Computational Antibody Papers
Filter by tags
structure prediction
Filter by published year
2026
TitleKey points
- Evaluated ten co-folding protocols on a benchmark of 412 human monomeric antigen complexes
- They demonstrate that recent architectures like Protenix v2 (52% top-1 success on post-cutoff Fv complexes) substantially outperform earlier methods like AlphaFold-Multimer (20%) and Protenix v0.5, which serves as the open-source AlphaFold3 reproduction baseline.
- Modeling accuracy scaled inversely with CDR-H3 loop length. Short loops (<11 residues) were predicted accurately across all methods (0.4–0.7 Å Calpha RMSD), whereas long loops (>16 residues) remained challenging but showed distinct improvement in Protenix v2 (median 2.67 Å RMSD vs. 3.22–3.88 Å in older methods).
- CDR-H3 accuracy was identified as the primary structural feature distinguishing successful complex predictions DockQ \ge 0.49 from failures, demonstrating a strong inverse correlation with overall DockQ scores (Spearman rho = -0.74) and high metric discrimination (AUROC = 0.91).
- Prediction failures in unconstrained models were dominated by sampling limitations (failure to generate native-like poses) rather than ipTM ranking failures; providing idealized epitope constraints or increasing seed depth successfully rescued many of these failures by guiding the search space.
2026-08-21
Benchmarking Antibody Modeling Tools across Structure Prediction, Docking, and Paratope–Epitope Interface Analysis
- structure prediction
- Benchmarking of some structural prediction methods on antibody-related tasks.
- Benchmarked five computational tools across two core tasks: AlphaFold3 (AF3), ImmuneBuilder (ABodyBuilder2/ABB2), and IgFold for antibody variable fragment (Fv) structure prediction, as well as AF3, dyMEAN, and GRAMM for antigen–antibody complex structure prediction and docking.
- Tools were tested on 50 non-redundant humanized antibody–antigen Fab complexes from the Protein Data Bank (PDB), filtered for resolution < 3.0Å and released after December 19, 2023, to eliminate training data overlap.
- All Fv predictors achieved high backbone accuracy (mean TM-score > 0.97), but AF3 demonstrated statistically significant advantages in global Fv geometry and hypervariable CDR-H3 loop modeling (median CDR-H3 RMSD of 0.86 Å, compared to 1.34 Å for ABB2 and 1.52 Å for IgFold). In complex prediction, AF3 substantially outperformed dyMEAN and GRAMM, generating reliable docking poses for 46% of complexes, whereas classical rigid-body docking (GRAMM) and epitope-guided modeling (dyMEAN) almost completely failed.
- Paratope and epitope residue recovery is strictly dependent on initial docking accuracy. When docking succeeds, AF3 reliably identifies interface residues (F1 score ~ 0.85/0.87), salt bridges (84% recall), and non-bonded contacts (75% recall).
2026-08-21
High-accuracy structural modeling of antibody-antigen complexes
- binding prediction
- structure prediction
- Novel method, DeepAAAssembly, to model antibody antigen complexes, beating AF3.
- DeepAAAssembly introduces a hybrid framework that pairs deep learning inter-chain distance predictions with flexible Monte Carlo sampling, overcoming the lack of strong co-evolutionary signals in antibody-antigen interfaces.
- The pipeline feeds Voronoi tessellation contact geometry, AntiBERTy embeddings, and a Triangular Awareness module into a multi-column CNN, creating continuous multi-peak distance energy landscapes to guide both global orientation sampling and local CDR loop refinement.
- Evaluated on Docking Benchmark 5.5, it surpassed AF3 with a 12.9% higher average DockQ score on best-generated models (0.454 vs. 0.402) and boosted the overall modeling success rate (DockQ > 0.23) from 47.8% to 56.7%.
- Demonstrates strong generalization on CASP15/16 blind targets by successfully correcting severe initial orientation errors, elevating previously failed structural predictions into acceptable, physically plausible models
2026-07-13
Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine
- structure prediction
- Novel structural co-folding method that rivals AF3 architectures whilst being completely open sourced.
- OpenDDE replaces direct coordinate regression with atomic latent reasoning over granular structural tokens and scales the standard Pairformer hidden dimension from 128 to 384, expanding parameter capacity.
- The model implements a differentiable shape-complementarity objective to physically fit interfaces and naturally unifies structure prediction and de novo design inside a single conditional diffusion framework via masking.
- The model uses a 4-stage precision-breadth-precision data distribution to master geometry , consuming ~414k GPU-hours handled efficiently via Fold-CP context parallelism.
- The model sets state-of-the-art antibody-antigen success rates (e.g., 70.0% on FoldBench-AB and 66.4% on 2026ARK-AB) , easily outperforming Protenix-v1 (beating/equalling AF3!), and climbs to nearly 90% oracle accuracy when scaled with test-time seeds.
- Very fast antibody modeling, FlashABB, with applications to developability screening.
- FlashABB accurately performs sota antibody antibody structure prediction in just 5 ms, making it faster than protein language models can even generate sequence embeddings, by completely discarding traditional pair representations in favor of an inner-product-based, linear-memory "Flashpoint Attention" (FPA) algorithm.
- Featuring roughly 7.2 million parameters trained on 8,395 SAbDab structures, the model is strictly forced to run on float32 precision to avoid the catastrophic numerical cancellation errors that heavily degrade 16-bit float implementations during distance calculations.
- Eliminating the structural compute bottleneck allows developers to scale downstream analysis to millions of sequences; using the variant tool FlashTAP, teams can filter out liability flags and predict therapeutic developability metrics for 100 antibodies per second.
- Having said that, CDR-H3 is still sota - so in the region of 2.5A, but it gets there way faster.
2026-06-23
AbCVista: a deep learning framework for predicting antibody conformational ensembles
- structure prediction
- Deep learning framework designed to predict both highly accurate single antibody structures and biologically realistic 3D conformational ensembles.
- It combines a fast, antibody-specific sequence alignment tool (Abalign) with an optimized OpenFold engine, utilizing a novel "split-and-intersect" cross-chain clustering strategy to properly model heavy-light chain coordination without chain-segregation bias.
- In static benchmarks, it outperforms top-tier models like AlphaFold3 and IgFold, especially on the notoriously difficult hypervariable CDR-H3 loop, while cutting down structure inference time to roughly 2 minutes per antibody.
- Validated against extensive Molecular Dynamics (MD) simulations, its generated ensembles accurately capture true biological flexibility rather than random sampling noise, which significantly improves downstream therapeutic evaluations like antibody aggregation and developability mapping.
2026-05-29
ABRefine: An Accurate Antibody Structure Refinement Method by Equivariant Graph Transformer with Rigid Body Constraint
- structure prediction
- An end-to-end structural refinement method specifically developed for antibodies and nanobodies.
- Curates structures from SAbDab to train a pure equivariant graph transformer (not a standard EGNN) that directly predicts 3D coordinate shift vectors for all backbone atoms (N, Ca, C, O).
- Benchmarked against five baseline methods, achieving an average Ca RMSD improvement typically on the order of 0.01 Å to 0.05 Å - which essentially is raising questions of statistical significance (as the error bars are order of 1.0A at least).
2026-05-29
Language Modeling Materializes a World Model of Protein Biology
- language models
- protein design
- structure prediction
- New versions of ESM and ESMFold, ESMC (ESM Cambrian) and ESMFold2 to model protein sequence, structure, and function.
- Like previous versions, ESMC is trained entirely on sequences using a masked language modeling (MLM) objective. However, it scales up to 2.8 billion metagenomic sequences, nearly a 100-fold increase over the 50 million sequences used for ESM2.
- ESMFold2 achieves state-of-the-art atomic resolution and directly outperforms AlphaFold3 on complex antibody-antigen predictions (even when AlphaFold3 is given multiple sequence alignments and ESMFold2 operates from sequence alone).
- To design therapeutic antibody fragments (scFvs), they input the target sequence and lock in a stable, known antibody framework template, leaving only the target-recognizing loops (CDRs) to be filled in.
- AI-guided optimization: The system uses mathematical backpropagation through both ESMC and ESMFold2 to iteratively optimize the CDR sequences. It automatically mutates the loops to maximize structural interface confidence scores.
- They synthesized and tested these computational designs in the wet lab, achieving high experimental hit rates and discovering entirely novel binders with therapeutically relevant nanomolar affinities.
- PromptMOL: a PyMOL plugin that enables direct interaction with molecular structures using natural language commands.
- While PyMOL is a gold standard in structural biology, its interface can be complex; PromptMOL removes the barrier to entry by replacing convoluted script syntax with simple, descriptive prompts.
- By hooking directly into LLMs (via local models like LM Studio, or cloud providers like OpenAI and Anthropic), the plugin intelligently handles selections, coloring, structural analysis, and rendering tasks on the fly.
2026-04-17
Sampling antibody conformational ensembles with ABodyBuilder4-STEROIDS
- structure prediction
- Fourth version of ABodyBuilder: Now uses a generative flow matching model (ABB4-STEROIDS) specifically designed to sample antibody conformational ensembles.
- Trained on FlAbDab and all-atom MD, utilizes 4.2 million frames from ~136,000 coarse-grained simulations, plus a new fine-tuning set of 83 all-atom MD simulations.
- Shows marginal improvements over ABB3 but is less accurate than Boltz-1 for single-structure RMSD; it also exhibits a higher number of atomic clashes than Boltz-1.
- Outperforms alternative models (including Boltz-1 and AlphaFlow) at reproducing MD ensemble metrics and matching experimental evidence of CDR loop diversity.