Computational Antibody Papers
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TitleKey points
- 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.
2025-07-25
AntiDIF: Accurate and Diverse Antibody Specific Inverse Folding with Discrete Diffusion
- generative methods
- AntiDIF, a diffusion-based inverse folding method specialized for antibodies, built on the RL-DIF framework.
- It is trained using antibody-specific data (from SAbDab and OAS) to generate diverse and accurate antibody sequences for a given backbone structure.
- Unlike prior methods like AntiFold, which trade off diversity for recovery, AntiDIF achieves a better trade-off: it produces substantially higher sequence diversity across CDRs while maintaining comparable or higher sequence recovery.
- Forward folding (via ABodyBuilder2) confirms that AntiDIF's sequences fold into structures that match the native antibody backbones with low RMSD, demonstrating structural plausibility.
- Introduced a novel model, Chai-2, that shows over 100× improvement in de novo antibody design success rates compared to prior methods.
- The model is prompted with the structure of the target, epitope residues, and desired antibody format (e.g., scFv or VHH).
- Benchmarking was performed on 52 antigens that had no known antibodies in the PDB, ensuring evaluation on novel, unbiased targets.
- Generated antibodies were structurally and sequentially dissimilar to any known antibodies, indicating that Chai-2 designs novel binders, not memorized ones.
- For VHH (nanobody) formats, the model achieved an experimental hit rate of 20%, validated in a single experimental round.
2025-06-24
Benchmark for Antibody Binding Affinity Maturation and Design
- binding prediction
- language models
- generative methods
- databases
- Benchmark of machine learning models for antibody-antigen binding affinity.
- A curated dataset of over 150,000 antibody-antigen complexes with associated experimental affinity values is compiled from literature.
- The benchmark compares a wide range of model types: language models, inverse folding models, graph-based, and diffusion-based generative models.
- Inverse folding models that are globally structure-aware perform best.
- General protein models like ESM-IF and ProteinMPNN outperform antibody-specific models such as AntiFold, DiffAb, and dyMEAN.
- Surprisingly, ESM-3 underperforms relative to ESM-IF, despite incorporating structural signals and improving upon earlier ESM models.
2025-06-24
REPURPOSING ALPHAFOLD3-LIKE PROTEIN FOLDING MODELS FOR ANTIBODY SEQUENCE AND STRUCTURE CO-DESIGN
- generative methods
- protein design
- Novel method to design antibodies based on boltz-1.
- They added a sequence head to boltz-1 to perform simultaneous sequence/structure co-design.
- They employed data from SAbDab to fine-tune boltz-1 on antibody-antigen complexes.
- They compared to dyMEAN and DiffAB looking at amino acid recovery, RMSD and Rosetta InterfaceAnalyzer energy - their model does better on these computational benchmarks.
2025-06-05
Adapting ProteinMPNN for antibody design without retraining
- protein design
- generative methods
- Novel method to bias ProteinMPNN for antibody design, without modifying model weights.
- Logits from protein-general ProteinMPNN and antibody-specific AbLANG are added and softmaxed. Addition of AbLANG is supposed to push the model into the antibody-acceptable space.
- On in-silico experiments ProteinMPNN+AbLang outperformed ProteinMPNN alone and rivalled antibody-specific AbMPNN.
- Authors designed 96 variants of Trastuzumab CDR-H3 using ProteinMPNN, AbLang and ProteinMPNN+AbLang each. AbLANG and ProteinMPNN produced 1 and 3 successful variants respecitively (both out of 96) whereas their combination produced 36 successful variants.
- None of the variants were better variants than WT Trastuzumab.
2025-06-05
AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks
- developability
- generative methods
- protein design
- Novel generative modeling framework (AbBFN2) using Bayesian Flow Networks (BFNs) for antibody sequence optimization.
- Trains on sequences from Observed Antibody Space (OAS) combined with genetic and biophysical annotations, leveraging a denoising approach for both conditional and unconditional sequence generation. Targets include optimizing Therapeutic Antibody Profiler (TAP) annotations.
- Computationally validated for germline assignment accuracy, species prediction (humanness), and TAP parameter optimization.
- Combines multiple antibody design objectives into a unified, single-step optimization process, unlike existing software methods which are typically specialized for individual tasks.
2025-03-31
IgCraft: A versatile sequence generation framework for antibody discovery and engineering
- generative methods
- language models
- Novel generative model for antibodies that allows one to fill in, inpaint inverse fold etc.
- The model employs Bayesian Flow Networks which is somewhat similar to diffusion.
- The model is trained on data from OAS - unpaired data as a first pass and paired data as a second pass.
- Models are benchmarked on a range of computational metrics, chiefly sequence recovery (for infiling/inverse folding).
- Developability is checked by computational prediction of solubility (CamSol) and humanness (AbNativ)
2025-03-21
How well do contextual protein encodings learn structure, function, and evolutionary context?
- generative methods
- protein design
- Novel inverse folding algorithm, studying the effect of pretraining on the effectiveness of Antibody design
- Authors check multiple inverse folding regimens, pretraining on general proteins, ppi interfaces and antibody-antigen interfaces and likewise finetuning on these.
- They only use the backbone atoms (N,C,Ca), with special provisions for Cb.
- They mask portion of the sequence and have the model guess its amino acids.
- The 37% recovery at 100% masking appears slightly lower than the same feat for proteinMPNN.
- Pretraining on antibodies still holds a signal towards antibody-antigen complexes, showing the power of such pre-training.
2025-03-11
Fast and accurate antibody sequence design via structure retrieval
- databases
- generative methods
- structure prediction
- Inverse folding and thus antibody design via database search.
- Authors train a vector retrieval database on SAbDab. In this way for a single sequence one can figure out where it falls structurally.
- They benchmark against state of the art inverse folding tools such as AbMPNN, AntiFold, ProteinMPNN and ESM-IF - their tools comes on top in terms of sequence retrieval.
- The database search is orders of magnitude faster than the state of the art inverse folding tools.
- They compare IgSeek versus FoldSeek - their tool gets a higher accuracy in sequence retrieval, for most CDRs, but CDR-H3. Therefore FoldSeek seems like a very good choice alongside IgSeek for such a database-driven inverse folding protocol.