The structural basis of antibody-antigen recognition

Paratope states in solution improve structure prediction and docking

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.

Functional antibody characterization via direct structural analysis and information-driven protein-protein docking

Detailed description of the mechanism of action of the therapeutic antibodies is essential for the functional characterization and future optimization of potential clinical agents. We recently developed KD035, a fully human antibody targeting vascular endothelial growth factor receptor 2 (VEGFR2). KD035 blocked VEGF‐A, and VEGF‐C‐mediated VEGFR2 activation, as demonstrated by the in vitro binding and competition assays and functional cellular assays. Here, we report a computational model of the complex between the variable fragment of KD035 (KD035(Fv)) and the domains 2 and 3 of the extracellular portion of VEGFR2 (VEGFR2(D2‐3)). Our modeling was guided by a priori experimental information including the X‐ray structures of KD035 and related antibodies, binding assays, target domain mapping and comparison of KD035 affinity for VEGFR2 from different species. The accuracy of the model was assessed by molecular dynamics simulations, and subsequently validated by mutagenesis and binding analysis. Importantly, the steps followed during the generation of this model can set a precedent for future in silico efforts aimed at the accurate description of the antibody–antigen and more broadly protein–protein complexes.

Small-Sized Co-Polymers for Targeted Delivery of Multiple Imaging and Therapeutic Agents

Research has increasingly focused on the delivery of high, often excessive amounts of drugs, neglecting negative aspects of the carrier's physical preconditions and biocompatibility. Among them, little attention has been paid to "small but beautiful" design of vehicle and multiple cargo to achieve effortless targeted delivery into deep tissue. The design of small biopolymers for deep tissue targeted delivery of multiple imaging agents and therapeutics (mini-nano carriers) emphasizes linear flexible polymer platforms with a hydrodynamic diameter of 4 nm to 10 nm, geometrically favoring dynamic juxtaposition of ligands to host receptors, and economic drug content. Platforms of biodegradable, non-toxic poly(β-l-malic acid) of this size carrying multiple chemically bound, optionally nature-derived or synthetic affinity peptides and drugs for a variety of purposes are described in this review with specific examples. The size, shape, and multiple attachments to membrane sites accelerate vascular escape and fast blood clearance, as well as the increase in medical treatment and contrasts for tissue imaging. High affinity antibodies routinely considered for targeting, such as the brain through the blood-brain barrier (BBB), are replaced by moderate affinity binding peptides (vectors), which penetrate at high influxes not achievable by antibodies.

Germline polymorphisms and alternative splicing of human immunoglobulin light chain genes

Inference of germline polymorphisms in immunoglobulin genes from B cell receptor repertoires is complicated by somatic hypermutations, sequencing/PCR errors, and by varying length of reference alleles. The light chain inference is particularly challenging owing to large gene duplications and absence of D genes. We analyzed the light chain cDNA sequences from naïve B cell receptor repertoires from 100 individuals. We optimized light chain allele inference by tweaking parameters of the TIgGER functions, extending the germline reference sequences, and establishing mismatch frequency patterns at polymorphic positions to filter out false-positive candidates. We identified 48 previously unreported variants of light chain variable genes. We selected 14 variants for validation and successfully validated 11 by Sanger sequencing. Clustering of light chain 5'UTR, L-PART1, and L-PART2 revealed partial intron retention in 11 kappa and 9 lambda V alleles. Our results provide insight into germline variation in human light chain immunoglobulin loci.

Identification of Unique Peptides for SARS-CoV-2 Diagnostics and Vaccine Development by an In Silico Proteomics Approach

Ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus strains is posing new COVID-19 diagnosis and treatment challenges. To help efforts to meet these challenges we examined data acquired from proteomic analyses of human SARS-CoV-2-infected cell lines and samples from COVID-19 patients. Initially, 129 unique peptides were identified, which were rigorously evaluated for repeats, disorders, polymorphisms, antigenicity, immunogenicity, toxicity, allergens, sequence similarity to human proteins, and contributions from other potential cross-reacting pathogenic species or the human saliva microbiome. We also screened SARS-CoV-2-infected NBHE and A549 cell lines for presence of antigenic peptides, and identified paratope peptides from crystal structures of SARS-CoV-2 antigen-antibody complexes. We then selected four antigen peptides for docking with known viral unbound T-cell receptor (TCR), class I and II peptide major histocompatibility complex (pMHC), and identified paratope sequences. We also tested the paratope binding affinity of SARS-CoV T- and B-cell peptides that had been previously experimentally validated. The resultant antigenic peptides have high potential for generating SARS-CoV-2-specific antibodies, and the paratope peptides can be directly used to develop a COVID-19 diagnostics assay. The presented genomics and proteomics-based in-silico approaches have apparent utility for identifying new diagnostic peptides that could be used to fight SARS-CoV-2.

Critical issues in molecular recognition: the enzyme-substrate association

Chemical and shape recognition are the main assembling mechanisms of complex bio-structures. A refined assessment of relevant thermodynamic parameters includes the consideration of a variety of contributions from different molecular motions and electronic interactions. An additional refinement includes the analysis of recognition involving multiple distant partners. This perspective note highlights some of the above issues in the cases of fibrillogenesis, and enzyme-substrate and antigen-antibody associations. Translational motions are found to be particularly relevant to the fibrillogenesis process. The assembly of enzyme-substrate complexes is discussed in terms of a dynamic equilibrium process.

Quantitative Description of Surface Complementarity of Antibody-Antigen Interfaces

Antibodies have the remarkable ability to recognise their cognate antigens with extraordinary affinity and specificity. Discerning the rules that define antibody-antigen recognition is a fundamental step in the rational design and engineering of functional antibodies with desired properties. In this study we apply the 3D Zernike formalism to the analysis of the surface properties of the antibody complementary determining regions (CDRs). Our results show that shape and electrostatic 3DZD descriptors of the surface of the CDRs are predictive of antigen specificity, with classification accuracy of 81% and area under the receiver operating characteristic curve (AUC) of 0.85. Additionally, while in terms of surface size, solvent accessibility and amino acid composition, antibody epitopes are typically not distinguishable from non-epitope, solvent-exposed regions of the antigen, the 3DZD descriptors detect significantly higher surface complementarity to the paratope, and are able to predict correct paratope-epitope interaction with an AUC = 0.75.

A 250-kDa glycoprotein of Naegleria fowleri induces protection and modifies the expression of α4β1 and LFA-1 on T and B lymphocytes in mouse meningitis model

The aims of this work were to evaluate the protective role of the 250-kDa polypeptide band of Naegleria fowleri. We designed an immunization strategy in Balb/c mice which were inoculated by i.n. route with an electrocuted 250-kDa polypeptide band of N. fowleri. We observed that the 250-kDa band induced 80% of protection, whereas the coadministration with Cholera Toxin induced 100% of protection. Moreover, high levels of IgA- and IgG-specific antibodies were detected by ELISA assay. We also analysed migration molecules (α4β1 and LFA-1) on T and B lymphocytes in nose-associated lymphoid tissue (NALT), cervical lymph nodes (CN) and nasal passages (NP) by flow cytometry. We observed that the percentage of B cells (B220/α4β1) and T cells (CD4/α4β1) in NP were higher in all immunized groups compared with the other compartments analysed. Finally, we detected by immunohistochemistry ICAM-1 and V-CAM-1 in the nasal cavity. The immunization with the 250-kDa polypeptide band, protect mice against N. fowleri challenge and modifies migration molecules and their ligands.

Multiple myeloma: therapeutic delivery of antibodies and aptamers

Multiple myeloma is the second most common hematological malignancy in adults, accounting for 2% of all cancer-related deaths in the UK. Current chemotherapy-based regimes are insufficient, as most patients relapse and develop therapy resistance. This review focuses on current novel antibody- and aptamer-based therapies aiming to overcome current therapy limitations, as well as their respective limitations and areas of improvement. The use of computer modeling methods, as a tool to study and improve ligand-receptor alignments for the use of novel therapy development will also be discussed, as it has become a rapid, reliable and comparatively inexpensive method of investigation.

Supramolecular "Click Chemistry" for Targeting in the Body

The fields of precision imaging and drug delivery have revealed a number of tools to improve target specificity and increase efficacy in diagnosing and treating disease. Biological molecules, such as antibodies, continue to be the primary means of assuring active targeting of various payloads. However, molecular-scale recognition motifs have emerged in recent decades to achieve specificity through the design of interacting chemical motifs. In this regard, an assortment of bioorthogonal covalent conjugations offer possibilities for in situ complexation under physiological conditions. Herein, a related concept is discussed that leverages interactions from noncovalent or supramolecular motifs to facilitate in situ recognition and complex formation in the body. Classic supramolecular motifs based on host-guest complexation offer one such means of facilitating recognition. In addition, synthetic bioinspired motifs based on oligonucleotide hybridization and coiled-coil peptide bundles afford other routes to form complexes in situ. The architectures to include recognition of these various motifs for targeting enable both monovalent and multivalent presentation, seeking high affinity or engineered avidity to facilitate conjugation even under dilute conditions of the body. Accordingly, supramolecular "click chemistry" offers a complementary tool in the growing arsenal targeting improved healthcare efficacy.

In vivo trafficking of a tumor-targeting IgE antibody: molecular imaging demonstrates rapid hepatobiliary clearance compared to IgG counterpart

IgE antibodies elicit powerful immune responses, recruiting effector cells to tumors more efficiently and with greater cytotoxicity than IgG antibodies. Consequently, IgE antibodies are a promising alternative to conventional IgG-based therapies in oncology (AllergoOncology). As the pharmacokinetics of IgE antibodies are less well understood, we used molecular imaging in mice to compare the distribution and elimination of IgE and IgG antibodies targeting the human tumor-associated antigen chondroitin sulfate proteoglycan 4 (CSPG4). Anti-CSPG4 IgE and IgG1 antibodies with human Fc domains were radiolabeled with 111In. CSPG4-expressing A375 human melanoma xenografts implanted in NOD-scid IL2rg-/- mice were also engrafted with human immune cells by intravenous administration. 111In-anti-CSPG4 antibodies were administered intravenously. Their distribution was determined by single-photon emission computed tomography (SPECT) and ex vivo gamma-counting over 120 h. SPECT imaging was conducted from 0 to 60 min after antibody administration to precisely measure the early phase of IgE distribution. 111In-labeled anti-CSPG4 IgG and IgE showed serum stability in vitro of >92% after 5 days. In A375 xenograft-bearing mice, anti-CSPG4 IgE showed much faster blood clearance and higher accumulation in the liver compared to anti-CSPG4 IgG. However, tumor-to-blood and tumor-to-muscle ratios were similar between the antibody isotypes and higher compared with a non-tumor-targeting isotype control IgE. IgE excretion was much faster than IgG. In non-tumor-bearing animals, early SPECT imaging revealed a blood clearance half-life of 10 min for IgE. Using image-based quantification, we demonstrated that the blood clearance of IgE is much faster than that of IgG while the two isotypes showed comparable tumor-to-blood ratios.

A CDR-based approach to generate covalent inhibitory antibody for human rhinovirus protease

Covalent target modulation with small molecules has been emerging as a promising strategy for drug discovery. However, covalent inhibitory antibody remains unexplored due to the lack of efficient strategies to engineer antibody with desired bioactivity. Herein, we developed an intracellular selection method to generate covalent inhibitory antibody against human rhinovirus 14 (HRV14) 3C protease through unnatural amino acid mutagenesis along the heavy chain complementarity-determining region 3 (CDR-H3). A library of antibody mutants was thus constructed and screened in vivo through co-expression with the target protease. Using this screening strategy, six covalent antibodies with proximity-enabled bioactivity were identified, which were shown to covalently target HRV14-3C protease with high inhibitory potency and exquisite selectivity. Compared to structure-based rational design, this library-based screening method provides a simple and efficient way for the discovery and engineering of covalent antibody for enzyme inhibition.

Modeling of protein complexes in CASP14 with emphasis on the interaction interface prediction

The goal of CASP experiments is to monitor the progress in the protein structure prediction field. During the 14th CASP edition we aimed to test our capabilities of predicting structures of protein complexes. Our protocol for modeling protein assemblies included both template‐based modeling and free docking. Structural templates were identified using sensitive sequence‐based searches. If sequence‐based searches failed, we performed structure‐based template searches using selected CASP server models. In the absence of reliable templates we applied free docking starting from monomers generated by CASP servers. We evaluated and ranked models of protein complexes using an improved version of our protein structure quality assessment method, VoroMQA, taking into account both interaction interface and global structure scores. If reliable templates could be identified, generally accurate models of protein assemblies were generated with the exception of an antibody‐antigen interaction. The success of free docking mainly depended on the accuracy of initial subunit models and on the scoring of docking solutions. To put our overall results in perspective, we analyzed our performance in the context of other CASP groups. Although the subunits in our assembly models often were not of the top quality, these models had, overall, the best‐predicted intersubunit interfaces according to several accuracy measures. We attribute our relative success primarily to the emphasis on the interaction interface when modeling and scoring.

Development of a structure-based computational simulation to optimize the blocking efficacy of pro-antibodies

The on-target toxicity of monoclonal antibodies (Abs) is mainly due to the fact that Abs cannot distinguish target antigens (Ags) expressed in disease regions from those in normal tissues during systemic administration. In order to overcome this issue, we “copied” an autologous Ab hinge as an “Ab lock” and “pasted” it on the binding site of the Ab by connecting a protease substrate and linker in between to generate a pro-Ab, which can be specifically activated in the disease region to enhance Ab selectivity and reduce side effects. Previously, we reported that 70% of pro-Abs can achieve more than 100-fold blocking ability compared to the parental Abs. However, 30% of pro-Abs do not have such efficient blocking ability. This is because the same Ab lock linker cannot be applied to every Ab due to the differences in the complementarity-determining region (CDR) loops. Here we designed a method which uses structure-based computational simulation (MSCS) to optimize the blocking ability of the Ab lock for all Ab drugs. MSCS can precisely adjust the amino acid composition of the linker between the Ab lock and Ab drug with the assistance of molecular simulation. We selected αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab as models and attached the Ab lock with various linkers (L1 to L7) to form pro-Abs by MSCS, respectively. The resulting cover rates of the Ab lock with various linkers compared to the Ab drug were in the range 28.33–42.33%. The recombinant pro-Abs were generated by MSCS prediction in order to verify the application of molecular simulation for pro-Ab development. The binding kinetics effective concentrations (EC-50) for αPD-1 (200-250-fold), αIL-1β (152-186-fold), αCTLA-4 (68-150-fold) and αTNFα Ab (20-123-fold) were presented as the blocking ability of pro-Ab compared to the Ab drug. Further, there was a positive correlation between cover rate and blocking ability of all pro-Ab candidates. The results suggested that MSCS was able to predict the Ab lock linker most suitable for application to αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab to form pro-Abs efficiently. The success of MSCS in optimizing the pro-Ab can aid the development of next-generation pro-Ab drugs to significantly improve Ab-based therapies and thus patients' quality of life.

The pro-Ab blocks the Ag binding site using an Ab lock. We designed a method which uses structure-based computational simulation (MSCS) to predict the cover rate of Ab locks with various linkers and select the suitable linker for each Ab.

e-Membranome: A Database for Genome-Wide Analysis of Escherichia coli Outer Membrane Proteins

OBJECTIVE

Lectin-like adhesins of enteric bacterial pathogens such as Escherichia coli are an attractive target for vaccine or drug development. Here, we have developed e-Membranome as a database of genome-wide putative adhesins in Escherichia coli (E. coli).

METHODS

The outer membrane adhesins were predicted from the annotated genes of Escherichia coli strains using the PSORTb program. Further analysis was performed using Interproscan and the String database. The candidate proteins can be investigated for homology modeling of the Three-Dimensional (3D) structure (I-TASSER version 5.1), epitope region (ABCpred), and the glycan array.

RESULTS

e-Membranome is implemented using the Django (version 2.2.5) framework. The Web Application Server Apache Tomcat 6.0 is integrated into the platform on Ubuntu Linux (version 16.04). MySQL database (version 5.7) is used as a database engine. The information on homology model of the 3D structure, epitope region, and affinity information from the glycan array will be stored in the e- Membranome database. As a case study, we performed a genome-wide screening of outer membraneembedded proteins from the annotated genes of E. coli using the e-Membranome pipeline.

CONCLUSION

This platform is expected to be a valuable resource for advancing research of outer membrane proteins for the construction of lectin-glycan interaction network of E. coli. In addition, the e- Membranome pipeline can be extended to other similar biological systems that need to address hostpathogen interactions.

Nano-layered surface plasmon resonance-based highly sensitive biosensor for virus detection: A theoretical approach to detect SARS-CoV-2

The outbreak of the coronavirus disease (COVID-19) pandemic has become a worldwide health catastrophe instigated by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Countries are battling to slow the spread of this virus by testing and treating patients, along with other measures such as prohibiting large gatherings, maintaining social distance, and frequent, thorough hand washing, as no vaccines or medicines are available that could effectively treat infected people for different types of SARS-CoV-2 variants. However, the testing procedure to detect this virus is lengthy. This study proposes a surface plasmon resonance-based biosensor for fast detection of SARS-CoV-2. The sensor employs a multilayered configuration consisting of TiO₂-Ag-MoSe₂ graphene with a BK7 prism. Antigen-antibody interaction was considered the principle for this virus detection. Immobilized CR3022 antibody molecules for detecting SARS-CoV-2 antigens (S-glycoprotein) are used for this sensor. It was found that the proposed sensor's sensitivity (194°/RIU), quality factor (54.0390 RIU^-1), and detection accuracy (0.2702) outperformed those of other single and multilayered structures. This study could be used as a theoretical base and primary step in constructing an actual sensor.

Computational-Driven Epitope Verification and Affinity Maturation of TLR4-Targeting Antibodies

Toll-like receptor (TLR) signaling plays a critical role in the induction and progression of autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematous, experimental autoimmune encephalitis, type 1 diabetes mellitus and neurodegenerative diseases. Deciphering antigen recognition by antibodies provides insights and defines the mechanism of action into the progression of immune responses. Multiple strategies, including phage display and hybridoma technologies, have been used to enhance the affinity of antibodies for their respective epitopes. Here, we investigate the TLR4 antibody-binding epitope by computational-driven approach. We demonstrate that three important residues, i.e., Y328, N329, and K349 of TLR4 antibody binding epitope identified upon in silico mutagenesis, affect not only the interaction and binding affinity of antibody but also influence the structural integrity of TLR4. Furthermore, we predict a novel epitope at the TLR4-MD2 interface which can be targeted and explored for therapeutic antibodies and small molecules. This technique provides an in-depth insight into antibody-antigen interactions at the resolution and will be beneficial for the development of new monoclonal antibodies. Computational techniques, if coupled with experimental methods, will shorten the duration of rational design and development of antibody therapeutics.

Alemtuzumab scFv fragments and CD52 interaction study through molecular dynamics simulation and binding free energy

Specific antibody-antigen recognition is crucial for the immune response. Knowledge of molecular interaction details in the recognition process is fundamental for the rational design of antibodies with improved properties. We used state-of-the-art computer simulation tools to deepen the molecular-level understanding of the interactions between the monoclonal antibody Alemtuzumab and its antigen, the CD52 membrane receptor, of great biotechnological importance. Thus, we seek such responses by modeling the interaction of native and known mutants single-chain fragment variable (scFv) of Alemtuzumab with CD52 inserted in a membrane model to mimic the physiological conditions of antibody-antigen binding. Extensive molecular dynamics simulations of the interaction between Alemtuzumab's scFvs and CD52 promoted greater understanding of the structural and energetic bases, which can be translated into the biological action and affinity of this antibody. The quantification of the scFv-CD52 complexes binding free energy (ΔG_bind) by Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) correlated with the experimental binding energies described before. Thus, the mutants D53K, K54D, and K56D resulted in less attractive ΔG_bind, therefore lower scFv-CD52 affinity than the native scFv. On the other hand, K56D and K54D/K56D showed lower binding to CD52. These Results revealed that the model system mimicking an environment close to the physiological with the presence of the CD52 in a membrane model proved essential for this system's study. The present study allowed to unveil the molecular mechanisms involved in antigen-antibody interaction and the effects of mutations. Thus, these mechanisms may be explored in the Alemtuzumab variants' rational design with enhanced properties.

The Anti-Proliferative Effect of a Newly-Produced Anti-PSCA-Peptide Antibody by Multiple Bioinformatics Tools, on Prostate Cancer Cells

BACKGROUND

Prostate Stem Cell Antigen (PSCA) is a small cell surface protein, overexpressed in 90% of prostate cancers. Determination of epitopes that elicit an appropriate response to the antibody generation is vital for diagnostic and immunotherapeutic purposes for prostate cancer treatment. Presently, bioinformatics B-cell prediction tools can predict the location of epitopes, which is uncomplicated, faster, and more cost-effective than experimental methods.

OBJECTIVE

We aimed to predict a novel linear peptide for Prostate Stem Cell Antigen (PSCA) protein in order to generate anti-PSCA-peptide (p) antibody and to investigate its effect on prostate cancer cells.

METHODS

In the current study, a novel linear peptide for PSCA was predicted using in silico methods that utilize a set of linear B-cell epitope prediction tools. Polyclonal antibody (anti-PSCA-p antibody "Patent No. 99318") against PSCA peptide was generated. The antibody reactivity was determined by the Enzyme-Linked Immunosorbent Assay (ELISA) and its specificity by immunocytochemistry (ICC), immunohistochemistry (IHC), and Western Blotting (WB) assays. The effect of the anti-PSCA-p antibody on PSCA-expressing prostate cancer cell line was assessed by Methylthiazolyldiphenyl- Tetrazolium bromide (MTT) assay.

RESULTS

New peptide-fragment of PSCA sequence as "N-CVDDSQDYYVGKKN-C" (PSCA-p) was selected and synthesized. The anti-PSCA-p antibody against the PSCA-p showed immunoreactivity with PSCA-p specifically bound to PC-3 cells. Also, the anti-PSCA-p antibody strongly stained the prostate cancer tissues as compared to Benign Prostatic Hyperplasia (BPH) and normal tissues (P < 0.001). As the degree of malignancy increased, the staining intensity was also elevated in prostate cancer tissue (P < 0.001). Interestingly, the anti-PSCA-p antibody showed anti-proliferative effects on PC-3 cells (31%) with no growth inhibition effect on PSCA-negative cells.

CONCLUSION

In this study, we developed a new peptide sequence (PSCA-p) of PSCA. The PSCA-p targeting by anti-PSCA-p antibody inhibited the proliferation of prostate cancer cells, suggesting the potential of PSCA-p immunotherapy for future prostate cancer studies.

Development of Label-Free Immunoassays as Novel Solutions for the Measurement of Monoclonal Antibody Drugs and Antidrug Antibodies

BACKGROUND

Immunoassays based on label-free technologies (label-free immunoassay [LFIA]) offer an innovative approach to clinical diagnostics and demonstrate great promise for therapeutic drug monitoring (TDM) of monoclonal antibody (mAb) drugs. An LFIA measures immunocomplex formation in real time and allows for quantification on initial binding rate, which facilitates fast measurement within a few minutes.

METHODS

Based on thin-film interferometry (TFI) technology, open-access LFIAs were developed for the quantification of the mAb drugs adalimumab (ADL) and infliximab (IFX) and for the detection of the antidrug antibodies (ADAs) to the mAb drugs (ADL-ADAs and IFX-ADAs).

RESULTS

The LFIAs for active mAb drugs (ADL and IFX) and for ADAs (ADL-ADAs and IFX-ADAs) were validated. The analytical measurement range (AMR) for both ADL and IFX was from 2 to 100 μg/mL. The AMR for ADL-ADAs was from 5 to 100 μg/mL and for IFX-ADAs was 10 to 100 μg/mL. In the comparison of LFIAs and reporter gene assays, the correlation coefficient was 0.972 for the quantification of ADL and 0.940 for the quantification of IFX. The concordance rate was 90% for the detection of ADL-ADAs and 76% for the detection of IFX-ADAs.

CONCLUSIONS

The LFIAs for active mAb drugs and ADAs were appropriate for the TDM of ADL and IFX. The TFI technology has unique advantages compared with other technologies used for the measurement of mAb drugs. Label-free technologies, especially those allowing for open-access LFIAs, have great potential for clinical diagnostics.

Protein L—More Than Just an Affinity Ligand

In the past 30 years, highly specific drugs, known as antibodies, have conquered the biopharmaceutical market. In addition to monoclonal antibodies (mAbs), antibody fragments are successfully applied. However, recombinant production faces challenges. Process analytical tools for monitoring and controlling production processes are scarce and time-intensive. In the downstream process (DSP), affinity ligands are established as the primary and most important step, while the application of other methods is challenging. The use of these affinity ligands as monitoring tools would enable a platform technology to monitor process steps in the USP and DSP. In this review, we highlight the current applications of affinity ligands (proteins A, G, and L) and discuss further applications as process analytical tools.

The potential applications of T cell receptor (TCR)-like antibody in cervical cancer immunotherapy

Cervical cancer is ranked as the fourth most common cancer in women worldwide. Monoclonal antibody has created a new dimension in the immunotherapy of many diseases, including cervical cancer. The antibody's ability to target various aspects of cervical cancer (oncoviruses, oncoproteins, and signaling pathways) delivers a promising future for efficient immunotherapy. Besides, technologies such as hybridoma and phage display provide a fundamental platform for monoclonal antibody generation and create the opportunity to generate novel antibody classes including, T cell receptor (TCR)-like antibody. In this review, the current immunotherapy strategies for cervical cancer are presented. We have also proposed a novel concept of T cell receptor (TCR)-like antibody and its potential applications for enhancing cervical cancer therapeutics. Finally, the possible challenges in TCR-like antibody application for cervical cancer therapeutics have been addressed, and strategies to overcome the challenges have been highlighted to maximize the therapeutic benefits.

Potent SARS-CoV-2 neutralizing antibodies directed against spike N-terminal domain target a single supersite

Numerous antibodies that neutralize SARS-CoV-2 have been identified, and these generally target either the receptor-binding domain (RBD) or the N-terminal domain (NTD) of the viral spike. While RBD-directed antibodies have been extensively studied, far less is known about NTD-directed antibodies. Here, we report cryo-EM and crystal structures for seven potent NTD-directed neutralizing antibodies in complex with spike or isolated NTD. These structures defined several antibody classes, with at least one observed in multiple convalescent donors. The structures revealed that all seven antibodies target a common surface, bordered by glycans N17, N74, N122, and N149. This site-formed primarily by a mobile β-hairpin and several flexible loops-was highly electropositive, located at the periphery of the spike, and the largest glycan-free surface of NTD facing away from the viral membrane. Thus, in contrast to neutralizing RBD-directed antibodies that recognize multiple non-overlapping epitopes, potent NTD-directed neutralizing antibodies appear to target a single supersite.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

Motivation

Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein–protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

Results

We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody–antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research.

Availability and implementation

Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Design of a Trispecific Checkpoint Inhibitor and Natural Killer Cell Engager Based on a 2 + 1 Common Light Chain Antibody Architecture

Natural killer cell engagers gained enormous interest in recent years due to their potent anti-tumor activity and favorable safety profile. Simultaneously, chicken-derived antibodies entered clinical studies paving the way for avian-derived therapeutics. In this study, we describe the affinity maturation of a common light chain (cLC)-based, chicken-derived antibody targeting EGFR, followed by utilization of the same light chain for the isolation of CD16a- and PD-L1-specific monoclonal antibodies. The resulting binders target their respective antigen with single-digit nanomolar affinity while blocking the ligand binding of all three respective receptors. Following library-based humanization, bispecific and trispecific variants in a standard 1 + 1 or a 2 + 1 common light chain format were generated, simultaneously targeting EGFR, CD16a, and PD-L1. The trispecific antibody mediated an elevated antibody-dependent cellular cytotoxicity (ADCC) in comparison to the EGFR×CD16a bispecific variant by effectively bridging EGFR/PD-L1 double-positive cancer cells with CD16a-positive effector cells. These findings represent, to our knowledge, the first detailed report on the generation of a trispecific 2 + 1 antibodies exhibiting a common light chain and illustrate synergistic effects of trispecific antigen binding. Overall, this generic procedure paves the way for the engineering of tri- and oligospecific therapeutic antibodies derived from avian immunizations.

Functional and in silico Characterization of Neutralizing Interactions Between Antibodies and the Foot-and-Mouth Disease Virus Immunodominant Antigenic Site

Molecular knowledge of virus–antibody interactions is essential for the development of better vaccines and for a timely assessment of the spread and severity of epidemics. For foot-and-mouth disease virus (FMDV) research, in particular, computational methods for antigen–antibody (Ag–Ab) interaction, and cross-antigenicity characterization and prediction are critical to design engineered vaccines with robust, long-lasting, and wider response against different strains. We integrated existing structural modeling and prediction algorithms to study the surface properties of FMDV Ags and Abs and their interaction. First, we explored four modeling and two Ag–Ab docking methods and implemented a computational pipeline based on a reference Ag–Ab structure for FMDV of serotype C, to be used as a source protocol for the study of unknown interaction pairs of Ag–Ab. Next, we obtained the variable region sequence of two monoclonal IgM and IgG antibodies that recognize and neutralize antigenic site A (AgSA) epitopes from South America serotype A FMDV and developed two peptide ELISAs for their fine epitope mapping. Then, we applied the previous Ag–Ab molecular structure modeling and docking protocol further scored by functional peptide ELISA data. This work highlights a possible different behavior in the immune response of IgG and IgM Ab isotypes. The present method yielded reliable Ab models with differential paratopes and Ag interaction topologies in concordance with their isotype classes. Moreover, it demonstrates the applicability of computational prediction techniques to the interaction phenomena between the FMDV immunodominant AgSA and Abs, and points out their potential utility as a metric for virus-related, massive Ab repertoire analysis or as a starting point for recombinant vaccine design.

Determination of the Rituximab Binding Site to the CD20 Epitope Using SPOT Synthesis and Surface Plasmon Resonance Analyses

Antibodies not only play a major role in clinical diagnostics and biopharmaceutical analysis but also are a class of drugs that are regularly used to treat numerous diseases. The identification of antibody-epitope binding sites is then of great interest to many emerging medical and bioanalytical applications, particularly to design monoclonal antibodies (mAb) mimics taking advantage of amino acid residues involved in the binding. Among relevant antibodies, the monoclonal antibody rituximab has received significant attention as it is exploited to treat several cancers including non-Hodgkin's lymphoma and chronic lymphocytic leukemia, as well as some autoimmune disorders such as rheumatoid arthritis. The binding of rituximab to the targeted cells occurs via the recognition of the CD20 epitope. A crystallographic study has shown that the binding area, named paratope, is located at the surface of rituximab. Combining the SPOT method and the complementary surface plasmon resonance technique allowed us to detect an extended recognition domain buried in the pocket of the rituximab Fab formed by four β-sheets. More generally, the present study offers a comprehensive approach to identify antibody-epitope binding sites.

Pharmacokinetic-Pharmacodynamic Modelling of Systemic IL13 Blockade by Monoclonal Antibody Therapy: A Free Assay Disguised as Total

A sequential pharmacokinetic (PK) and pharmacodynamic (PD) model was built with Nonlinear Mixed Effects Modelling based on data from a first-in-human trial of a novel biologic, MEDI7836. MEDI7836 is a human immunoglobulin G1 lambda (IgG1λ-YTE) monoclonal antibody, with an Fc modification to reduce metabolic clearance. MEDI7836 specifically binds to, and functionally neutralizes interleukin-13. Thirty-two healthy male adults were enrolled into a dose-escalation clinical trial. Four active doses were tested (30, 105, 300, and 600 mg) with 6 volunteers enrolled per cohort. Eight volunteers received placebo as control. Following single subcutaneous administration (SC), individual time courses of serum MEDI7836 concentrations, and the resulting serum IL13 modulation in vivo, were quantified. A binding pharmacokinetic-pharmacodynamic (PK-PD) indirect response model was built to characterize the exposure-driven modulation of the target over time by MEDI7836. While the validated bioanalytical assay specification quantified the level of free target (i.e., a free IL13 assay), emerging clinical data suggested dose-dependent increase in systemic IL13 concentration over time, indicative of a total IL13 assay. The target time course was modelled as a linear combination of free target and a percentage of the drug-target complex to fit the clinical data. This novel PK-PD modelling approach integrates independent knowledge about the assay characteristics to successfully elucidate apparently complex observations.

Revising Berg-Purcell for finite receptor kinetics

From nutrient uptake to chemoreception to synaptic transmission, many systems in cell biology depend on molecules diffusing and binding to membrane receptors. Mathematical analysis of such systems often neglects the fact that receptors process molecules at finite kinetic rates. A key example is the celebrated formula of Berg and Purcell for the rate that cell surface receptors capture extracellular molecules. Indeed, this influential result is only valid if receptors transport molecules through the cell wall at a rate much faster than molecules arrive at receptors. From a mathematical perspective, ignoring receptor kinetics is convenient because it makes the diffusing molecules independent. In contrast, including receptor kinetics introduces correlations between the diffusing molecules because, for example, bound receptors may be temporarily blocked from binding additional molecules. In this work, we present a modeling framework for coupling bulk diffusion to surface receptors with finite kinetic rates. The framework uses boundary homogenization to couple the diffusion equation to nonlinear ordinary differential equations on the boundary. We use this framework to derive an explicit formula for the cellular uptake rate and show that the analysis of Berg and Purcell significantly overestimates uptake in some typical biophysical scenarios. We confirm our analysis by numerical simulations of a many-particle stochastic system.

Integrative proteomics identifies thousands of distinct, multi-epitope, and high-affinity nanobodies

The antibody immune response is essential for the survival of mammals. However, we still lack a systematic understanding of the antibody repertoire. Here, we developed a proteomic strategy to survey, at an unprecedented scale, the landscape of antigen-engaged, circulating camelid heavy-chain antibodies, whose minimal binding fragments are called VHH antibodies or nanobodies. The sensitivity and robustness of this approach were validated with three antigens spanning orders of magnitude in immune responses; thousands of distinct, high-affinity nanobody families were reliably identified and quantified. Using high-throughput structural modeling, cross-linking mass spectrometry, mutagenesis, and deep learning, we mapped and analyzed the epitopes of >100,000 antigen-nanobody complexes. Our results revealed a surprising diversity of ultrahigh-affinity camelid nanobodies for specific antigen binding on various dominant epitope clusters. Nanobodies utilize both shape and charge complementarity to enable highly selective antigen binding. Interestingly, we found that nanobody-antigen binding can mimic conserved intracellular protein-protein interactions. A record of this paper's Transparent Peer Review process is included in the Supplemental information.

Development of a monoclonal-based ic-ELISA for the determination of kitasamycin in animal tissues and simulation studying its molecular recognition mechanism

To monitor the residue of kitasamycin (KIT), a monoclonal antibody against KIT was prepared, and a 50% inhibition concentration (IC₅₀) of 5.7 ± 1.4 μg/L was achieved with the most sensitive antibody, KA/2A9, by optimizing ELISA conditions. The LODs for KIT in different animal tissues ranged from 22.47 μg/kg to 29.32 μg/kg, and the recoveries of the fortified tissues were 70% ~ 120% with coefficients of variation below 20%. Then, KIT-specific scFv KA/2A9/3 was prepared for the first time. Homologous modeling and molecular docking results indicated that the key amino acids of KA/2A9/3 scFv are TYR-92 (CDRL3), SER-93 (CDRL3), ASP-155 (CDRH1) and GLY-226 (CDRH3), and the hydrogen bond is the main force. And then, virtual mutation provides a method to evolve KA/2A9/3 scFv antibodies. These results contribute to comprehending the antigen-antibody binding mechanism and provide effective information for in vitro affinity maturation of anti-KIT scFv.

Affinity maturation: highlights in the application of in vitro strategies for the directed evolution of antibodies

Affinity maturation is a key technique in protein engineering which is used to improve affinity and binding interactions in vitro, a process often required to fulfil the therapeutic potential of antibodies. There are many available display technologies and maturation methods developed over the years, which have been instrumental in the production of therapeutic antibodies. However, due to the inherent limitations in display capacity of these technologies, accommodation of expansive and complex library builds is still a challenge. In this article, we discuss our recent efforts in the affinity maturation of a difficult antibody lineage using an unbiased approach, which sought to explore a larger sequence space through the application of DNA recombination and shuffling techniques across the entire antibody region and selections using ribosome display. We also highlight the key features of several display technologies and diversification methods, and discuss the strategies devised by different groups in response to different challenges. Particular attention is drawn to examples which are aimed at the expansion of sequence, structural or experimental diversity through different means and approaches. Here, we provide our perspectives on these methodologies and the considerations involved in the design of effective strategies for the directed evolution of antibodies.

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 10⁴ motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.

Immunogenicity study of a Streptococcus suis autogenous vaccine in preparturient sows and evaluation of passive maternal immunity in piglets

Background

Streptococcus suis is an important pathogen that causes severe diseases mostly in weaned piglets. Only available vaccines in the field are those composed of killed bacteria (bacterins) but data about their effectiveness are missing. We report here a field study on the immunological response induced by an autogenous vaccine applied in pre-parturient sows. Using a farm with recurrent S. suis serotype 7 problems, the study was divided in three experiments: (I) Sows received the vaccine at 7 and 3 weeks pre-farrowing. (II) Replacement gilts introduced to the herd received the vaccine at 4 and 7 weeks after their entry in quarantine and a boost 3 weeks pre-farrowing. (III) Gilts from experiment II received another boost 3 weeks pre-farrowing at their 3rd/4th parity. Levels, isotype profile and opsonophagocytosis capacity of the serum antibodies induced by vaccination were evaluated in sows and maternal immunity in piglets.

Results

In sows (I), the vaccine induced a slight, albeit significant, increase in anti-S. suis total antibodies after 2 doses when compare to basal levels already present in the animals. These antibodies showed a high opsonic capacity in vitro, highlighting their potential protective capacity. A gilt vaccination program of 3 doses (II) resulted in a significant increase in anti-S. suis total antibodies. Levels of maternal immunity transferred to piglets were high at 7 days of age, but rapidly decreased by 18 days of age. A gilt vaccination program ensued a higher transfer of maternal immunity in piglets compared to control animals; nevertheless duration was not improved at 18 day-old piglets. The vaccine response in both gilts and sows was mainly composed of IgG1 subclass, which was also the main Ig transferred to piglets. IgG2 subclass was also found in piglets, but its level was not increased by vaccination. Finally, a recall IgG1 response was induced by another boost vaccination at 3rd/4th parity (III), indicating that the vaccine induced the establishment of a lasting memory response in the herd.

Conclusions

Overall, an optimal gilt/sow vaccination program might result in increased antibody responses; nevertheless duration of maternal immunity would not last long enough to protect post-weaned piglets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-02774-4.

Immune Response to SARS-CoV-2 Infection in Obesity and T2D: Literature Review

In December 2019, a novel coronavirus known as SARS-CoV-2 was first detected in Wuhan, China, causing outbreaks of the coronavirus disease COVID-19 that has now spread globally. For this reason, The World Health Organization (WHO) declared COVID-19 a public health emergency in March 2020. People living with pre-existing conditions such as obesity, cardiovascular diseases, type 2 diabetes (T2D), and chronic kidney and lung diseases, are prone to develop severe forms of disease with fatal outcomes. Metabolic diseases such as obesity and T2D alter the balance of innate and adaptive responses. Both diseases share common features characterized by augmented adiposity associated with a chronic systemic low-grade inflammation, senescence, immunoglobulin glycation, and abnormalities in the number and function of adaptive immune cells. In obese and T2D patients infected by SARS-CoV-2, where immune cells are already hampered, this response appears to be stronger. In this review, we describe the abnormalities of the immune system, and summarize clinical findings of COVID-19 patients with pre-existing conditions such as obesity and T2D as this group is at greater risk of suffering severe and fatal clinical outcomes.

Fiber-Optic Localized Surface Plasmon Resonance Sensors Based on Nanomaterials

Applying fiber-optics on surface plasmon resonance (SPR) sensors is aimed at practical usability over conventional SPR sensors. Recently, field localization techniques using nanostructures or nanoparticles have been investigated on optical fibers for further sensitivity enhancement and significant target selectivity. In this review article, we explored varied recent research approaches of fiber-optics based localized surface plasmon resonance (LSPR) sensors. The article contains interesting experimental results using fiber-optic LSPR sensors for three different application categories: (1) chemical reactions measurements, (2) physical properties measurements, and (3) biological events monitoring. In addition, novel techniques which can create synergy combined with fiber-optic LSPR sensors were introduced. The review article suggests fiber-optic LSPR sensors have lots of potential for measurements of varied targets with high sensitivity. Moreover, the previous results show that the sensitivity enhancements which can be applied with creative varied plasmonic nanomaterials make it possible to detect minute changes including quick chemical reactions and tiny molecular activities.

bbeaR: an R package and framework for epitope-specific antibody profiling

SUMMARY

Analysis of epitope-specific antibody repertoires has provided novel insights into the pathogenesis of inflammatory disorders, especially allergies. A novel multiplex immunoassay, termed Bead-Based Epitope Assay (BBEA), was developed to quantify levels of epitope-specific immunoglobulins, including IgE, IgG, IgA and IgD isotypes. bbeaR is an open-source R package, developed for the BBEA, provides a framework to import, process and normalize .csv data files exported from the Luminex reader, evaluate various quality control metrics, analyze differential epitope-binding antibodies with linear modelling, visualize results, and map epitopes' amino acid sequences to their respective primary protein structures. bbeaR enables streamlined and reproducible analysis of epitope-specific antibody profiles.

AVAILABILITY AND IMPLEMENTATION

bbeaR is open-source and freely available from GitHub as an R package: https://github.com/msuprun/bbeaR; vignettes included.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NHDL, a recombinant VL/VH hybrid antibody control for IgG2/4 antibodies

The mechanism of action of recombinant IgG2/4 antibodies involves blocking of their target without the induction of effector functions. Examples are eculizumab (Soliris®), which is used clinically to block complement factor C5, as well as anti-human CD14 (r18D11) and anti-porcine CD14 (rMIL2) produced in our laboratory. So far, no proper IgG2/4 control antibody has been available for controlled validation of IgG2/4 antibody functions. Here, we describe the design of a recombinant control antibody (NHDL), which was generated by combining the variable light (V_L) and heavy (V_H) chains from two unrelated specificities. NHDL was readily expressed and purified as a stable IgG2/4 antibody, and showed no detectable specificity toward any putative antigen present in human or porcine blood. The approach of artificial V_L/V_H combination may be adopted for the design of other recombinant control antibodies.

The Allosteric Effect in Antibody-Antigen Recognition

We studied the molecular details of the recognition of antigens by the variable domain of their cognate antibodies in as well as those elicited by the constant domains, which do not directly interact with antigens. Such effects are difficult to study experimentally; however, molecular dynamics simulations and subsequent residue interaction network analysis provide insight into the allosteric communication between the antigen-binding CDR region and the constant domain. We performed MD simulations of the complex of Fab and prion-associated peptide in the apo and bound forms and follow the conformational changes in the antibody and cross-talk between its subunits and with antigens. These protocols could be generally applied for studies of other antigens-antibody recognition systems.

SARS-CoV-2 recombinant proteins stimulate distinct cellular and humoral immune response profiles in samples from COVID-19 convalescent patients

OBJECTIVES

In this preliminary study we investigated cellular and humoral immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens in blood samples from 14 recovered coronavirus disease 2019 (COVID-19) patients and compared them to those in samples from 12 uninfected/unvaccinated volunteers.

METHODS

Cellular immunity was assessed by intracellular detection of IFN-γ in CD3+ T lymphocytes after stimulation with SARS-CoV-2 spike (S1), nucleocapsid (NC), or receptor-binding domain (RBD) recombinant proteins or overlapping peptide pools covering the sequence of SARS-CoV-2 spike, membrane and nucleocapsid regions. The humoral response was examined by ELISAs and/or chemiluminescence assays for the presence of serum IgG antibodies directed to SARS-CoV-2 proteins.

RESULTS

We observed differences between humoral and cellular immune profiles in response to stimulation with the same proteins. Assays of IgG antibodies directed to SARS-CoV-2 NC, RBD and S1/S2 recombinant proteins were able to differentiate convalescent from uninfected/unvaccinated groups. Cellular immune responses to SARS-CoV-2 protein stimuli did not exhibit a specific response, as T cells from both individuals with no history of contact with SARS-CoV-2 and from recovered donors were able to produce IFN-γ.

CONCLUSIONS

Determination of the cellular immune response to stimulation with a pool of SARS-CoV-2 peptides but not with SARS-CoV-2 proteins is able to distinguish convalescent individuals from unexposed individuals. Regarding the humoral immune response, the screening for serum IgG antibodies directed to SARS-CoV-2 proteins has been shown to be specific for the response of recovered individuals.

Role of artificial intelligence in peptide vaccine design against RNA viruses

RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

The CH1α domain of mucosal gp41 IgA contributes to antibody specificity and antiviral functions in HIV-1 highly exposed Sero-Negative individuals

The antibody molecule comprises a variable domain conferring antigen specificity and affinity distinct from the heavy chain constant (CH) domains dictating effector functions. We here interrogate this paradigm by evaluating the unique influence of the CH1α domain on epitope specificity and functions using two mucosal gp41-specific Fab-IgAs (FabA) derived from HIV-1 highly-exposed but persistently seronegative individuals (HESN). These HESN develop selectively affinity-matured HIV-1-specific mucosal IgA that target the gp41 viral envelope and might provide protection although by unclear mechanisms. Isotype-switching FabAs into Fab-IgGs (FabGs) results in a >10-fold loss in affinity for HIV-1 clade A, B, and C gp41, together with reduced neutralization of HIV-1 cross-clade. The FabA conformational epitopes map selectively on gp41 in 6-Helix bundle and pre-fusion conformations cross-clade, unlike FabGs. Finally, we designed in silico, a 12 amino-acid peptide recapitulating one FabA conformational epitope that inhibits the FabA binding to gp41 cross-clade and its neutralizing activity. Altogether, our results reveal that the CH1α domain shapes the antibody paratope through an allosteric effect, thereby strengthening the antibody specificity and functional activities. Further, they clarify the mechanisms by which these HESN IgAs might confer protection against HIV-1-sexual acquisition. The IgA-specific epitope we characterized by reverse vaccinology could help designing a mucosal HIV-1 vaccine.

Design and characterization of a recombinant immunotoxin for targeted therapy of breast cancer cells: In vitro and in silico analyses

AIMS

GnRH-DFF40 (gonadotropin releasing hormone-DNA fragmentation factor 40) humanized recombinant immunotoxin serves as a prospective candidate for targeted therapy of malignancies with over-expressed gonadotropin releasing hormone receptor (GnRHR). In this study, we attempted to generate a GnRH-based chimeric protein composed of human DFF40 fused with GnRH which encodes an apoptotic nuclease and specifically targets cancer cells displaying GnRH receptor overexpression.

MATERIALS AND METHODS

A codon optimized, synthetic GnRH-DFF40 fusion gene and its single counterpart (DFF40) were constructed in pET28a expression vector. Cytotoxicity of these expressed proteins were evaluated on three breast cancer cell lines (MCF7, MDA-MB231, and SKBR3). The stability and biological activity of the recombinant proteins were investigated in the treated cell line and cell-free system. Also, the ability of this fusion and its single form in inducing apoptosis, and inhibiting metastasis and migration were evaluated by flow cytometry, migration assay and wound healing analysis, respectively. In silico analyses were also done to understand the specific interactions between GnRH and its receptor.

KEY FINDINGS

GnRH-DFF40 fusion protein and DFF40 were successfully expressed. The purified chimeric protein showed dose-dependent cytotoxicity against all three cell lines. The recombinant fusion protein was biologically active with nucleolytic functionality and apoptosis induction ability. Moreover, the fusion could inhibit the invasion property of MDA-MB-231 cells. In silico analysis also showed that four residues from GnRH domain and 11 GnRHR residues had the most interaction sites for specific targeted delivery of the immunotoxin in cancer cells.

SIGNIFICANCE

Fusion construct could be a prospective candidate for targeted therapy of cancers upregulating GnRH receptor.

Exploitation of Elevated Extracellular ATP to Specifically Direct Antibody to Tumor Microenvironment

The extracellular adenosine triphosphate (ATP) concentration is highly elevated in the tumor microenvironment (TME) and remains tightly regulated in normal tissues. Using phage display technology, we establish a method to identify an antibody that can bind to an antigen only in the presence of ATP. Crystallography analysis reveals that ATP bound in between the antibody-antigen interface serves as a switch for antigen binding. In a transgenic mouse model overexpressing the antigen systemically, the ATP switch antibody binds to the antigen in tumors with minimal binding in normal tissues and plasma and inhibits tumor growth. Thus, we demonstrate that elevated extracellular ATP concentration can be exploited to specifically target the TME, giving therapeutic antibodies the ability to overcome on-target off-tumor toxicity.

Generation and reactivity analysis of human recombinant monoclonal antibodies directed against epitopes on HLA-DR

In kidney transplantation, eplet mismatches between donor and recipient have been associated with de novo donor-specific antibody development. Eplets are theoretically defined configurations of polymorphic amino acids and require experimental verification to establish whether they can be bound by alloantibodies. Human HLA-specific monoclonal antibodies (mAbs) have been instrumental for this purpose but are largely lacking for HLA class II. In this study, we isolated single HLA-DR-specific memory B cells from peripheral blood of immunized individuals (n = 3) using HLA class II tetramers to generate recombinant human HLA-DR antigen-reactive mAbs (n = 5). Comparison of the amino acid composition of the reactive HLA alleles in relation to the antibody reactivity patterns led to identification of 3 configurations, 70Q 73A, 31F 32Y 37Y, and 14K 25Q recognized, respectively, by HLA-DRB1*01:01, HLA-DRB1*04:01, and HLA-DRB1*07:01 antigen-reactive mAbs. The first 2 correspond to eplets 70QA and 31FYY and can now be considered antibody verified. The latter indicates that eplet 25Q needs to be redefined before being considered as antibody verified. Generation and reactivity analysis of human HLA-DR mAbs allowed for identification of amino acid configurations corresponding to known eplets, whereas the other patterns may be used to redefine eplets with similar, but not identical predicted amino acid composition.

Near-infrared fluorescence imaging in immunotherapy

Near-infrared (NIR) light possesses many suitable optophysical properties for medical imaging including low autofluorescence, deep tissue penetration, and minimal light scattering, which together allow for high-resolution imaging of biological tissue. NIR imaging has proven to be a noninvasive and effective real-time imaging methodology that provides a high signal-to-background ratio compared to other potential optical imaging modalities. In response to this, the use of NIR imaging has been extensively explored in the field of immunotherapy. To date, NIR fluorescence imaging has successfully offered reliable monitoring of the localization, dynamics, and function of immune responses, which are vital in assessing not only the efficacy but also the safety of treatments to design immunotherapies optimally. This review aims to provide an overview of the current research on NIR imaging of the immune response. We expect that the use of NIR imaging will expand further in response to the recent success in cancer immunotherapy. We will also offer our insights on how this technology will meet rapidly growing expectations in the future.

TCRBuilder: multi-state T-cell receptor structure prediction

MOTIVATION

T-cell receptors (TCRs) are immune proteins that primarily target peptide antigens presented by the major histocompatibility complex. They tend to have lower specificity and affinity than their antibody counterparts, and their binding sites have been shown to adopt multiple conformations, which is potentially an important factor for their polyspecificity. None of the current TCR-modelling tools predict this variability which limits our ability to accurately predict TCR binding.

RESULTS

We present TCRBuilder, a multi-state TCR structure prediction tool. Given a paired αβTCR sequence, TCRBuilder returns a model or an ensemble of models covering the potential conformations of the binding site. This enables the analysis of structurally driven polyspecificity in TCRs, which is not possible with existing tools.

AVAILABILITY AND IMPLEMENTATION

http://opig.stats.ox.ac.uk/resources.

CONTACT

deane@stats.ox.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Defining and Manipulating B Cell Immunodominance Hierarchies to Elicit Broadly Neutralizing Antibody Responses against Influenza Virus

The antibody repertoire possesses near-limitless diversity, enabling the adaptive immune system to accommodate essentially any antigen. However, this diversity explores the antigenic space unequally, allowing some pathogens like influenza virus to impose complex immunodominance hierarchies that distract antibody responses away from key sites of virus vulnerability. We developed a computational model of affinity maturation to map the patterns of immunodominance that evolve upon immunization with natural and engineered displays of hemagglutinin (HA), the influenza vaccine antigen. Based on this knowledge, we designed immunization protocols that subvert immune distraction and focus serum antibody responses upon a functionally conserved, but immunologically recessive, target of human broadly neutralizing antibodies. We tested in silico predictions by vaccinating transgenic mice in which antibody diversity was humanized to mirror clinically relevant humoral output. Collectively, our results demonstrate that complex patterns in antibody immunogenicity can be rationally defined and then manipulated to elicit engineered immunity.

Development of Rabbit Monoclonal Antibodies for Quantitation of Therapeutic Human Antibodies in Preclinical Non-Human Primate Studies

During preclinical studies, there is a great need to develop monoclonal antibodies (mAbs) that are specific to human immunoglobulin (IgG), without binding to monkey IgG, to detect therapeutic human mAb in non-human primates. We took advantage of the latest rabbit B cell cloning technology to develop six unique rabbit anti-human IgG mAb clones for this purpose. These clones are capable of binding to both human IgG and Fab with high affinity without nonspecific binding to cynomolgus monkey IgG. These clones have been evaluated as a generic capture reagent for the detection of human IgG and Fab, in the presence of cynomolgus monkey serum, by Gyrolab™ immunoassay. They may be used in singlet or as pairs for the detection of human IgG, in any host animal, to meet the need for therapeutic mAb development in preclinical studies.