Sequence Tolerance of a Single-Domain Antibody with a High Thermal Stability: Comparison of Computational and Experimental Fitness Profiles

A Multivalent and Thermostable Nanobody Neutralizing SARS-CoV-2 Omicron (B.1.1.529)

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variants have risen to dominance, which contains far more mutations in the spike protein in comparison to previously reported variants, compromising the efficacy of most existing vaccines or therapeutic monoclonal antibodies. Nanobody screened from high-throughput naïve libraries is a potential candidate for developing preventive and therapeutic antibodies.

Methods

Four nanobodies specific to the SARS-CoV-2 wild-type receptor-binding domain (RBD) were screened from a naïve phage display library. Their affinity and neutralizing activity were evaluated by surface plasmon resonance assays, surrogate virus neutralization tests, and pseudovirus neutralization assays. Preliminary identification of the binding epitopes of nanobodies by peptide-based ELISA and competition assay. Then four multivalent nanobodies were engineered by attaching the monovalent nanobodies to an antibody-binding nanoplatform constructed based on the lumazine synthase protein cage nanoparticles isolated from the Aquifex aeolicus (AaLS). Finally, the differences in potency between the monovalent and multivalent nanobodies were compared using the same methods.

Results

Three of the four specific nanobodies could maintain substantial inhibitory activity against the Omicron (B.1.1.529), of them, B-B2 had the best neutralizing activity against the Omicron (B.1.1.529) pseudovirus (IC₅₀ = 1.658 μg/mL). The antiviral ability of multivalent nanobody LS-B-B2 was improved in the Omicron (B.1.1.529) pseudovirus assays (IC₅₀ = 0.653 μg/mL). The results of peptide-based ELISA indicated that LS-B-B2 might react with the linear epitopes in the SARS-CoV-2 RBD conserved regions, which would clarify the mechanisms for the maintenance of potent neutralization of Omicron (B.1.1.529) preliminary.

Conclusion

Our study indicated that the AaLS could be used as an antibody-binding nanoplatform to present nanobodies on its surface and improve the potency of nanobodies. The multivalent nanobody LS-B-B2 may serve as a potential agent for the neutralization of SARS-CoV-2 variants.

Delicate balance among thermal stability, binding affinity, and conformational space explored by single-domain VHH antibodies

The high binding affinities and specificities of antibodies have led to their use as drugs and biosensors. Single-domain V_HH antibodies exhibit high specificity and affinity but have higher stability and solubility than conventional antibodies as they are single-domain proteins. In this work, based on physicochemical measurements and molecular dynamics (MD) simulations, we have gained insight that will facilitate rational design of single-chain V_HH antibodies. We first assessed two homologous V_HH antibodies by differential scanning calorimetry (DSC); one had a high (64.8 °C) and the other a low (58.6 °C) melting temperature. We then generated a series of the variants of the low stability antibody and analyzed their thermal stabilities by DSC and characterized their structures through MD simulations. We found that a single mutation that resulted in 8.2 °C improvement in melting temperature resulted in binding affinity an order of magnitude lower than the parent antibody, likely due to a shift of conformational space explored by the single-chain V_HH antibody. These results suggest that the delicate balance among conformational stability, binding capability, and conformational space explored by antibodies must be considered in design of fully functional single-chain V_HH antibodies.

NbX: Machine Learning-Guided Re-Ranking of Nanobody-Antigen Binding Poses

Modeling the binding pose of an antibody is a prerequisite to structure-based affinity maturation and design. Without knowing a reliable binding pose, the subsequent structural simulation is largely futile. In this study, we have developed a method of machine learning-guided re-ranking of antigen binding poses of nanobodies, the single-domain antibody which has drawn much interest recently in antibody drug development. We performed a large-scale self-docking experiment of nanobody–antigen complexes. By training a decision tree classifier through mapping a feature set consisting of energy, contact and interface property descriptors to a measure of their docking quality of the refined poses, significant improvement in the median ranking of native-like nanobody poses by was achieved eightfold compared with ClusPro and an established deep 3D CNN classifier of native protein–protein interaction. We further interpreted our model by identifying features that showed relatively important contributions to the prediction performance. This study demonstrated a useful method in improving our current ability in pose prediction of nanobodies.

Experimental evaluation of single-domain antibodies predicted by molecular dynamics simulations to have elevated thermal stability

Recently Bekker et al. [Bekker G-J et al. Protein Sci. 2019;28:429-438.] described a computational strategy of applying molecular-dynamics simulations to estimate the relative stabilities of single-domain antibodies, and utilized their method to design changes with the aim of increasing the stability of a single-domain antibody with a known crystal structure. The structure from which they generated potentially stabilizing mutations is an anti-cholera toxin single domain antibody selected from a naïve library which has relatively low thermal stability, reflected by a melting point of 48°C. Their work was purely theoretical, so to examine their predictions, we prepared the parental and predicted stabilizing mutant single domain antibodies and examined their thermal stability, ability to refold and affinity. We found that the mutation that improved stability the most (~7°C) was one which changed an amino acid in CDR1 from an asparagine to an aspartic acid. This change unfortunately was also accompanied by a reduction in affinity. Thus, while their modeling did appear to successfully predict stabilizing mutations, introducing mutations in the binding regions is problematic. Of further interest, the mutations selected via their high temperature simulations, did improve refolding, suggesting that they were successful in stabilizing the structure at high temperatures and thereby decrease aggregation. Our result should permit them to reassess and refine their model and may one day lead to a usefulin silico approach to protein stabilization.