Highly protective antimalarial antibodies via precision library generation and yeast display screening

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Molecular determinants of cross-reactivity and potency by VH3-33 antibodies against the Plasmodium falciparum circumsporozoite protein

IGHV3-33-encoded antibodies are prevalent in the human humoral response against the Plasmodium falciparum circumsporozoite protein (PfCSP). Among VH3-33 antibodies, cross-reactivity between PfCSP major repeat (NANP), minor (NVDP), and junctional (NPDP) motifs is associated with high affinity and potent parasite inhibition. However, the molecular basis of antibody cross-reactivity and the relationship with efficacy remain unresolved. Here, we perform an extensive structure-function characterization of 12 VH3-33 anti-PfCSP monoclonal antibodies (mAbs) with varying degrees of cross-reactivity induced by immunization of mice expressing a human immunoglobulin gene repertoire. We identify residues in the antibody paratope that mediate cross-reactive binding and delineate four distinct epitope conformations induced by antibody binding, with one consistently associated with high protective efficacy and another that confers comparably potent inhibition of parasite liver invasion. Our data show a link between molecular features of cross-reactive VH3-33 mAb binding to PfCSP and mAb potency, relevant for the development of antibody-based interventions against malaria.

Antibody-directed evolution reveals a mechanism for enhanced neutralization at the HIV-1 fusion peptide site

The HIV-1 fusion peptide (FP) represents a promising vaccine target, but global FP sequence diversity among circulating strains has limited anti-FP antibodies to ~60% neutralization breadth. Here we evolve the FP-targeting antibody VRC34.01 in vitro to enhance FP-neutralization using site saturation mutagenesis and yeast display. Successive rounds of directed evolution by iterative selection of antibodies for binding to resistant HIV-1 strains establish a variant, VRC34.01_mm28, as a best-in-class antibody with 10-fold enhanced potency compared to the template antibody and ~80% breadth on a cross-clade 208-strain neutralization panel. Structural analyses demonstrate that the improved paratope expands the FP binding groove to accommodate diverse FP sequences of different lengths while also recognizing the HIV-1 Env backbone. These data reveal critical antibody features for enhanced neutralization breadth and potency against the FP site of vulnerability and accelerate clinical development of broad HIV-1 FP-targeting vaccines and therapeutics.

Large-scale antibody immune response mapping of splenic B cells and bone marrow plasma cells in a transgenic mouse model

Molecular characterization of antibody immunity and human antibody discovery is mainly carried out using peripheral memory B cells, and occasionally plasmablasts, that express B cell receptors (BCRs) on their cell surface. Despite the importance of plasma cells (PCs) as the dominant source of circulating antibodies in serum, PCs are rarely utilized because they do not express surface BCRs and cannot be analyzed using antigen-based fluorescence-activated cell sorting. Here, we studied the antibodies encoded by the entire mature B cell populations, including PCs, and compared the antibody repertoires of bone marrow and spleen compartments elicited by immunization in a human immunoglobulin transgenic mouse strain. To circumvent prior technical limitations for analysis of plasma cells, we applied single-cell antibody heavy and light chain gene capture from the entire mature B cell repertoires followed by yeast display functional analysis using a cytokine as a model immunogen. We performed affinity-based sorting of antibody yeast display libraries and large-scale next-generation sequencing analyses to follow antibody lineage performance, with experimental validation of 76 monoclonal antibodies against the cytokine antigen that identified three antibodies with exquisite double-digit picomolar binding affinity. We observed that spleen B cell populations generated higher affinity antibodies compared to bone marrow PCs and that antigen-specific splenic B cells had higher average levels of somatic hypermutation. A degree of clonal overlap was also observed between bone marrow and spleen antibody repertoires, indicating common origins of certain clones across lymphoid compartments. These data demonstrate a new capacity to functionally analyze antigen-specific B cell populations of different lymphoid organs, including PCs, for high-affinity antibody discovery and detailed fundamental studies of antibody immunity.

Rapid engineering of SARS-CoV-2 therapeutic antibodies to increase breadth of neutralization including BQ.1.1, CA.3.1, CH.1.1, XBB.1.16, and XBB.1.5

SARS-CoV-2 Omicron variant XBB.1.5 has shown extraordinary immune escape even for fully vaccinated individuals. There are currently no approved antibodies that neutralize this variant, and continued emergence of new variants puts immunocompromised and elderly patients at high risk. Rapid and cost-effective development of neutralizing antibodies is urgently needed. Starting with a single parent clone that neutralized the Wuhan-Hu-1 strain, antibody engineering was performed in iterative stages in real time as variants emerged using a proprietary technology called STage-Enhanced Maturation. An antibody panel that broadly neutralizes currently circulating Omicron variants was obtained by in vitro affinity maturation using phage display. The engineered antibodies show potent neutralization of BQ.1.1, XBB.1.16, and XBB.1.5 by surrogate virus neutralization test and pM K_D affinity for all variants. Our work not only details novel therapeutic candidates but also validates a unique general strategy to create broadly neutralizing antibodies to current and future SARS-CoV-2 variants.

Challenges in antibody structure prediction

Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. In 2020 AlphaFold2 revolutionized the field using a combination of artificial intelligence and the evolutionary information contained in multiple sequence alignments. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Specialized tools used to predict antibody structures based on different principles have profited from current advances in protein structure prediction based on artificial intelligence. Here, we emphasize the importance of reliable protein structure models and highlight the enormous advances in the field, but we also aim to increase awareness that protein structure models, and in particular antibody models, may suffer from structural inaccuracies, namely incorrect cis-amide bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the importance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool "TopModel" to validate structure models.

Saccharomyces cerevisiae cell surface display technology: Strategies for improvement and applications

Microbial cell surface display technology provides a powerful platform for engineering proteins/peptides with enhanced properties. Compared to the classical intracellular and extracellular expression (secretion) systems, this technology avoids enzyme purification, substrate transport processes, and is an effective solution to enzyme instability. Saccharomyces cerevisiae is well suited to cell surface display as a common cell factory for the production of various fuels and chemicals, with the advantages of large cell size, being a Generally Regarded As Safe (GRAS) organism, and post-translational processing of secreted proteins. In this review, we describe various strategies for constructing modified S. cerevisiae using cell surface display technology and outline various applications of this technology in industrial processes, such as biofuels and chemical products, environmental pollution treatment, and immunization processes. The approaches for enhancing the efficiency of cell surface display are also discussed.