A window into the human immune system: comprehensive characterization of the complexity of antibody complementary-determining regions in functional antibodies

A synthetic heavy chain variable domain antibody library (VHL) provides highly functional antibodies with favorable developability

Synthetic antibody libraries have been developed as an efficient source for the discovery of the heavy chain variable (VH) domain, which exhibits low immunogenicity, high tissue penetration, and diverse binding epitopes in therapeutic biopharmaceuticals. In this study, the human IGHV3-23*04 germline gene was chosen as the scaffold with a high expression level and favorable thermal stability. Amino acid diversity was introduced into the complementarity determining region 3 (CDR3) to exclude potential sequence liabilities. A library containing 2.6 × 1011 independent clones was successfully constructed. The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, interleukin-17A (IL17A), B-cell maturation antigen (BCMA), and G-protein coupled receptor family C group 5 member D (GPRC5D) were used as target antigens to screen and identify VHs. In each case, Thirty-one to fifty-five VHs were screened out. The VH-Fc antibodies showed superior affinities (as high as 4.6 nM) to the corresponding antigens but did not bind to antigen-irrelevant cell CHO-S. Furthermore, the anti-RBD and anti-IL17A VH-Fc antibodies showed strong functional activity in the receptor-blocking assays. The VH-Fc antibodies from the synthetic library exhibited favorable developability (thermal stability, colloidal stability, hydrophilicity, anti-aggregation ability, and no interaction with human IgGs). We demonstrated that high-affinity and highly functional VH domain antibodies were generated from the rationally designed library with desired physicochemical properties. This approach is generally universal to target any antigen and has significant potential to accelerate candidate selection.

Identification of B-cell epitopes of Indian Zika virus strains using immunoinformatics

Introduction

The Zika virus is an emerging Flavivirus known to cause Zika infection in humans. It is associated with severe health problems such as microcephaly and Guillain-Barré syndrome post the Brazilian epidemic in 2015-16. The spread of the Zika virus to the Asian subcontinent, especially to India is a matter of great concern. Two recent co-circulating Indian Zika virus strains such as Rajasthan and Maharashtra detected in 2018 and 2021 were studied to identify B-cell epitopes in the envelope and non-structural 1 protein as these epitopes are major indicators of robust humoral immune response. The study aimed at identifying novel epitopes, followed by molecular docking with potent Zika virus-specific monoclonal antibodies. The novel epitopes identified in this study shall be essential in designing multi-epitope vaccines capable of inducing antibody response against Zika virus infection.

Methods

ABCpred, BepiPred 2.0 and Kolaskar-Tongaonkar methods were used for predicting the linear B-cell epitopes, and Discotope 2.0 and ElliPro were used for the prediction of conformational epitopes. Linear epitopes were further checked for protective antigenicity, allergenicity and toxicity. Based on the stringent study design criteria, only the novel epitopes were considered for molecular docking with complementary determining regions of potent Zika virus-specific monoclonal antibodies.

Results

Nineteen linear and five conformational epitopes were shortlisted based on protective potential, non-allergic and non-toxic properties for Zika virus E protein, from which nine linear and three conformational epitopes were identified as novel. Molecular docking studies revealed that the novel linear epitopes, one each from EDIII, EDII, EDI and EDI/DIII hinge were involved in epitope-CDR interactions with potent neutralizing Zika virus E-specific mouse monoclonal antibody ZV-67. Moreover, the novel EDII epitope was exclusively engaged in epitope-CDR interactions of potent neutralizing Zika virus E-specific human monoclonal antibody Z3L1. None of the linear epitopes of Zika virus NS1 were ascertained as novel based on our study criteria. Conformational epitopes were identified as novel for NS1 protein.

Conclusion

This study identified Zika virus-specific novel epitopes of envelope and non-structural -1 proteins in the currently co-circulating Indian strains. Furthermore, in-silico validation through molecular docking added insight into antigen-antibody interactions, paving way for future in vitro and in vivo studies.

Amino acids characterization based on frequency and interaction analysis in human antigen-antibody complexes from Thera-SAbDab

BackgroundAntibodies are composed of light and heavy chains, both of which have constant and variable regions. The diversity, specific binding ability and therapeutic potential of antibodies are determined by hypervariable loops called complementarity-determining regions (CDRs), with the other regions being the framework regions.ObjectiveTo investigate the key amino acid patterns in various antibody regions in the human therapeutic antigen-antibody (Ag-Ab) complexes collected from the Thera-SAbDab database.MethodThe study focuses on identifying the amino acid frequency, diversity index in CDRs, paratope-epitope amino acid interactions, amino acid bond formation frequency, and bond types among selected therapeutic Ag-Ab complexes.ResultsThe results revealed that Ser is highly distributed in the overall light chain CDRs while Gly is highly distributed in the heavy chain CDRs. CDR profiling analysis indicated that the average amino acid diversity in heavy chain CDRs is 60% to 70%, while in the light chain, it is 50% to 60%. Aromatic residues such as Tyr, Trp and Phe are the top contributors to these paratope-epitope interactions in the light and heavy chains. Moreover, we examined the frequency of amino acids in light and heavy chains of Ag-Ab complexes. Importantly, the outcome of this study leverages the in depth analysis on single residues, dipeptides, and tripeptides for the therapeutic Ag-Ab complexes.ConclusionWe conclude that the amino acid frequency and interaction analysis centered on therapeutic Ag-Ab complexes will benefit antibody engineering parameters such as antibody design, optimization, affinity maturation, and overall antibody development.

Amino Acids Frequency and Interaction Trends: Comprehensive Analysis of Experimentally Validated Viral Antigen-Antibody Complexes

Antibodies have specific binding capabilities and therapeutic potential for treating various diseases, including viral infections. The amino acid composition of the hypervariable complementarity determining regions (CDR) loops and the framework regions (FR) are the determining factors for the affinity and therapeutic efficacy of the antibodies. In this study selected and curated, 77 viral antigen-human antibody complexes from Protein data bank from the Thera-SAbdab database were analyzed. The results revealed diversity indices within specific CDR regions, amino acid frequencies, paratope-epitope interactions, bond formations, and bond types among the analyzed viral Ag-Ab complexes. The finding revealed that Ser, Gly, Tyr, Thr, and Phe are prominently present in the antibody CDRs. Analysis of CDR profiles indicated an average amino acid diversity of 60-80% in heavy chain CDRs and 45-60% in light chain CDRs. Aromatic residues, particularly Tyr, Phe, and Trp showed significant involvement in the paratope-epitope interactions in the heavy chain, while Tyr, Ser, and Thr were key contributors in the light chain. Furthermore, the study examined the occurrence of amino acids in both light and heavy chains of viral Ag- human Ab complexes, analyzing the presence of amino acids as single residues, dipeptides and tripeptides. The analysis is crucial for enhancing the antibody engineering processes including, design, optimization, affinity enhancement, and overall antibody development.

Structural trends in antibody-antigen binding interfaces: a computational analysis of 1833 experimentally determined 3D structures

Antibodies are attractive therapeutic candidates due to their ability to bind cognate antigens with high affinity and specificity. Still, the underlying molecular rules governing the antibody-antigen interface remain poorly understood, making in silico antibody design inherently difficult and keeping the discovery and design of novel antibodies a costly and laborious process. This study investigates the characteristics of antibody-antigen binding interfaces through a computational analysis of more than 850,000 atom-atom contacts from the largest reported set of antibody-antigen complexes with 1833 nonredundant, experimentally determined structures. The analysis compares binding characteristics of conventional antibodies and single-domain antibodies (sdAbs) targeting both protein- and peptide antigens. We find clear patterns in the number antibody-antigen contacts and amino acid frequencies in the paratope. The direct comparison of sdAbs and conventional antibodies helps elucidate the mechanisms employed by sdAbs to compensate for their smaller size and the fact that they harbor only half the number of complementarity-determining regions compared to conventional antibodies. Furthermore, we pinpoint antibody interface hotspot residues that are often found at the binding interface and the amino acid frequencies at these positions. These findings have direct potential applications in antibody engineering and the design of improved antibody libraries.

Assessing AF2's ability to predict structural ensembles of proteins

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.

Generation of binder-format-payload conjugate-matrices by antibody chain-exchange

The generation of antibody-drug conjugates with optimal functionality depends on many parameters. These include binder epitope, antibody format, linker composition, conjugation site(s), drug-to-antibody ratio, and conjugation method. The production of matrices that cover all possible parameters is a major challenge in identifying optimal antibody-drug conjugates. To address this bottleneck, we adapted our Format Chain Exchange technology (FORCE), originally established for bispecific antibodies, toward the generation of binder-format-payload matrices (pair-FORCE). Antibody derivatives with exchange-enabled Fc-heterodimers are combined with payload-conjugated Fc donors, and subsequent chain-exchange transfers payloads to antibody derivatives in different formats. The resulting binder-format-conjugate matrices can be generated with cytotoxic payloads, dyes, haptens, and large molecules, resulting in versatile tools for ADC screening campaigns. We show the relevance of pair-FORCE for identifying optimal HER2-targeting antibody-drug conjugates. Analysis of this matrix reveals that the notion of format-defines-function applies not only to bispecific antibodies, but also to antibody-drug conjugates.

A review of in vitro stochastic and non-stochastic affinity maturation strategies for phage display derived monoclonal antibodies

Monoclonal antibodies identified using display technologies like phage display occasionally suffers from a lack of affinity making it unsuitable for application. This drawback is circumvented with the application of affinity maturation. Affinity maturation is an essential step in the natural evolution of antibodies in the immune system. The evolution of molecular based methods has seen the development of various mutagenesis approaches. This allows for the natural evolutionary process during somatic hypermutation to be replicated in the laboratories for affinity maturation to fine-tune the affinity and selectivity of antibodies. In this review, we will discuss affinity maturation strategies for mAbs generated through phage display systems. The review will highlight various in vitro stochastic and non-stochastic affinity maturation approaches that includes but are not limited to random mutagenesis, site-directed mutagenesis, and gene synthesis.

Deep mining of antibody phage-display selections using Oxford Nanopore Technologies and Dual Unique Molecular Identifiers

Antibody phage-display technology identifies antibody-antigen interactions through multiple panning rounds, but traditional screening gives no information on enrichment or diversity throughout the process. This results in the loss of valuable binders. Next Generation Sequencing can overcome this problem. We introduce a high accuracy long-read sequencing method based on the recent Oxford Nanopore Technologies (ONT) Q20 + chemistry in combination with dual unique molecular identifiers (UMIs) and an optimized bioinformatic analysis pipeline to monitor the selections. We identified binders from two single-domain antibody libraries selected against a model protein. Traditional colony-picking was compared with our ONT-UMI method. ONT-UMI enabled monitoring of diversity and enrichment before and after each selection round. By combining phage antibody selections with ONT-UMIs, deep mining of output selections is possible. The approach provides an alternative to traditional screening, enabling diversity quantification after each selection round and rare binder recovery, even when the dominating binder was > 99% abundant. Moreover, it can give insights on binding motifs for further affinity maturation and specificity optimizations. Our results demonstrate a platform for future data guided selection strategies.

Structural analysis of light chain-driven bispecific antibodies targeting CD47 and PD-L1

In contrast to natural antibodies that rely mainly on the heavy chain to establish contacts with their cognate antigen, we have developed a bispecific antibody format in which the light chain (LC) drives antigen binding and specificity. To better understand epitope-paratope interactions in this context, we determined the X-ray crystallographic structures of an antigen binding fragment (Fab) in complex with human CD47 and another Fab in complex with human PD-L1. These Fabs contain a κ-LC and a λ-LC, respectively, which are paired with an identical heavy chain (HC). The structural analysis of these complexes revealed the dominant contribution of the LCs to antigen binding, but also that the common HC provides some contacts in both CD47 and PD-L1 Fab complexes. The anti-CD47 Fab was affinity optimized by diversifying complementary-determining regions of the LC followed by phage display selections. Using homology modeling, the contributions of the amino acid modification to the affinity increase were analyzed. Our results demonstrate that, despite a less prominent role in natural antibodies, the LC can mediate high affinity binding to different antigens and neutralize their biological function. Importantly, Fabs containing a common variable heavy (VH) domain enable the generation of bispecific antibodies retaining a truly native structure, maximizing their therapeutic potential.