Computational Antibody Papers

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epitope prediction
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2024
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  • 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.

  • 2024-07-16

    PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces

    • binding prediction
    • epitope prediction
    • Geometric transformer that receives a single structure at input and annotates residues with likelihood that they are part of a binding site.
    • The geometric transformer only uses the atom names, no mass, charge etc.
    • Similar to convolution their geometric attention mechanism focuses on 8 nearest neighbors (3.2A) and then increases up to 64 nn (8.2A).
    • They use ~300,000 chains from the PDB for training (!). This is because they have extracted all the bio assemblies at 30% sequence identity.
    • They defined the most common atom names for all molecule types which gave them 79 elements. Interactions between these elements can be given as a 79x79 matrix.
    • Interaction cutoff is taken as 5A.
    • Pesto outperforms Scannet by a large margin, 0.93 vs 0.87 ROC AUC.
    • In some cases, processing MD trajectories of unbound proteins with PeSTo identifies certain interfaces better than when PeSTo is run on the starting static structure.

  • 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

    Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies

    • clustering
    • structural modeling
    • epitope prediction
    • They show that structurally clustering six CDRs allows for binning anti-COVID antibodies by their domain including grouping together antibodies from different lineages (clonotypes). The method offers a way of deeper characterization of convergent epitope responses as well as allowing for more targeted efforts in determination of novel structures maximally contributing to plugging the structural holes. The introduced structural clustering algorithm is SPACE.
    • Serum baiting - an extracellular coronavirus antigen is used to pan donated blood serum directly for complementary antibodies
    • They modeled and structurally clustered thousands of antibody Fv sequences in CoV-AbDab and show that 92% of multiple-occupancy structural clusters bin together antibodies that bind to consistent coronavirus antigens/domains, the antibodies within these structural clusters frequently transcend clonal lineages.
    • Employed homology modeling using AbodyBuilder.
    • The 2,063 full variable domain (Fv) sequences in CoV-AbDab were submitted to the ABodyBuilder antibody modeling tool. To ensure high model quality, only the 1,500 models for which ABodyBuilder used FREAD to homology model all six CDR loops were carried forward for structural clustering
    • SPACE: The antibodies are split by the six lengths of the CDRs. The score is calculated as the length-weighted sum of individual Ca CDR RMSDs.
    • For each length combination they pick a first CDR in the list. If the Score equals to <0.75A the new structure is added to the list, otherwise it is left for the next iteration. In this form it is a greedy algorithm.
    • Their lenient VH-clonotyping protocol groups Fvs with matching IGHV genes, the same length CDRH3, and ≥ 80% CDRH3 sequence identity. Their lenient Fv-clonotyping protocol additionally requires cluster members to have a matching IG[K/L]V gene, the same length CDRL3, and ≥ 80% CDRL3 sequence identity.
    • As a measure of whether an antibody binds to the same region they used the definition of ‘domain’ consistent.
    • A total of 184/200 (92%) of our multiple-occupancy structural clusters were domain-consistent, indicating that structural clustering with another member of CoV-AbDab is likely to be highly predictive of function.
    • A total of 88 (47.8%) contained at least one pair of antibodies from different lenient Fv clonotypes and 73 (39.7%) of the structural clusters contained at least two lenient VH-only clonotypes.

  • 2024-05-15

    NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

    • t-cell
    • epitope prediction
    • Software for predicting T-cell epitopes. The one relevant to antibodies is netMHC2pan - it will predict 15-mer epitopes for each of human MHCs, giving it a score whether it is a strong or weak binder as opposed to naturally occurring peptides.
    • MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins
    • People use the binding affinity or eluted binding data.
    • Predictions can be done on either multi allelic or single allele binding.
    • The combined dataset used for training of NetMHCpan-4.1 consists of 13 245 212 data points covering 250 distinct MHC class I molecules, and the combined dataset used for training of NetMHCIIpan-4.0 consists of 4 086 230 data points covering a total of 116 distinct MHC class II molecules
    • The core improvement is integration of the NNALIgn_MA in netmhci/iiPAN

  • 2024-05-15

    EpiCluster: end-to-end deep learning model for B cell epitope prediction designed to capture epitope clustering property

    • epitope prediction
    • Antibody-less epitope prediction. Outperforms other methods by a large margin, is free to use and offers the training loop.
    • Approximately 90% of B cell epitopes are conformational epitopes
    • EpiCluster encodes the structure of antigen as EGNN and combines this embedding with ESM embedding. Epitopes as clustered using k-nearest neighbors.
    • They define contacts as any heavy atom within 4A
    • They used the graphein library to encode the antigen - edge is drawn between two residue nodes if they are within 5A of each other.
    • On a 2023 test set EpiCluster achieves PR-AUC of 0.72 with the closest contender BePro at 0.27