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

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.

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.

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.

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.