Computational Antibody Papers

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2024
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  • 2024-08-15

    EuDockScore: euclidean graph neural networks for scoring protein-protein interfaces

    • docking
    • Novel docking score based on graph model and language model embeddings, with an antibody-specific variety.
    • It is based on the Equiformer models, giving the structure an equivariant representation. With respect to previous work, authors moved from atom to residue-level representations, input complexes rather than single chains and provide NLP embeddings.
    • They employ DistilBert for embeddings, training it on interaction data from BioGrid.
    • The antibody specific model is trained on antibody data downloaded from ABDB to distinguish native from non-native poses after local docking with RosettaFold.
    • The antibody-specific model is in fact heavy-antigen as the authors did not see benefit in the light-only model and having it three-way heavy-light-antigen was too computationally expensive.
    • Though the antibody-specific scoring method has predictive power in distinguishing native from non-native poses.
    • The antibody-specific model did not outperform AF2-multimer, however authors note that it has predictive power that can be harnessed to rescoring AF2-multimer outputs.
    • The code is available in https://gitlab.com/mcfeemat/eudockscore

  • 2024-07-30

    Fast and accurate modeling and design of antibody-antigen complex using tFold

    • binding prediction
    • structure prediction
    • docking
    • Update on tfold-AB including modeling of the complex with the antigen, with applications to virtual screening.
    • They mostly use SABDAB/Covabdab as reference datasets.
    • The modeling happens by generating antibody & antigen features supplemented by a large language model, followed by flexible docking.
    • For antigen feature generation, they use AF2
    • They are training on several tasks simultaneously, ab structure prediction, complex prediction etc. making it a multi-task training.
    • On docking their method achieves DockQ 0.217 vs AlphaFold-Multimer DockQ score of 0.158 - that is global docking.
    • Whe local docking information is given, constraining paratope/epitope sites, their algorithm achieves DockQ of 0.416.
    • They demonstrate that filtering antibodies by their predicted modeling confidence score gives moderate enrichment against PD1 and Sars-cov-2 antigens, showing promise for virtual screening.

  • 2024-07-09

    Improved prediction of protein-protein interactions using AlphaFold2

    • binding prediction
    • docking
    • Using AF2 they developed a pipeline to fold and dock proteins simultaneously. The pipeline shows good performance in distinguishing interacting and non-interacting proteins.
    • Acceptable models are those with DockQ > 0.23. Success rate is defined as percentage of acceptable poses.
    • The best version of their model achieves a 39.4 success rate.
    • AlphaFold2 outperforms other docking methods.
    • Using the number of Cb in contact (within 8A) or plDDT of the interface results in ROC AUC in the region of .9 distinguishing interacting and non-interacting proteins.
    • As input they insert a chain break of 200 residues to model the interaction.
    • They note that it is very important to create the right MSAs for AF2.
    • As negative cases for interactions (non-interacting proteins) they employ data from Negatome.

  • 2024-07-02

    Flexible Protein-Protein Docking with a Multi-Track Iterative Transformer

    • docking
    • Mildly flexible docking tools that runs very fast, as compared to traditional docking methods.
    • They used the DIPS datasets of about 42,000 binary complexes from the PDB
    • They represent proteins as graphs. Nodes are given the ESM2 650M embeddings and the edges the distances alongside orientation distributions from trRosetta.
    • The graph module serves as input to the structural module that, similar to AF2, performs recycling of the rotation of the two proteins.
    • The number of trainable parameters is 4.3m.
    • Losses are from AF2-multimer, FAPE, IDDT-ca and structure violation loss.
    • DockQ score of 0.23 is seen as a successful dock.
    • The method runs in seconds, which is significantly faster than typical docking methods.
    • Though faster, it does not perform better than traditional docking methods.
    • They have successfully illustrated GeoDock's capability to induce minimal backbone movement, even though its training data exclusively comprises bound protein complexes. Notably, the resultant predicted structures bear a striking resemblance to the initial unbound structures, underscoring the method's ability to generate structurally consistent outcomes despite its limited training scope.

  • 2024-06-25

    The ClusPro AbEMap web server for the prediction of antibody epitopes

    • epitope prediction
    • docking
    • ClusPro server with the AbeMap module for epitope mapping. It employs homology modeling if antibody structure is unavailable and makes it possible to predict epitopes by ranking the most common contacting residues in its docking poses.
    • For epitope prediction, the 1,000 structures are used to calculate the frequency of each antigen surface atom’s occurrence in the antibody–antigen interface. To map an epitope, AbEMap defines the atomic epitope likelihood score as the Boltzmann weighted atomic interface occurrence frequency averaged over the ensemble of antibody structures.
    • If the structure of the antibody is not known, the structure is modeled using homology methods, with the completion by MODELLER
    • They count contact as 5Å away.
    • The epitope frequency/energy scores are calculated for each atom.
    • To assess the precision of epitope prediction, they transform atom likelihoods into residue likelihoods through the summation of atomic contributions attributed to each residue. While incorporating atomic likelihood values implies that larger residues with a greater number of surface-accessible atoms receive higher scores, it's important to note that the residue likelihood values remain unadjusted for size, and therefore, users may need to address this potential bias.
    • AbEmap gets F1 ~.2 for the top 10 residues ranked by scores.

  • 2024-05-29

    Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2

    • docking
    • They benchmark AF2 ability to improve docking of antibody-antigen complexes.
    • They check whether giving docked antibody-antigen to AF2 would improve the initial quality of the docking pose & whether it can be used for better rescoring.
    • No MSA is used, instead sidestepping it and just providing AF2 with sequence and ‘docked template’.
    • Side chains are stripped as it was found they provide too many constraints and AF2 is tasked in putting them back in place.
    • Four docking algorithms were used, Propose, ZDOCK, Piper and Cluspro (for processing PIPER results).
    • The docks are run in bound (231 complexes) and unbound state (25 complexes). The pulled apart bound complexes had side chains repackaged using SCWRL.
    • They find that AF2 retains ~50% of decoy contacts and moves the interface by ~1.24A, indicating that it does modify the input structure.
    • For rescoring they use AF2 composite score which is composed of pLDDT and pTMscore. However, both scores are given as z-scores normalized to each ab-ag complex - to allow for comparisons.
    • The rescoring using AF2composite helps in both bound and unbound docking cases, however much more in bound. But it deteriorates with model quality.
    • The least performance improvement for rescoring is noted for ClusPro.
    • So, AF2 can improve rescoring of docked poses by combining pLDDT and pTMscore, but the models need to be good to fully benefit from it.