Llamanade: An open-source computational pipeline for robust nanobody humanization

Nanoscale warriors against viral invaders: a comprehensive review of Nanobodies as potential antiviral therapeutics

Viral infections remain a significant global health threat, with emerging and reemerging viruses causing epidemics and pandemics. Despite advancements in antiviral therapies, the development of effective treatments is often hindered by challenges, such as viral resistance and the emergence of new strains. In this context, the development of novel therapeutic modalities is essential to combat notorious viruses. While traditional monoclonal antibodies are widely used for the treatment of several diseases, nanobodies derived from heavy chain-only antibodies have emerged as promising "nanoscale warriors" against viral infections. Nanobodies possess unique structural properties that enhance their ability to recognize diverse epitopes. Their small size also imparts properties, such as improved bioavailability, solubility, stability, and proteolytic resistance, making them an ideal class of therapeutics for viral infections. In this review, we discuss the role of nanobodies as antivirals against various viruses. Techniques used for developing nanobodies, delivery strategies are covered, and the challenges and opportunities associated with their use as antiviral therapies are discussed. We also offer insights into the future of nanobody-based antiviral research to support the development of new strategies for managing viral infections.

Nanobodies: From Discovery to AI-Driven Design

Nanobodies, derived from naturally occurring heavy-chain antibodies in camelids (VHHs) and sharks (VNARs), are unique single-domain antibodies that have garnered significant attention in therapeutic, diagnostic, and biotechnological applications due to their small size, stability, and high specificity. This review first traces the historical discovery of nanobodies, highlighting key milestones in their isolation, characterization, and therapeutic development. We then explore their structure-function relationship, emphasizing features like their single-domain architecture and long CDR3 loop that contribute to their binding versatility. Additionally, we examine the growing interest in multiepitope nanobodies, in which binding to different epitopes on the same antigen not only enhances neutralization and specificity but also allows these nanobodies to be used as controllable modules for precise antigen manipulation. This review also discusses the integration of AI in nanobody design and optimization, showcasing how machine learning and deep learning approaches are revolutionizing rational design, humanization, and affinity maturation processes. With continued advancements in structural biology and computational design, nanobodies are poised to play an increasingly vital role in addressing both existing and emerging biomedical challenges.

Pure drug nanomedicines - where we are?

Pure drug nanomedicines (PDNs) encompass active pharmaceutical ingredients (APIs), including macromolecules, biological compounds, and functional components. They overcome research barriers and conversion thresholds associated with nanocarriers, offering advantages such as high drug loading capacity, synergistic treatment effects, and environmentally friendly production methods. This review provides a comprehensive overview of the latest advancements in PDNs, focusing on their essential components, design theories, and manufacturing techniques. The physicochemical properties and in vivo behaviors of PDNs are thoroughly analyzed to gain an in-depth understanding of their systematic characteristics. The review introduces currently approved PDN products and further explores the opportunities and challenges in expanding their depth and breadth of application. Drug nanocrystals, drug-drug cocrystals (DDCs), antibody-drug conjugates (ADCs), and nanobodies represent the successful commercialization and widespread utilization of PDNs across various disease domains. Self-assembled pure drug nanoparticles (SAPDNPs), a next-generation product, still require extensive translational research. Challenges persist in transitioning from laboratory-scale production to mass manufacturing and overcoming the conversion threshold from laboratory findings to clinical applications.

A synthetic heavy chain variable domain antibody library (VHL) provides highly functional antibodies with favorable developability

Synthetic antibody libraries have been developed as an efficient source for the discovery of the heavy chain variable (VH) domain, which exhibits low immunogenicity, high tissue penetration, and diverse binding epitopes in therapeutic biopharmaceuticals. In this study, the human IGHV3-23*04 germline gene was chosen as the scaffold with a high expression level and favorable thermal stability. Amino acid diversity was introduced into the complementarity determining region 3 (CDR3) to exclude potential sequence liabilities. A library containing 2.6 × 1011 independent clones was successfully constructed. The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, interleukin-17A (IL17A), B-cell maturation antigen (BCMA), and G-protein coupled receptor family C group 5 member D (GPRC5D) were used as target antigens to screen and identify VHs. In each case, Thirty-one to fifty-five VHs were screened out. The VH-Fc antibodies showed superior affinities (as high as 4.6 nM) to the corresponding antigens but did not bind to antigen-irrelevant cell CHO-S. Furthermore, the anti-RBD and anti-IL17A VH-Fc antibodies showed strong functional activity in the receptor-blocking assays. The VH-Fc antibodies from the synthetic library exhibited favorable developability (thermal stability, colloidal stability, hydrophilicity, anti-aggregation ability, and no interaction with human IgGs). We demonstrated that high-affinity and highly functional VH domain antibodies were generated from the rationally designed library with desired physicochemical properties. This approach is generally universal to target any antigen and has significant potential to accelerate candidate selection.

Nanobodies: From High-Throughput Identification to Therapeutic Development

The camelid single-domain antibody fragment, commonly referred to as a nanobody, achieves the targeting power of conventional monoclonal antibodies (mAbs) at only a fraction of their size. Isolated from camelid species (including llamas, alpacas, and camels), their small size at ∼15 kDa, low structural complexity, and high stability compared with conventional antibodies have propelled nanobody technology into the limelight of biologic development. Nanobodies are proving themselves to be a potent complement to traditional mAb therapies, showing success in the treatment of, for example, autoimmune diseases and cancer, and more recently as therapeutic options to treat infectious diseases caused by rapidly evolving biological targets such as the SARS-CoV-2 virus. This review highlights the benefits of applying a proteomic approach to identify diverse nanobody sequences against a single antigen. This proteomic approach coupled with conventional yeast/phage display methods enables the production of highly diverse repertoires of nanobodies able to bind the vast epitope landscape of an antigen, with epitope sampling surpassing that of mAbs. Additionally, we aim to highlight recent findings illuminating the structural attributes of nanobodies that make them particularly amenable to comprehensive antigen sampling and to synergistic activity-underscoring the powerful advantage of acquiring a large, diverse nanobody repertoire against a single antigen. Lastly, we highlight the efforts being made in the clinical development of nanobodies, which have great potential as powerful diagnostic reagents and treatment options, especially when targeting infectious disease agents.

On the humanization of VHHs: Prospective case studies, experimental and computational characterization of structural determinants for functionality

The humanization of camelid-derived variable domain heavy chain antibodies (VHHs) poses challenges including immunogenicity, stability, and potential reduction of affinity. Critical to this process are complementarity-determining regions (CDRs), Vernier and Hallmark residues, shaping the three-dimensional fold and influencing VHH structure and function. Additionally, the presence of non-canonical disulfide bonds further contributes to conformational stability and antigen binding. In this study, we systematically humanized two camelid-derived VHHs targeting the natural cytotoxicity receptor NKp30. Key structural positions in Vernier and Hallmark regions were exchanged with residues from the most similar human germline sequences. The resulting variants were characterized for binding affinities, yield, and purity. Structural binding modes were elucidated through crystal structure determination and AlphaFold2 predictions, providing insights into differences in binding affinity. Comparative structural and molecular dynamics characterizations of selected variants were performed to rationalize their functional properties and elucidate the role of specific sequence motifs in antigen binding. Furthermore, systematic analyses of next-generation sequencing (NGS) and Protein Data Bank (PDB) data was conducted, shedding light on the functional significance of Hallmark motifs and non-canonical disulfide bonds in VHHs in general. Overall, this study provides valuable insights into the structural determinants governing the functional properties of VHHs, offering a roadmap for their rational design, humanization, and optimization for therapeutic applications.

Single-Domain Antibodies-Novel Tools to Study and Treat Allergies

IgE-mediated allergies represent a major health problem in the modern world. Apart from allergen-specific immunotherapy (AIT), the only disease-modifying treatment, researchers focus on biologics that target different key molecules such as allergens, IgE, or type 2 cytokines to ameliorate allergic symptoms. Single-domain antibodies, or nanobodies, are the newcomers in biotherapeutics, and their huge potential is being investigated in various research fields since their discovery 30 years ago. While they are dominantly applied for theranostics of cancer and treatment of infectious diseases, nanobodies have become increasingly substantial in allergology over the last decade. In this review, we discuss the prerequisites that we consider to be important for generating useful nanobody-based drug candidates for treating allergies. We further summarize the available research data on nanobodies used as allergen monitoring and detection probes and for therapeutic approaches. We reflect on the limitations that have to be addressed during the development process, such as in vivo half-life and immunogenicity. Finally, we speculate about novel application formats for allergy treatment that might be available in the future.

PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering

Protein scaffolds with small size, high stability and low immunogenicity show important applications in the field of protein engineering and design. However, no relevant computational platform has been reported yet to mining such scaffolds with the desired properties from massive protein structures in human body. Here, we developed PROSCA, a structure-based online platform dedicated to explore the space of the entire human proteome, and to discovery new privileged protein scaffolds with potential engineering value that have never been noticed. PROSCA accepts structure of protein as an input, which can be subsequently aligned with a certain class of protein structures (e.g. the human proteome either from experientially resolved or AlphaFold2 predicted structures, and the human proteins belonging to specific families or domains), and outputs humanized protein scaffolds which are structurally similar with the input protein as well as other related important information such as families, sequences, structures and expression level in human tissues. Through PROSCA, the user can also get excellent experience in visualizations of protein structures and expression overviews, and download the figures and tables of results which can be customized according to the user's needs. Along with the advanced protein engineering and selection technologies, PROSCA will facilitate the rational design of new functional proteins with privileged scaffolds. PROSCA is freely available at https://idrblab.org/prosca/.

Functional insights of Tyr37 in framework region 2 directly contributing to the binding affinities and dissociation kinetics in single-domain VHH antibodies

Single-domain VHH antibody is regarded as one of the promising antibody classes for therapeutic and diagnostic applications. VHH antibodies have amino acids in framework region 2 that are distinct from those in conventional antibodies, such as the Val37Phe/Tyr (V37F/Y) substitution. Correlations between the residue type at position 37 and the conformation of the CDR3 in VHH antigen recognition have been previously reported. However, few studies focused on the meaning of harboring two residue types in position 37 of VHH antibodies, and the concrete roles of Y37 have been little to be elucidated. Here, we investigated the functional states of position 37 in co-crystal structures and performed analyses of three model antibodies with either F or Y at position 37. Our analysis indicates that Y at position 37 enhances the dissociation rate, which is highly correlated with drug efficacy. Our findings help to explain the molecular mechanisms that distinguish VHH antibodies from conventional antibodies.

Prospects for the computational humanization of antibodies and nanobodies

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space

Motivation

Nanobodies are a subclass of immunoglobulins, whose binding site consists of only one peptide chain, bestowing favorable biophysical properties. Recently, the first nanobody therapy was approved, paving the way for further clinical applications of this antibody format. Further development of nanobody-based therapeutics could be streamlined by computational methods. One of such methods is infilling-positional prediction of biologically feasible mutations in nanobodies. Being able to identify possible positional substitutions based on sequence context, facilitates functional design of such molecules.

Results

Here we present nanoBERT, a nanobody-specific transformer to predict amino acids in a given position in a query sequence. We demonstrate the need to develop such machine-learning based protocol as opposed to gene-specific positional statistics since appropriate genetic reference is not available. We benchmark nanoBERT with respect to human-based language models and ESM-2, demonstrating the benefit for domain-specific language models. We also demonstrate the benefit of employing nanobody-specific predictions for fine-tuning on experimentally measured thermostability dataset. We hope that nanoBERT will help engineers in a range of predictive tasks for designing therapeutic nanobodies.

Availability and implementation

https://huggingface.co/NaturalAntibody/.

Developability considerations for bispecific and multispecific antibodies

Bispecific antibodies (bsAb) and multispecific antibodies (msAb) encompass a diverse variety of formats that can concurrently bind multiple epitopes, unlocking mechanisms to address previously difficult-to-treat or incurable diseases. Early assessment of candidate developability enables demotion of antibodies with low potential and promotion of the most promising candidates for further development. Protein-based therapies have a stringent set of developability requirements in order to be competitive (e.g. high-concentration formulation, and long half-life) and their assessment requires a robust toolkit of methods, few of which are validated for interrogating bsAbs/msAbs. Important considerations when assessing the developability of bsAbs/msAbs include their molecular format, likelihood for immunogenicity, specificity, stability, and potential for high-volume production. Here, we summarize the critical aspects of developability assessment, and provide guidance on how to develop a comprehensive plan tailored to a given bsAb/msAb.

Heavy chain-only antibodies with a stabilized human VH in transgenic chickens for therapeutic antibody discovery

Heavy chain-only antibodies have found many applications where conventional heavy-light heterodimeric antibodies are not favorable. Heavy chain-only antibodies with their single antigen-binding domain offer the advantage of a smaller size and higher stability relative to conventional antibodies, and thus, the potential for novel targeting modalities. Domain antibodies have commonly been sourced from camelids with ex-vivo humanization or transgenic rodents expressing heavy chains without light chains, but these host species are all mammalian, limiting their capacity to elicit robust immune responses to conserved mammalian targets. We have developed transgenic chickens expressing heavy chain-only antibodies with a human variable region to combine the superior target recognition advantages of a divergent, non-mammalian host with the ability to discover single-domain binders. These birds produce robust immune responses, consisting of antigen-specific antibodies targeting diverse epitopes with a range of affinities. Biophysical attributes are favorable, with good developability profiles and low predicted immunogenicity.

Evaluation of the Potential Impact of In Silico Humanization on VHH Dynamics

Camelids have the peculiarity of having classical antibodies composed of heavy and light chains as well as single-chain antibodies. They have lost their light chains and one heavy-chain domain. This evolutionary feature means that their terminal heavy-chain domain, VH, called VHH here, has no partner and forms an independent domain. The VHH is small and easy to express alone; it retains thermodynamic and interaction properties. Consequently, VHHs have garnered significant interest from both biotechnological and pharmaceutical perspectives. However, due to their origin in camelids, they cannot be used directly on humans. A humanization step is needed before a possible use. However, changes, even in the constant parts of the antibodies, can lead to a loss of quality. A dedicated tool, Llamanade, has recently been made available to the scientific community. In a previous paper, we already showed the different types of VHH dynamics. Here, we have selected a representative VHH and tested two humanization hypotheses to accurately assess the potential impact of these changes. This example shows that despite the non-negligible change (1/10th of residues) brought about by humanization, the effect is not drastic, and the humanized VHH retains conformational properties quite similar to those of the camelid VHH.

A comparison of the binding sites of antibodies and single-domain antibodies

Antibodies are the largest class of biotherapeutics. However, in recent years, single-domain antibodies have gained traction due to their smaller size and comparable binding affinity. Antibodies (Abs) and single-domain antibodies (sdAbs) differ in the structures of their binding sites: most significantly, single-domain antibodies lack a light chain and so have just three CDR loops. Given this inherent structural difference, it is important to understand whether Abs and sdAbs are distinguishable in how they engage a binding partner and thus, whether they are suited to different types of epitopes. In this study, we use non-redundant sequence and structural datasets to compare the paratopes, epitopes and antigen interactions of Abs and sdAbs. We demonstrate that even though sdAbs have smaller paratopes, they target epitopes of equal size to those targeted by Abs. To achieve this, the paratopes of sdAbs contribute more interactions per residue than the paratopes of Abs. Additionally, we find that conserved framework residues are of increased importance in the paratopes of sdAbs, suggesting that they include non-specific interactions to achieve comparable affinity. Furthermore, the epitopes of sdAbs are only marginally less accessible than those of Abs: we posit that this may be explained by differences in the orientation and compaction of sdAb and Ab CDR-H3 loops. Overall, our results have important implications for the engineering and humanization of sdAbs, as well as the selection of the best modality for targeting a particular epitope.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Nanobodies: Robust miniprotein binders in biomedicine

Variable domains of heavy chain-only antibodies (VHH), also known as nanobodies (Nbs), are monomeric antigen-binding domains derived from the camelid heavy chain-only antibodies. Nbs are characterized by small size, high target selectivity, and marked solubility and stability, which collectively facilitate high-quality drug development. In addition, Nbs are readily expressed from various expression systems, including E. coli and yeast cells. For these reasons, Nbs have emerged as preferred antibody fragments for protein engineering, disease diagnosis, and treatment. To date, two Nb-based therapies have been approved by the U.S. Food and Drug Administration (FDA). Numerous candidates spanning a wide spectrum of diseases such as cancer, immune disorders, infectious diseases, and neurodegenerative disorders are under preclinical and clinical investigation. Here, we discuss the structural features of Nbs that allow for specific, versatile, and strong target binding. We also summarize emerging technologies for identification, structural analysis, and humanization of Nbs. Our main focus is to review recent advances in using Nbs as a modular scaffold to facilitate the engineering of multivalent polymers for cutting-edge applications. Finally, we discuss remaining challenges for Nb development and envision new opportunities in Nb-based research.

Local delivery of optimized nanobodies targeting the PD-1/PD-L1 axis with a self-amplifying RNA viral vector induces potent antitumor responses

Despite the success of immune checkpoint blockade for cancer therapy, many patients do not respond adequately. We aimed to improve this therapy by optimizing both the antibodies and their delivery route, using small monodomain antibodies (nanobodies) delivered locally with a self-amplifying RNA (saRNA) vector based on Semliki Forest virus (SFV). We generated nanobodies against PD-1 and PD-L1 able to inhibit both human and mouse interactions. Incorporation of a dimerization domain reduced PD-1/PD-L1 IC50 by 8- and 40-fold for anti-PD-L1 and anti-PD-1 nanobodies, respectively. SFV viral particles expressing dimeric nanobodies showed a potent antitumor response in the MC38 model, resulting in >50% complete regressions, and showed better therapeutic efficacy compared to vectors expressing conventional antibodies. These effects were also observed in the B16 melanoma model. Although a short-term expression of nanobodies was observed due to the cytopathic nature of the saRNA vector, it was enough to generate a strong proinflammatory response in tumors, increasing infiltration of NK and CD8⁺ T cells. Delivery of the SFV vector expressing dimeric nanobodies by local plasmid electroporation, which could be more easily translated to the clinic, also showed a potent antitumor effect.

Therapeutic Phage Display-Derived Single-Domain Antibodies for Pandemic Preparedness

Driven by necessity, the COVID-19 pandemic caused by SARS-CoV-2 has accelerated the development and implementation of new vaccine platforms and other viral therapeutics. Among these is the therapeutic use of antibodies including single-domain antibodies, in particular the camelid variable heavy-chain fragment (VHH). Such therapies can provide a critical interim intervention when vaccines have not yet been developed for an emerging virus. It is evident that an increasing number of different viruses are emerging and causing epidemics and pandemics with increasing frequency. It is therefore imperative that we capitalize on the experience and knowledge gained from combatting COVID-19 to be better prepared for the next pandemic.

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

Single Domain Antibody application in bacterial infection diagnosis and neutralization

Increasing antibiotic resistance to bacterial infections causes a serious threat to human health. Efficient detection and treatment strategies are the keys to preventing and reducing bacterial infections. Due to the high affinity and antigen specificity, antibodies have become an important tool for diagnosis and treatment of various human diseases. In addition to conventional antibodies, a unique class of “heavy-chain-only” antibodies (HCAbs) were found in the serum of camelids and sharks. HCAbs binds to the antigen through only one variable domain Referred to as VHH (variable domain of the heavy chain of HCAbs). The recombinant format of the VHH is also called single domain antibody (sdAb) or nanobody (Nb). Sharks might also have an ancestor HCAb from where SdAbs or V-NAR might be engineered. Compared with traditional Abs, Nbs have several outstanding properties such as small size, high stability, strong antigen-binding affinity, high solubility and low immunogenicity. Furthermore, they are expressed at low cost in microorganisms and amenable to engineering. These superior properties make Nbs a highly desired alternative to conventional antibodies, which are extensively employed in structural biology, unravelling biochemical mechanisms, molecular imaging, diagnosis and treatment of diseases. In this review, we summarized recent progress of nanobody-based approaches in diagnosis and neutralization of bacterial infection and further discussed the challenges of Nbs in these fields.

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

VHH Structural Modelling Approaches: A Critical Review

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.