Computational Antibody Papers

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  • 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.

  • 2024-10-24

    Chai-1: Decoding the molecular interactions of life

    • structure prediction
    • Foundational model following in the footsteps of AlphaFold3 attempting prediction of molecular interactions.
    • Model architecture is closely modeled on this of AF3 - however it was not benchmarked against it (nor ESM3) because of use restrictions.
    • It takes 30 days on 128 A100s to train the model. Back of the envelope Google Colab pro+ pricing of 128 (which is NOT distributed) puts it at ca. 120k USD :)
    • Addressing antibodies, they introduce constraints (e.g. known epitope residue) to help the model out. Adding even a single residue makes a big difference with respect to baseline which is tantamount to global docking.
    • It only takes one residue as constraints to improve ab-ag complex prediction. Success rate for ‘local’ mode, with just one residue is about 50% in getting it with DockQ score >.21, 30% >.49 and less than 10% for high quality hits.
    • Success rate for ‘global’ mode, so without constraints is about 35% in getting it with DockQ score >.21, 20% >.49 and less than 5% for high quality hits.
    • So altogether if I want to ‘hit the epitope’, the model has ca. 30% success rate.
    • If I want a high quality ab-ag complex structure, unfortunately it seems that constraints do not help much currently.

  • 2024-10-08

    Accurate antibody loop structure prediction enables zero-shot design of target-specific antibodies

    • generative methods
    • structure prediction
    • Authors describe how using a structure predictor one can re-design the binding site, to maintain binding.
    • They use a proprietary GaluxDesign method, the method achieves 1.4 Å Ca RMSD in predicting CDR-H3 loop structures, leveraging a unique scoring metric (G-pass rate) that assesses both confidence and structural consistency for antibody design.
    • The method outperforms AlphaFold 2.3, ABlooper, and ImmuneBuilder in predicting CDR-H3 loop structures, with significantly lower RMSD values (1.4 Å compared to 2.4-3.7 Å), particularly on a more challenging, time-separated dataset.
    • The binding propensity to HER2 was evaluated using a large mutant library and calculated via the G-pass rate, outperforming AlphaFold's PAE-based scoring. The model showed strong discrimination with an AUROC of 0.758, compared to 0.529 for AlphaFold. The novel loop is scored using their metric (G-pass rate) in complex with Her2.
    • Novel antibody sequences were designed by predicting six CDR loops in antibody-protein complexes, using GaluxDesign models. These designs were experimentally tested, achieving high success rates, including a 13.2% success rate for HER2 antibody designs using yeast display methods.

  • 2024-09-04

    Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling

    • structure prediction
    • Novel CDR-H3 structure prediction method, ComMat based on ensemble sampling.
    • Rather than generating a single structure, the method generates several solutions that are then all informing the next iteration.
    • The method was integrated into the structure module of AlphaFold2.
    • Crucially, with the introduction of the second prediction into the ‘community’, the predictions become better. However these quickly plateau, showing the limits of the approach.
    • The method does not produce better results than ABodyBuilder2 and EquiFold.

  • 2024-08-21

    ABodyBuilder3: Improved and scalable antibody structure predictions

    • structure prediction
    • language models
    • Updated version of the popular ABodyBuilder2 program to model antibodies, that is more efficient.
    • Unlike ABB2 that did not employ language model embeddings, ABB3 does (ProtT5 to be precise).
    • Instead of using several models and aggregating the results for error prediction, they train pLDDT within the model.
    • ABB3 achieves CDR-H3 RMSD in the ballpark of 2.4Å whereas the previous version in the region of 2.5Å.

  • 2024-07-30

    Fast and accurate modeling and design of antibody-antigen complex using tFold

    • binding prediction
    • structure prediction
    • docking
    • Update on tfold-AB including modeling of the complex with the antigen, with applications to virtual screening.
    • They mostly use SABDAB/Covabdab as reference datasets.
    • The modeling happens by generating antibody & antigen features supplemented by a large language model, followed by flexible docking.
    • For antigen feature generation, they use AF2
    • They are training on several tasks simultaneously, ab structure prediction, complex prediction etc. making it a multi-task training.
    • On docking their method achieves DockQ 0.217 vs AlphaFold-Multimer DockQ score of 0.158 - that is global docking.
    • Whe local docking information is given, constraining paratope/epitope sites, their algorithm achieves DockQ of 0.416.
    • They demonstrate that filtering antibodies by their predicted modeling confidence score gives moderate enrichment against PD1 and Sars-cov-2 antigens, showing promise for virtual screening.

  • 2024-06-25

    Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind the same epitope

    • structure prediction
    • ngs
    • An update on SPACE1, employing ABodyBuilder2. Better coverage of the structural method.
    • They used binders against coronavirus, ebola, lysozyme among others.
    • Structures are modeled using ABodyBuilder2. Structures are sorted by CDR lengths, frameworks aligned by Cas and RMSD calculated for CDR loops. Finally a clustering algorithm is used.
    • The clustering algorithms benchmarked were DBSCAN, OPTICS-xi, OPTICS-DBSCAN, K-means, Butina clustering, greedy clustering.
    • Two more variants were developed, SPACE2-HC, for heavy chains only as well as SPACE2-Paratope, for paratyping.
    • Two accuracy metrics were used, the fraction of epitope-consistent clusters (number of epitope-consistent multiple-occupancy clusters / number of multiple-occupancy clusters) and the fraction of clustered antibodies in epitope-consistent clusters (number of antibodies in epitope-consistent multiple-occupancy clusters / number of antibodies in multiple-occupancy clusters)
    • Two coverage metrics were used, the number of multiple-occupancy clusters and the number of antibodies in multiple-occupancy clusters were used. In order to examine accuracy and coverage with one measure they calculated the number of antibodies in consistent multiple-occupancy clusters
    • They selected agglomerative clustering as best, though it is not better than Optics-XI, but it was providing larger clusters.
    • Space2 using all loops was better than SPACE2-HC or SPACE2-paratope
    • Space2 improves the coverage over SPACE1, thanks to the ABodyBuilder2 protocol.
    • Space2 increases coverage with respect to just clonotyping, but clonotyping remains much more accurate.