Computational Antibody Papers

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protein design
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2026
TitleKey points

  • 2026-02-05

    Iterative Epitope Expansion Enables Structure-Guided de novo Design of IgE-Binding Miniproteins Targeting the FcεRI Interface

    • non-antibody stuff
    • protein design
    • Describing a protocol to design mini-binders for a multi domain not that well characterized target using Latent-X1 and to lesser extent Chai.
    • The protocol used Latent-X1 to generate de novo sequences and initial poses, which were then refolded using Chai-1 to ensure the designs were structurally consistent and plausible.
    • The final rank was determined by the equation score = 2.0 * Binder PTM - 0.1 * min-iPAE - 0.1 * complex RMSD. This formula prioritized high global confidence (PTM) while penalizing designs where the Latent-X1 pose and Chai-1 refolded structure disagreed (iPAE and RMSD).
    • To handle the complex, multidomain IgE interface, they first designed binders against a smaller, stable seed on the epsilon3 domain before iteratively expanding the interface toward the full receptor-binding site.
    • Out of hundreds of generated designs, fewer than 80 candidates across two rounds were selected for wet-lab testing, resulting in a 6% hit rate and the identification of three specific IgE-binding miniproteins

  • 2026-01-20

    Origin-1: a generative AI platform for de novo antibody design against novel epitopes

    • protein design
    • De novo platform for epitope-specific antibody design against “zero-prior” targets, i.e. antigen sites with no known antibody–antigen or protein–protein complex structures and limited homology to previously solved interfaces.
    • The method combines three tightly integrated components: AbsciDiff, an all-atom diffusion model fine-tuned from Boltz-1 to generate epitope-conditioned antibody–antigen complex structures; IgDesign2, a structure-conditioned paired heavy–light CDR sequence design model; and AbsciBind, a modified AF-Unmasked / AlphaFold-Multimer–based scoring protocol using ipTM-derived interface confidence to rank and filter designs.
    • The platform was evaluated on 10 zero-prior protein targets, with fewer than 100 antibody designs per target advanced to experimental testing; specific binders were successfully identified for 4 targets (COL6A3, AZGP1, CHI3L2, IL36RA).
    • Experimental validation demonstrated both structural and functional accuracy, including cryo-EM confirmation at near-atomic resolution (DockQ 0.73–0.83) for two targets and AI-guided affinity maturation yielding a functional IL36RA antagonist with ~100 nM potency.

  • 2026-01-09

    Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2

    • protein design
    • All-atom, zero-shot generative model that designs antibody sequence and structure directly in complex with a target from epitope-conditioned prompts.
    • One specifies target, epitope, modality and the algorithm produces designs.
    • They tested 4–24 designs per target, achieving 50% target-level success, producing VHHs and scFvs with pico- to nanomolar affinities (best ≈ 26 pM).
    • Designed antibodies show therapeutic-grade developability (expression, aggregation, hydrophobicity, polyreactivity, stability) without optimization via wetlab validation.
    • Human PBMC assays (10 donors) show no detectable immunogenicity for representative de novo nanobodies.