Computational Antibody Papers

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structure prediction
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2025
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  • 2025-07-18

    Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins

    • structure prediction
    • Introduces IBEX, a pan‑immunoglobulin structure predictor for antibodies, nanobodies, and TCRs that explicitly models both bound (holo) and unbound (apo) conformations via a conformation token.
    • Training data comprise ~14 000 high‑quality antibody (SAbDab) and TCR (STCRDab) structures (including 760 matched apo/holo pairs), augmented by distillation from ~60 000 predicted immunoglobulin‑like structures to improve generalization (from OAS, modeled with ESMFOld and Boltz-1).
    • Architecture builds on AlphaFold2’s invariant‑point‑attention and the ABodyBuilder2 framework, adding a residual connection from the initial embedding into every structure module and feeding an apo/holo token at each block.
    • Performance on a private benchmark of 286 novel antibodies shows IBEX achieves mean CDR‑H3 RMSD = 2.28 Å, outperforming Chai‑1 (2.55 Å), Boltz‑1 (2.30 Å), and Boltz‑2 (2.42 Å). Most of its advantage arises from greater robustness to sequences whose CDR‑H3 loops have larger edit distances to any structure in the training set.

  • 2025-06-24

    AbEpiTope-1.0: Improved antibody target prediction byuse of AlphaFold and inverse folding

    • binding prediction
    • structure prediction
    • A novel method that repurposes AlphaFold-2.3 structure predictions and combines them with inverse folding–based machine learning models to assess antibody-antigen binding accuracy and specificity.
    • They generate antibody-antigen complex models using AlphaFold-2.3 and evaluate them using the 'AbAgIoU' metric, which measures the overlap between predicted and true epitope/paratope residues — penalizing both missing and extra contacts.
    • They demonstrate that the learned scores can distinguish true from incorrect antibody-antigen pairings (including swapped antibody scenarios), significantly outperforming random baselines.
    • The method relies only on antibody and antigen sequences as input, using AlphaFold to model structures — making it applicable in real-world settings where experimental structures are unavailable.

  • 2025-06-05

    PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models:

    • structure prediction
    • PSBench is a large benchmark dataset (>1M models) for training and evaluating model accuracy estimation (EMA) methods for protein complex structures, using data from CASP15 & CASP16.
    • Models were generated by AlphaFold2-Multimer and AlphaFold3 under blind prediction conditions and annotated with 10 detailed global, local, and interface quality scores.
    • The dataset enables development of advanced EMA methods (e.g. GATE), which showed top performance in blind CASP16 assessments.

  • 2025-03-31

    AI-Augmented Physics-Based Docking for Antibody-Antigen Complex Prediction

    • epitope prediction
    • docking
    • structure prediction
    • Benchmarking of the structure prediction/docking and co-folding methods for antibody design
    • Authors measure the impact of antibody-antigen model quality on the success rate of epitope prediction and antibody design.
    • For epitope prediction and antibody design they use a proxy measure of DockQ score - they call success when DockQ is better than 0.23, for antibody design they use a stricter threshold of 0.49.
    • Using these measures, AlphaFold3 comes out on top, and it would be successful roughly ~47% times.
    • THey introduce an approach where ProPOSE and ZDOCK decoys are refined using AlphaFold. With this combined protocol they reach success rates of 35% for epitope mapping and 30% for antibody design.

  • 2025-03-11

    Fast and accurate antibody sequence design via structure retrieval

    • databases
    • generative methods
    • structure prediction
    • Inverse folding and thus antibody design via database search.
    • Authors train a vector retrieval database on SAbDab. In this way for a single sequence one can figure out where it falls structurally.
    • They benchmark against state of the art inverse folding tools such as AbMPNN, AntiFold, ProteinMPNN and ESM-IF - their tools comes on top in terms of sequence retrieval.
    • The database search is orders of magnitude faster than the state of the art inverse folding tools.
    • They compare IgSeek versus FoldSeek - their tool gets a higher accuracy in sequence retrieval, for most CDRs, but CDR-H3. Therefore FoldSeek seems like a very good choice alongside IgSeek for such a database-driven inverse folding protocol.