Biology subjects

Emerson, R. O.

Publications and source records attributed to Emerson, R. O..

4 recordsLinked to original sources

bioRxiv2026

The Synthetic Epitope Atlas: High-Throughput Design and Validation of De Novo Antibody-Antigen Complexes

AO_SCPLOWBSTRACTC_SCPLOWDe novo antibody design models lack sufficient training data to reliably generalize. We demonstrate scalable generation of structural training data for machine learning-driven antibody design by linking in silico designs of antibody-antigen complexes to high-throughput experimental binding validation. Using AlphaSeq, a yeast-based platform for measuring protein binding affinities, we measure the affinity and specificity of thousands of de novo "synthetic epitope proteins" (SEPs) designed to bind to VHHs. The resulting Synthetic Epitope Atlas (SEPIA) pairs over 26 million on- and off-target affinity measurements with computationally designed VHH-SEP "pseudo-structures." We validate strong, specific binding for 1,161 pseudo-structures and >75,000 VHH and SEP mutational variants. We show that these pseudo-structures complement existing structural databases and enable ML models to outperform confidence metrics commonly used to rank de novo antibody designs. Taken together, SEPIA establishes a scalable framework for improving de novo antibody design by augmenting sparse structural data with large-scale experimental binding data.

synthetic biology↗

bioRxiv2024

AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity

Antibodies are versatile therapeutic molecules that utilize combinatorial sequence diversity to cover a vast fitness landscape. However, designing optimal antibody sequences remains a major challenge. Recent advances in deep learning provide opportunities to address this challenge by learning sequence-function relationships to accurately predict fitness landscapes. These models enable efficient in silico prescreening and optimization of antibody candidates. By focusing experimental efforts on the most promising candidates guided by deep learning predictions, antibodies with optimal properties can be designed more quickly and effectively. Here we present AlphaBind, a domain-specific model that utilizes protein language model embeddings and pre-training on millions of quantitative laboratory measurements of antibody-antigen binding strength to achieve state-of-the-art performance for guided affinity optimization of parental antibodies. We demonstrate that an AlphaBind-powered antibody optimization pipeline can deliver candidates with substantially improved binding affinity across four parental antibodies (some of which were already affinity-matured) and using two different types of training data. Resulting candidates, ranging up to 11 mutations from parental sequence, yield a sequence diversity that allows for optimization of other biophysical characteristics, all while using only a single round of data generation for each parental antibody. AlphaBind weights and code are publicly available at: https://github.com/A-Alpha-Bio/alphabind.

synthetic biology↗

bioRxiv2018

Longitudinal immunosequencing in healthy people reveals persistent T cell receptors rich in public receptors

BackgroundThe adaptive immune system maintains a diversity of T cells capable of recognizing a broad array of antigens. Each T cells specificity and affinity for antigens is determined by its T cell receptors (TCRs), which together across all T cells form a repertoire of tens of millions of unique receptors in each individual. Although many studies have examined how TCR repertoires change in response to disease or drugs, few have explored the temporal dynamics of the TCR repertoire in healthy individuals.\n\nResultsHere we report immunosequencing of TCR {beta} chains (TCR{beta}) from the blood of three healthy individuals at eight time points over one year. TCR{beta} repertoires from samples of all T cells and memory T cells clearly clustered by individual, confirming that TCR{beta} repertoires are specific to individuals across time. This individuality was absent from TCR{beta}s from naive T cells, suggesting that these differences result from an individuals antigen exposure history. Many characteristics of the TCR{beta} repertoire (e.g., alpha diversity, clonality) were stable across time, although we found evidence of T cell expansion dynamics even within healthy individuals. We further identified a subset of \"persistent\" TCR{beta}s present across all time points, and these receptors were rich in clonal and public receptors.\n\nConclusionsOur results revealed persistent receptors that may play a key role in immune system maintenance. They further highlight the importance of longitudinal sampling of the immune system and provide a much-needed baseline for TCR{beta} dynamics in healthy individuals. Such a baseline should help improve interpretation of changes in the TCR{beta} repertoire during disease or treatment.

immunology↗

bioRxiv2017

Unbiased Definition Of A Shared T-Cell Receptor Motif Enables Population-Based Studies Of Tuberculosis

Peptide-specific T cells that are restricted by highly polymorphic major histocompatibility complex (MHC) proteins express diverse T-cell receptors (TCRs) that are rarely shared among unrelated individuals. T-cells can also recognize bacterial lipid antigens that bind the relatively non-polymorphic CD1 family of proteins. However, genetic variation in human CD1 genes and TCR diversity expressed by CD1-restricted T-cells have not been quantitatively determined. Here, we show that CD1B is nearly nucleotide-identical across all five continental ancestry groups, providing evidence for purifying selection during human evolution. We used CD1B tetramers loaded with a mycobacterial glycolipid antigen to isolate T-cells from four genetically unrelated South African adults and cataloged thousands of TCRs from in-vitro expanded T-cells using immunosequencing. We identified highly conserved motifs that were co-expressed as a functional heterodimer and significantly enriched among tetramer-positive T-cells sorted directly from peripheral blood. Finally, we show that frequencies of these TCR motifs are increased in the blood of patients with active tuberculosis compared to uninfected controls, a finding that is confirmed by ex-vivo frequencies of tetramer-positive T-cells determined by flow cytometry. These data provide a framework for unbiased definition of TCRs targeting lipid antigens, which can be tested for clinical associations independently of host genetic background.\n\nBrief SummaryWe used human genetics and immunosequencing to define a shared T-cell receptor motif that is specific for a mycobacterial lipid antigen and associated with tuberculosis independently of host genetic background.

immunology↗