Computational Antibody Papers
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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
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
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.
2025-11-17
HeavyBuilder: Analysis of High-Throughput of Antibody Heavy Chain Repertoires in the Structural Space
- server/webapp
- structure prediction
- A heavy-chain–only version of ABodyBuilder2, removing the light-chain component entirely.
- The model is substantially faster than ABodyBuilder2 and comparable to IgFold or AlphaFold2 owing to (i) smaller embedding dimensions (128–256 vs 384 in ABB2), (ii) use of fewer submodels (3 vs 4), (iii) omission of the refinement step by default, and (iv) the inherently shorter sequence length of single heavy chains.
- Accuracy-wise, HeavyBuilder performs on par with ABodyBuilder2, IgFold, and AlphaFold2 for framework and CDRH1–H2, and is slightly better for CDRH3 (∼3.4 Å RMSD vs ∼4 Å for others). While ABodyBuilder2 achieves 2.99 Å on CDRH3, that figure depends on the inclusion of the paired light chain, so the authors note that it is not a fair comparison.
- Novel protein folding predictor that shows that using a simpler model architecture one can get quite far.
- Architecture/training: SimpleFold swaps AF2/RF-style pair reps, triangle updates, MSAs, and equivariant blocks for plain Transformer layers trained with a flow-matching objective to generate full-atom structures; rotational symmetry is handled via SO(3) augmentation.
- Training data: It is not crystals-only like previous predictors, the model mixes ~160k PDB experimental structures with large distilled sets from AFDB SwissProt (~270k) and AFESM (≈1.9M; 8.6M for the 3B model), then finetunes on PDB + SwissProt. So practically this is not a head-to-head comparison with other methods as they started from the smaller x-al dataset.
- Performance: It’s competitive but generally below AlphaFold2/RoseTTAFold2/ESMFold on CAMEO22, while on CASP14 the 3B model beats ESMFold but does not surpass AlphaFold2; overall they claim ~95% of AF2/RF2 on most metrics, with especially strong results for ensemble generation.
- Novel model for loop retrieval using embedded structural representation.
- It is a multimodal tokenizer at the antibody loop (CDR) level that fuses sequence with backbone dihedral-angle features and learns a latent space with a dihedral-distance contrastive loss—unlike residue-tokenizers and canonical clusters. It produces both continuous and quantized loop tokens that can plug into PLMs (IGLOOLM / IGLOOALM).
- Trained by self-supervised on ~807k loops from experimental (SAbDab/STCRDab) and Ibex-predicted structures, with four objectives: masked dihedral reconstruction, masked AA prediction, contrastive learning over dihedral distance (with DTW alignment), and codebook learning; followed by two-phase training and specific H100 settings.
- It was benchmarked on a set of computational goals: for H3 loops IGLOO beats the best prior tokenizer by +5.9% (dihedral-distance criterion). (2) Cluster recovery: high purity vs. canonical clusters across CDRs. (3) Downstream PLM task: IGLOOLM improves binding-affinity prediction on 8/10 AbBiBench targets, rivaling larger models. (4) Controllable sampling: IGLOOALM generates diverse sequences with more structure consistency than inverse-folding baselines.
2025-09-05
Assessing the Performance of AF2 and AF3-Implementations on Antibody-Antigen Complexes
- structure prediction
- Benchmarking of docking/complex prediction methods for antibody-antigen (Ab-Ag) complexes.
- Authors used 200 antibody-antigen and nanobody-antigen complexes curated from prior studies, specifically chosen to exclude any complexes present in the training data of the evaluated models.
- Evaluated methods: AF2 (v2.3.2), Protenix, ESMFold, Chai-1, Boltz-1, Boltz-1x, and Boltz-2. (Note: Boltz-2 was only tested on 18 complexes; Protenix failed on 26 large complexes.)
- DockQ and CAPRI criteria were used as primary metrics to assess structural prediction quality.
- AF2 performed best overall, especially for antibody-antigen complexes. Chai-1 outperformed AF2 on nanobody-antigen complexes.
- A composite confidence metric, AntiConf, was introduced, combining pTM and pDockQ2 scores to better assess the quality of Ab-Ag models. AntiConf = 0.3 × pDockQ2 + 0.7 × pTM
2025-07-18
IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
- structure prediction
- Introduced a novel foundational protein folding model, IntFold, designed for both general and specialized biomolecular structure prediction.
- Builds on architectural principles from AlphaFold 3, while adding key innovations like modular adapters, a custom attention kernel, and training-free structure ranking. While it does not outperform AlphaFold 3.
- While it does not outperform AlphaFold 3, IntFold exceeds all other public reproductions of AF3 across multiple biomolecular tasks.
- The fine-tuned IntFold+ model achieves 43.2% accuracy on antibody-antigen interfaces, approaching AlphaFold 3’s 47.9%.
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.