Biomolecular Recognition of the Glycan Neoantigen CA19-9 by Distinct Antibodies

Engineering affinity-matured variants of an anti-polysialic acid monoclonal antibody with superior cytotoxicity-mediating potency

Monoclonal antibodies (mAbs) that specifically recognize cell surface glycans associated with cancer and infectious disease hold tremendous value for both basic research and clinical applications. However, high-quality anti-glycan mAbs, especially those with sufficiently high affinity and specificity, remain scarce, highlighting the need for protein engineering approaches based on rational design or directed evolution that enable optimization of antigen-binding properties. To this end, we sought to enhance the affinity of a polysialic acid (polySia)-specific antibody called mAb735, which was raised by animal immunization and possesses only modest affinity, using a combination of rational design and directed evolution. The application of these approaches led to the discovery of affinity-matured IgG variants with up to ∼7-fold stronger affinity for polySia relative to the parental antibody. The higher affinity IgG variants were observed to opsonize polySia- positive cancer cells more avidly, which in turn resulted in significantly greater cytotoxicity as determined by both antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) assays. Collectively, these results demonstrate the effective application of both rational and random molecular evolution techniques to an important anti-glycan antibody, providing insights into its carbohydrate recognition while at the same time uncovering variants with greater therapeutic promise due to their enhanced affinity and potency.

htFuncLib: Designing Libraries of Active-site Multipoint Mutants for Protein Optimization

Protein function relies on accurate and densely packed constellations of amino acids within the active site. The high density in the active site optimizes activity but reduces tolerance to mutations, thereby frustrating efforts to engineer or design new or dramatically improved activity. Introducing new activities may therefore require simultaneous multipoint mutations. Still, in a phenomenon known as epistasis, the outcome of combinations of mutations can differ significantly-and even reverse-the impact of the individual mutations, limiting predictability. To address these challenges we previously developed FuncLib, a method for the computational design of multipoint mutants in active sites. We recently extended FuncLib to enable the design of large combinatorial mutation libraries for high-throughput screening in a method called htFuncLib that generates compatible sets of mutations likely to yield functional multipoint mutants. htFuncLib enables scalable library design and experimental screening of hundreds and up to millions of active-site variants. This approach has generated thousands of active enzymes and fluorescent proteins with diverse functional properties. We have updated the FuncLib web server (https://FuncLib.weizmann.ac.il/) to support htFuncLib and introduced an electronic notebook (https://github.com/Fleishman-Lab/htFuncLib-web-server) for customizable library design, making those tools easily accessible for protein engineering and design. The new FuncLib web server enables reliable and scalable design of function for low-, medium- and high-throughput experiments through a single computational platform. We envision that this server will accelerate the optimization and discovery of function in enzymes, antibodies, and other proteins.

Tumor-Associated Carbohydrate Antigen 19-9 (CA 19-9), a Promising Target for Antibody-Based Detection, Diagnosis, and Immunotherapy of Cancer

Carbohydrate antigen 19-9 (CA 19-9) also known as sialyl Lewis A is a tetrasaccharide overexpressed on a wide range of cancerous cells. CA 19-9 has been detected at elevated levels in sera of patients with various types of malignancies, most prominently pancreatic ductal adenocarcinoma. After its identification in 1979, multiple studies have highlighted the significant roles of CA 19-9 in cancer progression, including facilitating extravasation and eventually metastases, proliferation of cancer cells, and suppression of the immune system. Therefore, CA 19-9 has been considered an attractive target for cancer diagnosis, prognosis, and therapy. This review discusses the synthesis of CA 19-9 antigen, elicitation of antibodies through vaccination, development of anti-CA 19-9 monoclonal antibodies, and their applications as imaging tracers and therapeutics for a variety of CA 19-9-positive cancer.

Broad-Spectrum Legionaminic Acid-Specific Antibodies in Pooled Human IgGs Revealed by Glycan Microarrays with Chemoenzymatically Synthesized Nonulosonosides

The presence and the level of antibodies in human sera against bacterial glycans are indications of prior encounters with similar antigens and/or the bacteria that express them by the immune system. An increasing number of pathogenic bacteria that cause human diseases have been shown to express polysaccharides containing a bacterial nonulosonic acid called 5,7-di-N-acetyllegionaminic acid (Leg5,7Ac2). To investigate the immune recognition of Leg5,7Ac2, which is critical for the fight against bacterial infections, a highly effective chemoenzymatic synthon strategy was applied to construct a library of α2-3/6-linked Leg5,7Ac2-glycans via their diazido-derivatives (Leg5,7diN3-glycans) formed by efficient one-pot three-enzyme (OP3E) synthetic systems from a diazido-derivative of a six-carbon monosaccharide precursor. Glycan microarray studies using this synthetic library of a Leg5,7Ac2-capped collection of diverse underlying glycan carriers and their matched sialoside counterparts revealed specific recognition of Leg5,7Ac2 by human IgG antibodies pooled from thousands of healthy donors (IVIG), suggesting prior human encounters with Leg5,7Ac2-expressing pathogenic bacteria at the population level. These biologically relevant Leg5,7Ac2-glycans and their immune recognition assays are important tools to begin elucidating their biological roles, particularly in the context of infection and host-pathogen interactions.

CA19-9 and CEA biosensors in pancreatic cancer

Cancer is a complex pathophysiological condition causing millions of deaths each year. Early diagnosis is essential especially for pancreatic cancer. Existing diagnostic tools rely on circulating biomarkers such as Carbohydrate Antigen 19-9 (CA19-9) and Carcinoembryonic Antigen (CEA). Unfortunately, these markers are nonspecific and may be increased in a variety of disorders. Accordingly, diagnosis of pancreatic cancer generally involves more invasive approaches such as biopsy as well as imaging studies. Recent advances in biosensor technology have allowed the development of precise diagnostic tools having enhanced analytical sensitivity and specificity. Herein we examine these advances in the detection of cancer in general and in pancreatic cancer specifically. Furthermore, we highlight novel technologies in the measurement of CA19-9 and CEA and explore their future application in the early detection of pancreatic cancer.

Computational optimization of antibody humanness and stability by systematic energy-based ranking

Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il ), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.

Anti-glycan monoclonal antibodies: Basic research and clinical applications

Anti-glycan monoclonal antibodies have important applications in human health and basic research. Therapeutic antibodies that recognize cancer- or pathogen-associated glycans have been investigated in numerous clinical trials, resulting in two FDA-approved biopharmaceuticals. Anti-glycan antibodies are also utilized to diagnose, prognosticate, and monitor disease progression, as well as to study the biological roles and expression of glycans. High-quality anti-glycan mAbs are still in limited supply, highlighting the need for new technologies for anti-glycan antibody discovery. This review discusses anti-glycan monoclonal antibodies with applications to basic research, diagnostics, and therapeutics, focusing on recent advances in mAbs targeting cancer- and infectious disease-associated glycans.

Smaller size packs a stronger punch - Recent advances in small antibody fragments targeting tumour-associated carbohydrate antigens

Attached to proteins, lipids, or forming long, complex chains, glycans represent the most versatile post-translational modification in nature and surround all human cells. Unique glycan structures are monitored by the immune system and differentiate self from non-self and healthy from malignant cells. Aberrant glycosylations, termed tumour-associated carbohydrate antigens (TACAs), are a hallmark of cancer and are correlated with all aspects of cancer biology. Therefore, TACAs represent attractive targets for monoclonal antibodies for cancer diagnosis and therapy. However, due to the thick and dense glycocalyx as well as the tumour micro-environment, conventional antibodies often suffer from restricted access and limited effectiveness in vivo. To overcome this issue, many small antibody fragments have come forth, showing similar affinity with better efficiency than their full-length counterparts. Here we review small antibody fragments against specific glycans on tumour cells and highlight their advantages over conventional antibodies.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.