Computational Antibody Papers

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  • 2025-02-20

    A comparison of antibody–antigen complex sequence-to-structure prediction methods and their systematic biases

    • binding prediction
    • Benchmark of antibody-antigen docking and co-folding methods.
    • Methods compared were AF2-multimer, RoseTTAfold, AbAdapt, SnugDock and ClusPro.
    • ClusPro was given either AlphaFold or AbodyBuilder2 models of an antibody - it performed better when given ABodyBuilder2 models.
    • AlphaFOld multimer performed the best overall - however the top pose being close to correct only 19% of the time.
    • It appears that the best models reproduce correct side-chain binding geometries from the PDB.

  • 2025-02-17

    DyAb: sequence-based antibody design and property prediction in a low-data regime

    • binding prediction
    • language models
    • Method to employ low-N data for biologic engineering.
    • Assuming we have a dataset of ~100 affinity data points, we can choose (100 choose 2) pairs where we know which one has a larger readout than the other (e.g. stronger affinity) giving combinatorially larger amount of data points to train on.
    • The architecture used is CNN on top of a language model.
    • Benchmarked on three internal campaigns, Il6, EGFR and an undisclosed target.

  • 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.

  • 2025-01-21

    Antibody affinity engineering using antibody repertoire data and machine learning

    • binding prediction
    • ngs
    • Novel experimental/computational workflow that demonstrates how little data might be needed to develop antibody affinity predictors.
    • Mice were immunized with hen egg white lysozyme and via computational procedure of clustering with known binders 35 antibodies were characterized together with their affinities.
    • These 35 antibodies were used to train the methods: Gaussian Process (GP) models with Matern and RBF kernels, Kernel Ridge Regression (KRR), Random Forest (RF) and Linear Regression (used as a baseline).
    • Seed sequences were point or double-mutated and their affinity predicted using GP (that performed the best). Eight mutants predicted to span the whole range of affinities were selected for experimental testing and they had very good agreement with the predictions.

  • 2024-11-22

    AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity

    • binding prediction
    • language models
    • AlphaBind, a deep learning model designed to optimize antibody sequences, by leveraging large-scale pre-trained affinity datasets and fine-tuning on experimental data.
    • AlphaBind was pre-trained on a dataset of 7.5 million antibody-antigen affinity measurements, which includes data from yeast display systems and diverse antibody libraries obtained from multiple experimental sources, focusing on quantitative affinity measurements.
    • The model utilizes transformer-based architecture with protein sequence embeddings generated using ESM-2nv (Evolutionary Scale Model) embeddings. The model consists of 4 attention heads, 7 layers, and about 15 million parameters.
    • The model fine-tunes on specific antibody-antigen systems using AlphaSeq data, then performs stochastic greedy optimization by generating sequence mutations (using ESM-2nv logits) to explore sequence space and predict binding affinity. This process generates thousands of candidate sequences, which are filtered based on affinity predictions and developability metrics before in vitro validation.
    • The novel sequences based on three systems were verified in experimentally.

  • 2024-11-22

    Prediction of Paratope–Epitope Pairs Using Convolutional Neural Networks

    • binding prediction
    • epitope prediction
    • Novel epitope-paratope prediction method, ImaPEp.
    • The method projects the epitope and paratope onto 2D images and then uses a ResNET to predict the interacting vs non-interacting pairs.
    • Negative set was done by pairing non-cognate antibody-antigen pairs, rotations etc.
    • The method was not benchmarked against epitope predictions tools, that arguably do not take pairs into account, but against docking tools, scoring 13 out of 18 methods tested.

  • 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.

  • 2024-10-24

    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.

  • 2024-10-24

    Pretrainable geometric graph neural network for antibody affinity maturation

    • binding prediction
    • protein design
    • GearBind - Novel framework to predict the effect of mutations on an antibody-antigen complex
    • The architecture is graph-based, trained in a contrastive fashion on real atoms and their surroundings versus randomly samples (from rotamer libraries) atoms within the same environment. They use the real proteins from CATH for this purpose. The random points are serving as ‘negatives’ for contrastive learning whereas the real ones as positives.
    • The method shows improvement on previous datasets: SKEMPI and the Absci HER2 dataset.
    • The authors demonstrated the effectiveness of the method by performing in silico affinity maturation on two existing binders.

  • 2024-09-04

    Adapting protein language models for structure-conditioned design

    • language models
    • binding prediction
    • protein prediction
    • Novel language model incorporating structural information, with demonstrated experimental ability to improve design of therapeutic antibodies.
    • The new language model, ProseLM, builds upon Progen family of models from the same authors.
    • Structural information in the form of structural adapter layers after language model layers, encoding backbone and associated functional annotations.
    • Models with more parameters achieve much better perplexity. There is also some improvement by adding tangential context information such as ligands etc.
    • They trained an antibody-specific version of ProseLM, only on SABDAB data and it does much better on sequence recovery even than the larger models.
    • They use the model to propose mutations for Nivolumab ad Secukinumab, with mutations both in CDRs and Frameworks. THey used structures from the PDB as the basis for designs.
    • They found better binders, however if CDRs were re-designed the overall success rate of maintaining binding was lower (25% for Nivolumab) than when frameworks were redesigned (92%).