The structural basis of antibody-antigen recognition

Design and construction of a phage-displayed Camelid nanobody library using a simple bioinformatics method

BACKGROUND

Rational design of synthetic phage-displayed libraries requires the identification of the most appropriate positions for randomization using defined amino acid sets to recapitulate the natural occurrence. The present study uses position-specific scoring matrixes (PSSMs) for identifying and randomizing Camelidae nanobody (VHH) CDR3. The functionality of a synthetic VHH repertoire designed by this method was tested for discovering new VHH binders to recombinant coagulation factor VII (rfVII).

METHODS

Based on PSSM analysis, the CDR3 of cAbBCII10 VHH framework was identified, and a set of amino acids for the substitution of each PSSM-CDR3 position was defined. Using the Rosetta design SwiftLib tool, the final repertoire was back-translated to a degenerate nucleotide sequence. A synthetic phage-displayed library was constructed based on this repertoire and screened for anti-rfVII binders.

RESULTS

A synthetic phage-displayed VHH library with 1 × 108 variants was constructed. Three VHH binders to rfVII were isolated from this library with estimated dissociation constants (KD) of 1 × 10-8 M, 5.8 × 10-8 M and 2.6 × 10-7 M.

CONCLUSION

PSSM analysis is a simple and efficient way to design synthetic phage-displayed libraries.

Qualification of a LC-HRMS platform method for biosimilar development using NISTmab as a model

Biosimilars are a cost-effective alternative to biopharmaceuticals, necessitating rigorous analytical methods for consistency and compliance. Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is a versatile tool for assessing key attributes, encompassing molecular mass, primary structure, and post-translational modifications (PTMs). Adhering to ICH Q2R1, we validated an LC-HRMS based peptide mapping method using NISTmab as a reference. The method validation parameters, covering system suitability, specificity, accuracy, precision, robustness, and carryover, were comprehensively assessed. The method effectively differentiated the NISTmab from similar counterparts as well as from artificially introduced spiked conditions. Notably, the accuracy of mass error for NISTmab specific complementarity determining region peptides was within a maximum of 2.42 parts per million (ppm) from theoretical and the highest percent relative standard deviation (%RSD) observed for precision was 0.000219 %. It demonstrates precision in sequence coverage and PTM detection, with a visual inspection of total ion chromatogram approach for variability assessment. The method maintains robustness when subjected to diverse storage conditions, encompassing variations in column temperature and mobile phase composition. Negligible carryover was noted during the carryover analysis. In summary, this method serves as a versatile platform for multiple biosimilar development by effectively characterizing and identifying monoclonal antibodies, ultimately ensuring product quality.

A new era of antibody discovery: an in-depth review of AI-driven approaches

Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.

Comparative assessment of different anti-CD147/Basigin 2 antibodies as a potential therapeutic anticancer target by molecular modeling and dynamic simulation

Cluster of differentiation 147 (CD147) is an attractive target for anticancer therapy since it is pivotal in developing and progressing several of malignant tumors in the context of its high expression levels. Although anti-CD147 antibodies by different laboratories are designed for the Ig-like domains of CD147, there is a demand to provide priority among these anti-CD147 antibodies for developing of therapeutic anti-CD147 antibody before experimental validations. This study uses molecular docking and dynamic simulation techniques to compare the binding modes and affinities of nine antibody models against the Ig-like domains of CD147. After obtaining the model antibodies by homology modeling via Robetta, we predicted the CDRs of nine antibodies and the epitopes of CD147 to reach more accurate results for antigen affinity in molecular docking. Next, from HADDOCK 2.4., we meticulously handpicked the most superior model clusters (Z-Score: - 2.5 to - 1.2) and identified that meplazumab had higher affinities according to the success rate as the percentage of a scoring scale. We achieved stable simulations of CD147-antibody interaction. Our outcomes hold hypothetical importance for further experimental cancer research on the design and development of the relevant model antibodies.

Rapid and cost-effective epitope mapping using PURE ribosome display coupled with next-generation sequencing and bioinformatics

A novel, efficient and cost-effective approach for epitope identification of an antibody has been developed using a ribosome display platform. This platform, known as PURE ribosome display, utilizes an Escherichia coli-based reconstituted cell-free protein synthesis system (PURE system). It stabilizes the mRNA-ribosome-peptide complex via a ribosome-arrest peptide sequence. This system was complemented by next-generation sequencing (NGS) and an algorithm for analyzing binding epitopes. To showcase the effectiveness of this method, selection conditions were refined using the anti-PA tag monoclonal antibody with the PA tag peptide as a model. Subsequently, a random peptide library was constructed using 10 NNK triplet oligonucleotides via the PURE ribosome display. The resulting random peptide library-ribosome-mRNA complex was selected using a commercially available anti-HA (YPYDVPDYA) tag monoclonal antibody, followed by NGS and bioinformatic analysis. Our approach successfully identified the DVPDY sequence as an epitope within the hemagglutinin amino acid sequence, which was then experimentally validated. This platform provided a valuable tool for investigating continuous epitopes in antibodies.

Repeated vaccination with homologous influenza hemagglutinin broadens human antibody responses to unmatched flu viruses

The on-going diversification of influenza virus necessicates annual vaccine updating. The vaccine antigen, the viral spike protein hemagglutinin (HA), tends to elicit strain-specific neutralizing activity, predicting that sequential immunization with the same HA strain will boost antibodies with narrow coverage. However, repeated vaccination with homologous SARS-CoV-2 vaccine eventually elicits neutralizing activity against highly unmatched variants, questioning this immunological premise. We evaluated a longitudinal influenza vaccine cohort, where each year the subjects received the same, novel H1N1 2009 pandemic vaccine strain. Repeated vaccination gradually enhanced receptor-blocking antibodies (HAI) to highly unmatched H1N1 strains within individuals with no initial memory recall against these historical viruses. An in silico model of affinity maturation in germinal centers integrated with a model of differentiation and expansion of memory cells provides insight into the mechanisms underlying these results and shows how repeated exposure to the same immunogen can broaden the antibody response against diversified targets.

AbFlex: designing antibody complementarity determining regions with flexible CDR definition

MOTIVATION

Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.

RESULTS

We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody-antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model.

AVAILABILITY AND IMPLEMENTATION

The source code and implementation are available at https://github.com/wsjeon92/AbFlex.

Leveraging Cross-Linking Mass Spectrometry for Modeling Antibody-Antigen Complexes

Elucidating antibody-antigen complexes at the atomic level is of utmost interest for understanding immune responses and designing better therapies. Cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for mapping protein-protein interactions, suggesting valuable structural insights. However, the use of XL-MS studies to enable epitope/paratope mapping of antibody-antigen complexes is still limited up to now. XL-MS data can be used to drive integrative modeling of antibody-antigen complexes, where cross-links information serves as distance restraints for the precise determination of binding interfaces. In this approach, XL-MS data are employed to identify connections between binding interfaces of the antibody and the antigen, thus informing molecular modeling. Current literature provides minimal input about the impact of XL-MS data on the integrative modeling of antibody-antigen complexes. Here, we applied XL-MS to retrieve information about binding interfaces of three antibody-antigen complexes. We leveraged XL-MS data to perform integrative modeling using HADDOCK (active-passive residues and distance restraints strategies) and AlphaLink2. We then compared these three approaches with initial predictions of investigated antibody-antigen complexes by AlphaFold Multimer. This work emphasizes the importance of cross-linking data in resolving conformational dynamics of antibody-antigen complexes, ultimately enhancing the design of better protein therapeutics and vaccines.

Production of a Ribosome-Displayed Mouse scFv Antibody Against CD133, Analysis of Its Molecular Docking, and Molecular Dynamic Simulations of Their Interactions

The pentaspan transmembrane glycoprotein CD133, prominin-1, is expressed in cancer stem cells in many tumors and is promising as a novel target for the delivery of cytotoxic drugs to cancer-initiating cells. In this study, we prepared a mouse library of single-chain variable fragment (scFv) antibodies using mRNAs isolated from mice immunized with the third extracellular domain of a recombinant CD133 (D-EC3). First, the scFvs were directly exposed to D-EC3 to select a new specific scFv with high affinity against CD133 using the ribosome display method. Then, the selected scFv was characterized by the indirect enzyme-linked immunosorbent assay (ELISA), immunocytochemistry (ICC), and in silico analyses included molecular docking and molecular dynamics simulations. Based on ELISA results, scFv 2 had a higher affinity for recombinant CD133, and it was considered for further analysis. Next, the immunocytochemistry and flow cytometry experiments confirmed that the obtained scFv could bind to the CD133 expressing HT-29 cells. Furthermore, the results of in silico analysis verified the ability of the scFv 2 antibody to bind and detect the D-EC3 antigen through key residues employed in antigen-antibody interactions. Our results suggest that ribosome display could be applied as a rapid and valid method for isolation of scFv with high affinity and specificity. Also, studying the mechanism of interaction between CD133's scFv and D-EC3 with two approaches of experimental and in silico analysis has potential importance for the design and development of antibody with improved properties.

Computational identification of antibody-binding epitopes from mimotope datasets

Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods.

Synthetic Glycans Reveal Determinants of Antibody Functional Efficacy against a Fungal Pathogen

Antibodies play a vital role in the immune response to infectious diseases and can be administered passively to protect patients. In the case of Cryptococcus neoformans, a WHO critical priority fungal pathogen, infection results in antibodies targeting capsular glucuronoxylomannan (GXM). These antibodies yield protective, non-protective, and disease-enhancing outcomes when administered passively. However, it was unknown how these distinct antibodies recognized their antigens at the molecular level, leading to the hypothesis that they may target different GXM epitopes. To test this hypothesis, we constructed a microarray containing 26 glycans representative of those found in highly virulent cryptococcal strains and utilized it to study 16 well-characterized monoclonal antibodies. Notably, we found that protective and non-protective antibodies shared conserved reactivity to the M2 motif of GXM, irrespective of the strain used in infection or GXM-isolated to produce a conjugate vaccine. Here, only two antibodies, 12A1 and 18B7, exhibited diverse trivalent GXM motif reactivity. IgG antibodies associated with protective responses showed cross-reactivity to at least two GXM motifs. This molecular understanding of antibody binding epitopes was used to map the antigenic diversity of two Cryptococcus neoformans strains, which revealed the exceptional complexity of fungal capsular polysaccharides. A multi-GXM motif vaccine holds the potential to effectively address this antigenic diversity. Collectively, these findings underscore the context-dependent nature of antibody function and challenge the classification of anti-GXM epitopes as either "protective" or "non-protective".

Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation

COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.

Understanding Fc function for rational vaccine design against pathogens

Antibodies represent the primary correlate of immunity following most clinically approved vaccines. However, their mechanisms of action vary from pathogen to pathogen, ranging from neutralization, to opsonophagocytosis, to cytotoxicity. Antibody functions are regulated both by antigen specificity (Fab domain) and by the interaction of their Fc domain with distinct types of Fc receptors (FcRs) present in immune cells. Increasing evidence highlights the critical nature of Fc:FcR interactions in controlling pathogen spread and limiting the disease state. Moreover, variation in Fc-receptor engagement during the course of infection has been demonstrated across a range of pathogens, and this can be further influenced by prior exposure(s)/immunizations, age, pregnancy, and underlying health conditions. Fc:FcR functional variation occurs at the level of antibody isotype and subclass selection as well as post-translational modification of antibodies that shape Fc:FcR-interactions. These factors collectively support a model whereby the immune system actively harnesses and directs Fc:FcR interactions to fight disease. By defining the precise humoral mechanisms that control infections, as well as understanding how these functions can be actively tuned, it may be possible to open new paths for improving existing or novel vaccines.

Advances in electrochemical biosensor design for the detection of the stress biomarker cortisol

The monitoring of stress levels in humans has become increasingly relevant, given the recent incline of stress-related mental health disorders, lifestyle impacts, and chronic physiological diseases. Long-term exposure to stress can induce anxiety and depression, heart disease, and risky behaviors, such as drug and alcohol abuse. Biomarker molecules can be quantified in biological fluids to study human stress. Cortisol, specifically, is a hormone biomarker produced in the adrenal glands with biofluid concentrations that directly correlate to stress levels in humans. The rapid, real-time detection of cortisol is necessary for stress management and predicting the onset of psychological and physical ailments. Current methods, including mass spectrometry and immunoassays, are effective for sensitive cortisol quantification. However, these techniques provide only single measurements which pose challenges in the continuous monitoring of stress levels. Additionally, these analytical methods often require trained personnel to operate expensive instrumentation. Alternatively, low-cost electrochemical biosensors enable the real-time detection and continuous monitoring of cortisol levels while also providing adequate analytical figures of merit (e.g., sensitivity, selectivity, sensor response times, detection limits, and reproducibility) in a simple design platform. This review discusses the recent developments in electrochemical biosensor design for the detection of cortisol in human biofluids. Special emphasis is given to biosensor recognition elements, including antibodies, molecularly imprinted polymers (MIPs), and aptamers, as critical components of electrochemical biosensors for cortisol detection. Furthermore, the advantages and limiting factors of various electrochemical techniques and sensing in complex biofluid matrices are overviewed. Remarks on the current challenges and future perspectives regarding electrochemical biosensors for stress monitoring are provided, including matrix effects (pH dependence and biological interferences), wearability, and large-scale production.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

Strategies and rules for tuning TCR-derived therapy

Manipulation of T cells has revolutionized cancer immunotherapy. Notably, the use of T cells carrying engineered T cell receptors (TCR-T) offers a favourable therapeutic pathway, particularly in the treatment of solid tumours. However, major challenges such as limited clinical response efficacy, off-target effects and tumour immunosuppressive microenvironment have hindered the clinical translation of this approach. In this review, we mainly want to guide TCR-T investigators on several major issues they face in the treatment of solid tumours after obtaining specific TCR sequences: (1) whether we have to undergo affinity maturation or not, and what parameter we should use as a criterion for being more effective. (2) What modifications can be added to counteract the tumour inhibitory microenvironment to make our specific T cells to be more effective and what is the safety profile of such modifications? (3) What are the new forms and possibilities for TCR-T cell therapy in the future?

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

Prediction of protein assemblies by structure sampling followed by interface-focused scoring

Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their three-dimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIF-jury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody-antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies.

Unrepresented human leucocyte antigen alleles in single-antigen bead assays: A single-centre cohort study

Human leucocyte antigen (HLA) alleles may generate antibodies that are undetectable by routine single-antigen beads (SABs) assays if their unique epitopes are unrepresented. We aimed to describe the prevalence and explore the potential impact of unrepresented HLA alleles in standard SAB kits in our cohort. All individuals who had undergone two-field HLA typing (HLA-A/B/C/DRB1/DQA1/-DQB1/-DPA1/-DPB1) from February 2021 to July 2023 were included. Two-field HLA-DRB3/4/5 typing was imputed. Each unrepresented allele was compared with the most similar represented allele in the standard LABScreen, LABScreen ExPlex (One Lambda) and the LIFECODES (Immucor) SAB kits. Differences in eplet expression (HLA Eplet Registry) were identified. Differences in three-dimensional molecular structures were visualized using generated models (SWISS-MODEL). Two-field HLA typing was performed for 116 individuals. Overall, 16.7% of all HLA alleles, found in 36.2% of individuals, were unrepresented by all SAB test kits. Four eplets, found in 12.9% of individuals, were unrepresented in at least 1 SAB kit. Non-Chinese individuals were more likely to have unrepresented HLA alleles and eplets than Chinese individuals. There were differences in HLA allele and eplet representation amongst the different SAB test kits. Use of supplementary SAB test kits may improve HLA allele and eplet representation. Although some HLA alleles were unrepresented, most epitopes were represented in current SAB kits. However, some unrepresented alleles may contain epitopes which may generate undetectable antibodies. Further studies may be needed to investigate the potential clinical impact of these unrepresented alleles and eplets, especially in certain ethnic populations or at-risk individuals.

Strategies for the production of isotopically labelled Fab fragments of therapeutic antibodies in Komagataella phaffii (Pichia pastoris) and Escherichia coli for NMR studies

The importance and fast growth of therapeutic monoclonal antibodies, both innovator and biosimilar products, have triggered the need for the development of characterization methods at high resolution such as nuclear magnetic resonance (NMR) spectroscopy. However, the full power of NMR spectroscopy cannot be unleashed without labelling the mAb of interest with NMR-active isotopes. Here, we present strategies using either Komagataella phaffii (Pichia pastoris) or Escherichia coli that can be widely applied for the production of the antigen-binding fragment (Fab) of therapeutic antibodies of immunoglobulin G1 kappa isotype. The E. coli approach consists of expressing Fab fragments as a single polypeptide chain with a cleavable linker between the heavy and light chain in inclusion bodies, while K. phaffii secretes a properly folded fragment in the culture media. After optimization, the protocol yielded 10-45 mg of single chain adalimumab-Fab, trastuzumab-Fab, rituximab-Fab, and NISTmAb-Fab per liter of culture. Comparison of the 2D-1H-15N-HSQC spectra of each Fab fragment, without their polyhistidine tag and linker, with the corresponding Fab from the innovator product showed that all four fragments have folded into the correct conformation. Production of 2H-13C-15N-adalimumab-scFab and 2H-13C-15N-trastuzumab-scFab (>98% enrichment for all three isotopes) yielded NMR samples where all amide deuterons have completely exchanged back to proton during the refolding procedure.

Multimodal, broadly neutralizing antibodies against SARS-CoV-2 identified by high-throughput native pairing of BCRs from bulk B cells

TruAB Discovery is an approach that integrates cellular immunology, high-throughput immunosequencing, bioinformatics, and computational biology in order to discover naturally occurring human antibodies for prophylactic or therapeutic use. We adapted our previously described pairSEQ technology to pair B cell receptor heavy and light chains of SARS-CoV-2 spike protein-binding antibodies derived from enriched antigen-specific memory B cells and bulk antibody-secreting cells. We identified approximately 60,000 productive, in-frame, paired antibody sequences, from which 2,093 antibodies were selected for functional evaluation based on abundance, isotype and patterns of somatic hypermutation. The exceptionally diverse antibodies included RBD-binders with broad neutralizing activity against SARS-CoV-2 variants, and S2-binders with broad specificity against betacoronaviruses and the ability to block membrane fusion. A subset of these RBD- and S2-binding antibodies demonstrated robust protection against challenge in hamster and mouse models. This high-throughput approach can accelerate discovery of diverse, multifunctional antibodies against any target of interest.

C-MEMS-derived glassy carbon electrochemical biosensors for rapid detection of SARS-CoV-2 spike protein

According to a World Health Organization (WHO) report, the world has experienced more than 766 million cases of positive SARS-CoV-2 infection and more than 6.9 million deaths due to COVID through May 2023. The WHO declared a pandemic due to the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and the fight against this pandemic is not over yet. Important reasons for virus spread include the lack of detection kits, appropriate detection techniques, delay in detection, asymptomatic cases and failure in mass screening. In the last 3 years, several researchers and medical companies have introduced successful test kits to detect the infection of symptomatic patients in real time, which was necessary to monitor the spread. However, it is also important to have information on asymptomatic cases, which can be obtained by antibody testing for the SARS-CoV-2 virus. In this work, we developed a simple, advantageous immobilization procedure for rapidly detecting the SARS-CoV-2 spike protein. Carbon-MEMS-derived glassy carbon (GC) is used as the sensor electrode, and the detection is based on covalently linking the SARS-CoV-2 antibody to the GC surface. Glutaraldehyde was used as a cross-linker between the antibody and glassy carbon electrode (GCE). The binding was investigated using Fourier transform infrared spectroscopy (FTIR) characterization and cyclic voltammetric (CV) analysis. Electrochemical impedance spectroscopy (EIS) was utilized to measure the change in total impedance before and after incubation of the SARS-CoV-2 antibody with various concentrations of SARS-CoV-2 spike protein. The developed sensor can sense 1 fg/ml to 1 µg/ml SARS-CoV-2 spike protein. This detection is label-free, and the chances of false positives are minimal. The calculated LOD was ~31 copies of viral RNA/mL. The coefficient of variation (CV) number is calculated from EIS data at 100 Hz, which is found to be 0.398%. The developed sensor may be used for mass screening because it is cost-effective. A schematic representation of the SARS-CoV-2 spike protein sensing using surface functionalized glassy carbon electrode.

Identifying promising sequences for protein engineering using a deep transformer protein language model

Protein engineers aim to discover and design novel sequences with targeted, desirable properties. Given the near limitless size of the protein sequence landscape, it is no surprise that these desirable sequences are often a relative rarity. This makes identifying such sequences a costly and time-consuming endeavor. In this work, we show how to use a deep transformer protein language model to identify sequences that have the most promise. Specifically, we use the model's self-attention map to calculate a Promise Score that weights the relative importance of a given sequence according to predicted interactions with a specified binding partner. This Promise Score can then be used to identify strong binders worthy of further study and experimentation. We use the Promise Score within two protein engineering contexts-Nanobody (Nb) discovery and protein optimization. With Nb discovery, we show how the Promise Score provides an effective way to select lead sequences from Nb repertoires. With protein optimization, we show how to use the Promise Score to select site-specific mutagenesis experiments that identify a high percentage of improved sequences. In both cases, we also show how the self-attention map used to calculate the Promise Score can indicate which regions of a protein are involved in intermolecular interactions that drive the targeted property. Finally, we describe how to fine-tune the transformer protein language model to learn a predictive model for the targeted property, and discuss the capabilities and limitations of fine-tuning with and without knowledge transfer within the context of protein engineering.

Protocol to generate fast-dissociating recombinant antibody fragments for multiplexed super-resolution microscopy

Multiplexed high-density label super-resolution microscopy image reconstruction by integrating exchangeable single-molecule localization (IRIS) enables elucidating fine structures and molecular distribution in cells and tissues. However, fast-dissociating binders are required for individual targets. Here, we present a protocol for generating antibody-based IRIS probes from existing antibody sequences. We describe steps for retrieving antibody sequences from databases. We then detail the construction, purification, and evaluation of recombinant probes after site-directed mutagenesis at the base of complementarity-determining region loops. The protocol accelerates dissociation rates without compromising the binding specificity. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).1.

The Evolution of Polyclonal Antibody from Specific Epitope 47kDA for Detection of Bladder Cancer

OBJECTIVE

This study will identify specific epitopes from the 47kDa protein as the basis for making polyclonal antibodies to increasing sensitivity and specificity of 47kDa protein as bladder cancer biomarkers.

METHOD

The 47kDa protein epitope prediction was carried out using the in-silico method. The epitope with the highest and the lowest value was immunized to the mice for four weeks and was harvested at the fifth weeks. The antibody was tested with the patient's urine using western blotting. Total of  186 participants including in this study. For the first stage (antibody confirmation test)  test we have 18 participants, for the second stage (1st antibody diagnosis test) we have 72 participants and for the third stage (2nd antibody diagnosis test) we have 96 participants,  consist of total 64 BC patients 48 of healthy individuals and 74 participants with the other diseases.

RESULTS

Some epitopes from the sequenced protein are candidates for immunization, in the chain 108'-136' (with lowest Bepipred score: 0.53) named as peptide1 and chain 42'-56' (with highest bepipred score: 0.58) named as peptide2. In western blotting test, both antibodies showed detection at 47kDa. When examined with western blot using urine from BC patients, urine from other cancer patients (prostate, kidney, ureter, rectal, breast), and healthy persons, both antibodies were found to only express 47kDa in urine from BC patients. The diagnostic tests showed high sensitivity (91.67%) and specificity (94.44%) inAb2 in predicting bladder cancer.

CONCLUSSION

The evolution of the polyclonal antibody made from specific epitopes is proven to express specifically on bladder cancer patients and have high sensitivity and specificity to diagnose bladder cancer.

Preliminary characterization of Plasmodium vivax sporozoite antigens as pre-erythrocytic vaccine candidates

Plasmodium vivax pre-erythrocytic (PE) vaccine research has lagged far behind efforts to develop Plasmodium falciparum vaccines. There is a critical gap in our knowledge of PE antigen targets that can induce functionally inhibitory neutralizing antibody responses. To overcome this gap and guide the selection of potential PE vaccine candidates, we considered key characteristics such as surface exposure, essentiality to infectivity and liver stage development, expression as recombinant proteins, and functional immunogenicity. Selected P. vivax sporozoite antigens were surface sporozoite protein 3 (SSP3), sporozoite microneme protein essential for cell traversal (SPECT1), sporozoite surface protein essential for liver-stage development (SPELD), and M2 domain of MAEBL. Sequence analysis revealed little variation occurred in putative B-cell and T-cell epitopes of the PE candidates. Each antigen was tested for expression as refolded recombinant proteins using an established bacterial expression platform and only SPELD failed. The successfully expressed antigens were immunogenic in vaccinated laboratory mice and were positively reactive with serum antibodies of P. vivax-exposed residents living in an endemic region in Thailand. Vaccine immune antisera were tested for reactivity to native sporozoite proteins and for their potential vaccine efficacy using an in vitro inhibition of liver stage development assay in primary human hepatocytes quantified on day 6 post-infection by high content imaging analysis. The anti-PE sera produced significant inhibition of P. vivax sporozoite invasion and liver stage development. This report provides an initial characterization of potential new PE candidates for a future P. vivax vaccine.

ICOS and OX40 tandem co-stimulation enhances CAR T-cell cytotoxicity and promotes T-cell persistence phenotype

Chimeric Antigen Receptor (CAR) T-cell therapies have emerged as an effective and potentially curative immunotherapy for patients with relapsed or refractory malignancies. Treatment with CD19 CAR T-cells has shown unprecedented results in hematological malignancies, including heavily refractory leukemia, lymphoma, and myeloma cases. Despite these encouraging results, CAR T-cell therapy faces limitations, including the lack of long-term responses in nearly 50-70% of the treated patients and low efficacy in solid tumors. Among other reasons, these restrictions are related to the lack of targetable tumor-associated antigens, limitations on the CAR design and interactions with the tumor microenvironment (TME), as well as short-term CAR T-cell persistence. Because of these reasons, we developed and tested a chimeric antigen receptor (CAR) construct with an anti-ROR1 single-chain variable-fragment cassette connected to CD3ζ by second and third-generation intracellular signaling domains including 4-1BB, CD28/4-1BB, ICOS/4-1BB or ICOS/OX40. We observed that after several successive tumor-cell in vitro challenges, ROR1.ICOS.OX40ζ continued to proliferate, produce pro-inflammatory cytokines, and induce cytotoxicity against ROR1+ cell lines in vitro with enhanced potency. Additionally, in vivo ROR1.ICOS.OX40ζ T-cells showed anti-lymphoma activity, a long-lasting central memory phenotype, improved overall survival, and evidence of long-term CAR T-cell persistence. We conclude that anti-ROR1 CAR T-cells that are activated by ICOS.OX40 tandem co-stimulation show in vitro and in vivo enhanced targeted cytotoxicity associated with a phenotype that promotes T-cell persistence.

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.

Tuning Nanobodies' Bioactivity: Coupling to Ultrasmall Gold Nanoparticles Allows the Intracellular Interference with Survivin

Nanobodies are highly affine binders, often used to track disease-relevant proteins inside cells. However, they often fail to interfere with pathobiological functions, required for their clinical exploitation. Here, a nanobody targeting the disease-relevant apoptosis inhibitor and mitosis regulator Survivin (SuN) is utilized. Survivin's multifaceted functions are regulated by an interplay of dynamic cellular localization, dimerization, and protein-protein interactions. However, as Survivin harbors no classical "druggable" binding pocket, one must aim at blocking extended protein surface areas. Comprehensive experimental evidence demonstrates that intracellular expression of SuN allows to track Survivin at low nanomolar concentrations but failed to inhibit its biological functions. Small angle X-ray scattering of the Survivin-SuN complex locates the proposed interaction interface between the C-terminus and the globular domain, as such not blocking any pivotal interaction. By clicking multiple SuN to ultrasmall (2 nm) gold nanoparticles (SuN-N), not only intracellular uptake is enabled, but additionally, Survivin crosslinking and interference with mitotic progression in living cells are also enabled. In sum, it is demonstrated that coupling of nanobodies to nanosized scaffolds can be universally applicable to improve their function and therapeutic applicability.

Defining and Studying B Cell Receptor and TCR Interactions

BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.

Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 429 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. We show the importance of bound-like component modeling in complex assembly accuracy, and that the current version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training may further improve its performance.

Two MP2CL5 Antigen Vaccines from Naegleria fowleri Stimulate the Immune Response against Meningitis in the BALB/c Model

Naegleria fowleri is an etiological agent that generates primary amoebic meningoencephalitis; unfortunately, no effective treatment or vaccine is available. The objective of this work was to determine the immunoprotective response of two vaccine antigens, as follows: (i) the polypeptide band of 19 kDa or (ii) a predicted immunogenic peptide from the membrane protein MP2CL5 (Smp145). Both antigens were administered intranasally in mice using cholera toxin (CT) as an adjuvant. The survival rate and immune response of immunized mice with both antigens and challenged with N. fowleri trophozoites were measured in the nose-associated lymphoid tissue (NALT) and nasal passages (NPs) by flow cytometry and enzyme-linked immunosorbent assay (ELISA). We also determined the immunolocalization of both antigens in N. fowleri trophozoites by confocal microscopy. Immunization with the polypeptide band of 19 kDa alone or coadministered with CT was able to confer 80% and 100% of protection, respectively. The immunization with both antigens (alone or coadministered with CT) showed an increase in T and B lymphocytes. In addition, there was an increase in the expression of integrin α4β1 and IgA in the nasal cavity of protected mice, and the IgA, IgG, and IgM levels were increased in serum and nasal washes. The immunolocalization of both antigens in N. fowleri trophozoites was observed in the plasma membrane, specifically in pseudopod-like structures. The MP2CL5 antigens evaluated in this work were capable of conferring protection which would lead us to consider them as potential candidates for vaccines against meningitis caused by N. fowleri.

The ClusPro AbEMap web server for the prediction of antibody epitopes

Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.

AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities

Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs

Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.

Large scale plasma proteome epitome profiling is an efficient tool for the discovery of cancer biomarkers

Current proteomic technologies focus on the quantification of protein levels, while little effort is dedicated to the development of systems approaches to simultaneously monitor proteome variability and abundance. Protein variants may display different immunogenic epitopes detectable by monoclonal antibodies. Epitope variability results from alternative splicing, posttranslational modifications, processing, degradation, and complex formation and possess dynamically changing availability of interacting surface structures frequently serve as reachable epitopes, and often carry different functions. Thus, it is highly likely, that the presence of some of the accessible epitopes correlate with function under physiological and pathological conditions. To enable the exploration of the impact of protein variation on the immunogenic epitome first; here, we present a robust and analytically validated protein epitome profiling (PEP) technology for characterizing immunogenic epitopes of the plasma. To this end we prepared mAb libraries directed against the normalized human plasma proteome as a complex natural immunogen. Resulting hybridoma supernatants were selected for mAb production and the corresponding hybridomas were cloned. Monoclonal antibodies react with single epitopes, thus profiling with the libraries is expected to profile many epitopes which we define by the mimotopes, as we present here. Screening blood plasma samples from control subjects (n = 558) and cancer patients (n = 598) for merely 69 native epitopes displayed by 20 abundant plasma proteins resulted in distinct cancer-specific epitope panels that showed high accuracy (AUC 0.826-0.966) and specificity for lung, breast, and colon cancer. Deeper profiling (≈290 epitopes of approximately 100 proteins) showed unexpected granularity of the epitope-level expression data and detected neutral and lung-cancer associated epitopes of individual proteins. Biomarker epitope panels selected from a pool of 21 epitopes of 12 proteins were validated in independent clinical cohorts. The results demonstrate the value of PEP as a rich and thus far unexplored source of protein biomarkers with diagnostic potential.

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3, but also revealed how quickly viral escape can curtail effective options4,5. With the emergence of the SARS-CoV-2 Omicron variant in late 2021, many clinically used antibody drug products lost potency, including Evusheld™ and its constituent, cilgavimab4,6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies with a known clinical profile to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign COV2-2130 to rescue in vivo efficacy against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the contemporaneously dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and many variants of concern that subsequently emerged, and provides protection in vivo against the strains tested, WA1/2020, BA.1.1, and BA.5. Deep mutational scanning of tens of thousands pseudovirus variants reveals 2130-1-0114-112 improves broad potency without incurring additional escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Because our approach is computationally driven, not requiring experimental iterations or pre-existing binding data, it could enable rapid response strategies to address escape variants or pre-emptively mitigate escape vulnerabilities.

Clonal Characterization and Somatic Hypermutation (SHM) Assessment by Next Generation Sequencing in Chronic Lymphocytic Leukemia/ Small Lymphocytic Lymphoma (CLL/SLL): A Detailed Description of the Technical Performance, Clinical Utility, and Platform Comparison

Somatic hypermutation (SHM) status of the immunoglobulin heavy variable (IGHV) gene is essential for treating chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL) patients. Unlike the conventional low-throughput method, assessment of SHM by next generation sequencing (NGS) has potential for uniformity and scalability, however it lacks standardization or guidelines for routine clinical use. We critically assessed the performance of an amplicon-based NGS assay across 458 samples. Using a validation cohort (35 samples), the comparison of two platforms (Ion Torrent vs Illumina) and two primer sets (Leader vs FR1) in their ability to identify clonotypic IGHV rearrangement(s) revealed 97% concordance. The mutation rates were identical by both platforms when using the same primer set (FR1), while a slight overestimation bias (+0.326%) was found when comparing FR1 to Leader primers. However, for nearly all patients this did not affect the stratification into mutated or unmutated categories suggesting that use of FR1 may provide comparable results if Leader sequencing is not available, while also allowing for a simpler NGS laboratory workflow. In routine clinical practice (423 samples), the productive rearrangement was successfully detected by either primer set (Leader 97.7%, FR1 94.7%) and a combination of both in problematic cases reduced the failure rate to 1.2%. Higher sensitivity of the NGS-based analysis also detected a higher frequency of double IGHV rearrangements (19.1%) compared to traditional approaches.

Understanding repertoire sequencing data through a multiscale computational model of the germinal center

Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.

Identification and characterization of phage display-selected peptides having affinity to Peste des petits ruminants virus

Phage display is a well-established technique used for selecting novel ligands having affinity to a plethora of targets including proteins, viruses, whole bacterial and mammalian cells as well as lipid targets. In the present study, phage display technology was used to identify peptides having affinity to PPRV. The binding capacity of these peptides was characterized through various formats of ELISA using phage clones, linear and multiple antigenic peptides. The whole PPRV was used as an immobilized target in a surface biopanning process using a 12-mer phage display random peptide library. After five rounds of biopanning, forty colonies were picked and amplified followed by DNA isolation and amplification for sequencing. Sequencing suggested 12 different clones expressing different peptide sequence Phage-ELISA was performed using all 12 phage clones. Results indicated that four phage clones i.e., P4, P8, P9 and P12 had a specific binding activity to PPR virus. Linear peptides displayed by all 12 clones were synthesized using solid phase peptide synthesis and subjected to virus capture ELISA. No significant binding of the linear peptides with PPRV was evident which may be due to loss of conformation of linear peptide after coating. When the four selected phage clones displayed peptide sequences were synthesized in Multiple antigenic peptide (MAP) format and used in virus capture ELISA, the results indicated significant binding of PPRV to the MAPs. It may be due to increased avidity and/or better projection of binding residues in 4-armed MAPs as compared to linear peptides. MAP-peptides were also conjugated on gold nanoparticles (AuNPs). Visual colour change from wine red to purple was observed on addition of PPRV in MAP-conjugated AuNPs solution. This colour change may be attributable to the networking of PPRV with MAP -conjugated AuNPs resulting in aggregation of AuNPs. All these results supported the hypothesis that the phage display selected peptides were capable of binding to the PPRV. The potential of these peptides to develop novel diagnostic or therapeutic agents remains to be investigated.

DNA-Encoded Immunoassay in Picoliter Drops: A Minimal Cell-Free Approach

Immunoassays have emerged as indispensable bioanalytical tools but necessitate long preliminary steps for the selection, production, and purification of the antibody(ies) to be used. Here is explored the paradigm shift of creating a rapid and purification-free assay in picoliter drops where the antibody is expressed from coding DNA and its binding to antigens concomitantly characterized in situ. Efficient synthesis in bulk of various functional variable domains of heavy-chain only antibodies (VHH) using reconstituted cell-free expression media, including an anti-green fluorescent protein VHH, is shown first. A microfluidic device is then used to generate monodisperse drops (30 pL) containing all the assay components, including a capture scaffold, onto which the accumulation of VHH:antigen produces a specific fluorescent signal. This allows to assess, in parallel or sequentially at high throughput (500 Hz), the VHH-antigen binding and its specificity in less than 3 h, directly from a VHH-coding DNA, for multiple VHH sequences, various antigens and down to DNA concentrations as low as 12 plasmids per drop. It is anticipated that the ultraminiaturized format, robustness, and programmability of this novel cell-free immunoassay concept will constitute valuable assets in fields as diverse as antibody discovery, point-of-care diagnostics, synthetic biology, and/or bioanalytical assays.

Mapping of antibody epitopes based on docking and homology modeling

Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.

Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models

Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

Critical review of conformational B-cell epitope prediction methods

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.

IgG Fab Glycans Hinder FcRn-Mediated Placental Transport

Abs can be glycosylated in both their Fc and Fab regions with marked effects on Ab function and binding. High levels of IgG Fab glycosylation are associated with malignant and autoimmune conditions, exemplified by rheumatoid arthritis and highly Fab-glycosylated (∼90%) anti-citrullinated protein Abs (ACPAs). Important properties of IgG, such as long half-life and placental transport, are facilitated by the human neonatal Fc receptor (hFcRn). Although it is known that glycosylation of Abs can affect binding to Fc receptors, little is known on the impact of IgG Fab glycosylation on hFcRn binding and transplacental transport. Therefore, we analyzed the interaction between hFcRn and IgG with and without Fab glycans in vitro with various methods as well as in vivo by studying placental transfer of Fab-glycosylated Abs from mothers to newborns. No effect of Fab glycosylation on IgG binding to hFcRn was found by surface plasmon resonance and hFcRn affinity chromatography. In contrast, studies in a cell membrane context revealed that Fab glycans negatively impacted IgG-hFcRn interaction. In line with this, we found that Fab-glycosylated IgGs were transported ∼20% less efficiently across the placenta. This appeared to be a general phenomenon, observed for ACPAs, non-ACPAs, as well as total IgG in rheumatoid arthritis patients and healthy controls. Our results suggest that, in a cellular context, Fab glycans inhibit IgG-hFcRn interaction and thus negatively affect the transplacental transfer of IgG. As Fab-glycosylated Abs are frequently associated with autoimmune and malignant disorders and may be potentially harmful, this might encompass a regulatory mechanism, limiting the half-life and transport of such Abs.

Information-Driven Antibody-Antigen Modelling with HADDOCK

In the recent years, therapeutic use of antibodies has seen a huge growth, "due to their inherent proprieties and technological advances in the methods used to study and characterize them. Effective design and engineering of antibodies for therapeutic purposes are heavily dependent on knowledge of the structural principles that regulate antibody-antigen interactions. Several experimental techniques such as X-ray crystallography, cryo-electron microscopy, NMR, or mutagenesis analysis can be applied, but these are usually expensive and time-consuming. Therefore computational approaches like molecular docking may offer a valuable alternative for the characterization of antibody-antigen complexes.Here we describe a protocol for the prediction of the 3D structure of antibody-antigen complexes using the integrative modelling platform HADDOCK. The protocol consists of (1) the identification of the antibody residues belonging to the hypervariable loops which are known to be crucial for the binding and can be used to guide the docking and (2) the detailed steps to perform docking with the HADDOCK 2.4 webserver following different strategies depending on the availability of information about epitope residues.

The evolutionary and functional significance of germline immunoglobulin gene variation

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.