Computational Antibody Papers
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2025
TitleKey points
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
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.
- One of the first studies showing that introducing structure to protein language models, improves the predictive ability.
- They fed ProteinMPNN (structural) inputs to ESM-1B to show that it improved recovery as opposed to using ESM-1B mask alone.
- To marry ProteinMPNN and ESM-1B they use an ‘adapter’. Adapters in machine learning are lightweight modules that modify or extend a model’s functionality without retraining all parameters; in LM-DESIGN, a structural adapter integrates structural information into protein sequence predictions by bridging the structure encoder and a pretrained language model (pLM).
- LM-DESIGN benchmarked against state-of-the-art protein inverse folding models, including ProteinMPNN, PiFold, GVP-Transformer, Structured Transformer, and GVP, while utilizing pretrained language models such as ESM-1b 650M and the ESM-2 series.
- LM-DESIGN was evaluated on CATH 4.2 and CATH 4.3 datasets using sequence recovery rates and perplexity, compared against baselines.
- LM-DESIGN outperformed individual models, improving sequence recovery by 4-12% points, surpassing ProteinMPNN and PiFold.
2025-02-17
IgGM: A Generative Model for Functional Antibody and Nanobody Design
- protein design
- generative methods
- Novel method to design antibodies de novo.
- Architecturally, it is a mix of language models, diffusion and structure prediction methods.
- Training happens by noising diffusion, firstly perturbing structure and making the model get it right and afterwards doing the same thing for sequences.
- After these two steps the model is distilled into a consistency model. This results in a model that can get the final coordinates/sequence in a single step rather than iterative denoising.
- Method achieves comparable accuracy to many methods out there, such as DiffAb, dyMEAN and others.
- On docking, the best performance is in the order of 4A iRMSD when using an AlphaFold3 antibody model - so still some challenges remain.
- No wetlab validation.
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.