Heavy-chain-only antibodies (HcAbs), found naturally in camelids and cartilaginous fish, have been widely explored for therapeutic applications. The variable domain of these antibodies, known as VHH, has emerged as a promising scaffold due to its small size, stability, and high affinity for antigens. Despite these advantages, VHHs require humanization and sequence optimization to be suitable for clinical applications.
A recent study by Fernández-Quintero et al. (2024) investigated the structural determinants governing the function of VHHs, with a particular focus on humanization strategies and their impact on antigen binding, stability, and developability. Using experimental and computational methods, two llama-derived VHHs targeting NKp30 were analyzed to understand how Hallmark residues and non-canonical disulfide bonds influence VHH conformation and antigen binding.1
VHHs differ from conventional antibodies in that they lack a light chain, which results in structural adaptations to compensate for the missing variable light (VL) domain. One such adaptation is the presence of Hallmark residues in framework region 2 (FR2), which contribute to stability, solubility, and antigen binding. Additionally, VHHs frequently contain non-canonical cysteine pairings that introduce stabilizing disulfide bonds, often linking CDR3 to other regions of the antibody.
The role of Hallmark residues in VHH function has been partially characterized, but systematic humanization studies remain limited. In earlier work, Vincke et al. (2009) demonstrated that modifying certain Hallmark residues could alter VHH stability and binding affinity.2 More recent studies have proposed humanizing entire framework regions or selectively modifying key residues while preserving functional characteristics.
Related: VHH Antibody Discovery
To assess the impact of humanization, Fernández-Quintero et al. (2024) obtained co-crystal structures of NKp30-VHH2 complexes, while modeling the VHH1-NKp30 complex with AlphaFold2. The high-resolution structure of VHH2 provided direct insights into its binding interactions, while molecular dynamics (MD) simulations were used to predict conformational changes in humanized variants.
Key findings from their structural analysis include:
Role of Hallmark Residues: In VHH2, Hallmark residues do not directly contact the antigen but instead stabilize the bioactive conformation of CDR3. Conversely, in VHH1, Hallmark residues contribute to antigen interactions but do not stabilize CDR3.
Effect of Non-Canonical Disulfide Bonds: The presence of a non-canonical disulfide bond in VHH1 compensates for the absence of stabilizing Hallmark interactions, ensuring proper CDR3 orientation.
Binding Affinity and Conformational Stability: MD simulations revealed that humanization of specific Hallmark residues increased conformational entropy, leading to a broader distribution of CDR3 conformations and reduced antigen affinity.
These findings highlight the structural diversity of VHHs and emphasize the need for tailored humanization strategies based on individual antibody architectures.
Related: Future Directions for VHH Antibody Research and Development
Humanization of VHHs can reduce immunogenicity and improve clinical translation. However, applying conventional antibody humanization approaches to VHHs is challenging due to their unique structural features. Several considerations must be taken into account when designing humanized VHH variants:
1. Selection of Framework and CDR Definition
The definition of complementarity-determining regions (CDRs) varies between numbering schemes such as Kabat, Chothia, and IMGT. These discrepancies influence which residues are considered part of the framework and impact humanization strategies. In this study, a combined IMGT-Kabat-Chothia definition was used to minimize unintended structural disruptions.
2. Risk Assessment of Humanizing Key Residues
High-risk residues: Hallmark positions 42 and 52, which are critical for antigen binding or CDR3 stabilization.
Moderate-risk residues: Positions 49 and 50, which can affect hydrophobicity and solubility.
Framework residues adjacent to CDRs: Modifications in these regions may indirectly alter antigen-binding properties.
By prioritizing low-risk mutations and introducing only necessary modifications, humanization efforts can maintain the functional integrity of VHHs while reducing immunogenicity.
3. Balancing Human-Likeness and Developability
Humanizing VHHs to maximize sequence similarity to human germline antibodies does not always guarantee reduced immunogenicity. Other factors, including aggregation potential, chemical stability, and epitope presentation, contribute to immunogenic responses. Therefore, humanization should be combined with developability assessments to optimize antibody properties beyond sequence similarity.
Related: VHH Antibody Discovery
Current humanization protocols are labor-intensive and require extensive experimental validation. Integrating computational tools, such as AlphaFold2 and machine learning algorithms, can accelerate the identification of optimal humanized variants.
Potential advancements include:
Automated sequence optimization: Using AI-driven tools to predict optimal framework modifications while maintaining antigen affinity.
High-throughput stability screening: Implementing rapid assays to assess solubility, aggregation, and stability in early-stage humanization campaigns.
Machine learning-based immunogenicity prediction: Using large datasets to refine humanization strategies based on predicted immune responses.
These approaches will streamline VHH engineering and facilitate the rapid development of therapeutic candidates with improved clinical potential.
Early humanization strategies, such as CDR grafting, have been widely used to replace non-human residues with human sequences, but these methods often disrupt binding and require additional modifications to restore functionality. Advances in computational modeling have led to the development of humanness scoring metrics, such as the H-score, G-score, and T20, which quantify the similarity of an antibody sequence to human germline sequences. More recent machine learning-based tools, including Hu-mAb, BioPhi, and AbBERT, leverage large antibody sequence datasets to optimize humanization strategies. These models demonstrate promising correlations between humanness scores and reduced immunogenicity, though their effectiveness remains constrained by the availability and diversity of training datasets.3
Computational tools such as Llamanade and AbNatiV have been specifically designed to balance VHH antibody humanness with retention of structural integrity.4 These methods use deep learning and structural modeling to optimize humanization while preserving antigen-binding properties. However, due to the relatively limited availability of VHH sequence data compared to conventional antibodies, further validation and refinement of these models are necessary to ensure reliable humanization predictions.
Beyond reducing immunogenicity, humanization must be integrated into a broader multi-parameter optimization framework that considers factors such as aggregation propensity, stability, and pharmacokinetics. Machine learning approaches are now employed to simultaneously optimize multiple developability attributes, leveraging high-throughput experimental data to refine predictive models. However, a key challenge remains in validating these in silico humanization methods, as correlations between humanness scores and real-world immunogenicity responses are often weak. Future advances will depend on expanding diverse antibody and VHH datasets, improving experimental validation pipelines, and integrating computational humanization tools into antibody discovery workflows to enhance therapeutic success rates.
Related: Therapeutic Antibody Production: Future Projections
At Biointron, we are dedicated to accelerating antibody discovery, optimization, and production. Our team of experts can provide customized solutions that meet your specific research needs, including VHH Antibody Discovery. Contact us to learn more about our services and how we can help accelerate your research and drug development projects.
Fernández-Quintero, M. L., Guarnera, E., Musil, D., Pekar, L., Sellmann, C., Freire, F., Sousa, R. L., Santos, S. P., Freitas, M. C., Bandeiras, T. M., S. Silva, M. M., Loeffler, J. R., Ward, A. B., Harwardt, J., Zielonka, S., & Evers, A. (2024). On the humanization of VHHs: Prospective case studies, experimental and computational characterization of structural determinants for functionality. Protein Science, 33(11), e5176. https://doi.org/10.1002/pro.5176
Vincke, C., Loris, R., Saerens, D., Martinez-Rodriguez, S., Muyldermans, S., & Conrath, K. (2009). General Strategy to Humanize a Camelid Single-domain Antibody and Identification of a Universal Humanized Nanobody Scaffold. Journal of Biological Chemistry, 284(5), 3273–3284. https://doi.org/10.1074/jbc.m806889200
Gordon, G. L., Raybould, M. I., Wong, A., & Deane, C. M. (2024). Prospects for the computational humanization of antibodies and nanobodies. Frontiers in Immunology, 15, 1399438. https://doi.org/10.3389/fimmu.2024.1399438
Sang, Z., Xiang, Y., Bahar, I., & Shi, Y. (2022). Llamanade: An open-source computational pipeline for robust nanobody humanization. Structure, 30(3), 418-429.e3. https://doi.org/10.1016/j.str.2021.11.006
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