Antibody Binding Epitope Mapping (AbMap) of Hundred Antibodies in a Single Run

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Epitope landscape in autoimmune neurological disease and beyond

Autoantibody binding has a central role in autoimmune diseases and has also been linked to cancer, infections, and behavioral disorders. Autoimmune neurological diseases remain misclassified also due to an incomplete understanding of the underlying disease-specific epitopes. Such epitopes are crucial for both pathology and diagnosis, but have historically been overlooked. Recent technological advancements have enabled the exploration of these epitopes, potentially opening novel clinical avenues. The precise identification of novel B and T cell epitopes and their autoreactivity has led to the discovery of autoantigen-specific biomarkers for patients at high risk of autoimmune neurological diseases. In this review, we propose utilizing newly available synthetic and cellular-surface display technologies and guide epitope-focused studies to unlock the potential of disease-specific epitopes for improving diagnosis and treatments. Additionally, we offer recommendations to guide emerging epitope-focused studies to broaden the current landscape.

Phage immunoprecipitation and sequencing-a versatile technique for mapping the antibody reactome

Characterizing the antibody reactome for circulating antibodies provide insight into pathogen exposure, allergies and autoimmune diseases. This is important for biomarker discovery, clinical diagnosis, and prognosis of disease progression, as well as population-level insights into the immune system. The emerging technology phage display immunoprecipitation and sequencing (PhIP-seq) is a high-throughput method for identifying antigens/epitopes of the antibody reactome. In PhIP-seq, libraries with sequences of defined lengths and overlapping segments are bioinformatically designed using naturally occurring proteins and cloned into phage genomes to be displayed on the surface. These libraries are used in immunoprecipitation experiments of circulating antibodies. This can be done with parallel samples from multiple sources, and the DNA inserts from the bound phages are barcoded and subjected to next-generation sequencing for hit-determination. PhIP-seq is a powerful technique for characterizing the antibody reactome that has undergone rapid advances in recent years. In this review, we comprehensively describe the history of PhIP-seq and discuss recent advances in library design and applications.

Epitope binning for multiple antibodies simultaneously using mammalian cell display and DNA sequencing

Epitope binning, an approach for grouping antibodies based on epitope similarities, is a critical step in antibody drug discovery. However, conventional methods are complex, involving individual antibody production. Here, we established Epitope Binning-seq, an epitope binning platform for simultaneously analyzing multiple antibodies. In this system, epitope similarity between the query antibodies (qAbs) displayed on antigen-expressing cells and a fluorescently labeled reference antibody (rAb) targeting a desired epitope is analyzed by flow cytometry. The qAbs with epitope similar to the rAb can be identified by next-generation sequencing analysis of fluorescence-negative cells. Sensitivity and reliability of this system are confirmed using rAbs, pertuzumab and trastuzumab, which target human epidermal growth factor receptor 2. Epitope Binning-seq enables simultaneous epitope evaluation of 14 qAbs at various abundances in libraries, grouping them into respective epitope bins. This versatile platform is applicable to diverse antibodies and antigens, potentially expediting the identification of clinically useful antibodies.

A highly efficient blocking ELISA based on p72 monoclonal antibody for the detection of African swine fever virus antibodies and identification of its linear B cell epitope

Due to the absence of effective vaccine and treatment, African swine fever virus (ASFV) control is entirely dependent on accurate and early diagnosis, along with culling of infected pigs. The B646L/p72 is the major capsid protein of ASFV and is an important target for developing a diagnostic assays and vaccines. Herein, we generated a monoclonal antibody (mAb) (designated as 2F11) against the trimeric p72 protein, and a blocking ELISA (bELISA) was established for the detection of both genotype I and II ASFV antibodies. To evaluate the performance of the diagnostic test, a total of 506 porcine serum samples were tested. The average value of percent of inhibition (PI) of 133 negative pig serum was 8.4 % with standard deviation (SD) 6.5 %. Accordingly, the cut-off value of the newly established method was set at 28 % (mean + 3SD). Similarly, a receiver operating characteristic (ROC) was applied to determine the cut off value and the p72-bELISA exhibited a sensitivity of 100 % and a specificity of 99.33 % when the detection threshold was set at 28 %. The bELISA was also able to specifically recognize anti-ASFV sera without cross-reacting with other positive serums for other major swine pathogens. Moreover, by designing a series of overlapped p72 truncated proteins, the linear B cell epitope recognized by 2F11 mAb was defined to be 283NSHNIQ288. Amino acid sequence comparison revealed that the amino acid sequence 283NSHNIQ288 is highly conserved between different ASFV isolates. Our findings indicate that the newly established mAb based blocking ELISA may have a great potential in improving the detection of ASFV antibodies and provides solid foundation for further studies.

Molecular Mimicry as a Mechanism of Viral Immune Evasion and Autoimmunity

Mimicry of host protein structures ("molecular mimicry") is a common mechanism employed by viruses to evade the host's immune system. To date, studies have primarily evaluated molecular mimicry in the context of full protein structural mimics. However, recent work has demonstrated that short linear amino acid (AA) molecular mimics can elicit cross-reactive antibodies and T-cells from the host, which may contribute to development and progression of autoimmunity. Despite this, the prevalence of molecular mimics throughout the human virome has not been fully explored. In this study, we evaluate 134 human infecting viruses and find significant usage of linear mimicry across the virome, particularly those in the herpesviridae and poxviridae families. Furthermore, we identify that proteins involved in cellular replication and inflammation, those expressed from autosomes, the X chromosome, and in thymic cells are over-enriched in viral mimicry. Finally, we demonstrate that short linear mimicry from Epstein-Barr virus (EBV) is significantly higher in auto-antibodies found in multiple sclerosis patients to a greater degree than previously appreciated. Our results demonstrate that human-infecting viruses frequently leverage mimicry in the course of their infection, point to substantial evolutionary pressure for mimicry, and highlight mimicry's important role in human autoimmunity. Clinically, our findings could translate to development of novel therapeutic strategies that target viral infections linked to autoimmunity, with the goal of eliminating disease-associated latent viruses and preventing their reactivation.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays

Introduction

Lyme disease (LD), a rapidly growing public health problem in the US, represents a formidable challenge due to the lack of detailed understanding about how the human immune system responds to its pathogen, the Borrelia burgdorferi bacterium. Despite significant advances in gaining deeper insight into mechanisms the pathogen uses to evade immune response, substantial gaps remain. As a result, molecular tools for the disease diagnosis are lacking with the currently available tests showing poor performance. High interpersonal variability in immune response combined with the ability of the pathogen to use a number of immune evasive tactics have been implicated as underlying factors for the limited test performance.

Methods

This study was designed to perform a broad profiling of the entire repertoire of circulating antibodies in human sera at the single-individual level using planar arrays of short linear peptides with random sequences. The peptides sample sparsely, but uniformly the entire combinatorial sequence space of the same length peptides for profiling the humoral immune response to a B.burg. infection and compare them with other diseases with etiology similar to LD and healthy controls.

Results

The study revealed substantial variability in antibody binding profiles between individual LD patients even to the same antigen (VlsE protein) and strong similarity between individuals diagnosed with Lyme disease and healthy controls from the areas endemic to LD suggesting a high prevalence of seropositivity in endemic healthy control.

Discussion

This work demonstrates the utility of the approach as a valuable analytical tool for agnostic profiling of humoral immune response to a pathogen.

Mass Spectrometric ITEM-ONE and ITEM-TWO Analyses Confirm and Refine an Assembled Epitope of an Anti-Pertuzumab Affimer

Intact Transition Epitope Mapping-One-step Non-covalent force Exploitation (ITEM-ONE) analysis reveals an assembled epitope on the surface of Pertuzumab, which is recognized by the anti-Pertuzumab affimer 00557_709097. It encompasses amino acid residues NSGGSIYNQRFKGR, which are part of CDR2, as well as residues FTLSVDR, which are located on the variable region of Pertuzumab's heavy chain and together form a surface area of 1381.46 Å2. Despite not being part of Pertuzumab's CDR2, the partial sequence FTLSVDR marks a unique proteotypic Pertuzumab peptide. Binding between intact Pertuzumab and the anti-Pertuzumab affimer was further investigated using the Intact Transition Epitope Mapping-Thermodynamic Weak-force Order (ITEM-TWO) approach. Quantitative analysis of the complex dissociation reaction in the gas phase afforded a quasi-equilibrium constant (KD m0g#) of 3.07 × 10-12. The experimentally determined apparent enthalpy (ΔHm0g#) and apparent free energy (ΔGm0g#) of the complex dissociation reaction indicate that the opposite reaction-complex formation-is spontaneous at room temperature. Due to strong binding to Pertuzumab and because of recognizing Pertuzumab's unique partial amino acid sequences, the anti-Pertuzumab affimer 00557_709097 is considered excellently suitable for implementation in Pertuzumab quantitation assays as well as for the accurate therapeutic drug monitoring of Pertuzumab in biological fluids.

Detection of Antibodies against Endemic and SARS-CoV-2 Coronaviruses with Short Peptide Epitopes

(1) Background: Coronavirus proteins are quite conserved amongst endemic strains (eCoV) and SARS-CoV-2. We aimed to evaluate whether peptide epitopes might serve as useful diagnostic biomarkers to stratify previous infections and COVID-19. (2) Methods: Peptide epitopes were identified at an amino acid resolution that applied a novel statistical approach to generate data sets of potential antibody binding peptides. (3) Results: Data sets from more than 120 COVID-19 or eCoV-infected patients, as well as vaccinated persons, have been used to generate data sets that have been used to search in silico for potential epitopes in proteins of SARS-CoV-2 and eCoV. Peptide epitopes were validated with >300 serum samples in synthetic peptide micro arrays and epitopes specific for different viruses, in addition to the identified cross reactive epitopes. (4) Conclusions: Most patients develop antibodies against non-structural proteins, which are useful general markers for recent infections. However, there are differences in the epitope patterns of COVID-19, and eCoV, and the S-protein vaccine, which can only be explained by a high degree of cross-reactivity between the viruses, a pre-existing immune response against some epitopes, and even an alternate processing of the vaccine proteins.

Recent Progress in Antibody Epitope Prediction

Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.

Characterization and epitope mapping of monoclonal antibodies against PEDV N protein

Porcine epidemic diarrhea virus (PEDV) causes acute diarrhea, vomiting, dehydration and high mortality in neonatal piglets. The nucleocapsid (N) protein of PEDV is a highly conserved protein with strong immunogenicity and palys an important role in PEDV diagnosis. However, epitopes on the PEDV N protein have not yet been well characterized. Here, 32 monoclonal antibodies (mAbs) against the PEDV N protein were produced and identified. Six new epitopes were first identified by using a high-throughput epitope mapping method named AbMap. Sequence analysis revealed that among the six epitopes five epitopes were highly conserved among different PEDV strains. We also confirmed that the mAbs derived from the six epitopes of PEDV N protein, have no cross-reactivity with transmissible gastro enteritis virus or porcine delta coronavirus. These mAbs and their defined epitopes will help to understand the N protein structure and immunological characteristics, and to develop a rapid, accurate PEDV diagnosis method.

Next-Generation Phage Display to Identify Peptide Ligands of Deubiquitinases

Phage display (PD) is a powerful method and has been extensively used to generate monoclonal antibodies and identify epitopes, mimotopes, and protein interactions. More recently, the combination of next-generation sequencing (NGS) with PD (NGPD) has revolutionized the capabilities of the method by creating large data sets of sequences from affinity selection-based approaches (biopanning) otherwise challenging to obtain. NGPD can monitor motif enrichment, allow tracking of the selection process over consecutive rounds, and highlight unspecific binders. To tackle the wealth of data obtained, bioinformatics tools have been developed that allow for identifying specific binding sequences (binders) that can then be validated. Here, we provide a detailed account of the use of NGPD experiments to identify ubiquitin-specific protease peptide ligands.

Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold

Delineating the precise regions on an antigen that are targeted by antibodies has become a key step for the development of antibody therapeutics. X-ray crystallography and cryogenic electron microscopy are considered the gold standard for providing precise information about these binding sites at atomic resolution. However, they are labor-intensive and a successful outcome is not guaranteed. We used deep mutational scanning (DMS) of the human LAMP-1 antigen displayed on yeast surface and leveraged next-generation sequencing to observe the effect of individual mutants on the binding of two LAMP-1 antibodies and to determine their functional epitopes on LAMP-1. Fine-tuned epitope mapping by DMS approaches is augmented by knowledge of experimental antigen structure. As human LAMP-1 structure has not yet been solved, we used the AlphaFold predicted structure of the full-length protein to combine with DMS data and ultimately finely map antibody epitopes. The accuracy of this method was confirmed by comparing the results to the co-crystal structure of one of the two antibodies with a LAMP-1 luminal domain. Finally, we used AlphaFold models of non-human LAMP-1 to understand the lack of mAb cross-reactivity. While both epitopes in the murine form exhibit multiple mutations in comparison to human LAMP-1, only one and two mutations in the Macaca form suffice to hinder the recognition by mAb B and A, respectively. Altogether, this study promotes a new application of AlphaFold to speed up precision mapping of antibody-antigen interactions and consequently accelerate antibody engineering for optimization.

Unveiling the biological interface of protein complexes by mass spectrometry-coupled methods

Most biomolecules become functional and bioactive by forming protein complexes through interaction with ligands that are diverse in size, shape, and physicochemical properties. In the complex biological milieu, the interaction is ligand-specific, driven by molecular sensing, and involves the recognition of a binding interface localized within a protein structure. Mapping interfaces of protein complexes is a highly sought area of research as it delivers fundamental insights into proteomes and pathology and hence strategies for therapeutics. While X-ray crystallography and electron microscopy remain the gold standard for structural elucidation of protein complexes, their artificial and static analytic nature often produces a non-native interface that otherwise might be negligible or non-existent in a biological environment. Recently, the mass spectrometry-coupled approaches, chemical crosslinking (CLMS) and hydrogen-deuterium exchange (HDMS) have become valuable analytic complements to the traditional techniques. These methods explicitly identify hot residues and motifs embedded in binding interfaces, especially when the interaction is predominantly dynamic, transient, and/or caused by an intrinsically disordered domain. Here, we review the principal role of CLMS and HDMS in protein structural biology with a particular emphasis on the contribution of recent examples to exploring biological interfaces. Additionally, we describe recent studies that utilized these methods to expand our understanding of protein complex formation and the related biological processes, to increase the probability of structure-based drug design.

Screening of sperm antigen epitopes by phage display technique and its preliminary clinical application

Background

At present, there is a lack of standardized preparation methods of sperm antigen for the detection of antisperm antibody (AsAb). To screen sperm antigen mimotopes from a phage display random peptide library and use them to establish an enzyme-linked immunosorbent assay (ELISA) for the detection of AsAb, immunoglobulins were extracted from the sera of rabbits with positive AsAb and negative AsAb, respectively, by the saturated ammonium sulfate method, and a phage display 12-mer peptide library was affinity panned by the extracted immunoglobins coated on the ELISA plate. Then, the obtained positive phage clones were identified by ELISA and sent for sequencing and peptides synthesis. Last, a diagnostic ELISA was established to detect clinical serum and seminal plasma samples.

Results

A total of sixty phage clones were chosen by affinity panning, and sixteen of them reacted positively with AsAb in indirect ELISA and sandwich ELISA. Following DNA sequencing and translation, the peptide sequences of the sixteen positive clones were obtained. By comparison in Blast database, four of sixteen positive clones were found to be closely related to male reproduction. Two (#1 and #25) of four mimotopes were synthesized, and an ELISA method was established using the two mimotopes as sperm specific antigens. One hundred and thirty-four serum samples and seventy-four seminal plasma samples from infertile couples were analyzed by the established ELISA with #1 and #25 mimotopes, respectively. The positive rates of AsAb in serum samples were 20.15% (27/134) for #1 and 11.19% (15/134) for #25, respectively, and the coincidence rate between them was 91.04% (122/134). The positive rates of AsAb in seminal plasma samples were 1.35% (1/74) for both #1 and #25, and the coincidence rate was 100%.

Conclusion

Sperm antigen mimotopes can be obtained successfully by the phage display technique, and can be used as standard sperm specific antigens to establish an ELISA method for the detection of AsAb.

Current advances in antibody-based serum biomarker studies: From protein microarray to phage display

PURPOSE

This review aims to summarize the technological advances in the field of antibody-based biomarker studies by proteome microarray and phage display. In addition, the possible development directions of this field are also discussed.

EXPERIMENTAL DESIGN

We have focused on the antibody profiling by proteome microarray and phage display, including the technological advances, the tools/resources constructed, and the characteristics of both platforms.

RESULTS

With the help of tools/resources and technological advances in proteome microarray and phage display, the efficiency of profiling antibody-based biomarkers in serum samples has been greatly improved.

CONCLUSIONS

In the past few years, proteome microarray and phage display, especially the latter one, have already demonstrated their capacity and efficiency for biomarker identification. In the near future, we believe that more antibody-based biomarkers could be identified, and some of them could eventually be developed into real clinical applications.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Anti-Ro52/TRIM21 serological subsets identify differential clinical and laboratory parameters

Introduction

Anti-Ro52/tripartite motif-containing protein 21 (TRIM21) IgG is one of the most common autoantibodies found in systemic autoimmune diseases and is typically found in conjunction with anti-Ro60 and/or anti-La. A retrospective, cross-sectional study was undertaken to examine the clinical and laboratory features of two serological subsets: patients with anti-Ro52/TRIM21 autoantibodies in the absence of anti-Ro60 and anti-La (isolated anti-Ro52/TRIM21) and patients with anti-Ro52/TRIM21 in the presence of anti-Ro60 and/or anti-La.

Methods

Over a 12-month period, patients tested positive for anti-Ro52/TRIM21 via line immunoassay (LIA) at the Westmead Hospital (Australia) immunopathology laboratory were included. The presence of anti-Ro60 and/or anti-La via same LIA was noted. Associated laboratory and medical records were perused to extract demographic, laboratory, and clinical information.

Results

There were 346 patients within the study period, and 39.9% of the patients positive for anti-Ro52/TRIM21 lacked anti-Ro60/anti-La autoantibodies. Isolated anti-Ro52/TRIM21 patients tend to be older, have lower anti-Ro52/TRIM21 titres, have lower rheumatoid factors, and have lower proportions of neutropaenia compared to patients who were positive for anti-Ro52/TRIM21 and anti-Ro60/La. This occurred independent to diagnoses of Sjögren’s syndrome or systemic lupus erythematosus. Coexisting neurological syndromes, pulmonary pathologies, and malignancies were more prevalent in the isolated anti-Ro52/TRIM21 subset.

Conclusions

Patients with isolated anti-Ro52/TRIM21 tend to have distinct and important clinical and laboratory associations. It is unclear if these patients evolve or remain a stable subset and how they originate immunologically. Longitudinal and prospective studies are required to ascertain the overall predictive and prognostic value of this stratification.

Key Points • Anti-Ro52/TRIM21 is an autoantibody found in autoimmunity and non-immunological conditions. • Sixty percent of anti-Ro52/TRIM21 patients are positive for anti-Ro60. • Isolated anti-Ro52/TRIM21 has reduced anti-Ro52/TRIM21 and rheumatoid factor titres. • Isolated anti-Ro52/TRIM21 is associated with anaemia and malignancies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10067-022-06299-5.

Development and characterization of a camelid derived antibody targeting a linear epitope in the hinge domain of human PCSK9 protein

PCSK9 is an effective target for lowering LDL-c. Previously, a camelid-human chimeric heavy chain antibody VHH-B11-Fc targeting human PCSK9 was designed. It had a potent hypolipidemic effect. However, the nanobody VHH-B11 interacts with PCSK9 at low affinity, while camelid VHH exhibits some immunogenicity. Moreover, the interacting epitope is yet to be identified, although VHH-B11 was shown to have distinct hPCSK9-binding epitopes for Evolocumab. This might impede the molecule’s progress from bench to bedside. In the present study, we designed various configurations to improve the affinity of VHH-B11 with hPCSK9 (< 10 nM) that in turn enhanced the druggability of VHH-B11-Fc. Then, 17 amino acids were specifically mutated to increase the degree of humanization of the nanobody VHH-B11. Using phage display and sequencing technology, the linear epitope “STHGAGW” (amino acids 447–452) was identified in the hinge region of PCSK9 as the interacting site between VHH-B11-Fc and hPCSK9. Unlike the interaction epitope of Evolocumab, located in the catalytic region of PCSK9, the binding epitope of VHH-B11 is located in the hinge region of PCSK9, which is rarely reported. These findings indicated that a specific mechanism underlying this interaction needs to be explored.

Western blotting: a powerful staple in scientific and biomedical research

Western blotting (WB), also known as immunoblotting, is a well-known molecular biology method that biologists often use to investigate many features of the protein, ranging from basic protein analysis to disease detection. WB is simple, unique, rapid, widely used routine tool with easy interpretation and definite results. It is being used in various fields of science, research and development, diagnostic labs and hospitals. The principle of WB is to accomplish the separation of proteins based on molecular weight and charge. This review addresses in detail the individual steps involved in the WB technique, its troubleshooting, internal loading controls, total protein staining and its diverse applications in scientific research and clinical settings, along with its future perspectives.

Evaluation of Spike Protein Epitopes by Assessing the Dynamics of Humoral Immune Responses in Moderate COVID-19

The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike protein (S) of SARS-CoV-2 is a major target for diagnosis and vaccine development because of its essential role in viral infection and host immunity. Currently, time-dependent responses of humoral immune system against various S protein epitopes are poorly understood. In this study, enzyme-linked immunosorbent assay (ELISA), peptide microarray, and antibody binding epitope mapping (AbMap) techniques were used to systematically analyze the dynamic changes of humoral immune responses against the S protein in a small cohort of moderate COVID-19 patients who were hospitalized for approximately two months after symptom onset. Recombinant truncated S proteins, target S peptides, and random peptides were used as antigens in the analyses. The assays demonstrated the dynamic IgM- and IgG recognition and reactivity against various S protein epitopes with patient-dependent patterns. Comprehensive analysis of epitope distribution along the spike gene sequence and spatial structure of the homotrimer S protein demonstrated that most IgM- and IgG-reactive peptides were clustered into similar genomic regions and were located at accessible domains. Seven S peptides were generally recognized by IgG antibodies derived from serum samples of all COVID-19 patients. The dynamic immune recognition signals from these seven S peptides were comparable to those of the entire S protein or truncated S1 protein. This suggested that the humoral immune system recognized few conserved S protein epitopes in most COVID-19 patients during the entire duration of humoral immune response after symptom onset. Furthermore, in this cohort, individual patients demonstrated stable immune recognition to certain S protein epitopes throughout their hospitalization period. Therefore, the dynamic characteristics of humoral immune responses to S protein have provided valuable information for accurate diagnosis and immunotherapy of COVID-19 patients.

Epitope Analysis of Anti-SARS-CoV-2 Neutralizing Antibodies

Coronavirus disease 2019 is threatening thousands of millions of people around the world. In the absence of specific and highly effective medicines, the treatment of infected persons is still very challenging. As therapeutics, neutralizing antibodies (NAbs) have great potential. Many NAbs have been reported, and most target various regions on the receptor-binding domain of the spike (S) protein, or the N-terminal domain. Several NAbs and NAb cocktails have been authorized for emergency use, and more are in clinical trials or are under development. In this review, considering the angle of binding epitopes on the S protein, we summarize the functions and the underlying mechanisms of a set of well-recognized NAbs and provide guidance for vaccine design and the combinatorial use of these antibodies. In addition, we review the NAbs and NAb cocktails that have been approved for emergency use and discuss the effectiveness of these NAbs for combating severe acute respiratory syndrome coronavirus 2 mutants.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning

Antibodies provide a comprehensive record of the encounters with threats and insults to the immune system. The ability to examine the repertoire of antibodies in serum and discover those that best represent "discriminating features" characteristic of various clinical situations, is potentially very useful. Recently, phage display technologies combined with Next-Generation Sequencing (NGS) produced a powerful experimental methodology, coined "Deep-Panning", in which the spectrum of serum antibodies is probed. In order to extract meaningful biological insights from the tens of millions of affinity-selected peptides generated by Deep-Panning, advanced bioinformatics algorithms are a must. In this study, we describe Motifier, a computational pipeline comprised of a set of algorithms that systematically generates discriminatory peptide motifs based on the affinity-selected peptides identified by Deep-Panning. These motifs are shown to effectively characterize antibody binding activities and through the implementation of machine-learning protocols are shown to accurately classify complex antibody mixtures representing various biological conditions.

The binding epitope of sintilimab on PD-1 revealed by AbMap

PD-1 plays an important role as an immune checkpoint. Sintilimab is a newly approved PD-1 antibody for cancer immunotherapy with an unknown binding epitope on PD-1. In this study, to elucidate the molecular mechanism by which sintilimab blocks PD-1 activation, we applied Antibody binding epitope Mapping (AbMap) to identify the binding epitope of sintilimab. An epitope was successfully identified, i.e. SLAPKA, aa 127-132. By constructing a series of point mutations, the dominant residues S127, L128, A129, P130, and A132 of PD-1 were further validated by western blot analysis, biolayer interferometry, and flow cytometry. Structural analysis showed that the epitope is partially within the binding interface of PD-1 and PD-L1, and this epitope also partially overlaps with that of nivolumab and pembrolizumab. These results demonstrate that sintilimab can attenuate PD-1 activation by directly competing with the interaction between PD-1 and PD-L1 through binding with the key residues of the FG loop on PD-1. This study also demonstrates the high efficiency and accuracy of AbMap for determining the binding epitope of therapeutic antibodies.

Phage-Displayed Peptides for Targeting Tyrosine Kinase Membrane Receptors in Cancer Therapy

Receptor tyrosine kinases (RTKs) regulate critical physiological processes, such as cell growth, survival, motility, and metabolism. Abnormal activation of RTKs and relative downstream signaling is implicated in cancer pathogenesis. Phage display allows the rapid selection of peptide ligands of membrane receptors. These peptides can target in vitro and in vivo tumor cells and represent a novel therapeutic approach for cancer therapy. Further, they are more convenient compared to antibodies, being less expensive and non-immunogenic. In this review, we describe the state-of-the-art of phage display for development of peptide ligands of tyrosine kinase membrane receptors and discuss their potential applications for tumor-targeted therapy.

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.