Computational Antibody Papers
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2026-04-03
Evaluating Deep Learning Based Structure Prediction Methods on Antibody-Antigen Complexes
- structure prediction
- Authors benchmark co-folding methods on their ability to identify true positives given deep sampling.
- AlphaFold3 consistently outperforms AlphaFold2, Chai-1, and Boltz-1 in predicting antibody-antigen complexes, though its accuracy declines if the target lacks structural similarity to its training data.
- For all methods, increased sampling improves the probability of generating a correct model in a roughly log-linear manner; however, the improvement is limited by a significant gap between the "best" model generated and the "top-ranked" model.
- Internal confidence metrics (like ipTM) struggle to identify the most accurate structures for a given target, primarily because the models cannot yet accurately predict their own aligned errors.
2026-03-13
Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores
- binding prediction
- nanobodies
- structure prediction
- The authors evaluated AlphaFold3, Boltz-2, and Chai-1 on their ability to distinguish cognate (correct) nanobody-antigen pairs from incorrect, non-binding pairings.
- They used 106 experimental complexes and generated a combinatorial matrix of 11,132 shuffled non-cognate pairings to serve as ground-truth "incorrect" decoys.
- Internal confidence scores (specifically ipTM) were very weakly predictive of true binding. In terms of Average Precision (PR-AUC), AF3 performed best, followed by Chai-1 and then Boltz-2.
- Increased sampling improves structural geometry but does not help models "select" the correct binder. Most quality gains occur within 10–25 samples; deeper sampling primarily increases the number of plausible-looking false positives.
2026-02-26
Disulphide and sequence-encoded conformational priors guide nanobody structure prediction
- nanobodies
- structure prediction
- Authors propose a new method to train a nanobody structure predictor by using ‘blueprints’.
- They developed a classifier (NbFrame) to identify whether the HCDR3 loop adopts a kinked (framework-contacting) or extended (solvent-exposed) conformation. This allows the model to use sequence-encoded priors and explicit constraints during the folding process.
- The model itself is very lightweight and runs significantly faster than "heavy" models like AF or Boltz. NbForge achieves sub-second inference speeds, predicting structures in less than a second on both CPU and GPU. In comparison, models like AlphaFold3 or Boltz1 typically require tens of seconds to minutes per structure - MSA is a different story altogether.
- The model matches the HCDR3 prediction quality of heavy models while being much more efficient. While AF3 and Boltz1 are more accurate at modeling the rigid framework, NbForge achieves parity in the hypervariable HCDR3 region—the part most critical for binding. Its speed and high recovery of disulphide bonds make it ideal for triaging millions of candidates in large-scale discovery campaigns.
- ‘Evolution’ of AlphaFold from Isomorphic labs.
- IsoDDE achieves 39% accuracy in high fidelity regime ((DockQ > 0.8)) which corresponds to near-experimental precision with an interface RMSD (iRMSD) typically below 1.0Å. That’s a 2.3x improvement over AF3.
- Using a single model seed, IsoDDE successfully predicts 63% of interfaces DockQ > 0.23 (correlating to an iRMSD$ of roughly 4.0Å or less, which is a 1.4x improvement over AF3's single-seed performance.
- IsoDDE accurately models the backbone of the highly variable CDR-H3 loop for 70% of antibodies (<2Å) in the test set, outperforming AF3’s success rate of 58% 1.2x.
- When scaled to 1,000 seeds, IsoDDE reaches an 82% success rate for correct interfaces and 59% for high-accuracy predictions. So to get results one cannot exactly do it on a laptop.
- It is a technical report, architecture is not discussed.
2026-02-16
Surpassing AlphaFold3 via Architectural Refinement and Structural Consistency
- structure prediction
- New open source reproduction of AlphaFold3 that either matches or surpasses it.
- IntelliFold-2-Pro achieves a success rate of 58.2% (DockQ > 0.23 so about 4A irmsd) on antibody-antigen interactions, outperforming AlphaFold 3's 47.9%.
- For small molecule co-folding, IntelliFold-2-Pro reaches 67.7%, surpassing AlphaFold 3’s 64.9%.
- Interface Precision vs. Monomers: IntelliFold-2 shows marginal gains in protein monomer accuracy (LDDT of 0.89 vs AF3's 0.88).
2026-02-16
AbTune: layer-wise selective Fine-Tuning of protein language models for Antibodies
- structure prediction
- A strategy for layer-wise selective fine-tuning of general protein language models.
- Instead of full fine-tuning, they found that adapting only the first 50-75% of layers via LoRA provides optimal performance while saving computational costs.
- For example, they perform sequence-specific "test-time" training where they optimize the model using a Masked Language Modeling (MLM) objective on the target sequence itself before predicting its properties. This approach led to a 18.4% accuracy boost in predicting the notoriously difficult CDR-H3 antibody loop
2026-02-05
Multiple protein structure alignment at scale with FoldMason
- non-antibody stuff
- structure prediction
- Protocol for ultra fast protein structure alignment.
- FoldMason represents protein structures as 1D sequences using a structural alphabet (3Di+AA), which allows it to perform multiple alignments using fast string comparison algorithms and a parallelized progressive alignment following a minimum spanning tree.
- It operates two to three orders of magnitude faster than traditional structure-based methods, achieving a 722x speedup over tools like MUSTANG and scaling to align 10,000 structures in a fraction of the time required by competitors for just 100.
- It matches the accuracy of gold-standard structure aligners and exceeds sequence-based tools, particularly in aligning distantly related proteins or flexible structures that global superposition-based methods struggle to handle.
- It is used for large-scale structural analysis of massive databases like AlphaFoldDB, building structure-based phylogenies for proteins that have diverged past the "twilight zone" of sequence similarity, and providing interactive web-based visualizations of complex MSTAs
- First fully open-source reproduction of the diffusion-based AlphaFold3 architecture that matches or exceeds its performance while strictly adhering to the same training data cutoff and model scale (especially on antibodies!).
- Unlike previous open-source models, it exhibits a consistent improvement in accuracy as more computational budget is allocated (you sample more).
- Protenix-v1 beats others in antibody-antigen interface prediction, outperforming AlphaFold3 52.31% vs. 48.75% success rate (dockq better than .23). That is nearly doubling the accuracy of open-source like Chai-1 23.12%.
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
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.