Computational Antibody Papers
Filter by tags
nanobodies
Filter by published year
All
TitleKey points
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.
2026-01-09
NbBench: Benchmarking Language Models for Comprehensive Nanobody Tasks
- language models
- nanobodies
- Benchmarking of pretrained protein, antibody, and nanobody language model representations on a comprehensive suite of nanobody-specific tasks.
- The authors introduce eight tasks spanning variable-region annotation, CDR infilling, antigen binding prediction, paratope prediction, affinity prediction, polyreactivity, thermostability, and nanobody type classification (e.g. VHH, VNAR, conventional antibody chains).
- They evaluate generic protein LMs, antibody-specific LMs, and nanobody-specific LMs under a unified and standardized benchmark.
- All backbone models are kept frozen, with task-specific lightweight heads trained on top to isolate representational quality.
- No single model consistently outperforms others across all tasks, showing that nanobody-specific pretraining alone does not guarantee superior performance over antibody-specific or generic protein language models.
- Novel protein design framework based on a unified all-atom diffusion model that performs both structure prediction and binder generation.
- It is fully open and free.
- Training setup resembles recent diffusion architectures (e.g., AlphaFold3, Chai), but its distinguishing feature is broad wet-lab validation across diverse target types.
- Experimental scale: generated tens of thousands of nanobody and protein designs for 9 novel targets (no homologous complexes in PDB).
- Results: tested 15 designs per target, obtaining nanomolar binders for 6 of 9 targets (≈66% success rate) — a notably strong experimental outcome.
2025-09-30
Efficient generation of epitope-targeted de novo antibodies with Germinal
- generative methods
- nanobodies
- protein design
- Novel open nanobody design method with experimental validation.
- On the surface it might appear like a lot of methods stitched together. The magic sauce appears to be in the joint, gradient-based co-optimization: AF-Multimer and IgLM gradients are merged through a 3-phase schedule (logits → softmax → semi-greedy), with CDR-masking/framework bias and custom losses that force CDR-mediated, loop-like interfaces; then AbMPNN edits only non-contact CDR residues, and designs are filtered independently with AF3 + PyRosetta.
- All this is actually not a ‘trained’ model but rather a filtering pipeline that WAS NOT trained (using previous methods, gradients, weights etc.) Just validated experimentally.
- Experimental benchmark was ran on four targets: PD-L1, IL-3, IL-20, and BHRF1.
- Authors measured how different their designs weren’t just ‘regurgitations’ of known abs. CDR identities were computed against SAbDab and OAS (via MMseqs); many designs show <50% CDR identity to any public sequence.
2025-09-12
The Therapeutic Nanobody Profiler: characterising and predicting nanobody developability to improve therapeutic design
- developability
- nanobodies
- Introduces TNP, a nanobody-specific developability profiler inspired by TAP.
- Uses six metrics: total CDR length, CDR3 length, CDR3 compactness, and patch scores for hydrophobicity, positive charge, and negative charge.
- Thresholds are calibrated to 36 clinical-stage nanobodies.
- In vitro assays on 108 nanobodies (36 clinical-stage + 72 proprietary) show partial agreement with TNP flags, indicating complementary—but not perfectly correlated—assessments.
2025-06-24
NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores
- binding prediction
- nanobodies
- Introduced a novel machine learning method (NanoBinder) to predict the binding probability of nanobody-antigen structural complexes.
- Positive (binding) complexes were sourced from the SAbDab database, which contains experimentally validated nanobody-antigen interactions.
- Negative (non-binding) complexes were generated by structurally aligning nanobodies from different binding complexes (with RMSD < 2 Å) and recombining them with unrelated antigens to create likely non-binding pairs.
- Extracted Rosetta energy features from each complex and trained several machine learning models, including Random Forests, SVMs, AdaBoost, and Decision Trees, to classify binders vs. non-binders. Random Forests showed the best performance.
- They selected antibodies with known antigen targets (e.g., IL-6) and grafted their CDRs onto nanobody scaffolds using Rosetta-based protocols. The resulting nanobody-antigen complexes were evaluated in silico using NanoBinder, and selected candidates were experimentally validated. The predictions showed good correlation with binding outcomes, particularly for identifying non-binders.
2025-03-21
NanoDesigner: Resolving the complex–CDR interdependency with iterative refinement
- protein design
- nanobodies
- Novel pipeline for computational protein design of nanobodies
- Several tools are collated and adjusted to nanobody case - IgFold for structure prediction, HDOCK for docking and ABDESIGN, DiffAb and dyMEAN for backbone/sequence prediction.
- They chiefly perform computational validation showing the performance on the RMSD/DockQ (re-docking) and the amino acid recovery. Results indicate that focusing on nanobodies provides benefit.
- The entire pipeline can be used for de novo design and optimization.
2025-03-21
NanoGen: A High-affinity Nanobody Generation Model with Guided Diffusion
- protein design
- nanobodies
- Novel method for nanobody sequence re-design using quite a small network.
- The model was pre-trained using a large-scale collection of nanobody sequences from the INDI dataset, heavy-chain antibody sequences from the OAS, and antibody complex structures from SabDab. For fine-tuning, affinity data was generated by with 17,500 nanobody–antigen interaction data points—7,500 generated via the ANTIPASTI model and 10,000 through random pairing—with a CD45 patent dataset used for testing. So all computational predictions are not real affinity points.
- NanoGen uses a two-stage training framework with a shared encoder-decoder architecture based on CNN layers that learns sequence patterns via a Masked Language Modeling task. During generation, a guided discrete diffusion process, augmented with Discrete Bayesian Optimization, is employed to refine the sequence outputs for enhanced binding affinity.
- The model was tested using sequence recovery (REC) and binding affinity improvement (pKD improvement). Benchmarking involved comparing NanoGen against baseline models such as ESM-2 650M, AbLangHeavy, and nanoBERT under both random masking and CDR-specific masking strategies on the CD45 patent dataset.
2025-03-21
Nanobody engineering: computational modelling and design for biomedical and therapeutic applications
- binding prediction
- nanobodies
- Review on computational methods applied to nanobodies.
- The review covers databases, modeling and design methods.
- Much room is given to conformational sampling with molecular dynamics
- They highlight a special class of nanobodies, quench-bodies (Q-bodies) that can also detect small molecules alongside normal proteins.
- The focus presented is chiefly on binder design, rather than fine-tuning other biophysical properties.
2025-02-03
Benchmarking Inverse Folding Models for Antibody CDR Sequence Design
- generative methods
- protein design
- nanobodies
- Benchmarking of sequence design methods that are structure-conditioned
- ESM-IF, LM-Design, ProteinMPNN and AntiFold were benchmarked.
- On sequence recovery, AntiFold beats others on antibodies, but LM-Design is better when VHHs are considered.
- AntiFold makes minimal use of the antigen information.
- ESM-IF and ProteinMPNN have some weak correlation with affinity data.