Computational Antibody Papers

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2024
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  • 2024-11-22

    Antibody Library Design by Seeding Linear Programming with Inverse Folding and Protein Language Models

    • developability
    • Novel method for antibody library design.
    • Given a structural complex, they employ AntiFold and Protbert to explore the ‘fitness’ space, to generate a set of mutants.
    • To achieve multi-parameter optimization they use linear programming, rather than neural nets that are in vogue.
    • The method was not validated experimentally, rather using computational proxies.

  • 2024-11-14

    On the humanization of VHHs: Prospective case studies,experimental and computational characterization of structural determinants for functionality

    • developability
    • databases
    • Authors perform humanization of VHHs and generate experimental data confirming their designs.
    • The protocol involves grafting CDRs1-3 and then systematically modifying Hallmars/Verniers and others to make them more human.
    • Positions 49 and 50 (e.g., E49G, H50L in VHH1): These were generally well-tolerated, allowing for humanization without major impact on binding affinity or stability.
    • Position 52 (e.g., S52W in VHH2): In some cases, changing this residue even improved affinity.
    • Position 42: Humanizing residue F42 to a more human-like amino acid (e.g., F42V) in VHH2 led to a significant reduction in binding affinity. This residue plays a key role in stabilizing the CDR3 loop through interactions with other regions, making it essential for maintaining the bioactive conformation.
    • Position 52 (in some contexts): In VHH1, the mutation G52W led to a loss of binding due to steric clashes, demonstrating that this position can be critical depending on the structural context.
    • Measured binding affinities, expression yields, and purities of humanized variants. Crystal structures confirmed effects of humanization on binding; non-canonical disulfides stabilize CDR3

  • 2024-11-14

    Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations

    • developability
    • binding prediction
    • Pilot single-shot computational antibody design, where known binders were taken and new ones computationally generated on their basis, maintaining binding with a good developability profile.
    • The pipeline starts with known binders to the SARS-CoV-2 RBD
    • Novel binders were generated using a combination of computational approaches, including: Observed Antibody Space (OAS): Paired and unpaired sequences from the OAS dataset to identify antibody candidates within a certain edit distance from the starting antibodies. Inverse Folding Model (AbMPNN):generated new antibody sequences maintaining structural features compatible with binding to the SARS-CoV-2 RBD. ESM: guided the mutation of sequences to retain or improve binding affinity while enhancing developability.
    • The developability properties were assessed using Rosetta scoring to evaluate antibody stability and interface energetics, alongside TAP.
    • Experimental methods for screening included size-exclusion chromatography (SEC) to assess aggregation propensity and differential scanning fluorimetry (DSF) for thermal stability. Antibodies that passed these criteria were deemed suitable for development​.
    • Success rate of the method: The pipeline demonstrated a success rate of 54% for generating binding antibodies that retained affinity against escape mutations on the SARS-CoV-2 RBD.
    • Authors tested RFDiffusion for the design task but with poor success rate - albeit not the antibody-fine tuned version it appears, that should work better.

  • 2024-11-14

    An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody

    • developability
    • databases
    • Novel humanization method, employing diffusion.
    • The model first learns the diffusion of a human sequence (with CDRs intact). The framework residues are diffused back. The network is then fine-tuned on mouse sequences.
    • There are two flavors of the model - one nanobody, another antibody.
    • They curate a great dataset from patents with over 300 sequences of paired humanized/native seuqences.
    • They demonstrate in silico and in vitro that their designs make sense.

  • 2024-11-14

    Transfer learning to leverage larger datasets for improved prediction of protein stability changes

    • non-antibody stuff
    • developability
    • Novel method to predict stability of proteins, based on ProteinMPNN.
    • The method employs ProteinMPNN embeddings with a stability prediction module to gauge the effect of single point mutations on protein stability.
    • The Stability prediction module is composed of the light attention module (that figures out which parts of the embeddings should be upvoted) followed by shallow multi layer perceptron.
    • For training/evaluation they employed Megascale and Fireprot datasets with measured protein stability data. Though after much pre-processing because the original datasets either contained many unreliable data points, or there was a risk that the mutations would change the structure too much.
    • Ablations show that all the elements of the network are important and bring something to the prediction, with ProteinMPNN having quite some predictive power out-of-the box.

  • 2024-10-08

    Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains

    • developability
    • Novel humanization software, allowing for rapid re-design of both heavy and light chains.
    • Unlike other tools such as Hu-mAb and Sapiens, which humanize heavy and light chains separately, Humatch jointly humanizes both chains, improving stability and reducing the risk of immunogenic epitopes between chains.
    • Humatch consists of three lightweight Convolutional Neural Networks (CNNs). Each CNN is trained for a specific task: one for heavy chains (CNN-H), one for light chains (CNN-L), and one for assessing natural heavy/light chain pairing (CNN-P). The CNNs are designed to output multiclass predictions for identifying human V-genes and classifying chain pairings.
    • The CNNs were trained on data from the Observed Antibody Space (OAS), which includes millions of human and non-human antibody sequences.
    • Humatch's performance was measured through precision-recall and ROC-AUC metrics, achieving near-perfect accuracy in classifying human and non-human sequences. Performance was also tested by humanizing 25 precursor antibodies and comparing the mutations with experimentally derived humanized versions, showing high overlap (77-82%) with experimental designs.

  • 2024-10-08

    Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen

    • developability
    • generative methods
    • Authors demonstrate that using scores from DeepAb one can sort mutations in an antibody that improve affinity and a series of other properties.
    • The authors used the DeepAb structure prediction mode model to rank mutations based on their impact on structure prediction confidence, leading to the design of 200 novel anti-hen egg lysozyme (HEL) antibody variants.
    • Single-point mutations from a deep mutational scanning (DMS) dataset (Warszawski et al.) were combined into multi-mutation variants (up to 7 mutations), and these variants were selected based on DeepAb scores for experimental testing.
    • The designed variants were expressed and tested for thermostability, colloidal stability, and binding affinity to HEL.
    • Large percentage of the variants showed improved thermostability (91%) and affinity (94%), with 10% showing significant increases in binding affinity.
    • A subset of 27 high-performing variants was further tested for developability characteristics, including nonspecific binding, aggregation propensity, and self-association, ensuring their practical usability.

  • 2024-09-04

    p-IgGen: A Paired Antibody Generative Language Model

    • generative methods
    • language models
    • developability
    • Novel generative model for antibody sequences that supports Vh/Vl pairing and generation of developable sequences.
    • Three models were created, IgGen (unpaired model), p-IgGen (unpaired fine-tuned on pairs) and developable p-IgGen (paired fine-tuned on developable sequences).
    • They used ca. 250m unpaired sequences and 1.8m paired sequences for training.
    • The model is based on GPT-2 but with rotary position embedding.
    • Developable sequences were defined as structural models of the 1.8m that had good TAP metrics (900,000 in total).
    • The model is much smaller than many of the models out there, (17m params), so it is more lightweight in training and application.
    • The model performs better on immunogenicity prediction than other models but worse on expression prediction.

  • 2024-09-04

    Antibody Humanization via Protein Language Model and Neighbor Retrieval

    • language models
    • developability
    • Novel humanization protocol employing language models and large-scale repertoire data.
    • Human OAS and germline sequences are embedded using ESM2.
    • K-nearest neighbors algorithm is then used to introduce mutations into the ESM-2 embedded query sequence coming from closest functional neighbors in the ESM2-embedded OAS+germlinse space.
    • Results of humanized abs are validated experimentally via ELISA.

  • 2024-08-28

    AntiBARTy Diffusion for Property Guided Antibody Design

    • language models
    • generative methods
    • developability
    • Novel language model AntiBARTy with demonstration of how to use it to diffuse novel antibodies with favorable solubility properties.
    • The core model is a BART-based transformer, with 16m parameters.
    • It was firstly trained on all human heavy and light chains from OAS (254m heavies and 342m lights <- yes, more lights). This was followed by fine tuning on the higher quality paired data from OAS.
    • The diffusion model was based on U-net (CNN used for segmentation of medical images), totaling 3m parameters.
    • They define low and high solubility classes as predicted by protein-sol on paired OAS, with roughly 20k samples for each class.
    • Overall, one can sample from multivariate to get a vector in Antibarty latent space and use it to get an antibody sequence that is either high or low protein-sol predicted solubility.