Computational Antibody Papers

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generative methods
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2025
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  • 2025-02-20

    Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences

    • generative methods
    • ngs
    • Novel generative antibody method, CloneLM/CloneBO, following clonally plausible evolutionary paths.
    • They train CloneLM, an autoregressive language model, on antibody clonal family data from the OAS. There were two separate models for heavy and light sequences. They use FastBCR to call clonal families.
    • CloneLM generates new clonal families by conditioning on a given antibody sequence. They use a martingale posterior approach to ensure sampled sequences follow plausible evolutionary paths. So it takes antigen into account, but only by the virtue of the clonal family.
    • For benchmarking they train a language model oracle on a real human clonal family and use it as a simulated fitness function.
    • They further perform training on affinity and stability data to generate oracles for these and show that the newly generated sequences can be made to be more stable/have higher affinity.

  • 2025-02-17

    Structure-informed language models are protein designers

    • generative methods
    • protein design
    • 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.

  • 2025-02-03

    De novo design of epitope-specific antibodies against soluble and multipass membrane proteins with high specificity, developability, and function

    • binding prediction
    • generative methods
    • Novel method to design antibodies in silico with experimental validation.
    • The actual computational method is not disclosed.
    • The computational method takes target sequence/structure and constraints where the antibody should bind. The structure and sequence are then produced.
    • Method can generate nanomolar grade binders.
    • The main interesting take-away is test-time compute. By feeding the answers of the model back to itself, it produces better binders and does not compromise on diversity of the designs.