Computational Antibody Papers

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  • 2024-08-21

    Training data composition determines machine learning generalization and biological rule discovery

    • binding prediction
    • Authors investigate the role of the negative/positive binding data composition on predictive power of ab-ag interaction prediction.
    • They employ Absolut! Framework to generate synthetic binding data.
    • For binding prediction they use a 10 hidden neuron fully connected network.
    • CDR-H3 is used as a proxy for binding for computational expediency.
    • Altogether, if data in positive and negative are quite similar, there is better chance to out of distribution generalization.

  • 2024-08-21

    ABodyBuilder3: Improved and scalable antibody structure predictions

    • structure prediction
    • language models
    • Updated version of the popular ABodyBuilder2 program to model antibodies, that is more efficient.
    • Unlike ABB2 that did not employ language model embeddings, ABB3 does (ProtT5 to be precise).
    • Instead of using several models and aggregating the results for error prediction, they train pLDDT within the model.
    • ABB3 achieves CDR-H3 RMSD in the ballpark of 2.4Å whereas the previous version in the region of 2.5Å.

  • 2024-08-15

    Active learning for affinity prediction of antibodies

    • binding prediction
    • experimental techniques
    • An active learning framework is proposed to efficiently identify antibody mutations that enhance binding affinity, minimizing wet-lab experiments.
    • From the paper: “Active learning is a framework from experimental design that focuses on making informed decisions about which experiments to perform next”. That’s important these days to effectively generate prediction first data to maximize the effectiveness of models.
    • Bayesian optimization is used with relative binding free energy (RBFE) methods to iteratively propose and evaluate new antibody sequences, improving binding affinity predictions.
    • Various encoding schemes, including one-hot, bag of amino acids, BLOSUM, and AbLang2, are tested. The best performing methods are identified through validation with pre-computed data.
    • The study uses two RBFE methods: NQFEP for accurate but costly simulations, and Schrödinger Res Scan for faster but less precise results. The active learning loop consistently finds better binding sequences using these methods.
    • AbLang2 encoding with the Tanimoto kernel consistently outperformed other methods in the validation phase, indicating its effectiveness in predicting improved binding affinities.
    • NQFEP method provides more accurate but computationally expensive simulations, while the Schrödinger Res Scan offers quicker but less precise results. Despite the higher computational cost, the NQFEP method might be preferable when high accuracy is crucial.

  • 2024-08-15

    EuDockScore: euclidean graph neural networks for scoring protein-protein interfaces

    • docking
    • Novel docking score based on graph model and language model embeddings, with an antibody-specific variety.
    • It is based on the Equiformer models, giving the structure an equivariant representation. With respect to previous work, authors moved from atom to residue-level representations, input complexes rather than single chains and provide NLP embeddings.
    • They employ DistilBert for embeddings, training it on interaction data from BioGrid.
    • The antibody specific model is trained on antibody data downloaded from ABDB to distinguish native from non-native poses after local docking with RosettaFold.
    • The antibody-specific model is in fact heavy-antigen as the authors did not see benefit in the light-only model and having it three-way heavy-light-antigen was too computationally expensive.
    • Though the antibody-specific scoring method has predictive power in distinguishing native from non-native poses.
    • The antibody-specific model did not outperform AF2-multimer, however authors note that it has predictive power that can be harnessed to rescoring AF2-multimer outputs.
    • The code is available in https://gitlab.com/mcfeemat/eudockscore

  • 2024-08-15

    A generative foundation model for antibody sequence understanding

    • generative methods
    • language models
    • Authors introduce a large antibody-specific language model, Fabcon (2.4B) that demonstrably improves on predicting antibody specificity.
    • Model is based on the Falcon LLM and is trained no the CLM objective (predict next amino acid going N-to-C terminal)
    • The model was trained on paired (2.5m) and unpaired (821m) data from OAS.
    • The pre-trained model was tested on its ability to fine tune on binder prediction using three datasets anti-her2, anti-sars-cov2 and anti-il6.
    • When comparing against multiple other models on the binders prediction, the largest Fabcon model fares best, showing the benefit of overparametrization.
    • Since the model was trained on CLS objective, it can be used for sequence generation, producing sequences that are very human-like as compared to human PBMCs.

  • 2024-08-15

    De novo antibody design with SE(3) diffusion

    • protein design
    • 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

    Digger: directed annotation of immunoglobulin and T cell receptor V, D, and J gene sequences and assemblies

    • annotation/numbering
    • Novel tool to automatically annotate immunoglobulin (and t-cell) genes from assemblies.
    • They compared the human manual annotations from IMGT to what they identify and they were in close agreement for functional and open reading frame genes.
    • By automating annotation, strict rules can be enforced avoiding manual curation errors.
    • Software is available at: https://github.com/williamdlees/Digger

  • 2024-07-30

    Fast and accurate modeling and design of antibody-antigen complex using tFold

    • binding prediction
    • structure prediction
    • docking
    • Update on tfold-AB including modeling of the complex with the antigen, with applications to virtual screening.
    • They mostly use SABDAB/Covabdab as reference datasets.
    • The modeling happens by generating antibody & antigen features supplemented by a large language model, followed by flexible docking.
    • For antigen feature generation, they use AF2
    • They are training on several tasks simultaneously, ab structure prediction, complex prediction etc. making it a multi-task training.
    • On docking their method achieves DockQ 0.217 vs AlphaFold-Multimer DockQ score of 0.158 - that is global docking.
    • Whe local docking information is given, constraining paratope/epitope sites, their algorithm achieves DockQ of 0.416.
    • They demonstrate that filtering antibodies by their predicted modeling confidence score gives moderate enrichment against PD1 and Sars-cov-2 antigens, showing promise for virtual screening.

  • 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.