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.

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 (k_off). 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.

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:

1. Weak binding affinity
2. Inconsistent performance across proteins
3. Avidity
4. 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:

1. 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.
   
2. 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.
   
3. 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 k_on, k_off, or K_D 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 K_D and k_off 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 K_D and k_off of each of these tested proteins. We observed a relatively wide variability in both K_D and k_off 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: k_off and K_D 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.

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 k_on 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.

1. Beware of over-loading your biosensors
   Because 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.
   
2. Managing second-baseline dissociation
   Although 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.
   
3. Optimize regeneration conditions
   We 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

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:

1. Define your application: Determine whether you need quantitation, kinetics, or both
2. Select your biosensor: Choose the immobilization strategy that fits your molecules
3. Optimize your assay: Work with our applications team to dial in conditions
4. Scale up: Implement automation for high-throughput workflows

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