Computational Antibody Papers

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  • 2024-05-29

    Sequence-Based Nanobody-Antigen Binding Prediction

    • binding prediction
    • nanobodies
    • They introduce a classifier for specificity (binary) for antibody-antigen complexes.
    • They employ data from sdab-db, 47 antigens and 365 antibodies.
    • They extend the positive pairs set by assuming nanobodies would bind antigens with high sequence similarity.
    • They create a negative set by looking at similarity of antibodies and antigens and shuffling them if they are below similarity threshold to the complex.
    • They employ the k-mer gappe scheme as their embedding of choice.
    • They run a test on several embedding schemes versus several classification schemes (e.g. RF, SVM).
    • The best combination, k-mer gapped embedding and RF achieve accuracy ~90%.

  • 2024-03-13

    Efficient evolution of human antibodies from general protein language models

    • language models
    • binding prediction
    • They introduced a set of single and double mutations based on most likely variants proposed by an ensemble of language models (ESMs). Most of the mutations not only didn’t remove binding ability, but actually improved it.
    • They performed evolution with the ESM-1b language model and the ESM-1v ensemble of five language models (six language models in total)
    • In the first round of evolution, they measured the antigen interaction strength by biolayer interferometry (BLI) of variants that contain only a single-residue substitution from wild-type.
    • In the second round, they measured variants containing combinations of substitutions, where we selected substitutions that corresponded to preserved or improved binding based on the results of the first round.
    • They performed these two rounds for all seven antibodies, measuring 8–14 variants per antibody in round one and 1–11 variants per antibody in round two
    • Across all seven antibodies, they found that 71–100% of the first-round Fab variants (containing a single-residue substitution) retained sub-micromolar binding to the antigen, and 14–71% percent of first-round variants led to improved binding affinity (defined as a 1.1-fold or higher improvement in Kd compared to wild-type)
    • Thirty-six out of all 76 language-model-recommended, single-residue substitutions (and 18 out of 32 substitutions that lead to improved affinity) occur in framework regions.
    • They found that Fabs for 21 out of the 31 language-model-recommended, affinity-enhancing variants that we tested had a higher melting temperature (Tm) than wild-type, and all variants maintained thermostability (Tm > 70 °C).
    • They tested for polyspecificity but there were no off the chart changes in the poly profile.
    • Five out of 32 affinity-enhancing substitutions (~16%) involve changing the wild-type residue to a rare or uncommon residue
    • Approach based on general protein language models consistently outperformed all baseline methods, including the antibody-specific ones (!).

  • 2024-03-13

    Protein-Protein Interaction Prediction is Achievable with Large Language Models

    • language models
    • binding prediction
    • They developed a language model to predict protein protein interactions from sequence on the basis of a large language model.
    • They Train protBERT to predict PPI.
    • They use the BIOGRID dataset, where interactors are mapped if they are confirmed by two independent sources, such as two independent experimental techniques in two separate studies. In total they have 179,018 positive pairs.
    • They use negatome 2.0 as a negative dataset. It relies on various sources such as manual curation from the literature or subunits from the PDB that do not interact with each other. Total of 3,958 pairs were used.
    • They use ProtBERT-BFD to pretrain the model.
    • They mapped each protein pair as [CLS] Protein A [SEP] Protein B [SEP], mapping the final output to binary.
    • They achieve 92% accuracy on the test set.
    • They also perform well on annotating negative binders as coming from different subcellular compartments. On positive samples in this dataset, the model was 85% accurate. On negative samples, SYNTERACT was only 38% accurate, classifying many negatives from subcellular compartment sampling as interactors.