Computational Antibody Papers
Filter by tags
protein design
Filter by published year
All
TitleKey points
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.
2024-10-24
Pretrainable geometric graph neural network for antibody affinity maturation
- binding prediction
- protein design
- GearBind - Novel framework to predict the effect of mutations on an antibody-antigen complex
- The architecture is graph-based, trained in a contrastive fashion on real atoms and their surroundings versus randomly samples (from rotamer libraries) atoms within the same environment. They use the real proteins from CATH for this purpose. The random points are serving as ‘negatives’ for contrastive learning whereas the real ones as positives.
- The method shows improvement on previous datasets: SKEMPI and the Absci HER2 dataset.
- The authors demonstrated the effectiveness of the method by performing in silico affinity maturation on two existing binders.
2024-08-28
Unsupervised evolution of protein and antibody complexes with a structure-informed language model
- generative methods
- protein design
- Authors demonstrate that using inverse folding, one can affinity mature antibodies, confirmed experimentally.
- Authors employ ESM-IF as the inverse folding algorithm.
- They take two existing antibodies, bebletovimab and BD55-5840, both instrumental in COVID-19.
- They introduce all possible single point mutations to the Vh and Vl regions (about 4300). They pick the best perplexity for experimental characterization.
- The best perplexity ones have many framework mutations (bebletovimab 10/14 and BD55 5840 3/6). There was only one mutation to CDR-H3 in Bebletovimab.
- Inverse folding mother achieves much better performance when antigen is used as well.
- IgDiff - antibody-specific diffusion method to generate antibody-like coordinates.
- The method is the result of fine-tuning FrameDiff on ~150,000 antibody models (ABB2) from OAS.
- The method supports several design scenarios, such as generating the whole Vh/Vl, just the CDRs, CDR-H3 or light chain redesign.
- Several antibody coordinates that were generated using IgDiff and whose sequence was predicted using AbMPNN were successfully synthesized in the lab.
2024-07-30
AntiFold: Improved antibody structure design using inverse folding
- generative methods
- protein design
- Inverse folding method specific to antibodies.
- They employed ESM-IF as a base model for fine tuning.
- They made one pass through ABodyBuilder2 models of paired OAS sequences (~150k sequences) and then ~2000 crystal structures.
- They tested whether shotgun masking (random residues) is better than span-masking. Though shotgun performed better in general, span-masking is better in case the the entire CDRs are obscured (realistic case for design).
- AntiFold improves upon author’s earlier ab-specific inverse folding method AbMPNN (fine-tuned ProteinMPNN), 43 % vs 60% sequence recovery on CDR-H3.
- Authors took a handful of native structures, sampled sequences using different methods and modeled them using ABodyByuilder2 to see if the sampled sequences maintain the same fold. AntiFold achieves better (0.67) RMSD to the original backbone than AbMPNN (0.74) and ESM-IF (0.75).
2024-07-30
In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding
- protein design
- generative methods
- Inverse folding method for redesign of binding antibodies from crystal structure.
- IgDesign draws from LM-design which introduces structural conditioning of language models. They fine-tune ProteinMPNN on Sabdab to get an antibody specific IgMPNN. The structural embeddings from IgMPNN are then used in ESM2-3B. The method receives coordinates antigen & antibody but no CDRs, as these are designed.
- They experimentally validated the protocol on 8 antigens with co-complex in the PDB, some of which are given in the poster presenting the method: https://www.absci.com/antibody-inverse-folding/
- They design either the CDR-h3 only or all the heavy chain CDRs. They accuracy of designs to still bind after the CDR-H3 redesign is in the ballpark of 20/30% with two outliers with 70% and 90%. Success rate of redesign of all CDRs of heavy chain is lower but in some cases even comparable to the success rate of CDR-H3 redesign alone.