Computational Antibody Papers

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2026
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  • 2026-10-09

    Becoming the bull: Structural insights and engineering strategies for ultralong CDR-H3 paratopes from cattle

    • experimental techniques
    • structure prediction
    • Study of the bovine ultra long CDR-H3s.
    • Cattle produce unique immunoglobulins featuring ultralong CDR-H3 (uCDR-H3) loops that project a modular, disulfide-rich "knob" paratope from a conserved stalk, enabling access to recessed or cryptic epitopes and enabling reformatting into diverse therapeutic architectures.
    • Generated through restricted VDJ gene usage (IGHV1-7, IGHD8-2, and IGHJ2-4), cysteine-rich somatic hypermutation, and diverse intra-knob disulfide pairing topologies, uCDR-H3 regions achieve extensive structural diversity while relying on highly conserved V30 lambda light chains to provide framework stability and favorable biophysical traits.
    • Because antigen binding depends exclusively on the 4-6 kDa knob mini-domain, paratopes isolated via display technologies can be engineered into common light chain bispecific antibodies, loop-grafted Fc-knob variants, Knobbodies, framework-inserted Fabs/VHHs, artificial coiled-coil arrays, or autonomous solid-phase synthesized peptides.
    • While uCDR-H3 constructs exhibit favorable developability traits like high thermal stability and limited aggregation across broad pH ranges, translational challenges remain regarding potential non-human immunogenicity, sequence-dependent self-association tendencies, and structural instability when minimal knobs lack terminal spatial constraints.

  • 2026-08-10

    High-throughput machine learning-aided antibody discovery for cell surface antigens

    • databases
    • experimental techniques
    • ngs
    • ML-Optimized Paratope Representation (ARM).
    • The authors engineered a synthetic yeast-display Fab library centered on a compact "Antigen Recognition Module" (ARM), a <100 nt sequence pairing heavy-chain CDRH3 sequence diversity with a light-chain barcode, providing a computationally lightweight paratope representation tailored for high-throughput sequencing and ML workflows.
    • To overcome physical display biases (such as yeast growth rates or expression disparities), a k-mer-based logistic regression (LR) model was trained on sequence enrichment data from early sorting rounds (MACS to FACS1) to score clones based on sequence motifs.
    • Applying the LR model successfully rescued functional, high-affinity binders for ROBO2N (11 binders) and PD-L2 from early sequence pools that had been depleted or overshadowed by dominant clones in later experimental cell-sorting rounds.
    • The LR model trained on ROBO2N accurately predicted binding potency and epitope cluster preference for the closely related paralog ROBO1, while reliably scoring non-binding clones as low probability.
    • The paper provides a publicly available dataset of over 68,000 unique target-associated ARM sequences alongside comprehensive biophysical characterizations for 486 antibodies, offering a structured foundation for downstream predictive models, affinity maturation, and zero-shot antibody design.