Regulatory Approved Monoclonal Antibodies Contain Framework Mutations Predicted From Human Antibody Repertoires

Retrospective SARS-CoV-2 human antibody development trajectories are largely sparse and permissive

Immunological interventions, like vaccinations, are enabled by the predictive control of humoral responses to novel antigens. While the development trajectories for many broadly neutralizing antibodies (bnAbs) have been measured, it is less established how human subtype-specific antibodies develop from their precursors. In this work, we evaluated the retrospective development trajectories for eight anti-SARS-CoV-2 Spike human antibodies (Abs). To mimic the immunological process of BCR selection during affinity maturation in germinal centers (GCs), we performed deep mutational scanning on anti-S1 molecular Fabs using yeast display coupled to fluorescence-activated cell sorting. Focusing only on changes in affinity upon mutation, we found that human Ab development pathways have few mutations which impart changes in monovalent binding dissociation constants and that these mutations can occur in nearly any order. Maturation pathways of two bnAbs showed that while they are only slightly less permissible than subtype-specific Abs, more development steps on average are needed to reach the same level of affinity. Many of the subtype-specific Abs had inherent affinity for antigen, and these results were robust against different potential inferred precursor sequences. To evaluate the effect of differential affinity for precursors on GC outcomes, we adapted a coarse-grained affinity maturation model. This model showed that antibody precursors with minimal affinity advantages rapidly outcompete competitors to become the dominant clonotype.

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

Prospects for the computational humanization of antibodies and nanobodies

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery

The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.

Common framework mutations impact antibody interfacial dynamics and flexibility

Introduction

With the flood of engineered antibodies, there is a heightened need to elucidate the structural features of antibodies that contribute to specificity, stability, and breadth. While antibody flexibility and interface angle have begun to be explored, design rules have yet to emerge, as their impact on the metrics above remains unclear. Furthermore, the purpose of framework mutations in mature antibodies is highly convoluted.

Methods

To this end, a case study utilizing molecular dynamics simulations was undertaken to determine the impact framework mutations have on the VH-VL interface. We further sought to elucidate the governing mechanisms by which changes in the VH-VL interface angle impact structural elements of mature antibodies by looking at root mean squared deviations, root mean squared fluctuations, and solvent accessible surface area.

Results and discussion

Overall, our results suggest framework mutations can significantly shift the distribution of VH-VL interface angles, which leads to local changes in antibody flexibility through local changes in the solvent accessible surface area. The data presented herein highlights the need to reject the dogma of static antibody crystal structures and exemplifies the dynamic nature of these proteins in solution. Findings from this work further demonstrate the importance of framework mutations on antibody structure and lay the foundation for establishing design principles to create antibodies with increased specificity, stability, and breadth.

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 FvHallucinator 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, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator 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 FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.

Improved Characteristics of RANKL Immuno-PET Imaging Using Radiolabeled Antibody Fab Fragments

Purpose: RANKL expression in the tumor microenvironment has been identified as a biomarker of immune suppression, negating the effect of some cancer immunotherapies. Previously we had developed a radiotracer based on the FDA-approved RANKL-specific antibody denosumab, [⁸⁹Zr]Zr-DFO-denosumab, enabling successful immuno-PET imaging. Radiolabeled denosumab, however, showed long blood circulation and delayed tumor uptake, potentially limiting its applications. Here we aimed to develop a smaller radiolabeled denosumab fragment, [⁶⁴Cu]Cu-NOTA-denos-Fab, that would ideally show faster tumor accumulation and better diffusion into the tumor for the visualization of RANKL. Experimental design: Fab fragments were prepared from denosumab using papain and conjugated to a NOTA chelator for radiolabeling with ⁶⁴Cu. The bioconjugates were characterized in vitro using SDS-PAGE analysis, and the binding affinity was assessed using a radiotracer cell binding assay. Small animal PET imaging evaluated tumor targeting and biodistribution in transduced RANKL-ME-180 xenografts. Results: The radiolabeling yield of [⁶⁴Cu]Cu-NOTA-denos-Fab was 58 ± 9.2%, with a specific activity of 0.79 ± 0.11 MBq/µg (n = 3). A radiotracer binding assay proved specific targeting of RANKL in vitro. PET imaging showed fast blood clearance and high tumor accumulation as early as 1 h p.i. (2.14 ± 0.21% ID/mL), which peaked at 5 h p.i. (2.72 ± 0.61% ID/mL). In contrast, [⁶⁴Cu]Cu-NOTA-denosumab reached its highest tumor uptake at 24 h p.i. (6.88 ± 1.12% ID/mL). [⁶⁴Cu]Cu-NOTA-denos-Fab specifically targeted human RANKL in transduced ME-180 xenografts compared with the blocking group and negative ME-180 xenograft model. Histological analysis confirmed RANKL expression in RANKL-ME-180 xenografts. Conclusions: Here, we report on a novel RANKL PET imaging agent, [⁶⁴Cu]Cu-NOTA-denos-Fab, that allows for fast tumor imaging with improved imaging contrast when compared with its antibody counterpart, showing promise as a potential PET RANKL imaging tool for future clinical applications.

AbDiver-A tool to explore the natural antibody landscape to aid therapeutic design

Motivation

Rational design of therapeutic antibodies can be improved by harnessing the natural sequence diversity of these molecules. Our understanding of the diversity of antibodies has recently been greatly facilitated through the deposition of hundreds of millions of human antibody sequences in next-generation sequencing (NGS) repositories. Contrasting a query therapeutic antibody sequence to naturally observed diversity in similar antibody sequences from NGS can provide a mutational roadmap for antibody engineers designing biotherapeutics. Because of the sheer scale of the antibody NGS datasets, performing queries across them is computationally challenging.

Results

To facilitate harnessing antibody NGS data, we developed AbDiver (http://naturalantibody.com/abdiver), a free portal allowing users to compare their query sequences to those observed in the natural repertoires. AbDiver offers three antibody-specific use-cases: (i) compare a query antibody to positional variability statistics precomputed from multiple independent studies, (ii) retrieve close full variable sequence matches to a query antibody and (iii) retrieve CDR3 or clonotype matches to a query antibody. We applied our system to a set of 742 therapeutic antibodies, demonstrating that for each use-case our system can retrieve relevant results for most sequences. AbDiver facilitates the navigation of vast antibody mutation space for the purpose of rational therapeutic antibody design.

Availability and implementation

AbDiver is freely accessible at http://naturalantibody.com/abdiver.

Supplementary information

Supplementary data are available at Bioinformatics online.