Computational Antibody Papers

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experimental techniques
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2024
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  • 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.

  • 2024-03-13

    Language models enable zero-shot prediction of the effects of mutations on protein function

    • non-antibody stuff
    • language models
    • experimental techniques
    • They contrast ESM to some other language models and show that in zero shot fashion some correlations can be made with experimental measurements of variants.
    • They compare performance of ESM and DeepScan on 41 deep mutational scanning datasets collated in a single paper. They claim ESM has better overall correlations but it is not crystal clear from the graph and by their own admission by paired t-test.
    • They find that pretraining the data on Uniref30 gives worst performance. An ok performance is given for Uniref50 or Uniref70 with a dip again at Uniref100.
    • Binding sites have much higher conservation.
    • Core of the protein also appears to have lower conservation.