal. With Gator XT optics amplifying signals by 3x, you can load to the same nm shift as you are accustomed to and thereby load 3x fewer ligand molecules onto the surface without affecting analyte binding sensitivity. This can have a dramatic impact for applications that are affected by mass transport or avidity, like Fc receptors (see the His XT application note on Fc receptor studies). Increased sensitivity also gives you flexibility to study analytes down to 2 kDa. Load as much ligand as is needed to observe peptide binding clearly. For picomolar binders, a greater measurable amount of dissociation is helpful for calculating koff. With an XT biosensor, you can observe slow off rates more clearly because more signal is being removed from the sensor surface through dissociation, even if it’s the same number of molecules. This can improve accuracy of the modeling software as it tries to fit slow-off kinetics. How can I compare the difference between XT and standard BLI biosensors in my own lab? Gator’s XT biosensors are only compatible with Gator instruments, and not with any competitor’s instrumentation. However, if you’d like to see how Gator XT biosensors could improve your data quality, contact us and our team can arrange a demonstration in your lab. Works Cited [1] Lee, C.-C., Hsu, S.-F., Chang, Y.-W., Ho, M.-R., Sugiyama, H., Wang, W.-C., & Wang, A. H.-J. (2026). Structural basis of B-to-Z DNA transition mediated by an anti-Z-DNA antibody. Nucleic Acids Research, 54(5). DOI: 10.1093/nar/gkag160. Full text: academic.oup.com/nar/article/54/5/gkag160 [2] Zhu, Y., Liu, H., Qu, F., Qu, C., Wang, Y., Yao, J., Li, K., Wang, Z., You, S., Hua, L., Ge, C., Yao, H., & Li, T. (2025). De novo binders overcome the MMLV RT stability-activity trade-off. iScience, 28(12), 114140. DOI: 10.1016/j.isci.2025.114140 [3] Xiao, R., Zhou, W., Peng, J., Cai, S., Xie, J., Liao, W., Li, S., Ding, K., & He, F. (2025). AJDW, polysaccharide from Albizia julibrissin, binds to PI3K and induces cell cycle arrest and ROS-mediated pancreatic cancer cell apoptosis. Glycoscience & Therapy, 100003. DOI: 10.1016/j.glycos.2025.100003 [4] Stark, H., Faltings, F., Choi, M., Xie, Y., Hur, E., O’Donnell, T., Bushuiev, A., Uçar, T., Passaro, S., Mao, W., Reveiz, M., Bushuiev, R., Pluskal, T., Sivic, J., Kreis, K., Vahdat, A., et al. (2025). BoltzGen: Toward Universal Binder Design. bioRxiv. DOI: 10.1101/2025.11.20.689494 [5] Du, Y., Yu, B., Liu, T., Shen, T., Chen, J., Rittig, J. G., Sun, K., Zhang, Y., Song, Z., Zhou, B., Masschelein, C., Wang, Y., Wang, H., Jia, H., Zhang, C., Zhao, H., Ester, M., Head-Gordon, T., Gomes, C. P., Sun, H., Duan, C., & Schwaller, P., Jin, W. (2025). Accelerating Scientific Discovery with Autonomous Goal-evolving Agents. arXiv:2512.21782. Link: arxiv.org/abs/2512.21782 [6] Candido, S., Hayes, T., Derry, A., Rao, R., Lin, Z., Verkuil, R., Wu, B. Z., Lee, J. S., Bruguera, E. S., Keval, J. A., Kopylov, M., Pak, J. E., Wu, W., Thomas, N., Mataraso, S., Rives, A., et al. (2026). Language Modeling Materializes a World Model of Protein Biology. bioRxiv. DOI: 10.64898/2026.06.03.729735
Blog HIS XT Biosensor: How AI-Designed Protein Engineering Solved the Anti-His Baseline Drift Problem If you’ve ever struggled with baseline drift, slow loading times, or unreliable kinetics when working with His-tagged proteins, you’re not alone. These are longstanding pain points that have affected every anti-His biosensor on the market. In this post, we pull back the curtain on how Gator® Bio partnered with computational protein design company Monod Bio to develop HIS XT, a next-generation anti-His biosensor built around an AI-designed NovoBody™ protein. From the fundamental limitations of monoclonal antibodies to the in silico design process that replaced them, we walk through exactly why traditional anti-His probes fall short for BLI and how we engineered our way around those limitations to deliver faster loading, dramatically reduced baseline drift, and consistent performance across N- and C-terminal His-tagged proteins. Behind the Scenes Why Did Researchers Need a Better Anti-His Biosensor? The humble His tag (in its many variants) is likely the most common protein tag in existence, but as any user knows, its simplicity can sometimes come at a cost. In this post, we’ll walk through our multi-year journey to develop a better anti-His biosensor, starting from the fundamental problems we encountered to how we solved them by collaborating with AI protein design experts to generate the best possible anti-His biosensor on the market. Whether you use Gator instruments or not, we hope the technical insights are useful for anyone working with His-tagged proteins. Our traditional anti-His biosensor is used by hundreds of customers around the world. His-tagged proteins are ubiquitous in research for good reasons: expression plasmids often include the tag by default, the tag is small and typically doesn’t affect protein structure or activity, and purification resins from multiple manufacturers help keep costs competitive. Anti-His was one of our first biosensors because it allowed users to capture His-tagged proteins directly from crude lysate and obtain quantitation or kinetic information without complete purification. For many labs, this workflow represents a significant time savings. Even after we launched Ni-NTA biosensors, which have slightly better affinity for His-tagged ligands, anti-His remained the more popular choice. However, customer feedback revealed consistent pain points: Loading steps often required 5 minutes or longer Baseline drift interfered with accurate kinetic measurements Weak ligand binding signal made picomolar interactions with smaller analytes difficult Performance varied significantly between different His-tagged proteins These weren’t Gator-specific problems. Every BLI vendor’s anti-His probe suffered from similar issues because they all relied on the same fundamental approach: monoclonal antibodies. While Gator’s anti-His biosensor outperformed other commercially available options in baseline stability, we knew we could do better. Sidebar: About Anti-His and Baseline Drift What is Baseline Drift, and Why is Anti-His So Problematic? Anti-His biosensors use a monoclonal antibody to hold His-tagged ligands on the biosensor, but those antibodies have notoriously weak interactions. When the capture molecule slowly dissociates from the biosensor surface during measurement, we call the resulting downward slope “baseline drift” because we still see that downward movement on the baseline sensorgram. This interferes with accurate kinetic analysis because although we can subtract that baseline from the analyte binding data, the subtraction isn’t perfect – the analyte binding step contains analyte associating and dissociating from the ligand, as well as ligand dissociating (both with and without analyte attached). In many cases, this subtraction introduces large fitting errors that make accurate analysis difficult. This is particularly problematic for tight-binding protein interactions with slow dissociation rates. When the capture antibody-his-tagged ligand interaction has a faster off-rate than the his-tagged ligand-analyte interaction being measured, it becomes impossible to distinguish his-tagged ligand dissociation from analyte dissociation, compromising data quality. Figure 1: Gator anti-His vs another commercially available anti-His biosensor. Both sensors were loaded with his-tagged PD-L2 to ~0.5 nm, then both sensors were allowed to dissociate for 1800 seconds. In that 1800 second baseline, Gator’s traditional anti-His lost 18% of its binding signal, while the alternative lost 62% of bound protein. Why Can’t Traditional Anti-His Probes Measure Picomolar Interactions Reliably? Anti-His antibodies typically have relatively weak affinity for His-tags (micromolar to nanomolar range), which means they have fast off-rates (koff). When measuring picomolar protein-protein interactions that dissociate very slowly, even small amounts of probe dissociation create baseline artifacts that obscure the true signal. By using tight-binding capture molecules with slower off-rates, you’re more likely to accurate picomolar kinetics measurements. Making HIS XT a Truly Next-Generation Design What we looked for as we used computational protein design to solve persistent challenges in His-tag capture. What Makes a Good BLI Capture Molecule? Antibodies have been the standard capture molecules for decades because they’re straightforward to generate, commercially available, and work adequately for many applications. However, when we examined what makes an ideal BLI capture molecule specifically, we identified several inherent limitations with antibody-based approaches. They are: Weak binding affinity Inconsistent performance across proteins Avidity Regeneration efficacy Over several years, we evaluated dozens of anti-His antibodies from different manufacturers, hoping to find one with better baseline stability and faster association rates. What we discovered was that antibodies optimized for traditional immunoassays often lack the characteristics needed for high-performance BLI. Why are Anti-His Monoclonal Antibodies Consistently Weak Binders? There are three general reasons why anti-His antibodies aren’t the strongest: Manufacturers of monoclonal anti-His antibodies don’t have the same requirements that a BLI biosensor manufacturer does. Many anti-His manufacturers optimize for elution or stability, making the reagent good enough for a western and not a label-free technique. Gator biosensors, on the other hand, need a slow dissociation rate. Anti-His antibodies target a repeating motif; this means that there’s a high likelihood that an interaction can be “off-center” and weakly-binding when first presented with the tag. Additionally, many antibodies have a strong preference for C-terminal His tags because that helps with achieving a proper binding configuration, but this comes at the cost of versatility. Antibodies against His tag are hard to make because the tag is poorly immunogenic: it’s short, it can have variable charge depending on its environment, and it can be difficult to access when conjugated to a protein. Why Do Anti-His Antibodies Have Variable Affinity to Different Proteins and Tag Locations? Anyone who has shopped for antibodies knows that manufacturers rarely advertise kinetic parameters—the kon, koff, or KD of the antibody-antigen interaction. For most antibodies targeting consistent protein epitopes, this information would be relatively straightforward. However, with anti-His antibodies, achieving consistent binding across different proteins is extremely difficult. We generated single-point KD and koff estimates for our traditional anti-His and the new HIS XT biosensors across 20 different His-tagged proteins from 5 manufacturers. The variation was substantial. This is because the tags are in different electrochemical environments, some have likers (like G4S) between tag and protein, and the tags can be at either the C or N-terminus. When the tag has such low variability, the surroundings can play a large role in availability. Figure 2 contains the KD and koff of each of these tested proteins. We observed a relatively wide variability in both KD and koff rate with traditional Gator anti-His biosensors, which is dramatically improved with HIS XT. To end users, this means that HIS XT will be far more reliable, across a variety of experimental candidates. Figure 2: koff and KD estimates of 20 different his-tagged proteins to both the Gator anti-His and HIS XT biosensors. Results are single-point experiments with results generated via curve fitting. Anti-His has only 19 results plotted above because one failed to bind altogether. Note that the HIS XT biosensor shows a consistent improvement in overall binding, as well as interaction longevity, with far fewer instances of poor binding. What is Avidity? Why Would a Monoclonal Antibody as a Capture Ligand Cause This Problem? In the context of BLI, avidity usually refers to having two proteins on the surface of a biosensor interacting with a single protein in solution. When you use a bivalent capture molecule on the surface of the sensor, you have two capture sites in immediate proximity, meaning that captured ligands could be close enough to each other to “double-bind” to an analyte, or to prevent analyte from escaping because another binding site is immediately present. These artifacts are extremely common on BLI, and make the ligand-analyte interaction appear much tighter than actual 1:1 affinity, complicating accurate kinetic measurements. By using a small single-domain binding protein as capture molecule, we limit this issue. What is Regeneration? Why is Regeneration Performance Important for Biosensor Development? Regeneration efficacy (the ability to withstand short exposure to low pH conditions and successfully remove bound His-tagged protein) is critical for both practical and economic reasons. Being able to reuse the same biosensor for multiple binding cycles not only reduces cost per assay but also ensures consistency. Poor regeneration means either replacing biosensors mid-experiment or accepting incomplete ligand removal (which appears as inconsistent load and inconsistent analyte binding because bound ligand isn’t completely removed). For high-throughput applications, being able to run the entire plate (typically 12 columns) on a single sensor can dramatically simplify the workflow. Even after narrowing our list to a handful of high-performance, high consistency candidates, regeneration efficacy was one of the most important discriminators between the final candidates. Similar to the kinetics experiments above, we wanted to ensure that the final candidate for the new HIS XT would consistently work well for a variety of possible ligands. Figure 3 shows some examples of this process from the final candidate. Figure 3: Regeneration efficacy of His XT across four different his-tagged proteins. Top-left: His-HSA; top-right, PD1-his as ligand, Anti-PD1 as analyte; Bottom-left: MD1-his as ligand, PD-L2 as analyte; Bottom-right: Human Thrombopoietin Receptor-His as ligand, Human Thrombopoietin as analyte. Note: Regeneration efficacy may vary by protein and the chemistry surrounding the tag. An in silico Approach After years of screening antibodies from various suppliers, we reached a conclusion: there simply wasn’t a better antibody available. Gator’s traditional anti-His biosensor was already best-in-class among commercially available options, but it still had the fundamental limitations inherent to antibody-based capture. The breakthrough came from a different approach entirely. We partnered with Monod Bio, a company specializing in computational protein design. Their AI-driven methods had generated proof-of-concept results suggesting they could create a superior anti-His binding protein. How Did AI Design Accelerate Biosensor Development? Rather than relying on biological evolution (as in antibody generation from immunized animals) or combinatorial libraries (as in phage display), the only way to generate a truly new solution for anti-His binding was through an in silico approach. Using AI for protein design starts with the desired binding characteristics and works backward to design a protein sequence that should achieve them. For His-tag capture, we asked Monod Bio to explicitly optimize for strong binding to the poly-His motif at N- and C-terminal tags, single-domain architecture to avoid avidity, and stability under regeneration conditions. This approach bypassed months of screening and provided greater consistency for both association and dissociation kinetics. What are Monod Bio’s NovoBody™ Proteins? Rather than stick with the traditional antibody and nanobody scaffolds already on the market, Monod Bio moved in a new direction, creating NovoBody™ proteins, de novo computationally designed binding proteins. Unlike antibodies derived from animal immunization or VHH domains from camelids, NovoBody™ proteins are designed from scratch using AI algorithms. They can be optimized for specific requirements. In this case, they were designed for consistent His-tag binding, single-domain structure, and regeneration stability. Since they’re single-domain NovoBody™ proteins avoid the avidity complications inherent to bivalent antibodies. Over the course of a year, Monod Bio sent us several candidates as they refined their designs and we optimized our conjugation chemistry. Many candidates showed acceptable kinetics, but some had difficulty conjugating to biosensor surfaces, others showed inconsistent regeneration performance, and ultimately only one emerged as clearly superior. As part of our validation process, we tested 27 different His-tagged proteins representing a range of molecular weights, tag locations (N- and C-terminal), charge characteristics, and manufacturers. This panel allowed us to assess the pan-specificity of HIS XT, and the data confirmed dramatically improved binding activity, stability, and consistency compared to traditional antibody-based probes. Figure 4, below, shows the result, as well as the comparison against both anti-His and other ommercially-available anti-his biosensors on the market. Figure 4: Loading shift and loss in a sample experiment across 20 different his-tagged proteins of varying sizes, tag locations, and manufacturers. Why Call It HIS XT? What Does XT Mean for Gator Bio Biosensors? While the capture molecule is critical, biosensor performance also depends on surface chemistry and optical design. Biosensor development also requires immobilizing that capture molecule, optimizing its surface chemistry and tailoring the optical properties of the biosensor. One key innovation is preparing the biosensor using our proprietary XT optical coating, which dramatically increases the total signal per molecule on the surface of the sensor. The number of molecules on the surface at 1 nm shift on HIS XT is much lower than the number of molecules on a traditional anti-His at the same signal shift. This means you can still obtain usable data with smaller analytes without having to overload the biosensor for sensitivity. It also means that you can load far fewer proteins onto the surface and maintain good signal, reducing the risk of avidity further. As previously shown in Figure 4, in standardized 2-minute experiments, HIS XT sensors generated over 4× the total signal compared to other commercially available anti-His antibody biosensors when using the same ligand loading well. This is due to both an improved kon association rate, as well as this amplification of total signal. Technical Considerations for Optimal Performance As with any high-performance capture system, there are protocol optimization considerations to keep in mind. To experienced users, these points are nothing new, but it’s important to bear them in mind when optimizing your protocols. Beware of over-loading your biosensorsBecause HIS XT biosensors bind His-tagged proteins so efficiently, it’s easy to overload them using conventional protocols. For kinetic assays, we recommend using 10 nM of protein or less. The higher signal per molecule means less protein is needed to achieve excellent signal-to-noise ratios. Managing second-baseline dissociationAlthough the anti-His NovoBody™ remains bound with a very long interaction half-life, some ligands will dissociate a small amount at the beginning of second baseline (Figure 5). Using less protein during loading and avoiding saturation helps limit this effect. It is a good idea to include a 60-120 second baseline after loading allows this initial dissociation to plateau, though with proper reference subtraction this may not be necessary. Optimize regeneration conditionsWe screened the capture molecule used on the HIS XT biosensor for regeneration efficacy, and can confidently say that when using our Regen Buffer pH 2.0 (No Salt) (PN: 120008), you can typically expect 10+ regenerations with the HIS XT biosensor. However, His-tag chemistry can vary, and some proteins may require optimization of regeneration conditions beyond low-pH buffer wash.We do not recommend regenerating with low-pH buffers that contain high salt, as this may reduce the effectiveness of the biosensor. Figure 5: HIS XT biosensor loaded with his-tagged PD-L2, then allowed to dissociate for 1800 seconds. In that 1800 second baseline, only 9% of bound protein loss was observed, most of which occurred within the first 60-120 seconds. Frequently Asked Questions What is the difference between Ni-NTA and anti-His capture for BLI? Ni-NTA relies on metal chelation chemistry where nickel ions coordinate with histidine residues. This provides strong binding but can be sensitive to chelating agents, divalent cations, and pH variations in samples. Anti-His biosensors (including HIS XT) use protein-protein interactions, which are generally more tolerant of diverse sample conditions and provide more consistent performance across different experimental setups. Ni-NTA typically shows slightly higher affinity but greater promiscuity, making it less-than-ideal for complex matrices. How do you choose between N-terminal and C-terminal His-tags? HIS XT was specifically designed to eliminate this bias—it captures N- and C-terminal His-tags with equal efficiency, giving researchers flexibility in tag placement without sacrificing capture performance. As such, tag location choice entirely depends on protein folding, expression considerations, and whether the terminus is accessible. Adding a G4S linker can assist Can HIS XT biosensors detect interactions below 100 pM? Yes. The improved baseline stability (58% less drift) enables reliable measurement of very tight interactions. What applications benefit most from HIS XT compared to traditional anti-His probes? Applications involving tight-binding interactions (picomolar to low nanomolar), small analytes (<15 kDa), high-throughput epitope binning (faster loading speed), and quantitation of his-tagged proteins benefit most dramatically. The improved baseline stability is particularly valuable for extended kinetic measurements and the higher signal-per-molecule enables detection of interactions that would be below the noise threshold with traditional probes. Any workflow requiring multiple regeneration cycles also benefits from HIS XT’s robust regeneration performance. Conclusion After years of optimization and development, we’re proud to introduce the HIS XT biosensor. We believe it represents a significant advancement in His-tag capture technology, addressing the many fundamental limitations that have persisted across the field for years. Have you experienced similar challenges with His-tagged proteins and capture in your own work? We’d love to hear about your applications and learn what other capture molecule challenges the research community is facing. Share your thoughts via email to [email protected]. Getting Started with BLI Ready to implement BLI in your laboratory? Here’s how to get started: Define your application: Determine whether you need quantitation, kinetics, or both Select your biosensor: Choose the immobilization strategy that fits your molecules Optimize your assay: Work with our applications team to dial in conditions Scale up: Implement automation for high-throughput workflows Request Quote or Demo
Blog BLI Basics – What is BLI and How Does it Work? Understanding how proteins interact is fundamental to biologics development. Biolayer interferometry (BLI) makes these invisible interactions visible with real-time, precise visualization of binding interactions. What is BLI? Biolayer interferometry (BLI) is an optical, label-free technique that measures real-time binding at a sensor surface. BLI captures binding events as they happen, so it can measure both the rates and extent of molecular interactions, giving it broad appeal for quantitative kinetic and affinity characterization throughout biologics discovery and development. BLI works by analyzing interference patterns created when white light passes through specialized biosensors. Here’s how the technology works: The instrument shines white light through a fiber optic cable and into a glass biosensor. The biosensor tip is functionalized with capture molecules that bind target proteins of interest when placed into solution. As proteins bind and the layer of bound proteins grows, the instrument detects that change in distance through optical interference between two reflection points: a reference layer near the sensor surface and the outer edge of the protein layer. A spectrometer continuously monitors these reflections. After establishing a baseline measurement, the system tracks how the interference pattern shifts across the entire visible spectrum as the protein layer changes thickness. The instrument plots these changes on a graph called a sensorgram (a real-time binding curve), with time on the X-axis and “nm shift,” the measurable change in interference pattern, on the Y-axis. Watch how BLI translates molecular binding into actionable data Gator Bio’s BLI Platform Gator Bio’s BLI instruments allow users to analyze samples in standard 96-well or 384-well microplate formats, measuring up to 32 samples simultaneously. The plate-based design enables the instrument to move biosensors sequentially from well to well, capturing binding data at each step of your assay protocol. This high-throughput capability means you can screen hundreds of samples per day while maintaining the kinetic precision needed for confident decision-making. Explore Gator Bio BLI Instruments How BLI Measures Binding BLI transforms binding responses into quantitative data through two primary approaches: measuring binding kinetics or measuring total binding response. Quantitation Workflows For concentration measurements, BLI monitors the initial rate of binding (nm shift per second), which is directly proportional to both the association rate constant (kon) and analyte concentration. This relationship creates a concentration-dependent signal: higher concentrations produce steeper initial slopes. To quantify unknown samples, you run a series of known standard concentrations to generate a calibration curve, then compare your unknowns against this standard curve. Because the analysis focuses on initial binding rates, these quantitation experiments are remarkably fast, often finished in less than 2 minutes per sample. Rapid quantitation through initial rate analysis delivers results in under 2 minutes per sample Kinetics WorkflowsFor kinetic characterization, BLI provides real-time measurement of both binding (association) and unbinding (dissociation) between biomolecule pairs. This allows you to determine three critical parameters through curve fitting: – kon (association rate constant) – how fast molecules bind – koff (dissociation rate constant) – how fast molecules unbind – KD (equilibrium dissociation constant) – overall binding affinity. For interactions that reach equilibrium quickly, you can use endpoint binding measurements to determine KD . The minimal setup time and multi-channel format make BLI particularly attractive for high-throughput screening campaigns where you need to evaluate dozens or hundreds of candidates. Real-time kinetics reveal the complete binding profile—from initial association through full dissociation Key Terms in BLI Understanding BLI terminology helps you read and understand BLI literature: Capture Molecule: The reagent coated on the probe surface that is specifically designed to capture the first molecule of the binding pair Ligand: The first molecule of the binding pair captured onto the biosensor surface Analyte: The second molecule that binds to the immobilized ligand (your molecule of interest) Baseline: A buffer-only step that establishes the sensor’s stable starting point, allowing you to distinguish meaningful binding signal from instrument drift. A second baseline can also help to verify ligand binding to the biosensor. Loading: The kinetics assay step where biosensors are exposed to ligand-containing solution, allowing ligand to immobilize on the sensor surface Association: The kinetics assay step where analyte-containing solution contacts the ligand-loaded biosensor, enabling binding to occur Dissociation: The kinetics assay step where the biosensor returns to buffer-only conditions, allowing bound analyte to release from the sensor Assay Design Considerations Successful BLI experiments require a thoughtful experimental design. Here are the most critical considerations: Buffer Matching is Critical BLI is an optical technique that responds to changes in the refractive index. Variations in buffer composition between assay steps can create signal artifacts that overwhelm your binding data. For optimal performance: Maintain consistent buffer conditions across all assay steps Watch for refractive index shifts from pH changes, DMSO, glycerol, sucrose, and other solvents Remember that glycerol isn’t volatile and may remain in lyophilized proteins For quantitation assays, pre-soak biosensors in buffer similar to your samples to minimize non-specific signal. Choosing the Right Immobilization Strategy Kinetics assays require immobilizing your ligand to the biosensor without blocking the analyte binding site. The most common strategies target conserved domains or protein tags: Common capture approaches: Fc-binding biosensors: Protein A, Anti-Mouse Fc XT, Anti-Human Fc for antibodies Tag-based biosensors: Anti-His, Ni-NTA for His-tagged proteins; Strep-Tactin XT for twin-Strep-tagged proteins Biotin-Streptavidin: non-reversible, Streptavidin biosensors for biotinylated proteins (note: use a biotinylation protocol that limits the number of biotins per molecule – lysines are common in binding domains, and biotinylation can dramatically reduce analyte binding efficacy) Fc-binding and Tag-based biosensors offer the advantage of regeneration: a simple low pH wash typically removes bound analyte, allowing biosensor reuse and reduced costs. Browse Gator Bio Biosensors Optimizing Analyte Concentration Range Proper concentration selection is essential for accurate kinetic measurements. Your concentration series should: Upper range: Highest concentrations should show clear curve saturation (beginning to plateau) Lower range: Lowest concentrations should remain well below half-maximal binding Spacing: Use serial dilutions (typically 2-fold or 3-fold) to cover 2-3 orders of magnitude Concentration too high Concentration too low Optimal concentration Simulation of 1:1 binding interaction using Gator software’s built-in simulation feature for a 1 nM KD interaction, using 1 nm of response to represent 100% saturation of the available binding space in the simulation. Presented are several concentration series, each with a two-fold serial dilution of analyte. The sample labeled “too high” begins at 1000 nM, the “too low” sample begins at 2 nM, and the “optimal” sample begins at 50 nM. Minimize Ligand Loading A common mistake is overloading the ligand on the biosensor. More isn’t better—excessive ligand density can cause: Avidity effects that complicate analysis Reduced biosensor regeneration efficiency Use the minimum ligand loading level that still provides clear analyte binding signal. This typically means aiming for 0.5-1.0 nm loading signal for most antibody-based assays. Where BLI Fits in Your Analytical Toolkit Transitioning from ELISA Many laboratories upgrade from ELISA to BLI because real-time binding visualization provides complete kinetic context that endpoint assays simply can’t match. Instead of a single data point, you see the entire binding and unbinding process unfold. This makes troubleshooting straightforward; if something isn’t working, the sensorgram tells you exactly where the problem lies (poor binding, rapid dissociation, non-specific interactions, buffer effects). Key advantages over ELISA: – Real-time kinetic information, not just endpoint binding – No washing steps required – Label-free detection – Direct observation of binding specificity – 10-100x faster assay development Complementing SPR Workflows SPR laboratories often employ BLI for rapid candidate screening and prioritization. While SPR excels at detailed characterization of a few high-priority candidates, BLI’s multi-channel format enables you to quickly evaluate hundreds of candidates and identify the top 10-20 for deeper analysis. Why labs add BLI alongside SPR: Screen hundreds of samples per day (even base model instruments) Wider variety of off-the-shelf biosensors for diverse project types Plate-based format simplifies automation integration Faster setup reduces time from sample to data Lower cost per sample for routine screening Quality Control and Bioprocessing Applications BLI has become indispensable in manufacturing settings, delivering: Rapid quantitation: Real-time titer measurements that drive process decisions Potency assays: Activity measurements that meet regulatory requirements Clone selection: High-throughput screening during cell line development Formulation stability: Rapid assessment of protein aggregation and degradation In quality control environments, BLI’s speed and reproducibility make it ideal for routine lot release testing and in-process monitoring. Explore BLI in Manufacturing Quick Comparison: Gator BLI vs Other Binding Technologies FeatureGator Bio BLIELISASPRReal-Time Kinetics✓ Yes✗ No✓ YesLabel-Free✓ Yes✗ No✓ YesThroughputHigh (32 Channels)Medium-HighLow (1-8 channels)Setup Time< 15 mins2-4 Hours30-60 minAutomation-Ready✓ Plate-Based✓ Plate-BasedLimitedBiosensor VarietyVery HighN/AMediumSample ConsumptionLow (μL)Medium (μL-mL)Very low (μL)Best ForScreening & QCEndpoint AssaysDetailed characterization BLI: The Workhorse of Bioanalytical Labs BLI has earned its place as the workhorse platform in bioanalytical laboratories because it delivers the binding and quantitation data you need with unmatched speed and versatility. Whether you’re discovering new therapeutics, optimizing manufacturing processes, or ensuring product quality, BLI provides the analytical foundation for confident decision-making. Getting Started with BLI Ready to implement BLI in your laboratory? Here’s how to get started: Define your application: Determine whether you need quantitation, kinetics, or both Select your biosensor: Choose the immobilization strategy that fits your molecules Optimize your assay: Work with our applications team to dial in conditions Scale up: Implement automation for high-throughput workflows Request Quote or Demo
Blog Small Molecule Analysis via Biolayer Interferometry Why is small molecule analysis important? In pharmacology and molecular biology, “small molecules” have a molecular mass below around 900 Daltons.1 This distinction is far from arbitrary: The smaller a molecule is, the more easily it can permeate living systems. Small molecules are better able to diffuse across cell membranes, enabling them to reach intracellular action sites and giving them generally much higher bioavailability than larger “macromolecules” such as RNA or proteins.2 For this reason, the vast majority of pharmaceutical drugs are “small” molecules (with a few notable exceptions, such as insulin, which is a protein and consequently classed as a macromolecule). Small molecule analysis is therefore a key concern of pharmacology and molecular biology. In this context, “small molecule analysis” generally refers not only to the identification and quantification of small molecules themselves but to the analysis of their interactions with other molecules of interest (such as receptors). Yielding information on concentration, kinetics, binding and interaction affinities, small molecule analysis is fundamental to drug discovery and biomolecular research. What is biolayer interferometry and how does it work? First developed in the early 21st century, biolayer interferometry (BLI) is a relatively new technique for small molecule analysis.3–5 BLI uses the principle of optical interference to monitor binding activity between a pair of unique biomolecules.6 One of these molecules – the ligand – is bound (immobilized) to the surface of a fiber optic tip which has been coated with a biocompatible matrix to enable selective binding. Once the tip is prepared, it is “dipped” into a solution containing the other molecule in the pair (the analyte). As the analyte begins to interact with the ligand on the sensor tip, the optical thickness of the biological layer (consisting of ligand and analyte) on the surface of the sensing tip increases. Small molecule analysis is enabled by monitoring the precise thickness of this layer of interacting molecules using white light interferometry, which compares the spectral shift to that of a reference surface. How can biolayer interferometry be used for small molecule analysis? BLI provides real-time label-free monitoring of molecular interactions on the sensor surface by continuously measuring the thickness of the layer formed by ligand-analyte interactions. This is why BLI is such a powerful tool for small molecule analysis. Alongside surface plasmon resonance, BFI is one of only a few widely available biosensing techniques which doesn’t require labeling. However, BFI offers a few unique advantages for small molecule analysis. Crucially, only molecules binding to or dissociating from the sensor can shift the produced interference pattern and generate a response profile. This means that unbound molecules, changes in flow rate, or changes in the refractive index of the surrounding medium don’t influence measurements. BLI is also fluidics-free and can carry out small molecule analysis for either crude or purified samples. BLI is sensitive enough for peptide and small molecule analysis; and, thanks to its simple operation and the use of reusable fiber-optic tips, is well-suited to parallelized high-throughput applications. BLI Small Molecule Analysis from Gator Bio Founded by the inventors of biolayer interferometry, Gator Bio is a world-leading producer of BLI platforms for small molecule analysis applications.7 Combining a 1 mm glass rod with patented optical layers and specialized biosensor surface chemistry, Gator® BLI systems allow for high-capacity immobilization of biotinylated proteins for a wide range of molecular weights. Following immobilization, Gator® systems enable rapid and accurate small molecule analysis, determining critical interaction parameters such as kon, koff, and kD for molecules down to 150 Da. Are you interested in finding out more about the Gator BLI platform for small molecule analysis? Get in touch with us today. References and Further Reading 1. Dougherty, T. J. & Pucci, M. J. Antibiotic Discovery and Development. (Springer Science & Business Media, 2011). 2. Veber, D. F. et al. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. J. Med. Chem. 45, 2615–2623 (2002). 3. Label-free detection of biomolecular interactions using BioLayer interferometry for kinetic characterization – PubMed. https://pubmed.ncbi.nlm.nih.gov/19758119/. 4. Biosensor-based small molecule fragment screening with biolayer interferometry – PubMed. https://pubmed.ncbi.nlm.nih.gov/21660516/. 5. Biolayer Interferometry | Gator Bio. https://www.gatorbio.com/technology. 6. Apiyo, D. O. Chapter 10:Biolayer Interferometry (Octet) for Label-free Biomolecular Interaction Sensing. in Handbook of Surface Plasmon Resonance 356–397 (2017). doi:10.1039/9781788010283-00356. 7. About | Gator Bio. https://www.gatorbio.com/about.