ArticleBioinformatics advances2022
AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Improving B-cell epitope prediction.Drug discovery today · 2025Review
- AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.Science advances · 2025Article
- Bio-Inspired Mamba for Antibody-Antigen Interaction Prediction.Biomolecules · 2025Article
- EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information.NPJ systems biology and applications · 2024Article
- A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.Protein science : a publication of the Protein Society · 2024Article
- Complementary dual-virus strategy drives synthetic target and cognate T-cell engager expression for endogenous-antigen agnostic immunotherapy.Nature communications · 2024Article
- A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases.bioRxiv : the preprint server for biology · 2024Article
- Antibody design using deep learning: from sequence and structure design to affinity maturation.Briefings in bioinformatics · 2024Review
- Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.Briefings in bioinformatics · 2024Article
- Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences.mSystems · 2023Article
- SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs.Nucleic acids research · 2023Article
- Structure-free antibody paratope similarity prediction foriScience · 2023Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- Improved Antibody-Specific Epitope Prediction Using AlphaFold and AbAdapt.Chembiochem : a European journal of chemical biology · 2022Article
- Advances in antibody discovery from human BCR repertoires.Frontiers in bioinformatics · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences. Results: To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set. Availability and implementation: AbAdapt and related data are available at https://sysimm.org/abadapt/. Supplementary information: Supplementary data are available at
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.