Computational Antibody Papers
Filter by tags
nanobodies
Filter by published year
All
TitleKey points
- 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.
2024-12-18
IgBlend: Unifying 3D Structures and Sequences in Antibody Language Models
- language models
- nanobodies
- Novel language model for antibodies, blending sequence and structural information.
- The model encodes sequence ‘as usual’ and uses GVP-GNN (like esm-if) for structural representation. Only the three backbone atoms (C,N,Ca) are taken per residue to get the structural representation.
- The data is a mix of sequence data and X-ray structures. The sequence datasets were modeled using ImmuneBuilder to increase structural coverage.
- The model has an MLM objective on sequence & structure with three losses - sequence only, sequence + structure and structure only.
- On sequence infilling IgBLEND performs better than other methods (e.g. AbLang, Nanobert), though arguably CDR-H3 predictions look very ‘close’ across the board.
- On inverse folding the method performs quite a stretch better with large gaps in CDR-H3 with notable improvements for nanobodies - that other methods like ESM-IF or AntiFold did not handle natively.
- They introduce a classifier for specificity (binary) for antibody-antigen complexes.
- They employ data from sdab-db, 47 antigens and 365 antibodies.
- They extend the positive pairs set by assuming nanobodies would bind antigens with high sequence similarity.
- They create a negative set by looking at similarity of antibodies and antigens and shuffling them if they are below similarity threshold to the complex.
- They employ the k-mer gappe scheme as their embedding of choice.
- They run a test on several embedding schemes versus several classification schemes (e.g. RF, SVM).
- The best combination, k-mer gapped embedding and RF achieve accuracy ~90%.