Camelization of a murine single-domain antibody against aflatoxin B1 and its antigen-binding analysis

Optimization of synthetic human VH affinity and solubility through in vitro affinity maturation and minimal camelization

An attractive feature of human VHs over camelid VHHs as immunotherapeutics is their perceived lower risk of immunogenicity. While human VHs can readily be obtained from synthetic phage display libraries, they often suffer from low affinity and poor solubility compared to VHHs derived from immune libraries. Using SARS-CoV-2 spike protein as a model antigen, we screened a synthetic human VH phage display library and identified a diverse set of antigen-specific VHs. However, the VHs exhibited low affinity, and many had low solubility; that is, they were prone to aggregation. To explore the feasibility of improving the affinity, we subjected a representative VH to in vitro affinity maturation. We created a yeast surface display library of VH variants employing a site-saturated mutagenesis approach targeting complementarity-determining regions and selected against the target antigen. Next-generation sequencing of the selected variants, combined with structural modeling, identified a set of VHs as potentially improved candidates. Characterization of these candidates revealed several VHs with improved affinities of up to 100-fold (KDs as low as 3 nM) and potent neutralization capabilities; however, they still showed significant aggregation. By introducing as few as two camelid residues into the framework region 2 of a high-affinity VH (a process referred to as camelization), we were able to completely solubilize the VH without compromising its affinity and other important attributes, including thermostability and protein A binding. This study demonstrates the feasibility of generating high-affinity, -solubility, and -stability human VHs from synthetic libraries through a combination of in vitro affinity maturation and minimal camelization.

Characterization of a Novel Monoclonal Antibody with High Affinity and Specificity against Aflatoxins: A Discovery from Rosetta Antibody-Ligand Computational Simulation

Aflatoxin B1 (AFB1) accumulates in crops, where it poses a threat to human health. To detect AFB1, anti-AFB1 monoclonal antibodies have been developed and are widely used. While the sensitivity and specificity of these antibodies have been extensively studied, information regarding the atomic-level docking of AFB1 (and its derivatives) with these antibodies is limited. Such information is crucial for understanding the key interactions that are required for high affinity and specificity in aflatoxin binding. First, a 3D comparative model of anti-AFB1 antibody (Ab-4B5G6) was predicted from the sequence using RosettaAntibody. We then utilized RosettaLigand to dock AFB1 onto ten homology models, producing a total of 10,000 binding modes. Interestingly, the best-scoring mode predicted strong interactions involving four sites within the heavy chain: ALA33, ASN52, HIS95, and TRP99. Importantly, these strong binding interactions exclusively involve the variable domain of the heavy chain. The best-scoring mode with AFB1 was also obtained through AF multimer combined with RosettaLigand, and two interactions at TRP and HIS were consistent with those found by Rosetta antibody-ligand computational simulation. The role of tryptophan in π interactions in antibodies was confirmed through mutation experiments, and the resulting mutant (W99A) exhibited a >1000-fold reduction in binding affinity for AFB1 and analogs, indicating the effect of tryptophan on the stability of CDR-H3 region. Additionally, we evaluated the binding of two glycolic acid-derived molecular derivatives (with impaired hydrogen bonding potential), and these derivatives (AFB2-GA and AFG2-GA) demonstrated a very weak binding affinity for Ab-4B5G6. The heavy chain was successfully isolated, and its sensitivity and specificity were consistent with those of the intact antibody. The homology models of variable heavy (VH) single-domain antibodies were established by RosettaAntibody, and the docking analysis revealed the same residues, including Ala, His, and Trp. Compared to the potential binding mode of fragment variable (FV) region, the results from a model of VH indicated that there are seven models involved in hydrophobic interaction with TYR32, which is usually referred to as polar amino acid and has both hydrophobic and hydrophilic features depending on the circumstances. Our work encompasses the entire process of Rosetta antibody-ligand computational simulation, highlighting the significance of variable heavy domain structural design in enhancing molecular interactions.

ImmunoPET imaging of Trop2 expression in solid tumors with nanobody tracers

PURPOSE

The high expression of the transmembrane glycoprotein trophoblast cell-surface antigen 2 (Trop2) was strongly associated with the progression of solid tumors, including pancreatic and gastric cancers. Our study aimed to construct Trop2-specific immuno-positron emission tomography (immunoPET) probes and assess the diagnostic abilities in preclinical pancreatic and gastric cancer models.

METHODS

The expression of Trop2 in pancreatic cancer was determined by single-cell sequencing and immunohistochemistry on tissue microarray (TMA). Flow cytometry was used to screen the expression of Trop2 in pancreatic cancer cell lines. Two nanobodies (i.e., RTD98 and RTD01) targeting Trop2 were developed and labeled with gallium-68 (68Ga, T1/2 = 1.1 h) to construct immunoPET imaging probes. The agents were researched in cell-derived pancreatic and patient-derived gastric cancer models expressing varying Trop2.

RESULTS

Single-cell sequencing results showed high expression of Trop2 in pancreatic ductal cells as well as acinar cells and immunohistochemical staining of TMA from pancreatic cancers showed significantly higher expression of Trop2 in cancerous than in paracancerous tissues. ImmunoPET utilizing [68Ga]Ga-NOTA-RTD98 could clearly delineate subcutaneous tumors, both in cell-derived pancreatic cancer models and patient-derived gastric cancer models, superior to imaging using [18F]-FDG or a non-specific probe [68Ga]Ga-NOTA-RTD161. Another probe with improved pharmacokinetics targeting Trop2, [68Ga]Ga-NOTA-RTD01, was further prepared and showed advantageous diagnostic capabilities in preclinical pancreatic cancer models.

CONCLUSION

In the work, we reported two nanobody tracers targeting human Trop2 which may facilitate better use of Trop2-targeted therapeutics by noninvasively displaying expression dynamics of the target.

Overview-gold nanoparticles-based sensitive nanosensors in mycotoxins detection

Food-borne mycotoxins is one of the food safety concerns in the world. At present, nanosensors are widely used in the detection and analysis of mycotoxins due to their high specificity and sensitivity. In nanosensor-based mycotoxindetections, the sensitivity is mainly improved from two aspects. On the one hand, based on the principle of immune response, antigens and antibodies can be modified and developed. Such as single-domain heavy chain antibodies, aptamers, peptides, and antigen mimotopes. On the other hand, improvements and innovations have been made on signal amplification materials, including gold nanoparticles (AuNPs), quantum dots, and graphene, etc. Among them, gold nanoparticles can not only be used as a signal amplification material, but also can be used as carriers for identification elements, which can be used for signal amplification in detection. In this article, we systematically summarized the emerging strategies for enhancing the detection sensitivity of traditional gold nanoparticles-based nanosensors, in terms of recognition elements and signal amplification. Representative examples were selected to illustrate the potential mechanism of each strategy in enhancing the colorimetric signal intensity of AuNP and its potential application in biosensing. Finally, our review suggested the challenges and future prospects of gold particles in detection of mycotoxins.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

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.