Computational Antibody Papers

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  • 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.

  • 2024-08-28

    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.

  • 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.

  • 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.

  • 2024-07-30

    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.

  • 2024-07-23

    Baselining the Buzz Trastuzumab-HER2 Affinity, and Beyond

    • databases
    • protein design
    • generative methods
    • binding prediction
    • Novel dataset of 0.5m anti-trastuzumab antibodies together with benchmarking of affinity classification methods.
    • They generated a dataset of ~500,000 anti-Her2 trastuzumab variants by modifying the CDR-H3. The binding affinity is divided into high/medium/low with reasonably even split (178,160, 196,392, and 171,732 respectively).
    • They split their dataset into positives and negatives by putting medium and low binders into the negative set.
    • They contrast their dataset with this from Mason et al. (~39k variants vs theirs 0.5m), to show that in a small (relatively) number of cases, binders in one set can be labeled as negative in another.
    • They test that the predictor developed by Mason et al. for binding/non binding classification works as intended on the novel 0.5m dataset. Likewise their model trained on Mason data and tested on their ds (and vice versa) has predictive power. It does but to a much lesser extent than training on data from the same experiment.
    • As methods to benchmark they used FLAML https://arxiv.org/abs/1911.04706, CNN and EGNN.
    • CNN and FLAML are top performers, but CNN performs well on the small data (signal starting with ~170 sequences).
    • Performance drops radically when train/val splits are done with respect to the clonotype.
    • THey tested AbLang, ProteinMPNN, ESM and Blosum on their ability to generate binding trastuzumab variants. As control they also randomly generated sequences. They observed the following percentages of sequences exhibiting CNN-HER2-max binding probabilities greater than 90%: 13% for Random, 26% for BLOSUM, 27% for AbLang (when masking all ten residues simultaneously), 29% for AbLang (when masking one residue at a time), 19% for ProteinMPNN, and 30% for ESM (when masking one residue at a time), respectively.

  • 2024-07-09

    Robust deep learning–based protein sequence design using ProteinMPNN

    • protein design
    • generative methods
    • ProteinMPNN is a framework that receives a backbone and generates the most probable sequence that could fit it. This is firmly for protein design where you have a binding interface or a structure that you ‘need to fit’.
    • Using distances is better than using dihedral angles: This resulted in a sequence recovery increase from 41.2% (baseline model) to 49.0% (experiment 1), see Table 1 below; interatomic distances evidently provide a better inductive bias to capture interactions between residues than dihedral angles or N-Ca-C frame orientations
    • We found that training models on backbones to which Gaussian noise (std=0.02Å) had been added improved sequence recovery on confident protein structure models generated by AlphaFold (average pLDDT>80.0) from UniRef50, while the sequence recovery on unperturbed PDB structures decreased.
    • They employed a scaffold made by Rosetta, that was supposed to house a peptide recognizing some protein. The rosetta designs failed, but when they used ProteinMPNN to get sequences for it, they bound even better than the original bare-peptide.

  • 2024-03-13

    Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization

    • language models
    • generative methods
    • Proposing a Bayesian scheme to optimally select generated antibodies from a previously introduced language model (GLM).
    • They use the 1B GLM-AB model from BioMap. Training involves variation on MLM that masks entire spans of sequence.
    • The entire point is how to ‘select’ better antibodies according to some unknown ‘fitness function’. If you only get a few experimental data points at a time to evaluate f, you’d better make them count. Their combination of bayesian scheme and a language model optimizes how the ‘next generated sequence points’ are picked so that best approximation to f is reached.
    • They use (computational simulation) Absolut! framework rather than wet lab data.