Computational Antibody Papers

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  • 2025-03-11

    Generation of antigen-specific paired heavy-light chain antibody sequences using large language models

    • generative methods
    • New antigen-specific language model
    • Authors curated a dataset of antigen-specific antbiody seqeunces and fine tuned generic protein language model (don’t know which one) to it.
    • Dataset appears to be comprised mostly of plabdab and cov-abdab so very biased towards covid.
    • Antibodies are generated by prompting the model with the antigone sequence and generating the antibody on the basis of it.
    • Authors tested the generated antibodyes in the lab, including COVID antigens but also some that were less prevalent in the training set and they found binders.

  • 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.

  • 2024-11-22

    Structure Language Models for Protein Conformation Generation

    • generative methods
    • language models
    • non-antibody stuff
    • Novel model for sampling the structural space of proteins - applied to nanobodies.
    • Protein structures are encoded into latent tokens using a discrete variational auto-encoder (dVAE), which captures residue-level local geometric features in a roto-translation invariant manner.
    • The model combines a dVAE encoder-decoder with a Structure Language Model (SLM) to model sequence-to-structure relationships.
    • One can sample alternate conformations by providing an amino acid sequence as input and use the SLM to generate latent tokens representing potential conformations. Decode these latent tokens back into 3D structures using the dVAE decoder to generate diverse structural ensembles.
    • Altogether, though they develop ESMdiff in the context of the paper, it is rather the structural language in the context of the broader framework of language models that forms the core of the message, rather than any single method.
    • The model was evaluated on tasks like generating equilibrium dynamics, fold-switching, and intrinsically disordered proteins, using metrics like JS-divergence, TM-score, and RMSD. It outperformed existing methods in speed and accuracy, generating structures 20-100× faster.

  • 2024-10-24

    ESM3- Simulating 500 million years of evolution with a language model

    • language models
    • generative methods
    • An evolution of the ESM model family that scales up in terms of parameters, data, and computational power compared to ESM2, which allows it to improve on sequence, structure, and function representations of proteins.
    • ESM3 is a multimodal, bidirectional transformer that models sequence, structure, and function using discrete token representations for each modality. It merges these representations into a single latent space and is trained with a masked language model objective, allowing it to generate and predict across different modalities.
    • ESM3's largest model has 98 billion parameters.
    • The model was trained with 1.07 × 10²⁴ floating point operations (FLOPs) over a dataset of 771 billion tokens from 2.78 billion proteins.
    • Structural tokens in ESM3 are encoded by a discrete autoencoder that compresses three-dimensional protein structures into a sequence of discrete tokens. This is done by encoding local atomic environments around each amino acid and representing them in a simplified form that captures geometric properties.
    • There are a total of 4096 structural tokens to be had.
    • The structural autoencoder tokenizes protein structures by encoding local neighborhoods around each amino acid into discrete tokens. It uses a geometric attention mechanism that operates in local reference frames, based on bond geometry. This mechanism encodes and reconstructs the atomic structure, supervised by a geometric loss that preserves distances and orientations of bonds and atoms.
    • ESM3 can be used to generate novel sequences/proteins. generates protein sequences and structures by prompting the model with sequence or structural tokens. It uses iterative sampling, starting from a masked context, where tokens are predicted and unmasked progressively until a full sequence or structure is generated. This allows the model to create novel proteins that respect the given prompts or constraints.
    • The model was verified experimentally by generating novel proteins, including a green fluorescent protein, that was synthesized and tested for fluorescence in laboratory conditions. The novel protein had a sequence identity of 58% to the nearest known fluorescent protein.

  • 2024-10-24

    Benchmarking antibody generative models

    • generative methods
    • language models
    • binding prediction
    • Study evaluates a number of generative models on datasets of antibodies with reported affinities.
    • The methods tested were: MEAN, dyMEAN, IgBLEND, Ablang, Ablang2, AntiBerty, ESM, Antifold, ESM-IF, AbX, Diffab + their own version of Diffab.
    • Datasets used were the Absci HER2 datasets (100s of binders) and a number of datasets with tens of binders each.
    • All models have some correlation with the affinity data, though weak.
    • Adding epitope information is not a game changer, showing that information that is mostly captured is fitness of antibody first and antigen second, if at all.
    • Employing structural information helps as compared to purely sequence approaches.

  • 2024-10-08

    Accurate antibody loop structure prediction enables zero-shot design of target-specific antibodies

    • generative methods
    • structure prediction
    • Authors describe how using a structure predictor one can re-design the binding site, to maintain binding.
    • They use a proprietary GaluxDesign method, the method achieves 1.4 Å Ca RMSD in predicting CDR-H3 loop structures, leveraging a unique scoring metric (G-pass rate) that assesses both confidence and structural consistency for antibody design.
    • The method outperforms AlphaFold 2.3, ABlooper, and ImmuneBuilder in predicting CDR-H3 loop structures, with significantly lower RMSD values (1.4 Å compared to 2.4-3.7 Å), particularly on a more challenging, time-separated dataset.
    • The binding propensity to HER2 was evaluated using a large mutant library and calculated via the G-pass rate, outperforming AlphaFold's PAE-based scoring. The model showed strong discrimination with an AUROC of 0.758, compared to 0.529 for AlphaFold. The novel loop is scored using their metric (G-pass rate) in complex with Her2.
    • Novel antibody sequences were designed by predicting six CDR loops in antibody-protein complexes, using GaluxDesign models. These designs were experimentally tested, achieving high success rates, including a 13.2% success rate for HER2 antibody designs using yeast display methods.