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

Balancing the Affinity and Tumor Cell Binding of a Two-in-One Antibody Simultaneously Targeting EGFR and PD-L1

The optimization of the affinity of monoclonal antibodies is crucial for the development of drug candidates, as it can impact the efficacy of the drug and, thus, the dose and dosing regimen, limit adverse effects, and reduce therapy costs. Here, we present the affinity maturation of an EGFR×PD-L1 Two-in-One antibody for EGFR binding utilizing site-directed mutagenesis and yeast surface display. The isolated antibody variants target EGFR with a 60-fold-improved affinity due to the replacement of a single amino acid in the CDR3 region of the light chain. The binding properties of the Two-in-One variants were confirmed using various methods, including BLI measurements, real-time antigen binding measurements on surfaces with a mixture of both recombinant proteins and cellular binding experiments using flow cytometry as well as real-time interaction cytometry. An AlphaFold-based model predicted that the amino acid exchange of tyrosine to glutamic acid enables the formation of a salt bridge to an arginine at EGFR position 165. This easily adaptable approach provides a strategy for the affinity maturation of bispecific antibodies with respect to the binding of one of the two antigens.

A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment

Thymic stromal lymphopoietin is a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is increasingly recognized as an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental approaches holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

Hallucinating structure-conditioned antibody libraries for target-specific binders

Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the F_vHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, F_vHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the F_vHallucinator designs amino acid substitutions at the V_H-V_L interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens F_vHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the F_vHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.

Fast-tracking antibody maturation using a B cell-based display system

Affinity maturation, an essential component of antibody engineering, is crucial for developing therapeutic antibodies. Cell display system coupled with somatic hypermutation (SHM) initiated by activation-induced cytidine deaminase (AID) is a commonly used technique for affinity maturation. AID introduces targeted DNA lesions into hotspots of immunoglobulin (Ig) gene loci followed by erroneous DNA repair, leading to biased mutations in the complementary determining regions. However, systems that use an in vivo mimicking mechanism often require several rounds of selection to enrich clones possessing accumulated mutations. We previously described the human ADLib® system, which features autonomous, AID-mediated diversification in Ig gene loci of a chicken B cell line DT40 and streamlines human antibody generation and optimization in one integrated platform. In this study, we further engineered DT40 capable of receiving exogenous antibody genes and examined whether the antibody could be affinity matured. The Ig genes of three representative anti-hVEGF-A antibodies originating from the human ADLib® were introduced; the resulting human IgG1 antibodies had up to 76.4-fold improvement in binding affinities (sub-picomolar K_D) within just one round of optimization, owing to efficient accumulation of functional mutations. Moreover, we successfully improved the affinity of a mouse hybridoma-derived anti-hCDCP1 antibody using the engineered DT40, and the observed mutations remained effective in the post-humanized antibody as exhibited by an 8.2-fold increase of in vitro cytotoxicity without compromised physical stability. These results demonstrated the versatility of the novel B cell-based affinity maturation system as an easy-to-use antibody optimization tool regardless of the species of origin.Abbreviations: ADLib®: Autonomously diversifying library, ADLib® KI-AMP: ADLib® knock-in affinity maturation platform, AID: activation-induced cytidine deaminase, CDRs: complementary-determining regions, DIVAC: diversification activator, ECD: extracellular domain, FACS: fluorescence-activated cell sorting, FCM: flow cytometry, HC: heavy chainIg: immunoglobulin, LC: light chain, NGS: next-generation sequencing, PBD: pyrrolobenzodiazepine, SHM: somatic hypermutation, SPR: surface plasmon resonance.