Generative Language Modeling for Antibody Design

Successful development of monoclonal antibodies (mAbs) for therapeutic applications is hindered by developability issues such as low solubility, low thermal stability, high aggregation, and high immunogenicity. The discovery of more developable mAb candidates relies on high-quality antibody libraries for isolating candidates with desirable properties. We present Immunoglobulin Language Model (IgLM), a deep generative language model for generating synthetic libraries by re-designing variable-length spans of antibody sequences. IgLM formulates antibody design as an autoregressive sequence generation task based on text-infilling in natural language. We trained IgLM on approximately 558M antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species-of-origin. We demonstrate that IgLM can be applied to generate synthetic libraries that may accelerate the discovery of therapeutic antibody candidates

Antibody Libraries as Tools to Discover Functional Antibodies and Receptor Pleiotropism

Most antibodies currently in use have been selected based on their binding affinity. However, nowadays, antibodies that can not only bind but can also alter the function of cell surface signaling components are increasingly sought after as therapeutic drugs. Therefore, the identification of such functional antibodies from a large antibody library is the subject of intensive research. New methods applied to combinatorial antibody libraries now allow the isolation of functional antibodies in the cellular environment. These selected agonist antibodies have provided new insights into important issues of signal transduction. Notably, when certain antibodies bind to a given receptor, the cell fate induced by them may be the same or different from that induced by natural agonists. In addition, combined with phenotypic screening, this platform allows us to discover unexpected experimental results and explore various phenomena in cell biology, such as those associated with stem cells and cancer cells.

Parameters and determinants of responses to selection in antibody libraries

The sequences of antibodies from a given repertoire are highly diverse at few sites located on the surface of a genome-encoded larger scaffold. The scaffold is often considered to play a lesser role than highly diverse, non-genome-encoded sites in controlling binding affinity and specificity. To gauge the impact of the scaffold, we carried out quantitative phage display experiments where we compare the response to selection for binding to four different targets of three different antibody libraries based on distinct scaffolds but harboring the same diversity at randomized sites. We first show that the response to selection of an antibody library may be captured by two measurable parameters. Second, we provide evidence that one of these parameters is determined by the degree of affinity maturation of the scaffold, affinity maturation being the process by which antibodies accumulate somatic mutations to evolve towards higher affinities during the natural immune response. In all cases, we find that libraries of antibodies built around maturated scaffolds have a lower response to selection to other arbitrary targets than libraries built around germline-based scaffolds. We thus propose that germline-encoded scaffolds have a higher selective potential than maturated ones as a consequence of a selection for this potential over the long-term evolution of germline antibody genes. Our results are a first step towards quantifying the evolutionary potential of biomolecules.

High-throughput screening and validation of antibodies against synaptic proteins to explore opioid signaling dynamics

Antibodies represent powerful tools to examine signal transduction pathways. Here, we present a strategy integrating multiple state-of-the-art methods to produce, validate, and utilize antibodies. Focusing on understudied synaptic proteins, we generated 137 recombinant antibodies. We used yeast display antibody libraries from the B cells of immunized rabbits, followed by FACS sorting under stringent conditions to identify high affinity antibodies. The antibodies were validated by high-throughput functional screening, and genome editing. Next, we explored the temporal dynamics of signaling in single cells. A subset of antibodies targeting opioid receptors were used to examine the effect of treatment with opiates that have played central roles in the worsening of the ‘opioid epidemic.’ We show that morphine and fentanyl exhibit differential temporal dynamics of receptor phosphorylation. In summary, high-throughput approaches can lead to the identification of antibody-based tools required for an in-depth understanding of the temporal dynamics of opioid signaling.

Antibody Identification for Antigen Detection in Formalin-Fixed Paraffin-Embedded Tissue Using Phage Display and Naïve Libraries

Immunohistochemistry is a widely used technique for research and diagnostic purposes that relies on the recognition by antibodies of antigens expressed in tissues. However, tissue processing and particularly formalin fixation affect the conformation of these antigens through the formation of methylene bridges. Although antigen retrieval techniques can partially restore antigen immunoreactivity, it is difficult to identify antibodies that can recognize their target especially in formalin-fixed paraffin-embedded tissues. Most of the antibodies currently used in immunohistochemistry have been obtained by animal immunization; however, in vitro display techniques represent alternative strategies that have not been fully explored yet. This review provides an overview of phage display-based antibody selections using naïve antibody libraries on various supports (fixed cells, dissociated tissues, tissue fragments, and tissue sections) that have led to the identification of antibodies suitable for immunohistochemistry.

Phage Display Selection of an Anti-Idiotype-Antibody with Broad-Specificity to Deoxynivalenol Mycotoxins

The use of synthetic antibody libraries and phage displays provides an efficient and robust method for the generation of antibodies against a wide range of targets with highly specific binding properties. As the in vitro selection conditions can be easily controlled, these methods enable the rapid generation of binders against difficult targets such as toxins and haptens. In this study, we used deoxynivalenol mycotoxin as a target to generate anti-idiotype-antibodies with unique binding properties from synthetic antibody libraries. The binding of the selected anti-idiotype antibodies can be efficiently inhibited with the addition of free isoforms of deoxynivalenol. The antibody was consecutively used to develop deoxynivalenol-specific ELISA and TRF-immunoassays, which can detect deoxynivalenol and two of the most common metabolic isoforms in the range of 78–115 ng/mL.

Potent SARS-CoV-2 neutralizing antibodies selected from a human antibody library constructed decades ago

ABSTRACTCombinatorial antibody libraries not only effectively reduce antibody discovery to a numbers game, but enable documentation of the history of antibody responses in an individual. The SARS-CoV-2 pandemic has prompted a wider application of this technology to meet the public health challenge of pandemic threats in the modern era. Herein, we used a combinatorial human antibody library constructed 20 years before the COVID-19 pandemic to discover three highly potent antibodies that selectively bind SARS-CoV-2 spike protein and neutralize authentic SARS-CoV-2 virus. Compared to neutralizing antibodies from COVID-19 patients with generally low somatic hypermutation (SHM), these antibodies contain over 13-22 SHMs, many of which are involved in specific interactions in crystal structures with SARS-CoV-2 spike RBD. The identification of these somatically mutated antibodies in a pre-pandemic library raises intriguing questions about the origin and evolution of human immune responses to SARS-CoV-2.

Rapid identification of a human antibody with high prophylactic and therapeutic efficacy in three animal models of SARS-CoV-2 infection

Effective therapies are urgently needed for the SARS-CoV-2/COVID-19 pandemic. We identified panels of fully human monoclonal antibodies (mAbs) from large phage-displayed Fab, scFv, and VH libraries by panning against the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) glycoprotein. A high-affinity Fab was selected from one of the libraries and converted to a full-size antibody, IgG1 ab1, which competed with human ACE2 for binding to RBD. It potently neutralized replication-competent SARS-CoV-2 but not SARS-CoV, as measured by two different tissue culture assays, as well as a replication-competent mouse ACE2-adapted SARS-CoV-2 in BALB/c mice and native virus in hACE2-expressing transgenic mice showing activity at the lowest tested dose of 2 mg/kg. IgG1 ab1 also exhibited high prophylactic and therapeutic efficacy in a hamster model of SARS-CoV-2 infection. The mechanism of neutralization is by competition with ACE2 but could involve antibody-dependent cellular cytotoxicity (ADCC) as IgG1 ab1 had ADCC activity in vitro. The ab1 sequence has a relatively low number of somatic mutations, indicating that ab1-like antibodies could be quickly elicited during natural SARS-CoV-2 infection or by RBD-based vaccines. IgG1 ab1 did not aggregate, did not exhibit other developability liabilities, and did not bind to any of the 5,300 human membrane-associated proteins tested. These results suggest that IgG1 ab1 has potential for therapy and prophylaxis of SARS-CoV-2 infections. The rapid identification (within 6 d of availability of antigen for panning) of potent mAbs shows the value of large antibody libraries for response to public health threats from emerging microbes.