Computational Antibody Papers

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  • 2024-12-18

    Fast and accurate protein structure search with Foldseek

    • non-antibody stuff
    • Novel algorithm to perform structural search of proteins and at the same time introduces an innovative way to encode protein structures
    • It encodes protein structures as sequences over a 20-state 3Di alphabet, representing tertiary residue-residue interactions (Ca of neighboring residues) rather than backbone conformations, enabling faster sequence-based comparisons.
    • The 3Di alphabet and substitution matrix were trained on the SCOPe40 dataset (~11k structures), which consists of manually classified single-domain protein structures clustered at 40% sequence identity.
    • FoldSEEK is thousands of times faster than structural alignment tools like Dali, TM-align, and CE, being 184,600 times faster than Dali and 23,000 times faster than TM-align on the AlphaFoldDB.
    • FoldSEEK achieves sensitivities of 86%, 88%, and 133% relative to Dali, TM-align, and CE, respectively, and ranks among the top tools in precision-recall benchmarks.
    • FoldSEEK produces alignments with accuracy comparable to Dali and TM-align, is 15% more sensitive than CE, and excels in detecting homologous multi-domain structures efficiently.

  • 2024-11-22

    Structure Language Models for Protein Conformation Generation

    • generative methods
    • language models
    • non-antibody stuff
    • Novel model for sampling the structural space of proteins - applied to nanobodies.
    • Protein structures are encoded into latent tokens using a discrete variational auto-encoder (dVAE), which captures residue-level local geometric features in a roto-translation invariant manner.
    • The model combines a dVAE encoder-decoder with a Structure Language Model (SLM) to model sequence-to-structure relationships.
    • One can sample alternate conformations by providing an amino acid sequence as input and use the SLM to generate latent tokens representing potential conformations. Decode these latent tokens back into 3D structures using the dVAE decoder to generate diverse structural ensembles.
    • Altogether, though they develop ESMdiff in the context of the paper, it is rather the structural language in the context of the broader framework of language models that forms the core of the message, rather than any single method.
    • The model was evaluated on tasks like generating equilibrium dynamics, fold-switching, and intrinsically disordered proteins, using metrics like JS-divergence, TM-score, and RMSD. It outperformed existing methods in speed and accuracy, generating structures 20-100× faster.

  • 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.

  • 2024-03-13

    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.