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Antibody-antigen binding prediction

Antibody-antigen binding prediction involves using computational or experimental methods to assess how well an antibody interacts with its target antigen. This can be done by analyzing features such as sequence, structure, and physicochemical properties. These predictions aid in designing therapeutic antibodies, vaccines, and diagnostic tests. Validation through experimental assays ensures the accuracy of the predictions and their applicability in biomedical research and development.

The binding site prediction task is used to predict which antibody residues interact with an antigen. A residue is considered part of the paratope’s binding site if any of its heavy atoms (non-hydrogen atoms) is located within 4.5˚A of any antigen-heavy atom.

Papers

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Showing 1–10 of 10 papers

TitleStatusHype
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfacesCode1
Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variantCode1
Learning context-aware structural representations to predict antigen and antibody binding interfacesCode1
Paragraph—antibody paratope prediction using graph neural networks with minimal feature vectorsCode1
ParaSurf: A Surface-Based Deep Learning Approach for Paratope-Antigen Interaction PredictionCode1
Attentive cross-modal paratope prediction—0
Parapred: antibody paratope prediction using convolutional and recurrent neural networksCode0
Antibody interface prediction with 3D Zernike descriptors and SVMCode0
A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2Code0
Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness EstimationCode0
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