Computational Antibody Papers

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  • 2025-12-12

    Predicting Experimental Success in De Novo Binder 2 Design: A Meta-Analysis of 3,766 Experimentally 3 Characterised Binders

    • protein design
    • databases
    • Large-scale benchmarking of structural, energetic, and confidence metrics to distinguish protein binders from non-binders.
    • Curated 3,766 experimentally tested de novo binders across 15 targets from independent campaigns.
    • Of these, 436 were confirmed binders, the remainder non-binders.
    • Each design was re-modelled using AF2 (initial guess + ColabFold), Boltz-1, and AF3.
    • From these predictions they computed 200+ structural and confidence descriptors.
    • AF3-derived confidence scores (especially ipSAE_min) were the best single discriminators, although per-target precision still ranged widely (0.1–1.0), underscoring strong target dependence.

  • 2025-12-12

    CDR Conformation Aware Antibody Sequence Design with ConformAb

    • generative methods
    • structure prediction
    • ConformAb is a guided discrete-diffusion method for antibody lead optimization that preserves the seed binder’s CDR backbone conformation while introducing sequence diversity.
    • Structural preservation is enforced by steering the diffusion process to match the seed’s canonical CDR class probabilities, ensuring generated sequences retain the same canonical backbone geometry.
    • Canonical classes are assigned by folding SabDab and pOAS sequences with ABB2 and labeling them using the Kelow et al. dihedral-based canonical clustering scheme; ConformAb learns to predict these classes from sequence.
    • During generation, a KL-based guidance signal constrains mutations so that each CDR remains in the seed’s canonical class, enabling safe exploration of sequence space around the functional binder.
    • Although ConformAb does not model affinity directly, its structure-preserving diversification enables zero-shot affinity maturation: some variants emerge with improved binding despite using no antigen structure, no repertoire data, and no affinity labels.
    • The method was experimentally validated, generated sequences were expressed, tested by SPR on EGFR, IL-6, and a third target, achieving 15–60% binding rates and, for two targets, producing binders with 3–5x higher affinity than the seed.
    • Crystal structures of top EGFR and IL-6 binders confirmed that, despite substantial and non-conservative mutations, the CDR backbone conformations were preserved, validating the model’s structural guidance in wet-lab experiments.

  • 2025-12-12

    AntiBMPNN: Structure-Guided Graph Neural Networks forPrecision Antibody Engineering

    • protein design
    • Novel inverse folding algorithm for antibodies with experimental validation.
    • It uses atom-level graph MPNN, structured transformer, novel scoring and AF3 filtering; unlike ProteinMPNN/AbMPNN/AntiFold, which operate residue-level and lack downstream optimization.
    • Only experimental antibody structures (free antibodies + complexes) of antibodies were used for training.
    • AntiBMPNN uses a distinct dataset; AbMPNN and AntiFold rely heavily on modeled structures, unlike AntiBMPNN. So it might just be that the moderate gains in residue retrieval are due to a slightly bigger dataset used.
    • Unlike most models they actually performed experimental validation: ELISA assays on huJ3 (single-points, CDR1, CDR3) and D6 (CDR2), with multiple variants improving binding over wild type.

  • 2025-12-12

    Benchmarking all-atom biomolecular structure prediction with FoldBench

    • structure prediction
    • Benchmarking of all-atom biomolecular structure prediction methods, including AlphaFold 3 and open-source reproductions such as Boltz-1, Chai-1, HelixFold 3 and Protenix.
    • They introduced a large low-homology benchmark spanning nine tasks, including protein monomers, protein–protein, protein–ligand, nucleic acid systems, and antibody–antigen complexes.
    • Success is defined using DockQ ≥ 0.23 for protein–protein and antibody–antigen interfaces. This is actually a low bar, barely acceptable (4-6A interface RMSD roughly).
    • Antibody–antigen complexes remain particularly challenging, with AlphaFold 3 achieving only ~45–48% success and other methods performing substantially worse.
    • Antibodies have much lower proportion of high quality DOckQ in AF3 (~13%) versus a whopping 33% for nanobodies.
    • AlphaFold 3 consistently outperforms competing methods by roughly ten percentage points on antibody–antigen docking.
    • Increased sampling improves AlphaFold 3 predictions, whereas other methods show unstable or degrading performance, underscoring the importance of robust ranking and confidence calibration rather than sampling alone.

  • 2025-11-26

    Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions

    • structure prediction
    • Novel algorithm (ITsFlexible) for predicting conformational flexibility of antibody and TCR CDR3 loops and CDR3-like loops.
    • Dataset of 1.2M loops extracted from all antiparallel β-strand motifs in the PDB, including antibody and TCR CDR3s, representing the same secondary-structure pattern.
    • Flexibility defined by structural clustering: multiple conformations with pairwise Cα RMSD > 1.25 Å yield a “flexible” label.
    • Model is a three-layer equivariant GNN (EGNN) trained as a binary classifier (rigid vs flexible).
    • ITsFlexible is better than random, but it only just about outperforms loop-length, solvent exposure, pLDDT, RMSPE, and AF2-MSA-subsampling baselines. Best results are obtained when using crystal structures rather than predicted ones showing that modeling is still the roadblock for predictability. So the gain is very moderate against strong but simple baselines such as loop length.
    • They performed cryo-em validation which is a huge positive of the paper - three antibodies were experimentally solved; two predictions matched the data, one did not (likely due to antigen-binding-induced rigidification).

  • 2025-11-26

    JAM-2: Fully computational design of drug-like antibodies with high success rates

    • generative methods
    • protein design
    • JAM-2 is a novel method for de novo design of biologics that are then experimentally validated and show strong developability (expression, hydrophobicity, polyspecificity, monomericity). More than 57% of all designs pass all core developability criteria straight from the computer jam-2.
    • JAM-2 is a generative model, but the details are not revealed.
    • The most promising candidates (thousands per target in epitope-tiling mode, ~45 per format in target-level mode) are tested for binding by yeast display (epitope mode) or BLI (target mode) ; the entire discovery timeline is ≈ 1 month, with 2–3 days of fully computational design upfront, matching exactly what the paper reports jam-2.
    • Hit rates: Across 16 completely unseen targets: 39% average hit-rate for VHH-Fcs 18% average hit-rate for mAbs 100% of targets produced at least one binder These are all double-digit success rates from only 45 designs per format jam-2.
    • VHHs have higher hit rates but generally weaker affinities.
    • A panel of several hundred antibodies was assessed for: hydrophobicity, self-association (polyspecificity), expression titer, monomericity, thermostability. More than half (57%) met all pass criteria simultaneously, and 80%+ passed individual criteria such as expression or hydrophobicity. These molecules were not optimized, it was the first pass from the model.

  • 2025-11-26

    Drug-like antibody design against challenging targets with atomic precision

    • protein design
    • generative methods
    • developability
    • Update on earlier Chai-2 results adding developability and structural validations.
    • Previously generated scFv hits were reformatted into full-length IgGs; ~93% retained binding.
    • Developability was assessed using NanoDSF (Tm), HIC-HPLC, BVP ELISA, and AC-SINS, with Jain-style green-flag thresholds.
    • Most reformatted IgGs passed ≥3 of 4 developability flags, indicating good biophysical properties without further optimization.
    • Newly designed antibodies were generated for the GPCR benchmarks, showing successful in silico design against challenging targets.

  • 2025-11-26

    Uncovering the flexibility of CDR 2 loops in antibodies and TCRs through 3 large-scale molecular dynamics

    • structure prediction
    • Large-scale coarse-grained molecular dynamics of antibody and TCR Fvs.
    • They use CALVADOS 3-Fv, an antibody-specific modification of CALVADOS 3 with added CDR restraints, to simulate Fv motions at scale.
    • They benchmark CALVADOS 3-Fv against full all-atom MD for several antibodies/TCRs, showing that the coarse-grained simulations largely agree with atomistic dynamics. Treating CDRs as fully disordered causes overestimation of flexibility, so additional intra-CDR and framework-CDR restraints are required.
    • They simulate all non-redundant experimentally solved antibody structures (~3,140 Fvs) and show that even CDR-H3 is moderately flexible, typically around ~1 Å RMSD from the mean structure, with only a minority exceeding 2 Å.
    • Loop length is a major driver of flexibility: even short CDRs (<15 IMGT residues) show ~1 Å movement, consistent with well-modeled structural variability.
    • Curiously sometimes when they start the simulation from the bound structure they cannot sample the unbound state.
    • There was no correlation between distance from germline and flexibility (so an easy way to chek if very mature antibodies are more rigid), but they confirmed rigidity of some known 'rigidifiers'.
    • They release the full FlAbDab and FTCRDab datasets: ~3,140 experimental Fvs, ~148,000 predicted ABodyBuilder2 Fvs, and all available TCR Fvs (280 simulations).

  • 2025-11-17

    BLOSUM Is All You Learn — Generative Antibody Models Reflect Evolutionary Priors

    • generative methods
    • binding prediction
    • Benchmarking of 'old-school' substition matrices to see whether they can guide affinity maturation of antibodies.
    • they took several datasets wherte the parental antibody is known and the mutants affinities are known as well
    • they compared a diffusion based model, inverse folding and the substitution matrices.
    • they note that blosum performs surprisingly well.
    • 'training' the substitution matrix on the mutants with respect to the wild type yields better results than making it off sabdab or oas.

  • 2025-11-17

    ODesign: A World model for biomolecular interaction design.

    • generative methods
    • protein design
    • structure prediction
    • New biomolecular generative algorithm for protein/molecular design
    • It extends AlphaFold3 architecture into a generative “world model” that designs interactions across proteins, nucleic acids, and small molecules using a shared token space and conditional diffusion.
    • High-throughput in-silico design: It achieves up to 100- to 1000-fold higher computational throughput than diffusion or hallucination baselines (RFDiffusion, BoltzDesign, etc.) across 11 computational benchmark tasks.
    • Only computational benchmarks presented.