Homology Modeling-Based in Silico Affinity Maturation Improves the Affinity of a Nanobody

Novel bispecific nanobody mitigates experimental intestinal inflammation in mice by targeting TNF-α and IL-23p19 bioactivities

BACKGROUND

Inflammatory bowel diseases (IBDs) pose significant challenges in terms of treatment non-response, necessitating the development of novel therapeutic approaches. Although biological medicines that target TNF-α (tumour necrosis factor-α) have shown clinical success in some IBD patients, a substantial proportion still fails to respond.

METHODS

We designed bispecific nanobodies (BsNbs) with the ability to simultaneously target human macrophage-expressed membrane TNF-α (hmTNF-α) and IL-23. Additionally, we fused the constant region of human IgG1 Fc (hIgG1 Fc) to BsNb to create BsNb-Fc.  Our study encompassed in vitro and in vivo characterization of BsNb and BsNb-Fc.

RESULTS

BsNb-Fc exhibited an improved serum half-life, targeting capability and effector function than BsNb. It's demonstrated that BsNb-Fc exhibited superior anti-inflammatory effects compared to the anti-TNF-α mAb (infliximab, IFX) combined with anti-IL-12/IL-23p40 mAb (ustekinumab, UST) by Transwell co-culture assays. Notably, in murine models of acute colitis brought on by 2,4,6-trinitrobenzene sulfonic acid（TNBS) and dextran sulphate sodium (DSS), BsNb-Fc effectively alleviated colitis severity. Additionally, BsNb-Fc outperformed the IFX&UST combination in TNBS-induced colitis, significantly reducing colon inflammation in mice with colitis produced by TNBS and DSS.

CONCLUSION

These findings highlight an enhanced efficacy and improved biostability of BsNb-Fc, suggesting its potential as a promising therapeutic option for IBD patients with insufficient response to TNF-α inhibition.

KEY POINTS

A bispecific nanobody (BsNb) was created to target TNF-α and IL-23p19, exhibiting high affinity and remarkable stability. BsNb-Fc inhibited the release of cytokines in CD4+T cells during co-culture experiments. BsNb-Fc effectively alleviated colitis severity in mouse model with acute colitis induced by DSS or TNBS, outperforming the IFX&UST combination.

Improvement of the sensitivity of lateral flow systems for detecting mycotoxins: Up-to-date strategies and future perspectives

Mycotoxins are dangerous human and animal health-threatening secondary fungal metabolites that can be found in various food and agricultural products. Several countries have established regulations to restrict their presence in food and agricultural products destined for human and animal consumption. Consequently, the need to develop highly sensitive and smart detection systems was recognized worldwide. Lateral flow assay possesses the advantages of easy operation, rapidity, stability, accuracy, and specificity, and it plays an important role in the detection of mycotoxins. Nevertheless, strategies to comprehensively improve the sensitivity of lateral flow assay to mycotoxins in food have rarely been highlighted and discussed. In this article, a comprehensive overview was presented on the application of lateral flow assay in mycotoxin detection in food samples by highlighting the principle of lateral flow assay, presenting a detailed discussion on various analytical performance-improvement strategies, such as the development of high-affinity recognition reagents, immunogen immobilization methods, and signal amplification. Additionally, a detailed discussion on the various signal analyzers and interpretation approaches was provided. Finally, current hurdles and future perspectives on the application of lateral flow assay in the detection of mycotoxins were discussed.

Molecular Evolution of Antiparathion Nanobody with Enhanced Sensitivity and Specificity Based on Structural Analysis

Nanobody (Nb) has gained significant attention in immunoassays owing to its numerous advantages, particularly its ease of molecular evolution. However, the limited understanding of how high sensitivity and specificity attained for antihapten Nbs hamper the development of high-performance Nbs. Herein, the antiparathion Nb (Nb9) we prepared previously was chosen as the model, and an approach based on X-ray crystallography, molecular docking, and rational site-directed saturation mutation for constructing a rapid and effective platform for nanobody evolution was described. Based on the structural analysis, two mutants, namely Nb-D5 (IC50 = 2.4 ± 0.2 ng/mL) and Nb-D12 (IC50 = 2.7 ± 0.1 ng/mL), were selected out from a six-sites directed saturation mutation library, 3.5-fold and 3.1-fold sensitivity enhancement over Nb9 to parathion, respectively. Besides, Nb-D12 exhibited improved sensitivity for quinalphos, triazophos, and coumaphos (5.4-35.4 ng/mL), indicating its broader detection potential. Overall, our study advances an effective strategy for the future rational evolution of Nbs with desirable performance.

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.

Quantitative flow cytometric selection of tau conformational nanobodies specific for pathological aggregates

Single-domain antibodies, also known as nanobodies, are broadly important for studying the structure and conformational states of several classes of proteins, including membrane proteins, enzymes, and amyloidogenic proteins. Conformational nanobodies specific for aggregated conformations of amyloidogenic proteins are particularly needed to better target and study aggregates associated with a growing class of associated diseases, especially neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. However, there are few reported nanobodies with both conformational and sequence specificity for amyloid aggregates, especially for large and complex proteins such as the tau protein associated with Alzheimer's disease, due to difficulties in selecting nanobodies that bind to complex aggregated proteins. Here, we report the selection of conformational nanobodies that selectively recognize aggregated (fibrillar) tau relative to soluble (monomeric) tau. Notably, we demonstrate that these nanobodies can be directly isolated from immune libraries using quantitative flow cytometric sorting of yeast-displayed libraries against tau aggregates conjugated to quantum dots, and this process eliminates the need for secondary nanobody screening. The isolated nanobodies demonstrate conformational specificity for tau aggregates in brain samples from both a transgenic mouse model and human tauopathies. We expect that our facile approach will be broadly useful for isolating conformational nanobodies against diverse amyloid aggregates and other complex antigens.

Engineering of conserved residues near antibody heavy chain complementary determining region 3 (HCDR3) improves both affinity and stability

Affinity and stability are crucial parameters in antibody development and engineering approaches. Although improvement in both metrics is desirable, trade-offs are almost unavoidable. Heavy chain complementarity determining region 3 (HCDR3) is the best-known region for antibody affinity but its impact on stability is often neglected. Here, we present a mutagenesis study of conserved residues near HCDR3 to elicit the role of this region in the affinity-stability trade-off. These key residues are positioned around the conserved salt bridge between VH-K94 and VH-D101 which is crucial for HCDR3 integrity. We show that the additional salt bridge at the stem of HCDR3 (VH-K94:VH-D101:VH-D102) has an extensive impact on this loop's conformation, therefore simultaneous improvement in both affinity and stability. We find that the disruption of π-π stacking near HCDR3 (VH-Y100E:VL-Y49) at the VH-VL interface cause an irrecoverable loss in stability even if it improves the affinity. Molecular simulations of putative rescue mutants exhibit complex and often non-additive effects. We confirm that our experimental measurements agree with the molecular dynamic simulations providing detailed insights for the spatial orientation of HCDR3. VH-V102 right next to HCDR3 salt bridge might be an ideal candidate to overcome affinity-stability trade-off.

Solvated interaction energy: from small-molecule to antibody drug design

Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH.

Structural Modeling of Nanobodies: A Benchmark of State-of-the-Art Artificial Intelligence Programs

The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

Replica-exchange optimization of antibody fragments

In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.

Modeling and affinity maturation of an anti-CD20 nanobody: a comprehensive in-silico investigation

B-cell Non-Hodgkin lymphomas are the malignancies of lymphocytes. CD20 is a membrane protein, which is highly expressed on the cell surface of the B-cells in NHL. Treatments using monoclonal antibodies (mAbs) have resulted in failure in some cases. Nanobodies (NBs), single-domain antibodies with low molecular weights and a high specificity in antigen recognition, could be practical alternatives for traditional mAbs with superior characteristics. To design an optimized NB as a candidate CD20 inhibitor with raised binding affinity to CD20, the structure of anti-CD20 NB was optimized to selectively target CD20. The 3D structure of the NB was constructed based on the optimal templates (6C5W and 5JQH), and the key residues were determined by applying a molecular docking study. After identifying the key residues, some mutations were introduced using a rational protocol to improve the binding affinity of the NB to CD20. The rational mutations were conducted using the experimental design (Taguchi method). Six residues (Ser27, Thr28, Phe29, Ile31, Asp99, and Asn100) were selected as the key residues, and five residues were targeted for rational mutation (Trp, Phe, His, Asp, and Tyr). Based on the mutations suggested by the experimental design, two optimized NB structures were constructed. NB2 showed a remarkable binding affinity to CD20 in docking studies with a binding energy of - 853 kcal/mol. The optimized NB was further evaluated using molecular dynamics simulation. The results revealed that CDR1 (complementarity determining regions1) and CDR3 are essential loops for recognizing the antigen. NB2 could be considered as a potential inhibitor of CD20, though experimental evaluations are needed to confirm it.

Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders

Nanobodies (VHHs) are engineered fragments of the camelid single-chain immunoglobulins. The VHH domain contains the highly variable segments responsible for antigen recognition. VHHs can be easily produced as recombinant proteins. Their small size is a good advantage for in silico approaches. Computer methods represent a valuable strategy for the optimization and improvement of their binding affinity. They also allow for epitope selection offering the possibility to design new VHHs for regions of a target protein that are not naturally immunogenic. Here we present an in silico mutagenic protocol developed to improve the binding affinity of nanobodies together with the first step of their in vitro production. The method, already proven successful in improving the low Kd of a nanobody hit obtained by panning, can be employed for the ex novo design of antibody fragments against selected protein target epitopes.

Optimizing Antibody-Antigen Binding Affinities with the ADAPT Platform

The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform guides the selection of mutants that improve/modulate the affinity of antibodies and other biologics. Predicted affinities are based on a consensus z-score from three scoring functions. Computational predictions are interleaved with experimental validation, significantly enhancing the robustness of the design and selection of mutants. A key step is an initial exhaustive virtual single-mutant scan that identifies hot spots and the mutations predicted to improve affinity. A small number of proposed single mutants are then produced and assayed. Only the validated single mutants (i.e., having improved affinity) are used to design double and higher-order mutants in subsequent rounds of design, avoiding the combinatorial explosion that arises from random mutagenesis. Typically, with a total of about 30-50 designed single, double, and triple mutants, affinity improvements of 10- to 100-fold are obtained.

Design of nanobody-based bispecific constructs by in silico affinity maturation and umbrella sampling simulations

Random mutagenesis is the natural opportunity for proteins to evolve and biotechnologically it has been exploited to create diversity and identify variants with improved characteristics in the mutant pools. Rational mutagenesis based on biophysical assumptions and supported by computational power has been proposed as a faster and more predictable strategy to reach the same aim. In this work we confirm that substantial improvements in terms of both affinity and stability of nanobodies can be obtained by using combinations of algorithms, even for binders with already high affinity and elevated thermal stability. Furthermore, in silico approaches allowed the development of an optimized bispecific construct able to bind simultaneously the two clinically relevant antigens TNF-α and IL-23 and, by means of its enhanced avidity, to inhibit effectively the apoptosis of TNF-α-sensitive L929 cells. The results revealed that salt bridges, hydrogen bonds, aromatic-aromatic and cation-pi interactions had a critical role in increasing affinity. We provided a platform for the construction of high-affinity bispecific constructs based on nanobodies that can have relevant applications for the control of all those biological mechanisms in which more than a single antigen must be targeted to increase the treatment effectiveness and avoid resistance mechanisms.

Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV

The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

Vaccination has saved more lives than any other medical procedure. But, we do not have robust ways to develop vaccines against highly mutable pathogens. For example, there is no effective vaccine against HIV, and a universal vaccine against diverse strains of influenza is also not available. The development of immunization strategies to elicit antibodies that can neutralize diverse strains of highly mutable pathogens (so-called ‘broadly neutralizing antibodies’, or bnAbs) would enable the design of universal vaccines against such pathogens, as well as other viruses that may emerge in the future. In this paper, we present an agent-based model of affinity maturation–the Darwinian process by which antibodies evolve against a pathogen–that, for the first time, enables the in silico investigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins. Our results provide new insights into bnAb evolution against HIV, and can be used to qualitatively guide the future design of vaccines against highly mutable pathogens.

VHH Structural Modelling Approaches: A Critical Review

V_HH, 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 V_HH 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 V_HHs 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 V_HH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of V_HHs. 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 V_HH 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 V_HH from its sequence.

Affinity-based isolation of extracellular vesicles by means of single-domain antibodies bound to macroporous methacrylate-based copolymer

Correct elucidation of physiological and pathological processes mediated by extracellular vesicles (EV) is highly dependent on the reliability of the method used for their purification. Currently available chemical/physical protocols for sample fractionation are time-consuming, often scarcely reproducible and their yields are low. Immuno-capture based approaches could represent an effective purification alternative to obtain homogeneous EV samples. An easy-to-operate chromatography system was set-up for the purification of intact EVs based on a single domain (VHH) antibodies-copolymer matrix suitable for biological samples as different as conditioned cell culture medium and human plasma. Methacrylate-based copolymer is a porous solid support, the chemical versatility of which enables its efficient functionalization with VHHs. The combined analyses of morphological features and biomarker (CD9, CD63 and CD81) presence indicated that the recovered EVs were exosomes. The lipoprotein markers APO-A1 and APO-B were both negative in tested samples. This is the first report demonstrating the successful application of spherical porous methacrylate-based copolymer coupled with VHHs for the exosome isolation from biological fluids. This inexpensive immunoaffinity method has the potential to be applied for the isolation of EVs belonging to different morphological and physiological classes.

Research Progress and Applications of Multivalent, Multispecific and Modified Nanobodies for Disease Treatment

Recombinant antibodies such as nanobodies are progressively demonstrating to be a valid alternative to conventional monoclonal antibodies also for clinical applications. Furthermore, they do not solely represent a substitute for monoclonal antibodies but their unique features allow expanding the applications of biotherapeutics and changes the pattern of disease treatment. Nanobodies possess the double advantage of being small and simple to engineer. This combination has promoted extremely diversified approaches to design nanobody-based constructs suitable for particular applications. Both the format geometry possibilities and the functionalization strategies have been widely explored to provide macromolecules with better efficacy with respect to single nanobodies or their combination. Nanobody multimers and nanobody-derived reagents were developed to image and contrast several cancer diseases and have shown their effectiveness in animal models. Their capacity to block more independent signaling pathways simultaneously is considered a critical advantage to avoid tumor resistance, whereas the mass of these multimeric compounds still remains significantly smaller than that of an IgG, enabling deeper penetration in solid tumors. When applied to CAR-T cell therapy, nanobodies can effectively improve the specificity by targeting multiple epitopes and consequently reduce the side effects. This represents a great potential in treating malignant lymphomas, acute myeloid leukemia, acute lymphoblastic leukemia, multiple myeloma and solid tumors. Apart from cancer treatment, multispecific drugs and imaging reagents built with nanobody blocks have demonstrated their value also for detecting and tackling neurodegenerative, autoimmune, metabolic, and infectious diseases and as antidotes for toxins. In particular, multi-paratopic nanobody-based constructs have been developed recently as drugs for passive immunization against SARS-CoV-2 with the goal of impairing variant survival due to resistance to antibodies targeting single epitopes. Given the enormous research activity in the field, it can be expected that more and more multimeric nanobody molecules will undergo late clinical trials in the next future. Systematic Review Registration.

Native llama Nanobody Library Panning Performed by Phage and Yeast Display Provides Binders Suitable for C-Reactive Protein Detection

C-reactive protein (CRP) is an inflammation biomarker that should be quantified accurately during infections and healing processes. Nanobodies are good candidates to replace conventional antibodies in immunodiagnostics due to their inexpensive production, simple engineering, and the possibility to obtain higher binder density on capture surfaces. Starting from the same pre-immune library, we compared the selection output resulting from two independent panning strategies, one exclusively exploiting the phage display and another in which a first round of phage display was followed by a second round of yeast display. There was a partial output convergence between the two methods, since two clones were identified using both panning protocols but the first provided several further different sequences, whereas the second favored the recovery of many copies of few clones. The isolated anti-CRP nanobodies had affinity in the low nanomolar range and were suitable for ELISA and immunoprecipitation. One of them was fused to SpyTag and exploited in combination with SpyCatcher as the immunocapture element to quantify CRP using electrochemical impedance spectroscopy. The sensitivity of the biosensor was calculated as low as 0.21 μg/mL.

Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.

CDR1 Composition Can Affect Nanobody Recombinant Expression Yields

The isolation of nanobodies from pre-immune libraries by means of biopanning is a straightforward process. Nevertheless, the recovered candidates often require optimization to improve some of their biophysical characteristics. In principle, CDRs are not mutated because they are likely to be part of the antibody paratope, but in this work, we describe a mutagenesis strategy that specifically addresses CDR1. Its sequence was identified as an instability hot spot by the PROSS program, and the available structural information indicated that four CDR1 residues bound directly to the antigen. We therefore modified the loop flexibility with the addition of an extra glycine rather than by mutating single amino acids. This approach significantly increased the nanobody yields but traded-off with moderate affinity loss. Accurate modeling coupled with atomistic molecular dynamics simulations enabled the modifications induced by the glycine insertion and the rationale behind the engineering design to be described in detail.

VHH212 nanobody targeting the hypoxia-inducible factor 1α suppresses angiogenesis and potentiates gemcitabine therapy in pancreatic cancer in vivo

OBJECTIVE

We aimed to develop a novel anti-HIF-1α intrabody to decrease gemcitabine resistance in pancreatic cancer patients.

METHODS

Surface plasmon resonance and glutathione S-transferase pull-down assays were conducted to identify the binding affinity and specificity of anti-HIF-1α VHH212 [a single-domain antibody (nanobody)]. Molecular dynamics simulation was used to determine the protein-protein interactions between hypoxia-inducible factor-1α (HIF-1α) and VHH212. The real-time polymerase chain reaction (PCR) and Western blot analyses were performed to identify the expressions of HIF-1α and VEGF-A in pancreatic ductal adenocarcinoma cell lines. The efficiency of the VHH212 nanobody in inhibiting the HIF-1 signaling pathway was measured using a dual-luciferase reporter assay. Finally, a PANC-1 xenograft model was developed to evaluate the anti-tumor efficiency of combined treatment. Immunohistochemistry analysis was conducted to detect the expressions of HIF-1α and VEGF-A in tumor tissues.

RESULTS

VHH212 was stably expressed in tumor cells with low cytotoxicity, high affinity, specific subcellular localization, and neutralization of HIF-1α in the cytoplasm or nucleus. The binding affinity between VHH212 and the HIF-1α PAS-B domain was 42.7 nM. Intrabody competitive inhibition of the HIF-1α heterodimer with an aryl hydrocarbon receptor nuclear translocator was used to inhibit the HIF-1/VEGF pathway in vitro. Compared with single agent gemcitabine, co-treatment with gemcitabine and a VHH212-encoding adenovirus significantly suppressed tumor growth in the xenograft model with 80.44% tumor inhibition.

CONCLUSIONS

We developed an anti-HIF-1α nanobody and showed the function of VHH212 in a preclinical murine model of PANC-1 pancreatic cancer. The combination of VHH212 and gemcitabine significantly inhibited tumor development. These results suggested that combined use of anti-HIF-1α nanobodies with first-line treatment may in the future be an effective treatment for pancreatic cancer.

Designing and constructing a phage display synthesized single domain antibodies library based on camel VHHs frame for screening and identifying humanized TNF-α-specific nanobody

Tumor necrosis factor (TNF-α) is an important clinically tested cytokine that could induce autoimmune diseases and inflammation. Therefore, the anti-TNF-α therapy strategy was developed and used therapeutically in various diseases, especially in the cytokine storm associated chimeric antigen receptor (CAR) T-cell therapy and antiviral therapy. Compare with other anti-TNF-α inhibitors, anti-TNF-α Nb (nanobody) has many unique advantages. Herein, we reported a novel humanized scaffold for library construction, which could be soluble and expressed in Escherichia coli (E.coli), and the efficiency capacity could reach as high as 2.01 × 10⁹. Meanwhile, an anti-TNF-α Nb was selected for further study after 4 rounds of screening, NT-3, as the optimal Nb could effectively inhibit TNF-mediated cytotoxicity. The IC50 of NT-3 was determined as 0.804 μM, and its apoptosis inhibition rate was 62.47 % in L929 cells. Furthermore, the molecular docking results showed that complementarity-determining regions (CDRs) of NT-3 could connect to TNF for blocking function through strong hydrogen bonds and salt bridges. In general, our study not only provided a good Nb screening platform in vitro without animal immunization, but also generated a series of novel humanized anti-TNF-α Nb candidates with potential applications.

In vitro affinity maturation to improve the efficacy of a hypoxia-inducible factor 1α single-domain intrabody

Affinity is an important property of therapeutic antibodies, so improving affinity is critical to the biological activity and clinical efficacy. An anti-HIF-1α nanobody, VHH212, was screened via a native ribosome display library with a 26.6 nM of K_D value was used as the parent. In this paper, a Venn-intersection of multi-algorithms screening (VIMAS) strategy for computer-aided binding affinity prediction was designed. Homology modeling and protein docking methods were used to substitute the need for a crystal structure. Finally, a mutant with a 17.5-fold enhancement in binding affinity (1.52 nM) was obtained by using the VIMAS strategy. Furthermore, the biological activity of mutants was verified at the cellular level. Targeting HIF-1α can sensitize PDAC (pancreatic ductal adenocarcinoma) tumors to gemcitabine, which is a potential co-treatment method for pancreatic cancer patients. Our results showed that the cytotoxicity of gemcitabine on pancreatic cancer cell lines increased with the enhanced-affinity of an intrabody under combined treatment.

Protein electrostatics: From computational and structural analysis to discovery of functional fingerprints and biotechnological design

Computationally driven engineering of proteins aims to allow them to withstand an extended range of conditions and to mediate modified or novel functions. Therefore, it is crucial to the biotechnological industry, to biomedicine and to afford new challenges in environmental sciences, such as biocatalysis for green chemistry and bioremediation. In order to achieve these goals, it is important to clarify molecular mechanisms underlying proteins stability and modulating their interactions. So far, much attention has been given to hydrophobic and polar packing interactions and stability of the protein core. In contrast, the role of electrostatics and, in particular, of surface interactions has received less attention. However, electrostatics plays a pivotal role along the whole life cycle of a protein, since early folding steps to maturation, and it is involved in the regulation of protein localization and interactions with other cellular or artificial molecules. Short- and long-range electrostatic interactions, together with other forces, provide essential guidance cues in molecular and macromolecular assembly. We report here on methods for computing protein electrostatics and for individual or comparative analysis able to sort proteins by electrostatic similarity. Then, we provide examples of electrostatic analysis and fingerprints in natural protein evolution and in biotechnological design, in fields as diverse as biocatalysis, antibody and nanobody engineering, drug design and delivery, molecular virology, nanotechnology and regenerative medicine.

Recombinant expression of nanobodies and nanobody-derived immunoreagents

Antibody fragments for which the sequence is available are suitable for straightforward engineering and expression in both eukaryotic and prokaryotic systems. When produced as fusions with convenient tags, they become reagents which pair their selective binding capacity to an orthogonal function. Several kinds of immunoreagents composed by nanobodies and either large proteins or short sequences have been designed for providing inexpensive ready-to-use biological tools. The possibility to choose among alternative expression strategies is critical because the fusion moieties might require specific conditions for correct folding or post-translational modifications. In the case of nanobody production, the trend is towards simpler but reliable (bacterial) methods that can substitute for more cumbersome processes requiring the use of eukaryotic systems. The use of these will not disappear, but will be restricted to those cases in which the final immunoconstructs must have features that cannot be obtained in prokaryotic cells. At the same time, bacterial expression has evolved from the conventional procedure which considered exclusively the nanobody and nanobody-fusion accumulation in the periplasm. Several reports show the advantage of cytoplasmic expression, surface-display and secretion for at least some applications. Finally, there is an increasing interest to use as a model the short nanobody sequence for the development of in silico methodologies aimed at optimizing the yields, stability and affinity of recombinant antibodies.

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There is an increasing request for immunoreagents based on nanobodies.

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The multiplicity of their applications requires constructs with different structural complexity.

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Alternative expression methods are necessary to achieve such structural requirements.

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In silico optimization of nanobody biophysical characteristics becomes more and more reliable.

Structure and development of single domain antibodies as modules for therapeutics and diagnostics

Since their discovery just over 25 years ago, the single variable domain from heavy-chain-only antibodies plays a role in an increasing number of antibody-based applications. Structural and biophysical studies have revealed that the small, ∼15 kDa, single variable domain found in camelids displays versatility in target recognition. Such insight has served as the foundation to develop and engineer VHH domains with enhanced properties capable of targeting a range of therapeutically relevant protein antigens or low-molecular weight haptens. Furthermore, the modular nature of VHH domains allows them to be introduced into constructs that are simply not possible with conventional antibodies. Here, we review the structural and biophysical properties of VHH domains, highlight recent VHH-based therapeutics and diagnostics, and provide insight into VHH engineering that may pave the way to next-generation single domain antibody applications.