Biology subjects

Chungyoun, M.

Publications and source records attributed to Chungyoun, M..

2 recordsLinked to original sources

bioRxiv2025

Anti-citrullinated protein antibodies arise during affinity maturation of germline antibodies to carbamylated proteins in rheumatoid arthritis

Why autoantibodies in rheumatoid arthritis (RA) primarily target physiologically modified proteins, called citrullinated proteins, is unknown. Recognizing the inciting event in the production of anti-citrullinated protein antibodies (ACPAs) may shed light on the origin of RA. Here, we demonstrate that ACPAs originate from germline-encoded antibodies targeting a distinct but structurally similar modification, called carbamylation, which is pathogenic and environmentally driven. The transition from anti-carbamylated protein (anti-CarP) antibodies to ACPAs results from somatic hypermutations, indicating that the change in reactivity is acquired via antigen-driven affinity maturation. During this process, a single germline anti-CarP antibody transitions from anti-CarP to double positive (anti-CarP/ACPA) to ACPA according to the pattern and number of somatic hypermutations, explaining their coexistence and diverse specificity in RA. Artificial intelligence-based structural modeling revealed that an ACPA and its germline precursor exhibit distinct structural and biophysical properties, and pointed to heavy-chain tryptophan 48 (H-W48) as a critical residue in the differential recognition of citrullinated vs. carbamylated proteins. Indeed, a single methionine substitution in H-W48 changes the antibody specificity from ACPA to anti-CarP. These data indicate that the existence of germline-encoded anti-CarP antibodies is most likely the first event in the production of ACPAs during the early stages of RA development.

immunology↗

bioRxiv2024

FLAb: Benchmarking deep learning methods for antibody fitness prediction

The successful application of machine learning in therapeutic antibody design relies heavily on the ability of models to accurately represent the sequence-structure-function landscape, also known as the fitness landscape. Previous protein bench-marks (including The Critical Assessment of Function Annotation [33], Tasks Assessing Protein Embeddings [23], and FLIP [6]) examine fitness and mutational landscapes across many protein families, but they either exclude antibody data or use very little of it. In light of this, we present the Fitness Landscape for Antibodies (FLAb), the largest therapeutic antibody design benchmark to date. FLAb currently encompasses six properties of therapeutic antibodies: (1) expression, (2) thermosta-bility, (3) immunogenicity, (4) aggregation, (5) polyreactivity, and (6) binding affinity. We use FLAb to assess the performance of various widely adopted, pretrained, deep learning models for proteins (IgLM [28], AntiBERTy [26], ProtGPT2 [11], ProGen2 [21], ProteinMPNN [7], and ESM-IF [13]); and compare them to physics-based Rosetta [1]. Overall, no models are able to correlate with all properties or across multiple datasets of similar properties, indicating that more work is needed in prediction of antibody fitness. Additionally, we elucidate how wild type origin, deep learning architecture, training data composition, parameter size, and evolutionary signal affect performance, and we identify which fitness landscapes are more readily captured by each protein model. To promote an expansion on therapeutic antibody design benchmarking, all FLAb data are freely accessible and open for additional contribution at https://github.com/Graylab/FLAb.

bioinformatics↗